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Evaluating the role of energy system decarbonization and land cover properties on urban air quality in southern California
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Content
Evaluating the Role of Energy System Decarbonization and Land Cover Properties on Urban Air
Quality in Southern California
By
Yun Li
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
(ENVIRONMENTAL ENGINEERING)
August 2022
Copyright 2022 Yun Li
ii
Acknowledgements
During my Ph.D. study, I have met and worked with many great people without whom this journey
would have been much more difficult for me. Therefore, I would like to take this opportunity to
express my sincere gratitude to them.
Above all, I would like to thank my advisor Dr. George Ban-Weiss, who unfortunately passed
away in October 2021. George was a great advisor to work with because he had a broad vision and
came up creative and practical research questions to work on, and he was both rigorous and
encouraging when carrying out actual work. He was also a great advisor in life because he was
optimistic and lived his life full of passion. George influenced me a lot during my Ph.D. study and
will continuously influence me on how I conduct and convey research and how I want to live my
life.
I would also like to thank my co-advisor Dr. Kelly Sanders who generous took me as her student
after George’s passing and helped me go through the hard time. Kelly is also a great advisor to
work with. She is open to merging idea across disciplines, a quick thinker with a down-to-earth
personality. Although I just joined her group in late 2021, I feel I have known, appreciated and
learnt a lot from her style of conducting research and leading a research group for a long time.
I would like to thank my defense committee members: Dr. Garvin Heath (National Renewable
Energy Laboratory), Dr. Felipe De Barros and Dr. Sam Silva for taking time to review this
dissertation and for their valuable feedbacks on my research. I would also like to thank my
qualifying committee members: Dr. Constantinos Sioutas, Dr. Amy Childress and Dr. Bistra
Dilkina for their guidance during my Ph.D study.
iii
I would like to thank my collaborators across multiple agencies for their contribution to my
research. They are Dr. David Sailor at Arizona State University, Dr. Vikram Ravi at the National
Renewable Energy Laboratory, Dr. Pouya Vahmani the Lawrence Berkeley National Laboratory,
and Dr. Sang-Mi Lee and Dr. Xinqiu Zhang at South Coast Air Quality Management District.
I would also like to thank my collogues and friends I have met during my Ph.D. study. I would
like to give special thanks to my collogues and friends from the GBW group and the S3 group,
who are Jiachen Zhang, Arash Mohegh, Trevor Krasowsky, Mo Chen, Measrainsey Meng, Joseph
Ko, Hannah Schlaerth, Mckenna Peplinski, Andrew Jin, Stepp Mayes, Kayley Butler, Zoia
Comarova, Diego Ramos Aguilera. It was always a great time to brainstorm with them as well as
hanging out with them.
I would like to thank my parents and all my family members for their non-stopping love and
support to me. It feels both soft and strong to know that someone is always behind me no matter
what situation I’m in and no matter what type of person I am.
Finally, I would also like to thank myself. You are a much better person than you think, so believe
in yourself, and be proud of who you are.
iv
Table of Contents
Acknowledgements ......................................................................................................................... ii
List of Tables ................................................................................................................................ vii
List of Figures .............................................................................................................................. viii
Abstract ........................................................................................................................................... x
Chapter 1: Introduction ................................................................................................................... 1
1.1 Background ........................................................................................................................... 1
1.2 Definition of Research Goals ................................................................................................ 3
1.2.1 Evaluating the Role of Renewable Energy Adoption on Urban Air Quality and
Public Health in the City of Los Angeles ............................................................................... 3
1.2.2 Evaluating the Role of Land Cover Properties via Urbanization on Urban Air
Quality in Southern California ................................................................................................ 4
1.3 Structure of Document .......................................................................................................... 5
Chapter 2: Air Quality and Public Health Co-benefits of 100% Renewable Electricity
Adoption and Electrification Pathways in Los Angeles ................................................................. 6
2.1 Introduction ........................................................................................................................... 6
2.2 Materials and Methods ........................................................................................................ 10
2.2.1 Scenario Designs .......................................................................................................... 11
2.2.2 Air Quality Modeling ................................................................................................... 13
2.2.3 Emission Inventory Development ................................................................................ 16
2.2.4 Health Impact Estimation and Monetization ............................................................... 27
v
2.3 Results ................................................................................................................................. 28
2.3.2 Emission Inventory for Baseline and Future Scenarios ............................................... 28
2.3.3 Simulated Air Quality for the Baseline and Future Scenarios ..................................... 32
2.3.4 Public Health Impacts and Corresponding Monetization for the Baseline and Future
LA100 Scenarios ................................................................................................................... 37
2.4 Discussion ........................................................................................................................... 40
Chapter 3: Evaluating the Role of Land Cover Properties Changes via Urbanization on
Regional Meteorology and Air Quality in Southern California.................................................... 44
3.1 Introduction ......................................................................................................................... 44
3.2 Methodology and Data ........................................................................................................ 49
3.2.1 Model Description and Configuration ......................................................................... 49
3.2.2 Land Surface Property Characterization and Irrigation Parameterization ................... 51
3.2.3 Emission Inventories .................................................................................................... 53
3.2.4 Meteorology and Air Pollutant Observations .............................................................. 54
3.2.5 Simulation Scenarios.................................................................................................... 55
3.2.6 Uncertainties ................................................................................................................ 56
3.3 Results and Discussion ....................................................................................................... 57
3.3.1 Evaluation of Simulated Meteorology and Air Pollutant Concentrations ................... 57
3.3.2 Effects of Urbanization on Air Temperature and Ventilation Coefficient................... 59
3.3.3 Effects of Urbanization on NOx and O3 Concentrations due to Meteorological
Changes ................................................................................................................................. 65
vi
3.3.4 Effects of Urbanization on Total and Speciated PM2.5 Concentrations due to
Meteorological Changes ....................................................................................................... 70
3.4 Conclusion and Discussion ................................................................................................. 74
Chapter 4: Conclusion and Future Work ...................................................................................... 78
References ..................................................................................................................................... 81
Appendix A: Supplementary Information for Chapter 2: Air Quality and Public Health
Co-benefits of 100% Renewable Electricity Adoption and Electrification Pathways in Los
Angeles ......................................................................................................................................... 89
vii
List of Tables
Table 2- 1. Scenario names and key assumptions on power sector eligibility criteria and
electrification levels 13
Table 2- 2. Fraction of light-duty vehicles and buses that are assumed to be electric powered
in 2045 for the moderate and high electrification levels 19
Table 2- 3. Fraction of buildings/households by end use that are assumed to be electricity-
powered in 2045 for the moderate and high electrification levels 23
Table 2- 4. Fraction of port sources that are assumed to be electric powered in 2045 for both
moderate and high electrification load assumption 25
Table 3- 1. Summary statistics for model evaluation, which compares simulated hourly near-
surface air temperature (T2), hourly O3 and daily PM2.5 concentrations to observations. 58
Table A- 1. Emission factors (kg/MMBTU) for the LADWP-owned power plants applied in
the SB100 scenario. 89
Table A- 2. Emission factors (kg/MMBTU) for the LADWP-owned power plants applied in
the Early & No Biofuels Scenario. 89
Table A- 3. Scaling factors of fuel consumption in commercial buildings from 2020 to 2045
under moderate and high electrification load assumptions 89
Table A- 4. Scaling factors of fuel consumption in residential buildings from 2020 to 2045
under moderate and high electrification load assumptions 90
viii
List of Figures
Figure 2- 1. Three two-way nested domains used for all simulations (left). The innermost
domain (d03) encompasses Los Angeles and is the focus of our analysis (right). 14
Figure 2- 2. Annually averaged daily (a) NOx and (b) PM2.5 emissions from all anthropogenic
sources in the city of Los Angeles for all scenarios. 32
Figure 2- 3. Spatial patterns of simulated July daily maximum 8-hour average O3, and annual
daily average PM2.5) concentrations in Los Angeles for Baseline (2012), differences
between Baseline (2012) and SB100 – Moderate (2045), differences between SB100 –
Moderate (2045) and Early & No Biofuels – Moderate (2045), and differences between
SB100 – Moderate (2045) and Early & No Biofuels – High (2045). 33
Figure 2- 4. O3 isopleth diagrams to illustrate how 8-hour O3 concentration design values
(DV) at a location close to or in Los Angeles can change in response to decreases in
NOx and VOC emissions (in units of tons per day) in SoCAB. 35
Figure 2- 5. Health benefits and penalties due to air pollutant concentrations changes. 37
Figure 2- 6. Monetized health benefits (in 2019 U.S. dollars) for the 15 Los Angeles city
council districts across LA100 scenarios. 39
Figure 3- 1. Maps of the three nested WRF-UCM-Chem domains, and land cover types for the
innermost domain (d03) for the Present-day and Nonurban scenarios. 51
Figure 3- 2. Spatial patterns of differences (Present-day – Nonurban) in land surface
properties for urban grid cells. 56
Figure 3- 3. Comparison between modeled and observed (a) hourly near-surface air
temperature, hourly O3 concentrations, and daily PM2.5 concentrations. 58
ix
Figure 3- 4. Diurnal cycles for present-day, nonurban, and present-day – nonurban for air
temperature in the lowest atmospheric layer and ventilation coefficient. 60
Figure 3- 5. Spatial patterns of differences (Present-day – nonurban) in temporally averaged
values during morning, afternoon and nighttime for air temperature in the lowest
atmospheric layer, and ventilation coefficient. 61
Figure 3- 6. Diurnal cycles for present-day, nonurban, and present-day – nonurban for NOx
and O3 concentrations. 66
Figure 3- 7. Spatial patterns in differences (Present-day – nonurban) of temporally averaged
values during morning, afternoon and nighttime for NOx, CO, andO3 concentrations. 67
Figure 3- 8. Diurnal cycles for spatially averaged total PM2.5 and speciated PM2.5
concentrations. 72
Figure 3- 9. Spatial patterns in differences (Present-day – nonurban) of temporally averaged
values during morning, afternoon, and nighttime for total PM2.5 and speciated PM2.5. 74
Figure A- 1. Annually averaged daily CO, SOx, VOCs and NH3 emissions from all
anthropogenic sources in the city of Los Angeles for all scenarios. 91
Figure A- 2. Change in incidences of cardiovascular hospital admissions and heart attacks in
the city of Los Angeles for the scenarios compared 92
x
Abstract
Air pollution is among the biggest environmental concerns of our time. Exposure to air pollutants
including fine particulate matter (PM2.5) and ozone (O3) cause respiratory and other diseases,
making it a major concern for public health as well. Air pollution is caused primarily by fossil fuel
combustion from energy use and is affected by land—climate—air quality interactions via
chemical reactions, deposition and atmospheric transport. This dissertation investigates how
energy system decarbonization and land cover property changes affect air quality in Southern
California, which includes Los Angeles, one of the most polluted cities in the US. Exposure to air
pollution in Los Angeles has resulted in hundreds to thousands of hospitalizations and deaths
annually and billions of US dollars lost.
First, this dissertation analyzes the air quality impacts of the city of LA’s renewable energy
adoption plan, which aims to achieve 100% clean electricity generation in the Los Angeles
Department of Water and Power’s service territory by the mid-21
st
century, along with a push to
electrify the city’s buildings and transportation sectors. Using a detailed bottom-up approach and
a state-of-the-art regional meteorology-chemistry model (WRF-Chem), this work projects air
pollutant emission inventories for future renewable energy adoption scenarios and simulates
concentrations of major air pollutants including PM2.5 and O3 for Los Angeles in 2045. Overall
changes in air pollution from adopting clean energy in Los Angeles are found to avoid as many as
150 premature deaths annually, which is equivalent to $1.4 billion in savings. This work helped
form the scientific foundation for the Los Angeles City Council’s approval of its 100% clean
energy target by 2035, which is a decade ahead of its prior goal.
Second, this dissertation investigates how land surface property changes via historical urbanization
influence regional meteorology and air quality in Southern California. While the impact of land
xi
cover changes on regional meteorology has been widely researched, studies that quantify its impact
on regional air quality have been limited and typically fail to resolve the wide spatial heterogeneity
of the urban land surface Thus, this work fills this knowledge gap by incorporating satellite-derived
high-resolution real-world land cover representations into WRF-Chem and comparing the
simulated air pollutant concentrations between a present-day land cover scenario and a land cover
scenario representing conditions prior to any human perturbation (assuming identical
anthropogenic air pollutant emissions). Results from this work show the important role of urban
morphology, building materials, and urban irrigation on regional air quality, in addition to
affecting local meteorology.
The body of work summarized in this dissertation reveals the importance of the connection
between energy and air quality, and the interactions among land cover, climate, and air quality. It
suggests that policy makers should seek sustainable solutions that address air pollution, the energy
crisis, and climate change problems synergistically.
