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A pilot study of a global approach to assessing air pollution exposure in port communities: passive air monitoring of nitrogen dioxide concentrations

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A pilot study of a global approach to assessing air pollution exposure in port communities: passive air monitoring of nitrogen dioxide concentrations

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A PILOT STUDY OF A GLOBAL APPROACH TO ASSESSING AIR POLLUTION EXPOSURE
IN PORT COMMUNITIES: PASSIVE AIR MONITORING OF NITROGEN DIOXIDE CONCENTRATIONS

by

Wai Ping Athena Foong

A Thesis Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(APPLIED BIOSTATISTICS AND EPIDEMIOLOGY)

August 2013

Copyright 2013 Wai Ping Athena Foong
ACKNOWLEDGEMENTS
This thesis was completed under the direction of Heather Wipfli, Jonathan Samet, Junfeng (Jim)
Zhang, and Cecilia Patino-Sutton, of whom I am truly grateful for their guidance and support. I
am grateful to my parents for their unconditional support and love. A big thank you to all of my
friends, notably Lida Asatryan, Purple Hsu, Judith Linton, John Gil-Flamer, Anne Dee, and Sandra
Ackermann, for their unyielding love and encouragement throughout this project. Most
importantly, this thesis would not have been completed without the help I received from Amy
Zhu, Jessica Trimis, Cecilia Patino-Sutton, and Christine Lanois, who each sat by me as I worked
through the days and nights in bringing this project to fruition.
Thank you.
ii
TABLE OF CONTENTS
Acknowledgements.......................................................................................................................... ii
List of Tables ................................................................................................................................... iv
List of Figures ................................................................................................................................... v
Abstract ........................................................................................................................................... vi
Chapter 1: Introduction .................................................................................................................. 1
Chapter 2: Methods ........................................................................................................................ 9
Chapter 3: Results ......................................................................................................................... 15
Chapter 4: Discussion and future directions ................................................................................. 31
Bibliography ................................................................................................................................... 35
Appendix A: Locations of the three study sites ............................................................................ 37
Appendix B: Study questionnaire .................................................................................................. 38
iii
LIST OF TABLES
Table 1.1 Sources of air pollution in urban microenvironments ................................................. 5
Table 3.1 Baseline characteristics of study participants by location ......................................... 16
Table 3.2 Median and interquartile range (IQR) of NO
2
concentrations by
monitor type and study location ................................................................................ 19
Table 3.3 Associations between personal and outdoor NO
2
concentrations,
overall and by location ............................................................................................... 24
Table 3.4 Selected responses to questions on participants’ personal perceptions,
beliefs and behaviors on air pollution ........................................................................ 27
iv
LIST OF FIGURES
Figure 1.1 Estimated number of cardiopulmonary mortalities attributable to
shipping-related PM
2.5
emissions in 2002 .................................................................... 3
Figure 2.1 The Ogawa Sampler.................................................................................................... 10
Figure 2.2 The Ogawa Samplers with protective outdoor shelters installed outside
the homes of study participants in the Port of Los Angeles community ................... 11
Figure 3.1 Boxplots of personal and outdoor NO
2
concentrations in three selected
port communities of Los Angeles, Hong Kong, and Kuala Lumpur ............................ 18
Figure 3.2 Correlations of personal NO
2
levels with outdoor NO
2
levels in three selected
port communities of Los Angeles, Hong Kong, and Kuala Lumpur ............................ 20
Figure 3.3 Correlations of personal NO
2
levels with outdoor NO
2
levels by location ................. 21
v
ABSTRACT
Background: As a key node in the global goods movement network, sea ports are a significant
source of traffic and industrial emissions of air pollutants, which are shown to have an adverse
effect on human health. Accurate measurement of personal exposure to air pollutants in port
communities is valuable for informing local and international efforts to reduce air pollution in
and around ports.
Objective: To evaluate the feasibility of a coordinated global network approach to assess
personal exposure to nitrogen dioxide (NO
2
) levels, as well as perceptions, knowledge and
beliefs about air pollution among people living in communities located near large ports in the
Pacific Rim.
Methods: A total of 45 participants from port communities in Los Angeles, Hong Kong, and Kuala
Lumpur were assessed for personal and outdoor exposure to NO
2
by passive sampling using the
Ogawa Sampler. Participants' personal perceptions, knowledge, attitudes and beliefs about air
pollution were collected using an interviewer-administered questionnaire. Spearman correlation
and multiple linear regression were used to examine the association between personal and
outdoor NO
2
concentrations. Descriptive statistics were used to report responses to
questionnaire.
Results: Median NO
2
level was lowest in Kuala Lumpur, followed by Los Angeles and Hong Kong.
There was a positive correlation of personal NO
2
levels with outdoor NO
2
levels across all three
study sites (correlation coefficient 0.55, p=0.0001). This association was not confounded by age,
sex, or the reported traffic pattern near the participants’ homes. In general there were
differences in perceptions and knowledge of air pollution across the communities. Participants
in Los Angeles showed greater awareness of air quality issues and were more predisposed to
vi
acknowledge that port emissions were a source of local air pollution compared to those in Hong
Kong and Kuala Lumpur. However, participants in all 3 communities responded that they would
modify their behaviors in response to changes in air quality.
Conclusion: This study demonstrated a feasible protocol to characterize levels of personal and
outdoor NO
2
concentrations at port communities, as well as to assess the perceptions, beliefs
and behaviors of residents in these communities toward issues related to air pollution.

