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Effects of near-roadway air pollution exposure on obesity, obesity-related behavior, and neurobehavioral deficits during peripuberty

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Effects of near-roadway air pollution exposure on obesity, obesity-related behavior, and neurobehavioral deficits during peripuberty

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Effects of Near-Roadway Air Pollution Exposure on Obesity, Obesity-Related Behavior,
and Neurobehavioral Deficits during Peripuberty

by
Christopher Michael Warren

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(PREVENTIVE MEDICINE: HEALTH BEHAVIOR RESEARCH)

December 2019

ACKNOWLEDGEMENTS

I would like to express my gratitude to my dissertation committee members, Drs. Rob
McConnell, Britni Belcher, Nathaniel Riggs, and especially, my chair Dr. Mary Ann Pentz for
her guidance and support throughout my time at USC and especially throughout the dissertation
process. I am thankful to Dr. Chih Ping Chou for his statistical insights and Dr. Cousineau for
encouraging me to carefully consider the policy implications of my work. Each of these
remarkable individuals have constantly pushed me to think more deeply and grow my ambition
throughout my doctoral training.
I would like to give a shout out to the entire Health Behavior Research program, its students, and
administrators for providing a supportive, nurturing learning environment that allowed me to
thrive as a predoctoral student. I am especially thankful to Marny Barovich for all of her efforts
to ensure everyone’s success in so many ways.
Finally, I am profoundly grateful to my family for their support. I thank my parents, Jody and
Mike Warren, for their many sacrifices to provide me with every possible opportunity to
succeed. I am also appreciative of my sister Rebecca and her husband Mike for joining me in my
move to the Southwest and sharing my love for fresh mountain air. Of course I would be remiss
if I didn’t acknowledge my gratitude to my grandmother, Jo Marie Paul, who originally spurred
my interest in human health and has always encouraged me to have an open mind toward new
and innovative modalities of health promotion. I owe my greatest thanks to my partner Alyssa
Huff for everything she has done to help me through this journey. I could never have
accomplished so much without her unwavering patience, wisdom, kindness, humor and love.

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TABLE OF CONTENTS

PAGE
LIST OF TABLES ........................................................................................................................................ iii
LIST OF FIGURES ....................................................................................................................................... v
CHAPTERS
CHAPTER 1 – A brief introduction to air pollution in the Los Angeles basin ................................ 1
Chapter 1 Bibliography………………………………………………………. 40
CHAPTER 2 – Relationships between near-roadway air pollution exposure & obesity risk in a
sample of Southern California primary schoolchildren ...................................... 49
Chapter 2 Bibliography……………………………………………………….85
CHAPTER 3 – Neurobehavioral effects of near-roadway air pollution exposure among a sample
of Southern California primary schoolchildren .................................................. 91
Chapter 3 Bibliography……………………………………………………..130
CHAPTER 4 – Exploring obesogenic and neurobehavioral effects of near-roadway air pollution:
effect modification by chronic stress? .............................................................. 137
Chapter 4 Bibliography……………………………………………………...176
CHAPTER 5 – The feasibility and acceptability of assessing inhibitory control and
working memory among adolescents via an ecological momentary assessment
approach ............................................................................................................ 185
Chapter 5 Bibliography……………………………………………………...194

LIST OF TABLES

CHAPTER 2
Table 2.1. Comparing outcome and covariate values between final analytic sample and
participants lacking residential address data .................................................................. 67
Table 2.2. Distribution of annual air pollution exposures assigned to analytic sample (N=475) at
study baseline ...................................................................................................... 68
Table 2.3. Difference in baseline overweight/obesity status by total NRAP exposure .................. 70
Table 2.4. Comparing difference (SE) in baseline obesity status by freeway vs. non-freeway
source NRAP exposure across an inter-quartile range of exposures .................. 71
Table 2.5. Predicted probabilities of baseline obesity across IQR of total NRAP exposure by sex72
Table 2.6. Differences in BMI and waist circumference model parameters across total NRAP
exposure strata .................................................................................................... 73
Table 2.7. Frequency of active transportation to school ................................................................. 74
Table 2.8. Comparing 6 models of obesity-related behavioral outcomes by air pollution and
sociodemographic covariates .............................................................................. 76
Supplemental Table 1. Zero-order correlations between key study variables at study baseline
(N=475) ............................................................................................................... 84

CHAPTER 3
Table 3.1. Comparing outcome and covariate values between final analytic sample and
participants lacking residential address data ..................................................... 110
Table 3.2. Comparing outcome, exposure, and covariate values between final analytic sample
with exposure assignment (N=475) and participants with complete teacher-
assessed CBCL ................................................................................................. 111

Table 3.3. Comparing mean externalizing and internalizing behavior between upper vs lower
quartiles/deciles of NRAP exposure ................................................................. 116

CHAPTER 4
Table 4.1. Comparing outcome and covariate values between final analytic sample and
participants lacking residential address data ..................................................... 158
Table 4.2. Distribution of annual air pollution exposures assigned to analytic sample (N=475) at
study baseline (2009) ........................................................................................ 159
Table 4.3. Comparing neurodevelopmental outcomes across NRAP exposure strata by perceived
psychosocial stress ............................................................................................ 164
CHAPTER 5
Table 5.1. Zero-order correlations of key study variables at baseline .......................................... 192
Table 5.2. Momentary bivariate predictors of computerized flanker and complex symmetry span
task performance ............................................................................................... 192

LIST OF FIGURES

CHAPTER 1
Figure 1.1. Diagram of ‘Marine Layer’ phenomenon ...................................................................... 1
Figure 1.2. Diagram of regional subsidence inversion ..................................................................... 2
Figure 1.3. Diagram of North Pacific high ....................................................................................... 2
Figure 1.4. Diagram of photochemical smog production ................................................................. 3
Figure 1.5. Chemical composition of near-roadway air pollution .................................................... 7
Figure 1.6. Properties and typical applications of air filters by MERV rating ............................... 10
Figure 1.7. Recommendations for school siting issued by relevant government agencies ............ 24
Figure 1.8. Conceptual model for the assessment of individual and population-wide exposure to
air pollution including health effects and context ............................................... 32
Figure 1.9. Biomedical REAl-Time Health Evaluation (BREATHE) platform ............................. 34

CHAPTER 2
Figure 2.1. Neighborhood-level socioeconomic deprivation ......................................................... 61
Figure 2.2. Predicted probabilities of baseline overweight/obese by NRAP exposure .................. 69
Figure 2.3. Predicted BMI and waist circumference growth trajectories from 4
th
-6
th
grades by total
near-roadway modeled NOx exposure ............................................................... 73
Figure 2.4. Comparing predicted BMI and waist circumference growth trajectories from 4
th
-6
th

grades by freeway vs non-freeway near-roadway modeled NOx exposure ....... 74

CHAPTER 3
Figure 3.1. Baseline models predicting EF deficits ...................................................................... 113
Figure 3.2. Baseline CBCL outcomes among all participants ...................................................... 114
Figure 3.3. Pooled models predicting EF deficits across 4
th
-6
th
grades ........................................ 115
Figure 3.4. Pooled models predicting sex-specific EF deficits across 4
th
-6
th
grades .................... 116

Figure 3.5. Pooled models predicting externalizing and internalizing behavior problems .......... 117

CHAPTER 4
Figure 4.1. Baseline cross-sectional associations between NRAP exposure and
overweight/obesity: effect modification by stress ............................................ 160
Figure 4.2. Baseline cross-sectional associations between NRAP exposure and obesity: effect
modification by perceived stress ....................................................................... 161
Figure 4.3. Longitudinal trajectories of anthropometric weight-gain outcomes across NRAP and
perceived stress exposure strata ........................................................................ 162
Figure 4.4. Associations between NRAP exposure and neurobehavioral outcomes pooled across
4
th
-6
th
grades ...................................................................................................... 163

CHAPTER 5
Figure 5.1. Design of complex symmetry span task ..................................................................... 189
Figure 5.2. Participant-reported acceptability and study feedback .............................................. 190

CHAPTER 1—A brief introduction to air pollution in the Los Angeles basin

Compared to many other North American cities, the greater Los Angeles Metropolitan Area has
unique geographic, climatological, and historical characteristics that make air pollution a
particularly challenging issue. The Los Angeles Basin is ringed by high mountains to the north,
east, (and to a lesser degree the south), which trap pollution as it is blown eastward by onshore
breezes, which prevail during the day when pollutant concentrations are highest. Furthermore,
there is a persistent marine inversion later
that often settles over the Los Angeles
Basin, which occurs when cool, moist
marine air is blown overland by the
prevailing winds from the west (Figure 1.1).
Since this marine air is cooler than the
warmer, drier air sitting atop the basin, it tends to flow underneath and settle close to the land,
creating a “marine layer”, which can trap polluted basin air close to the ground and prevent it
from escaping the basin. This inversion pattern is exacerbated by Southern California’s
frequently sunny climate, given that sun warms the land surface more rapidly during the day than
the ocean surface, creating a situation where this additional heating leads to additional rising
warm air, thereby drawing even more cool, marine air onshore. This process is largely
responsible for the phenomena, colloquially known as “May Grey” and/or “June Gloom”.
(Huber, 2004)

Figure 1.1. Diagram of “Marine Layer” phenomenon
Phenomenon
#

1

Another common meteorological pattern
contributing to the high levels of air pollution in
the Los Angeles Metropolitan area is known as
the Regional Subsidence Inversion, which
happens when warm, dry air flows from the
high deserts (i.e. Mojave, Colorado) to the east,
and down into the Los Angeles Basin. Since as mentioned above, the LA Basin tends to have a
moist stagnant layer of marine area close to the ground, this warm dry air typically slides above
it, thereby creating a temperature inversion and further trapping the cold, polluted air low to the
ground. These warm, dry air masses typically arrive in the Los Angeles Basin as the “Santa Ana
Winds”. (Huber, 2004)

Finally, Southern California also tends to frequently
experience the phenomena known as high pressure
inversions, wherein warm dry air comprising a large
high pressure system sinks down and serves as a cap
atop the cooler, marine air already present in the LA
Basin. The “North Pacific” high is a semi-permanent subtropical system, which is strongest
during the summer months and is responsible for California’s typically dry summer and fall.
During these months, it generally sets up immediately to the west of the California coastline and
therefore, often brings it’s corresponding high pressure and mild weather into the Basin,
protecting it from the moisture and storms experienced by other regions of the country during
Figure 1.2. Diagram of Regional Subsidence inversion
Figure 1.3. Diagram of North Pacific high

2

this time. (Huber, 2004) (NB: Figures 1.1, 1.2, and 1.3, are adapted from the excellent “Earth
Under Siege”) (Turco, 2002)

Due to these geographic and meteorological factors, the Los Angeles Basin has been
characterized by high levels of visible, anthropogenic air pollution likely since its settlement by
indigenous groups capable of harnessing the power of fire. To wit, notes from a 1542 Spanish
Voyage referred to the Los Angeles Basin as the “Baya de los Fumos” or Bay of the Smokes—
attributed to campfire smoke of the many Tongva settlements in the region (Masters, 2013). The
abundance of sun in Southern California—about 75% of days are sunny—also contributes to the
development of secondary photochemical smog, of which ground-level ozone is a key
component (Figure 1.4).

Figure 1.4. Diagram of photochemical smog production

3

While the processes underlying the production of photochemical smog are complex, the key
inputs are solar radiation and nitrogen dioxides, of which the largest contributors are vehicular
traffic.(Miller & Hackett, 2011) In a recent summary the South Coast Air Quality Management
District estimated that 75% of such NOX pollution was created by mobile sources, chiefly on-
road vehicles. Among on-road vehicles, the majority of NOX emissions are attributable to
heavy-duty diesel trucks, followed by personal light-duty vehicles. Other major mobile NOX
sources include construction equipment, container ships, medium-duty trucks, trains, and buses
(Guerin, 2018). A substantial proportion of these emissions stem from the fact that the 2 busiest
container ports in the United States—The Port of Los Angeles, and Port of Long Beach—are
located in the Los Angeles Basin, comprising the largest port complex in North America. Given
its location on the Western periphery of the LA basin, this results in the resulting pollution from
activity at the port being blown onshore. Furthermore, the port is growing rapidly, resulting in
an increasing number of port container trucks on area roadways as well as increased vehicle
miles traveled per truck (Fischer, Hicks, & Cartwright, 2006). Again, given the location of the
port, these highly-polluting transport modalities must travel across the LA Basin to reach their
eventual destinations, increasing primary exposure to NRAP throughout the basin, as well as
contributing to the production of secondary pollutants like ozone. It is important to note that
while California has strict regulations regarding emissions controls for passenger vehicles, many
of the heavy-duty diesel trucks that service the ports and transport goods elsewhere in the
country are not subject to these controls. As a result, they emit a disproportionate share of near-
roadway pollutants and are therefore constitute an important target for regional air pollution
control efforts.

4

Other factors contributing to the relatively high levels of air pollution observed in Los Angeles,
relative to other major North American cities, include the relatively high rates of vehicle
ownership and low rates of public transit ridership compared to other major US cities. For
example, analysis of 2016 American Communities Survey data revealed an average of 1.6
vehicles per Los Angeles household compared to 0.6 per New York City household and 1.1 per
Chicago household. At the same time, the percentage of commuters who utilize public transit
was much lower in Los Angeles (10.6%) relative to other major cities (56.5% of New Yorkers
and 27.6% of Chicagoans) ("2016 American Communities Survey 1 year Estimates,").

Efforts to expand public transportation infrastructure in the Los Angeles Metro area have
accelerated in recent years due to the passage of Measure M in 2016, a sales tax increase
anticipated to generate $120 billion over the next 4 decades for expansion of rail, rapid bus, and
bike networks. However, it is important to note that such efforts are far from a panacea to the
current high levels of traffic and corresponding near-roadway exposures. For example, three
large sales tax increases were passed between 1980 and 2016, which funded the construction and
maintenance of over 110 miles of rail. However during the same period of time public
transportation ridership declined, while the number of annual private vehicle miles grew
substantially (Manville, Tayler, & Blumenberg, 2018). While it is tempting to think that this
time will be different, recent research suggests that this is unlikely—absent additional large-scale
transformative efforts to transform urban land use in a way that comprehensively supports transit
(i.e. denser housing development, less required parking, implementation of tolling) (Manville,
2019). Therefore, to maximize the probability of success, multi-pronged strategies are needed to
reduce near-roadway air pollution and the regional pollutants to which it contributes.
5

What is Near-Roadway Air Pollution?

Near-roadway air pollution is a complex mixture of pollutants, primarily composed of fresh
vehicle exhaust, which contains both particulate and gaseous pollutants (Figure 1.5). Key
pollutants, known to have adverse health effects include NO and NO (often characterized jointly
as NOX), CO (a byproduct of incomplete hydrocarbon combustion), polycyclic aromatic
hydrocarbons (e.g. benzo[a]pyrene), as well as a variety of volatile organic compounds (i.e. the
fuel additives benzene, 1,3 butadiene and xylene). NRAP also contains a wide variety of
particulate pollution, including PM10, PM2.5, and ultrafine PM (which is 1 micron in diameter).
In general, it is believed that in general, the smaller the particle size, the more hazardous to
human health (Understanding the
health effects of ambient ultrafine particles., 2013), given that the smaller particles can penetrate
deeper into the lungs, potentially enter the systemic circulation, pass through the blood-brain-
barrier, and have greater surface area in comparable concentrations (Kreyling, Semmler-Behnke,
& Möller, 2006). Moreover, black carbon (also known as elemental carbon or soot) is a type of
PM that often used by health effects studies due to the fact that it may have more deleterious
health effects than other PM components (Cassee, Héroux, Gerlofs-Nijland, & Kelly, 2013;
Grahame, Klemm, & Schlesinger, 2014). However, there are other components to NRAP that
are not combustion byproducts nor do they stem from tailpipe emissions. These include tire
debris, as well as metals, including the following, which can result from brake and engine wear:
copper, chromium, iron, manganese, antimony, and strontium (Kam, Delfino, Schauer, &
Sioutas, 2013).

6

Figure 1.5. Chemical composition of near-roadway air pollution

Owing to their common cause (vehicular roadway traffic), it is extremely difficult to tease apart
the differential effects of specific NRAP components. For this reason, researchers typically
attempt to assess exposure to the plume itself, using various proxy measures. Perhaps the easiest
proxy to calculate for such exposures is the distance between roadways and locations believed to
represent the majority of an individual’s NRAP exposure (i.e. home, school, work). However,
pure proximity-based models are quite limited as they are easily confounded by SES, noise, and
other factors associated with both near-roadway residence and true NRAP exposure. They also
do not reflect the meteorological and built environmental factors that are known to influence
exposure. These are also limitations of studies which attempt to estimate local traffic density via
traffic count data within specific buffers. However, such factors are taken into account via line-
source dispersion models like the CALINE4-based approach used in the present study, which
model exposure to important components of the near-roadway plume. However, given that these
pollutants (e.g. NOX, black and organic carbon, PM2.5, PM10) originate from the same line-source,
7

they are typically very highly correlated (e.g. r>.95; (McConnell et al., 2015)) and therefore a
single pollutant is sometimes chosen to reflect exposure to the complex mixture. This is how
NRAP modeling was addressed in the studies comprising the present dissertation. It is important
to note that many of these primary near-roadway pollutants also contribute to the formation of
regional pollution, so often exposure is modeled in terms of incremental increases attributable to
NRAP on top of background ambient levels—as was done here. Other, more sophisticated
hybrid approaches to NRAP exposure modeling, such as those incorporating personal monitoring
and land-use regression are discussed later.

What can be done to reduce NRAP exposure and its adverse health effects?

Household level Interventions:

Currently, California state law mandates that all new homes include air filtration meeting at least
the MERV 6 standard. Such filters remove approximately half of ambient particle pollution.
Here in Los Angeles, a 2016 city council ordinance mandated that new mechanically ventilated
buildings located within 1000 feet of a freeway utilize air filtration at MERV 13 or better—an
acknowledgement of the negative health impacts of exposure to the near-roadway plume.
However, it is important to note that there are substantial limitations to even the highest quality
air filtration systems, even if filters are constantly replaced, the building’s ventilation system
remains on full-time and all doors and windows are closed and well-sealed. As such, while such
methods may help reduce exposure to some air pollutants, they are far from a panacea to the
8

problem of ubiquitous NRAP and urban air pollution exposure. One concern is that they are
considerably more expensive than typical medium efficiency (MERV-6-8) filters, with costs per
high-quality filter (MERV-13-16) ranging from $20-$90 compared to $5-$10 for a lower
MERV-rated filter. Therefore, higher efficiency filters, which require more frequent
replacement to maintain effectiveness, may be less likely to be replaced due to cost—particularly
in low income households. Another concern is that, while high-quality air filters (i.e. MERV 13-
16) can remove a large proportion of particle air pollutants—recent work (Russell, Welch, &
Gutierrez) indicates that 70-90% of Ultrafine Particles and Black Carbon can be removed via
such devices—they do not remove toxic exhaust gases (i.e. benzene, carbon monoxide, volatile
organic compounds, 1,3-butadiene) which also can comprise part of the near-roadway plume and
are acknowledged to be hazardous to human health("Health Effects Notebook for Hazardous Air
Pollutants," 2019). An excellent summary figure published by the EPA’s Office of Air and
Radiation contrasting the properties and typical applications of different MERV standards is
found below. ("ASHRAE Handbook -- HVAC systems and equipment," 2016)

9

Figure 1.6. Properties and typical applications of air filters by MERV rating

Another option for improving air filtration at the household or school level is the utilization of
portable air filtration units with built-in HEPA filters. The presence of a single unit within the
10

home has been found to be effective in substantially reducing particle pollution within the indoor
environment (Macintosh et al., 2008)) Importantly, numerous studies have identified tangible
health benefits associated with use of such devices (Fisk, 2013). For example, one well-
controlled crossover study found that use of portable HEPA air filtration in a community
impacted by wildfire smoke for a 7 day period was associated with 60% reductions in indoor
PM2.5 and concomitant reductions in inflammatory biomarkers (i.e. interleukin-6, C-reactive
protein) as well as improvements in endothelial function (Allen et al., 2011). A follow-up
crossover study adding an additional sample of NRAP-exposed individuals also found portable
HEPA filtration to be associated with substantial reduction in indoor PM2.5 concentrations
(Kajbafzadeh et al., 2015). Importantly this study identified much stronger associations between
C-reactive protein and the presence of indoor traffic-related PM2.5 than were observed between
C-reactive protein levels and wildfire smoke, thereby provides additional evidence for the
particularly pro-inflammatory effects of NRAP.

Individual-Level Interventions

In high-pollution environments, properly fit face masks and respirators have been shown to offer
a substantial degree of protection from key near-roadway pollutants. For example, commercially
available respirators receiving the N95 designation from the National Institute of Occupational
Safety and Health (NIOSH) have been found to block ≥95% of very small (.3 micron) particles
under ideal circumstances, including black carbon and diesel aerosol (Janssen & Bidwell, 2006)
(Langrish et al., 2009; Penconek, Drążyk, & Moskal, 2013). Indeed, use of such masks has been
11

linked to acute improvements in important health indicators (i.e. blood pressure, heart rate,
airway inflammation) (Guan et al., 2018; S. Zhang, Li, Gao, Wang, & Yao, 2016) However,
research has shown that, even when used routinely, their filtration efficiency can be substantially
reduced in real-world conditions (Mueller et al., 2018; Steinle et al., 2018), owing to inward
leakage of polluted air via poorly sealed face masks (Grinshpun et al., 2009). Worryingly,
studies suggest that the effectiveness of these masks may be lower in children due to their
smaller, more variable facial shapes (van der Sande, Teunis, & Sabel, 2008) (Cherrie et al.,
2018)) and the fact that most recommended respirators were designed to mitigate workplace
exposures. Research also suggests that although surgical masks can provide excellent protection
against NRAP exposures under ideal conditions, the design of such masks generally leads to
poorer facial fit, and greater entry of polluted air into the mask, resulting in less effective
filtration compared to respirator-type masks (Gao, Koehler, Yermakov, & Grinshpun, 2016;
Oberg & Brosseau, 2008; Shakya, 2017).

While sustained air pollution control efforts should clearly be the most important target of efforts
to reduce adverse health effects associated with air pollution, regular use of properly fit N95
respirator masks may also be an effective modality for reducing NRAP exposure among highly
impacted groups. Use of such masks is increasingly commonplace in some Asian cities that
experience particularly high levels of pollution (i.e. Beijing). However, their use is far more less
widespread in the United States, although behavioral epidemiology studies are limited. One
notable recent study explored the motivations behind pollution-mask use in a sample of urban
Chinese young adults residing in Shanghai using the theory of planned behavior as a conceptual
framework (Hansstein & Echegaray, 2018).
12

The theory of planned behavior asserts that a given person’s intention to engage in a certain
behavior is influenced by 1) one’s attitude toward the behavior (i.e. what are the perceived
relevance and benefits of enacting such a behavior?), 2) the social norms associate with the
behavior (i.e. how socially acceptable, desirable or mandated is the target behavior within the
individual’s most salient social groups?, 3) one’s perceived control over the behavior (i.e. How
capable does one view oneself of overcoming relevant barriers and/or leveraging facilitators to
enact the target behavior?). (Glanz, Rimer, & Viswanath, 2008) Related to this last concept is
the idea of self-efficacy, which is a more general construct linked to one’s perceived capacity to
succeed in accomplishing a given task. Obviously, the theory of planned behavior also assumes
that intention is a key antecedent of deciding to engage in the target behavior.

In the study by Hansstein & Echegaray (2018), the authors concluded that perceived social
norms toward mask-wearing, self-efficacy, and positive attitudes toward the necessity, benefits,
effectiveness and utility of mask wearing each significantly predicted intentions to wear
pollution masks. These intentions in turn strongly predicted actual reported pollution mask-
wearing behavior. This suggests that public health promotion efforts aiming to improve such
practices that focus primarily on increasing awareness of the health impacts of air pollution
exposures should also consider ways to frame mask-wearing behaviors as socially- desirable,
necessary, effective and useful for reducing exposure. As options for effective pollution masks
and respirators begin to include more fashionable alternatives, as has occurred in East Asian
cities where their use is more commonplace, perceived barriers to use may decline further.
However, additional work is clearly needed—especially in the North American context where
such studies are absent, particularly in the pediatric population.

It is known that NRAP concentrations at a given location can exhibit high intraday variability,
owing to systematic differences in traffic counts, vehicle type, and weather patterns at different
times of day. For example, Modeled NOX concentrations are typically highest during
commuting hours, with the highest exposures occurring on or immediately proximal to high-
volume roadways. Recent work conducted in a sample of Spanish schoolchildren incorporating
personal monitoring of black carbon (BC) found that while these children spent only 6% of their
time commuting to and from school, they received 20% of their daily black carbon dose during
this time (Rivas et al., 2016). While this study suffered from high rates of missingness (45%)
regarding the transport mode of commuting, the available data suggested BC exposures were
highest among those commuting via bus or metro, with the lowest BC exposures observed
among those commuting via car—which was attributed to the likely use of air recirculation
within the vehicle. Not having to spend time waiting for the bus/train adjacent to high traffic-
density thoroughfares may also contribute to the lower exposures among youth who did not
utilize public transportation for their commute to school. While such practices were unassessed
by the aforementioned study, use of high-efficiency cabin air filters has been found to reduce in-
cabin concentrations of PM2.5 by 37% and Ultrafine PM by 47%, when car windows remained
closed over a 6 hour period (Yu et al., 2017). These reductions in PM were in turn linked to
reductions in associated lipid peroxidation levels (an indicator of oxidative stress), suggesting
that such pollution mitigation efforts may have beneficial health effects—even in the short term.

While unassessed in the aforementioned work by Rivas et al (2016), it has been posited that
bicycle commuters in high-traffic density communities may comprise a particularly high-risk
subgroup owing to the greater inhalation rates which accompany active transport modalities (De

Nazelle & D. Ripoll, 2012), thereby increasing NRAP doses relative to peers commuting by foot
or other less active modalities over a comparable temporospatial context. Consequently, these
individuals may be particularly ideal targets for exposure reduction efforts, including
encouraging respirator use. Again, children commuting to school via bike may comprise a
subpopulation of specific concern due to their greater basal rates of ventilation relative to adults,
exacerbated by the physical activity of riding a bicycle, as well as their lower stature which may
render them more proximal to tailpipe emissions.

Policy-based solutions to reduce traffic-related air pollution

Clearly the most direct way to reduce the adverse health effects of NRAP is through primary
reduction of vehicle emissions and there are many strategies through which this can be achieved.
At a national level, these strategies include economic policies like taxation of fossil fuels,
subsidies for cleaner vehicles, and the establishment of ambient air quality standards for
pollutants like NO2, PM2.5, PM10 and O3, much of which originates from vehicular sources
(2016). At the regional level, numerous efforts to implement low-emissions zoning policies have
been found to be effective, both with respect to reducing emissions and their concomitant health
effects. Some convincing data have emerged from studies examining the effectiveness of
emissions control policies implemented during recent Olympic games. Regional efforts to
prepare for the Olympic Games and the corresponding massive influx of visitors often require
implementing comprehensive strategies for minimizing traffic congestion, not only for pollution-
reduction, but simply to ensuring that spectators can reach Olympic events in a reasonable

amount of time. Consequently these settings can serve as excellent natural experiments to
examine the effects of emission-controls policies.

For example, it was feared that the 1996 Summer Olympics and its corresponding > 1 million
visitors, would overwhelm regional transportation infrastructure and exacerbate the region's
ongoing summertime air quality violations for high ambient ozone concentrations. Therefore,
the following air pollution-control policies were implemented prior to the start of the games: 1)
A 24-hour-a-day public transportation system was initiated, which including dramatically
enhanced park-and-ride services, 2) local industry was encouraged to utilize alternative work
hours and telecommuting, 3) the downtown area was closed to private vehicular traffic, and 4)
public education efforts warned the public about potential traffic and air quality problems.
(Balagas, 1996; Porter, 1997) Studies found that these efforts were not only successful in
reducing traffic density during the game, but also a corresponding 30% reduction in ground-level
ozone concentrations, smaller reductions in PM10, and NO2 as well as a significantly reduced rate
of both acute and non-acute childhood asthma care events, particularly among the Medicaid-
eligible population (Friedman, Powell, Hutwagner, Graham, & Teague, 2001; Peel, Klein,
Flanders, Mulholland, & Tolbert, 2010). It is worth noting that the 2028 Olympic Games will
take place in Los Angeles-- and have the potential to be a transformative event with respect to air
pollution control and green infrastructure investment. However, for such efforts to be maximally
impactful, planning should start now not only to leverage these enormous expenditures for public
health gains, but also to ensure that such efforts are rigorously evaluated and therefore can
inform future, similar efforts in other cities.

Other efforts to reduce traffic-related emissions have been undertaken in major metropolitan
areas like London, where a “Congestion Charging Scheme” was first implemented in 2003. It
currently covers a 21 km
2
region of Central London. Subsequent evaluations have found this tax
on entering central London during peak hours has been associated with substantial reductions in
traffic-related pollutant concentrations (Green, Heywood, & Navarro, 2018) as well as increased
life expectancy, particularly within the most socioeconomically deprived communities with the
greatest NRAP exposure (Tonne, Beevers, Armstrong, Kelly, & Wilkinson, 2008). However,
concerns have been raised about the failure to decrease NO2 to the same extent as other traffic-
related pollutants (e.g. PM), which has been attributed to the disproportionate share of diesel
vehicles exempt from the congestion charge. By exempting buses and taxis from the congestion
surcharge, the associated transfer of commuters from personal vehicles to these public transit
modalities resulted in a shift in the fuel-mix of vehicles in the congestion charging zone toward
diesel, and an increase in the number of diesel miles driven in Central London relative to traffic
patterns before the charging scheme was established. This observation clarifies the important
issue that substantially reducing vehicular congestion may not always be accompanied by
corresponding reductions in emissions. This has also been proposed as a possible explanation
for the failure of recent studies to identify clear protective effects of the low emissions zone on
children’s respiratory health (Mudway et al., 2019). However, it is important to point out that no
such studies have examined the effects of such policy interventions on the neurodevelopmental
or metabolic outcomes assessed in the present study. Relating to this issue of substitution
effects, some European municipalities (i.e. the cities of Dusseldorf and Stuttgart) have begun to
enforce outright bans on certain diesel vehicles. These bans recently declared constitutional by

the German high court (Connolly, 2018), opening the door for further, more aggressive
regulatory efforts to remove high emissions vehicles from the roads entirely.

A related set of policies implemented in Stockholm, Sweden, were also found to be effective in
reducing congestion, lowering ambient concentrations of key traffic-related pollutants and
improving health outcomes. For example, in Stockholm, a variable toll was implemented in
2006 which charged vehicles entering and exiting high-congestion regions of the city. During a
6 month implementation period, modeled NOX and PM10 emissions declined by 8.5 and 13%
respectively, an improvement that was linked to estimated improvements in mortality
outcomes(C. B. Johansson, L. Forsberg, B., 2009). The toll has since been expanded to cover a
35km
2
region of the city, which encompasses 2/3 of residents. Subsequent work has modeled the
health benefits corresponding to a hypothesized switching of car commuters to bike-commuters
and found that the effects of such switching on mortality among Stockholm citizens would be
even greater than the aforementioned mortality benefits associated with implementing the
congestion toll (C. Johansson et al., 2017).

However, it’s important to note that not all policy-based efforts to reduce traffic congestion
and/or pollutant exposure have been successful. For example, Mexico City implemented its
“Hoy No Circula” policy in 1989, which prevented vehicles with certain license plate numbers
from circulating on certain days. Subsequent analysis of this policy suggest that it did not in fact
improve air quality (L. W. Davis, 2017), likely due to an increase in the total number of vehicles
in circulation as well as a shift toward utilization of higher-emitting vehicles (L. Davis, 2008).

Prior to the 2008 Beijing Olympic games, besides mandating the installation of pollution control
devices on local coal-burning power plants, closing local cement, concrete, and lime plants, and
requiring power and chemical plants to decrease emissions by 30%, a number of policies were
enacted simultaneously to specifically address traffic-related pollutants. For example, public
transportation infrastructure was expanded, with particularly high-emitting vehicles removed
from circulation. Fuel taxes were also increased to discourage private vehicle use. During the
Olympics, vehicles with odd-numbered license plates were only permitted to operate on odd
numbered days, while cars with even numbered plates were only allowed to circulate on even
days. As a result of these policies, Beijing experienced substantial improvements in urban air
quality, with PM10 decreased by approximately 1/3 during the Olympic Games and nearly 20%
across 2008 (He & G. Fan, 2016). This decrease was associated with a substantial (6.6%)
reduction in all-cause mortality, which was driven primarily by fewer cardio-cerebrovascular and
respiratory deaths. Additional studies observed substantial improvements in birthweight
outcomes associated with the improvements in air quality (Rich et al., 2015), as well as
cardiovascular health among young adults (Rich et al., 2012). Taken together these findings
suggests that concerted, multicomponent emission-controls efforts can have substantial health
benefits, both in the short and long terms.