1
Chapter 1
Introduction
1.1 Background
Air pollution is among the biggest environmental and public health concerns of our time. Severe
air quality can degrade visibility, interact with regional meteorology and global climate, and
adversely affect human health (1–3). The World Health Organization (WHO) estimates that
outdoor air pollution accounted for approximately 4.2 million premature deaths globally in 2016
1
.
Thus, it is important to investigate the direct causes of air pollution, as well at the processes that
affect air pollutant levels, to build a better understanding of air pollution mitigation.
Anthropogenic air pollutant emissions (including carbon monoxide (CO), nitrogen oxides (NOx),
sulfur oxides (SOx), total organic gases (TOG), total suspended particles (TSP), and ammonia
(NH3), etc.) from fossil fuel combustion together with some non-combustion processes are major
sources of urban air pollution. The abundance of anthropogenic emissions is determined by the
emission factor of a unit of activity (e.g., fuel combustion) and the total amount of emitting
activities (e.g., total fuel combusted). The implementation of emission control policies and
advancements in control technologies have led to emission reductions in recent 20 years (4, 5). In
more recent years, driven by the urgent need for renewable energy adoption and electrification for
mitigating climate change, research has quantified how energy system transitions can reduce air
pollutant emissions by reducing fossil fuel consumption, in addition to reducing greenhouse gas
1
WHO, Ambient (outdoor) air pollution, available at: https://www.who.int/news-room/fact-sheets/detail/ambient-
(outdoor)-air-quality-and-health
2
emissions (6, 7). These studies differ according to their investigated geographic area and included
sectors, as well as their assumptions made regarding future energy scenarios.
Aside from air pollutant emissions, the physical properties (e.g., surface roughness, thermal inertia
and albedo) of land surfaces can also affect air quality by altering meteorology (8, 9). Urbanization
can lead to profound changes in these land surface properties, which then drive changes in urban
meteorology such as air temperature, wind speed and planetary boundary layer (PBL) height.
Changes in meteorological conditions can in turn influence concentrations of regional air
pollutants via processes/phenomena such as atmospheric transport (which determines how
efficiently pollutants get diluted), and chemical reactions (through which secondary pollution is
formed from air pollutant emissions). (10, 11) Thus, it is important to investigate the driving forces
among all processes/phenomena and determine the overall effect of how land surface properties
change air pollutant concentrations. Building a better understanding of these processes is important
for informing sustainable urban planning, as well as quantifying the air quality tradeoffs of climate
adaptation strategies that modify land surfaces at regional scales to protect cities from the
consequences of climate change.
This dissertation addresses urban air pollution in Southern California, with a focus on Los Angeles
city. Los Angeles is the second largest metropolitan city in the U.S and has been suffering from
severe air pollution issues for decades (12). Among all air pollutants, ozone (O3) and fine
particulate matter (PM2.5) are the two major species that are regulated by the U.S. Environmental
Protection Agency (EPA), by which Los Angeles is out of compliance. In order to achieve better
air quality and avoid adverse impacts on public health, it is important to investigate what factors
are affecting the generation/fate of air pollutant in Los Angeles.
3
1.2 Definition of Research Goals
This dissertation evaluates the urban air pollution changes that occur as a result of 1) renewable
energy adoption and 2) land surface property changes using a regional meteorology—chemistry
model, which is applied to Southern California as a case study.
1.2.1 Evaluating the Role of Renewable Energy Adoption on Urban Air Quality and Public
Health in the City of Los Angeles
For decades, the city of Los Angeles has been suffering from increasing temperatures due to
climate change and urbanization, in addition to its severe air pollution. To combat these dilemmas,
Los Angeles has established ambitious renewable energy adoption goals in recent years. In 2017,
the Los Angeles City Council passed a series of motions directing the Los Angeles Department of
Water and Power (LADWP) to determine the technical feasibility and investment pathways of a
100% renewable energy power system, along with the electrification of several sectors. Aside from
reducing greenhouse gas (GHG) emissions, renewable energy adoption can also reduce air
pollutant emissions due to less fossil fuel combustion, which in turn will affect ambient air
pollutant concentrations in Los Angeles. Thus, it is important to quantitatively analysis the air
quality benefits and penalties that follow various renewable energy adoption pathways in Los
Angeles and their resulting impact on public health. Thus, the research questions that this
dissertation addresses within this research topic include:
• How might future scenarios of renewable electricity adoption and electrification by
LADWP change Los Angeles’s air pollutant emissions from a historical reference year
(2012) to a future year (2045)?
4
• How could changes in air pollutant emissions from renewable electricity adoption and
electrification affect ambient air pollutant concentrations (i.e., PM2.5 and O3) within Los
Angeles?
• How could changes in air pollutant concentrations, resulting from renewable electricity
adoption and electrification, alter deleterious health consequences from air pollution
exposures within Los Angeles?
1.2.2 Evaluating the Role of Land Cover Properties via Urbanization on Urban Air Quality
in Southern California
Southern California, which includes the metropolitan regions of Los Angeles and San Diego, has
undergone accelerated urbanization for a century. Along with urbanization, there has been an
expansion of urban area and a profound anthropogenic modification of the land surface due to the
construction of buildings and roads, etc. These modifications have altered land physical properties
such as surface roughness, thermal inertia (i.e., the capacity of a material to store heat and to delay
its transmission), and albedo (i.e., the ratio of reflected to incident sunlight) in urban areas. Land
physical properties can affect regional meteorology, which in turn influences regional air quality;
thus, it is crucial to investigate how land cover properties that changed as a result of historical
urbanization have affected regional air quality in Southern California, both spatially and
temporally. Thus, the research questions that this dissertation addresses under this research topic
include:
• How have land cover property changes, resulting from historical urbanization, altered
regional meteorological conditions such as temperature and atmospheric ventilation in
5
Southern California? What are the spatial and temporal patterns of these changes? And
what are the driving factors on those changes in meteorology?
• How have changes in regional meteorology from land cover property changes altered
regional air quality in Southern California, assuming identical anthropogenic emissions?
What are the spatial and temporal patterns of these changes? And what are the driving
factors on those changes in air pollution?
1.3 Structure of Document
This document is organized into four chapters. Chapter 2 and 3 each corresponds to one set of
research questions raised above.
Chapter 2 quantifies the air pollutant emission (e.g., NOx, primary PM2.5) reductions from multiple
renewable energy adoption pathways in the city of Los Angeles, simulates regional air pollutant
concentration (i.e., O3 and PM2.5) changes using a state-of-the-art regional meteorology and
chemistry model (WRF-Chem), and estimates avoided premature mortality and morbidity
incidences, along with overall monetized benefits.
Chapter 3 compares a present-day urban land cover scenario with a hypothetical non-urban land
cover scenario that assumes conditions prior to any human perturbation, simulates the differences
in their resulting regional meteorology and air quality (with the assumption that anthropogenic
emissions are kept the same) using the WRF-Chem model, and analyzes the driving factors of
those differences.
Finally, Chapter 4 summarizes the main findings and significance of this dissertation and proposes
directions for future work based on the conclusions drawn from this body of work.
6
Chapter 2
Air Quality and Public Health Co-benefits of 100% Renewable Electricity
Adoption and Electrification Pathways in Los Angeles
2.1 Introduction
Energy is central to modern society and is essential for most daily activities – travel, cooking,
residential heating and cooling, entertainment, etc. However, combustion processes that are
traditionally used to produce energy release emissions that lead to climate change and air pollution.
An important strategy for solving the climate crisis involves transforming our power systems by
replacing combustion of fossil fuels with renewable electricity sources such as solar and wind.
Renewable energy adoption can also have important co-benefits on urban air quality including
potential reductions in air pollutant emissions and corresponding changes in ambient air pollutant
concentrations (6, 7, 13–15). These changes in air pollutant concentrations are a crucial aspect to
consider in renewable energy adoption because exposure to air pollutants such as ozone (O3) and
fine particulate matter (particles with aerodynamic diameter of 2.5 micrometer or less, PM2.5) is
associated with premature mortality and numerous deleterious health consequences like asthma (1,
3, 16, 17).
The city of Los Angeles, the second largest city in the U.S., has been suffering from increasing
temperatures and severe air pollution for decades (18–20). Los Angeles has been experiencing
higher temperatures from climate change as well as urbanization (19). Along with increased
average temperatures, there have been increases in the frequency and duration of extreme heat
7
events in cities like Los Angeles (20). Los Angeles is also among the most polluted U.S. cities
with high O3 and PM2.5 concentrations (18), which are out of compliance with National Ambient
Air Quality Standards (NAAQS) set by the U.S. Environmental Protection Agency (U.S. EPA) for
decades (12, 21, 22). Current design values (2018-2020)
2
of PM2.5 and 8-hour O3 concentrations
are 18% and 63% above the NAAQS in Los Angeles area, respectively.
To combat the climate crisis and urban air pollution, Los Angeles has set ambitious goals for
renewable energy adoption in recent years. In 2017, the Los Angeles City Council passed a series
of motions directing the Los Angeles Department of Water and Power (LADWP, the municipal
utility responsible for supplying power and water to Los Angeles) to determine the feasible
pathways of an electricity system powered by 100% renewable energy (23), along with the
electrification of several sectors. Thus, a large-scale study was conducted—the City of Los
Angeles 100% Renewable Energy Study (LA100)—to determine the renewable resource mix and
system upgrades required to achieve 100% renewable electricity and increased electrification in
transportation and end-use sectors while maintaining the current high degree of reliability by 2045
(24).
LA100 is the first-of-its-kind effort to create a bottom-up, science and policy-driven roadmap of
various feasible technical and economic pathways to achieve a 100% renewable energy power
system by 2045, through the collaboration of researchers across the National Renewable Energy
Laboratory and academia, in close partnership with local authorities, and can serve as an example
for other cities (25). The broader LA100 effort evaluated four scenarios across three potential
trajectories of LADWP customer electricity demand. Scenarios varied in their definition of
2
Data available at: https://www3.epa.gov/airquality/greenbook/kdtc.html and
https://www3.epa.gov/airquality/greenbook/jdtc.html
8
achieving “100% renewable electricity” (e.g., if renewable energy certificates (RECs) could be
used to offset fossil fuels; if LADWP is allowed to make purchase of existing nuclear generation)
(23). Customer electricity demand varied in levels of energy efficiency, electrification and demand
flexibility. LA100 is a systems-level study that includes electricity demand modeling (i.e.,
residential and commercial buildings, electric vehicle and electric bus charging, water
infrastructure and other industrial loads), power system investments and operations, distributed
energy resources and distribution grid modeling, economic impact analysis, life-cycle greenhouse
gas (GHG) analysis, and analysis of the environmental justice implications of select results
including of the air quality and public health consequences.
This chapter focuses on one aspect of the LA100 study: the co-benefits for air pollution and public
health. Only a few past studies have investigated air quality and public health implications of
renewable energy adoption in California (6, 7, 15). Some of these past studies have showed that
reductions in air pollutant emissions due to low- or zero-carbon energy can lead to decreases in
PM2.5 concentrations and increases in O3 concentrations in the Los Angeles Basin. Zapata et al.
(2018) reported a 14% reduction in annual population-weighted PM2.5 concentrations and a 3.2%
increase in summertime 8-hour O3 concentrations in the South Coast Air Basin (SoCAB, where
Los Angeles is located) when comparing a low-carbon (i.e., statewide GHG emissions across the
entire economic sectors are 80% below 1990 level) energy scenario to a business-as-usual (BAU)
scenario in year 2050 (6). Wang et al. (2020) showed the same trends in PM2.5 and O3 concentration
changes comparing a net-zero GHG pathway to a BAU scenario in year 2050 in the Los Angeles
Basin (7). The net-zero GHG pathway in Wang et al. (2020) assumes high energy efficiency,
electrification among demand sectors and 85% renewable electricity generation, and requires
offsetting the GHGs emissions using carbon capture and sequestration technology.
9
While previous studies proposed theoretical low- or net-zero GHG pathways based on national or
state level regulations, this study for the first-time modeled the air quality and public health impacts
of 100% renewable electricity adoption, developed in coordination with local policy
implementation, which demonstrate how science can be more closely integrated with policy at the
city scale. First, the air quality and public health modeling was done for scenarios that were
comprehensively investigated and modeled to ensure their feasibility and their attainment of other
performance metrics like grid reliability and adequate energy resources, including local distributed
resources. Second, the assumptions regarding future emission projections of fuel transitions in the
power sector and the electrification of multiple sectors are based on detailed grid modeling and
load modeling, region-specific activities changes and emission factors changes, in order to ensure
the feasibility and accuracy of such projections. In addition, while reductions in PM2.5
concentrations are intuitive, increases in O3 concentrations are associated with more complex
nonlinear photochemistry. Past studies generally lack details that explain this phenomenon, and
thus, omit insights into pathways to avoid these O3 increases, which are critical for policy making.