vii
Chapter 1: Introduction
Air pollution has long shown to have adverse effects on human health. While the link
between air pollution exposure and illness has been known for centuries, findings from a broad
range of research in recent decades, including epidemiological and toxicological studies, provide
scientific evidence that shows air pollution continues to damage the health of the public (Pope
et al. 2002; Katsouyanni et al. 2009). These health effects include increased risk for respiratory
symptoms, such as coughing and wheezing, increased risk for cardiovascular diseases, and
increased risk for hospitalizations and mortality (National Center for Environmental Assessment
2009; World Health Organization 2006). In cities and urban areas, where large and dense human
populations exist alongside numerous combustion sources such as vehicles for transportation
and infrastructure for industry and power generation, air pollution is a clear threat to health.
One key activity in urban areas is goods movement, which involves the transport of
manufactured goods from the point of production to the point of consumption through an
expansive network of seaports, airports, highways, rails, warehouses, distribution centers, and
other cargo facilities (Matsuoka et al. 2011). This system of goods movement, strongly
influenced by the expansion of global trade, has a significant impact on the environment and
health of the communities it serves (Matsuoka et al. 2011).
Contribution of Ports to Air Pollution
Sea ports are a key node in the goods movement network; but they are also a significant
source of both traffic and industrial emissions of air pollutants. Sea ports contribute to air
pollution in a number of ways: 1) by commercial ships operating in and around the port, 2) by
the fleets of ferries, tugboats, trucks, trains, forklifts, and other combustion-powered vehicles
1
that transfer freight, many of which are powered by diesel fuel, and 3) by emissions of air toxins
from the loading and unloading of cargo. According to a study led by the National Oceanic and
Atmospheric Administration and the University of Colorado at Boulder, commercial ships alone
emit almost half as much particulate matter pollution into the air globally as the total released
by the world's cars, and contribute almost 30 percent of smog-forming nitrogen oxide gases
(Lack et al. 2009). Half of ships’ total particle emissions are made up of organic pollutants and
black carbon, which Lack and colleagues found remain suspended for longer periods of time,
providing more time for the pollutants to travel and be inhaled by humans. More than 70
percent of shipping-related traffic takes place within 250 miles of the coastline, making it a
significant health concern for coastal communities (Lack et al. 2009). A 2007 study estimated
that global shipping-related particulate matter (PM) emissions from marine shipping alone
contribute to approximately 60,000 annual deaths worldwide, with the impact concentrated in
coastal regions along major trade routes in Europe, East Asia, and South Asia (Corbett et al.
2007) (Figure 1.1).
2
Figure 1.1. Estimated number of cardiopulmonary mortalities attributable to shipping-related
PM
2.5
emissions in 2002

Note: Case 2b refers to PM
2.5
concentrations from ship emissions modeled using the geospatial emissions
inventory AMVER 2001 and global aerosol model E5/M1-MADE. Data presented as range of estimated
number of deaths. (Corbett et al. 2007)
In addition to the ships, port trucking carries 80 percent of the shipping containers
between ports, warehouses and distribution centers (Bensman 2009). These massive fleets of
trucks often pass directly through urban communities. While some efforts have been made in
select ports in high-income countries, most trucking systems of ports have not kept pace with
advances in clean truck technologies. Diesel emissions, especially from older trucks, release
particles and nitrogen oxides into the air that are carcinogenic and dangerous to the
environment and the health of nearby residents.
Communities near ports are also more likely to be exposed to toxic emissions from
cargo ships. For example, asthma epidemics in Barcelona, Spain and elsewhere have been
traced to atmospheric distribution of soybean antigen during shipping port cargo transfers
(Soriano et al. 1995).
3
Measures of Air Pollution
Personal Exposure and the Microenvironmental Model. The concept of personal
exposure is central to characterizing the health risks of air pollution. Personal exposure, defined
as the contact of a person with the pollutant of concern, is calculated for air pollutants as the
product of the concentration of the pollutant in the place(s) where time is spent and the time
spent in that place (Samet 2011). For example, a person working outdoors at a concentration of
particulate matter less than 2.5 microns in aerodynamic diameter (PM
2.5
) of 100 µg per m
3
for 8
hours would have an exposure of 800 µg/m
3
-hours.
The microenvironmental model is useful for estimating personal exposure; this concept
defines total personal exposure as the sum of exposures received in the various
microenvironments where time is spent. A microenvironment is defined as a place with a
particular pollutant concentration profile; for example, microenvironments may include a motor
vehicle, representing a microenvironment during time spent commuting, or a restaurant where
a meal is eaten (Samet 2011). Table 1.1 lists some of these microenvironments along with
pollution sources.
Considered within the framework of the microenvironmental model, there are a
number of specific microenvironments of particular relevance to the health of those living near
ports. The residence is particularly important because most people spend the majority of their
time at home. In areas around ports, the air contaminants in the home include those generated
by indoor sources, such as cooking and smoking of tobacco, and the penetration of outdoor air
pollutants indoors, including those pollutants generated by ships and other traffic and by nearby
and more distant sources.
4
Table 1.1. Sources of air pollution in urban microenvironments
Microenvironment Sources Pollutants
Home Cooking, space heating, parked
vehicles, hobbies, smoking, household
products, pets, rodents, insects
PM, CO, NO
x
, VOCs, allergens
Transportation
environments
Vehicle and industrial emissions, road
dust, background pollution, smoking
PM, including ultrafine PM,
CO, NO
x
, O
3
, VOCs,
aeroallergens, carcinogens
Streets Vehicle emissions, road dust,
background pollution
PM, including ultrafine PM,
CO, NO
x
, O
3
, VOCs,
carcinogens, lead
Work environments Industrial processes, smoking,
background pollution
PM, CO, VOCs, NO
x
,
carcinogens
Entertainment
environments
Cooking and space heating, background
pollution, smoking
PM, VOCs, carcinogens
PM=particulate matter, CO=carbon monoxide, NO
x
=nitrogen oxides, O
3
=ozone, VOCs= volatile organic
compounds. Source: Samet 2011
Air Pollutant Measurements. Many different ambient air pollutants can be measured.
The primary pollutants associated with shipping are those resulting from combustion, primarily
from diesel fuels (Soriano et al. 1995). These include sulfur dioxide, carbon monoxide,
particulate matter, nitrogen oxides, black carbon, and toxic emissions. Nitrogen dioxide (NO
2
) is
a particularly attractive tracer because ships, cars and trucks are its predominant source (U.S.
Environmental Protection Agency 2008) and NO
2
concentration can be easily measured with
small passive badges that are relatively simple, accurate and inexpensive. Recent advances in
active sampling of black carbon have also made it an increasingly inexpensive and widely used
tracer of combustion generated PM.
5
Clean Air Port Policies
Policies aimed at reducing the pollution emissions in port areas can be generated at the
local, state, national, and international level. In the US, efforts to reduce air pollution at ports
have largely been undertaken at the local level. Local policies include vessel speed reduction,
provision of electric shore power for docked vessels, retrofitting and auxiliary fuel
improvements, upgrading of harbor crafts, tractors and other cargo handling equipment to
clean fuels, locomotive modernization with ultra-low emission switchers, and clean truck
programs. In Long Beach, California, for example, the Clean Trucks Program was launched in
2009 to reduce air pollution by establishing “clean truck” standards, requiring truck owners to
replace old diesel-powered trucks with newer models that meet the federal 2007 emission
standard. In a little over three years, the Port of Long Beach authority announced that air
pollution from harbor trucks has been reduced by more than 90% due to the Clean Trucks
Program (Port of Long Beach 2013).
Nonetheless, given the global nature of the commercial shipping industry,
uncoordinated local approaches are unlikely to result in major systematic changes that are
needed to protect many communities located near large ports throughout the world. The
industry-sponsored International Maritime Organization (IMO) is currently responsible for
regulating international shipping. The IMO has been slow to take advantage of the best available
technologies and fuels. Its current regulations for emissions, adopted in 1997 and implemented
in 2005, are outdated and woefully inadequate (International Maritime Organization 2013).