Research has also shown that the presence of strategically located green urban vegetation can
substantially reduce street-level concentrations of NRAP—both through increasing particle
deposition and modifying the degree of mixing between street-level concentrations and the upper
boundary layer (McDonald et al., 2007; Pugh, Mackenzie, Whyatt, & Hewitt, 2012). For
example, plants can filter air pollutants directly via dry pollutant deposition through stomata

uptake or other deposition on plant surfaces (Pugh et al., 2012). This removes pollutants from the
air and may directly reduce NRAP exposure among individuals residing in more densely
vegetated areas. Previous work suggests that a greater proportion of near-roadway pollutants is
likely to be deposited when vegetation is located close enough to the road to ensure that the
entirety of the near-roadway plume passes through the vegetation while the particle
concentration is high (Janhall, 2015) It is also clear that, to maximize pollution reduction
benefits, the size and species of vegetation planted is crucial. (McDonald et al., 2007)

Research has demonstrated that vegetation with greater surface area has a correspondingly
greater potential to filter PM. For example, studies have found that tall cedar and oak trees
located 25 meters from a major roadway reduced airborne levels of PM10 by up to 67% and 50%
respectively, while the presence of tall prairie grass led to corresponding reductions of up to 45%
(Cowherd & Grelinger, 2006). In contrast, these data found that short trees only reduced PM10
concentrations by 29%. Other, more recent research has shown that roadside hedges are more
effective than trees or combinations of trees and hedges in reducing NRAP. In this study up to
63% reductions in black carbon concentrations were observed at breathing height, with smaller
reductions detected for other particulate air pollutants (e.g. PM10, PM2.5, PM1) (Abhijith &
Kumar, 2019). This is consistent with previous data indicating that near-roadway NO2 and PM10
concentrations can be reduced by up to 40%, and 60% by planting roadside greenery and that
benefits are likely to be particularly pronounced in the urban canyons that are ubiquitous
throughout most global cities (Currie & Bass, 2008; Tallis, Taylor, Sinnett, & Freer-Smith,
2011). Multiple indirect mechanisms may also reduce air pollution exposure. First, increased
vegetation can improve urban ventilation, thereby increasing pollutant dispersal across a wider

area. Second, increased vegetation reduces urban heat islands and the resulting decrease in
ambient temperature, can reduce smog formation (Akbari, 2002). Consequently, systematic
efforts by to plant low, leafy green vegetation along major traffic thoroughfares may help reduce
NRAP exposure and should be incorporated alongside other pollution reduction strategies.

Besides directly reducing NRAP concentrations, it is also possible that efforts to increase leafy
vegetation and expand access to urban greenspace may reduce stress, and thereby indirectly
mitigate some adverse health effects of NRAP exposure. Numerous studies have linked
exposure to greenspace with reductions in perceived stress (Barton & Rogerson, 2017; Ewert &
Chang, 2018), as well as stress biomarkers (Roe et al., 2013). A variety of plausible pathways
have been proposed for how such greenspace-related stress buffering may occur (van den Berg,
Maas, Verheij, & Groenewegen, 2010). For example, access to nearby parks and greenspace
may bring children together and provide opportunities to make friends, and deepen interpersonal
relationships, which may in turn provide social support, thereby socially buffering the adverse
effects of life stressors (Gunnar & Donzella, 2002; Hennessy, Kaiser, & Sachser, 2009).
Similarly, social support can also be facilitated by participation in team sports, which often take
place in neighborhood parks. The physical activity inherent in many team sports may also
reduce stress more directly, as demonstrated by previous work.

Another putative mechanism for how greenspace may buffer stress is drawn from “attention
restoration theory”, which posits that directed attention can become fatigued and subsequently
restored through spending time in restorative environments, such as natural greenspace (Kaplan,
1995). In their seminal typology of executive functions, Miyake et al posited that directed, or in

their terminology “controlled” attention is a domain-free cognitive process that is involved in
most executive functions insofar as it is required to actively maintain or suppress specific
representations in working memory. Per Miyake et al, cognitive processes like goal maintenance,
conflict resolution, resistance to or suppression of distracting information, error monitoring, and
effortful memory search all are likely to require this attentional capacity, irrespective of the
specific task to be performed (Miyake et al., 2000). According to this perspective, there are
multiple characteristics of the natural environment that facilitate restoration of
directed/controlled attention. For instance, nature has a tendency to draw one’s attention without
requiring effortful cognition. To use Kaplan’s terminology, it is innately fascinating. In this
way, it allows one to escape from their stressors by providing an immersive context in which one
can be away from their daily concerns. Restoration of one’s attentional and other cognitive
control capacities can allow children to be less reactive to emotions or external stimuli, more
planful, focused and equipped to cope with daily challenges. Similarly, research suggests that
exposure to nature may also support state mindfulness through these bottom-up, restorative
processes (Lymeus, Lindberg, & Hartig, 2018).

Exposure reduction at schools:

It is increasingly acknowledged that school-based air pollution exposures are an important
determinant of a variety of health outcomes including obesogenic outcomes. It is noteworthy

that, in the present dissertation, schools were an important source of modeled NRAP exposure,
and a similar magnitude of obesogenic effects were observed when only school-based exposures
were assessed (in lieu of combined residential and school-based exposures). A recent Spanish
study found that children exposed to higher levels (top 30%) of ultrafine particulate matter at
school had 30% greater odds of childhood overweight or obesity compared to children in the
lowest tertile of exposure (de Bont et al., 2019). Another study found slower development of
working memory across a 3.5 year follow-up period among children exposed to greater levels of
NRAP at school (Forns et al., 2017). Consequently, schools are an important venue for NRAP
exposure reduction efforts—particularly in Southern California where mild weather patterns
mean more of the school-day is spent outdoors (i.e. passing periods, recess, lunch…etc)
compared to other regions. Furthermore, the mild climate is also likely to lead to ventilation
practices resulting in greater NRAP infiltration (e.g. windows left open) .

Arguably the most important way to reduce NRAP exposures at school is to ensure that schools
are sited as distally as feasible from major roadways, while at the same time not unduly
increasing average commute time. A recent report by the US EPA aggregated guidance issued
by key regional agencies regarding school siting, summarized in Figure 1.7. (Best Practices for
Reducing Near-Road Pollution Exposure at Schools, 2015)

Figure 1.7. Recommendations for school siting issued by relevant government agencies

Another key consideration is ensuring that a continuously operated, well-maintained, high
quality air filtration system is employed, along with a structurally sound building envelope (one
which minimizes air leaks/infiltration) and incorporates strategically placed intake/exhaust. A
recent school-based study found that indoor concentrations of PM2.5, PM10, Ultrafine PM, Black
Carbon and Volatile Organic Compounds were reduced by approximately 90% after installation
of a MERV-16 filter into the HVAC system (Polidori, Fine, White, & Kwon, 2013). For schools
that employ natural ventilation, simply ensuring that all windows are closed during peak periods
of air pollution can substantially reduce indoor exposures (Meng, Spector, Colome, & Turpin,
2009).

Given that many students utilize school buses for transportation to school, and such buses are
mostly diesel—implementation of no-idling policies near student pick-up/drop-off areas has been
found to substantially reduce outdoor concentrations of particulate pollution at schools with large
bus fleets (Ryan et al., 2013). Besides being largely diesel-powered, many bus fleets are over 12
years old, and therefore lack the emissions controls of newer buses manufactured since 2007,
when EPA instituted stricter emissions standards for these vehicles("Clean School Bus
Replacement," 2019). Consequently, updating school district bus fleets is another way to reduce
school-based NRAP exposures. Such efforts would be particularly beneficial for the school bus
commuters who generally ride these buses up to two hours daily.

Ecological Momentary Assessment and Personal Exposure Monitoring:
The next frontier of air pollution health effects research

Paper 4 (Warren & Pentz, 2018) describes a pilot study assessing the feasibility and acceptability
of using a smartphone-based ecological momentary assessment (EMA) paradigm to collect data
on key components of children’s executive function via performance-based inhibitory control
and working memory tasks as well as its momentary behavioral determinants. While further
work is clearly needed to establish the validity of this approach for more naturalistic EF
assessment among school-age children, it clearly holds promise. EMA is increasingly being
utilized by researchers for ambulatory assessment of a wide variety of health behaviors and
psychological constructs. When well-implemented, important advantages of EMA include
greater external validity of research findings, given that data are collected in real-time, and
within the real-world microenvironments of interest. By evaluating behaviors and psychological

constructs as they occur, through “pinging” participants at particular times during the day, it is
asserted that EMA can reduce measurement error induced by recall biases—a major threat to
validity (Shiffman, Stone, & Hufford, 2008).

EMA approaches are also better equipped to examine the momentary antecedents of behaviors or
psychological constructs of interest in the real-world contexts in which they occur given the
intensive, repeated nature of measurement. Beyond executive functioning, constructs relevant
to the present project which have been assessed in EMA studies include physical activity
(Dunton, Liao, Kawabata, & Intille, 2012), sedentary behavior (Liao, Intille, & Dunton, 2015),
eating behavior (Thomas, Doshi, Crosby, & Lowe, 2011)), smoking (Stennett, Krebs, Liao,
Richie, & Muscat, 2018) (an important covariate for evaluating the health effects of air
pollution), perceived stress, as well as other psychological outcomes and affective states (Yang,
Ryu, & Choi, 2019). Given the ongoing, growing popularity of smart-phones across all
sociodemographic strata as well as their growing computational power, EMA holds tremendous
promise as an environmental research method for examining both the neurodevelopmental and
obesogenic effects of near-roadway air pollution, as well as psychosocial factors that may
modify such effects (e.g. stress).

Human air pollution exposure assessment is a challenging endeavor due to its many
heterogenous sources, spatiotemporal variability, and complex interactions between the
microenvironments in which exposure occurs and human systems (Steinle, Reis, & Sabel, 2013).
Traditionally, regional air pollutants have been modeled by leveraging data from stationary air
monitoring stations, such as the state-wide air monitoring network coordinated by the California

Air Resources Board. Then statistical models can be applied to create interpolated exposure
surfaces via methods like Empirical Bayesian kriging, or inverse distance weighting, which can
be in tern be applied to estimate exposures at given geocoded street addresses, (i.e. sets of
longitude, latitude coordinates). This is how the mean annual concentrations of regional ambient
PM2.5, PM10, NO2, and O3 were assigned to participant residences in the present study. In an
effort to reduce measurement error, some well-resourced studies have incorporated direct
monitoring of key air pollutants to their exposure assessment protocols. For example, a recent
school-based Spanish study of obesogenic effects of air pollution measured PM2.5, elemental
carbon, ultrafine PM and NO2 on the premises of participating schools, via high-volume
sampling, aerosol monitoring and passive dosimetry, respectively (de Bont et al., 2019).

Another air pollution assessment modality that is becoming increasingly relevant is satellite-
based-remote sensing. For example, the TROPOMI instrument, which was launched in
collaboration with the European Space Agency in 2017 as part of their SENTINEL-4 initiative,
provides estimates of ground-level NO2 and O3 concentrations at a spatial resolution of 3.5km X
7km every 16 days. While the timing of TROPOMI’s launch rendered it useless for the present
study, which was carried out from 2009-2011, it will serve as a valuable resource for subsequent
studies aiming to characterize exposures to common regional air pollutant—particularly in areas
that lack fixed monitoring stations. A related project, currently being undertaken by NASA, is
known as Tropospheric Emissions: Monitoring of Pollution (TEMPO), which will be the first
space-based instrument to monitor major air pollutants across the North American continent
every daylight hour at high spatial resolution. It is estimated to launch in the early 2020s, and
when it does, it will permit estimation of hourly ground-level NO2, O3, and PM concentrations at

a 2km X 5km resolution. When online, these data will provide a valuable, standardized resource
for researchers seeking to comprehensively characterize air pollution exposures at a population
level. Given that its products will be publicly-accessible in near real-time, TEMPO will also
help improve air quality forecasting and associated exposure reduction-efforts.

In contrast to the aforementioned stationary monitors and remote-sensing modalities, in health
effects studies, exposure to near-roadway pollutants is perhaps most frequently characterized
using roadway air dispersion modeling—as was done in the CALINE-4-based modeling
performed for the present study ("User's Guide for CL4: A user-friendly interface for the
CALINE4 model for transportation project impact assessments," 1998). Such models calculate
concentrations of key air pollutants (e.g. NOX, elemental carbon, CO, PM2.5) in the vicinity of a
highway or arterial roadway by considering them as line sources—and taking into account
important local roadway (e.g. roadway volume, truck mix, vehicle speeds), topographic (e.g.
canyons, mountains), and meteorological characteristics (e.g. temperature, wind) that impact
their concentrations at a given point in space.

However, even the most sophisticated air pollution exposure models rely on assumptions that an
individual’s relevant exposure history occurs at one or more fixed locations (i.e. home and
school), when in fact people spend their lives in a more diverse set of microenvironmental
contexts, which can vary dramatically in terms of air pollutant concentrations. Moreover, as
reviewed earlier, even if outdoor ambient exposure concentrations are assigned with no error to a
given street address, there are many structural and behavioral factors that influence the actual
uptake dose of study participants. Not only are average levels of exposure to near-roadway air

pollution likely to differ across time and space for a given individual, but the relative
concentrations of NRAP components may also be heterogenous and have different health effects.
In the present study, we used modeled NOX as an indicator of exposure to the near-roadway
plume, due to its high correlations with other traffic-related pollutants. However, it is likely that
the magnitude of these correlations vary spatiotemporally, thereby rendering NOX an imperfect
proxy for other near-roadway pollutants known to have adverse health effects (e.g. PM2.5,
Ultrafine PM) and resulting in further measurement error.

In order to address the aforementioned limitations in air pollution exposure assessment stemming
from modeled, and/or site-based exposures, health effects researchers are increasingly seeking to
integrate personal exposure assessment into their assessment protocols. These approaches
incorporate direct monitoring of real-time pollutant concentrations—and can therefore provide
more accurate estimates of the true exposures to specific monitored pollutants during the time
they are wearing the monitor. If the participant is monitored during a representative period with
respect to their exposure-related behaviors and microenvironments, such estimates can be
excellent proxies for chronic outdoor and indoor exposures as well. For this reason, personal
monitoring is generally considered to be the gold-standard for air pollution exposure assessment
(Larkin & Hystad, 2017).

Unfortunately, the high cost of personal air pollution monitoring devices and the logistical
constraints involved in integrating them into epidemiological studies have limited their use.
However, this is likely to be changing in light of the rapid technological advances being made
with respect to wearable sensors, many of which are relatively small and unobtrusive and can

interface with smartphones (e.g via Bluetooth) (Wang & Brauer, 2014). Given that smartphones
are already being carried by a large majority of the public, spanning all socioeconomic strata,
this has the potential to lower the burden of adherence to assessment protocols that previously
required carrying both an additional sensor and hub. This has the potential to render such
personal exposure assessments more feasible and the resulting data of higher quality.

Importantly, the cost of personal air pollution sensors has also been dropping, with numerous
sensors currently available for <$300 and dozens currently under development on product-
development/fundraising websites like Kickstarter (Thompson, 2016). To help guide consumers
and so-called “citizen-scientists”, the South Coast Air Quality Management District has
developed an Air Quality Sensor Performance Evaluation Center (AQ-SPEC) program, which
evaluates the performance of these new “low-cost” sensors under ambient (field) and controlled
(laboratory) conditions as they become available . The stated goal of this program is to “catalyze
the successful evolution, development, and use of sensor technology”. There are currently 12
available PM sensors and 4 available gaseous sensors (i.e. NO2, O3, CO, SO2) which have been
validated via both field- and laboratory testing (R
2
=.87-1.00) ("Air Quality Sensor Performance
Evaluation Center (AQ-SPEC) "). As utilization of these sensors increases, both through studies
and their use by the general public, many have proposed that the resulting data could augment
pre-existing fixed-site air pollution monitoring networks. To wit, platforms already exist online
(e.g. www.aircasting.org) where data from certain personal sensors (e.g. the Airbeam) are
automatically uploaded and made available for real-time air quality data at times and in locations
that are currently not actively monitored. Similarly, a recent project in the Netherlands found
that over 6000 measurements collected by citizen scientists via a low‐cost, optical add‐on for

smartphones with a corresponding app could be used to derive aerosol optical thickness maps
that were successfully spatiotemporally validated against satellite imagery and ground‐based
precision photometry (Snik et al., 2014).

Another example of recent health effects research integrating “crowdsourced” smartphone-based
personal air pollution exposure assessment is the Prospective Urban and Rural Epidemiological
(PURE)-Air Study ("PURE-AIR: A Global Assessment of Air Pollution and Cardiopulmonary
Disease,"). This project aims to characterize the effects of air pollution on cardiopulmonary
health in a large, diverse global sample, using relatively low-cost filter-based Ultrasonic Personal
Air Samplers (Volckens et al., 2017) to develop exposure models for a planned cohort of
approximately 200,000 participants. However, given that this project is being implemented in
many rural, low/middle income countries, rather than linking the samplers to a participants’
personal smartphones, they are instead linked to smartphones carried by local field staff, who can
then upload the data to secure servers for further processing and analysis. These recent
advancements in sensor technology, along with the growing popularity of the “quantified self”
movement (Hoy, 2016) (wherein adherents seek to empirically assess their own exposures and
health-related behavior) suggests that personal air pollution exposure monitoring will likely
continue to increase, to the mutual benefits of researchers and the general public.

While clearly EMA and smartphone-linked personal air pollution monitoring technologies each
have the potential to strengthen future studies of the health effects of near-roadway air pollution,
they may be particularly powerful when combined. For example, as outlined in (Steinle et al.,
2013) it is now possible to imagine a future where time-activity patterns and air pollution are

both personally monitored via smartphone-based platforms and then transformed via GIS to
create context-specific, population-level exposure assessments in near-real time (Figure 1.8).

Figure 1.8. Conceptual model for the assessment of individual and population-wide exposure to
air pollution including health effects and context (Adapted from Steinle et al (2013))

Such exposure data can be easily aggregated and linked to health outcomes at the census
geometry level (e.g. census block-group, tract) via population-based health surveys (i.e.
NHANES, ACS). Even more promisingly, exposure, outcome, and covariate data outcome data
could be assessed directly via a combination of smartphone-based sensors, EMA, and other more
traditional, structured survey-based assessments. Such a migration towards studies where
smartphones are used as primary assessment modalities is consistent with recent efforts by NIH
leadership to establish a participant technology systems center ("Participant Technology
32

Systems Center," 2019) that aims to facilitate the integration of these assessment technologies
into their precision medicine cohort initiative (Collins & Varmus, 2015). In this initiative, NIH
Director Francis Collins envisions a longitudinal “cohort” of over a million US resident, who
will consent to extensive characterization of biologic specimens (i.e. cell populations, proteins,
metabolites, RNA, and DNA — including whole-genome sequencing, when costs permit) as well
as behavioral and other data collected via smartphone, which in turn would be linked to their
electronic health records. However, the existence of such a cohort remains hypothetical,
although it would be a tremendous resource for examining the health effects of air pollution were
it to come to fruition.

While such efforts have not yet addressed the primary health outcomes of interest in the present
set of studies (i.e. childhood obesity, executive function), considerable work is ongoing in the
field of asthma. For example, a recent report described the Biomedical REAl-Time Health
Evaluation (BREATHE) platform, which incorporates sensor-based assessment of physical
activity, sedentary behavior, and geographic location, with momentary questionnaire-based
assessment of contextual information and air pollution exposures (Buonocore, Rocchio, Roman,
King, & Sarrafzadeh, 2017). The entire BREATHE platform is pictured in Figure 1.9, and is
comprised of two air pollution sensors linked to a smartphone, smart-watch, and spirometer.

33

Figure 1.9. Biomedical REAl-Time Health Evaluation (BREATHE) platform

Moreover, through real-time personal monitoring of air pollution exposure, physical activity,
heart-rate, time-activity patterns, and lung function, the BREATHE platform aims to detect both
environmental and physiological antecedents of asthma exacerbations and administer context-
dependent EMA prompts to collect additional information about the most relevant triggers. As
described by (Buonocore et al., 2017) there are numerous predefined situations that trigger EMA
prompts, which include increasing levels of sensor-detected PM2.5, dust density, elevated heart
rate, and accelerometry-measured energy expenditure. Additionally, a random forest machine
learning classifier is then utilized to combine sensor and EMA data, with publicly available
traffic, weather, and air quality monitoring information to provide a constantly updated estimate
of a child’s minute-by-minute risk of experiencing an asthma attack. Importantly, the
34

BREATHE platform also contains a user dashboard, which permits direct monitoring of sensor
data by patients, caregivers, and physicians so that they can work together to make more
informed inferences regarding the impact of recent exposures on their asthma status.

It is easy to imagine how the integrated technologies of personal air pollution monitoring, EMA
and smartphone-linked sensors could be leveraged by future research to better understand the
obesogenic and/or neurodevelopmental effects of near roadway air pollution. For example, a
considerable literature has documented the advantages of accelerometry and other sensor-based
approaches for assessment of physical activity (Dunton, Liao, Intille, Spruijt-Metz, & Pentz,
2011; McClain & Tudor-Locke, 2009), a key covariate to consider in health effects studies of
both outcomes. Given that numerous health preventive and risk behaviors have also been shown
to be validly-and reliably-assessed via sensor-linked EMA (Goldstein et al., 2018), it may also be
possible to examine the interrelationships of such behaviors across space and time and
incorporate these data into models aiming to better understand if and how these patterned
behaviors correlate or interact with the adverse health effects of NRAP. At the same time, a
major advantage of incorporation of personal air pollution monitoring would be its
corresponding reduction in exposure measurement error. However, by providing fine-grained
spatiotemporal information about air pollution exposure, as well as permitting more frequent
outcome and covariate assessment, it would also allow researchers to better understand the acute
effects of air pollution in real-world contexts.

For example, while acute effects studies have examined the effects of air pollution on a variety
of cardiopulmonary outcomes (i.e. lung function (Yoda et al., 2017), stroke (R. Zhang et al.,
35

2018)), myocardial infarction (Nawrot, Perez, Künzli, Munters, & Nemery, 2011) mortality (Mo
et al., 2018; Qian et al., 2010) and health-care utilization (i.e. outpatient visits (Mo et al., 2018)),
hospitalizations (Huang et al., 2016) remarkably little is known about its acute effects on
cognitive outcomes. Previous human studies have examined the short term cognitive effects of a
variety of transient environmental factors, including heat stress, cold stress (Muller et al., 2012),
and hypoxia (de Aquino Lemos et al., 2012; Virués-Ortega, Buela-Casal, Garrido, & Alcázar,
2004), finding that passive exposures to each can have detrimental effects on both simple and
complex cognitive tasks. One double-blind randomized crossover EEG study found that
exposing human volunteers to diesel exhaust led to short-term increases in functional activity in
the left frontal cortex both during and immediately after diesel exposure (Crüts et al., 2008).
Subsequent murine studies found that acute exposure to diesel exhaust (250-300 μg/m
3
over a 6
hour period) leads to greater neuroinflammation, oxidative stress, microglia activation, and
impaired hippocampal and subventricular neurogenesis (Costa et al., 2017). However to my
knowledge, only a single study to date has examined acute effects of NRAP on human cognition
outside of the laboratory.

A school-based study, by (Sunyer et al., 2017) administered a computerized attention task (the
child Attention Network Test) to 2687 children, from 39 Barcelona schools. Attention outcomes
were assessed 4 times over a 12 month period, while school-based monitors measured daily
concentrations of NO2 and elemental carbon while children were at school. Previous work found
that these specific pollutants are excellent indicators of near-roadway air pollution in Barcelona
(Reche et al., 2011). This resulted in approximately 10,000 completed ANTs on 177 different
days from January 2012-March 2013, which could be linked to daily exposure data for
36

examination of acute effects. After testing a series of lagged exposure models, the authors found
that children’s reaction times were consistently slower and more variable on days with higher
levels of NRAP—suggesting that such exposures have acute effects on childhood
inattentiveness. Interestingly, no acute effects of NRAP exposure were identified on working
memory, despite previous work in this cohort reporting chronic adverse effects of NRAP
exposure on working memory development across this same 1 year period (Sunyer et al., 2015).
In their discussion, the authors justifiably suggest that assessment of attention—which is likely to
exhibit substantial spatiotemporal fluctuations within and across subjects (Ballard, 1996;
Castellanos et al., 2005)—at 4 time points is insufficient to obtain a comprehensive
understanding of how cognition is modulated by environmental factors (i.e. NRAP). However,
given the well-acknowledged difficulties of implementing repeated assessments within the
constraints of school-based research (e.g. 40 minute classroom sessions requiring computer
access for each student), adding additional assessments is likely to be infeasible in many
contexts.

As suggested by Dissertation Paper 4 (Warren & Pentz, 2018) EMA offers a unique opportunity
to administer a variety of performance-based EF tasks via smartphone, in a more ecologically
valid context than the classroom or computer laboratory-based data collection paradigms
currently utilized. Integration of EMA into future studies also has the potential to reduce burden
on partner school teachers and administrators by reducing the amount of valuable instructional
time dedicated to assessment. Perceptions that school-based research study protocols will take
away instructional time have been repeatedly found to be a major barrier to participation in
prevention research (Girio-Herrera, Ehrlich, Danzi, & La Greca, 2019; Hall et al., 2014).
37

Consequently, efforts to reduce the necessity of using class-time for assessment via technology-
based modalities like EMA hold promise for increasing school participation in future studies and
in turn the generalizability of the resulting samples. Additionally, as mentioned above,
integration of mobile sensor-based air pollution personal monitors can also improve exposure
assessment by measuring exposures in the many microenvironments that students inhabit each
day. The concomitant increases in exposure variability and more granular data would also likely
increase statistical power to detect true effects, which as Sunyer et al (2018) mention, are likely
to be small.

Another exciting aspect of leveraging emerging technologies like personal, smartphone-enabled
air pollution monitoring and EMA is the ability to evaluate the effectiveness of prevention
interventions in real-life environments, in near real-time. For example, many ongoing health
promotion efforts to reduce air pollution exposures rely on issuing air quality alerts (e.g. “Spare
the Air” days, smog days) when pollutant concentrations exceed preestablished thresholds along
with forecasts that warn of upcoming poor air quality. These alerts generally have
accompanying advisories containing behavioral recommendations, both with respect to reducing
pollution generating activities (e.g. burn bans, reduction in use of gasoline-powered vehicles and
yard equipment) as well as personal exposure reduction (e.g. remaining indoors, reducing
outdoor physical activity). However, remarkably little remains known about the effect of these
alerts on reducing the health effects of air pollution exposure (Chen et al., 2018; Mullins &
Bharadwaj, 2015). Studies incorporating the aforementioned assessment technologies (e.g.
personal air pollution monitoring, accelerometry for physical activity detection) could not only
examine whether these alerts are effective in modifying the individual behaviors and/or air
38

pollution exposures, but could also shed light into affective, attitudinal, and/or psychosocial
determinants of such behavior change through the administration of context-dependent EMA
prompts.

Furthermore, given the adverse health impacts of air pollution and the health benefits of physical
activity, particularly during childhood, much remains unknown about the appropriate balance to
strike with public health messaging and school policies regarding physical activity. For
example, it is not uncommon for school-based physical activity (i.e. recess, physical education)
to be reduced or cancelled on days where air quality is poor in order to avoid unnecessary
exposure (Dong et al., 2018). However, the health benefits of physical activity are myriad and
widely acknowledged (Warburton, Nicol, & Bredin, 2006). Similarly, it remains unknown at
what point the health benefits of cycling to work are contraindicated by the elevated levels of
NRAP one is exposed to via the elevated inhalation and particle deposition rates inherent in near-
roadway physical activity at peak traffic times (Tainio et al., 2016). Personal exposure
monitoring and outcome assessment via smartphone-based platforms like the BREATHE
platform have the potential to shed light on these important questions and as such, their
development, validation, and implementation into future work into these pressing public health
issues should be prioritized.

39

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48
CHAPTER 2 – Relationships between near-roadway air pollution
exposure and obesity risk in a sample of Southern California primary
schoolchildren

BACKGROUND

Childhood Obesity and the “Environmental Obesogen” Hypothesis
Childhood overweight and obesity have emerged as a major public health crisis that currently
impacts approximately one-third of US children and adolescents (Fryar, Carroll, Ogden, & CL.,
2018). Childhood metabolic dysregulation often persists into adulthood where it has serious
health consequences, including increased risk of hypertension (Re, 2009), heart disease (Poirier
et al., 2006), many cancers (Bhaskaran et al., 2014), depression (Luppino et al., 2010) and
premature death (Global BMI Mortality Collaboration et al., 2016). Consequently, obesity is a
major cause of preventable death both in the US and globally (Hruby & Hu, 2015). While
national data indicate that pediatric rates may be beginning to stabilize in the US, this is not the
case for other parts of the world—especially Asia, where rates have increased rapidly in recent
years((NCD-RisC), 2017).

To date, a dominant approach toward obesity prevention has been the energy balance paradigm
involving individual-level behaviors, specifically promoting substitution of healthy foods for
highly-caloric low nutrient foods and promoting physical activity in lieu of inactivity (Romieu et
al., 2017). However, in recent years, growing attention has also been paid to environmental
factors, exposure to which may increase childhood obesity risk (Holtcamp, 2012; Kelishadi,
49
Poursafa, & Jamshidi, 2013). It has been proposed that such environmental “obesogens” include
both regional ambient air pollutants monitored under the purview of the Clean Air Act by the
Environmental Protection Agency (i.e. PM, NO2, O3 ) as well as near-roadway air pollution
(McConnell et al., 2016). For example, a recent murine study found that pregnant rats breathing
unfiltered ambient Beijing air for 2 weeks were heavier, more insulin resistant, had higher
cholesterol, triglycerides, and inflammatory biomarkers, than otherwise identical rats breathing
HEPA filtered air (Wei et al., 2016). Remarkably, at 8 weeks of age, the pups of these same
pregnant rats breathing unfiltered Beijing air were 10-18% heavier, with greater inflammation
than their less exposed counterparts. Additional murine studies have clearly shown that PM2.5
and other diesel exhaust particles early in life can increase diet-induced weight gain (Bolton et
al., 2012), and lead to greater systemic and adipose inflammation (Sun et al., 2009; Xu et al.,
2010).

Near Roadway Air Pollution—A Plume of Environmental Obesogens?
Near-roadway air pollution (NRAP) is a complex mixture of particulate and gaseous combustion
products from fresh vehicle emissions, debris from tires and brake wear, and metals from engine
wear (Karner, Eisinger, & Niemeier, 2010), which comprises a distinct chemical mixture from
that found in regional air pollution and may be uniquely deleterious to human health (Zhang &
Batterman, 2013). Recent epidemiological research has linked children’s NRAP exposure to
steeper BMI trajectories across elementary school (Jerrett et al., 2014). Prospective relationships
have been found between prenatal exposure to polycyclic aromatic hydrocarbons (PAH) and
increased obesity risk at age seven (Rundle et al., 2012). Additionally, regional air pollution has
50
been linked to adverse metabolic effects. For example, a 2014 meta- analysis of 10 studies
concluded that evidence supports a prospective association between exposure to criterion air
pollutants and increased risk for type 2 diabetes (Balti, Echouffo-Tcheugui, Yako, & Kengne,
2014). A subsequent study of overweight/obese Latino youth in Los Angeles found greater
exposure to NO2 and PM2.5 during adolescence was linked to higher BMI at age 18 as well as
impairments in insulin sensitivity and B-cell function—increasing risk of type 2 diabetes
(Alderete et al., 2017). Large population-level studies conducted in Denmark provide further
support for the adverse metabolic effects of long-term air pollution exposure to PM2.5 and NO2 at
the residential level—at least among adults (Hansen et al., 2016; Strak et al., 2017).