To fill this gap, we include discussion on achieving O3 reductions on pathways to clean energy
futures.
In this study, we investigate how future renewable electricity adoption and electrification pathways
could change air pollutant emissions, resulting air pollutant concentrations, and public health. We
focus on O3 and PM2.5 concentrations because these species (1) continue to exceed NAAQS in Los
Angeles, and (2) are major contributors to air pollutant-caused human health impacts (1, 3). To
achieve this goal, we first establish a baseline inventory of emissions from a historical year (2012)
that accounts for all known anthropogenic sources. Next, we quantify changes to air pollutant
emissions under selected future LA100 scenarios projected to year 2045. We select two of the four
10
LA100 scenarios including one that is compliant with meeting California’s Senate Bill 100 goals
by 2045 and a second that achieves the 100% renewable electricity target by 2035 with more
stringent criteria defining how the target is met. Each scenario is evaluated across two customer
electricity demand assumptions, namely moderate and high electrification load assumptions (see
Table 2- 1). The load assumptions directly affect portions of the transportation sector (i.e., light-
duty vehicles (LDVs) and buses), residential and commercial buildings, and the Port of Los
Angeles and Port of Long Beach (which are located adjacent to each other at the south corner of
Los Angeles and referred to hereafter singularly as the Ports). The scenario and electrification load
assumption pairs were carefully chosen to reflect the lower and upper bounds of LA100-induced
air quality and public health changes and to isolate the contribution of certain sectors to those
changes. The baseline and future emissions inventories are then used as inputs to a state-of-the-
science regional meteorology-atmospheric chemistry model, the Weather Research and
Forecasting Model coupled with chemistry (WRF-Chem) (26), to quantify spatiotemporal patterns
in air pollutant concentrations. Finally, simulated air pollutant concentrations are used to estimate
the health impacts and economic valuation from exposure changes to air pollutants using EPA’s
Benefit Mapping and Analysis Program – Community Edition (BenMAP-CE) (27).
2.2 Materials and Methods
LA100 presented an analytical undertaking of unprecedented scale and complexity, incorporating
over a dozen individual models and tools, pushing power systems analysis to new levels of
sophistication. The methods and results presented here relied on LA100’s uniquely integrated
power grid and load modeling activities that aimed to identify where, when, how much, and what
types of infrastructure and operational changes would achieve reliable electricity at least cost,
taking into consideration factors such as renewable energy policies and requirements,
11
technological advancement, fuel prices, and electricity demand projections. Grid modeling
incorporated detailed AC powerflow modeling of the system (including contingency and stability
analysis), high fidelity chronological simulations of grid operations including provision of
operating reserves and realistic constraints on the operation of hydroelectric and thermal
generators, probabilistic outage simulations and long term outage scenarios to assess resource
adequacy, and detailed analysis of both transmission and distribution networks to ensure that new
resources do not overload lines. Load modeling scenarios included millions of simulations of
thousands of buildings to examine how the adoption of new design elements, equipment, and
appliances might change how much and when people use electricity; detailed charging simulations
of electrified transportation futures; and customer adoption models of rooftop solar potential at the
individual rooftop level based on sophisticated aerial scans. Full modeling details can be found at
the LA100 website
3
.
2.2.1 Scenario Designs
We assess air quality and public health co-benefits of LA100 scenarios by first considering 2012
as a baseline, and then comparing among the future LA100 scenarios in year 2045, which was the
final year for the power sector to achieve 100% renewable energy within the LA100 study. The
future LA100 scenarios of focus represent different power sector eligibility criteria and 100%
renewable electricity achievement year; each scenario is then modeled for two levels of
electrification, which ultimately affect emissions from power plants, transportation, buildings, and
the Ports.
3
https://maps.nrel.gov/la100/report
12
Table 2- 1 summarizes the LA100-assigned scenario names and assumptions for energy supply
and demand. The SB100 scenario is aligned with California Senate Bill 100, which requires 100%
zero-carbon electricity in California by 2045 but allows the use of renewable energy credits (RECs,
represents energy generated by renewable energy sources) to offset a portion of power generation
provided by fossil fuel combustion. In addition, biofuels can be used as transitioning fuel prior to
2045, but are not allowed starting in 2045. Early & No Biofuels represents a scenario that achieves
compliance with a more stringent 100% renewable energy definition among LADWP-owned
power generation utilities (e.g., no RECs are allowed, nor biofuel combustion for power generation)
in 2035, 10 years earlier than for SB100. The comparison between these two scenarios with the
same electrification assumption isolates the effect of removing natural gas power plants at
LADWP-owned sites in the target year 2045.
Each of these two scenarios is evaluated at two levels of load electrification (moderate and high)
for emission sources within the transportation sector (including LDVs and buses), the building
sector (including water heating and spacing heating in commercial buildings, as well as space
heating, water heating, cloth drying and cooking in residential buildings), and the Ports (including
shore power usage from ocean-going vessels at berth, and cargo handling equipment and heavy-
duty vehicles operating at the ports). The definitions of moderate and high electrification levels
for these emission sources are described in Chapter 2.2.3. In short, while moderate electrification
assumptions are generally consistent with current regulatory requirements in California, high
electrification assumptions are more aggressive on electrification and match most of the goals set
by the city
4
. The comparison between the two electrification levels within the same scenario can
isolate the effect of greater electrification of multiple sectors.
4
“L.A.'s Green New Deal: Sustainability pLAn 2019,” https://plan.lamayor.org/
13
Table 2- 1. Scenario names and key assumptions on power sector eligibility criteria and electrification levels
2.2.2 Air Quality Modeling
In this study, we use a state-of-the-science regional meteorology and chemistry model, the Weather
Research and Forecasting model coupled with Chemistry Version 3.7 (WRF-Chem v3.7) (26).
This model uses an emissions inventory as an input, and then simulates pollutant transport (both
in the horizontal and vertical direction) and gas- and particle-phase chemistry that result in the
formation of secondary pollutants like O3 and secondary components of PM2.5. As shown by Figure
2- 1, all simulations are performed using three, two-way nested domains at horizontal resolutions
of 18 km, 6 km and 2 km, respectively. The outer two domains encompass most of California and
Current time
reference
Baseline
(2012)
(Baseline
(2012))
SB100 -
Moderate
(SB100 – M)
SB100 - High
(SB100 – H)
Early & No Biofuels –
Moderate (Early & No
Biofuels – M)
Early & No Biofuels –
High (Early & No
Biofuels – H)
Electrification
Level
Electrification Level for LDVs and Buses,
Commercial and Residential Buildings, and
the Ports
Moderate High Moderate High
LADWP achieves 100% renewable electricity
by 2045
LADWP achieves 100% renewable electricity
by 2035
Based on retail sales of electricity (less
stringent)
Based on generation (as opposed to retail
sales)
LADWP-owned power plants can burn natural
gas
a
No natural gas generation allowed in target
year
No biofuels allowed for power generation in
any year
b
LADWP-owned power plants can burn
hydrogen
c
Existing nuclear generation purchases allowed
Allows upgrades to transmission beyond
planned projects
Builds new transmission cooridors
c
LADWP-owned power plants are assumed to burn 100% hydrogen by 2045 to the extent they are utilized. Hydrogen fuel is produced and stored on-site at the
plant using electricity.
Early Target & No Biofuels Allowed
a
Burning natural gas would necessitate the utility to purchase RECs to meet the requirements of SB100.
b
While biofuels are allowed in years prior to the target year 2045 in the SB100 scenario, they are not allowed starting in 2045.
Target
Compliance
Target
Measurement
Power Generation
Source
Constraints
Transmission
Constraints
Compliant w/ Senate Bill 100
Scenario Name (and Abbreviation)
Scenario Definition
N/A
: Included in the scenario : Not included in the scenario
14
provide boundary conditions to the innermost domain, which covers Southern California. Los
Angeles is located in the innermost domain (d03) and is the focus of our model analysis. Each
domain uses 29 layers in the vertical from the ground to 100 hPa, although only the lowest
atmospheric layer is used for analysis of pollutant concentrations. For each scenario we simulate
January (winter), April (spring), July (summer), and October (autumn) as representative months
per season in Southern California based on meteorology in year 2012. All simulations start 5 days
before the beginning of the month (i.e., from the 25th day of the previous month at 0100 Pacific
Standard Time, PST) and end at 2300 PST of the last day in the modeled month. The first five
days of the simulation (i.e., results prior to the 1st day (at 0000 PST) of the modeled month) are
discarded as “spin-up”.
Figure 2- 1. Three two-way nested domains used for all simulations (left). The innermost domain (d03) encompasses
Los Angeles and is the focus of our analysis (right). In the right panel, the red line shows the boundary of the city of
Los Angeles, the black line shows the boundary of the South Coast Air Basin, and the grey line shows the county
boundaries.
d03
RESE
GLEN
SFV
15
The physics schemes used in our model configuration are as follows: the Lin cloud microphysics
scheme (28), the RRTM longwave radiation scheme (29), the Goddard shortwave radiation scheme
(30), the MM5 similarity surface layer scheme (31, 32), the unified Noah land surface model (33),
the YSU boundary layer scheme (34), the Grell 3D ensemble cumulus cloud scheme (35), and the
urban canopy model (UCM) (36–38). The chemistry schemes we adopt in this study include the
TUV photolysis scheme (39), RACM-ESRL gas phase chemistry (40, 41), and the MADE/VBS
aerosol scheme (42, 43). The meteorological initial and boundary conditions used in both the 2012
baseline and the future scenarios are from the Global Forecast System Analysis Data (GFS-ANL)
Historical Archive for July (44), and the North American Regional Reanalysis (NARR) data set
for all other three months to achieve better model evaluation with historical observations (45). The
chemistry initial and boundary conditions for all scenarios are from the Model for Ozone and
Related chemical Tracers, version 4 (MOZART-4) (46). We use all available gas-phase species in
MOZART-4.
Following our previous publications Vahmani and Ban-Weiss (2016) (47), we modify WRF-Chem
v3.7 to include a realistic representation of land surface properties and processes within urban
areas. Accurately characterizing the land surface is critical for attaining good model performance
of urban meteorology and air quality (47, 48). For all simulations we used the 33-category National
Land Cover Database (NLCD) for determining land cover type for all model domains (49), the
National Urban Database and Access Portal Tool (NUDAPT) for building morphology where
available for the innermost domain (50), and MODIS-retrieved albedo, green vegetation fraction
(GVF), and leaf area index (LAI) for the innermost domain. Data are available for download at
http://earthexplorer.usgs.gov. In addition, we also incorporate a Los Angeles Basin-specific
irrigation scheme (51).
16
2.2.3 Emission Inventory Development
Gridded hourly emissions for the Baseline (2012) scenario for the innermost domain are
constructed based on the most recent gridded source-specific raw emissions obtained from the
South Coast Air Quality Management District (SCAQMD) (5). Annual-averaged daily emissions
are provided for carbon monoxide (CO), nitrogen oxides (NOx), sulphur oxides (SOx), total
organic gases (TOG), total suspended particles (TSP), and ammonia (NH3), and are processed to
gridded hourly emissions. Raw emissions were processed to gridded hourly emissions from all
sources using associated temporal profiles per emission source, and to chemical species in the
SAPRC chemical mechanism using associated speciation profiles per source (2). There are
differences in grid settings and speciation schemes between the SCAQMD emission inventory and
our model set-up. Thus, we re-gridded the inventory and converted from SAPRC speciation to the
Regional Acid Deposition Model, version 2 (RADM2) speciation (3).
Gridded hourly emissions in the innermost domain for the future scenarios can be classified into
two categories. First, for emissions sources within the city of Los Angeles that are directly
influenced by LA100, we project emissions for future scenarios based on four factors: (1) gridded,
source-apportioned, raw emissions (either from the 2012 baseline, or 2031 projections from
SCAQMD if factors for scaling activity from 2031 to 2045 are available) (5); (2) activity
projections (either from LA100 energy model input assumptions and outputs or, when not
available, from regulatory agencies) (24); (3) emission factor projections (from regulatory
agencies and past studies); and (4) electrification projections (from LA100 electricity demand
projections). Second, for emissions sources not directly influenced by LA100 (within Los Angeles),
and all emissions sources outside Los Angeles, we adopt emissions projections to year 2031
17
available from SCAQMD’s 2016 AQMP. Given that SCAQMD has not developed projections for
pollutant emissions past 2031, we assume these emissions stay constant from 2031 to 2045.