6
Objectives
The overall objective of this study was to evaluate the feasibility of a coordinated global
network approach to assess personal exposure to air pollution among people living in
communities located near large ports throughout the Pacific Rim in order to characterize the
public health impact. The elements of the study included measurement of port-related
environmental pollutants (specifically NO
2
and black carbon), generation of locally relevant data
to support community-based efforts to change port policies, and creation an international
dataset and network to advocate for greater international regulation of the global shipping
industry. This project involved collaborative relationships of the faculty of the Department of
Preventive Medicine at the University of Southern California with leading institutions in Hong
Kong and Malaysia. This network included Dr. Heather Wipfli, Mr. Ed Avol, Ms. Andrea Hricko,
and Dr. Scott Fruin within the Department of Preventive Medicine and other members of the
Southern California Environment Health Sciences Center (SCEHSC)’s Exposure Assessment and
GIS Facility Core, and collaborating investigators Dr. Tze Wai Wong, Professor at the Chinese
University of Hong Kong, and Dr. Jamal Hisham Hashim, Professor at United Nations University
in Kuala Lumpur, Malaysia. This effort built upon previous work with these investigators in
implementing joint research protocols and the translation of results for effective public health
policies.
Potential Impact
The generation of an international dataset on air pollution near ports, and the
characterization of the public health impact, will be valuable for informing local and
international efforts to reduce air pollution in and around ports throughout the Pacific Rim.
7
These locally-relevant data would support community-based advocacy efforts to change port
policies, and create a global network to advocate for greater global regulation of port emissions.

8
Chapter 2: Methods
Study locations
Three cities located in the Pacific Rim with large international ports and different
environmental port policies were selected for this study: Port of Los Angeles in California,
United States, Port of Hong Kong in Hong Kong Special Administration Region, and Port Klang
near Kuala Lumpur, Malaysia (Appendix A). Existing collaborations were already in place
between the investigators at each city, thus enabling the feasible conduct of this study. Data
collection began in Los Angeles from November 2010 through January 2011, where the protocol
was first tested for feasibility and adapted for logistical improvement. A workshop was
conducted in Los Angeles by the principal investigators of USC to train the collaborating
investigators from Hong Kong and Kuala Lumpur on all aspects of the protocol, including
collection of the measurements and data entry and analysis.
In each location, the collaborating investigator identified one community located within
a half-mile to one-mile radius downwind from the port. The study communities were selected
based on their accessibility and the collaborator’s prior experience working with that
community. The USC principal investigators reviewed each country’s protocol prior to the start
of data collection.
Data collection took place in Hong Kong from April 2011 through May 2011, and
continued in Kuala Lumpur throughout February 2012 after the Malaysian rainy season.

9
Study population
A total of 15 individuals between the ages of 18 and 64 years and residing in different
areas of the selected community in each city were selected using a convenience sampling
method. This sample size was judged sufficient to develop the protocol and establish the
study’s feasibility within the constraint of resources. Individuals taking medication(s) or
diagnosed with any health conditions that could influence normal physical activity or exposure
to outdoor air were excluded. Individuals who self-reported smoking or living with persons who
smoked indoors were also excluded from the study.
Informed consent was obtained from all participants by the field personnel. The study
protocol was approved by the Institutional Review Board of the University of Southern California
and by the review boards of the Chinese University of Hong Kong and the United Nations
University in Kuala Lumpur, Malaysia.
Data collection
Passive air monitoring of NO
2
concentrations. Ambient NO
2
concentrations were
estimated by passive sampling using the Ogawa Sampler, a holder reusable, low-cost air monitor
for personal and outdoor use (Figure 2.1,
OGAWA and Co. 2013). The two-sided passive
samplers contained chemically-impregnated
filters with a compound-specific response to the
pollutant under investigation, in this case NO
2
.
Each monitor was labeled with two
identification codes, one provided by the
Figure 2.1. The Ogawa Sampler
10
laboratory and another provided by the lead study coordinator at USC.
Two monitors were used for each individual, with 2 randomly selected households
having duplicate or blank sampling. Both monitors (outdoor and personal) were employed for 7
days. One monitor was placed outside of the home (such as on the balcony or patio), with the
monitor facing away from the house, to sample ambient concentration at a stationery location
(Figure 2.2). The selected participant wore the second monitor on the outside of his/her
clothing at all times during the day and next to their bed while sleeping in order to sample the
levels of NO
2
in their microenvironment.