Despite this compelling evidence of its adverse metabolic consequences, a systematic review of
16 epidemiological studies exploring the impact of ambient air pollution on obesity characterized
the extant evidence as mixed, with the majority of included studies identifying either null
findings or negative associations (An, Ji, Yan, & Guan, 2018). However, it is noteworthy that
both of the reviewed studies identifying negative effects in pediatric populations had substantial
methodological limitations. For example, one such study employed an ecological study design
comparing children living in two Serbian cities, which was unable to take into account potential
individual-level confounders and variability in exposure, (Nikolić, Stanković, Jović, Kocić, &
Bogdanović, 2014) while the other assessed PM10 alone (E. Kim et al., 2016). Furthermore, all
but three of the included studies assessed adverse health effects of regional ambient air
pollutants, or composite measures of overall air quality (i.e. Air Quality Index), in lieu of
estimating concentrations of key pollutants). Among the three included studies assessing
obesogenic effects of NRAP exposure, each identified positive longitudinal associations between
51
greater NRAP exposure and attained BMI in their respective pediatric cohorts (Jerrett et al.,
2010; Jerrett et al., 2014; McConnell et al., 2015). Importantly, all three studies, which
examined the effects of NRAP exposure during childhood among school-aged children, also
received the highest score among included studies on the NIH Quality Assessment Tool for
Observational Cohort and Cross-Sectional Studies, indicative of methodological rigor and strong
internal validity (NIH, 2014). While the obesogenic effects identified in each of these three
studies were invariant to the sex of study participants, other studies have identified significant
effects only in females (Jerrett et al., 2010; Li et al., 2015) —underlying the potential importance
of examining effect modification by sex

A recent study of the Boston-area Project Viva pre-birth cohort, which was not included in the
aforementioned review, incorporated spatiotemporal model-based assessment of
prenatal/perinatal residential PM2.5 and Black Carbon as well as neighborhood traffic-density and
residential roadway proximity. BMI was assessed every 6 months from birth through age 10.
Despite observing effect estimates in the hypothesized direction for covariate-adjusted models
estimating obesogenic effects of traffic density and home roadway proximity (i.e. lower BMI at
birth, higher peak BMI in infancy, and a higher BMI throughout childhood), the authors
concluded that no evidence was found for persistent effects of prenatal exposures to traffic
pollution from birth through mid-childhood in a population exposed to modest levels of air
pollution (Fleisch et al., 2018). Similarly, a recent study of an Italian birth cohort also found no
association between exposure to vehicular traffic (as assessed via total traffic load of all roads in
a 100m residential buffer around the birth address) and numerous obesity-related outcomes
52
assessed at 4 and 8 years of age (Fioravanti et al., 2018). Notably, this study was the first to
examine effects of estimated NRAP exposure on abdominal adiposity (as assessed via waist
circumference), in addition to BMI and blood lipids, which have been used in previous work.

There are numerous pathways through which NRAP has been hypothesized to increase risk for
childhood obesity both directly and indirectly. Such putative mechanisms include the promotion
of systemic inflammation, increased stress, endocrine disruption (Grün & Blumberg, 2009),
reduction of physical activity, increasing sedentary behavior, increased consumption of high-
calorie/low nutrient “fast” foods and trans-fats (Chen et al., 2019) and increased risk of chronic
comorbidities associated with obesity (e.g. asthma (McConnell et al., 2010). See (Jerrett et al.,
2014; McConnell et al., 2016) for more detailed reviews. However, US studies to date have not
been able to adequately characterize these putative mediators of NRAP’s obesogenic effects—
particularly obesity-related behaviors of activity and eating, given that they have mostly
leveraged preexisting cohorts originally established to examine the effects of NRAP on
respiratory outcomes (Jerrett et al., 2010; McConnell et al., 2015).

School-based Studies and Proposed Obesogenic Pathways
Another recent study of the obesogenic effects of ambient air pollution examined cross-sectional
associations between NRAP exposure and childhood obesity outcomes among a large sample of
7-10 year old Barcelona school-children. This study is noteworthy as it was the first to account
for both school- and home-based air pollution exposures via both direct monitoring and land use
regression methods (de Bont et al., 2019). School-based exposures are important since school-
aged children spend roughly one-quarter to one-third of their time at school and school hours
53
coincide with the times of day where traffic volumes and ambient air pollution concentrations are
highest (Mazaheri et al., 2014) (Pañella et al., 2017). Furthermore, students engage in physical
activity during school hours (e.g. PE and recess), both of which generally occur outside and
result in increased ventilation and inspiratory flow rates—thereby increasing inhaled doses of air
pollution (Pasalic, Hayat, & Greenwald, 2016). This study found that children exposed to high
levels of UFP at school had greater odds of being overweight/obese than children exposed to low
levels, accounting for key sociodemographic characteristics (e.g. SES, maternal smoking,
exposure to environmental tobacco smoke, and physical activity). They also found that children
exposed to higher doses of NO2, PM2.5 and EC at school were more likely to be
overweight/obese, but in a non-linear fashion (i.e. only at moderate levels of exposure).

Clearly, much remains unknown about the putative effects of NRAP exposure on childhood
obesity in the US context, particularly with respect to abdominal adiposity and the effects of
school-based exposures. Consequently, the present study tested the hypothesis that greater
combined residential and school-based exposure to near-roadway air pollution is associated with
greater risk of overweight/obesity as well as steeper gains in BMI and waist circumference
across early adolescence. Relationships between air pollution exposure and key obesity-related
behaviors, including multiple types of physical activity, sedentary behavior, and eating behavior
were examined as they may also contribute to the obesogenic effects of NRAP exposure. By
characterizing the effects of NRAP exposure on obesity-related behavior and anthropometric
outcomes alike, this study aims to inform future obesity prevention efforts—in particular the
54
extent to which they may benefit from broadening their scope beyond the energy balance
paradigm to address NRAP as an environmental obesogen.

METHODS

This study is a secondary analysis of data drawn from the fourth grade semester 1 (baseline),
fourth grade semester 2, fifth grade semester 2, and sixth grade semester 2 waves of assessment
in Pathways to Health, a cluster randomized control trial for the prevention of childhood obesity
and substance use. This study was carried out in 28 Southern California public elementary
schools that were selected for study participation based on racial/ethnic diversity and low
income. Participating elementary schools had an average of one 4th grade class per school. All
human subjects procedures were approved by the University of Southern California Institutional
Review Board.

Participants were a longitudinal cohort of 1005 students with both full active parental consent
and self-assent to participate in the study from 4
th
through 6
th
grade with four waves of
measurement. Of these, 709 had complete data at all four waves, and 475 of the 709 completed a
Student Information Sheet reporting residential address information. To assign near-roadway
and regional air pollution exposures, 2009 residential and school addresses were geocoded using
ArcGIS World Geocoder. Geographic distances between each child’s school and residence were
also calculated. Geocode quality was high (>90% matched to building centroid). Participants
resided in San Bernardino, Orange, and Los Angeles counties.
55
Near-roadway Air Pollution Exposure
Estimated average NOX exposures from local on-road motor vehicle emissions were assigned to
geocoded residential and school locations for the year 2009 using CALINE4 line-source
dispersion models. These estimates represent the incremental contribution of local traffic above
and beyond background NOX concentrations, which include both primary and secondary
pollution and regional atmospheric photochemistry. Link-based traffic was based on Caltrans +
ESRI Premium StreetMap traffic count and roadway geometry. Vehicle emission factors as a
function of speed and LDV/HDT fleet mix were determined from CARB's EMFAC2014 model
for 2009. Vehicle NOX exhaust emissions were modeled from local on-road traffic/roads within
5 km of residences and schools. Link-specific exhaust emission factors based on average vehicle
speed and HD truck fraction were applied using Caltrans post-mile truck count data by year.
Day-of week and hour of day volume adjustments from WIM data Freeway (FCC1) and Non-
Freeway (FCC2-FCC4) road classes were also applied. Regarding meteorology, surface wind
data from the closest AQS monitor was used. Monthly wind speed and direction frequencies
were determined from 1997-2014 surface wind data. Mixing heights [morning, afternoon, and
nighttime climatological values (200m, 400m, 100m)], and atmospheric stability based on time
of day and wind speed were also incorporated into the model.
Exposures for freeway and non-freeway sources were assigned to participants’ baseline
residential and school addresses. Freeway and non-freeway Total NOX exposure was estimated
by adding freeway and non-freeway estimates for each participant. Residential and school-based
Total NOX exposures were combined using both a 79% residential/ 21% school ratio as well as
an 84% residential/16% school ratio, as in previous work (McConnell et al., 2010). These ratios
were calculated to approximate the relative proportion of time students spent in each location.
56
Previous work have shown that failing to incorporate residential and school-based NOX
exposures results in a systematic underestimation of health effects by 11-14% compared to
models incorporating both exposure venues (Ragettli et al., 2015). The author also considered
attempting to model NOX exposure along likely commuting routes to- and from school, however,
no data were collected regarding the specific routes taken by students, only the typical
commuting modality. Therefore, such exposure assignments were deemed to entail an
unacceptable amount of measurement error. Furthermore, recent data suggest that even when
commuting exposures are appropriately modeled, their contribution to total weekly exposures are
minimal—comprising approximately 3% of total NRAP exposure (Ragettli et al., 2015)
assuming a roughly 15 minute commute. Given that most students resided near their school
(median distance=1.1km; IQR=1.5km), it was assumed that most home-school commutes would
be 15 minutes or less within the present analytic sample.

Regional Air Pollution Exposure
Regional air pollution exposures for years 2000-2009 were modeled and retrospectively assigned
to participant residential addresses as most students resided within 1.5 km of school. This range
approximates the lifetime exposures of cohort members, 98.73% of whom were ages 9 or 10 at
study baseline. Monthly average 24-hour concentrations were calculated for NO2, O3, PM2.5,
PM10. Values were estimated from monthly air quality data measured at 4 closest locations
within 50 km of the grid point by inverse distance squared interpolation. In 2009, regional PM2.5,
PM10, and NO2 were highly correlated (r>.9) whereas O3 was less strongly correlated with PM2.5
(r=.23), PM10 (r=.36), and NO2 (r=.04). Similar patterns were observed at other years.
Consequently, only 2009 PM2.5 and O3 were included as covariates in final adjusted models.
57
Outcomes
There were two primary childhood obesity outcomes measured by the present study: BMI and
waist circumference. Participant height, weight, and waist circumference were also assessed by
two trained data collectors at each of the four in-school assessment waves. Each measurement
was averaged at each time point per CDC protocol to reduce measurement error (National Health
and Nutrition Examination Survey (NHANES): Anthropometry Procedures Manual, 2007).
BMI was calculated using participant height and weight measured twice at each assessment
wave. Height and weight anthropometric data was converted to BMI (kg/m2) percentile with
Center for Disease Control (CDC) reference charts of BMI percentile-for-age-and-sex. BMI z-
scores were also calculated using the CDC age-and sex-specific growth charts ("Clinical Growth
Charts,"). For analyses of dichotomous outcomes, children at or above the 85
th
percentile of
age/sex-specific BMI were classified as overweight, while children at or above the 95
th

percentile of age/sex-specific BMI were classified as obese. Two outliers were excluded from
BMI analysis due to implausibly high BMI values for their waist circumference, (e.g. BMIs of 77
and 145). The same criteria were utilized for categorization of overweight and obesity using the
waist circumference outcomes, which were also measured twice at each assessment wave using
standardized protocols (National Health and Nutrition Examination Survey
(NHANES): Anthropometry Procedures Manual, 2007).

58
Obesity-related Behavioral Covariates
The following obesity-related behaviors were assessed as part of a 145-item self-report survey
administered to children during four 45 minute in-school assessment periods from 4
th
grade
through 6
th
grade (Shin et al., 2014). Previous research has found each of the following energy
balance behavioral constructs to contribute to childhood obesity (Romieu et al., 2017). The
survey was administered verbally by a trained data collector during each session.

Physical Activity Inside- and Outside-of-School Multiple aspects of physical activity were
assessed in the present study at 4
th
, 5
th
, and 6
th
grades using adapted versions of the Physical
Activity Questionnaire for Older Children (PAQ-C) (Crocker, Bailey, Faulkner, Kowalski, &
McGrath, 1997) and the Self-Administered Physical Activity Checklist (Sallis et al., 1996). Both
scales have been shown to accurately and reliably assess physical activity among elementary
school-children as early as 3
rd
grade (Bailey, McKay, Mirwald, Crocker, & Faulkner, 1999;
Sallis et al., 1996). Each has established internal consistency and validity (Janz, Lutuchy,
Wenthe, & Levy, 2008), including external validations established through comparison with
teacher observations, motion, 7-day recall, and leisure time activities (Crocker et al., 1997; Sallis
et al., 1996).

Moderate/Vigorous Physical activity was assessed at grades 5 and 6 by the NutritionQuest
Block Kids Physical Activity Screener (Drahovzal, Bennett, Campagne, Vallis, & Block, 2003).
Designed for children ages 8-17, this instrument asks about frequency and duration of physical
activities in the past 7 days. Its nine items address both leisure and school-time activities
including sports and active household chores. It also asks about amount of time per day spent
59
watching television, playing video games and using the internet. Average daily moderate and
vigorous activity minutes were calculated for inclusion as a covariate. The instrument was
administered at 5
th
and 6
th
grades and the mean of both administrations was calculated for
inclusion in adjusted models.

Sedentary Behavior was assessed using the following items: “On a regular school day, how
many hours per day do you (a) usually watch TV or video movies at home or away from school,
(b) spend on a computer at home or away from school and (c) play video games that you sit
down to play like PlayStation, Xbox, GameBoy, or arcade games. Responses ranged from 0 (“I
don’t watch TV”) to 6 (“6 or more hours”). As in previous work, a composite sedentary behavior
score was created by taking the mean across these three items (Hoelscher, Day, Kelder, & Ward,
2003; Huh et al., 2011).

To assess child High-Calorie/Low-Nutrient (HCLN) food intake, five items were taken from a
validated open-source food frequency questionnaire (Willett et al., 1985) that has been used
successfully in previous studies (Nguyen-Michel, Unger, & Spruijt-Metz, 2007; Pentz, Spruijt-
Metz, Chou, & Riggs, 2011; Riggs, Spruijt-Metz, Sakuma, Chou, & Pentz, 2010); and has been
validated for fourth grade children (Field et al., 1999). The items assessed consumption of
French fries, chips, doughnuts, candy, and non-diet soda (e.g., “How often do you eat corn chips,
potato chips, popcorn, crackers?”). Two additional items assessed the frequency of fruit intake
over the past week (e.g., How often did you eat any fruit, fresh, or canned?), and four assessed
the frequency of vegetable intake (e.g., How often do you eat green salad?). Response choices
were as follows: 1 = Less than once a week, 2 = Once a week, 3 = 2-3 times a week, 4 = 4-6
60
times a week, 5 = Once a day, 6 = 2 or more of these a day. Internal reliability for snack food
items was acceptable (4
th
grade, α = .80; 5
th
grade, α = .79; 6
th
grade, α = .81).

Individual Demographic Characteristics
Self-reported demographic characteristics were also obtained via survey at each of the 4
assessments. These data included participant age, gender, race/ethnicity, and free school lunch
eligibility (a proxy for socioeconomic status), variables which have been shown to be associated
with child risk for obesity.

Community-level Covariates

Neighborhood-level Socioeconomic Deprivation
As an additional
measure of
community-level
socioeconomic
privation, which has
been linked to both
childhood obesity
and NRAP exposure,
an Area Deprivation
Index (ADI) was
assigned to each
Census Block Group Components
Factor Score
Coefficients
Percent of the block group’s population aged ≥ 25 years with < 9 years of education 0.0849
Percent aged ≥ 25 years with greater than or equal to a high school diploma −0.0970
Percent of employed persons ≥16 years of age in white-collar occupations −0.0874
Median family income −0.0977
Income disparity
†
0.0936
Median home value −0.0688
Median gross rent −0.0781
Median monthly mortgage −0.0770
Percent owner-occupied housing units (home ownership rate) −0.0615
Percent of civilian labor force population ≥ 16 years of age unemployed 0.0806
Percent of families below the poverty level 0.0977
Percent of population below 150% of the poverty threshold 0.1037
Percent of single-parent households with children < 18 years of age 0.0719
Percent of occupied housing units without a motor vehicle 0.0694
Percent of occupied housing units without a telephone 0.0877
Percent of occupied housing units without complete plumbing (log) 0.051
Percent of occupied housing units with more than one person per room (crowding) 0.0556
† Income disparity defined as the log of 100*ratio of the # of households with <$10,000 income
to the # of households with >$50,000 income.
Components and factor score coefficients drawn from Singh (2013)
All coefficients are multiplied by −1 such that higher ADI = higher disadvantage.
Figure 2.1. Neighborhood-level socioeconomic deprivation
61
participant (Singh, 2003). The ADI was developed by the Health Resources & Services
Administration (HRSA) and has been refined and validated as a composite index of
neighborhood-level deprivation calculated using American Communities Survey 5 year estimates
corresponding to the current study period ("Area Deprivation Index,"). The items and
corresponding factor scores utilized to calculate the composite index corresponding to the
present
study period (2009-2011) are provided here. For consistency with previous work, variables were
created which reflecting the state-specific deciles and national percentiles of each Block Group
(Kind et al., 2014)). The median ADI decile among study participants was 4 (IQR=3) although
student residential block group-level ADI deciles ranged from 1-10.

Environmental Vegetation Index
Previous research suggests that NRAP exposure can be associated with aspects of the built
environment which demonstrate substantial, non-random spatial variability. Since the urban and
suburban Southern California environments in which study participants generally reside are
heavily landscaped, vegetation density is one aspect of the built environment which previous
work indicates may reduce air pollution through both direct and indirect pathways. The presence
of green vegetation, which may be an important covariate given its plausible associations with
both NRAP exposure and obesity outcomes (through behavioral pathways) (Lachowycz & Jones,
2011) can be detected via remote sensing methods, most notably satellite imagery from NASA’s
Moderate Resolution Imaging Spectroradiometer (MODIS) (Huete, Miura, Rodriguez, & Gao,
2002). The MODIS instrument is installed on two earth-viewing satellites, Terra and Aqua,
62
which orbit the Earth in a complementary manner, ensuring that the entire earth’s surface is
imaged every 24-48 hours. For years, the principal method for estimating vegetation density via
remote sensing was calculation of the Normalized Difference Vegetation Index (NDVI). This
measure is based upon the principal that chlorophyll absorbs light from the visible spectrum (i.e.
0.4 to 0.7 µm), while the other leaf cells reflect near-infrared light (i.e. 0.7 to 1.1 µm).
Consequently, the relatively density of vegetation in a given satellite image can be approximated
by calculating the satellite-measured near-infrared radiation minus visible radiation (assessed by
the RED band of the MODIS) divided by near-infrared radiation plus visible radiation (NDVI =
(NIR — RED)/(NIR + RED). In recent years, an alternative measure, known as the Enhanced
Vegetation Index (EVI) has been developed, which improves upon some well-acknowledged
limitations of the NDVI by taking into account an additional MODIS band (Wang, Ma, & Sun,
2014).

It is important to acknowledge that NDVI was only designed to detect living vegetation and
differentiate it from other material (i.e. rocks, soil, dead vegetation, building). As such,
compared to NDVI, the EVI is more sensitive to differences in important vegetation
characteristics like canopy structure and density, as well as seasonal variation and stress
compared to NDVI which simply assesses the amount of chlorophyll present. Importantly, the
EVI also reduces the variability of vegetation density estimates due to differences in atmospheric
condition (including concentrations of regional air pollution (i.e. PM) –and has been shown to
outperform NDVI in urban environments (Huete, Miura, Rodriguez, & Gao, 2002). Another
key advantage of EVI is that its estimates are not dependent on the time of day when the satellite
63
images were captured, because—unlike NDVI—it is able to account for changes in the angle at
which the sun shines on the earth’s surface (i.e. the “solar incidence angle”). Consequently, EVI
was used in the present study as an estimate of the green vegetation present in the area proximal
to each participant’s residence. NASA produces aggregate EVI estimates every 16 days at a
spatial resolution of 250m X 250m. To account for monthly, seasonal and annual variations in
vegetation, these estimates were averaged over the entire study period, which lasted from fall
2009 to spring 2011. Such averaging is believed to reduce measurement error stemming from the
relatively high variability in precipitation in Southern California relative to the rest of the
country. For example, between 2006-2011 the annual precipitation measured in Downtown Los
Angeles ranged from 3.2 inches in the 2006-2007 water year to 20.2 inches in the 2010-2011
water year—a >500% change in annual precipitation.

Environmental Noise Exposure
Residential modeled noise exposure was also tested as a covariate owing to a growing body of
evidence linking it to a variety of adverse health consequences (Hammer, Swinburn, & Neitzel,
2014), as well as the fact that traffic noise is a leading source of noise in urban environments
(Hansell, Cai, & Gulliver, 2017) . In the present study, noise pollution exposure was estimated at
the residential level via assignment of Soundscores™ (Soundscore, 2019). These estimates are
determined by a combination of the following three key contributors to noise pollution in the
urban environment: vehicle traffic, air traffic and local sources (which include establishments
such as bars, restaurants, and stadiums). Soundscore™ developers apply the Federal Highway
Authority’s Traffic Noise Model for estimation of traffic noise—which is the greatest contributor
to residential noise estimates of the three factors overall, and particularly in the present dataset
64
given that the analytic sample did not reside near major metropolitan airports. Their proprietary
model also incorporates airport noise and other local sources. As anticipated, owing to their
common causes, residential NRAP exposure and noise pollution (per Soundscore™ estimates),
were highly correlated (r=.65).

Statistical Methods
A generalized linear mixed effects modeling approach was utilized in the present longitudinal
analysis via the Stata 14 Mixed command. For each obesity-related outcome and air pollution
exposure of interest, the following set of models was fit. First, unadjusted models were
specified, followed by models adjusting for the following basic socio-demographics: gender, age,
intervention condition, White race/ethnicity, Black race/ethnicity, Hispanic race/ethnicity, Asian
race/ethnicity, and residential area deprivation index (national ADI percentile). Additional
models also tested the impact of the following continuous covariates: residential noise pollution,
neighborhood green, executive function deficits, and obesity-related behavior. One-sided p
values were utilized for all tests of obesogenic effects, per a priori directional hypotheses.
Baseline differences in normal vs. overweight/obese weight status were assessed using multilevel
generalized linear effects modeling, where a logit link function was used to model each
dichotomous outcome and cluster robust standard errors accounted for school-level clustering.
Random intercepts were included in each longitudinal model at the individual level to account
for baseline BMI and waist circumference. Random intercepts were included at the school level
to account for between-school variability in baseline BMI and waist circumference. Random
coefficients for time were also included such that unique growth curves were fit for each
participant. Although, previous work did not identify confounding of associations between air
65
pollution exposure and childhood BMI growth by school-level variables (Jerrett et al., 2014),
random school-level coefficients were nonetheless tested via likelihood-ratio tests in all models,
but neither substantially influenced estimates nor provided a significant improvement in model
fit over the models with random intercepts alone.

RESULTS

Sample Characteristics and Representativeness
When compared by obesity status and all tested covariates, the 475 students with complete
address information were significantly more likely to be White, and less likely to be Hispanic
than the remaining 234 participants whose parents did not report residential address data. The
two groups did not differ by weight status or any other assessed covariates (Table 2.1). The
mean age of children at the first study assessment was 9.27 years (SD=.47). Zero-order
correlations at study baseline between all key study variables are reported in Supplemental Table
1

66
Table 2.1. Comparing outcome and covariate values between final analytic sample and
participants lacking residential address data
Dichotomous Baseline
Variable
N=709
Longitudinal
Sample
Participants
with geocoded
home address
(N=475)
%
Participants
with no home
address
provided
(N=234)
%
Two-
sided
P Value
(N=475 vs.
N=234)
N=212
Control
Group
%
Male gender 49.9 48.4 53.0 .25 45.3
Free lunch eligibility 20.9 19.4 23.9 .16 24.5
White race/ethnicity 32.9 35.8 26.9 .02 39.5
Hispanic race/ethnicity 25.7 22.1 32.9 .02 21.3
Black race/ethnicity 2.1 1.7 3.0 .26 1.0
Asian race/ethnicity 7.9 9.3 5.1 .06 10.4
Program vs. Control 53.2 55.4 48.7 .10 100
Overweight BMI 17.5 18.0 16.4 .60 15.2
Obese BMI 22.9 21.7 25.2 .30 23.0
Continuous Baseline
Variable
N=709
Longitudinal
Sample
Mean (SD) Mean (SD)
Two-
sided
P Value
(N=475 vs.
N=234)
N=212
Control
Group
%

Age 9.3 (.5) 9.3 (.5) 9.3 (.5) .22 9.3 (.5)
Out-of-School Physical
Activity
3.1 (1.1) 3.1 (1.2) 3.1 (1.1) .57 3.1 (1.2)
MVPA (5
th
and 6
th
) 100.7 (89.2) 100.3 (86.5) 101.4 (94.8) .88 98.4 (83.9)
Fruit and Vegetable Intake 2.9 (.9) 2.9 (.9) 2.8 (.9) .22 2.9 (.9)
Sedentary Behavior Hours 4.8 (3.7) 4.7 (3.8) 5.0 (3.6) .35 5.3 (4.2)
HCLN Food Intake 2.4 (1.0) 2.4 (1.1) 2.4 (1.0) .99 2.4 (1.1)
Perceived Stress 2.0 (.3) 2.0 (.3) 2.0 (.3) .67 2.0 (.3)
BMI 19.1 (3.8) 19.0 (3.8) 19.4 (3.9) .21 18.9 (3.7)
Waist Circumference 69.1 (10.4) 68.8 (10.4) 69.7 (10.5) .28 67.5 (9.5)
Executive Function Deficits 1.66 (.33) 1.65 (.33) 1.69 (.32) .07 1.72 (.34)
Externalizing Behavior
#
1.54 (2.65) 1.48 (2.70) 1.67 (2.54) .47 1.79 (2.91)
Internalizing Behavior
#
.86 (1.45) .92 (1.51) .74 (1.29) .23 .89 (1.38)

#
A subset of the Longitudinal Sample (N=488) had complete data on these teacher-report measures

67
Table 2.2. Distribution of annual air pollution exposures assigned to analytic sample
(N=475) at study baseline (2009)
Mean Median IQR Min Max Range
Total Modeled NRAP 10.50 9.74 6.0 1.00 47.63 46.62
Freeway NRAP 7.42 6.05 5.38 .29 44.19 43.89
Non-Freeway NRAP 3.08 2.95 1.46 .61 12.4 11.76
Regional PM2.5 (µg/m
3
) 12.18 9.71 5.71 8.99 16.10 7.11
Regional PM10 (µg/m
3
) 30.97 26.42 13.25 23.29 48.14 24.85
Regional O3 (ppb) 40.88 40.28 1.75 35.83 54.67 18.84
Regional NO2 (ppb) 16.84 15.51 10.70 8.43 25.40 16.97
+
NRAP exposures combined modeled residential and school exposures via a 79%/21% ratio. (McConnell et al.,
2010).

Regional Air Pollution Exposure
In general, participants’ estimated residential exposures to regional air pollutants were lower
than the average concentrations measured across the entire Los Angeles-South Coast Air Basin
for the study period. This basin includes most of the Greater Los Angeles Metropolitan area and
includes all of Orange County and the non-desert regions of Los Angeles County, Riverside
County, and San Bernardino Counties. For example, average regional PM2.5 concentrations
across the entire basin reported by the EPA for the study period exceeded the ones observed in
our sample by approximately one interquartile range (5.7 µg/m
3
).

68
Figure 2.2. Predicted probabilities of baseline overweight/obese by NRAP exposure

Baseline Associations between NRAP and Childhood Overweight/Obesity Outcomes
Table 2.3 provides the differences and associated standard errors between estimated predicted
probabilities of BMI- and waist circumference-specific overweight and obesity outcomes at
study baseline (1
st
semester 4
th
grade) across two sets of exposure strata. Estimates are provided
comparing the upper and lower quartiles of Total NRAP exposure, as well as the upper and
lower deciles. For all comparisons, estimates were in the anticipated direction, where greater
NRAP exposure was associated with greater probability of overweight/obesity—although not all
contrasts reached statistical significance at p<.05). Figure 2.2 visualizes how these covariate-
69
adjusted predicted probabilities vary across NRAP exposure deciles. Significant differences in
both sets of outcomes were observed between upper and lower quartiles of NRAP exposure in
models adjusting for key sociodemographic differences and regional air pollutant concentrations.

Table 2.3. Difference in baseline overweight/obesity status by total NRAP exposure

Unadjusted
1
Demographics
2

Demographics +
Regional AP
3

Demographics
+
Regional AP
+
EVI
4

Demographics
+
Regional AP
+
Soundscore
5

Demographics
+
Regional AP
+ EF +
Obesity-
related
Behavior
7

BMI | NRAP [³75
th
vs. ≤25
th
] .17 (.06)* .15 (.06)* .14 (.06)* .10 (.07)+ .11 (.10) .14 (.06)*
BMI | NRAP [³90
th
vs. ≤10
th
] .19 (.08)* .19 (.09)* .16 (.10)+ .12 (.11) .10 (.14) .15 (.10)+
WC | NRAP [³75
th
vs. ≤25
th
] .15 (.06)* .14 (.06)* .12 (.07)* .08 (.07) .14 (.10)+ .12 (.07)*
WC | NRAP [³90
th
vs. ≤10
th
] .15(.10)+ .11(.11) .05(.12) .00 (.12) .00 (.16) .05 (.12)
BMI | NRAP [³75
th
vs. ≤25
th
] .11 (.06)* .09 (.05)* .07 (.05)+ .05 (.05) .08 (.07) .07 (.05)+
BMI | NRAP [³90
th
vs. ≤10
th
] .13 (.09)+ .13 (.09) .09 (.08) .07 (.09) .13 (.12) .08 (.08)
WC | NRAP [³75
th
vs. ≤25
th
] .11 (.05)* .10(.05)* .09 (.05)* .08 (.05)+ .08 (.07) .09 (.05)*
WC | NRAP [³90
th
vs. ≤10
th
] .16 (.08)* .16 (.09)* .13 (.09)+ .11 (.09) .13 (.11) .11 (.09)+
+ p<.1; *p<.05 (one-tailed)

Table 2.4 summarizes differences in baseline obesity status by modeled exposure to NOX arising
from freeway vs. non-freeway sources. While significant differences were observed between the
most and least exposed quartiles in unadjusted models, and in fully adjusted models of Non-
Freeway exposures, these differences were attenuated after covariate adjustment in the models of
Freeway-based exposures.

Overweight
/Obese
Obese
70
Table 2.4. Comparing difference (SE) in baseline obesity status by freeway vs. non-freeway
source NRAP exposure across an inter-quartile range of exposures

Unadjusted
1
Demographics
2

Demographics
+
Regional AP
3

Demographics
+
Regional AP
+
EVI
4

Demographics
+
Regional AP
+
Soundscore
5

Demographics
+
Regional AP
+ EF +
Obesity-related
Behavior
BMI | Total NRAP
FREEWAY
.13 (.06)* .11 (.06)* .08 (.05)* .06 (.06) .09 (.06)+ .08 (.05)+
BMI | Total NRAP NON-
FREEWAY
.11 (.07)* .09 (.05)* .13 (.04)** .12 (.04)** .14 (.05)** .13 (.04)**
WC | Total NRAP
FREEWAY
.10 (.06)* .08 (.06)+ .06 (.06) .05 (.06) .03 (.07) .06 (.06)
WC | Total NRAP NON-
FREEWAY
.14 (.08)* .13 (.07)* .16 (.07)* 15 (.07)* .18 (.08)* .16 (.07)*
+ p<.1, * p<.05 ** p<.01 (one-tailed)

Table 2.5 reports the sex-specific predicted probabilities of baseline obesity (using BMI and
Waist Circumference-specific cutoffs, adjusting for various sets of covariates. While in general,
predicted probabilities of obesity were higher in males irrespective of covariate adjustment, no
sex differences were present when these contrasts were compared across the highest and lowest
quartiles of NRAP exposure strata (Gender X NRAP exposure p>.10 for all contrasts).