Details on future emission inventory development for each influenced sector (i.e., electricity
generation, transportation, buildings and the Ports) are described in the Chapter 2.2.3.1 to Chapter
2.2.3.4. In short, we project absolute emissions based on fuel consumption and emission factors
for the power sector, and scale baseline emissions (from year 2012 or 2031, depending on source)
using activity, emissions factors, and electrification levels for emissions from the transportation
sector, residential and commercial buildings, and the Ports. While the scaling method aligns with
our emissions data and is the method applied in past relevant studies (7, 13), we calculate emissions
from LADWP-owned power plants directly from fuel consumption and emission factors for the
following two reasons. First, some LADWP-owned power plant units have been modified since
2012 and thus these changes to emissions have not been considered in the SCAQMD emission
inventories. Second, there is no consistent data source for generating scaling factors for fuel
consumption of power plants.
Anthropogenic emissions for the two outer domains in all scenarios are adopted from the 2012
emissions inventory from the California Air Resource Board (CARB) for areas within California
(52), and from the 2011 National Emission Inventory (NEI) for regions outside California (53).
Biogenic emissions are generated by the Model of Emissions of Gases and Aerosols from Nature
(MEGAN) coupled to WRF-Chem using model predicted meteorological conditions (54). In
addition, emissions from wildfire are not included in our simulations.
2.2.3.1 Emission Inventory Development for Electricity Generation
The future LA100 scenarios differ in their assumptions on fuel eligibility for LADWP-owned
power plants. Natural gas combustion turbine and combined cycle power plants are allowed in the
18
SB100 scenario, but not in the Early & No Biofuels scenario. Instead, they are substituted by
hydrogen combustion turbines in Early & No Biofuels. To project power plant activity in 2045,
we used hourly fuel consumption projected by the PLEXOS
®
Electricity Market Simulation
model
5
(55). Emission factors for power plants in 2045 are based on data from current power plants
along with regulatory limits. Emissions in 2045 from power plants owned by utilities other than
LADWP follow the SCAQMD projection to year 2031, and assume they are constant from 2031
to 2045.
Emissions generated from LADWP-owned power plants in 2045 for a given scenario are
quantified as
𝐸𝑚𝑖𝑠𝑠 _𝑃𝑜𝑤𝑒𝑟 𝑝 ,𝑝𝑜𝑤𝑒𝑟 ,2045
= 𝐹𝐶 _𝑃𝑜𝑤𝑒𝑟 𝑝𝑜𝑤𝑒𝑟 ,2045
× 𝐸𝐹 _𝑃𝑜𝑤𝑒𝑟 𝑝 ,𝑝𝑜𝑤𝑒𝑟 ,2045
(Eq. 2-1)
where 𝐸𝑚𝑖𝑠𝑠 _𝑃𝑜𝑤𝑒𝑟 𝑝 ,𝑝 𝑜 𝑤𝑒𝑟 ,2045
(kg/hour) represents emissions of pollutant p from a specific
power plant power at hourly resolution, 𝐹𝐶 _𝑃𝑜𝑤𝑒𝑟 𝑝𝑜𝑤𝑒𝑟 ,2045
(one million British Thermal Units
(MMBTU)/hour) is fuel consumption (natural gas or hydrogen, depending on scenario) of the
power plant at hourly resolution from the PLEXOS
®
model, and 𝐸𝐹 _𝑃𝑜𝑤𝑒𝑟 𝑝 ,𝑝𝑜𝑤𝑒𝑟 ,2045
(kg/MMBTU) is the emission factor for pollutant p from the power plant in year 2045. Values for
each term vary by scenario.
Emission factors for natural gas power plants in SB100 are based on calculated values from the
latest available (year 2019) LADWP emissions reporting system. Thus, for SB100 we assume that
current power plants will maintain constant emission factors until 2045, except for NOx and NH3
emissions. For these two pollutants, if the calculated emission factor exceeds SCAQMD current
regulation limits RULE 1135
6
, we modify the emission factor according to the applicable
5
“PLEXOS
®
Market Simulation Software,” Energy Exemplar, https://energyexemplar.com/products/plexossimulation-software/
6
Emissions of oxides of nitrogen from electricity generating facilities, South Coast Air Quality Management District,
19
regulatory limits assuming that the emissions will be brought back into compliance. For hydrogen-
fueled combustion turbines, we assumed that emission factors for SOx, TSP, TOG and CO are
zero since hydrogen fuel does not contain carbonaceous or sulfuric species. For NOx and NH3
emission factors from hydrogen combustion, we followed the current regulation limit for natural
gas combustion turbines as an upper bound based on the assumption that future hydrogen power
plants will at least meet the emissions regulations of current power plants. Table A- 1 and Table
A- 2 include assumed emission factors for every LADWP-owned power plant in the SB100 and
Early & No Biofuels scenarios, respectively.
2.2.3.2 Emission Inventory Development for the Transportation Sector
Table 2- 2. Fraction of light-duty vehicles and buses that are assumed to be electric powered in 2045 for the moderate
and high electrification levels
Emission Source Moderate Electrification High Electrification
Light-duty vehicles
30% of stock is plug-in electric
vehicles
a
(PEV)
80% of stock is PEV
School and urban
buses
100% 100%
a
PEVs consist of 50% plug-in hybrid vehicles and 50% battery electric vehicles
Assumptions on the moderate and high electrification levels of the transportation sector are shown
in Table 2- 2. For the transportation sector, we projected emissions to 2045 for sources included
in LA100 load modeling: light-duty vehicles (LDVs, including passenger cars and light-duty
trucks) and buses (including school bus and urban bus) in Los Angeles (56). The projected
emissions are based on (a) source-specific emissions projections to 2031 from SCAQMD (5), (b)
assumptions on vehicle activity (i.e., vehicle populations and vehicle miles traveled) from the
http://www.aqmd.gov/docs/default-source/rule-book/reg-xi/rule-1135.pdf
20
California Air Resources Board (CARB) EMission FACtor (EMFAC) model
7
, (c) emission factor
projections also from the CARB EMFAC model, and (d) assumptions on electrification level
changes from LA100 electricity demand projections (56). Emissions in 2045 for vehicle types that
are not influenced by LA100 (i.e., motorcycles, medium-duty vehicles and heavy-duty vehicles,
and bus types other than school and urban buses), follow the SCAQMD projection to year 2031,
and are assumed to be constant from 2031 to 2045.
Emissions per grid cell for pollutant p from LDVs and buses in Los Angeles for year 2045
(𝐸𝑚𝑖𝑠𝑠 _𝑉𝑒 ℎ
𝑝 ,2045
, in unit of kg/day) are calculated as the sum of emissions from non-electric
vehicles (𝐸𝑚𝑖𝑠𝑠 _𝑁𝑜𝑛𝐸𝑙𝑒𝑐 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2045
) and electric vehicles (𝐸𝑚𝑖𝑠𝑠 _𝐸𝑙𝑒𝑐 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑒𝑙𝑒𝑐 ,𝑝𝑐 ,2045
)
as shown by Eq. 2-2.
𝐸𝑚𝑖𝑠𝑠 _𝑉𝑒 ℎ
𝑝 ,2045
= ∑ 𝐸𝑚𝑖𝑠𝑠 _𝑁𝑜𝑛𝐸𝑙𝑒𝑐 _𝑉𝑒 ℎ
𝑣 ,𝑓 ,𝑝𝑐
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2045
+
∑ 𝐸𝑚𝑖𝑠𝑠 _𝐸𝑙𝑒𝑐 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑒𝑙𝑒𝑐 ,𝑝𝑐 ,2045 𝑣 ,𝑝𝑐
(Eq. 2-2)
where v stands for a vehicle type, f is the fuel type (i.e., gasoline or diesel) used by the non-electric
vehicle, and pc represents the process that emits pollutants (i.e., diurnal evaporative emissions, hot
soak evaporative emissions, running evaporative emissions, rest evaporative emissions, start
exhaust emissions, running exhaust emissions, idling exhaust emissions, tire wear emissions and
break wear emissions).
Gridded emissions for pollutant p in year 2045 from a non-electric vehicle
(𝐸𝑚𝑖𝑠𝑠 _𝑁𝑜𝑛𝐸𝑙𝑒𝑐 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2045
) are projected by applying a scaling factor to the SCAQMD
projections for year 2031. The scaling factor is calculated as the product of four terms as shown in
7
EMFAC2014 Web Database (v1.0.7), California Air Resources Board (CARB), https://arb.ca.gov/emfac/2014/
21
Eq. 2-3: (a) the ratio of emissions from a specific vehicle type to the total emissions from all vehicle
types in 2031 (from EMFAC), (b) the ratio of vehicle activity projected for a specific vehicle type
between year 2045 to 2031 (from EMFAC), (c) the ratio of emission factors for an emitting process
of a specific vehicle type between 2045 to 2031 (from EMFAC), and (d) one minus the fraction of
vehicles registered in Los Angeles that are electric vehicles (from LA100 load modeling). More
specifically, 𝐸𝑚𝑖𝑠𝑠 _𝑁𝑜𝑛𝐸𝑙𝑒𝑐 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2045
is calculated as
𝐸𝑚𝑖𝑠𝑠 _𝑁𝑜𝑛𝐸𝑙𝑒𝑐 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2045
= 𝐸𝑚𝑖𝑠𝑠 _𝑉𝑒 ℎ
𝑝 ,2031
×
𝐸𝑚𝑖𝑠𝑠 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2031
∑ 𝐸𝑚𝑖𝑠𝑠 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2031 𝑣 ,𝑓 ,𝑝𝑐
×
𝐴 _𝑉𝑒 ℎ
𝑣 ,𝑓 ,𝑝𝑐 ,2045
𝐴 _𝑉𝑒 ℎ
𝑣 ,𝑓 ,𝑝𝑐 ,2031
×
𝐸𝐹 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2045
𝐸𝐹 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2031
× (1 − 𝐸𝑙𝑒𝑐 _𝑉𝑒 ℎ
𝑣 ,2045
)
(Eq. 2-3)
where 𝐸𝑚𝑖𝑠𝑠 _𝑉𝑒 ℎ
𝑝 ,2031
(kg/day) is the gridded emission for pollutant p from all vehicle types in
year 2031 provided by SCAQMD,
𝐸𝑚𝑖𝑠𝑠 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2031
∑ 𝐸𝑚𝑖𝑠𝑠 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2031 𝑣 ,𝑓 ,𝑝𝑐
(unitless) is the ratio of emissions from
vehicle type v using fuel type f to all transportation emissions based on EMFAC, 𝐴 _𝑉𝑒 ℎ
𝑣 ,𝑓 ,2031
and 𝐴 _𝑉𝑒 ℎ
𝑣 ,𝑓 ,2045
are activity projections per vehicle and fuel type in year 2031 and 2045
respectively (units for A_Veh are number of vehicles for vehicle population and miles/day for
vehicle miles traveled,
𝐴 _𝑉𝑒 ℎ
𝑣 ,𝑓 ,𝑝𝑐 ,2045
𝐴 _𝑉𝑒 ℎ
𝑣 ,𝑓 ,𝑝𝑐 ,2031
is unitless), 𝐸𝐹 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2031
and 𝐸𝐹 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2045
are
emission factors per vehicle, fuel type and emitting process in year 2031 and 2045 respectively
(units for 𝐸𝐹 _𝑉𝑒 ℎ are kg/vehicle/day for vehicle-population-based calculation and kg/mile for
VMT-based calculation,
𝐸𝐹 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2045
𝐸𝐹 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2031
is unitless), and 𝐸𝑙𝑒𝑐 _𝑉𝑒 ℎ
𝑣 ,2045
is the fraction of
vehicles per vehicle type that are electric in year 2045 which are based on the transportation model
Electric Vehicle Infrastructure Projection Tool (EVI-Pro) (57). Values for 𝐸𝑚𝑖𝑠𝑠 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2031
,
∑ 𝐸𝑚𝑖𝑠𝑠 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2031 𝑣 ,𝑓 ,𝑝𝑐
, 𝐴 _𝑉𝑒 ℎ
𝑣 ,𝑓 ,2031
, 𝐴 _𝑉𝑒 ℎ
𝑣 ,𝑓 ,2045
, 𝐸𝐹 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2031
and
22
𝐸𝐹 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2045
are determined using annual averaged data from the EMFAC2014 model
applied to Los Angeles County (except the Mojave Desert region). We chose EMFAC2014 (rather
than newer EMFAC versions) because SCAQMD used this version for developing the inventories
used in our baseline (year 2012). The electrification level 𝐸𝑙𝑒𝑐 _𝑉𝑒 ℎ
𝑣 ,2045
for all vehicle types
within the LDV category (i.e., passenger cars and light-duty trucks) have the same value for each
electrification load level assumption.