Figure 2.2. The Ogawa Samplers with protective outdoor shelters installed outside the homes
of study participants in the Port of Los Angeles community.

11
For each monitor, the location, date and time was recorded on the sampling sheet that
was completed at the time when the monitors were installed. Once sampling was complete,
each monitor was returned to its plastic holding cup and securely capped, and the date and time
of removal was recorded. The country coordinators have examined the labels for accuracy and
stored the monitors in a smoke-free location at room temperature. Once all the samples for
one country were collected, the samples were transported to USC where the lead coordinator
would then send to a commercial laboratory for analysis (Research Triangle Institute, Research
Triangle Park, NC).
Active air monitoring for black carbon. Black carbon (BC) exposure was assessed by
active sampling using the microAeth® AE51 from AethLabs (originally developed at Magee
Scientific Company). The handheld personal monitor uses optical techniques to determine the
mass concentration of BC particles collected from an air stream passing through a filter. At each
city, 5 participants were selected using a fixed random sampling procedure of every 3 individuals
and carried the microAeth for 48 hours. For the analyses presented in this study, only NO
2

exposure data were included.
Questionnaire. The field personnel administered a questionnaire to each individual on
the day the passive air monitors were deployed, if possible. The interviews were conducted in
the native languages of the participants at each location – English in Los Angeles, Chinese in
Hong Kong, and Malay in Kuala Lumpur. The questionnaire included demographic information
and questions to assess participants’ personal perceptions, knowledge, attitudes and beliefs
about air pollution (both in general and specific to port environments). Participants also
recorded their personal time-activity over the period when the monitors were deployed.
12
All study data were recorded on data collection sheets and entered into the REDCap
electronic data capture tools (Harris et al. 2009) hosted at the University of Southern California.
REDCap (Research Electronic Data Capture) is a secure, web-based application designed to
support data capture for research studies, providing: 1) an intuitive interface for validated data
entry; 2) audit trails for tracking data manipulation and export procedures; 3) automated export
procedures for seamless data downloads to common statistical packages; and 4) procedures for
importing data from external sources (Harris et al. 2009). Appendix B shows the study
questionnaire.
Laboratory analysis
NO
2
. Time-weighted average NO
2
concentration was assayed at a commercial
laboratory (Research Triangle Institute, Research Triangle Park, NC) with broad experience in
analyses of the Ogawa Sampler (the designated laboratory by Ogawa USA for laboratory
support). Before sending the monitors to the laboratory, the lead study coordinator at USC
removed the code that identifies the monitor location while keeping the laboratory code. To
minimize the potential for sample degradation, fresh filters were purchased on a “just-in-time”
basis, used in the field and sent to the laboratory for timely analyses in convenience batches.
NO
2
was measured in nanograms per cubic meter (ng/m
3
).
Quality Control. A total of 10% of the outdoor and personal NO
2
monitors had an
accompanying duplicate and blank for quality control purposes. Duplicates and blanks were
selected using a fixed random sampling procedure of every 10 individuals. The normal and
duplicate monitors were located on the same individual. The duplicates were used to estimate
fieldwork reliability. The blank samples were handled in a similar manner as the rest of the
13
samples and were sent along with the other monitors for analysis. Blank samples were used to
determine blank-corrected NO
2
concentrations and to calculate the method limit of detection.
However, due to the small sample size in this pilot study, formal measure of agreement
between the selected monitors and their duplicates was not calculated.
Statistical analysis
Demographic and home characteristics of participants as well as NO
2
levels across study
locations (Los Angeles, Kuala Lumpur, and Hong Kong) were evaluated. Frequency distributions
of responses to questionnaire by location were also examined. Correlation between personal
and outdoor NO
2
levels was assessed using Spearman correlation. To explore the association
between factors previously reported in the literature (including age, sex, and time spent
outdoors) and personal NO
2
levels, an explanatory model was constructed using univariate and
multivariate linear regression analyses. All analyses were performed using SAS version 9.2 (SAS
Institute Inc., North Carolina, USA). Statistical tests were two-sided and an alpha of 0.05 was
used to assess statistical significance.
14
Chapter 3: Results
Demographic characteristics of study participants
Table 3.1 presents the baseline characteristics of participants at each of the three study
locations. Per the study protocol, there were 15 participants in Los Angeles, Hong Kong, and
Kuala Lumpur, respectively. The mean age of participants was comparable across the three
sites. Men made up more than half of the respondents from Los Angeles while only one-fifth of
the respondents from Kuala Lumpur were men. Of the three sites, participants from Kuala
Lumpur reported the fewest years of formal education (mean of 9.8 years which approximates a
middle school-level education), while participants from Hong Kong and Los Angeles reported
means of 12.1 years and 14.1 years, respectively, which approximate high-school to some
college-level education.
Participants in Kuala Lumpur reported doing the most amount of strenuous exercise,
although participants in all three locations did some form of outdoor physical activity of over 30
minutes at least 4 times a week. The majority of people in Los Angeles and Hong Kong perceived
themselves to be in good to very good health, while many in Kuala Lumpur reported fair to good
health on average.
A handful of participants reported being exposed to secondhand smoke at various
locations in their communities, including the home and workplace for those from Los Angeles
and Kuala Lumpur. Secondhand smoke exposure in restaurants and other hospitality venues was
reported by some participants from Los Angeles and Hong Kong, and there were reports of SHS
exposure in other locations across all three cities.
15
Among those who reported working outside of their homes, the majority were
employed in indoor workplaces. Working respondents from Hong Kong all reported having
workplaces with policy that bans smoking in all indoor areas. In Los Angeles, if a policy was in
place, smoking in indoor areas was also prohibited. In Kuala Lumpur, workplaces of participants
did not necessarily have comprehensive smoking policy, particularly for indoor areas.
The type of traffic near homes of participants in Kuala Lumpur was mostly residential,
and was fairly infrequent. There were more mixed traffic types near the homes of participants in
Los Angeles and Hong Kong, and the traffic patterns were much busier.