71
Table 2.5. Predicted probabilities of baseline obesity across IQR of total NRAP exposure by
sex

Unadjusted
1
Demographics
2

Demographics
+
Regional AP
3

Demographics
+
Regional AP
+
EVI
4

Demographics
+
Regional AP
+
Soundscore
5

Demographics
+
Regional AP
+ EF +
Obesity-related
Behavior
6

Obese BMI | Females .17 (.07) .16 (.07) .14 (.06) .13 (.06) .15 (.07) .13 (.06)
Obese BMI | Males .35 (.04) .34 (.05 .32 (.05) .31 (.05) .33 (.07) .31 (.04)
Obese WC | Females .24 (.05) .23 (.05) .22 (.05) .20 (.05) .19 (.06) .20 (.05)
Obese WC | Males .33 (.04) .33 (.04) .31 (.04) .30 (.03) .31 (.06) .32 (.04)
Obese BMI | Females .12 (.03) .12 (.03) .11 (.03) .11 (.03) .11 (.03) .10 (.03)
Obese BMI | Males .19 (.07) .19 (.06) .20 (.06) .21 (.07) .19 (.06) .19 (.06)
Obese WC | Females .15 (.04 .15 (.04) .14 (.04) .14 (.04) .15 (.04) .13 (.04)
Obese WC | Males .20 (.07) .21 (.07) .22 (.07) .23 (.08) .21 (.08) .24 (.0
Pr(male-female) contrast is not significantly different across upper/lower quartiles of NRAP exposure in any
model (p>.10)
1
Accounts for school-level clustering only via cluster-robust SEs
2
Adjusts for Age, Gender, Race/Ethnicity (White, Black, Asian, Hispanic), Free Lunch Eligibility, Area Deprivation, clustering
by school
3
Adjusts for all above covariates + Regional PM 2.5 and O 3
4
Adjusts for all above covariates + EVI within residential 250m X 250m grid (modeled as continuous)
5
Adjusts for covariates in model 3 + residential Soundscore
TM
(modeled as continuous)
6
Adjusts for covariates in model 3 + the four composite scores representing baseline physical activity, sedentary behavior, high-
calorie/low-nutrient food intake, and fruit/vegetable intake.

Longitudinal Associations between NRAP and Childhood Overweight/Obesity Outcomes
Table 2.6 reports key parameters from linear growth curve models estimating longitudinal BMI
and Waist Circumference trajectories across the 4 waves of measurement. Greater NRAP
exposure was associated with greater BMI and Waist Circumference at 4
th
grade. For example
estimated differences in BMI between the upper and lower exposure quartiles varied between 1
and 1.4 BMI points, depending on covariate adjustment, while estimated differences in waist
circumference between the upper and lower exposure quartiles varied between 3.8 and 2.5cm.
Greater baseline differences in waist circumference (3.7-4.3cm) were estimated between the
highest and lowest deciles of NRAP exposure in adjusted models.
³ 75
th
Pct
Total NRAP
≤25
th
Pct
Total NRAP
72
These models are visualized in Figure 2.3. Figure 2.4 presents identically adjusted models to
those presented in Figure 2.3, but only estimates effects for modeled NOx arising from Freeway
and Non-Freeway-sources, respectively. Similarly obesogenic effects were observed for NOX
originating from both sources when the highest and lowest exposed quartiles were compared.

Table 2.6. Differences in BMI and waist circumference model parameters across total
NRAP exposure strata (³90
th
vs. ≤10
th
)

or IQR

Unadjusted
1
Demographics
2

Demographics
+
Regional AP
3

Demographics
+
Regional AP
+
EVI
4

Demographics
+
Regional AP
+
Soundscore
5

Demographics
+
Regional AP
+ EF +
Obesity-
related
Behavior
7

BMI | NRAP [³75
th
vs. ≤25
th
] 1.44 (.70)* 1.14 (.62)* 1.11 (.63)* 1.11 (.67)+ 1.32 (.90) 0.98 (.64)+
BMI | NRAP [³90
th
vs. ≤10
th
] 1.22 (1.06) 1.35 (.94)+ 1.19 (1.00) 1.10 (1.03) .84 (1.37) 1.02 (.99)
WC | NRAP [³75
th
vs. ≤25
th
] 3.75 (1.73)* 2.64 (1.68)+ 2.61 (1.69)+ 2.52 (1.79)+ 3.37 (2.37)+ 2.52 (1.71)+
WC | NRAP [³90
th
vs. ≤10
th
] 3.70 (2.78)+ 3.93 (2.50)+ 3.92 (2.63)+ 3.65 (2.72)+ 4.28 (3.62) 3.81 (2.63)+
BMI | NRAP [³75
th
vs. ≤25
th
] -.03 (.11) -.04 (.11) -.04 (.11) -.04 (.11) -.06 (.11) -.04 (.11)
BMI | NRAP [³90
th
vs. ≤10
th
] -.02 (.16) .02 (.16) .02 (.16) .02 (.16) .01 (.17) .03 (.16)
WC | NRAP [³75
th
vs. ≤25
th
] .34 (.36) .32 (.36) .32 (.36) .32 (.36) 42 (.37) .31 (.36)
WC | NRAP [³90
th
vs. ≤10
th
] -.07 (.53) -.07 (.52) -.07 (.53) -.07 (.53) .17 (.54) .06 (.53)
+ p<.1, * p<.05 ** p<.01

Figure 2.3. Predicted BMI and waist circumference growth trajectories from 4
th
-6
th
grades
by total near-roadway modeled NOX exposure (SE)

#
Models adjust for Age, Gender, Race/Ethnicity (White, Black, Asian, Hispanic), Free Lunch Eligibility, Area Deprivation Index,
Regional PM 2.5, Regional O 3 and clustering by school

D Baseline
Intercept
D Growth
X Time
Figure 2.4. Comparing predicted BMI and waist circumference growth trajectories from
4
th
-6
th
grades by freeway vs non-freeway near-roadway modeled NOX exposure (SE)

Table 2.7. Frequency of active transportation to school

≥90
th

Percentile
NRAP
10
th
-90
th

Percentile
NRAP
≤10
th

Percentile
NRAP
Two-
sided X
2
P Value
All Children
11% 11% 29% <.01
Children Residing
within 1 km of school
21% 24% 50% .03
Children Residing
within 2 km of school
12% 15% 33% .01
Children Residing
within 3 km of school
11% 13% 33% <.01

74
Active Transportation between Home and School
Table 2.7 reports the proportion of children reporting that they usually bike or walk to school.
Other response options included car, or bus. X
2
tests compared the relative frequency of each
response based on the Euclidian distance between each participant’s geocoded primary residence
and their school. A clear pattern emerged where individuals residing closer to school were more
likely to use active transit across all exposure strata. However, children in the lowest NRAP-
exposure strata were more likely to report active transit to school, even if they resided more than
twice as far from school than their more highly exposed counterparts (p<.05). The above
findings were nearly identical when participants were characterized separately according to their
exposure to freeway- and non-freeway-sources of NRAP.
Finally, Table 2.8 demonstrates the associations, or lack thereof, between modeled NRAP
exposure and the key obesity-related behavioral outcomes assessed in the present study.
Separate models were fit for each obesity-related behavioral outcome. All models employ
multiple linear regression with cluster-robust errors accounting for school-level clustering,
except the model of MVPA, which employs a negative binomial distribution to reflect the count
outcome. Only HCLN intake, out-of-school physical activity, and MVPA were associated with
NRAP exposure in these adjusted models.

75
Table 2.8. Comparing 6 models of obesity-related behavioral outcomes by
air pollution and sociodemographic covariates

HCLN
Intake
Fruit/Veg
Intake
Out-of-
School PA
In-school
PA
MVPA
(5
th
/6
th
only)
Sedentary
Behavior
B(SE) B(SE) B(SE) B(SE) B(SE) B(SE)
Modeled NRAP -0.01(.01)* 0.01 (.01) 0.01(.006)* 0.004(.005) .02 (.005)*** -0.007 (.01)
Regional PM 2.5 0.25(.24) 0.09(.15) -0.52(.24) -0.05(.12) -.68 (.25)** 0.58(.33)+
Regional O 3 -0.01(.33) -0.01(.20) 0.40(.34) 0.26(.18) -.02 (.48) 0.28(.56)
Male Gender 0.21(.13) 0.07(.13) 0.20(.11) 0.16(.05)** -.12 (.11) 0.72(.18)**
Low SES 0.23(.08)* -0.05(.14) 0.04(.13) -0.05(.09) .26 (.15)+ 0.51 (.14)**
Area Deprivation 0.002(.003) -0.01(.002)** -0.01(.004) -0.000(.001) .001 (.003) -0.001 (.005)
White 0.15(.13) -0.09(.05)+ -0.14(.19) 0.12(.08) -.19 (.18) 0.032 (.20)
Hispanic 0.14(.11) 0.34(.21) 0.03 (.19) 0.05 (.11) .01 (.15) 0.14 (.21)
Black 2.41 (.17)** -0.06(.23) 1.62(.35)** 0.23(.09)* .96 (.62) 2.77 (.75)**
Asian 0.17(.21) -0.25(.11)* -0.57(.18)** 0.03 (.12) -.47 (.20)* -0.15 (.18)
Age 0.06(.14) -0.01(.12) -0.20(.07)** 0.09 (.07) -.18 (.09)* -0.02 (.14)

Regional PM 2.5 modeled as unit change per (10 ug/m
3
)
Regional O3 modeled as unit change per (10ppb)
NRAP is modeled as a continuous variable with a unit change indicating a 1ppb increase in modeled NO X.

Two-sided p<.1, * p<.05 ** p<.01

DISCUSSION

Overview of Primary Study Findings
This study examined the effects of near-roadway air pollution exposure on attained childhood
BMI and waist circumference at 4
th
grade, as well as developmental trajectories from 4
th
-6
th

grade among a diverse sample of late-elementary school youth. Among 4
th
graders assessed at
study baseline, a roughly 2.5 cm difference in waist circumference and a >1 point difference in
BMI was identified between children in the upper and lower quartiles of modeled NRAP
exposure after accounting for estimated exposure to regional PM2.5 and O3, as well as other key
sociodemographic covariates, with larger differences observed when comparing children in the
most vs. least- NRAP exposed deciles. When predicted probabilities of overweight/obesity vs.
76
normal weight at 4
th
grade were compared, students in the upper quartile of NRAP exposure
were at significantly increased risk of childhood overweight and obesity compared to their
counterparts in the lowest exposed quartile. Estimated obesogenic effects were similar for boys
and girls, in contrast to past work demonstrating substantial effect modification by sex
(Clougherty, 2010). Furthermore, adjustment for environmental noise and nearby green
vegetation (i.e. enhanced vegetation index) did not reliably attenuate the estimated magnitude of
point estimates, suggesting that the observed obesogenic effects may be robust to the presence of
these potentially confounding variables.

Findings also suggest that waist circumference growth trajectories may be steeper among more
highly-exposed youth after accounting for numerous potential confounders, including both
personal- and community-level demographics, as well as exposures to regional ambient air
pollution. Taken together, these findings are consistent with the study hypothesis that greater
childhood NRAP exposure may be obesogenic. Previous work comparing the 10
th
and 90
th

percentiles of NRAP exposure in a separate large Southern California cohort estimated that a
difference of 16.8ppb in near-roadway modeled NOX was associated with a 1.13 point difference
in attained BMI over an 8 year period (McConnell et al., 2015). This is comparable to the
difference in attained BMI observed in the present study baseline assessment between the 10
th

and 90
th
percentiles of NRAP exposure (a difference of 14.2ppb).

Examining Obesity-related Behavioral Pathways from NRAP Exposure à Obesity
It has been hypothesized that individuals living in areas with high levels of air pollution may be
less likely to engage in regular physical activity and more likely to participate in sedentary
77
behavior. While a recent systematic review of seven adult studies—six of which were cross-
sectional—concluded that the reviewed data were consistent with this hypothesis (An, Zhang, Ji,
& Guan, 2018), little is known about how air pollution influences physical activity patterns in
pediatric populations. Indeed, public health messaging on days with poor air quality often
recommends that sensitive groups (including children) stay indoors and refrain from physical
activity. However, such advisements generally refer to regional air pollutants, such as PM2.5 and
O3 which were assessed as covariates in the present study and were not of primary interest here
due to homogeneity of exposures. Nevertheless, it is worth noting that in our sample, greater
modeled regional PM2.5 exposure was negatively associated with each of the assessed types of
physical activity, as well as increased sedentary behavior across 4
th
-6
th
grade.

This study focused on the obesogenic effects of NRAP, levels of which are generally higher
along major, multi-lane transit thoroughfares with high traffic density and speed. Previous work
has found that children are less likely to utilize local parks and engage in physical activity within
communities characterized by high traffic density and speed (Kaczynski, Koohsari, Stanis,
Bergstrom, & Sugiyama, 2014). Consequently, air pollution-related changes in obesogenic
behavior have been proposed as a putative mechanism linking increased NRAP exposure to
childhood obesity (An, Ji, et al., 2018). However, results of the present study suggest that
systematic differences in physical activity and/or sedentary behavior are unlikely to fully explain
either the substantial baseline differences in BMI the steepening of BMI and Waist
Circumference trajectories observed among the more highly NRAP-exposed youth in our
sample. After adding additional indicators for sedentary behavior and physical activity, each of
which were assessed across the study period, the observed associations between NRAP exposure
78
and obesity were comparable to models omitting these obesity-related behavioral covariates.
Similarly, estimates of obesogenic effects of NRAP exposure and baseline overweight/obesity
status were nearly identical with and without additional adjustment for baseline obesity-related
behavioral covariates.

Moreover, as evidenced by Table 2.8, greater NRAP exposure was not associated with less
physical activity during- or outside-of-school, nor was it associated with greater sedentary
behavior (as assessed by self-reported daily screen time). This was the case with respect to
bivariate correlations at baseline, as well as covariate-adjusted models where outcomes were
pooled across 4
th
-6
th
grades. If anything, the observed directionality of effects suggests a
tendency for greater NRAP exposure to be associated with greater physical activity and less
sedentary behavior in this sample. One possible explanation for this would be that children
living near busy traffic thoroughfares may be more likely to take active transport, rather than
being driven around via car. However, the limited data collected by our study indicate that in
fact, children who are more highly exposed to NRAP were substantially less likely to report
biking or walking to school “most of the time”. Specifically, when compared to children in the
90
th
percentile of modeled NRAP exposure, children in the 10
th
percentile were significantly
more likely to report walking or biking to school most of the time at 4
th
, 5
th
, and 6
th
grades (11%
vs. 29% of all students; X
2
=12.7; p<.01 & 21% vs 50% of children residing within 1km of their
school; X
2
=6.9; p<.01). Remarkably, the group of children residing up to 3km from school who
were least exposed to NRAP had higher rates of active transit than those residing less than 1km
from school, but with the highest exposures to NRAP.

79
Given that the bulk of modeled NRAP exposure in the present sample originated from freeway-
based sources, this suggests that residential proximity to freeways may impede use of active
transport modalities to- and from-school among study participants, possibly due to safety
concerns. Nevertheless, given the lack of inverse associations identified between NRAP
exposure and large battery of physical activity measures utilized here, which include multiple
validated assessments of physical activity, it is likely that other factors besides local walkability
and active transportation opportunities are driving the observed differences in weight gain
outcomes.

Previous studies identifying obesogenic effects of NRAP have identified poor diet (e.g.
consumption of high-calorie/low nutrient foods and sugar sweetened beverages) as a possible
unmeasured confounder, given that it has been linked to both childhood obesity (Ludwig,
Peterson, & Gortmaker, 2001) (Davis, Whaley, & Goran, 2012) and school attendance/residence
near busy Southern California roadways as a function of lower SES in the Southern California
context (Green, Smorodinsky, Kim, McLaughlin, & Ostro, 2004) (Morello-Frosch, Pastor,
Porras, & Sadd, 2002). For instance, emerging evidence from murine models suggests that air
pollution exposure may have effects on metabolism and appetite control, as well as possibly
predispose individuals to diet-induced weight gain (Bolton et al., 2012). It has also been
proposed that NRAP exposure may lead to neuroinflammation in brain regions involved in
appetite and dietary decision-making (Tzameli, 2013), thereby predisposing youth to less healthy
dietary intake. Recent data from the Children’s Health Study found that early exposure to
modeled NRAP as well as regional air pollutants (e.g. NO2, NO, acid vapor, PM2.5, EC and OC)
were each longitudinally associated with increased consumption of trans-fats and fast food in a
80
large cohort of adolescents followed from 4-8 years (Chen et al., 2019). However, no
relationships were identified here between NRAP exposure and consumption of either high-
calorie/low-nutrient food intake or fruits/vegetables, either at study baseline or across 4
th
-6
th

grades.

Obesogenic Effects of Freeway vs. Non-Freeway Sources
Notably, a previous study, which also employed CALINE4-modeled NOx as an indicator of
NRAP exposure identified significant obesogenic effects of NRAP among 5-11 year-olds in
multivariate adjusted models only for non-Freeway modeled NOx, while modeled NOx from
freeway sources was only significantly associated with steeper BMI trajectories in unadjusted
models. In the current study, modeled NOX from Non-Freeway sources also demonstrated a
stronger, more robust relationship with baseline risk of overweight/obesity relative to Freeway-
sources, as well as a slightly more pronounced dose-response relationship at lower exposures in
the longitudinal models. In contrast to participants in Southern California Children’s Health
Study (Jerrett et al., 2014), which included both rural and urban-dwelling participants, just over
half of participants in the current study resided within 1km of a major freeway with 25% living
within 500m. Therefore it is perhaps unsurprising that the bulk of participant modeled near-
roadway NOX exposures were attributable to Freeway-based sources. Notably, further analysis
of Southern California Children’s Health Study data concluded that greater exposure to freeway
NOX in-utero and during the first year of life was associated with faster BMI growth during
childhood, but no effect was found for greater non-Freeway NOX exposure (J. S. Kim et al.,
2018). Together these findings suggest that future work examining obesogenic effects of NRAP
81
exposure during childhood should incorporate assessment of both freeway- and non-freeway
NRAP sources.

Limitations and Conclusions
Biological exposure to urban air pollution has been shown to be influenced by behavioral factors
that were not able to be fully assessed in the current study. For example, beyond the
measurement error introduced by assigning NRAP and regional air pollution exposures to fixed
street addresses, exposure assignment was also unable to take into account the presence/absence
of pollution mitigation strategies (i.e. air filtration, ventilation) that can substantially modify
particle concentrations within each structure (Tong, Chen, Malkawi, Adamkiewicz, & Spengler,
2016). Characterization of exposure via personal monitoring is one way researchers can estimate
and integrate exposures across multiple locations and times (Tonne, Whyatt, Camann, Perera, &
Kinney, 2004) and is likely to more comprehensively assess the variety of air pollution
exposures that children encounter in their daily lives. Such approaches can also be augmented
by ecological momentary assessment methods, which can leverage smartphone sensors to detect
physical activity and other obesity-related behavior, administer short questionnaire-based
assessments that participants can complete in real-time as well as leverage GPS for linkage to
relevant temporo-spatially determined exposures. Furthermore, evidence from the Southern
California Children’s Health Study as well as a New York City birth cohort indicates that
prenatal and/or early-life NRAP exposure (Rundle et al., 2012) may lead to greater childhood
body size, as assessed by BMI. Unfortunately, the study design utilized here made it impossible
to characterize such exposures prior to grade 4. Other relevant unassessed exposures include
environmental tobacco smoke, which is associated with increased obesity risk and may
82
potentiate adverse health effects of air pollution (Qureshi et al., 2018), as well as breastfeeding,
which research suggests may mitigate some adverse health effects of particulate air pollution
(Dong et al., 2013). However, it is worth noting that recent work in the comparably-aged, school-
based Spanish BREATHE cohort found that exposure to environmental tobacco smoke did not
moderate the observed obesogenic effects of NRAP exposure (de Bont et al., 2019).

Furthermore, due to the high correlations observed in previous work between CALINE4
modeled NOX and other near-roadway pollutants (e.g. CO, UFPM, NOx) Total NOX is
conceptualized as an indicator of broader exposure to the complex mixture of pollutants
comprising the near-roadway plume, rather than the independent effects of NOX alone. Previous
work has demonstrated very strong correlations (r >.90) between CALINE4 modeled NOX and
other near-roadway pollutants (e.g. NO2, CO, PM2.5). Consequently, it was impossible to
differentiate the obesogenic effects of specific near-roadway pollutants.

Finally, it is also important to note that these study data were not originally collected with the
aim of examining the health effects of air pollution, and as such the variability in air pollution
exposure is not as large as it would have been had a more purposive sampling method been
utilized. Moreover, while both traffic volumes and regional air pollution are relatively high in
Southern California compared to much of the rest of the US, the pollutant concentrations
observed here are much lower than those experienced by many residents of rapidly
industrializing and urbanizing African and Asian cities (Greenstone & Hanna, 2014; Marlier,
Jina, Kinney, & DeFries, 2016), which contain a large proportion of the world population (Rohde
& Muller, 2015). For example, average PM2.5 concentrations in many Chinese cities often
83
exceed US levels by an order of magnitude (Air pollution prevention and control progress in
Chinese cities, 2016). Unfortunately, very few studies examining obesogenic effects of air
pollution have been conducted outside of North America and Europe (An, Ji, et al., 2018).
Consequently, further work is needed to assess the obesogenic effects of both regional and
NRAP exposure in the global cities where exposures are highest, and where effective regulatory
regimes have yet to be established to mitigate some of the population-level obesogenic effects
suggested by the present study.

Supplemental Table 1. Zero-order correlations between key study variables at study
baseline (N=475)

84
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CHAPTER 3 – Neurobehavioral effects of near-roadway air pollution
exposure among a sample of Southern California primary schoolchildren

BACKGROUND

An Overview of Executive Function
The construct of executive function refers to a set of higher-order mental processes that are
critical for planning and coordination of other cognitive abilities and the adaptive behaviors that
permit us to confront life’s myriad challenges. Executive functions facilitate goal-directed
behavior across both familiar and unfamiliar contexts, including complex problem-solving under
particular environmental constraints (Diamond, 2013). The underlying structure of executive
function remains under debate (McKenna, Rushe, & Woodcock, 2017), with differing views on
the extent to which specific cognitive processes are indicative of a single “unitary” executive
function capacity or rather are indicative of “dissociable” component processes (Best, Miller, &
Jones, 2009; Miyake et al., 2000). While the specific terminology used to describe these
executive function processes differs somewhat among researchers, these processes typically
include working memory (Baddeley, 1998), which permits the maintenance and manipulation of
information in the “mind’s eye”, as well as inhibitory control, which allows one to regulate
attention, thoughts and/or behavior so as to override internal predispositions or impulsive
responses which may be suboptimal for achieving the task at hand. Conversely, executive
functioning deficits have been consistently associated with a wide variety of adverse health risk
behavioral (Allan, McMinn, & Daly, 2016) and psychosocial outcomes, including externalizing
behavior (Poon, 2017). Evidence suggests that, while some components of executive function
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are neuroanatomically and functionally dissociable, broadly speaking, these processes are largely
dependent on the prefrontal cortex, whose maturation and functional connectivity are each
delayed relative to more caudal regions (Shanmugan & Satterthwaite, 2016). Furthermore it has
been shown that executive functions are malleable (Diamond & Lee, 2011) and vulnerable to
environmental insult (Best et al., 2009).

Urban Air Pollution—Neurotoxicants in our Air?
In recent years, evidence has accumulated suggesting that urban air pollution can adversely
impact the central nervous system, contributing to neurodegeneration and neurocognitive
impairment across the lifespan (Allen et al., 2017; Babadjouni et al., 2017). Putative pathways
for how air pollutants such as particulate matter (PM) may exert neurotoxic effects include direct
uptake into the brain via olfactory and cranial nerves, as well as direct transfer of small particles
to brain endothelial cells from the systemic circulation/erythrocytes laden with PM (Elder et al.,
2006; Lewis et al., 2005). Ultrafine PM (<100nm) has been found to enter the systemic
circulation via macrophage-like cells loaded with PM from the lung capillary bed (Calderón-
Garcidueñas, Mora-Tiscareño, et al., 2008). Systemic inflammation has been found to result
from the pulmonary effects of PM (Tsai et al., 2012) as well as exposure to other air pollutants
(e.g. nitrogen dioxide, carbon monoxide) (Rich et al., 2012). Further evidence suggest that glial
activation and white matter injury may also present plausible cellular mechanisms for air
pollution-related neurotoxicity in exposed individuals, in addition to neuroinflammation
(Levesque, Surace, McDonald, & Block, 2011; Qin et al., 2007) and oxidative stress (J. O.
Anderson, Thundiyil, & Stolbach, 2012; Lodovici & Bigagli, 2011).

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Given the rapid rates of neurological development occurring at these ages and concomitant
increases in susceptibility to environmental toxicants, most pediatric research into the
neurocognitive impacts of air pollution has focused on prenatal/early-life exposures. However,
research has shown that, although infancy and young childhood constitutes a critical period for
sensorimotor neurodevelopment—a second critical period exists throughout puberty during
which myelination of the prefrontal cortices reaches adult levels (Blakemore & Choudhury,
2006). This myelination facilitates the transmission of nerve impulses between these areas—
implicated in complex, executive cognitive functioning—and the rest of the brain. Histological
studies of both human and non-human primate prefrontal cortex suggest a second wave of
synaptic proliferation coincides with pubertal onset (Bourgeois, Goldman-Rakic, & Rakic, 1994;
Woo, Pucak, Kye, Matus, & Lewis, 1997; Zecevic & Rakic, 2001). This pattern of
neurodevelopment in the prefrontal cortex is distinct from the rest of the brain, which undergoes
a single wave of synaptogenesis in early childhood followed by gradual pruning of these
connections throughout adolescence. These concurrent progressive and regressive modifications
of prefrontal neural architecture are believed to be influenced by the child’s life experiences and
ongoing environmental exposures (O'Hare, Sowell, & Luciana). It has been suggested that this,
more protracted period of executive cognitive development lasting through the high school years
is both beneficial and necessary given its complexity, and non-automaticity, relative to lower-
level cognitive processes (Best et al., 2009). For instance, work by Diamond indicates that as
children advance through adolescence, they not only demonstrate greater performance on tasks
designed to assess individual aspects of executive function (e.g. inhibition, working memory),
but also substantially improve their performance on tasks which leverage multiple EF
components simultaneously (Diamond, 2013).
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A landmark neuroimaging study of late-elementary schoolchildren found that students exposed
to high levels of urban air pollution present within the urban core of Mexico City had greater
neuroinflammation and more extensive prefrontal lesioning than did their counterparts residing
in a less polluted nearby city (Calderón-Garcidueñas, Mora-Tiscareño, et al., 2008). Children
exposed to more air pollution performed significantly worse on tests of fluid cognition, memory
and executive functioning. This study extended earlier postmortem studies which found similar
patterns of prefrontal white matter lesions and neuroinflammation in highly-exposed canines
(Calderón-Garcidueñas et al., 2002), children, and young adults (Calderón-Garcidueñas, Solt, et
al., 2008). However, such studies are limited by the small number of participants assessed, the
dichotomous characterization of children as highly exposed or not—with no direct assessment or
modeling of children’s actual exposure to individual regional ambient pollutants (i.e. PM2.5,
PM10, NO2, O3) or near-roadway air pollutants, which may have particularly deleterious health
effects—owing to the greater presence of smaller, more reactive particles which can translocate
across the olfactory epithelium directly into the brain (Allen et al., 2017).

Near-Roadway Air Pollution—A particularly unhealthy mixture
Near-roadway air pollution (NRAP) is a complex mixture, including particulate and gaseous
combustion products in fresh vehicle emissions, debris from tires and brake wear, and metals
from engine wear—which is a distinct chemical mixture from that found in regional air
pollution. Multiple studies have also identified associations between NRAP exposure and
decreased neurocognitive function, however, these studies have largely examined the impact of
prenatal or early-life exposures and have generally not assessed functional ECF or
neurobehavioral deficits apart from psychopathological constructs of autism or attention deficit
neurobehavioral deficits apart from psychopathological constructs of autism or attention deficit
hyperactivity disorder (ADHD) (Guxens et al., 2014; Perera et al., 2012; Perera et al., 2013;
Suglia, Gryparis, Wright, Schwartz, & Wright, 2008). For example, a Spanish study identified
negative associations between prenatal residential N02 exposure and infant cognitive
development at 14 months (Guxens et al., 2012). Another recent study assessing PAH exposure
in New York City identified associations between prenatal exposure and impaired cognitive
development at age three (Perera et al., 2006). Subsequent analysis of this cohort found
increased prenatal PAH exposure was associated with higher symptom scores on the
Anxious/Depressed and Attention Problems subscales of the Childhood Behavior Checklist
(CBCL) at age 6-7 (Perera et al., 2012; Perera et al., 2013). Further study utilizing both prenatal
and early-life exposure assessment linked high PAH exposure to significantly increased risk of
ADHD diagnosis at age nine (Perera et al., 2014). Another study of Boston children aged 8-11
identified significant associations between performance on memory/nonverbal IQ tasks and
exposure to black carbon—a proxy for NRAP exposure (Suglia et al., 2008; Volk, Lurmann,
Penfold, Hertz-Picciotto, & McConnell, 2013). Importantly, a variety of potential confounders
were assessed and adjusted for in this cohort (e.g. prenatal environmental tobacco smoke
exposure, maternal IQ, maternal education, maternal ADHD symptoms, and other aspects of the
home environment), thereby strengthening inferences drawn regarding the causal role of NRAP.
Furthermore, a pair of case-control studies conducted in California identified links between
prenatal/early-life NRAP exposure and subsequent autism risk (Volk, Hertz-Picciotto, Delwiche,
Lurmann, & McConnell, 2011; Volk et al., 2013). A recent Spanish study also reported
longitudinal associations between impaired performance on computerized working
memory/attention assessments and exposure to NRAP at the school level (Sunyer et al., 2015)
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(Basagaña et al., 2016). Finally, a recent report from the Project VIVA cohort found that
children with greater Black Carbon exposure had greater problems with behavioral regulation as
assessed by the teacher reported- Behavioral Regulation Inventory of Executive Function
(BRIEF), but not parent-reported BRIEF (Harris et al., 2016). Notably, associations between
greater NRAP exposure and behavioral regulation problems grew stronger as the cohort aged,
reaching statistical significance at ages 4-6. These findings are consistent with the hypothesis
that mid-childhood constitutes a critical period for adverse neurodevelopmental effects of NRAP.

On the other hand, other studies examining the effects of NRAP exposure and executive function
in pediatric samples have not identified significant associations. For example, a British Study of
9-10 year-olds did not find any associations between performance on a modified attention task
intended to assess working memory (Hygge, Boman, & Enmarker, 2003) and modeled
concentrations of nitrogen dioxide (μg/m3) representing traffic-related air pollution for 22
London schools (C. Clark et al., 2012). Furthermore, findings from the well-characterized
Project VIVA prospective birth cohort concluded that prenatal and childhood exposure to greater
traffic density and PM2.5 was not associated with poorer performance on a variety of
neurobehavioral measures, including executive functioning (Harris et al., 2015) during mid-
childhood. However, as only a small number of this relatively affluent, highly-educated
Massachusetts-area cohort resided near a major roadway, the authors suggest that the resulting
estimates warrant replication in a more-highly exposed and diverse US cohort. Findings from
another New England study of lower SES, more highly exposed children ranging in age from 7-
14, did identify significant associations between adverse effects of NRAP exposure on a
computerized attention task, which were more pronounced in boys, relative to girls (Chiu et al.,
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2013) but were limited by their relatively small sample. The study authors also suggested that
future work in larger samples should further examine the hypothesis that boys may be more
susceptible to the neurotoxic effects of NRAP exposure during peripuberty.

Consequently, the present longitudinal study investigates the neurodevelopmental effects of
exposure to NRAP among a diverse sample of urban-dwelling Southern California children
during a putative critical period of prefrontal synaptic proliferation—the mid to late-elementary
school years. More specifically, cross-sectional and longitudinal associations were estimated
between participant exposure to line-source dispersion modeled NRAP at 4
th
grade and both
executive functioning and neurobehavioral deficits from 4
th
-6
th
grades.

METHODS

This study is a secondary analysis of data drawn from the fourth grade semester 1 (baseline),
fourth grade semester 2, fifth grade semester 2, and sixth grade semester 2 waves of assessment
in Pathways to Health, a cluster-randomized control trial for the prevention of childhood obesity
and substance use. This study was carried out in 28 Southern California schools from 2009-2011
(Pentz & Riggs, 2013; N. R. Riggs, Spruijt-Metz, Chou, & Pentz, 2012; Sakuma, Riggs, &
Pentz, 2012; Warren, Riggs, & Pentz, 2017), with an average of one 4th grade class per school.
All human subjects procedures were approved by the University of Southern California
Institutional Review Board.