Emissions from electric LDVs and buses in year 2045 need to be considered since they have non-
zero emission factors for PM from brake wear and tire wear. Note that EMFAC2014 suggests that
electric LDVs have non-zero evaporative TOG emissions, but we assume these emissions are zero
since they are negligible in magnitude. We assume that tire wear PM emission factors per vehicle
category are identical for electric versus non-electric vehicles in 2045. Brake wear emission factors
per vehicle category are reduced by 59% for electric vehicles (relative to non-electric vehicles)
due to regenerative braking system (58). Eq. 2-4 is used for quantifying tire and brake wear
emissions from electric LDVs and buses.
𝐸𝑚𝑖𝑠 𝑠 𝐸𝑙𝑒 𝑐 𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑒𝑙𝑒𝑐 ,𝑝𝑐 ,2045
= 𝐸𝑚 𝑖 𝑠 𝑠 𝑉𝑒 ℎ
𝑝 ,2031
×
𝐸𝑚𝑖𝑠 𝑠 𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2031
∑ 𝐸𝑚𝑖𝑠 𝑠 𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2031
𝑣 ,𝑓 ,𝑝𝑐
×
𝐴 𝑉𝑒 ℎ
𝑣 ,𝑓 ,2045
𝐴 𝑉𝑒 ℎ
𝑣 ,𝑓 ,2031
×
𝐸 𝐹 𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2045
𝐸 𝐹 𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2031
×
𝐸 𝐹 𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑒𝑙𝑒𝑐 ,𝑝𝑐 ,2045
𝐸 𝐹 𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2045
× 𝐸𝑙𝑒 𝑐 𝑉𝑒 ℎ
𝑣 ,2045
(Eq. 2-4)
where the new term
𝐸𝐹 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑒𝑙𝑒𝑐 ,𝑝𝑐 ,2045
𝐸𝐹 _𝑉𝑒 ℎ
𝑝 ,𝑣 ,𝑓 ,𝑝𝑐 ,2045
represents the ratio of emission factors for electric vehicles
to non-electric vehicles.
23
2.2.3.3 Emission Inventory Development for Commercial and Residential Buildings
Table 2- 3. Fraction of buildings/households by end use that are assumed to be electricity-powered in 2045 for the
moderate and high electrification levels
Emission Source End Use Moderate Electrification High Electrification
Commercial building
Water heating 72% 100%
Space heating 81% 96%
Residential building
Water heating 50% 100%
Space heating 49% 91%
Clothes drying 93% 100%
Cooking 53% 100%
Assumptions on the moderate and high electrification levels for the building sectors are shown in
Table 2- 3. Emissions projections for commercial and residential buildings account for reductions
in natural gas consumption from various end uses as electrification is increased. Natural gas fuel
use was projected using ComStock and ResStock within the demand-side grid model, dsgrid, for
residential buildings and commercial buildings, respectively (56, 59). ComStock and ResStock use
year 2020 as an initial state and project natural gas consumption to year 2045 in 5-years increments
for Los Angeles under moderate and high electrification load assumptions. We projected emissions
from commercial and residential buildings to 2045 in two steps. First, we projected emissions from
these sectors in Los Angeles from year 2012 to 2020 using gridded source-specific 2012 emissions
by applying scaling factors representing activity growth and emission factor changes between 2012
and 2020, both taken from SCAQMD (5). We did this first step to match the 2020 base year of the
dsgrid simulations. Secondly, we projected emissions from year 2020 to 2045 based on scaling
factors that represent changes in activity (i.e., natural gas consumption from dsgrid) and emission
factors. Scaling factors for emission factors come from SCAQMD’s emissions inventory
24
projections (60). All emissions from commercial and residential buildings outside of Los Angeles
but within our model domain follow the SCAQMD projection to year 2031, assuming they stay
constant from 2031 to 2045.
Emissions (kg/day) per grid cell for pollutant p from commercial and residential buildings
(𝐸𝑚𝑖𝑠𝑠 _𝐶𝑅
𝑝 ,2045
) in Los Angeles for year 2045 are calculated as Eq. 2-5.
𝐸𝑚𝑖𝑠𝑠 _𝐶𝑅
𝑝 ,2045
= ∑ 𝐸𝑚𝑖𝑠𝑠 _𝐶𝑜𝑚 𝑝 ,𝑐 ,2045 𝑐 + ∑ 𝐸𝑚𝑖𝑠𝑠 _𝑅𝑒𝑠 𝑝 ,𝑟 ,2045 𝑟 (Eq. 2-5)
where 𝐸𝑚𝑖𝑠𝑠 _𝐶𝑜𝑚 𝑝 ,𝑐 ,2045
is the emission for pollutant p from end use c in commercial buildings
projected to year 2045, and 𝐸𝑚𝑖𝑠𝑠 _𝑅𝑒𝑠 𝑝 ,𝑟 ,2045
is the emission for pollutant p from end use r in
residential buildings projected to year 2045. 𝐸𝑚𝑖𝑠𝑠 _𝐶𝑜𝑚 𝑝 ,𝑐 ,2045
and 𝐸𝑚𝑖𝑠𝑠 _𝑅𝑒𝑠 𝑝 ,𝑟 ,2045
are
computing using Eq. 2-6 and Eq. 2-7, respectively.
𝐸𝑚𝑖𝑠𝑠 _𝐶𝑜𝑚 𝑝 ,𝑐 ,2045
= 𝐸𝑚𝑖𝑠𝑠 _𝐶𝑜𝑚 𝑝 ,𝑐 ,2012
×
𝐴 _𝐶𝑜𝑚 𝑐 ,2020
𝐴 _𝐶𝑜𝑚 𝑐 ,2012
×
𝐸𝐹 _𝐶𝑜𝑚 𝑝 ,𝑐 ,2020
𝐸𝐹 _𝐶𝑜𝑚 𝑝 ,𝑐 ,2012
×
𝐴 _𝐶𝑜𝑚 𝑐 ,2045
𝐴 _𝐶𝑜𝑚 𝑐 ,2020
×
𝐸𝐹 _𝐶𝑜𝑚 𝑝 ,𝑐 ,2045
𝐸𝐹 _𝐶𝑜𝑚 𝑝 ,𝑐 ,2020
(Eq. 2-6)
𝐸𝑚𝑖𝑠𝑠 _𝑅𝑒𝑠 𝑝 ,𝑟 ,2045
= 𝐸𝑚𝑖𝑠𝑠 _𝑅𝑒𝑠 𝑝 ,𝑟 ,2012
×
𝐴 _𝑅𝑒𝑠 𝑟 ,2020
𝐴 _𝑅𝑒𝑠 𝑟 ,2012
×
𝐸𝐹 _𝑅𝑒𝑠 𝑝 ,𝑟 ,2020
𝐸𝐹 _𝑅𝑒 𝑠 𝑝 ,𝑟 ,2012
×
𝐴 _𝑅𝑒𝑠 𝑟 ,2045
𝐴 _𝑅𝑒𝑠 𝑟 ,2020
×
𝐸𝐹 _𝑅𝑒𝑠 𝑝 ,𝑟 ,2045
𝐸𝐹 _𝑅𝑒𝑠 𝑝 ,𝑟 ,2020
(Eq. 2-7)
where 𝐸𝑚𝑖𝑠𝑠 _𝐶𝑜𝑚 𝑝 ,𝑐 ,2012
(𝐸𝑚𝑖𝑠𝑠 _𝑅𝑒𝑠 𝑝 ,𝑟 ,2012
) is emission (kg/day) for pollutant p from end use
c (r) in commercial (residential) buildings from the 2012 SCAQMD emission inventory,
𝐴 _𝐶𝑜𝑚 𝑐 ,2020
𝐴 _𝐶𝑜𝑚 𝑐 ,2012
(
𝐴 _𝑅𝑒𝑠 𝑟 ,2020
𝐴 _𝑅𝑒𝑠 𝑟 ,2012
) is the ratio of natural gas consumption between year 2020 and 2012 from
the SCAQMD projection for end use c (r),
𝐸𝐹 _𝐶𝑜𝑚 𝑝 ,𝑐 ,2020
𝐸𝐹 _𝐶𝑜𝑚 𝑝 ,𝑐 ,2012
(
𝐸𝐹 _𝑅𝑒𝑠 𝑝 ,𝑟 ,2020
𝐸𝐹 _𝑅𝑒𝑠 𝑝 ,𝑟 ,2012
) and
𝐸𝐹 _𝐶𝑜𝑚 𝑝 ,𝑐 ,2045
𝐸𝐹 _𝐶𝑜𝑚 𝑝 ,𝑐 ,2020
(
𝐸𝐹 _𝑅𝑒𝑠 𝑝 ,𝑟 ,2045
𝐸𝐹 _𝑅𝑒𝑠 𝑝 ,𝑟 ,2020
) are the scaling factors accounting for changes in emission factors for 2012 to 2020,
and 2020 to 2045, respectively, and
𝐴 _𝐶𝑜𝑚 𝑐 ,2045
𝐴 _𝐶𝑜𝑚 𝑐 ,2020
(
𝐴 _𝑅𝑒𝑠 𝑟 ,2045
𝐴 _𝑅𝑒𝑠 𝑟 ,2020
) is the scaling factor accounting for
25
changes in natural gas consumption from 2020 to 2045 determined from the ComStock (ResStock)
model. Note that
𝐴 _𝐶𝑜𝑚 𝑐 ,2045
𝐴 _𝐶𝑜𝑚 𝑐 ,2020
and
𝐴 _𝑅𝑒𝑠 𝑟 ,2045
𝐴 _𝑅𝑒𝑠 𝑟 ,2020
are the same for all commercial and residential end uses,
respectively, but varies by the month of projection. Those scaling factors are listed in Table A- 3
and Table A- 4 for commercial buildings and residential buildings, respectively.
2.2.3.4 Emission Inventory Development for Sources at the Ports of Los Angeles and Long
Beach
Table 2- 4. Fraction of port sources that are assumed to be electric powered in 2045 for both moderate and high
electrification load assumption
a
Emission Sources
Moderate
Electrification
High
Electrification
Ocean-going vessels (OGVs, shore power
at berth)
80% 90%
Cargo handling equipment (CHE) 100% 100%
Heavy-duty vehicles (HDVs) 100% 100%
a
The assumptions of electrification levels for emissions from cargo handling equipment and heavy-duty vehicles at
the Ports are based on the most up-to-date 2017 Clean Air Action Plan (https://cleanairactionplan.org/), and they differ
from what is used in the rest of the LA100 study. In addition, we assume the moderate and high electrification are
applicable to both the Port of Los Angeles and the Port of Long Beach instead of just the Port of Los Angeles. These
changes at the Ports are expected to have minimal effect on power generation projected overall in 2045.
Emissions projections for the Ports account for increased electrification of three source types: (a)
hoteling emissions from ocean-going vessels (OGVs, including container ships and tankers) at
berth, (b) cargo handling equipment (CHE), and (c) heavy-duty vehicles (HDVs) operating at the
Ports, which are shown in Table 2- 4. Thus, we projected emissions from these three sources to
year 2045 based on the 2031 projections from SCAQMD, scaling factors that account for changes
in emission factors from 2031 to 2045 using CARB OGV model for OGVs emissions at berth
8
,
8
Available at “MSEI - Documentation - Off-Road - Diesel Equipment,” California Ari Resources Board,
https://ww2.arb.ca.gov/our-work/programs/mobile-source-emissions-inventory/road-documentation/msei-
26
and the EMFAC model for HDVs at the Ports, scaling factors accounting for changes from 2031
to 2045 in activity and electrification levels for OGVs based on the Port Master Plan (61) and
California Transportation Electrification Assessment (62), respectively, and electrification levels
for CHE and HDVs at the Ports from the 2017 Clean Air Action Plan Update (63). For other source
types at the Ports, we adopt 2031 SCAQMD projections and assume that emissions remain the
same from 2031 to 2045.
For ocean going vessels, LA100 scenarios assume that 80% and 90% of OGV fleet visits use shore
power in moderate and high electrification levels, respectively, which reduces at berth (i.e.,
hoteling) emissions from auxiliary engines and boilers. Projected emissions (kg/day) for pollutant
p in 2045 (𝐸𝑚𝑖𝑠𝑠 _𝑂𝐺𝑉 𝑝 ,𝑖 ,𝑝𝑐 ,2045
) from emitting process pc (i.e., auxiliary engine or boiler) of OGV
type i (i.e., container ship or tanker) are quantified using Eq. 2-8.