Table 3.1. Baseline characteristics of study participants by location
Los Angeles
(n=15)
Hong Kong
(n=15)
Kuala Lumpur
(n=15)
Age, mean (SD) 47.8 (19.5) 48.0 (18.3) 48.9 (16.6)
Gender, % male 60.0 46.7 20.0
Education, mean years (SD) 14.1 (2.4) 12.1 (4.5) 9.8 (4.4)
Outdoor physical activity >30 minutes,
mean # of times per week (SD)

Strenuous exercise 2.3 (2.6) 1.6 (2.1) 5.4 (6.3)
Moderate/Mild activities 4.5 (2.6) 4.5 (3.5) 6.1 (4.9)
Self-perception of general health, %
Excellent 6.7 6.7 0
Very good 53.3 26.7 13.3
Good 13.3 40.0 40.0
Fair 26.7 26.7 40.0
Poor 0 0 6.7
16
Table 3.1. Baseline characteristics of study participants by location
Los Angeles
(n=15)
Hong Kong
(n=15)
Kuala Lumpur
(n=15)
Secondhand smoke exposure*, % yes
At home 6.7 0 13.3
At work 26.7 0 13.3
Other people’s homes 20.0 0 6.7
Restaurants, cafes, bars or clubs 20.0 20.0 0
Other locations 6.7 26.7 13.3
Workplace location†, %
Primarily indoors 77.8 100.0 50.0
Primarily outdoors 22.2 0 33.3
Both indoors and outdoors 0 0 16.7
Workplace smoking policy†, %
No 11.1 0 33.3
Yes
Not allowed in any indoor areas 100.0 100.0 50.0
Allowed in some indoor areas 0 0 50.0
Traffic type near home*, %
Residential 80.0 53.3 93.3
Commercial 66.7 53.3 6.7
Commuter 46.7 40.0 0
Traffic pattern near home, %
No traffic 0 0 40.0
Infrequent traffic 33.3 80.0 33.3
Frequent traffic 46.7 20.0 26.7
Constant traffic 20.0 0 0
*More than one response was given.
†Among participants who reported working outside of home: 9 in Los Angeles, 6 in Hong Kong, and 6 in
Kuala Lumpur.

17
Concentrations of NO
2

At each location, the 7-day mean NO
2
levels measured using personal monitors were
lower than the levels measured using outdoor monitors (Figure 3.1). Figure 3.1 shows the
distribution of personal and outdoor NO
2
levels by study location. In each boxplot the blue star
indicates the mean NO
2
level, the center line the median, and the lower and upper edges of the
box the 25
th
and 75
th
percentile values, respectively. The extreme NO
2
levels are at the end of
the lines extending from the lower and upper edges of each box.
Figure 3.1. Boxplots of personal and outdoor NO
2
concentrations in three selected port
communities of Los Angeles, Hong Kong, and Kuala Lumpur

1 2 3 4 5 6
0
5000
10000
15000
20000
N
O
Loc ation
NO
2
concentrations (ng/m
3
)
Los Angeles Kuala Lumpur Hong Kong
Personal Outdoor Personal Outdoor Personal Outdoor
18
The median personal NO
2
level was lowest in Kuala Lumpur, followed by Los Angeles
and Hong Kong (Table 3.2). Similarly, median outdoor NO
2

level was lowest in Kuala Lumpur,
followed by Los Angeles and Hong Kong. One exceptionally high reading of outdoor NO
2

concentration was measured at one household in Hong Kong (16738.0 ng/m
3
).
Table 3.2. Median and interquartile range (IQR) of NO
2
concentrations by monitor type and
study location
Monitor
type
Median (IQR), ng/m
3

Los Angeles Hong Kong Kuala Lumpur
Personal 4049.1 (3415.5-4876.4) 5800.5 (4532.4-7315.4) 3563.5 (2526.0-4642.1)
Outdoor 5695.0 (4617.6-6286.0) 7433.9 (7147.9-8273.2) 4616.1 (3742.9-4894.8)

Figure 3.2 shows the correlation of personal NO
2
levels with outdoor NO
2
levels across
all three communities, with a Spearman correlation coefficient of 0.53, p=0.0002 (Figure 3.2,
top). The correlation between personal and outdoor concentrations strengthens somewhat
when the one outlier of outdoor NO
2
level in Hong Kong (circled) was omitted, resulting in a
Spearman correlation coefficient of 0.55, p=0.0001 (Figure 3.2, bottom).