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Participants were a longitudinal cohort of 709 assented students with full active parental consent
and self-assent to participate in the study, and who had complete data at 4
th
, 5
th
and 6
th
grade
waves. Of these students, 475 students completed an optional Student Information Sheet
reporting residential address information. To assign near-roadway and regional air pollution
exposures, 2009 residential and school addresses were geocoded using ArcGIS World Geocoder.
Geographic distances between each child’s school and residence were also calculated. Geocode
quality was high (>90% matched to building centroid).

Near-roadway Air Pollution Exposure
Estimated average NOX exposures from local on-road motor vehicle emissions were assigned to
geocoded residence and school locations for the year 2009 using CALINE4 line-source
dispersion models (Wu, Wilhelm, Chung, & Ritz, 2011). These estimates do not include the
estimated regional background air pollution levels. In similar contexts, modeled NOX has been
found to be very highly correlated (r>.95) with other near-roadway pollutions modeled by
CALINE4 (R > 0.95), and as such, represents primary local NOx from vehicular traffic along
with these other correlated pollutants in the fresh traffic exhaust plume. Link-based traffic was
based on Caltrans + ESRI Premium StreetMap traffic count and roadway geometry. Vehicle
emission factors as a function of speed and LDV/HDT fleet mix were determined from CARB's
EMFAC2014 model for 2009. Vehicle NOX exhaust emissions were modeled from local on-road
traffic/roads within 5 km of residences and schools. Link-specific exhaust emission factors based
on average vehicle speed and HD truck fraction were applied using Caltrans post-mile truck
count data by year. Day-of week and hour of day volume adjustments from WIM data Freeway
(FCC1) and Non-Freeway (FCC2-FCC4) road classes were also applied. Regarding
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meteorology, surface wind data from the closest AQS monitor was used. Monthly wind speed
and direction frequencies were determined from 1997-2014 surface wind data. Mixing heights
[morning, afternoon, and nighttime climatological values (200m, 400m, 100m)], and
atmospheric stability based on time of day and wind speed were also incorporated into the
model. Exposures for freeway and non-freeway sources were assigned to participants’ baseline
residential and school addresses. Freeway and non-freeway Total NOX exposure was estimated
by adding freeway and non-freeway estimates for each participant. Residential and school-based
Total NOX exposures were combined using both a 79% residential/ 21% school ratio as well as
an 84% residential/16% school ratio, as in previous work (McConnell et al., 2010). These ratios
were calculated to approximate the relative proportion of time students spent in each location.
A series of buffers were also applied, which indicated residential proximity to the nearest arterial
roadway (CalTrans functional class 1, 2 or 3), which included 300m, 175m, and 50m buffers for
consistency with previous work (Batterman et al., 2014; Harris et al., 2016). A second set of
buffers was also created at 100m, 300m and 500m, distances which have also been used in
previous work and which permit categorization of more participants. These functional classes
represent Interstate highways/freeways/ expressways or other principal arterials characterized by
high traffic volumes. Distances from each residence to the nearest arterial roadway were
calculated using ESRI ArcMap (version 10.4) “NEAR” function via the Proximity toolset, 2012
Topologically Integrated Geographic Encoding and Referencing (TIGER) 2012 road shape files,
and the 1983 North American Datum. These buffers were intended to serve as a more
parsimonious, additional indicator of NRAP exposure apart from modeled NOX and facilitate
comparability with other studies.

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Regional Air Pollution Exposure
Regional air pollution exposures for years 2000-2009 were modeled and retrospectively assigned
to participant residential addresses as most students resided within 1.5 km of school. This range
approximates the lifetime exposures of cohort members, 98.73% of whom were ages 9 or 10 at
study baseline. Monthly average 24-hour concentrations were calculated for NO2, O3, PM2.5,
PM10. Values were estimated from monthly air quality data measured at 4 closest locations
within 50 km of the grid point by inverse distance squared interpolation. In 2009, regional PM2.5,
PM10, and NO2 were highly correlated (r>.9) whereas O3 was less strongly correlated with PM2.5
(r=.23), PM10 (r=.36), and NO2 (r=.04). Similar patterns were observed at other years.
Consequently, only PM2.5 and O3 were included as covariates in final adjusted models.

Outcomes

Executive Function
Items from four of eight clinical sub-scales of the Behavioral Rating Inventory of Executive
Function-Self-Report (BRIEF-SR) (Guy & Gioia, 2004)were used to assess EF. The BRIEF was
designed to be an appropriate and ecologically valid measure of EF skills associated with goals
and actions in everyday problem solving situations encountered by school-age children, rather
than as an indicator of laboratory or structured task performance. The abbreviated BRIEF-SR
measure was used to prevent child test fatigue and time-constraints imposed by partner schools
for in-class survey administration. For the present study, abbreviated scales were constructed
using the highest loading index items from a pilot study and demonstrated acceptable internal
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consistency (α=0.63–0.74) and predictive validity when compared to full BRIEF-SR scales (N.
Riggs, Chou, Spruijt-Metz, & Pentz, 2010).

The four selected EF sub-scales were inhibitory control, emotional control, working memory,
and organization of materials. The 23-item abbreviated questionnaire was prompted by the
following text: “In the past month, how often has each of the following behaviors been a
problem?” Response choices ranged from 1=Never, 2=Sometimes, 3=Often. The following
items from each subscale were included:

Inhibitory Control:
I have trouble sitting still;
I do things without thinking first;
I interrupt others;
I get out of control more than my friends;
I say things without thinking;
I talk a lot at the wrong time (for example, like during class);
I don’t think ahead of what will happen before I do something
Emotional Control:
I make a big deal out of small problems;
When I get angry, I yell, scream or cry (or I do all of them);
I yell, scream, or cry for no reason;
I get more upset than most of my friends do;
I get bothered easily;
I get nervous about stuff easily;
Working Memory:
I have problems completing my work;
I forget what I’m doing in the middle of things;
When I am sent to get something, I forget what I am supposed to get;
I have trouble remembering things, even for a few minutes (such as directions, phone numbers, etc.);
I forget instructions easily;
I forget things all the time;
Organization of Materials:
My desk/workspace is a mess;
I lose things (such as keys, money, wallet, homework, etc);
My backpack/school bag is disorganized;
I have difficulty finding my clothes, glasses, shoes, books, pencils, etc.

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Problem Behavior

Child behavior problems were assessed via abbreviated versions of internalizing and
externalizing scales from the Child Behavioral Checklist - Teacher Report Form (CBCL-
TF;(Achenbach, 2001)). To complete the CBCL-TF, teachers were given a list of symptomatic
behaviors and asked to rate each child on each item by denoting whether a given behavior was
“not true” (0), “sometimes or somewhat true” (1), or “very true or often true” (2), “now or
within the past 6 months.” This reduced set of CBCL items was selected to reduce the response
burden of teachers, who had up to 30 students per classroom and the time constraint of a single
class period during which to complete all surveys for
consented students. Due to these time-constraints, this teacher-report measure only provided
behavior reports on the subset of all students a teacher could complete within a single classroom
period (N=331 of the N=475 with complete residential address data).

Eight items were drawn from the CBCL externalizing subscale (α = .90) which assesses behavior
problems such as aggression and conduct problems (i.e.“disobedient at school”, “does poor
school work”, “argues a lot”, “bullies or is mean to others”, “demands a lot of attention”,
“gets in many fights”, “hangs around kids that get into trouble”, “has temper tantrums”). Four
items were also drawn from the CBCL internalizing subscale (α = .78) which assesses behavior
problems such as anxiety, depression, and somatic problems (i.e. “is too fearful or anxious”, “is
self-conscious or easily embarrassed”, “is shy or timid”, “is worried”).

102
Covariates

Individual Demographic Characteristics

Self-reported demographic characteristics were also obtained via survey at each of the 4
assessments. These data included participant age, gender, race/ethnicity, and free school lunch
eligibility, which served as a proxy for socioeconomic status (SES). This SES proxy was
utilized because it was believed that directly querying primary school-children about parental
income, education, and employment would result in unacceptable levels of measurement error.

Community-level Covariates

Neighborhood-level Socioeconomic Deprivation

Self-reported demographic characteristics were also obtained via survey at each of the four
assessments.
These data included
participant age,
gender,
race/ethnicity, and
free school lunch
eligibility, which
served as a proxy for
socioeconomic status. As an additional measure of community-level socioeconomic privation,
which has been linked to both childhood obesity and NRAP exposure, an Area Deprivation
Index (ADI) was assigned to each participant. The ADI was developed by the Health Resources
Census Block Group Components Factor Score
Coefficients
Percent of the block group’s population aged ≥ 25 years with < 9 years of education 0.0849
Percent aged ≥ 25 years with greater than or equal to a high school diploma −0.0970
Percent of employed persons ≥16 years of age in white-collar occupations −0.0874
Median family income −0.0977
Income disparity
†
0.0936
Median home value −0.0688
Median gross rent −0.0781
Median monthly mortgage −0.0770
Percent owner-occupied housing units (home ownership rate) −0.0615
Percent of civilian labor force population ≥ 16 years of age unemployed 0.0806
Percent of families below the poverty level 0.0977
Percent of population below 150% of the poverty threshold 0.1037
Percent of single-parent households with children < 18 years of age 0.0719
Percent of occupied housing units without a motor vehicle 0.0694
Percent of occupied housing units without a telephone 0.0877
Percent of occupied housing units without complete plumbing (log) 0.051
Percent of occupied housing units with more than one person per room (crowding) 0.0556
† Income disparity defined as the log of 100*ratio of the # of households with <$10,000 income
to the # of households with >$50,000 income.
Components and factor score coefficients drawn from Singh (2013)
All coefficients are multiplied by −1 such that higher ADI = higher disadvantage.
103
& Services Administration (HRSA) and has been refined and validated as a composite index of
neighborhood-level deprivation calculated using American Communities Survey 5 year estimates
(Singh, 2003). The items and corresponding factor scores utilized to calculate the composite
index
corresponding to the present study period (2009-2011) are provided here. For consistency with
previous work, variables were created which reflecting the state-specific deciles and national
percentiles of each
Block Group (Kind et al., 2014). The median ADI decile among study participants was 4
(IQR=3) although student residential block group-level ADI deciles ranged from 1-10.

Environmental Vegetation Index
Previous research suggests that NRAP exposure can be associated with aspects of the built
environment which demonstrate substantial, non-random spatial variability. Since the urban and
suburban Southern California environments in which study participants generally reside are
heavily landscaped, vegetation density is one aspect of the built environment which previous
work indicates may reduce air pollution through both direct and indirect pathways. The presence
of green vegetation, which may be an important covariate given its plausible associations with
both NRAP exposure and obesity outcomes (through behavioral pathways) can be detected via
remote sensing methods, most notably satellite imagery from NASA’s Moderate Resolution
Imaging Spectroradiometer (MODIS) (Huete, Miura, Rodriguez, & Gao, 2002). The MODIS
instrument is installed on two earth-viewing satellites, Terra and Aqua, which orbit the Earth in a
complementary manner, ensuring that the entire earth’s surface is imaged every 24-48 hours.
For years, the principal method for estimating vegetation density via remote sensing was
104
calculation of the Normalized Difference Vegetation Index (NDVI). This measure is based upon
the principal that chlorophyll absorbs light from the visible spectrum (i.e. 0.4 to 0.7 µm), while
the other leaf cells reflect near-infrared light (i.e. 0.7 to 1.1 µm). Consequently, the relatively
density of vegetation in a given satellite image can be approximated by calculating the satellite-
measured near-infrared radiation minus visible radiation (assessed by the RED band of the
MODIS) divided by near-infrared radiation plus visible radiation (NDVI = (NIR — RED)/(NIR
+ RED). In recent years, an alternative measure, known as the Enhanced Vegetation Index (EVI)
has been developed, which improves upon some well-acknowledged limitations of the NDVI by
taking into account an additional MODIS band (J. Wang, Ma, & Sun, 2014).

Compared to NDVI, the EVI is more sensitive to differences in important vegetation
characteristics like canopy structure and density, as well as seasonal variation and stress
compared to NDVI which simply assesses the amount of chlorophyll present. Importantly, the
EVI also reduces the variability of vegetation density estimates due to differences in atmospheric
conditions (including concentrations of regional air pollution (i.e. PM) –and has been shown to
outperform NDVI in urban environments (Huete, Miura, Rodriguez, & Gao, 2002). Another key
advantage of EVI is that its estimates are not dependent on the time of day when the satellite
images were captured, because—unlike NDVI—it is able to account for changes in the angle at
which the sun shines on the earth’s surface (i.e. the “solar incidence angle”). Consequently, EVI
was used in the present study as an estimate of the green vegetation present in the area proximal
to each participant’s residence. NASA produces aggregate EVI estimates every 16 days at a
spatial resolution of 250m X 250m. To account for monthly, seasonal and annual variations in
105
vegetation, these estimates were averaged over the entire study period, which lasted from fall
2009 to spring 2011. Such averaging is believed to reduce measurement error stemming from the
relatively high variability in precipitation in Southern California relative to the rest of the
country. For example, between 2006-2011 the annual precipitation measured in Downtown Los
Angeles ranged from 3.2 inches in the 2006-2007 water year to 20.2 inches in the 2010-2011
water year—a >500% change in annual precipitation.

Statistical Methods

A multilevel mixed effects modeling approach was utilized in the present longitudinal analysis
via the Stata 14 Mixed command. Baseline associations at 4
th
grade were also examined using
the Mixed command to account for random effects at the school and classroom levels. For each
longitudinal neurobehavioral outcome and air pollution exposure of interest, the following set of
models was fit. First, unadjusted models were specified, which included cluster-robust standard
errors to account for the fact that this is a secondary analysis of data collected during a school-
based cluster-randomized control prevention trial. Then socio-demographically adjusted models
adjusted for the following individual-level demographics: gender, free lunch eligibility (a proxy
for socioeconomic status) age, White race/ethnicity, Hispanic race/ethnicity, Asian
race/ethnicity, Black race/ethnicity. Finally, fully-adjusted models were fit with the
aforementioned covariate set, in addition to exposure to regional PM2.5 and O3. These
sociodemographic covariates were selected as previous work suggests that they may potentially
confound associations between air pollution exposure and a variety of adverse health outcomes
106
(Sheppard et al., 2012). For example, a substantial literature has documented how
“environmental racism” can result in low-SES and/or minority communities being more likely to
reside and attend school in areas with greater risk of exposure to environmental toxicants
(Pulido, Sidawa, Vos, & Department of Geography, Volume 17, 1996 - Issue 5 : Environmental
Racism) (Morello-Frosch, Pastor, Porras, & Sadd, 2002). We also adjusted for systematic
differences in regional ambient air pollution given that traffic is a key contributor to these
criterion air pollutants, which 2 studies have linked to a variety of adverse neurological outcomes
in children (Xu, Ha, & Basnet, 2016).
The first set of models examined associations at the first wave of data collection only (Semester
1, 4
th
grade), and did not adjust for group assignment, given that it was cluster-randomized and
no program content had yet been delivered. The second set of models examined pooled EF
outcomes across all 4 assessment waves and considered individuals in the control condition only
(N=212). Such participant restriction is important as this study is a secondary analysis of data
originally collected as part of a two-arm, school-based multiple health risk behavior prevention
trial intended to target executive function as a program mediator. Multilevel linear regression
models were fit for the continuous EF outcomes via the mixed command. For each linear model,
model residuals were examined and their normality was confirmed before proceeding.
Multilevel mixed effects negative binomial regression was used for analysis of the externalizing
and internalizing behavior subscales of the CBCL due to the underlying data generating process
which in many cases is known to result in conditionally over-dispersed count data.
Random intercepts were included in each model at the individual level to account for outcome
status at baseline. Random slopes were also included at the individual subject level as it was
assumed a priori that neurotoxic effects of air pollution may vary based on individual-level
107
characteristics (Barr, Levy, Scheepers, & Tily, 2013). Random intercepts were also included at
the school level to account for between-school variability in students’ neurobehavioral
characteristics. Additional models incorporating random slopes at the school level were also fit,
as it is plausible that the effects of NRAP on the assessed outcomes may vary based on
unmeasured school-level factors (e.g. ventilation practices, air filtration quality, siting of air
intakes, indoor/outdoor time during the school day). However, the final models did not
incorporate this additional random effects term as its inclusion led to occasional convergence
issues in fully adjusted models, while neither improving fit, nor explaining any additional
variance in study outcomes. As such it is unlikely that its omission has any influence on the
resulting estimates or inferences drawn.
Consistent with previous work, an additional series of models was also fit to examine a priori
hypotheses that adverse effects of NRAP on neurobehavioral outcomes would be more
pronounced among boys, relative to girls. This was tested by incorporation of an interaction
term by sex and subsequent comparison of sex-stratified estimates.

108
RESULTS

Participant Characteristics
The mean age of children at the first study assessment was 9.27 years (SD=.47). Among students
included in the present study, approximately half (48%) were male, 36% were White, 20% were
Hispanic, and 38% reported eligibility for the federal free lunch program at one or more study
assessments, an indicator of low socioeconomic status (Harwell & LeBeau., 2010).

When compared by neurobehavioral outcomes and all tested covariates, the 475 students with
complete address information were significantly more likely to be White, and less likely to be
Hispanic than the remaining 234 participants whose parents did not report residential address
data. The two groups did not significantly differ by other sociodemographic factors, perceived
stress, executive function, externalizing behavior or internalizing behavior (Table 3.1). Among
the 475 students for whom residential address data were available, no significant
sociodemographic differences were observed between the 331 students whose teachers
completed the abbreviated CBCL measure at 4
th
, 5
th
, and 6
th
grades and the 144 students whose
teachers did not. While children with complete CBCL data had significantly lower estimated
exposure to all four of the regional air pollutants (i.e. PM2.5, PM10, O3, and NO2), modeled
exposures to near-roadway air pollution did not significantly differ between students in our
analytic sample with and without complete CBCL data.

109
Table 3.1. Comparing outcome and covariate values between final analytic sample and
participants lacking residential address data

Dichotomous Baseline Variable
Participants with
geocoded home
address (N=475)
%
Participants with
no home address
provided (N=234)
%
P
Male gender 48.42% 52.99% .25
Free lunch eligibility 19.37% 23.93% .16
White race/ethnicity 35.79% 26.92% .02*
Hispanic race/ethnicity 22.11% 32.91% .02*
Black race/ethnicity 1.68% 2.99% .26
Asian race/ethnicity 9.26% 5.13% .06
Program vs. Control 55.37% 48.72% .10
Continuous Baseline Variable Mean (SD) Mean (SD) P

Age 9.26 (.46) 9.28 (.49) .22
Stress 1.96 (.33) 1.95 (.33) .67
Mean CBCL Externalizing Behavior^ 1.48 (2.70) 1.67 (2.54) .06^
Mean CBCL Internalizing Behavior^ .92 (1.51) .75 (1.29) .47^
Mean baseline EF 2.35 (.33) 2.31 (.32) .07
Mean difference 6
th
-4
th
grade EF deficits -.005 (.32) -.009 (.038) .88
^ Data only collected on a random subsample of 331 children
p value reflects Wilcoxon Rank-Sum test as outcomes are non-normally distributed

110
Table 3.2. Comparing outcome, exposure, and covariate values between final analytic
sample with exposure assignment (N=475) and participants with complete teacher-assessed
CBCL

Dichotomous Baseline Variable
Participants
with NRAP
Exposure
Assignment
and CBCL
N=331
%
Participants
with NRAP
Exposure
Assignment and
no CBCL
N=144
%
P
Male gender 50.76% 43.06% .12
Free lunch eligibility 17.52% 23.61% .12
White race/ethnicity 37.16% 32.64% .34
Hispanic race/ethnicity 21.15% 24.31% .45
Black race/ethnicity 1.81% 1.39% .74
Asian race/ethnicity 7.85% 12.50% .11
Program vs. Control 66.16% 30.56% <.001
Continuous Baseline Variable Mean (SD) Mean (SD) P

Age 9.28 (.49) 9.28 (.49) .56
Stress 1.98 (.33) 1.93 (.32) .17
Mean baseline EF 2.36 (.33) 2.34 (.36) .47
Mean difference 6
th
-4
th
grade EF deficits -.02 (.02) .02 (.03) .22
Residential Regional PM2.5 (2009 in μg/m
3
) 11.9 (2.9) 12.9 (2.8) <.001
Residential Regional PM10 (2009 in μg/m
3
) 30.3 (6.9) 32.6 (6.5) <.001
Residential Regional NO2 (2009 in ppb) 16.1 (5.8) 18.5 (4.8) <.001
Residential Regional O3 (2009 in ppb) 40.7 (2.0) 41.3 (2.4) .006
Total Near-roadway Modeled NOX 10.7 (6.8) 10.0 (7.3) .29
Freeway Near-roadway Modeled NOX 7.6 (6.5) 7.1 (6.9) .47
Non-Freeway Near-roadway Modeled NOX 3.2 (1.2) 2.9 (1.7) .06

Zero-order correlations between students’ self-reported composite EF deficits and teacher-
reported problem behavior assessed via CBCL were generally low. For externalizing behavior,
correlations ranged from r =.25 at 4
th
grade to r=.19 at 6
th
grade, while correlations were of a
lower magnitude for internalizing behavior (r=.02 at 4
th
grade to r=-.08 at 6
th
grade).

111
Measurement
Executive Function
The BRIEF-SR measure of composite EF deficits exhibited excellent reliability at each of the 4
assessment waves, both among the full N=709 analytic sample and the N=475 for whom
residential NRAP could be calculated/assigned (Cronbach’s Alpha=.86-.88). Confirmatory
factor analysis confirmed adequate fit of a 1-factor model of composite EF at each assessment
wave (RMSEA< .08 ; SRMR <.08)(Rigdon, 1996). However, model fit improved when a two
factor CFA model was fit, with the inhibitory control and emotional control subscale items
specified to load onto a latent factor representing “Hot EF”, and the working memory and
organization of materials subscale items specified to load onto a “Cool EF” factor ( RMSEA<
.065 ; SRMR <.06). Model fit was further improved when each of the 4 BRIEF subdomains was
treated as a factor in a 4- factor CFA model (RMSEA< .05 ; SRMR <.05), suggesting that further
examination of specific EF subdomains as unique outcomes may be warranted. Consequently, as
in previous work (Warren et al., 2017) (Warren et al., 2017) (Pentz & Riggs, 2013), summary
mean scores were calculated reflecting composite EF deficits, as well as deficits within each of
the 4 assessed subdomains (i.e. inhibitory control, emotional control, working memory,
organization of materials).

Childhood Behavior Checklist
In contrast to the mean scores for each of the 4 EF subdomains, which exhibited significant
pairwise correlations at each wave (r>.35; p<.01) the mean scores of the internalizing and
externalizing behavior subdomains from the assessed BRIEF items were not significantly
correlated (r<.10; p>.05). Unsurprisingly, a one-factor CFA model provided very poor fit to the
112
CBCL data (RMSEA=.157 (90% CI:.146-.147); SRMR=.132). In contrast, a two-factor CFA
model including both a latent internalizing and externalizing factor provided significantly
improved fit to the data at each wave (Wave A: RMSEA=.092 (90% CI:.082-.103); SRMR=.067;
X
2
=426.38; p<.001; Wave C: RMSEA .099 (90%CI .089-.110); SRMR=.069 X
2
=410.60;
p<.001; Wave D: CFI=.899 ; RMSEA .095 (90%CI .084-.106); SRMR=.076; X
2
=354.48;
p<.001). This confirms that the internalizing and externalizing subscales of the CBCL should
not be pooled, but rather considered as distinct outcomes.

As seen in Figure 3.1, baseline unadjusted associations between modeled NRAP exposure (in
ppb) and each of the 4 assessed EF subdomains were small and mostly statistically
indistinguishable from zero both overall (rspearman=-.03 to -.09; p>.05 for all except emotional
control (p=.049), as well as when only controls were analyzed (rspearman=-.05 to .09; p>.05 for
all).

Figure 3.1. Baseline models predicting EF deficits

113
Figure 3.2 reports the distribution of the count outcomes assessed by each subscale of the
childhood behavior checklist (CBCL) at study baseline. As expected among normative
populations, the majority of students had zero teacher-reported externalizing (53%) or
internalizing behaviors (62%) at 4
th
grade. The lowess curves superimposed upon the
distributions of CBCL outcomes suggest that there were no substantial differences in count
distributions by NRAP exposure strata at 4
th
grade.

Furthermore, when the frequency of internalizing and externalizing behaviors was compared at
baseline over NRAP exposure strata (not shown), no significant differences in mean counts were
found between the highest and lowest quartiles of NRAP exposure in either minimally- or
maximally adjusted models (p>.05).

Figure 3.2. Baseline CBCL outcomes among all participants

After examining cross-sectional associations at baseline, minimally, and maximally-adjusted
multilevel regression models were fit among control participants , which incorporated random
effects at the individual and school level and pooled EF outcomes across all 4 waves of
measurement to reduce measurement error. However, as seen in Figure 3.3, findings were
inconsistent with the study hypothesis that greater EF deficits would be observed among students
more highly exposed to near-roadway air pollution. In light of previous work identifying effect
modification of associations by sex, models were also fit incorporating additional interaction
terms by sex. In fully adjusted models, the sex X NRAP interaction term did not approach
significance when examining composite EF, or specific subdomains (p>.50 for all models) As
seen in Figure 3.4, a clear dose-response relationship between NRAP exposure strata and EF
outcomes was not present either overall nor in sex-stratified models pooling outcomes across all
four waves of measurement.

Figure 3.3. Pooled models predicting EF deficits across 4
th
-6
th
grades,

115
Figure 3.4. Pooled models predicting sex-specific EF deficits across 4
th
-6
th
grades,

Similarly, no significant differences were observed over the various NRAP exposure strata with
respect to externalizing or internalizing behavior (Figure 3.5) pooled across the 4 measurement
waves. This was true whether the mean counts were modeled directly via multilevel negative
binomial regression, or whether the data were dichotomized into any teacher-reported
externalizing/internalizing behavior vs none and then modeled via mixed effects logistic
regression (analyses not shown). Again, no effect modification by sex was observed.

Table 3.3 Comparing mean externalizing and internalizing behavior between upper vs
lower quartiles/deciles of NRAP exposure

Undjusted
1
Adjusted
2

Mean Externalizing Behavior
D([³90
th
-. ≤10
th
Pct Total NRAP (SE)
.15(1.58) 2.45(2.07)
Mean Externalizing Behavior
D([³75
th
-. ≤25
th
Pct Total NRAP (SE)
-.20 (.93) .52 (.94)
Internalizing Behavior
D([³90
th
-. ≤10
th
Pct Total NRAP (SE)
-.27 (.34) -.62 (.47)
Internalizing Behavior
D([³90
th
-. ≤10
th
Pct Total NRAP (SE)
.34 (.35) .38 (.38)
1
Accounts for school-level and within-subject clustering only via random effects
2
Also adjusts for Age, Gender, Race/Ethnicity (White, Black, Asian, Hispanic), Free Lunch Eligibility,
Area Deprivation Index, Enhanced Vegetation Index, Regional PM 2.5 and O 3

116
Figure 3.5. Pooled models predicting externalizing and internalizing behavior problems

The above models accounts for school-level and within-subject clustering via random effects, as well as
incorporate fixed effects of Age, Gender, Race/Ethnicity (White, Black, Asian, Hispanic), Free Lunch Eligibility,
Area Deprivation Index, green vegetation, Regional PM 2.5 and O 3.

Finally, for comparability with other studies that have relied on proximity to major roadways as
an indicator of NRAP exposure, as opposed to the plume modeling methods utilized in the
current study, mean values of EF were compared based on residential proximity to a major
roadway (Caltrans classes 1, 2, or 3("Functional Classification,")). First, buffers were applied at
50m, 175m, and 300m per previous work (Batterman et al., 2014). However, only 112 of the
475 study participants lived within the 300 meter buffer, so additional buffers were applied at
100m, 300m, 500m, >500m—values which have also been used in previous studies (Jerrett et al.,
2010). One-way ANOVA found no significant differences in composite EF scores by major
117
roadway proximity at 4
th
or 5
th
grade assessments (F(77 df)<=2.25 ; p>.05). A significant overall
difference by (50m, 175m, 300m) near-roadway proximity strata was found at 6
th
grade only [F
(77 df) = =4.59; p=.01), however the directionality was in the opposite direction from that which
was hypothesized. That is to say, the fewest deficits were observed among participants with the
greatest roadway proximity. No differences were found when using the more inclusive roadway
proximity categories at 6
th
grade (F=.68 (474 df) ; p=.56).

Similarly, Pearson chi-squared tests were used to compare the presence/absence of externalizing
and internalizing symptoms at 4
th
, 5
th
, and 6
th
grades by roadway proximity strata. No evidence
of a dose-response relationship was observed for either CBCL outcome. All chi-square tests
were non-significant when using the 50m, 175m, 300m buffers. When considering the more
inclusive buffers, non-significant chi-squared tests were observed at 5
th
(X
2
(3df)=1.87; p=.61), and
6
th
(X
2
(3df)=2.35; p=.50) grades for externalizing behavior and 5
th
grade for internalizing
behavior (X
2
(3df)=0.65; p=.89). However, once again, in each case where X
2
tests were
significant at p<.05, the pattern of results again was in the opposite direction from that which
was hypothesized.

118
DISCUSSION

Overview of Main Findings
The present study systematically examined hypothesized adverse neurodevelopmental effects of
NRAP exposure among a sample of Southern California elementary schoolchildren. Observed
effects of modeled NRAP exposure on study outcomes, which include the self-reported behavior
inventory of executive function and a teacher-report behavioral inventory of externalizing and
internalizing behaviors, were inconsistent with the study hypothesis that executive functioning
and neurobehavioral deficits would be greater among more highly exposed children. Both cross-
sectional and longitudinal associations were examined providing no significant evidence to
support the study hypothesis, either in unadjusted or covariate-adjusted models accounting for
participant age, race/ethnicity, gender, federal free lunch eligibility, regional air pollution
exposure, residential neighborhood deprivation and greenery, as well as participant clustering at
the school and individual levels. Teacher-reported externalizing behaviors were twice as
frequent among the most highly NRAP exposed decile, relative to the least-exposed decile.
However this difference did not reach statistical significance and must be considered in the
context of the highly inconsistent relationships observed beyond these particular exposure strata.

Air Pollution Exposure Assessment—Challenges and Suggestions for Future Work
While care was taken in the present study to apply state-of-the-art line-source dispersion models
to estimate exposures to the near-roadway plume, accounting for key meteorological factors, as
well as background concentrations of regional air pollutants, the validity of such GIS-based
approaches is dependent on key assumptions that are difficult to assess in the context of the
present school-based prevention trial. Given that this is a secondary analysis of data which were
119
not originally intended to examine health effects of air pollution, exposures were assigned based
upon school and primary residential address provided by parents at study enrollment, rather than
a comprehensive list of previous addresses. Therefore, it is likely that some degree of
nondifferential misclassification of NRAP exposure occurred among children who had had
resided at other addresses, or were currently residing in multiple addresses (e.g. children of
divorced parents). It is probable that in some cases the exposures at these alternative addresses
were substantially higher or lower than those assigned to the primary address, introducing
additional measurement error to presented estimates of recent/current NRAP exposure.
Furthermore, in light of previous work linking early-life/prenatal exposures to subsequent
neuropsychological outcomes (Clifford, Lang, Chen, Anstey, & Seaton, 2016), it is possible that
study estimates are confounded by the presence/absence of prenatal/early life air pollution
exposures, which were also unassessed by the present study. Such nondifferential
misclassification of exposure is well-known to bias estimates of exposure-outcome
associations—most frequently toward the null (Jurek, Greenland, Maldonado, & Church, 2005).

To date, among the best-characterized cohorts established to study adverse neurodevelopmental
effects of NRAP in an urban US context is the Columbia Center for Children’s Environmental
Health NYC birth cohort, which recruited healthy African-American or Dominican Women
living in the Bronx, through local prenatal clinics (Perera et al., 2014). Since participants were
recruited during pregnancy, the authors were able to characterize prenatal exposure to polycyclic
aromatic hydrocarbons (PAH) via analysis of maternal and umbilical cord blood collected
immediately after delivery. While PAHs can result from a variety of incomplete combustion
reactions, including regional industrial processes and food preparation, vehicle traffic is a major
120
source of urban PAH exposure (Boström et al., 2002). Therefore, PAH adduct levels in
maternal/cord blood present a useful biomarker for characterization of prenatal NRAP exposure.
Similarly the CCCEH cohort also characterized PAH exposure throughout childhood via spot
urine analysis of PAH metabolites, permitting more comprehensive characterization of this time-
varying exposure than the single, model-based measure used in the present study. However, it is
worth noting that our study did attempt to account for systematic differences in exposure to key
regional air pollutants, which may act on similar pathways as NRAP and thus constitute an
important potential confounder when estimating unique effects of NRAP exposure on
neurodevelopment based exclusively on modeled exposure to the near-roadway plume. This was
achieved through GIS-based assignment of residential exposures to PM2.5, PM10, O3, and NO2
using monitoring data collected by the EPA and South Coast Air Quality Management District
between 1999 and 2010, and their incorporation as covariates in adjusted models.