𝐸𝑚𝑖𝑠𝑠 _𝑂𝐺𝑉 𝑝 ,𝑖 ,𝑝𝑐 ,2045
= 𝐸𝑚𝑖𝑠𝑠 _𝑂𝐺 𝑉 𝑝 ,𝑖 ,𝑝𝑐 ,2031
×
𝐴 _𝑂𝐺𝑉 𝑖 ,2045
𝐴 _𝑂𝐺𝑉 𝑖 ,2031
×
𝐸𝐹 _𝑂𝐺𝑉 𝑝 ,𝑖 ,𝑝𝑐 ,2045
𝐸𝐹 _𝑂𝐺𝑉 𝑝 ,𝑖 ,𝑝𝑐 ,2031
× (1 −
𝐸𝐿𝐸𝐶 _𝑂𝐺𝑉 𝑖 ,2045
)
(Eq. 2-8)
where 𝐸𝑚𝑖𝑠𝑠 _𝑂𝐺𝑉 𝑝 ,𝑖 ,𝑝𝑐 ,2031
is the gridded emissions for pollutant p from OGV type i, emitting
process pc at berth in year 2031 provided by SCAQMD,
𝐴 _𝑂𝐺𝑉 𝑖 ,2045
𝐴 _𝑂𝐺𝑉 𝑖 ,2031
(= 1.18 for container ships and
= 1.03 for tankers, unitless) is the ratio of activity change (i.e., number of shore visits) for 2045 to
2031 based on the Port Master Plan,
𝐸𝐹 _𝑂𝐺𝑉 𝑝 ,𝑖 ,𝑝𝑐 ,2045
𝐸𝐹 _𝑂𝐺𝑉 𝑝 ,𝑖 ,𝑝𝑐 ,2031
(unitless) is the scaling factor for emission
factors based on the CARB OGV At Berth Emissions Inventory Model (assumed to be 1 for all
pollutants), and 𝐸𝐿𝐸𝐶 _𝑂𝐺𝑉 𝑖 ,2045
is the percentage of shore visits of OGV type i that use shore
power assumed in port electrification scenarios in California Transportation Electrification
documentation-road
27
Assessment. Note that 2031 OGV emissions provided by SCAQMD are based on the CARB OGV
model version 2014, which does not consider future regulations for electrification of OGVs at
berth.
Cargo handling equipment is assumed to achieve 100% electrification in both moderate and high
electrification level based on the 2017 Clean Air Action Plan Update. Non-electric CHE has only
exhaust emissions and no evaporative emissions. Thus, we assume that electric CHE in 2045 has
no associated air pollutant emissions.
The projection of emissions from HDVs within the Ports (i.e., under the Ports’ control) follows
the method used for quantifying emissions from LDVs (see Chapter 2.2.3.2). However, we made
a couple of additional assumptions. First, HDVs within the Ports do not travel outside the Ports,
and thus only grid cells overlapped with the Ports are affected. Second, HDVs within the Ports are
classified as the “Heavy-Heavy Duty Diesel Drayage Truck near South Coast (T7 POLA - DSL)”
category from EMFAC2014. Thus, the scaling factors for activity change and emission factor
change are determined using this category from EMFAC2014 model output.
2.2.4 Health Impact Estimation and Monetization
Our collaborators at NREL use the Benefit Mapping and Analysis Program – Community Edition
(BenMAP-CE) (27) to estimate the health impacts (i.e., mortality and mobility) and economic
valuation from exposure changes to PM2.5 and O3 pollution. The benefits associated with a
pollutant reduction can be calculated based on a health impact function which describes the
relationship between changes in the observed adverse health impact incidences and changes in the
pollutant concentrations (64, 65). The confidence intervals are calculated using the Monte-Carlo
method for uncertainties in this concentration-response function. In this analysis, all-cause
28
mortality due to long-term exposure to PM2.5 and short-term exposure to O3 are estimated. As for
morbidity estimation, PM2.5-associated morbidities include hospital admissions, asthma
emergency department visits, and acute myocardial infarction, and O3-associated morbidity is
asthma emergency department visits. Since our future year of focus is 2045, population in Los
Angeles is projected to 2045 in the quantification of health impact incidences. Monetizing these
health benefits depends on the cost of illness (COI, including the total medical costs plus the value
of the lost productivity) for avoiding a disease incidence (i.e., morbidity), and the value of
statistical life (VSL, the willingness to pay for a marginal reduction in risk of death in a society)
for avoiding a fatality (i.e., mortality) in BenMAP.
2.3 Results
2.3.2 Emission Inventory for Baseline and Future Scenarios
We find that citywide total air pollutant emissions from all economic sectors will decrease between
2012 and future LA100 scenarios in 2045. Figure 2- 2 shows the emissions of NOx and primary
PM2.5 as examples. Emissions of other air pollutants including carbon monoxide (CO), sulfur
oxides (SOx), volatile organic compounds (VOCs) and ammonia (NH3) are shown by Figure A-
1. Citywide total emissions of NOx are reduced by 60%, 62%, 60% and 62% under the 2045
SB100 – Moderate, SB100 – High, Early & No Biofuels – Moderate, and Early & No Biofuels –
High scenarios, respectively, as compared to the 2012 baseline. Citywide total emissions of PM2.5
are reduced by 6%, 11%, 6% and 11% for the same scenarios analyzed.
Citywide total air pollutant emissions can be divided into two groups. The first group includes
emissions from sources directly influenced by LA100 (i.e., LDVs and buses, buildings, the Ports,
and the LADWP-owned power plants; hereafter referred to as “LA100-influenced sources”). We
29
assume that the LA100 scenarios themselves only cause future emissions changes from these
sources. The second group includes emissions from sources that might be indirectly influenced by
LA100 (e.g., changes to refining volumes in the Oil and Gas Industry) or that are not influenced
by LA100 (i.e., “Other” and “Medium and Heavy-Duty Vehicles” in Figure 2- 2); changes in these
categories are outside of the scope of this LA100 analysis. Any differences in emissions from these
sources between 2012 and future LA100 scenarios result from projections in SCAMQD’s 2016
Air Quality Management Plan (AQMP) (66).
When considering just LA100-influenced sources, NOx emissions from 2012 to 2045 decrease by
88%, 95%, 88%, and 95% from Baseline (2012) to SB100 – Moderate, SB100 – High, Early &
No Biofuels – Moderate, and Early & No Biofuels – High, respectively; PM2.5 emissions from
2012 to 2045 decrease by 38%, 61%, 38%, and 62% for the same scenarios analyzed. Note that
these reductions from Baseline (2012) to future LA100 scenarios primarily occur due to two
factors: (a) simultaneous increases in renewable electricity penetration and increases in
electrification of LA100-influenced sources, and (b) assumed decreases in pollutant emission
factors projected by SCAQMD’s 2016 AQMP (which is outside the scope of LA100).
LDVs are the dominant contributor to LA100-influenced NOx (52%) and primary PM2.5 (54%)
emissions in Baseline (2012) and the largest contributor to reductions in LA100-influenced
emissions from the current baseline to future LA100 scenarios. Reductions in LDVs emissions are
due to electrification and decreased emission factors despite increased vehicle population and
vehicle miles travelled. Moderate and high electrification load trajectories assume 30% and 80%
of LDV stock in 2045 are plug-in electric vehicles (assuming 50% are plug-in hybrid and 50% are
battery electric vehicles in either scenario), respectively. These LDV fleet modifications in future
LA100 scenarios result in NOx (PM2.5) emission reductions of more than 50% (30%) below the
30
2012 baseline. There are smaller reductions in PM2.5 emissions than NOx for two reasons: (a) non-
electric LDV-related PM2.5 emissions mainly come from brake wear that has an emission factor
that is assumed to be identical in year 2012 and 2045, and (b) while electric vehicles are assumed
to have zero NOx emissions, they still have brake wear and tire wear PM2.5 emissions (67).
In addition to LDVs, residential buildings and the Ports also contribute to NOx and PM2.5
emissions, and reductions in LA100 scenarios. In year 2045 for scenarios with moderate
electrification, residential buildings are the largest LA100-influenced source of emissions (~35%
of LA100-influenced emissions) for NOx. In the high electrification scenarios, the Ports are the
largest source of NOx (~45% of LA100-influenced emissions). Residential buildings and the Ports
are also the second largest LA100-influenced source type for PM2.5 emissions in scenarios with
moderate and high electrification following LDVs, respectively. Residential buildings and the
Ports collectively make up 30-40% of the LA100-influenced NOx and PM2.5 emissions reductions
that occur between the Baseline (2012) scenario and future LA100 scenarios. Emission reductions
from residential buildings are mainly attributable to the electrification of multiple end uses (mainly
space heating and water heating). Emission reductions at the Ports are due to the adoption of the
2017 Clean Air Action Plan Update between the Baseline (2012) scenario and 2045 for scenarios
with moderate electrification (63). High electrification assumes increases in ocean-going vessels’
(OGV) shore power usage compared to moderate electrification in 2045, leading to further
emission reductions (62).
LADWP-owned power plants, buses and commercial buildings are minor contributors to citywide
NOx and PM2.5 emissions. Note that while power plants are not a prominent emission source at
the city scale, they can still be important neighborhood scale emission sources. LADWP-owned
power plants emit less NOx and PM2.5 in the SB100 scenario than the Baseline (2012) scenario
31
mostly due to reductions in natural gas combustion. Less natural gas is used for electricity
generation in year 2045 as predicted by the PLEXOS
®
power sector production cost model because
clean energy such as solar and wind dominate electricity production and gas combustion at
LADWP power plants is reserved for meeting peak demand (55). Further reductions occur in the
Early & No Biofuels scenario relative to the SB100 scenario. We assume that NOx emission
factors are similar for natural gas and hydrogen power plants (based on lack of evidence to the
contrary and the assumption that any new source would be required to emit no more than the
regulatory limits applicable to current natural gas-fired combined-cycle power plants in Los
Angeles)
9
, so NOx reductions between the SB100 and Early & No Biofuels scenarios are
dominated by decreases in simulated fuel consumption. PM2.5 emissions from these power plants
become zero in the Early & No Biofuels scenario since hydrogen power plants are assumed to emit
no PM2.5. NOx emissions from buses are also reduced to zero in all year 2045 scenarios due to
100% electrification. However, electrified buses still emit PM2.5 emissions from brake wear and
tire wear, similar to electrified LDVs.
In addition, NOx emissions from sectors not directly affected by LA100 (“Other”, “Medium and
Heavy-Duty Vehicles” and “Oil and Gas Industry” in Figure 2- 2) decrease by 45% from the
baseline to future scenarios owing to decreased emission factors projected by SCAQMD’s 2016
AQMP. Primary PM2.5 emissions from sectors not directly affected by LA100 increase slightly
(<1%) from the baseline to future scenarios due to increases in related activities.
9
RULE 1135. Emissions of Oxides of Nitrogen From Electricity Generating Facilities. Available at:
http://www.aqmd.gov/docs/default-source/rule-book/reg-xi/rule-1135.pdf
32
Figure 2- 2. Annually averaged daily (a) NOx and (b) PM 2.5 emissions from all anthropogenic sources in the city of
Los Angeles for all scenarios. Emissions for future scenarios (SB100 – Moderate, SB100 – High, Early & No Biofuels
– Moderate and Early & No Biofuels – High) are projected to year 2045. Emissions that are directly influenced by
LA100 include those from buses, LDVs, commercial buildings, residential buildings, the ports of Los Angeles and
Long Beach, and LADWP-owned power plants. Note that ‘LADWP-owned Power Plants’ include only those located
in SoCAB. The sources labeled “Other”, “Medium and Heavy-duty Vehicles” and “Oil and Gas Industry” are not
directly influenced by LA100, but are shown for context. Examples of major contributing sources to the “Other”
category include off-road equipment, Regional Clean Air Incentives Market (RECLIAM) and aircraft for NOx; and
commercial cooking, road dust and industrial processes for primary PM 2.5. (“Other”, “Medium and Heavy-duty
Vehicles” and “Oil and Gas Industry” are included in the emissions inventories used in air quality modeling.)
2.3.3 Simulated Air Quality for the Baseline and Future Scenarios
Reductions in air pollutant emissions can lead to simulated increases in O3 concentrations for most
parts of Los Angeles and simulated reductions in PM2.5 concentrations across all of Los Angeles
in 2045 comparing to the 2012 baseline as shown by Figure 2- 3, which include simulated
summertime daily maximum 8-hour average O3 (panel a-d) and annual daily averaged PM2.5 (panel
e-h) concentrations for all scenarios.
33
Figure 2- 3. Spatial patterns of simulated July daily maximum 8-hour average O 3 (panels a-d), and annual daily
average PM 2.5 (panel e-h) concentrations in Los Angeles for Baseline (2012) (panels a and e), differences between
Baseline (2012) and SB100 – Moderate (2045) (panels b and f), differences between SB100 – Moderate (2045) and
Early & No Biofuels – Moderate (2045) (panels c and g), and differences between SB100 – Moderate (2045) and
Early & No Biofuels – High (2045) (panels d and h).