19
Figure 3.2. Correlations of personal NO
2
levels with outdoor NO
2
levels in three selected port
communities of Los Angeles, Hong Kong, and Kuala Lumpur, with (top) and without (bottom)
one outlier of outdoor NO
2
level

*Note change of scale between top and bottom graphs.

NO2PER
1000
2000
3000
4000
5000
6000
7000
8000
9000
NO2OUT
2000 3000 4000 5000 6000 7000 8000 9000 10000 11000 12000 13000 14000 15000 16000 17000
NO2PER
1000
2000
3000
4000
5000
6000
7000
8000
9000
NO2OUT
2000 3000 4000 5000 6000 7000 8000 9000 10000 11000
Personal NO
2
concentration (ng/m
3
) Personal NO
2
concentration (ng/m
3
)
Outdoor NO
2
concentration (ng/m
3
)
Outdoor NO
2
concentration (ng/m
3
)
20
When personal and outdoor NO
2
concentrations were examined by location, different
correlation patterns emerged (Figure 3.3). In both Los Angeles and Hong Kong, the levels of
personal NO
2
concentration increased as the outdoor levels increased. However, correlation was
inversed in Kuala Lumpur, where personal NO
2
levels decreased as outdoor levels increased.
Figure 3.3. Correlations of personal NO
2
levels with outdoor NO
2
levels by location

NO2PER
1000
2000
3000
4000
5000
6000
7000
8000
NO2OUT
3000 4000 5000 6000 7000 8000
country=Los Angeles
Outdoor NO
2
concentration (ng/m
3
)
Los Angeles
Personal NO
2
concentration (ng/m
3
)
21

*Note change of scale between graphs.
NO2PER
4000
5000
6000
7000
8000
9000
NO2OUT
6000 7000 8000 9000 10000 11000
country=Hong Kong
NO2PER
1000
2000
3000
4000
5000
6000
7000
NO2OUT
2000 3000 4000 5000 6000
country=Kuala Lumpur
Hong Kong
Kuala Lumpur
Outdoor NO
2
concentration (ng/m
3
)
Outdoor NO
2
concentration (ng/m
3
)
Personal NO
2
concentration (ng/m
3
) Personal NO
2
concentration (ng/m
3
)
22
Table 3.3 shows the result of correlations between personal and outdoor NO
2
levels
using linear regression analysis. In Los Angeles, there was a positive correlation between
personal and outdoor NO
2
levels (β=0.7, p=0.04), and this association did not change after
adjusting for age, sex, or air quality behavior. After adjusting for traffic pattern near home, the
association appeared stronger (β=0.9, p=0.01).
In Hong Kong, there was also a positive correlation between personal and outdoor NO
2
levels but this association was not statistically significant (β=0.6, p=0.13). The association
appeared to be confounded by age, sex, and working outside of home, although the results
were also not significant (Table 3.3).
In Kuala Lumpur, the correlation between personal and outdoor NO
2
levels was negative
(β=–0.9, p=0.04). The effect estimate was larger after adjusting for traffic pattern near the
home, although the resulting model did not reach statistical significance (β=–1.0, p=0.06) (Table
3.3).
Overall, when including data from all three cities the correlation between personal and
outdoor levels of NO
2
was significant (β=0.6, p<0.001), and was not confounded by age, sex, or
the reported traffic pattern near the participants’ homes. The association changed slightly and
was no longer significant when adjusted for country or participants’ behavior in response to
poor air quality (Table 3.3). The level of outdoor NO
2
concentration explains about 30% of the
variability in personal NO
2
levels.

23

Table 3.3. Associations between personal and outdoor NO
2
concentrations, overall and by
location
Los Angeles
(N=15)
Hong Kong
(N=14)
Kuala Lumpur
(N=15)
Overall
(N=44)
Base model
R
2

β
p-value

0.30
0.68
0.037

0.18
0.63
0.129

0.28
-0.89
0.043

0.31
0.59
<0.001
After adjusting for age
R
2

β
p-value

0.34
0.69
0.036

0.28
0.50
0.231

0.34
-0.87
0.047

0.31
0.59
<0.001
After adjusting for sex
R
2

β
p-value

0.30
0.68
0.049

0.37
0.71
0.072

0.34
-0.86
0.052

0.31
0.59
<0.001
After adjusting for
working outside home
R
2

β
p-value

0.30
0.69
0.04

0.69
0.21
0.44

0.32
-0.95
0.04

0.33
0.56
0.002
After adjusting for
traffic pattern near
home
R
2

β
p-value

0.49
0.90
0.01

0.21
0.60
0.161

0.30
-1.02
0.06

0.36
0.59
<0.001
After adjusting for air
quality behavior*
R
2

β
p-value

0.31
0.70
0.039

0.19
0.58
0.193

0.33
-0.90
0.042

0.37
0.22
0.35
24
Table 3.3. Associations between personal and outdoor NO
2
concentrations, overall and by
location
Los Angeles
(N=15)
Hong Kong
(N=14)
Kuala Lumpur
(N=15)
Overall
(N=44)
After adjusting for
country
R
2

β
p-value
– – –

0.37
0.24
0.28
*Changing plans to stay indoors if air quality is poor.