Assessment of Executive Function in Population-based Studies—
Challenges and Suggestions
Another well-characterized cohort that has been utilized to study the neurodevelopmental effects
of air pollution exposure in a school-based context employed simultaneous direct measurement
of NRAP (i.e. Environmental Carbon, NO2, and Ultrafine particles) via indoor and outdoor
monitors at matched pairs of Barcelona schools (Sunyer et al., 2015). Validated land use
regression models were also used to characterize residential exposures (M. Wang et al., 2013).
This study found that children at schools with higher levels of each of the aforementioned near-
roadway pollutants (indoors and outdoors) had substantially smaller growth in all three of the
cognitive assessments administered. In contrast to the present study, which administered
121
behavioral ratings inventories of executive function and problem behavior, the Spanish cohort
utilized three computerized performance-based tasks, which were combined into a 40 minute
assessment battery administered in small groups by trained examiners. Specific validated
working memory (i.e. “n-back” tasks) and attention tasks (i.e. the attentional network task) were
chosen for administration at 3 month intervals due to previous work indicating that task
performance improves reliably across pre-adolescence (P. Anderson, 2002; Rueda, Rothbart,
McCandliss, Saccomanno, & Posner, 2005), unlike the BRIEF-SR and CBCL which were not
designed to be developmental measures in the same fashion. Furthermore, the large number of
blocks of each task administered during the 40 minute assessment batteries reduces the
variability of resulting estimates, permitting greater statistical power to detect significant effects
in their larger (N>2400) longitudinal cohort than afforded by the abridged, self-report measures
utilized in the present study. Consequently, future studies should consider augmenting behavior
ratings inventories with computerized, performance-based tasks where feasible, as such measures
may be better equipped to detect NRAP-induced cognitive deficits.

While administration of performance-based testing batteries may be infeasible in the school-
based context—recent work suggests that it may be possible to deliver these instruments via
smartphone-based assessment platforms outside of school hours (Warren & Pentz, 2018). This
recent study found that delivering the child flanker task and complex symmetry span working
memory tasks to 7
th
graders via an iPhone app, was both acceptable and feasible to a group of 7
th

grade youth, who were administered an assessment battery including both tasks and other survey
measures a total of six times over a 72 hour period. If these mobile, performance-based EF tasks
are found to be valid when delivered in this ecological momentary assessment context, such
122
measures have the potential to increase the quality and quantity of EF assessments utilized in
future work. Notably, the National Institutes of Health “Cognition Toolbox”—which contains a
series of standardized, validated cognitive assessment measures, including the aforementioned
child flanker task—was recently adapted for mobile administration via an iPad app (Brearly et
al., 2019). Given that behavioral self-report measures like the BRIEF-SR can also be
administered via EMA, future work should consider if/how such mobile EF assessment methods
can augment more traditional assessment modalities, given their potential for greater ecological
validity and participants’ growing preference for electronically administered instruments.

Recent findings from the Project VIVA cohort study, which like the present study also utilized
the BRIEF to characterize EF among an elementary-school aged sample, found significant EF
impairment among children with greater cumulative childhood black carbon exposure as well as
greater black carbon exposure within the previous year (Harris et al., 2016). However
relationships were only observed for the teacher-reported BRIEF- Behavioral Regulation Index
(which includes the subscales of Inhibitory Control and Working Memory used in the Pathways
study), but also two additional scales (Shift, and Self-Monitoring) which were not included in the
Pathways study. No associations were identified by Harris et al between black carbon exposure
and the Metacognition Index (which includes the subscales of Working Memory and
Organization of Materials utilized in the Pathways study). While the BRIEF has been validated
for use in both teacher-report and self-report formats, previous work suggests that teacher-
reported instruments may be more strongly associated with student behavioral outcomes than
students’ own self-report (Buckley & Krachman, 2016). Future work should consider comparing
teacher-, parent-, and child self-reported EF measures where feasible, as they may reduce
123
measurement error and bias stemming from use of self-report scales only. While they did not
rise to the level of statistical significance, the pattern of results observed with respect to
externalizing behavior outcomes within the highest decile of NRAP exposure was the most
consistent with previous findings of adverse neurodevelopmental effects of NRAP exposure.

Unassessed Household-level Factors Influencing Cumulative Air Pollution Exposures
Another limitation of an exclusively GIS-based strategy for air pollution exposure assignment is
their inability to account for household-level factors known to modify NRAP exposure. While
indoor concentrations of urban air pollutants such as PM2.5 and PM10 have been found to be
positively associated with outdoor concentrations (Kuo, 2010), the levels of NRAP in ambient
air outside a residence can differ dramatically from the levels indoors where people spend most
of their time (Jenkins, Phillips, Mulberg, & Hui, 1992). For example a recent study found that
indoor concentrations of PM2.5, black carbon ultrafine PM and ozone in a home located 250m
downwind of a California Freeway could be reduced to under 10% and 5% of outdoor ambient
levels with use of widely-available MERV13 and MERV16 home air filters, respectively
(Singer, Delp, Black, & Walker, 2017). Unfortunately, the present study did not assess the
presence or type of air filtration used in participant residences or schools, nor the nature of the
building’s ventilation system. For instance research indicates that the siting of supply air intakes
at street level, can result in elevated indoor levels of traffic-related pollutants, relative to intakes
sited in locations more distal from the near-roadway plume (Fung, Yang, & Zhu, 2014). Besides
the location of intakes, whether a building utilizes supply ventilation (where the building is
pressurized via an intake fan), exhaust ventilation (where the building is depressurized via an
124
exhaust fan), or balanced ventilation (which incorporates both supply and exhaust fans) has
impacts on the concentrations of indoor NRAP. Generally speaking, research suggests that
indoor concentrations of traffic-related air pollutants are greater in buildings lacking exhaust
ventilation.

Apart from mechanical ventilation, which can occur through air conditioning units or fans,
outdoor NRAP can also enter the indoor environment via natural ventilation processes, such as
open windows or doors. Such ventilation practices that readily circulate unfiltered outdoor air
within the home can result in comparable levels of NRAP inside the home relative to outdoor
concentrations (Fuller et al., 2013). In some cases, indoor concentrations may even exceed
outdoor concentrations due to particulate accumulation within the home (Singer et al., 2017).
Future work relying on plume modeling of NRAP exposure at participant residences should
consider incorporating assessment of both mechanical and natural ventilation processes in order
to reduce measurement error in exposure assessment as well as to permit analysis of effect
modification by behavioral and built-environmental factors intended to reduce exposure to
NRAP and other ambient air pollutants. For example, routinely closing windows can reduce
indoor-outdoor air exchange by 50% (Meng, Spector, Colome, & Turpin, 2009).

The other principal route through which NRAP may enter a building is through a mechanism
known as infiltration. Infiltration occurs when outdoor air seeps through holes, cracks, or leaks
in the exterior shell of a building. Infiltration is more likely to occur in poorly sealed structures,
such as under doors and around windows. Given that routine, often-costly maintenance can be
required to maintain air-tightness of a building’s envelope (the barrier that separates indoor and
125
outdoor air), it is likely that low SES populations may experience greater infiltration of outdoor
air pollutants into the indoor environment. Low SES populations may also be less likely to live
in households with the aforementioned ventilation and air filtration characteristics that can
reduce indoor NRAP exposure, and consequently may be more likely to employ natural
ventilation methods, particularly in Southern California where temperatures are generally mild
and precipitation infrequent (Hajat, Hsia, & O'Neill, 2015). Furthermore, air filtration systems
generally require relatively frequent replacement of filters, which can be costly—particularly the
MERV13 and MERV16 filters which provide the greatest protection against particulate air
pollution.

Environmental Tobacco Smoke and Socioeconomic Status—Potential lurking confounders
Another potentially important unassessed confounder of associations between near-roadway air
pollution exposure and neurodevelopment that should be accounted for in future work is
environmental tobacco smoke (ETS) exposure. Numerous studies have linked greater ETS
exposure, both during childhood and in-utero—to worse neurodevelopmental outcomes across
adolescence (Pagani, 2014) (Kabir, Connolly, & Alpert, 2011). Findings from a previous cohort
study suggest that ETS exposure may potentiate the adverse health effects of NRAP among
Southern California schoolchildren, possibly due to their effects on shared inflammatory immune
pathways (McConnell et al., 2015). Studies have also identified associations between ETS and
NRAP exposure, which may share a common cause of socioeconomic deprivation. In general,
rates of tobacco use are higher among lower SES Southern California residents, whose
households tend to be both larger in terms of number of inhabitants per household and smaller in
126
terms of overall square footage. Both of these factors are likely to result in greater concentrations
of this important indoor air pollutant. Self-reported lifetime cigarette use was assessed in the
current study, which was positively associated with free lunch eligibility and greater EF deficits,
respectively at 5
th
and 6
th
grades (rspearman= 0.10-0.16), as reported in previous work (Pentz &
Riggs, 2013). However, it was uncorrelated with modeled-NRAP exposure (rspearman=.004).
Furthermore, given the relatively young age of our sample, and the fact that 21 of the 31
participants reporting any cigarette use by 6
th
grade only reported a single puff, this is likely to
be a poor proxy for ETS exposure.

As in many studies of spatially-varying exposures, there were concerns about residual
confounding by SES in the present study, particularly given the nature of the self-report measure
utilized for individual-level assessment. Many studies have observed that exposure to N0X and
other primary air pollutants like PM2.5 is greater among low-income and non-White populations
in the US relative to their higher income and White counterparts (Bell & Ebisu, 2012; L. P.
Clark, Millet, & Marshall, 2014; Marshall, Brauer, & Frank, 2009). This has been demonstrated
both with regard to average pollutant concentrations in low-SES vs. high SES communities as
well as with respect to housing quality and differential engagement in behaviors which may
modify air pollution exposure. For example, lower-income individuals are less likely to own a
car, and thus more likely to walk or use public transit, thereby increasing exposure to NRAP
relative to higher SES neighbors (Pratt, Vadali, Kvale, & Ellickson, 2015). Even among children
from low-income, car-owning families, it is possible that their exposure to on-roadway pollutants
may be greater than their higher-income peers, due to more frequent transit in older passenger
vehicles with less efficient/frequently-maintained cabin air filtration.
127
Strong associations between low SES and impaired executive functioning have been reported in
previous literature from infancy through late childhood (Lipina, Vuelta, & Colombo, 2005).
Conversely, numerous studies have concluded that greater SES (as indicated by a variety of
proxies, including maternal educational attainment, and family income-to-needs) predicts
improved working memory and inhibitory control among young children (Sarsour et al., 2011).
In the present study, as in many other survey studies of children, SES was characterized by a
single question at each assessment wave, which asked whether students were eligible for the
federal free lunch program. While the repeated assessments help reduce measurement error,
clearly a single dichotomous measure is insufficient to fully explain the variability in SES among
our cohort. Furthermore, such measures may be increasingly misinterpreted by students as more
schools expand free lunch to all students, per the recent Community Eligibility Provision of the
National School Lunch Program (Vilsack & Duncan, 2015). A validated measure of
community-level deprivation—the area deprivation index—was also added into each model in an
effort to capture some of the residual variance in SES unmeasured by the free lunch eligibility
items—which was supported by the moderate baseline correlation observed between the two
SES indicators (rspearman=.36 ). While both SES items were somewhat correlated with Total
NRAP (rspearman=.08-.10), the magnitude of their correlations with EF deficits and externalizing
behavior were considerably lower (rspearman<.06). To more comprehensively assess SES, future
work should consider incorporating assessment of “the big 3” indicators of SES: family income,
parental educational attainment, and parental employment status in order to increase precision
and explain more of the true underlying variability in individual SES (Cowan et al., 2012).
While SES (like NRAP exposure, and the neurodevelopmental outcomes studied here), may have
been subject to measurement error in the current study, it is highly unlikely that its
mischaracterization is the primary contributor to the null effects observed here since stratified
effects were comparable in magnitude and direction across SES strata.

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CHAPTER 4—Exploring obesogenic and neurobehavioral effects of near-
roadway air pollution: effect modification by chronic stress?

BACKGROUND

Psychosocial Stress: Friend and Foe
Psychosocial stress has been conceptualized as resulting from demanding life conditions that
exceed one’s ability to cope (Goldstein & Kopin, 2007). During an acute stress response, the
sympathetic nervous system is activated, resulting in the so-called “fight-or-flight” response,
wherein catabolic pathways are activated, and respiration, blood pressure, and heart rate are
increased-thereby mobilizing valuable physiological resources (e.g. oxygen and glycogen) to
address this external threat. One of the principal pathways involved in this so called “stress
response” is a hormonal network known as the hypothalamic-pituitary-adrenal (HPA) axis,
which integrates both physical and psychosocial inputs so as to permit adaptation of an organism
to its environment and successful confrontation of challenges perceived as necessary for survival
(Stephens & Wand, 2012) . This occurs both through facilitating the aforementioned
physiological responses which are likely to help the organism immediately deal with the stressor,
as well as through diversion of biological resources from processes that are less pressing in the
short-term. For example, during acute stress, these multi-pronged stress responses lead to short-
term immunosuppression and release of anti-inflammatory cytokines, while concomitantly
reducing digestion and appetite (Dhabhar, 2014).

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Briefly, initiation of the human stress response generally involves secretion of the following two
peptides from the paraventricular nucleus of the hypothalamus: cortocotropin-releasing hormone
(CRH) and vasopressin. These peptides stimulate secretion of adrenocorticotropic hormone,
which in turn acts on the adrenal cortex, which in turn releases cortisol, a steroid hormone, which
is a key mediator of the human stress response. Importantly, the HPA axis is comprised of a
series of negative-feedback loops which protect against its extended activation and serve to
regulate hormone levels near an individual’s “set-point”, thereby achieving homeostasis. One
particularly important mechanism for homeostatic regulation of cortisol is exerted by the
presence of two types of receptors throughout the HPA axis: types I and II. Type I receptors
have higher affinity for cortisol than type II receptors and the activation of the latter leads to
more rapid termination of the stress response. (Besedovsky, H., Chrousos, & Rey, 2008) This
discrepancy between the affinity of cortisol receptor types I and II promotes homeostasis of
circulating cortisol, which is crucial, as both chronically high and low cortisol have been linked
to negative health effects across the lifespan (McEwen, 2008). While previous work has
identified genetic factors that can increase risk of such HPA axis dysregulation, both among
adults (Bartels, Van den Berg, Sluyter, Boomsma, & de Geus, 2003) and children (Bartels, de
Geus, Kirschbaum, Sluyter, & Boomsma, 2003), chronic psychosocial stress, has been identified
by many studies as a key determinant of HPA axis dysfunction (Farag et al., 2008).

In recent decades, a large and growing literature has linked chronic psychosocial stress and its
corresponding HPA axis dysregulation to a variety of adverse developmental health effects,
including both increased risk of metabolic disease as well as neurodevelopmental problems.
Within the brain, the prefrontal cortex (PFC) is the region most sensitive to the adverse effects of
137
stress (Arnsten, 2009). Consequently, the most pronounced stress-related cognitive impairments
observed to date have been in the PFC-mediated systems responsible for executive functioning
(EF)—top-down cognitive processes, which support emotional regulation and behavioral
inhibition as well as more complex behavioral decision-making (Diamond, 2013). EF deficits
have been previously linked to a variety of increased risk-taking and problem behaviors within
pediatric populations, as well as reduced engagement in health promoting behaviors (Pentz &
Riggs, 2013), which require the suppression of prepotent, default, responses in favor of more
effortful and/or planned alternatives. For example, engagement in physical activity and
consumption of fruits and vegetables among children has been shown to be predicted by their
levels of executive functioning, given that screen time and consumption of high-calorie/low-
nutrient foods often constitute default modes of behavior in today’s obesogenic society (N. R.
Riggs, Spruijt-Metz, Chou, & Pentz, 2012).

However, other studies have linked stress and obesity more directly, demonstrating that
chronically stressed humans and animals alike are more likely to prefer highly caloric, less
nutritious foods (i.e. foods higher in fat and sucrose) when stressed in laboratory paradigms
where both healthy and unhealthy foods are equivalently accessible (Yau & Potenza, 2013).
Research has shown that elevated glucocorticoid levels (e.g. cortisol) after stress exposure are
associated with increased food intake more generally and reduced satiety as a function of
reduced sensitivity to leptin and enhanced insulin resistance (Spencer & Tilbrook, 2011).
Epidemiological studies have also identified associations between obesity status and higher long-
term (i.e. hair) cortisol levels in pediatric and adult samples (Jackson, Kirschbaum, & Steptoe,
2017; Noppe et al., 2016; Wester et al., 2014). Remarkably, elevated hair cortisol levels have
138
also been linked to greater central adiposity in children (Noppe et al., 2016)), which has been
attributed to the greater number of glucocorticoid receptors present in visceral adipose tissue,
relative to other adipose tissues—leading to an unhealthier redistribution of body fat (Fardet &
Fève, 2014; Rebuffé-Scrive et al., 1990; van der Valk, Savas, & van Rossum, 2018).

Synergistic Effects of Stress and Environmental Toxicants
Findings from mechanistic studies suggest that the adverse health effects of chronic exposure to
psychosocial stress and environmental toxicants such as exposure to traffic-related, near-
roadway air pollution may operate through similar biological pathways (e.g. HPA axis
dysregulation, oxidative stress, upregulation of inflammatory immune responses). As such it is
reasonable to expect that their effects may be additive and/or multiplicative (Wright, 2011). For
example, a cohort study of military found that associations between blood lead concentrations
and cognitive impairments were significantly stronger among more highly stressed participants
(Peters et al., 2010). Further pediatric studies have found that the adverse health effects of
chronic exposure to traffic-related air pollution on increasing asthma risk (Clougherty et al.,
2007; Shankardass et al., 2009), frequency of exacerbations and levels of proinflammatory
cytokines (E. Chen, Schreier, Strunk, & Brauer, 2008) were greater among more chronically
stressed children. Near-roadway air pollution is an increasingly ubiquitous exposure both
globally and in the US, in light of trends of growing urbanization and its accompanying traffic
volumes. Greater NRAP exposure has been linked not only to increased respiratory morbidity,
but also to increased risk of childhood obesity and neurodevelopmental problems such as
executive functioning deficits (Harris et al., 2016), ADHD, and problem behavior (Xu, Ha, &
139
Basnet, 2016). However, to date, the issue of whether high levels of chronic stress may
potentiate NRAP’s putative obesogenic or neurotoxic effects remains unexamined.

Nearby Nature— A Potentially Important Covariate
An established and growing body of research has linked exposure to the natural environment
such as green spaces with improved physical health, psychological well-being, and cognitive
function (Barton & Rogerson, 2017; Markevych et al., 2017; Wells & Evans, 2003). Many
studies have also found stress reduction to be a significant benefit associated with spending time
in areas with green vegetation (Amicone et al., 2018; Berto, 2014). Furthermore, exposure to
“nature” has been conceptualized as a moderator of life stress on elementary schoolchildren .
For example, a large, population-based Dutch health survey found that observed associations
between the number of stressful life events and both the number of self-reported health issues,
and self-reported general health, were moderated by the amount of green vegetation near the
home (van den Berg, Maas, Verheij, & Groenewegen, 2010).

A cross-sectional study of New York City children identified associations greater local
greenspace and lower asthma prevalence among 4 and 5 year old children. The authors also
identified a similar relationship between greater greenspace and lower asthma hospitalization
rates, although it did not reach statistical significance after adjustment for sociodemographic
confounders. While it was not formally tested, the authors proposed that the likely mechanism
for this effect was the local improvements in air quality resulting from increased nearby green
vegetation (Lovasi, Quinn, Neckerman, Perzanowski, & Rundle, 2008). Consistent with this
hypothesis, a more recent study employing personal air pollution monitoring of urban-dwelling
140
pregnant women found that participants living in areas with more greenspace—as assessed via
satellite imagery—had lower personal exposures to PM2.5 than their counterparts residing in less
green areas (Dadvand et al., 2012). Further work examining the health effects of air pollution
have incorporated measures of residential/neighborhood greenness as covariates to adjust for
their potential confounding role—given that residential greenness has been shown to be
negatively associated with air pollution exposure and obesity (Villeneuve, Jerrett, Su,
Weichenthal, & Sandler, 2018), as well as healthy neurodevelopmental outcomes (Dadvand et
al., 2015). This suggests that local greenspace may be important to consider as a covariate in
future work examining the health effects of air pollution in urban settings.

Consequently, the current study examines the extent to which the effects of chronic exposure to
NRAP on multiple obesogenic/neurodevelopmental outcomes may vary by chronic stress in a
diverse sample of Southern California schoolchildren, accounting for key covariates including
the presence of nearby green vegetation and exposure to regional air pollution.

METHODS

Data for study outcomes and key covariates were collected between 2009-2011 as part of the
routine assessments administered to participants in the Pathways to Health trial, a cluster
randomized control trial for the prevention of multiple health risk behaviors, including
obesogenic behavior and substance use. The study was carried out in 28 Southern California
schools from 2009-2011 (Little, Riggs, Shin, Tate, & Pentz, 2015; N. R. Riggs et al., 2012;
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Sakuma, Riggs, & Pentz, 2012; C. Warren, Riggs, & Pentz, 2016) with an average of one 4th
grade class per school. Human subjects procedures were approved by the University of Southern
California Institutional Review Board.

Pathways to health participants include a longitudinal cohort of 709 students with both full active
parental consent and self-assent to participate in the study from 4
th
through 6
th
grade with four
waves of measurement. At each assessment, a survey was administered verbally during a 45-
minute classroom session, with trained staff present in order to answer comprehension questions.
Due to the limited amount of classroom time for survey administration, abbreviated versions of
the survey instruments were piloted, shown to be valid and reliable, and subsequently used in
this study, Use of abridged versions of surveys are commonplace in school-based research with
youth (Gortmaker et al., 1999).

Among students participating at each assessment wave, at total of 475 students had complete
“Student Information Sheets”, which included the student’s current residential street address at
the time of the study. Completion of these forms was optional. These residential addresses were
geocoded using ArcGIS World Geocoder and the geocoded coordinates were utilized to assign
near-roadway and regional air pollution exposures to each student’s residence and school.
Geographic distances between each child’s school and residence were also calculated. The
quality of geocoded addresses was high (>90% matched to building centroid).

All 475 participants in this cluster-randomized control trial were included in analyses of baseline
4
th
grade data, which were collected before the prevention program was implemented. However,
142
longitudinal analyses incorporating data across 5
th
and 6
th
grades were performed on the 212
participants randomized to the control group—receiving health education as usual. Study
participants resided in San Bernardino, Orange, and Los Angeles counties.

Primary Exposures Of Interest:

Near-roadway Air Pollution Exposure
Average NOX exposures from local on-road motor vehicle emissions were estimated for each
geocoded residence and school locations for the year 2009 using CALINE4 line-source
dispersion models. These estimates do not include estimated regional background air pollution
levels, which were accounted for via inclusion of additional covariates. Link-based traffic was
based on Caltrans + ESRI Premium StreetMap traffic count and roadway geometry. Vehicle
emission factors as a function of speed and LDV/HDT fleet mix were determined from CARB's
EMFAC2014 model for 2009. Vehicle NOX exhaust emissions were modeled from local on-road
traffic/roads within 5 km of residences and schools. Link-specific exhaust emission factors based
on average vehicle speed and HD truck fraction were applied using Caltrans post-mile truck
count data by year. Day-of week and hour of day volume adjustments from WIM data Freeway
(FCC1) and Non-Freeway (FCC2-FCC4) road classes were also applied. Regarding
meteorology, surface wind data from the closest AQS monitor was used. Monthly wind speed
and direction frequencies were determined from 1997-2014 surface wind data. Mixing heights
[morning, afternoon, and nighttime climatological values (200m, 400m, 100m)], and
atmospheric stability based on time of day and wind speed were also incorporated into the
model. Exposures for freeway and non-freeway sources were assigned to participants’ baseline
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residential and school addresses. Freeway and non-freeway Total NOX exposure was estimated
by adding freeway and non-freeway estimates for each participant. Residential and school-based
Total NOX exposures were combined using both a 79% residential/ 21% school ratio as well as
an 84% residential/16% school ratio, as in previous work (McConnell et al., 2010). These ratios
were calculated to approximate the relative proportion of time students spent in each location
annually.
Psychosocial Stress
Perceived psychosocial stress was assessed via selected items taken from Cohen’s Perceived
Stress Scale, a well-validated measure of the degree to which situations in one’s life are
appraised as stressful (Cohen, Kamarck, & Mermelstein, 1983). In order to help ensure
comprehensibility among our elementary-school aged sample, some minor changes in item
wording were made. To ensure that the measure was completed within the time-constraints
imposed by partner schools, the following six items were selected for administration at all four
assessment waves in the current study. They comprise an abridged version of the full 10 item
scale.
• I got upset because of something that happened all of a sudden.
• I felt nervous and “anxious.”
• I was angry because of things that happened that were outside of my control.
• I felt that I could handle important changes that were happening in my life.
• I felt good about my ability to handle my problems.
• I found that I could handle all the things that I had to do.

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Response options, which prompted students with respect to the past week (7 days) were: “1 =
Never; 2 = Sometimes; 3 = Often”. Positively-worded items were reverse-coded such that
higher scores represented greater stress.

Longitudinal measurement invariance of the perceived stress measures was assessed in order to
ensure that the indicators used to comprise the perceived stress construct at baseline were
consistent across subsequent administrations of the stress measure. By confirming that the
measurement model is consistent across time, this helps ensure that the same construct is being
assessed at each assessment wave therefore indicating that pooling each administration of the
perceived stress scale into a composite score across all 4 waves is warranted. The degree of
measurement invariance present in a given longitudinal construct can be determined by imposing
equality constraints on model parameters and testing to see whether the imposition of additional
restrictions leads to a significant decrease in model fit. A model with weak factorial invariance
has factor loadings that are the same over time for all indicators, while a model with strong
factorial invariance implies both factor loadings and intercepts of indicators are consistent across
time (Putnick & Bornstein, 2016). In the case of the stress construct utilized here, strong
measurement invariance was established across all waves, indicating that the construct of
perceived stress was reliably assessed, with comparable item-level residual error and means
across all administrations [X
2
difference tests: Dconfigural-metric =24.7(15df); p=.06 | D metric-scalar
=12.8 (15df); p=.62 | D scalar-strict factorial (residuals) = 26.9(18df); p=.08] | Dstrict factorial (residuals)-strict factorial
(means) = 5.0(3df); p=.18].

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Anthropometric Obesity-related Outcomes
Participant height, measurement error (National Health and Nutrition Examination Survey
(NHANES): Anthropometry Procedures Manual, 2007). BMI was calculated using participant
height x weight at each assessment wave. Height and weight anthropometric data was converted
to BMI (kg/m2) percentile with Center for Disease Control (CDC) reference charts of BMI
percentile-for-age-and-sex. For analyses of dichotomous outcomes, children at or above the 85
th

percentile of age/sex-specific BMI and Waist Circumference were classified as
overweight/obese. Similarly, children at or above the corresponding 95
th
percentile were
classified as obese. Two weight, and waist circumference were also assessed by two trained data
collectors at each of the four in-school assessment waves. Each measurement was averaged at
each time point per CDC protocol to reduce outliers were excluded from BMI analysis due to
implausibly high BMI values for their waist circumference, (e.g. BMIs of 77 and 145).

Neurobehavioral Outcomes

Executive Function
Items from four of eight clinical sub-scales of the Behavioral Rating Inventory of Executive
Function-Self-Report (BRIEF-SR) were used to assess EF (Guy & Gioia, 2004). The BRIEF was
designed to be an appropriate and ecologically valid measure of EF skills associated with goals
and actions in everyday problem solving situations encountered by school-age children, rather
than an indicator of laboratory or structured task performance (Toplak, West, & Stanovich,
2013). Our decision to abbreviate the BRIEF-SR measure was motivated by time-constraints
imposed by partner schools for in-class survey administration. However, previous work has
146
found the abbreviated BRIEF-SR scales demonstrate predictive validity when compared to full
BRIEF-SR scales.

The four selected EF sub-scales were inhibitory control, emotional control, working memory,
and organization of materials. The 23-item abbreviated questionnaire was prompted by the
following text: “In the past month, how often has each of the following behaviors been a
problem?” Item response choices ranged from 1=Never, 2=Sometimes, 3=Often. For the
present study, abbreviated scales were constructed using the highest loading index items from a
pilot study and demonstrated acceptable internal consistency (α=0.63–0.74) (N. Riggs, Chou,
Spruijt-Metz, & Pentz, 2010). The following items from each subscale were included:
Inhibitory Control:
• I have trouble sitting still;
• I do things without thinking first;
• I interrupt others;
• I get out of control more than my friends;
• I say things without thinking;
• I talk a lot at the wrong time (for example, like during class);
• I don’t think ahead of what will happen before I do something
Emotional Control:
• I make a big deal out of small problems;
• When I get angry, I yell, scream or cry (or I do all of them);
• I yell, scream, or cry for no reason;
• I get more upset than most of my friends do;
• I get bothered easily;
• I get nervous about stuff easily;
Working Memory:
• I have problems completing my work;
• I forget what I’m doing in the middle of things;
• When I am sent to get something, I forget what I am supposed to get;
• I have trouble remembering things, even for a few minutes (such as directions, phone numbers,
etc.);
• I forget instructions easily;
• I forget things all the time;
Organization of Materials:
• My desk/workspace is a mess;
• I lose things (such as keys, money, wallet, homework, etc);
• My backpack/school bag is disorganized;
• I have difficulty finding my clothes, glasses, shoes, books, pencils, etc.

147
This abridged version of the BRIEF has been extensively utilized as an indicator of executive
function and the aforementioned subdomains by previous studies (Pentz & Riggs, 2013; Pentz,
Riggs, & Warren, 2016; N. R. Riggs et al., 2012; Tate et al., 2015; C. M. Warren, Riggs, &
Pentz, 2017).

Problem Behavior
Child behavior problems were assessed via abbreviated versions of internalizing and
externalizing scales from the Child Behavioral Checklist - Teacher Report Form (CBCL-TF)
(Achenbach, 2001). To complete the CBCL-TF, teachers were given a list of symptomatic
behaviors and asked to rate each child on each item by denoting whether a given behavior was
not true (0), “sometimes or somewhat true” (1), “or very true or often true” (2), “now or within
the past 6 months.” This reduced set of CBCL items was selected to reduce the response burden
of teachers, who had up to 30 students per classroom and the time constraint of a single class
period during which to complete all surveys. Eight items were drawn from the CBCL
externalizing subscale (α = .90) which assesses behavior problems such as aggression and
conduct problems (i.e.“disobedient at school”, “does poor school work”, “argues a lot”,
“bullies or is mean to others”, “demands a lot of attention”, “gets in many fights”, “hangs
around kids that get into trouble”, “has temper tantrums”). Four items were also drawn from the
CBCL internalizing subscale (α = .78) which assesses behavior problems such as anxiety,
depression, and somatic problems (i.e. “is too fearful or anxious”, “is self-conscious or easily
embarrassed”, “is shy or timid”, “is worried”). Given the aforementioned time constraints for
administration of this teacher-report assessment, teachers were instructed to complete as many
148
assessments as possible during the day of classroom-based assessment on a randomly-selected
subset of students.

Due to the high correlations (r=.5-.7) observed between subsequent administrations of the EF
and Problem Behavior scales, and their stability over time (in contrast to the obesity-outcomes
which demonstrated linear growth), these outcomes were pooled across grades for the control
group participants [Mean(SD) EF deficits at all waves =1.7 (.3); Mean(SD) Externalizing
behavior at 4th=1.7(2.8); 5
th
=1.9(3.3); 6
th
=1.8(2.9); Mean(SD) Internalizing behavior at
4
th
=0.9(1.4); 5
th
=0.9(1.5); 6
th
=1.1(1.4)].