Simulated increases in daily maximum 8-hour average O3 concentrations occur across Los Angeles
for July except in the northwest area (the San Fernando Valley region (SFV), where the highest
O3 concentrations was simulated in Baseline (2012)) due to a combined effect of changes made by
LA100 and changes outside the scope of LA100. City averaged O3 concentrations increase by 5%
from Baseline (2012) (43.8 ppb) to SB100 – Moderate (46.0 ppb) in year 2045. Our simulated city
averaged changes in O3 concentrations are consistent with the magnitude of changes reported by
previous studies (6, 7). Zapata et al. (2018) reported a 3.2% increase in summertime 8-hour O3
concentrations in SoCAB, and Wang et al. (2020) reported a 2.5 ppb increase in population-
weighted annual average O3 concentration in Los Angeles County when comparing a low/net-zero
carbon energy scenario to a BAU scenario in year 2050. Note that this study differs from these
O
3
PM
2.5
a)
e)
Baseline(2012)
b)
f)
SB100 –M
relativeto
Baseline(2012)
c)
g)
Early/NoBio –M
relativeto
SB100 –M
d)
h)
Early/NoBio –H
relativeto
SB100 –M
60
55
50
45
40
35
30
ppb
4
3
2
1
0
-1
-2
-3
-4
ppb
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
ppb
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
ppb
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
μg/m
3
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
μg/m
3
20.0
17.5
15.0
12.5
10.0
7.5
5.0
2.5
0.0
μg/m
3
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
μg/m
3
34
previous studies in terms of the included sectors and assumptions on activity, fuels, and technology
changes adopted in the future scenarios, which may lead to differences in simulated changes in air
pollutant concentrations.
When comparing among the future LA100 scenarios, Early & No Biofuels – High shows slight
increases in daily maximum 8-hour O3 concentrations across Los Angeles except the northwest
area relative to SB100 – Moderate (Figure 2- 3d), leading to 0.2% increase in city averaged O3
concentrations. These differences in O3 are dominated by increased electrification levels;
removing natural gas power plants plays a very small role as indicated by Figure 2- 3c. Figure 2-
3c shows that O3 concentrations are nearly identical for SB100 – Moderate and Early & No
Biofuels – Moderate, with the maximum difference being less than 0.001 ppb.
Increases in O3 concentrations despite reductions in NOx emissions simulated in most parts of Los
Angeles are due to complex O3 chemistry and can be though as a temporary “growing pain” that
the city is likely to go through. Los Angeles is known to be in the “NOx-saturated” regime (68),
meaning the NOx/VOC (volatile organic compounds) ratio is relatively high (69). In this regime,
reaction with NO2 is the main sink pathway for the hydroxyl radical and thus decreases in NOx
lead to increases in the hydroxyl radical. Since the hydroxyl radical is an important O3 precursor,
reductions in NOx ultimately lead to increases in O3. The O3 increases simulated in most of Los
Angeles can be thought of as following line “A” downward in Figure 2- 4a because LA100
scenarios affect VOC emissions less than NOx emissions. There are increases in O3 until NOx
emissions are sufficiently low, at which point further NOx reductions lead to marked O3 reductions
(i.e., transition to the “NOx-limited” regime). Alternatively, we could avoid O3 increases on a
pathway to O3 reductions by having sufficiently large reductions in emissions of VOC, as
illustrated by line B in Figure 2- 4a. By contrast, a few regions in Los Angeles, like ‘RESE’ in
35
SFV, have already shifted from the “NOx-saturated” regime to the “NOx-limited” regime where
reductions in NOx emission can lead to reductions in O3 concentrations (see Figure 2- 4b,
following line “A” downward). This explains why O3 decreases in 2045 for the northwest region
of Los Angeles (Figure 2- 3b,d).
Figure 2- 4. O 3 isopleth diagrams to illustrate how 8-hour O 3 concentration design values (DV) at a location close to
(panel a) or in (panel b) Los Angeles can change in response to decreases in NOx and VOC emissions (in units of tons
per day) in SoCAB. ‘GLEN’ represents foothill areas located to the northest of Los Angeles, while ‘RESE’ is located
within SFV. Line A indicates a pathway of reducing only NOx emissions, line B indicates a pathway of reducing NOx
and VOC emissions simultaneously, and line C indicates a pathway of reducing only VOC emissions. Note that this
figure was from the 2016 Air Quality Management Plan regional modeling platform using a different emission
inventory and model at SCAQMD (66), and thus is for illustration purpose only.
In contrast with O3 concentrations, reductions in PM2.5 concentrations are simulated for future
LA100 scenarios in 2045 across Los Angeles due to lowered primary PM2.5 emissions and other
precursor emissions contributing to secondary PM2.5 when comparing to Baseline (2012). The city
averaged annual daily PM2.5 concentration in 2045 is reduced by 6% when comparing SB100 –
Moderate (10.0 µg/m
3
) to the Baseline (2012) (10.6 µg/m
3
) and is reduced by 8% when comparing
Early & No Biofuels – High (9.8 µg/m
3
) to the Baseline (2012). These PM2.5 concentration changes
are consistent in direction with previous studies (6, 7). Zapata et al. (2018) reported a 14%
reduction in population-weighted annual average PM2.5 concentration in SoCAB, and Wang et al.
36
(2020) reported an 8.4 µg/m
3
reduction in population-weighted annual average PM2.5
concentration in Los Angeles County when comparing a low/net-zero carbon energy scenario to a
BAU scenario in year 2050.
The differences in simulated PM2.5 concentrations among future LA100 scenarios are mainly
attributable to electrification load levels. PM2.5 for Early & No Biofuels – Moderate is similar to
SB100 – Moderate with absolute differences within ±0.06 µg/m
3
, indicating a very small impact
of transitioning from natural gas to hydrogen-powered power plants in 2045. The comparison
between Early & No Biofuels – High and SB100 – Moderate shows decreases in PM2.5
concentrations across Los Angeles (maximum reduction = 0.45 µg/m
3
) and are dominated by
increases in electrification levels.
Changes in air pollutant concentrations are slightly more beneficial to disadvantaged communities
(DAC) than non-disadvantaged communities (non-DAC) within Los Angeles as indicated by
Hettinger et al. (2021) who used the California Community Environmental Health Screening Tool
(CalEnviroScreen) based DAC and non-DAC designation of census tracts and statistically
analyzed the relative benefits to DAC and non-DAC using air pollutant concentrations in this study
(70). Hettinger et al. (2021) shows that reductions in annual averaged population-weighted PM2.5
concentrations are slightly greater in disadvantaged DAC communities (0.42–0.62 µg/m
3
) than
non-DAC communities (0.39–0.56 µg/m
3
) in all future LA100 scenarios when comparing to the
Baseline (2012) scenario. By contrast, increases in summertime population-weighted daily
maximum 8-hour average O3 concentration in DAC communities (5.1–5.3 ppb) are greater than
non-DAC communities (4.2–4.4 ppb) when comparing the future scenarios to Baseline (2012).
37
2.3.4 Public Health Impacts and Corresponding Monetization for the Baseline and Future
LA100 Scenarios
Results on annual mortality and morbidity incidences due to O3 and PM2.5 concentration changes
are aggregated to each of the 15 city council districts constituting the city of Los Angeles as shown
by Figure 2- 5a. Since transitioning from natural gas to hydrogen yields only small influences on
O3 and PM2.5 concentrations in year 2045 (Figure 2- 3), we only discuss the comparison between
Baseline (2012) and SB100 – Moderate (2045) and between SB100 – Moderate (2045) and Early
& No Biofuels – High (2045) in here.
Figure 2- 5. Health benefits and penalties due to air pollutant concentrations changes. Fifteen Los Angeles city council
districts annotated by district number are shown in panel (a). Avoided mortality and ER visits for SB100 – Moderate
relative to Baseline (2012) are in panel (b) and (c), and for Early & No Biofuels – High relative to SB100 – Moderate
in panel (d) and (e).
Changes in air pollutant exposures from current to future LA100 scenarios lead to overall public
health benefits except for increased asthma-related ER visits. Figure 2- 5 includes simulated
changes in PM2.5- and O3-induced annual all-cause premature mortality incidences and asthma-
related emergency room (ER) visits at district level. The effects of altered PM2.5 concentrations on
a
38
cardiovascular hospital admissions and heart attacks are shown by Figure A- 2. When comparing
the SB100 – Moderate scenario to the Baseline (2012) scenario, about 96 deaths
10
(95% confidence
interval (CI) of 67–130) are avoided annually in Los Angeles because when aggregating the effect
on premature mortality across the two pollutants, PM2.5 changes more than offset the O3 disbenefit.
Avoided deaths due to reductions in PM2.5 are largest in district 14, although this is also where
maximum deaths due to an increase in O3 occur, which offsets some benefits from decreased
concentration of PM2.5. In 2045, asthma-related ER visits increase in most districts because the
disbenefit from increases in O3 concentrations outweighs the benefit from PM2.5 reductions. A
total of 30 additional ER visits occur in 2045 (95% CI of 20–40) compared to 2012. In addition,
cardiovascular hospital admissions and heart attacks incidences are reduced in all districts due to
decreased PM2.5 concentrations. When comparing SB100 – Moderate with Early & No Biofuels –
High, about 53 deaths are avoided (95% CI of 36–71) in year 2045, and 19 fewer ER visits take
place (95% CI of -10–48). Maximum health benefits occur in districts 1, 10, and 14 due to the
larger population and reductions in PM2.5 concentration. Although the grid cells around the Port
of LA (district 15) have some of the largest reduction in PM2.5, the health benefits are not as large
because of district 15’s smaller population.
The changes in public health are comparable between DAC communities and non-DAC
communities as shown in Hettinger et al. (2021) (70). While there are slightly greater increases in
ER visits at DAC than non-DAC communities for Baseline (2012) versus SB100 – Moderate, and
Early & No Biofuels – High versus SB100 – Moderate, changes in avoided deaths do not show
statistical differences between the two types of communities.
10
As a reference, an average of 200 traffic accident fatalities occurred in LA per year (based on 2012 – 2017 data from
Los Angeles Department of Transportation).
39
As shown by Figure 2- 6, when combining all public health endpoints together, future LA100
scenarios could yield nearly a billion U.S. dollars of avoided health impacts (thus monetized net
benefits) in 2045 alone as compared to current air pollution because the overall benefit from PM2.5
reduction outweighs the disbenefit from O3 increases. (Note: all benefits are reported in 2019 U.S.
dollars). A comparison of Baseline (2012) and future reference (SB100 – Moderate) yield average
monetized net benefits of $900 million in 2045. Early & No Biofuels – High yields maximum air
quality improvements, and thus the corresponding air quality benefits are largest, reaching $1.4
billion compared to the 2012 baseline scenario. Avoided mortalities in Early & No Biofuels – High
compared to SB100 – Moderate provide approximately $500 million net benefit in 2045 and is
mainly attributed to the higher electrification levels. Among all districts, the districts on the east
end of the city and central LA (districts 1, 10, 13 and 14) benefit most in the three scenario
comparisons as shown by Figure 2- 6. This observed pattern is a combination of high population
density and relatively larger concentration changes for these districts. In addition, the valuation of
avoided mortality accounts for about 99% of the valuation, while benefits associated with other
health endpoints (e.g., hospital admissions, emergency room visits) are much smaller.
Figure 2- 6. Monetized health benefits (in 2019 U.S. dollars) for the 15 Los Angeles city council districts across
40
LA100 scenarios. Note that these benefits are in 2045 alone, and do not include any cumulative benefits since 2012.
2.4 Discussion
Overall, our results suggest that the renewable energy adoption pathways investigated in LA100
can lead to reductions in major air pollutant emissions including NOx and PM2.5. Reduced
emissions contribute to citywide reductions in PM2.5 concentrations and slight increases in O3
concentrations in Los Angeles (owing to the complex chemistry of ozone formation). These
concentration changes yield significant citywide benefits in public health including avoided
premature deaths, which can lead up to millions of U.S. dollars of monetized net benefits in the
single modeling year (2045) we analyzed. DAC communities benefit slightly more from PM2.5
concentration reductions than non-DAC communities, but public health savings are similar
between the two classifications of communities.
Changes in air pollution concentrations and public health that result from the LA100 scenarios are
dominated by increases in electrification in transportation and end-use sectors as compared to the
fuel transition in the power generation sector. Among the electrified sectors, the electrification of
LDVs is crucial for citywide emission reductions, because LDVs with internal combustion engines
are a large contributor to GHGs emissions and air pollutant emissions. PM2.5 emissions from brake
wear and tire wear are even more dominant in total LDVs-generated PM2.5 emissions in future
LA100 scenarios in 2045, thus worth attention during the process of increasing electric LDVs sales.
We assume reduced PM2.5 emissions factors from brake wear for electric vehicles (relative to non-
electric vehicles) due to the use of regenerative braking system in our calculation (67, 71, 72).