Perceptions, knowledge, attitudes and beliefs about air pollution
Table 3.4 shows the distribution of responses to selected questions from the standard
questionnaire administered across all three port communities. From responses to questions in
the "Perception of air quality" category, participants in Los Angeles had a somewhat negative
perception of their local air quality, where the majority agreed that air quality was either hazy or
unhealthy, and on average considered over 4 days a week as hazy days. In general, participants
in Los Angeles were aware of issues concerning air quality in their community. On the other
hand, participants in Hong Kong and Kuala Lumpur provided somewhat mixed responses to
questions in this category. Participants from these two communities perceived fewer hazy days
in a week compared to participants in Los Angeles.
In the next category of questions, "Air pollution beliefs", participants from the Los
Angeles port community held much stronger beliefs that the port and ships were sources of
local air pollution than the participants from Hong Kong or Kuala Lumpur. Respondents from
both Los Angeles and Kuala Lumpur believed that strong relations are necessary in the effort to
25
control port pollution. In general, there was no specific pattern of responses from the
participants in Hong Kong to suggest their beliefs in air pollution issues.
In the "Air pollution behaviors" category, the majority of participants across all three
port communities responded yes to spending more time outdoors when air quality was good,
and conversely, reducing amount of exercise done outdoors when air quality was poor. Most
people in Los Angeles would not alter their plans to stay indoors if the air quality was poor,
while more than half of participants in Hong Kong and Kuala Lumpur reported that they would
do so in the same situation. Finally, no one reported wearing a face mask if air quality was poor
in Los Angeles, while some participants in Hong Kong and Kuala Lumpur did report wearing face
masks.
26
Table 3.4. Selected responses to questions on participants’ personal perceptions, beliefs and behaviors on air pollution
Los Angeles
(n=15)
Hong Kong
(n=15)
Kuala Lumpur
(n=15)
Perception of air quality

How many days per week (SD) do you consider the air in
your community to be “hazy”?
a
4.1 (2.3) 2.4 (1.1) 2.1 (1.3)
The air quality in my community is usually hazy.
b
Strongly disagree
Disagree
Agree
Strongly agree

0
28.6%
50.0%
21.4%

40.0%
40.0%
20.0%
0

0
57.1%
42.9%
0
The air quality in my community is usually unhealthy.

Strongly disagree
Disagree
Agree
Strongly agree

0
0
53.3%
46.7%

33.3%
33.3%
20.0%
13.3%

0
53.3%
46.7%
0
Have you ever heard or read about “air quality index” or
“air quality alerts” where you live?
Yes
No
Don’t know/unsure

66.7%
33.3%
0

33.3%
60.0%
6.7%

26.7%
33.3%
40.0%
27
Table 3.4. Selected responses to questions on participants’ personal perceptions, beliefs and behaviors on air pollution
Los Angeles
(n=15)
Hong Kong
(n=15)
Kuala Lumpur
(n=15)
I usually pay attention to news announcement about air
pollution.
Strongly disagree
Disagree
Agree
Strongly agree

0
6.7%
60.0%
33.3%

20.0%
26.7%
13.3%
40.0%

0
0
80.0%
20.0%
Air pollution beliefs

The port is a major source of air pollution in my
community.
Strongly disagree
Disagree
Agree
Strongly agree

0
0
66.7%
33.3%

26.7%
33.3%
26.7%
13.3%

0
26.7%
60.0%
13.3%
Ships are a major source of air pollution in my
community.
Strongly disagree
Disagree
Agree
Strongly agree

0
13.3%
66.7%
20.0%

26.7%
40.0%
20.0%
13.3%

13.3%
40.0%
40.0%
6.7%
28
Table 3.4. Selected responses to questions on participants’ personal perceptions, beliefs and behaviors on air pollution
Los Angeles
(n=15)
Hong Kong
(n=15)
Kuala Lumpur
(n=15)
There is more pollution in my community than in other
communities further away from the port.
Strongly disagree
Disagree
Agree
Strongly agree

0
26.7%
46.7%
26.7%

33.3%
26.7%
20.0%
20.0%

0
60.0%
33.3%
6.7%
Strong relations controlling port pollution is needed.
Strongly disagree
Disagree
Agree
Strongly agree

0
0
60.0%
40.0%

13.3%
0
40.0%
46.7%

0
0
53.3%
46.7%
Air pollution behaviors

If the air quality is good, I spend more time outdoors.
Yes
No

86.7%
13.3%

60.0%
40.0%

66.7%
33.3%
If the air quality is poor, I reduce the amount of exercise I
do outdoors.
Yes
No

60.0%
40.0%

60.0%
40.0%

86.7%
13.3%
29
Table 3.4. Selected responses to questions on participants’ personal perceptions, beliefs and behaviors on air pollution
Los Angeles
(n=15)
Hong Kong
(n=15)
Kuala Lumpur
(n=15)
If the air quality is poor, I will change my plans to stay
indoors.
Yes
No