Covariates

Regional Exposure to Criterion Air Pollutants
Regional air pollution exposures for years 2000-2009 were modeled and retrospectively assigned
to participant residential addresses as most students resided within 1.5 km of school. This range
approximates the lifetime exposures of cohort members, 98.73% of whom were ages 9 or 10 at
study baseline. Monthly average 24-hour concentrations were calculated for NO2, O3, PM2.5,
PM10. Values were estimated from monthly air quality data measured at 4 closest locations
within 50 km of the grid point by inverse distance squared interpolation. In 2009, regional PM2.5,
PM10, and NO2 were highly correlated (r>.9) whereas O3 was less strongly correlated with PM2.5
(r=.23), PM10 (r=.36), and NO2 (r=.05). Similar patterns were observed at other years.
Consequently, only PM2.5 and O3 were included as covariates in final adjusted models.

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The following obesity-related behaviors were drawn from a 145-item self-report survey
administered to children during four in-school assessment periods from 4
th
grade through 6
th

grade.

Overall Physical Activity Inside- and Outside-of-School Multiple aspects of physical activity
were assessed in the present study at 4
th
, 5
th
, and 6
th
grades using adapted versions of the
Physical Activity Questionnaire for Older Children (PAQ-C); (Crocker, Bailey, Faulkner,
Kowalski, & McGrath, 1997) and the Self-Administered Physical Activity Checklist (Sallis et
al., 1996). Both scales have been shown to accurately and reliably assess physical activity among
elementary school-children as early as 3
rd
grade both inside and outside of school (Bailey,
McKay, Mirwald, Crocker, & Faulkner, 1999; Sallis et al., 1996). Each has established internal
consistency and validity (Janz, Lutuchy, Wenthe, & Levy, 2008), including external validations
established through comparison with teacher observations, motion, 7-day recall, and leisure time
activities (Crocker et al., 1997; Sallis et al., 1996).

Moderate/Vigorous Physical activity was assessed at grades 5 and 6 by the NutritionQuest
Block Kids Physical Activity Screener (Drahovzal, Bennett, Campagne, Vallis, & Block, 2003).
Designed for children ages 8-17, this instrument asks about frequency and duration of physical
activities in the past 7 days. Its nine items address both leisure and school-time activities
including sports and active household chores. It also asks about amount of time per day spent
watching television, playing video games and using the internet. Average daily moderate and
vigorous activity minutes were calculated for inclusion as a covariate. The instrument was
150
administered at 5
th
and 6
th
grades and the mean of both administrations was calculated for
inclusion in adjusted models.

Sedentary Behavior was assessed using the following items: “On a regular school day, how
many hours per day do you (a) usually watch TV or video movies at home or away from school,
(b) spend on a computer at home or away from school and (c) play video games that you sit
down to play like PlayStation, Xbox, GameBoy, or arcade games. Responses ranged from 0 (“I
don’t watch TV”) to 6 (“6 or more hours”). As in previous work, a composite sedentary behavior
score was created by taking the mean across these three items (Hoelscher, Day, Kelder, & Ward,
2003; Huh et al., 2011).

High-Calorie/Low-Nutrient (HCLN) Food and Fruit/Vegetable Intake
To assess child HCLN food intake, five items were taken from a validated open-source food
frequency questionnaire (Willett et al., 1985) that has been used successfully in previous studies
((Nguyen-Michel, Unger, & Spruijt-Metz, 2007; Pentz, Spruijt-Metz, Chou, & Riggs, 2011; N.
R. Riggs, Spruijt-Metz, Sakuma, Chou, & Pentz, 2010) and has been validated for fourth grade
children (Field et al., 1999). The items assessed consumption of French fries, chips, doughnuts,
candy, and non-diet soda (e.g., “How often do you eat corn chips, potato chips, popcorn,
crackers?”). Two additional items assessed the frequency of fruit intake over the past week (e.g.,
How often did you eat any fruit, fresh, or canned?), and four assessed the frequency of vegetable
intake (e.g., How often do you eat green salad?). Response choices were as follows: 1 = Less
than once a week, 2 = Once a week, 3 = 2-3 times a week, 4 = 4-6 times a week, 5 = Once a day,
151
6 = 2 or more of these a day. Internal reliability for snack food items was acceptable (4
th
grade, α
= .80; 5
th
grade, α = .79; 6
th
grade, α = .81).

Environmental Vegetation Index

The presence of green vegetation, which may be an important covariate given its plausible
associations with both NRAP exposure and obesity outcomes through behavioral pathways
(Lachowycz & Jones, 2011) can be detected via remote sensing methods, most notably satellite
imagery from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). The MODIS
instrument is installed on two earth-viewing satellites, Terra and Aqua, which orbit the Earth in a
complementary manner, ensuring that the entire earth’s surface is imaged every 24-48 hours.
For years, the principal method for estimating vegetation density via remote sensing was
calculation of the Normalized Difference Vegetation Index (NDVI) (Almanza, Jerrett, Dunton,
Seto, & Pentz, 2012). This measure is based upon the principal that chlorophyll absorbs light
from the visible spectrum (i.e. 0.4 to 0.7 µm), while the other leaf cells reflect near-infrared light
(i.e. 0.7 to 1.1 µm). Consequently, the relatively density of vegetation in a given satellite image
can be approximated by calculating the satellite-measured near-infrared radiation minus visible
radiation (assessed by the RED band of the MODIS) divided by near-infrared radiation plus
visible radiation (NDVI = (NIR — RED)/(NIR + RED). In recent years, an alternative measure,
known as the Enhanced Vegetation Index (EVI) has been developed, which improves upon some
well-acknowledged limitations of the NDVI by taking into account an additional MODIS band
(Wang, Ma, & Sun, 2014).
152

It is important to acknowledge that NDVI was only designed to detect living vegetation and
differentiate it from other material (i.e. rocks, soil, dead vegetation, building). As such,
compared to NDVI, the EVI is more sensitive to differences in important vegetation
characteristics like canopy structure and density, as well as seasonal variation and stress
compared to NDVI which simply assesses the amount of chlorophyll present. Importantly, the
EVI also reduces the variability of vegetation density estimates due to differences in atmospheric
condition (including concentrations of regional air pollution (i.e. PM) –and has been shown to
outperform NDVI in urban environments (Huete, Miura, Rodriguez, & Gao, 2002).. Another
key advantage of EVI is that its estimates are not dependent on the time of day when the satellite
images were captured, because—unlike NDVI—it is able to account for changes in the angle at
which the sun shines on the earth’s surface (i.e. the “solar incidence angle”). Consequently, EVI
was used in the present study as an estimate of the green vegetation present in the area proximal
to each participant’s residence. NASA produces aggregate EVI estimates every 16 days at a
spatial resolution of 250m X 250m. To account for monthly, seasonal and annual variations in
vegetation, these estimates were averaged over the entire study period, which lasted from fall
2009 to spring 2011. Such averaging is believed to reduce measurement error stemming from the
relatively high variability in precipitation in Southern California relative to the rest of the
country. For example, between 2006-2011 the annual precipitation measured in Downtown Los
Angeles ranged from 3.2 inches in the 2006-2007 water year to 20.2 inches in the 2010-2011
water year—a >500% change in annual precipitation.

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Environmental Noise Exposure
Residential modeled noise exposure was also tested as a covariate owing to a growing body of
evidence linking it to a variety of adverse health consequences (Hammer, Swinburn, & Neitzel,
2014), including metabolic dysregulation/ childhood obesity (Jerrett et al., 2014) as well as the
fact that traffic is a leading source of noise in urban environments (Hansell, Cai, & Gulliver,
2017) . In the present study, noise pollution exposure was estimated at the residential level via
assignment of Soundscores™. These estimates are determined by a combination of the
following three key contributors to noise pollution in the urban environment: vehicle traffic, air
traffic and local sources (which include establishments such as bars, restaurants, and stadiums).
Soundscore™ developers apply the Federal Highway Authority’s Traffic Noise Model for
estimation of traffic noise—which is the greatest contributor to residential noise estimates of the
three factors overall, and particularly in the present dataset given that the analytic sample did not
reside near major metropolitan airports. Their proprietary model also accounts for aircraft noise
and other local sources (Soundscore, 2019). As anticipated, owing to their common causes,
residential NRAP exposure and noise pollution (per Soundscore estimates), were highly
correlated (rpearson=.65).

Individual and Community-level Demographic Characteristics
Self-reported demographic characteristics were also obtained via survey at each of the 4
assessments. These data included participant age, gender, race/ethnicity, and free school lunch
eligibility, which served as a proxy for socioeconomic status. As an additional measure of
community-level socioeconomic privation, which has been linked to both childhood obesity and
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NRAP exposure, an Area Deprivation Index (ADI) was assigned to each participant (Singh,
2003). The ADI was developed by the Health Resources & Services Administration (HRSA)
and has been refined and validated as a composite index of neighborhood-level deprivation
calculated using American Communities Survey 5 year estimates. The items and corresponding
factor scores utilized to calculate the composite index corresponding to the present study period
(2009-2011) can be found in (Singh, 2003). For consistency with previous work, variables were
created which reflecting the state-specific deciles and national percentiles of each Block Group
(Kind et al., 2014). The median ADI decile among study participants was 4 (IQR=3) although
student residential block group-level ADI deciles ranged from 1-10.

Statistical Methods

A mixed effects modeling approach was utilized in the present longitudinal analysis via the Stata
14 Mixed command. For each outcome and air pollution exposure of interest, all models
adjusted for the following covariate set, used in previous work to attempt to control as well as
possible for possible confounding by sociodemographic factors, as well as systematic differences
in regional air pollution: gender, free lunch eligibility, residential area deprivation index, age,
White race/ethnicity, Hispanic race/ethnicity, Asian race/ethnicity, Black race/ethnicity, regional
PM2.5 and regional O3. Models incorporating the aforementioned covariates, as well as
residential environmental noise and green vegetation, were also fit. Baseline differences in
normal vs. overweight/obese weight status were assessed using multilevel generalized linear
effects modeling, where a logit link function was used to model each dichotomous outcome and
155
cluster robust standard errors accounted for school-level clustering. Interaction terms by
baseline perceived stress were incorporated to compare levels of association across the lower
quartile, middle 50% and upper quartile of perceived stress.
Since EF deficits and problem behavior were stable across 4
th
-6
th
grades, multilevel models were
fit which pooled all measurement waves in order to reduce measurement error and increase
precision of the resulting estimates. However, due to the approximately linear increases in
weight gain outcomes observed over time, a linear growth curve modeling was also employed
for these outcomes to examine if and how the previously-reported associations between NRAP
and obesity outcomes varied by perceived stress. Again, interaction terms by perceived stress
were incorporated to compare levels of association across the lower quartile, middle 50% and
upper quartile of perceived stress, which was averaged across all 4 measurement waves in these
longitudinal models. Additionally, since internalizing and externalizing behaviors as assessed by
the childhood behavior checklist are count variables, a mixed effects negative binomial modeling
approach was used.
Random intercepts were included in each longitudinal model at the individual level to account
for baseline BMI and waist circumference, as well as the within-subjects nesting of assessments
across waves. Growth curve models also incorporated random slopes by time. Random
intercepts were included at the school level to account for between-school variability in baseline
BMI and waist circumference. Although, previous work did not identify confounding of
associations between air pollution exposure and childhood BMI growth by school-level variables
(Jerrett et al., 2014), random school-level coefficients were nonetheless tested via likelihood-
ratio tests in all converging models. However, along with introducing computational difficulties,
their addition neither substantially influenced estimates nor provided a significant improvement
156
in model fit over the models with random school-level intercepts alone so they were omitted.

RESULTS

Table 4.1. Comparing outcome and covariate values between final analytic sample and
participants lacking residential address data
Dichotomous Baseline Variable
Participants with
geocoded home
address (N=475)
%
Participants with
no home address
provided (N=234)
%
Two-
sided
P Value
Male gender 48.42% 52.99% .25
Free lunch eligibility 19.37% 23.93% .16
White race/ethnicity 35.79% 26.92% .02*
Hispanic race/ethnicity 22.11% 32.91% .02*
Black race/ethnicity 1.68% 2.99% .26
Asian race/ethnicity 9.26% 5.13% .06
Program vs. Control 55.37% 48.72% .10
Continuous Baseline Variable Mean (SD) Mean (SD)
Two-
sided
P Value

Age 9.26 (.46) 9.28 (.49) .22
Out-of-School Physical Activity 3.06 (1.15) 3.12 (1.14) .57
MVPA (5
th
and 6
th
) 100.30 (86.52) 101.43 (94.76) .88
Fruit and Vegetable Intake 2.89 (.89) 2.80 (.88) .22
Sedentary Behavior Hours 4.71 (3.83) 5.00 (3.55) .35
High-Calorie/Low-Nutrient Food
Intake
2.35 (1.05) 2.35 (.95) .99
Perceived Stress 1.96 (.33) 1.95 (.33) .67
BMI 18.98 (3.80) 19.37 (3.87) .21
Waist Circumference 68.82 (10.36) 69.73 (10.54) .28
Executive Function Deficits 1.65 (.02) 1.69 (.02) .07
Externalizing Behavior
#
1.48 (2.70) 1.67 (2.54) .47
Internalizing Behavior
#
.92 (1.51) .74 (1.29) .23

#
A subset of the Longitudinal Sample (N=488) had complete CBCL Data

157
Table 4.2. Distribution of annual air pollution exposures assigned to analytic sample
(N=475) at study baseline (2009)
Mean Median IQR Min Max Range
Total Near-Roadway Modeled NO X 10.50 9.74 6.0 1.00 47.63 46.62
Freeway Near-Roadway Modeled NO X 7.42 6.05 5.38 .29 44.19 43.89
Non-Freeway Near-Roadway Modeled
NO X
3.08 2.95 1.46 .61 12.4 11.76
Regional PM 2.5 (µg/m
3
) 12.18 9.71 5.71 8.99 16.10 7.11
Regional PM 10 (µg/m
3
) 30.97 26.42 13.25 23.29 48.14 24.85
Regional O 3 (ppb) 40.88 40.28 1.75 35.83 54.67 18.84
Regional NO 2 (ppb) 16.84 15.51 10.70 8.43 25.40 16.97
+
As in previous work, NRAP exposures combined modeled residential and school exposures via a 79%/21% ratio.
(20371422)

In general, participants’ estimated residential exposures to regional air pollutants were lower
than the average concentrations measured across the entire Los Angeles-South Coast Air Basin
for the study period. This basin includes most of the Greater Los Angeles Metropolitan area and
includes all of Orange County and the non-desert regions of Los Angeles County, Riverside
County, and San Bernardino Counties. For example, average regional PM2.5 concentrations
across the entire basin reported by the EPA for the study period exceeded the ones observed in
our sample by approximately an IQR (5.7 µg/m
3
). No significant differences in NRAP or
regional pollutants were identified between participants assigned to the Pathways to Health
program and control groups.

When compared by obesity status and all tested covariates, the 475 students with complete
address information were significantly more likely to be White, and less likely to be Hispanic
than the remaining 234 participants whose parents did not report residential address data. The
two groups did not differ by weight status, perceived stress at any assessment wave, or other
assessed covariates (Table 4.1). With respect to the longitudinal sample, the 212 members of the
control group did not substantially differ from their counterparts in the program group with
158
respect to baseline socio-demographics, obesity-related behavior or neurobehavioral
characteristics. The mean age of children in our sample at the first study assessment was 9.27
years (SD=0.47).

Zero-order correlations between key study variables at study baseline (4
th
grade) are presented in
Supplemental Table 1. Perceived stress was positively correlated with Total NRAP exposure
(rspearman=.08) as well as baseline BMI (rspearman=.08) waist circumference (rspearman=.04) and
executive function deficits ((rspearman=.20). Smaller or negative correlations were observed
between perceived stress and student-reported free lunch eligibility (rspearman=.01), area
deprivation index (rspearman=-.09), as well as regional PM2.5 (e.g. rspearman=-.06).

Figure 4.1. Baseline cross-sectional associations between NRAP exposure and overweight/obesity:
effect modification by stress

+Error bars reflect 1 SE

When the probability of children falling into the ≥85
th
percentiles of BMI and Waist
Circumference, was modeled by NRAP exposure strata, adjusting for regional air pollution,
gender, free lunch eligibility, race/ethnicity, age, and neighborhood deprivation, accounting for
classroom level clustering, predicted probabilities of overweight BMI were only significantly
159
higher (DPr(overweight BMI)=.25; p<.05) among the most NRAP exposed children in the upper
quartile of perceived stress (vs. those in the 10
th
-90
th
percentiles of NRAP exposure). Contrasts
between the ≥90
th
and ≤10
th
Percentiles of NRAP exposure couldn’t be calculated due to sparse
data. When the same model was fit predicting overweight using measured baseline waist
circumference, a significantly greater probability of overweight was observed among the most
highly NRAP-exposed children relative to the least NRAP-exposed children (DPr(overweight
WC)=.45; p<.05). These observed obesogenic effects were only slightly attenuated after
additional covariate adjustment for environmental noise pollution and green vegetation near
participant residences (DPr(overweight BMI)=.22; p<.05); (DPr(overweight WC)=.41; p<.05).

Figure 4.2. Baseline cross-sectional associations between NRAP exposure and obesity: effect
modification by perceived stress

+Error bars reflect 1 SE

Similar effects were also observed when the probability of children falling into the ≥95
th

percentiles of BMI and Waist Circumference, was modeled by NRAP exposure strata, adjusting
for the same set of covariates. Here, predicted probabilities of obese BMI were more than
double those observed among the most NRAP exposed children (Pr(obese BMI)=.50) in the upper
quartile of perceived stress (vs. those in the 10
th
-90
th
percentiles of NRAP exposure(Pr(obese
160
BMI)=.22). However the difference did not reach statistical significance (DPr(obese BMI)=.25; p=.12).
Nearly identical results were observed when waist circumference percentile-based cutoffs were
employed. (DPr(obese WC)=.26; p=.10). Here, adding additional covariate adjustment for
environmental noise pollution and green vegetation near participant residences did not influence
the corresponding estimates (DPr(overweight BMI)=.25; p<.14); (DPr(overweight WC)=.26; p<.10).

Figure 4.3. Longitudinal trajectories of anthropometric weight-gain outcomes across NRAP and
perceived stress exposure strata

+Error bars reflect 1 SE

Multilevel growth curve models estimated adjusted BMI and waist circumference trajectories from 4
th
-6
th

grades by NRAP exposure strata and perceived stress averaged across all 4 assessments. As seen in
Figure 4.3, significant differences in adjusted mean BMI and waist circumference between the highest
and lowest NRAP exposure quartiles were observed among more highly stressed children across all study
waves. However, mean BMI and waist circumference did not significantly differ at any study wave
among students in the lower quartile of perceived stress. The slopes of the estimated BMI and waist
circumference trajectories did not significantly differ within each model across NRAP or perceived stress
strata [X
2
BMI=.23(1 df); p=.63] [X
2
Waist Circumference =.79(1 df); p=.37].

161
In contrast to the above findings reporting reliable unadjusted and adjusted associations between
NRAP exposure and obesity outcomes among highly stressed children, no such associations
were identified with respect to the assessed neurobehavioral outcomes. For example, while
mean EF deficits were significantly greater among children in the most vs. least stressed
quartiles (DEF unadjusted=.25; p<.05 / (DEF adjusted=.37; p<.05), mean EF deficits were invariant
within each stress strata. In contrast, no unadjusted (Figure 4.4) or covariate-adjusted positive
associations between NRAP exposure and EF deficits, externalizing behavior or internalizing
behavior were identified within any stress strata (Table 4.3).

Figure 4.4. Associations between NRAP exposure and neurobehavioral outcomes pooled across 4
th
-
6
th
Grades

+Error bars reflect 1 SE

162
Table 4.3. Comparing neurodevelopmental outcomes across NRAP exposure strata by
perceived psychosocial stress
Unadjusted
1
Adjusted
2

Within Least
Stressed Quartile
Within Most
Stressed Quartile
Within Least
Stressed Quartile
Within Most
Stressed Quartile
Executive Function Deficits
D([³90
th
-. ≤10
th
Pct Total NRAP
-.04 (.14) .02 (.13) -.07 (.15) .02 (.13)
Externalizing Behavior
D([³90
th
-. ≤10
th
Pct Total NRAP
.57(3.21) .16 (5.36) 2.36 (6.00). 5.09 (5.94)
Internalizing Behavior
D([³90
th
-. ≤10
th
Pct Total NRAP
-.35 (.49) -.22 (.53) -.42 (.49) -.28 (.61)
1
Accounts for school-level and within-subject clustering only via random effects
2
Also adjusts for Age, Gender, Race/Ethnicity (White, Black, Asian, Hispanic), Free Lunch Eligibility,
Area Deprivation Index, Enhanced Vegetation Index, Regional PM 2.5 and O 3

DISCUSSION

Overview of Principal Findings

Previous work from this cohort of Southern California schoolchildren has identified significant
positive associations between NRAP exposure and predicted probabilities of childhood obesity at
4
th
grade (study baseline) (Dissertation Paper #1). The present study suggests that these
obesogenic effects may be stronger among participants with greater levels of perceived
psychosocial stress. As observed in Figure 4.1 adjusted probabilities of obesity at 4
th
grade were
similar among individuals in the lower and moderate stress and exposure strata—and comparable
to national prevalence estimates(Fryar, Carroll, Ogden, & CL., 2018). However, obesity risk
was doubled (112%Waist Circumference Criteria/127%BMI Criteria) among the most highly stressed/highly
NRAP-exposed children relative to their highly stressed/moderately NRAP exposed
counterparts—even after adjustment for individual and neighborhood level sociodemographic
covariates and regional concentrations of PM2.5. Similar findings, though smaller in magnitude,
were also observed when 4
th
grade childhood overweight/obesity were considered together. For
example, depending on whether waist circumference or BMI-based overweight cutoffs were
163
utilized, within the most highly stressed quartile, the most highly NRAP exposed children had a
30% Waist Circumference Criteria to 65% BMI Criteria increase in predicted probabilities of obesity or
overweight relative to the 80% children in the middle of the NRAP exposure distribution.
Growth curve models estimating adjusted longitudinal trajectories indicate that the
aforementioned baseline differences in outcomes observed at 4
th
grade were maintained through
the end of 6
th
grade. Remarkably, among students in the upper quartile of NRAP exposure, the
adjusted mean waist circumference (73.3cm) and BMI (21.0) values among the most highly
stressed students at study baseline (the first semester of 4
th
grade) were comparable to the waist
circumference (73.8cm) and BMI (20.9) values attained by the second semester of 6
th
grade.
This suggests that, among highly NRAP-exposed late-elementary schoolchildren, an interquartile
range difference in perceived stress corresponds to approximately a 2.5 year difference in weight
gain. These findings extend previous research indicating that psychosocial stress may potentiate
adverse health effects of urban air pollution (Clougherty & Kubzansky, 2009) in the context of
other chronic inflammatory childhood conditions (e.g. asthma).

A large and growing literature supports the existence of causal relationships between executive
function deficits and increased engagement in obesity-related behavior (Miller et al., 2018), as
well as greater obesity risk (Eichen, Matheson, Appleton-Knapp, & Boutelle, 2017; Liang,
Matheson, Kaye, & Boutelle, 2014). In light of previous work linking urban air pollution
exposure to developmental neurotoxicity and impairments in executive function specifically
(Allen et al., 2017; Costa et al., 2017), it was hypothesized that the observed obesogenic effects
of NRAP exposure may be mediated through changes in EF. However, as reported in
Dissertation Paper 2, greater NRAP exposure—as indicated by CALINE4 modeled NOX was not
164
reliably associated with deficits in EF—suggesting that it is unlikely to mediate the observed
obesogenic effects of NRAP. More generally, the baseline obesogenic effects of NRAP detailed
in Dissertation Paper 1 were not attenuated after adjustment for obesity-related behavioral
covariates and/or executive function deficits. The differential effects of NRAP exposure by
stress on obesity status reported in the present manuscript were also not attenuated after such
covariate adjustment—indicating that these observed effects are unlikely to be operating
predominantly through behavioral pathways.

Implications for Future Stress Reduction Interventions
The present findings, which link greater perceived psychosocial stress and NRAP exposure to
increased risk of childhood overweight/obesity, indicate that efforts to prevent both air pollution
exposure and stress may reap substantial public health dividends. A detailed discussion of air
pollution prevention and mitigation strategies is provided on pages 8-25 of the Supplemental
Discussion component of this dissertation. With respect to stress, a variety of effective stress-
reduction modalities exist, including cognitive-behavioral therapies (CBT) (Hofmann, Asnaani,
Vonk, Sawyer, & Fang, 2012) and mindfulness-based stress reduction (MBSR) (Khoury,
Sharma, Rush, & Fournier, 2015). While CBT is typically delivered in a traditional, office-based
context, by a clinical therapist and directed at clinical populations, mindfulness-based
interventions are increasingly being delivered in school-based settings to non-clinical
populations (Zenner, Herrnleben-Kurz, & Walach, 2014) in a health promotion context.
Generally speaking, such mindfulness-based interventions encourage participants to engage
165
potentially stressful situations via a nonjudgmental and nonreactive approach—thereby
modifying the participant’s relationship with stressful thoughts such that cognitive appraisal is
increased and emotional reactivity is reduced (Teasdale, Segal, & Williams, 1995). Besides
reducing stress, mindfulness-based interventions have also been found to have additional
benefits, including improved well-being (Huppart & Johnson, 2010; Spijkerman, Pots, &
Bohlmeijer, 2016), self-regulation (Tang et al., 2007), attentional processes (Hodgins & Adair,
2010; Jha, Krompinger, & Baime, 2007), inhibitory control (Zanesco, King, Maclean, & Saron,
2013), and working memory (Mrazek, Franklin, Phillips, Baird, & Schooler, 2013; Zeidan,
Johnson, Diamond, David, & Goolkasian, 2010). Furthermore, mindfulness-based approaches
have been successfully adapted to target obesity-related eating behaviors (e.g. binge eating
(Woolhouse, Knowles, & Crafti, 2012), emotional eating (Alberts, Thewissen, & Raes, 2012),
dietary intake (Timmerman & Brown, 2012), as well as to promote weight loss and/or
maintenance Encouragingly, studies have found such approaches to be effective both in the
context of obesity prevention (O'Reilly, Cook, Spruijt-Metz, & Black, 2014) and treatment
(Dunn et al., 2018). Given growing evidence for the benefits of mindfulness, efforts are ongoing
to adapt traditional mindfulness-based interventions, which tend to involve a series of individual
or group sessions lasting at least 30 min or more, into school-based curricula that can be
delivered in short “mini-lessons” that can augment daily lesson plans without displacing
instructional content (Ritt-Olsen, Warren… Pentz IN PREP). By reducing perceived barriers to
implementation, such efforts are likely to help further facilitate the ongoing integration of
mindfulness-based prevention interventions into the school-setting (Semple, Droutman, & Reid,
2017).

166
As summarized above, there is promising evidence that individual or group-level mindfulness
interventions may be efficacious in bolstering socio-emotional resiliencies, improving stress
management, improving neuropsychological functioning, and reducing obesogenic behavior in
some contexts. While it is tempting to focus on these more constrained arenas, which are often
easier to target and cleanly evaluate via interventions, it is also important for prevention
scientists to acknowledge and attempt to address the broader upstream determinants of health at
the population level. According to the American Psychological Association’s annual
population-based Stress in America poll, the economic precarity of many communities and
associated workplaces stresses are consistently at the top of participant’s most commonly
reported sources of stress (Stress in America: The State of Our Nation. Stress in America Survey,
2017), with parents reporting greater stress than adults without children (Paying With Our
Health: Stress in America Survey, 2014)

Major Strengths of the Present Study
Recent studies are deepening our understanding of the extent to which parental stress and child
stress are linked. For example, a recent birth cohort study found that maternal urinary cortisol
following conception was positively associated with biological stress responses of the child to an
experimental stressor administered at age 12 (Barha et al., 2019). However, the authors did not
find evidence for mediation through epigenetic mechanisms, suggesting that shared
environmental exposures between parent and child may explain the observed associations. To
date, work examining the role of psychosocial stress as a potential effect modifier of the adverse
167
health effects of NRAP has been limited to characterizing child/household stress via the proxy of
parental report on Cohen’s Perceived Stress Scale (Islam et al., 2011; Lee et al., 2018;
Shankardass et al., 2009). However, while shared environmental stressors may lead to
correlations between parental and child stress levels early in life when children cannot reliably
complete self-report measures (Islam et al., 2011), such correlations are likely to diverge as
children age and begin to spend much of their day outside of the home (i.e. at school).
Consequently, since perceived stress is fundamentally a subjective construct—per Cohen "the
degree to which situations in one’s life are appraised as stressful” (Cohen et al., 1983), it would
appear advantageous to assess these perceptions directly, as we did, rather than rely on parent
proxy, as in previous work. Furthermore, perceived stress was assessed at 4 separate time points
in our study, which were averaged to reduce measurement error and create an indicator of
chronic stress throughout the study period.

Another advantage of the present study design over previous work with respect to measurement
is its repeated assessments of BMI and waist circumference outcomes by trained study staff
using standardized CDC protocols. Previous US studies examining obesogenic effects of NRAP
have relied upon BMI assessment as the primary indicator of obesity—despite the fact that there
are other measures (e.g. waist circumference, waist-to-hip ratio), which may be uniquely
predictive of cardiometabolic risk in certain contexts. For example, a study of young adult
women exposed to a laboratory stressor found that inflammatory cytokine responses were
significantly higher in participants with greater waist circumference, independent of BMI and
other covariates (Brydon et al., 2008). However, the direction and magnitude of findings in the
present study were comparable whether BMI or Waist Circumference-based criteria were used—
168
which is logical given that BMI and waist circumference were highly correlated at each wave
(rspearman=.86-.90). This is also consistent with previous work in pediatric populations concluding
that BMI and waist circumference are roughly equivalent in their observed associations with
cardiometabolic risk (Sardinha et al., 2016)—although their predictive value may be improved
by combining these assessments with waist-to-hip ratio (Buchan & Baker, 2017; Buchan,
McLellan, Donnelly, & Arthur, 2017) which was not assessed in the present study.

Environmental Noise—A stress-inducing, potential confounder
Previous research reporting obesogenic effects of NRAP exposure in Southern California
schoolchildren identified exposure to noise—a known stressor—as an important potential
covariate to consider in future work (Jerrett et al., 2014). This is particularly in urban
environments like the one inhabited by participants in the present study, where traffic noise is
generally the largest contributor to environmental noise—a nonspecific stressor that may activate
neuroendocrine and immune pathways also involved in the adverse health effects of NRAP
(Münzel et al., 2018). As anticipated, owing to their common causes, residential NRAP
exposure and noise pollution (per Soundscore
TM
estimates), were highly correlated (r=.65).
However cross-sectional correlations between noise pollution and EF deficits at each wave were
small and in the opposite direction as hypothesized (r4th grade=-.08 r5th grade=-.05 r6th grade=-.03).
Patterns of cross-sectional spearman correlations between noise pollution and neurobehavioral
deficits, were similarly small and/or in the opposite direction as hypothesized, for both
externalizing (r4th grade=-.13 r5th grade=.04 r6th grade=.01 ) and internalizing behavior (r4th grade=-.02
r5th grade=.04 r6th grade=-.04). Soundscore-modeled environmental noise was also uncorrelated
with baseline BMI andwaist circumference (r ≤.03). While most previous health effects studies
169
in this area have not attempted to model environmental noise exposure, it is notable that our
approach differed from the strategy used to characterize the Spanish BREATHE cohort (Sunyer
et al., 2015). In that study, noise levels were directly measured within school classrooms at
multiple times before children arrived at school—which was intended to assess environmental
noise arising from both traffic and other background sources. However, no attempt was made to
model residential exposures. In contrast, our study solely modeled residential exposures, as this
was believed to be more representative of their chronic exposures to environmental noise. It is
important to note that while the Soundscore
TM
utilizes validated Federal traffic noise models, the
fact that it applies a closed-source, proprietary model to incorporate other noise contributors
means that—while it may be a promising tool for characterizing environmental noise exposures
at geocoded locations in California—future validation studies are clearly needed before it can be
unconditionally recommended

Aircraft traffic is another important source of environmental noise to take into account in our
sample, given that the 5 international airports in the metropolitan Los Angeles area alone render
it the 5
th
largest airport system in the world in terms of passenger volume. There are also over a
dozen regional airports, including some quite proximal to participant residences, which also are
likely to contribute to environmental noise. By assigning exposure estimates of residential
environmental noise (Soundscore, 2019) to each study participant and including them as
covariates in the present regression models, we were able to at least somewhat address the
potentially important issue of confounding by differential exposure to environmental noise
arising from traffic, aircraft, and other local sources (e.g. stadiums, bars). Since the contrasts in
point estimates across NRAP exposure strata corresponding to predicted probabilities of
170
overweight/obesity were not impacted by addition of Soundscores
TM
as a covariate, we can
thereby conclude that the observed effects are unlikely to be primarily driven by environmental
noise exposure.