26.7%
73.3%

53.5%
46.7%

66.7%
33.3%
I usually wear a face mask if the air quality is poor.
Yes
No

0
100.0%

40.0%
60.0%

13.3%
86.7%
SD=standard deviation;
a
Respondents answering “Don’t know”: Los Angeles, n=4; Hong Kong, n=8; Kuala Lumpur, n=4;
b
Missing responses: Los Angeles,
n=1, Hong Kong, n=1; Kuala Lumpur, n=0
30
Chapter 4: Discussion and Future Directions
In this pilot study that assessed levels of the air pollutant NO
2
in port communities
across three countries, we found differences between the ambient concentrations measured
outside of participants’ homes and concentrations measured on participants who wore personal
monitors over 7 days. In Los Angeles and Hong Kong, a positive correlation between personal
NO
2
and outdoor ambient NO
2
levels was observed. However, the reverse was seen in Kuala
Lumpur, where personal NO
2
levels decreased as the outdoor levels increased. Across the three
study sites, the median levels of NO
2
concentrations also varied, with Hong Kong showing the
highest measures of both personal and outdoor levels.
From the responses to the selected questions, we saw different patterns of perceptions
and beliefs on the topic of air pollution between those living in Los Angeles, and those in Hong
Kong and Kuala Lumpur. Participants in Los Angeles generally showed greater awareness of air
quality issues and were more predisposed to acknowledge that port emissions were a source of
local air pollution. Participants from the other two study sites gave mixed responses to the
same questions. Despite the difference in awareness of air quality issues, participants across all
three places did in general suggest that they would modify their behaviors in response to
changes in air quality. Interestingly, we noticed that while more participants in Los Angeles than
Hong Kong or Kuala Lumpur perceived the air quality of their community as polluted, the levels
of NO
2
concentrations were in fact the lowest in Los Angeles compared to the other two
locations. This reflects country or regional variations in personal assessment of air quality and
interpretation of air pollution. As the project is expanded to more communities with larger
31
sample sizes and greater cultural heterogeneity, we will be able to assess possible determinants
of air pollution knowledge and beliefs using the data collected through this questionnaire.
Studies have suggested that the difference between outdoor and personal NO
2
levels
may be due to several factors including ventilation of the home, use of a gas stove and proximity
of home to major roads (Rotko et al. 2001; Brown et al. 2009). Homes with limited indoor
ventilation and presence of gas appliances used for cooking or heating have higher
concentrations of NO
2
compared to those with better ventilation and electric stoves (U.S.
Environmental Protection Agency 2008). In terms of the observed geographic variability on NO
2
levels across the three countries, studies have suggested that this may reflect actual differences
in levels of pollution, but may also be influenced by seasonal differences which include
variations in temperature and precipitation (Rotko et al. 2001; Brown et al. 2009). In a study of
personal exposure to several particulate and gaseous pollutants including NO
2
in Boston during
1999-2000, Brown et al. reported higher concentrations of NO
2
recorded on personal monitors
during the warmer summer months (July-Aug) compared to the winter months (Nov-Jan). Such
variability may be attributed to indoor ventilation patterns including use of air-conditioning
units and opening of windows in the summer (Brown et al. 2009). In our study, data collection
did not take place at around the same time across all three locations. The study was first
conducted in Los Angeles through the winter months of November through January (average
temperature 58 degrees Fahrenheit and 1.9 inches of rain), followed by Hong Kong in late spring
with comparatively warmer temperatures during April to May (average temperature 76 degrees
Fahrenheit with 9.4 inches of rain). Meanwhile, data collection in Kuala Lumpur took place in
32
February of the following year after the rainy season (mean temperature 81 degrees Fahrenheit
with 6.5 inches of rain).
Due to the nature of its design as a pilot study of feasibility, the findings presented here
may not be representative of the port communities at large due to the small sample sizes at
each location. The negative correlation of personal and outdoor NO
2
levels in Kuala Lumpur
may have been an artifact of small sample size. We were also underpowered by the small
sample size to understand how well outdoor NO
2
concentrations and other covariates can
predict levels of personal NO
2
concentrations. We are interested in constructing a predictive
model for personal NO
2
to better understand the microenvironment model of exposure to air
pollution among persons living in port communities. In order to achieve this, we will need to
expand the protocol to include more microenvironments (e.g., inside home), more accurate
assessment of time/activities in each microenvironment, and increase sample sizes, and use
population-based sampling. Information on home ventilation or gas stove use was not collected
in this pilot study but will be included in the expanded study.
In carrying out this study, we learned to overcome the many challenges of conducting a
cross-country project. The successful implementation of this pilot study shows that the protocol
can be feasibly conducted in countries with different languages, cultures, socio-demographics,
as well as environmental policies and legislature, as represented by the three selected study
sites. One of the lessons learned include incorporating more web-based technology to
streamline the data collection and data management processes (e.g., using a standard data
entry web portal). Another part of the protocol that can be improved would be the addition of
questions on home ventilation including use of air conditioning and use of gas stoves for heating
33
or cooking, and the documentation of microenvironment temperatures. For now, the remaining
steps for this study would be to analyze the subset of participants with data on black carbon
concentrations, GPS, and daily time-activity.
In summary, using a standard data collection protocol, the study characterized the levels
of NO
2
concentrations, at both the personal monitoring level and the microenvironmental
(residential outdoor) level, across port communities in Los Angeles, Hong Kong, and Kuala
Lumpur. Questionnaire data showed study participants’ perceptions, beliefs and behaviors
toward issues related to air pollution in their local communities. The data collected through this
pilot study, along with practical knowledge learned during the data collection process, provide
useful information that can help the design and implementation of future studies to assess air
pollution exposures in port communities especially in the Pacific Rim. The generation of an
international dataset on air pollution near ports, and the characterization of the public health
impact, will be valuable for informing local and international efforts to reduce air pollution in
and around ports. These locally-relevant data would support community-based advocacy
efforts to change port policies, and create a global network to advocate for greater global
regulation of port emissions.
34
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36
Appendix A. Locations of the three study sites – Port of Los Angeles in California, United States (A); Port of Hong Kong in Hong Kong
Special Administrative Region (B); and Port Klang near Kuala Lumpur, Malaysia (C)

Image source: Google Maps, 2013. http://goo.gl/maps/KCBc7
37
Appendix B. Study questionnaire

38

39

40

41

42

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Asset Metadata

Creator Foong, Wai Ping Athena (author)

Core Title A pilot study of a global approach to assessing air pollution exposure in port communities: passive air monitoring of nitrogen dioxide concentrations

School Keck School of Medicine

Degree Master of Science

Degree Program Applied Biostatistics and Epidemiology

Publication Date 07/23/2013

Defense Date 06/11/2013

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

Tag Air pollution,global health,nitrogen dioxide,OAI-PMH Harvest,passive air monitoring,Ports

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Wipfli, Heather L. (committee chair), Patino Sutton, Cecilia M. (committee member), Samet, Jonathan M. (committee member), Zhang, Junfeng (Jim) (committee member)

Creator Email wfoong@med.usc.edu,wfoong@usc.edu

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