While airports, and the aircraft that service them are important sources of environmental noise,
emerging research suggests that they are also important sources of urban air pollution that may
be underestimated by current monitoring methods. While previous work demonstrated
substantial increases in particulate air pollution on and immediately proximal to airport runways
(Carslaw, 2006; Fanning, Yu, Lu, & Froines, 2007), a landmark 2014 Southern California
examining the spatial distribution of particulate air pollution near the Los Angeles International
Airport along primary takeoff/landing vectors concluded that concentrations of particulate air
pollutants were elevated 2-5 fold above baseline concentrations up to 10km away from LAX
(Hudda, Gould, Hartin, Larson, & Fruin, 2014). Remarkably, the authors concluded that the
amount of particulate pollutants emitted by LAX aircraft traffic alone are only slightly lower
than those produced by the entire Los Angeles freeway network. Given that these particulate
pollutants appear to be emitted along line-source takeoff/landing vectors, the true PM exposures
of individuals underneath these major flight paths may be underestimated by methods which
interpolate regional PM using fixed monitoring stations assuming a relatively smooth gradient of
particle concentrations between stations. Furthermore, aircraft are known to produce relatively
high levels of ultrafine particles (Hudda, Simon, Zamore, & Durant, 2018), which may be
uniquely deleterious to human health due to their small particle size (Oberdörster, Oberdörster,
& Oberdörster, 2005)) and are less frequently monitored by regional air quality surveillance
networks than their larger counterparts PM2.5, and PM10. While participants in the present study
171
did not reside within 10km of LAX, many did reside proximal to Ontario International Airport,
and therefore may have received additional, unmodeled exposures to aircraft-source pollutants.
Consequently, future work examining the effects of near-roadway air pollution should also
consider “near-airway” exposures along major takeoff/landing vectors as well, given their similar
determinants, composition, and possible health effects.

Study Limitations
Limitations of the present study include utilization of an adapted version of the perceived stress
scale, which was abbreviated and modified to improve comprehension among our 4
th
-6
th
grade
sample. Future work should consider administration of the full 10-item Cohen’s Perceived
Stress instrument as well as incorporation of stress biomarkers, which can augment such self-
report measures. While measures such as serum cortisol may be infeasible in school- or other
population-based survey research settings, less invasive measures such hair cortisol have been
demonstrated to be valid indicators of chronic stress (Noppe, de Rijke, Dorst, van den Akker, &
van Rossum, 2015; Veldhorst et al., 2014)) (i.e. long-term HPA-axis activation) as assessed via
other standard measures like urinary cortisol (Reinehr et al., 2014). Salivary cortisol has also
been found to be a valid, useful, and relatively non-invasive method for assessment of stress in
population-based studies when carefully implemented ((Adam & Kumari, 2009; Hellhammer,
Wüst, & Kudielka, 2009), although it has greater short-term, including intra-day variability.
However, the psychometrics, and strong positive associations between psychosocial stress and
EF deficits in the present study—which were expected based on past work linking stress to
impaired working memory, cognitive flexibility, attentional control, and prefrontal function more
172
generally (Liston, McEwen, & Casey, 2009; Shields, Sazma, & Yonelinas, 2016)—support the
validity of our stress measure.

It is also important to note that the behavioral ratings-based neuropsychological measures
utilized here may be ill-suited for detecting the neurotoxic effects of NRAP. For example, the
limited response options offered (i.e. “never”, “sometimes”, “often”), the abridged version of the
BRIEF we utilized (i.e. selected items from 4/8 clinical subscales), and the fact that this self-
report instrument is designed for ages 11-18 are each likely to render the resulting instrument
less sensitive than the full version utilized in the Project VIVA cohort where significant
associations were detected (Harris et al., 2016). However, the Project VIVA birth cohort is
approximately five times larger than the analytic sample in the present study, and has very well-
characterized prenatal and postnatal NRAP exposures. It is also important to note that the
magnitude of the largest observed association in the study by Harris et al (2016) only reflects a
<4% change in BRIEF Behavioral Regulation Index scores per IQR of Black Carbon exposure in
the year prior to assessment. True population-level effects of this size, while typical of the air
pollution health effects literature (B. Chen & Kan, 2008), would be challenging to detect—
particularly in the context of the present study, which aims to test interactions by stress. Besides
improving exposure assessment and/or recruiting more participants, future work may benefit
from employing additional, performance-based EF tasks, which not only may assess
complementary cognitive constructs (Toplak et al., 2013), but may be better able to detect the
relatively small magnitude of cognitive impairment associated with NRAP exposure in previous
work. Given that both NRAP exposure and psychosocial stress are increasingly ubiquitous, and
173
are acknowledged to independently contribute to myriad adverse health effects, more high-
quality studies are clearly warranted to further ascertain their joint effects.

174
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183
The feasibility and acceptability of assessing inhibitory
control and working memory among adolescents via an
ecological momentary assessment approach
Christopher M. Warren and Mary Ann Pentz
Department of Preventive Medicine, Division of Health Behavior Research, University of Southern
California Keck School of Medicine, Los Angeles, CA, USA
ABSTRACT
Early adolescence is a critical period for the development of
executive function (EF). EF deficits are associated with increased
engagement in multiple health risk behaviors and may be influ-
enced by momentary factors, such as state mindfulness and phy-
sical activity. Ecological momentary assessment (EMA) leverages
the increasing ubiquity of smart-phones to assess moment-to-
moment changes in neurocognition and behavior with minimal
recall bias and high ecological validity. As such, EMA is a promising
method for delivering performance-based EF tasks and assessing
the degree to which EF is influenced by momentary variation in its
putative antecedents (e.g. state mindfulness and PA). This study
adapts the (1) State Mindfulness Attention Awareness Scale, (2)
physical activity/sedentary behavior recall items, (3) complex sym-
metry span working memory task, and (4) the child flanker inhibi-
tory control task into an EMA tool delivered via iPhone using
Inquisit Web. This tool was piloted with a sample of 32 seventh
graders over a 72 h period. Participants’ posttest survey responses
indicated that they found these study activities to be enjoyable,
non-burdensome, not overly difficult, and preferable to pencil-and
-paper instruments. Baseline correlations between flanker task
performance and both BRIEF inhibitory control (σ = .23) and work-
ing memory subscales (σ = .34) were moderate. Correlations
between symmetry span working memory task performance and
BRIEF inhibitory control (σabsolute = .28; σpartial = .16) and work-
ing memory subscales (σabsolute = .19; σpartial = .15) were
slightly lower, demonstrating associationsconsistent with previous
studies. This study supports the feasibility and acceptability of
administering two common performance-based EF tasks to ado-
lescents via an EMA approach.
ARTICLE HISTORY
Received 26 February 2018
Accepted 1 December 2018
KEYWORDS
Executive function;
ecological momentary
assessment; state
mindfulness
Early adolescence is a critical period for the development of executive function (EF)
(Best & Miller, 2010), a cluster of top-down cognitive processes mobilized whenever it
is necessary to adapt one’s response to contextual cues while engaging in goal-directed
behavior (Diamond, 2013). EF facilitates mental phenomena such as selective attention,
self-control, and complex problem-solving. While debate persists regarding the
CONTACT Christopher M. Warren cmwarren@usc.edu 2001NSotoSt.,Suite312-18,LosAngeles,CA90032,USA
CHILD NEUROPSYCHOLOGY
https://doi.org/10.1080/09297049.2018.1556624
© 2018 Informa UK Limited, trading as Taylor & Francis Group
184
underlying structure of EF, most current typologies assert the existence of working
memory and inhibitory control as dissociable subdomains (Diamond, 2013). Working
memory permits immediate maintenance and manipulation of information in mind
(e.g. behavioral plans) whereas inhibitory control allows individuals to override auto-
matic reactions to stimuli in favor of alternative responses.
Giventheimportantrolesthatinhibitorycontrolandworkingmemoryplayinfacilitating
complex human behavior, numerous instruments have been developed to assess these
different aspects of EF.Two frequently usedtypesof instruments include: (1) computerized
performance-based reaction-time tasks designed to measure EF via structured assessments,
and(2)assessmentsratingthefrequencyofday-to-daybehaviorsthatinvolvemobilizingEF
resources, which can be administered either electronically or via pencil-and-paper. Well-
studiedcomputerizedtasksmeasuringinhibitorycontrolincludethe“flanker”and“go/no-go
” tasks, whereas computerized performance-based working memory tasks include complex
span tasks and “N-back” paradigms. On the other hand, the Behavior Rating Inventory of
ExecutiveFunction(BRIEF)(Gioia,Isquith,Guy,& Kenworthy,2000),isa commonlyused
real-world behavioral measure of executive control. A recent review found that scores on
performance-based measures generally have positive, relatively small correlations with rat-
ings-based measures of EF, since they measure distinct aspects of children’sEF(Toplak,
West,&Stanovich,2013)byassessingdifferentlevelsofbehavior.However,asbothtypesof
measures assert to measure EF, there have been increasing efforts to understand their
observed lack of strong concordance in order to improve EF assessment in specificpara-
digms. For example, given the standardized structure of mostperformance-basedtasks,
researchers have suggested that they may provide greater insight into EF capacity and/or
processing efficiency under ideal, highly-structured circumstances (i.e. when competing
demands for attention, social pressures or emotional valence are artificially low) (Silver,
2014).Assuch,theyhavethepotentialtobothunderestimatereal-worldEFdeficits–dueto
the unrealistic lack of distractors in the assessment setting – as well as overestimate deficits
(e.g. when tasks are novel and children cannot utilize the compensatory strategies to which
they are accustomed. Conversely, behavioralratingmeasuresmaybemoreindicativeof
individual goal pursuit in less structured, more ecologically-valid contexts, where children
can draw upon a variety of cognitive resources to execute behavioral plans. Therefore,
behavioral-rating measures may better reflect a child’sabilitytosuccessfullyapplyextant
EF resources, rather than their underlyingEF capacity.
LongitudinalstudiesofchildrenhavefoundgreaterEF–asassessedbybehavioralratings
measures – to predict both higher levels of physical activity (Pentz & Riggs, 2013)(Isasi&
Wills, 2011)(PA)andlesssedentarybehavior(Riggs,Spruijt-Metz,Chou,&Pentz, 2012).
Moreover, two recent reviews found acute PA – defined as a single, short-term bout of
exercise – to transiently improve performance on a variety of performance-based EF tasks
(Smith, Blumenthal, & Hoffman, 2010)(Verburgh,Konigs,Sherder,&Oosterlaan, 2014).
However,theeffectsofacutePAonEFwereonlysignificantinthesubdomainofinhibitory
control and had no impact on working memory. Recently, the construct of mindful aware-
ness has been increasingly incorporated into health behavior change interventions due to
worklinkinggreatermindfulnesstoimprovedhealth.Forexample,mindfulnesstraininghas
been found to increase children’sabilitytoengageinbehavioralregulation,emotional
regulation, meta-cognition and executive control (Singh et al., 2007)(Singhetal., 2011)
(Flooketal.,2010).ResearchsuggestsEFisinfluencedbynumerousfactors,whichmayvary
2 C. M. WARREN AND M. A. PENTZ
185
substantiallyacrossthedayandweek.Thesemomentaryfactorsincludemindfulness(Teper
&Inzlicht, 2013)(Gallant, 2016)andPA(Verburgh,Konigs,Sherder,&Oosterlaan, 2014)
(Best, 2012)(Daly,McMinn,&Allan, 2014)(Ziereis&Jansen, 2015).
ExtantworkinvestigatingtheeffectsofEFonhealthbehaviorhavereliedoneithersingle,
cross-sectionalorpre-postlongitudinaladministrationsofcomputerizedtasks,completedin
laboratoryenvironments,whichareunrepresentativeofparticipants’day-to-dayexperiences.
Furthermore,assessmentschedulesutilizedwithinthesestudiesdonotpermitevaluationof
shorter-term and/or within-subjects variability in EF, which has been identified in previous
work with clinical populations (Gonzalez-Gadea, Baez, & Torralva, 2013)(VanDeVoorde,
Roeyers, Verte, & Wiersema, 2010). Ecological momentary assessment (EMA) (Shiffman,
Stone, & Hufford, 2008)isanincreasinglypopularparadigminbehavioralresearch(Burke
et al., 2017)(Moskowitz&Young, 2006)thatcanleveragetheincreasingubiquityofsmart-
phonestoquicklyassessmoment-to-momentchangesinneurocognitionandbehaviorwith
minimal recall bias and high ecological validity. These potential advantages of using EMA
approachestomeasureEFwerehighlightedinarecentcommentary(Silver,2014)suggesting
that such assessments could be contrasted, and potentially integrated, with responses on
globalbehavioralratingscalestogleancomplementaryinsightsintoachild’sneurocognitive
function,andtheirabilitytoapplythesefunctionsinmultiplerelevantcontexts.Forexample,
if a performance-based EF assessment is delivered via mobile phone during the course of
achild’snormalday,successfultaskcompletionwouldtheoreticallyrequireboththeabilityto
mobilizeEFresourcestoovercomecompetingmomentarydemandsforattention,aswellas
the underlying EF capacity purportedly assessed by such measures.
Given the potential benefits of delivering performance-based EF tasks via EMA, the
present pilot study adapted the (1) State Mindfulness Attention Awareness Scale
(MAAS), (2) PA/sedentary behavior recall items, (3) shortened complex symmetry
span task, and (4) the child flanker task into an EMA tool delivered via iPhone using
Inquisit’s mobile data collection platform (Millisecond Software, 2017). We hypothe-
sized that (a) it would be feasible to repeatedly administer these tasks via EMA over
a 72-hour period, (b) students would report the EMA tasks to be acceptable and would
complete at least 50% of administered assessment batteries, and (c)students’ baseline
scores on the symmetry span and flanker tasks administered via EMA would signifi-
cantly correlate with the corresponding BRIEF subdomain scores and state mindfulness
at magnitudes comparable to past work (Toplak, West, & Stanovich, 2013).
Methods
Participants
This study recruited a convenience sample of 32 seventh grade student participants
from the home economics classroom at a local middle school (N = 26) and a girl scout
troop (N = 6). Participants were 22% White, 47% Hispanic and 25% male.
Study measures and procedure
The inhibit (13 items) and working memory (12 items) subscales of the BRIEF-SR
(Gioia, Isquith, Guy, & Kenworthy, 2000) were administered at the initial study session
CHILD NEUROPSYCHOLOGY 3
186
via pencil-and-paper. Possible responses to each BRIEF item ranged from 1 to 3.
Relevant items were averaged within each subdomain for analysis. All participants
were then asked to download the Inquisit player app and a text message was sent to
each participant containing a link to complete study activities. Additional links were
texted to participants between 3-10pm over the following 72 h (2 prompts/day) for
a total of 6 prompts. Each prompt included four assessments.
First, the State-MAAS was administered (Brown & Ryan, 2003) – a five-item scale
designed to measure mindful attention and awareness as they are experienced within
daily life. Possible mean MAAS scores range from 1 to 6. Then a single health behavior
recall question was administered, which asked: “Which of the following have you done in
the past 2 hours? [select all that apply]”. The following response options were provided:
(1) Watched TV or online videos;(2) Played videogames;(3) Read or did homework;(4)
Played sports or other activities that made me breathe hard;(5) Exercised.
(Hoelscher, Day, Kelder, & Ward, 2003) (Sallis et al., 1996) The behavioral recall item
was included not to estimate causal effects, but rather to represent the type of self-
report health behavioral inventory that we are considering for inclusion in future trials.
Next, a complex symmetry span task (Foster et al., 2015) was administered (Figure 1).
Such tasks have been found to be valid, reliable indicators of working memory perfor-
mance when administered electronically in a laboratory context (Redick et al., 2012),
(Fosteretal.,2015)(Ma,Chang,Chen,&Zhou,2017).Incomplexspantasksparticipants
are shown a sequence of to-be-remembered items. However, participants must also
complete distractor tasks between presentations of each sequential to-be-remembered
item. The number of to-be-remembered items, and the corresponding distractor tasks,
varies each trial (Unsworth, Heitz, Schrock, & Engle, 2005). Subjects were presented with
a sequence ranging from two tofive to-be-remembered items. Recent work supports the
validity of abridged symmetry span tasks (Oswald, McAbee, Redick, & Hambrick, 2015)
(Foster et al., 2015). In this task, the to-be-remembered items were locations of a red
squareappearingina4×4gridofpossiblelocations.Thedistractortaskinvolvedjudging
whether a displayed shape is vertically symmetrical. Absolute and partial storage scores
were calculated (Redick et al., 2012). Absolute scores summed the sets of red squares
correctly recalled in the correct order at each assessment. Partial scores summed the
number of red squares correctly recalled in the correct order, regardless of whether the
entire trial was correctly recalled. Possible absolute scores ranged from 0 to 4, while
possible partial scores ranged from 0 to 11.
Finally, the childflanker task was administered. This task has been found to be a valid,
reliable assessment of inhibitory control and attention when electronically administered to
children (Weintraub et al., 2014)(Ruedaetal., 2004). The task was scored using the two-
vector NIH Toolbox method (Zelazo et al., 2013)whichincorporatesaccuracyand,for
participants responding with high accuracy (≥80% correct), reaction time (RT). Accuracy
scores were calculated based on congruent and incongruent trials using .125 X#ofcorrect
responses.Forparticipantswithhighaccuracy,themeanobservedRToncorrectincongruent
trialsanRTscorewasalsoincludedintheirscore.RTscoreswerethenrescaledfrom0to5
and added to the accuracy scores of participants achieving the accuracy criterion (≥80%)
(Hanley, Negassa, & Forrester, 2003). Consequently, the range of possible flanker scores
was 0–10.
4 C. M. WARREN AND M. A. PENTZ
(Figure 5.1).
187
A five-question survey (Figure 2)wasadministereduponcompletionofthestudyto
determineparticipant satisfaction andburdenregarding the protocol. Each survey item was
deliveredviaavisualanalogscalerangingfrom0to100in1unitincrementswiththeresponse
optionsindicatedinFigure2.Freeresponseitemsalsoinquiredaboutdifficultiesparticipants
encounteredwhilecompletingtheactivities,iftheyhadanysuggestionsforimprovement,or
additionalcomments.
Figure 1. Design of complex symmetry span task.
CHILD NEUROPSYCHOLOGY 5
Figure 5.1. Design of complex symmetry span task
Figure 5.2.
(Figure 5.2)
188
Statistical analysis
Spearman correlations were used to estimate baseline associations between the
BRIEF subscales and both computerized task performance and state mindfulness.
Figure 2. Participant-reported acceptability and study feedback.
M = Mean; SD = Standard Deviation
6 C. M. WARREN AND M. A. PENTZ
Figure 5.2. Participant-reported acceptability and study feedback
189
Responses to each BRIEF item were reverse-coded such that greater values indicate
better EF. Stata’sxtgeecommandwithrobuststandarderrors(Hanley,Negassa,&
Forrester, 2003)addressed the non-independence of repeated measuresnested
within individual subjects via generalized estimating equations. Two-sided p < .05
indicated statistical significance.
Results
The EMA assessment battery took an average of 5.24 (SD = 2.38) min and was
completed 3.4 times/participant over the study period – a57% compliance rate.
However, when participants clicked the text message link to begin an assessment
battery, the battery was fully completed 94% of the time. No participants were
excluded from analysis for any reason. At the conclusion of the study, students
expressed strong preferences for completingfuturehypotheticalstudyactivitieson
their phone (0) rather than using pencil-and-paper (100) (M = 16; SD = 16).
Students also reported that study activities were enjoyable (M = 76 SD = 21) and
that they would be willing to complete additional activities if asked (M = 84;
SD = 21). Students found the activities to be only moderately tough (M = 34;
SD = 20) and reported that in general the activities did not prevent participants
from doing other things (M = 26; SD = 23) [Figure 2].
On the free-response question asking students about any problems they may have
encountered while completing the tasks, no students reported any issues with the
assessments, with typical responses like “I had no trouble”; “No, everything seemed to
go OK”; and “No trouble”. Typical participant suggestions for improvement of the study
activities included “i would like them more if they were not the same every time” and
“mix them up”. The remaining suggestions/comments were positive including mention
of their favorite activity (frequently referred to as “games”), though one student,
referring to the five-item MAAS, wrote “Maybe not the survey part it’s kinda boring
cause you just want to get to the games”.
In addition to examining the feasibility and acceptability of administering per-
formance-based assessments via EMA, exploratory analyses also compared baseline
EF scores to paper-and-pencil BRIEF subdomain scores. At baseline, zero-order
correlations between flanker task performance and both BRIEF subscales were
moderate (σ
inhibit
=.23)(σ
working memory
=.34). Correlations between symmetry
span working memory task performance and BRIEF inhibitory control (σ
absolute
=.28; σ
partial
=.16)andworkingmemorysubscales(σ
absolute
=.19; σ
partial
=.15)
were slightly lower, demonstrating associations consistent with previous studies
(Toplak, West, & Stanovich, 2013). Correlations between absolute and partial sym-
metry span tasks were high (σ=.88).Atbaseline,statemindfulnesswasstrongly
associated with both the inhibitory control (σ=.47)andworkingmemory(σ=.60)
subdomains of the BRIEF. Table 1 reports the aforementioned zero-order correla-
tions between key study variables. Exploratory analyses of repeated measures found
that state mindfulness failed to significantly predict flanker (B = .14; p = .095) or
symmetry span (B = .01; p = .590) task performance.
CHILD NEUROPSYCHOLOGY 7
(Figure 5.2).
Table 5.1
190
Discussion
This study provides evidence for the feasibility and acceptability of administering
common neuropsychological assessment measures for inhibitory control, working
memory, and state mindfulness to adolescents via an EMA paradigm. In general,
participants overwhelmingly reported that they preferred completing assessments, like
the State MAAS, via smartphone compared to paper-and-pencil. Given the ubiquity of
computationally powerful smartphones among US children – administration of perfor-
mance-based EF tasks is no longer necessarily limited to laboratory settings. This study
suggests that participantsfind such activities to be enjoyable, non-burdensome, and not
overly difficult (Figure 2), as evidenced by the high (94%) completion rates among
initiated assessment batteries. Moreover, in qualitative responses, many respondents
noted that they particularly enjoyed the complex symmetry span working memory task,
which they perceived to be engaging and game-like. This is notable given that, unlike
the child flanker task, which utilized small fish to render the task more salient for
pediatric participants, the symmetry span task was not developed or adapted specifically
for administration to children, but rather was developed for and has been used
predominantly in adult populations.
Table 1. Zero-order correlations of key study variables at baseline.
Male White Hispanic
BRIEF-
Inhibit
BRIEF-
Working
Memory Flanker
Symmetry
Span
(Partial)
Symmetry
Span
(Absolute)
State
Mindfulness
Male 1
White .34 1
Hispanic −.44 −.71 1
BRIEF-Inhibit .11 −.17 .13 1
BRIEF-Working
Memory
.37 .01 −.10 .85 1
Flanker .21 .05 .06 .23 .34 1
Symmetry Span
(Partial)
.11 −.15 .34 .16 .15 −.08 1
Symmetry Span
(Absolute)
.09 −.14 .36 .28 .19 −.12 .90 1
State Mindfulness .36 .31 −.20 .47 .60 .35 −.04 −.05 1
Table 2. Momentary bivariate predictors of computerized flanker and complex symmetry span task
performance.
Flanker Symmetry Span (Partial) Symmetry Span (Absolute)
BP B P B P
State Mindfulness .14 .097 .11 .588 .002 .98
Exercise −0.05 0.8 .04 .95 −.14 .58
Sports 0.18 0.3 −.24 .64 −.17 .43
TV or Videos 0.03 0.87 −.004 .99 .06 .80
Videogames 0.54 0.000 1.36 .001 .63 .001
Reading/Homework −.13 .48 .06 .91 −.01 .96
8 C. M. WARREN AND M. A. PENTZ
Table 5.2. Momentary bivariate predictors of computerized flanker
and complex symmetry span task performance
Table 5.1 Zero-order correlations of key study variables at baseline
(Figure 5.2)
191
As hypothesized, observed correlations between computerized EF task perfor-
mance and traditionally administered paper-and-pencil BRIEF-SR subdomain scores
were comparable to past work in laboratory settings. This is consistent with the idea
that the EMA-administered flanker and complex symmetryspantasksweremeasur-
ing underlying inhibitory control and working memory skills. Converging evidence
is provided by the observation that participants’ state mindfulness was strongly
associated at baseline with both the BRIEF-inhibit subscale and flanker task perfor-
mance, which is consistent with both theory (Petersen & Posner, 2012)andrecent
studies linking early adolescents’ mindful attention and awareness to their ability to
inhibit irrelevant stimuli (Sanger & Dorjee, 2016)(Oberle,Schonert-Reichl,Lawlor,
&Thomson, 2012).
While this pilot study was not designed to detect significant effects of PA on
momentary cognition, but rather to test the feasibility/acceptability of delivering such
items via EMA – a strong positive association was identified between recent video-
gaming and performance on both computerized EF tasks (Table 2). We cannot rule out
a selection effect where individuals with better EF are more likely to regularly engage in
video-gaming, however, this finding is consistent with previous work linking video-
gaming to short-term improvement in EF, including working memory (Best, 2012)
(Nouchietal.,2013).Interestingly,oursamplewas75%female,thusextendingprevious
work conducted with predominantly male participants (Bueow, Okdie, & Cooper,
2015). Together, this suggests that future work employing performance-based tasks to
examine relationships between EF and health behavior, may want to consider assessing
videogaming behavior as a potential confounder if the behavior of interest is also
associated with videogaming (e.g. sedentary behavior, high-calorie/low-nutrient food
intake).
Limitations and future directions
Limitations of the present study include its small, convenience sample, and the short
assessment period. While the sample was designed to be representative of the target
population of a future intervention, the over-representation of Hispanic and female
students relative to the US population may impact the generalizability of these
findings. Furthermore, while students reported positive experiences with the data
collection process, which presented the tasks in the same order at all six assessment
periods, numerous participants echoed one student’ssentimentthat “One way to
improve mobile minds is to have different activities each day or each text you send
them”.Consequently,itislikelythatourfailure to obtain complete responses from
all students at all six assessments is at leastpartiallyduetotherepetitivenatureof
the tasks. Suboptimal adherence stemming from participant fatigue has been
observed in many EMA studies (Wen, Schneider, Stone, & Spruijt-Metz, 2017)
and the observed compliance rate of 57% of all administered prompts completed
is suboptimal relative to the 80% compliance rates recommended by Stone and
Shiffman (Stone & Shiffman, 2002). Maximizing EMA compliance is an emerging
research topic (Wen, Schneider, Stone, & Spruijt-Metz, 2017), and evolving tech-
nologies are beginning to permit researchers to leverage background sensor data
collected by mobile phones to identify optimal times to prompt participants when
CHILD NEUROPSYCHOLOGY 9
(Table 5.2)
192
they are most likely to be available and such that interference with other activities is
minimized. However, to date such work haslargelyfocusedonadultpopulations,
although it may be particularly useful for work focusing on youth, whose mobile
device use is generally more restricted than their adult counterparts (Poppinga,
Heuten, & Boll, 2014)(Sarker,Sharmin,Ali,Rahman,&Bari, 2014). Moreover, in
an attempt to standardize our assessment protocol and improve generalizability,
unlike previous EMA studies with youth thathavereportedhighercompliancerates
(Heron, Everhart, McHale, & Smyth, 2017), we did not employ response-based
incentives nor did we follow-up individually with non-compliant students to encou-
rage response. However, future work shouldconsideremployingsuchstrategiesas
appropriate in order to help promote compliance .
Finally, researchers seeking to further validate an EMA approach to EF assess-
ment should consider both a more comprehensive battery of cross-validation
measures than the BRIEF subscales utilized here as well as adapting additional
EF measures for EMA assessment. For example, the symmetry span task was
selected as an ideal first candidate measure for adaptation to EMA due to its
game-like appearance, however researchshowsthatcombiningmultiplecomplex
span working memory tasks (e.g. operation span, rotation span) in the same
assessment leads to a greater proportion ofvarianceexplainedthananequivalent
number of administrations of a single complex span task (Foster et al., 2015).
Inclusion of additional complex span working memory task types, ideally delivered
in counterbalanced order, would also address participant desires to have a more
varied, less predictable assessment battery – factors which past studies suggest may
increase EMA compliance.
Disclosure statement
The authors declare that they have no conflicts of interest to disclose.
Funding
This work was supported by the National Institutes of Health [R01 HD 052107, T32 CA009492-
31, F31 ES026482.].
ORCID
Christopher M. Warren http://orcid.org/0000-0002-8924-3329
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Abstract (if available)

Abstract Near-roadway air pollution (NRAP) is a complex mixture of particulate and gaseous combustion products from fresh vehicle emissions, debris from tires and brake wear, and metals from engine wear. It comprises a distinct chemical mixture from that found in regional air pollution and may be uniquely deleterious to human health. This is potentially worrisome in light of global trends toward greater urbanization and substantial increases in total vehicle miles travelled within the world’s most densely populated metropolitan areas. Recent epidemiological research has linked greater residential NRAP exposure to steeper BMI trajectories across elementary school, as well as increased risk of neurocognitive deficits—particularly in the domains of working memory and executive attention. Furthermore, findings from mechanistic studies suggest that the adverse health effects of chronic psychosocial stress and environmental toxicants such as NRAP exposure may operate through similar biological pathways (e.g. HPA axis dysregulation, oxidative stress, upregulation of inflammatory immune responses). As such it is reasonable to expect that their effects may be additive and/or multiplicative. Consequently, the present dissertation comprises a series of studies investigating the health effects of NRAP exposure on a socioeconomically and geographically diverse cohort of urban-dwelling Southern California children during the mid to late-elementary school years. After providing an introduction to key determinants of air pollution exposure in the Los Angeles Basin and emerging exposure assessment modalities in chapter one, chapter two investigates putative obesogenic effects of NRAP exposure in a cohort of 709 schoolchildren assessed four times across 4th-6th grades. This study tested the hypotheses that greater combined residential and school-based NRAP exposure is associated with greater risk of overweight/obesity as well as steeper gains in BMI and waist circumference across early adolescence. Relationships between air pollution exposure and key obesity-related behaviors, including multiple types of physical activity, sedentary behavior, and eating behavior are also examined. Following this, chapter three investigates the neurodevelopmental effects of NRAP exposure within this same sample during a putative critical period of prefrontal synaptic proliferation—the mid to late-elementary school years. Cross-sectional and longitudinal associations are estimated between participant NRAP exposure at 4th grade and both executive functioning and neurobehavioral deficits across 4th-6th grades. After examining main effects of NRAP on numerous obesity-related and neurodevelopmental outcomes, chapter 4 explores the extent to which these effects vary by chronic psychosocial stress, accounting for key covariates including sociodemographic factors, the presence of nearby green vegetation and exposure to regional air pollution. Finally chapter 5 describes the feasibility and acceptability of a novel mobile platform for ecological momentary assessment of inhibitory control and working memory, which has the potential to improve the validity of neurocognitive assessment in future work.

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Asset Metadata

Creator Warren, Christopher Michael (author)

Core Title Effects of near-roadway air pollution exposure on obesity, obesity-related behavior, and neurobehavioral deficits during peripuberty

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Preventive Medicine (Health Behavior Research)

Publication Date 12/04/2019

Defense Date 06/27/2019

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag developmental cognition,ecological momentary assessment,emotional control,executive function,inhibitory control,near-roadway air pollution,neurocognition,OAI-PMH Harvest,obesity risk,obesity-related behavior,obesogenic behavior,problem behavior,psychosocial stress,traffic-related air pollution,urban air pollution,working memory

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Pentz, Mary Ann (committee chair), Belcher, Britni (committee member), McConnell, Rob (committee member), Riggs, Nathaniel (committee member)

Creator Email cmwarren@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-243377

Unique identifier UC11674124

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Legacy Identifier etd-WarrenChri-7988.pdf

Dmrecord 243377

Document Type Dissertation

Rights Warren, Christopher Michael

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

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