However, some literature also suggests the same or even higher PM2.5 emission factors for electric
vehicles (if using friction braking system) than non-electric vehicles (67, 73), which could result
in increased PM2.5 emissions with higher vehicle population and miles traveled in the future. In
41
contrast with electrification of multiple sectors, transitioning four LADWP-owned power plants
within SoCAB (totaling annual generation of ≤2 TWh) from natural gas to hydrogen had a very
small impact on city-scale air pollution because those plants are not a large contributor to LA-wide
emissions in future LA100 scenarios. Nevertheless, gas-fired power plants can be an important
contributor to near-source air pollutant exposure and the effects of combustion of hydrogen on
emissions of NOx and the health of adjacent communities should be further studied.
It is also worth noting that while we show reduction in air pollutant emissions from LA100
renewable energy adoption pathways, ~90% of citywide primary PM2.5 emissions remain in the
future LA100 scenarios due to three main reasons. First, we only included LA100-induced changes
to sources that are directly influenced by LA100 in this analysis. Including any indirect changes
that LA100 might cause in categories such as the oil and gas industry (“Oil and Gas Industry” in
Figure 2- 2), which currently supplies fossil fuel based energy inputs, such as gasoline, to sectors
directly affected by LA100, were beyond the scope of this analysis. However, in reality,
electrifying a large fraction of the LDV fleet would be likely to cause changes in oil and gas
demand, which could impact future emissions in this category indirectly. Secondly, regulations
that are not enforced by the city, thus not considered in the analysis, will lead to higher emissions
reductions. For example, the California Air Resources Board’s Advanced Clean Trucks
Regulation
11
requires increases in the percentage of zero-emission trucks in manufacturers’ annual
California sales and will reduce emissions from “Medium and Heavy-Duty Vehicles” in Figure 2-
2. Thirdly, LA100 scenarios are designed to target sectors that maximize GHGs reductions, which
are not necessarily the sectors with the largest air pollutant emissions. The fractions of PM2.5
emissions from LA100-influenced sources to all anthropogenic emissions in Los Angeles are
11
More information available at: https://ww2.arb.ca.gov/our-work/programs/advanced-clean-trucks
42
below 20% for all current and future scenarios. Controlling other major PM2.5 emission sources
such as commercial cooking, road dust and industrial processes (included in the “Other” category
in Figure 2- 2) will be important to achieve further air quality improvements.
Nevertheless, reductions in PM2.5 concentrations induced by LA100 are beneficial for the city to
meet the NAAQS and yield public health benefits, but increases in O3 concentrations are
temporarily counter productive. Increases in O3 concentrations due to NOx reductions is a
widespread issue for the greater Los Angeles basin because of the complexity of ozone chemistry
and the state of the atmosphere in SoCAB (74, 75). This phenomenon is also found in other
metropolitan area such as the San Francisco Bay Area, Denver and Houston (7, 73, 76). The
increase in O3 concentrations despite decreases in NOx emissions can be thought of as a temporary
“growing pain” that the city is likely to deal with to ultimately reduce O3 from decreasing NOx
emissions. Once NOx emissions get sufficiently low, further emissions decreases will lead to O3
reductions when the atmosphere transitions from the “NOx-saturated” regime to the “NOx-
limited” regime. Alternatively, cities could avoid these O3 increases on the path to O3 reductions
by having sufficiently large and simultaneous reductions in emissions of VOC in addition to NOx
reductions. Additionally, reducing air pollution emissions from the greater Los Angeles Basin is
crucial for improving air quality in the City of Los Angeles. CARB and SCAQMD are pursuing
aggressive air quality and climate mitigation plans, including the 2022 State Strategy for the State
Implementation Plan, the 2022 Climate Change Scoping Plan Updates, and the 2022 Air Quality
Management Plan (77–79). In addition to developing and implementing mitigation measures
similar to what is being modeled in this study, these plans will reduce NOx emissions in sectors
not directly influenced by LA100 and emissions from surrounding areas of Los Angeles. With the
additional NOx emission reductions in Los Angeles and its neighboring regions, it is likely that
43
Los Angeles could transition from the “VOC-limited” regime to the “NOx-limited” regime earlier
than 2045, leading to reductions in O3 concentrations from 2012 to 2045. Thus, joint effort by city,
district, and state governments can substantially reduce greenhouse gas and air pollution emissions
from different sectors, accelerate the transition to “NOx-limited” regime of ozone chemistry, and
bring further health benefits.
This study is the first-ever 100% renewable electricity adoption study to be initiated by a U.S.
utility system the size of LADWP that reflects detailed, science and policy-driven scenarios based
on bottom-up, sector-specific grid/load modeling and that investigates co-benefits on air quality
and public health. While GHGs reductions require global effort to curb warming, air pollutant
reductions provide immediate co-benefits to local communities, which could reinforce the
motivation of renewable energy adoption at local scale. Note that impacts on public health
presented here are only evaluated for a single year 2045, so cumulative benefits from renewable
energy adoption proposed in LA100 are likely to be much higher. The outcomes of this study,
together with other aspects of LA100 study were referenced by the city of Los Angeles leadership
as providing a blueprint for its recently announced goal to achieve 100% renewable electricity by
2035, one decade earlier than originally planned.
12
As the Intergovernmental Panel on Climate
Change (IPCC) updates its goal of limiting global warming from 2 °C to 1.5 °C in its most recent
assessment (80), localized action on greenhouse gas emission reductions and renewable energy
adoption are even more prudent than ever. Thus, LA100 also serves as a blueprint for other cities
seeking science-based and cross-sector strategies for creating deep regional decarbonization plans,
while maintaining the reliability of energy systems.
12
“100 Percent Carbon-neutral Power By 2035: Los Angeles City Council Approves Landmark Initiative”, available
at: https://www.ladwpnews.com/100-percent-carbon-neutral-power-by-035-los-angeles-city-council-approves-
landmark-initiative/
44
Chapter 3
Evaluating the Role of Land Cover Properties Changes via Urbanization
on Regional Meteorology and Air Quality in Southern California
This chapter is based on work published in Atmospheric Chemistry and Physics in 2019 (48).
3.1 Introduction
The world has been undergoing accelerated urbanization since the industrial revolution in the 19
th
Century (81, 82). Urbanization leads to profound human modification of the land surface and its
associated physical properties such as roughness, thermal inertia, and albedo (83), and land surface
processes like irrigation (84). These changes in land surface physical properties and processes alter
corresponding surface-atmosphere coupling including exchange of water, momentum and energy
in urbanized regions (47, 85), which exerts an important influence on regional meteorology and
air quality (8, 9).
Land surface modifications from urbanization drive changes in urban meteorological variables
such as temperature, wind speed and planetary boundary layer (PBL) height, which result in urban
– rural differences. Differences in surface temperature and near surface air temperature have been
widely studied for decades. The urban heat island (UHI) effect, a phenomenon in which
temperatures within an urban area are higher than surrounding rural areas (86), has been
extensively studied using models and observations for a great number of urban regions (87–89).
A contrary phenomenon, namely the urban cool island (UCI), under which urban temperatures are
lower than surrounding rural temperatures, has also been investigated recently in some studies
45
(90–92). Urban – rural contrast in temperature (i.e. both UHI and UCI) is mainly attributable to
differences in thermal properties and energy fluxes due to heterogeneous land surface properties.
For urban areas, buildings and roads (i.e., impervious surfaces) are generally made from
manufactured materials (e.g., asphalt concrete) with low albedo and thus high solar absorptivity
(93). These materials also have high thermal inertia, which can lead to reductions in diurnal
temperature range due to heat storage and consequent temperature reductions during the day and
heat release and consequent temperature increases at night (94). Street canyons, which we refer to
as the U-shaped region between buildings, can trap longwave energy fluxes within the canyon
because of reductions of sky-view factors (95). On the other hand, shading in street canyons during
the day can reduce absorption of shortwave radiation (90, 96). Pervious surfaces within urban areas
such as irrigated urban parks and lawns can lead to the urban oasis effect in which evaporative
cooling occurs due to increases in evapotranspiration. In addition, soil thermal properties depend
on their water content, which ultimately affects ground heat fluxes and thus surface and air
temperatures. Land surface properties in surrounding rural areas can also affect urban – rural
differences in temperature (88, 97, 98). In particular, urban regions built in semi-arid or arid
surroundings tend to have a weak daytime UHI or even a UCI, whereas those built in moist regions
tend to have a larger daytime UHI (83, 88). Lastly, factors such as anthropogenic heat and
atmospheric aerosol burdens can play an important role in urban heat/cool island formation in
some regions (86, 93).
Urbanization can also cause differences between urban and rural areas for meteorological variables
other than surface and air temperatures. Changes in regional near-surface wind speed and direction
can occur in urban areas because of spatially varying modifications in surface roughness (8, 99).
Changes in near surface winds in coastal urban areas can also be affected by modifying land-sea
46
temperature contrast (8). The characteristics of the PBL are dependent on the magnitude of
turbulent kinetic energy (TKE). Higher (lower) TKE will lead to deeper (shallower) PBLs. During
daytime, the magnitude of TKE is driven by buoyancy production contributed mainly by sensible
heat flux (with clear skies); at night, TKE is driven by shear production associated with variance
in wind speed. Thus, temperature and surface roughness play an important role on the depth of the
PBL during daytime and nighttime, respectively. Lastly, changes in relative humidity, precipitation,
and other meteorological variables due to land surface changes can also be significant in some
regions (100, 101).
Changes in meteorological conditions due to urbanization can influence concentrations of air
pollutants including oxides of nitrogen (NOx), ozone (O3) and fine particulate matter (PM2.5). NOx
and O3 pollution are major public health concerns in megapolitan regions (102). PM2.5 reduces
visibility, causes adverse health effects, and alters regional and global climate via direct and
indirect effects (2, 3). Meteorology can affect emission rates (e.g., biogenic volatile organic
compounds (BVOCs) and evaporative emissions of gasoline), chemical reaction rates, gas-particle
phase partitioning of semi-volatile species, pollutant dispersion, and deposition; thus, it plays an
important role in determining air pollutant concentrations. Variations in air temperatures together
with vegetation types affect the production of BVOCs, which are important precursors for ground-
level O3 and secondary organic aerosols (SOA) (103). Gas-phase chemical reactions that form
secondary pollutants are also temperature-dependent. Higher (lower) air temperatures in general
lead to higher (lower) photolysis reaction rates and atmospheric oxidation rates, which enhance
the production of tropospheric O3, secondary inorganic aerosols (e.g., nitrate, sulfate, and
ammonium aerosols) and SOA (11, 104). In addition, concentrations of semi-volatile compounds
are affected by equilibrium vapor pressure under various temperature conditions (10, 105). Higher
47
(lower) temperatures favor phase-partitioning to the gas (particle) phase. Ventilation, which is the
combined effect of vertical mixing and horizontal dispersion, can also influence pollutant
concentrations (106). Higher (lower) ventilation rates lead to lower (higher) pollutant
concentrations especially in coastal cities like Los Angeles where upwind air under typical
meteorological conditions is clean relative to urban air. Lastly, changes in surface roughness may
affect loss of pollutants via surface deposition, which in turn alters air pollutant concentrations
(107).
A number of previous studies have investigated the impacts of land surface changes on regional
meteorology in a variety of urban regions around the world (100, 108, 109). However, limited
studies have quantified the impact of land surface changes on regional air quality, and most of
these studies have focused on changes in surface O3 concentrations. Civerolo et al. (2007)
estimated that land-use changes via urban expansion in New York City can cause increases in near-
surface air temperature of 0.6 ℃ as well as increases in episode-maximum 8h O3 concentrations
of 6 ppb. Jiang et al. (2008) focused on the Houston, Texas area, and found similar relationships
between urban expansion, near-surface air temperatures, and O3 (110). Nevertheless, only a few
studies have included changes in PM2.5 concentrations. Tao et al. (2015) simulated that spatially
averaged surface O3 concentrations slightly increased (+0.1 ppb) in eastern China due to
urbanization, whereas PM2.5 concentrations decreased by –5.4 μg/m
3
at the near surface (111).
Chen et al. (2018) studied urbanization in Beijing and found that modification of rural to urban
land surfaces has led to increases in near-surface air temperature and PBL height, which in turn
led to increases (+9.5 ppb) in surface O3 concentrations and decreases (–16.6 μg/m
3
) in PM2.5
concentrations (112). However, past studies that investigate interactions between land surface
changes and changes in meteorology and air quality generally do not identify the major processes
48
driving these interactions. They also do not resolve the wide spatial heterogeneity of urban land
surface properties, with most studies assuming that urban properties are homogenous throughout
the city. In addition, only few studies investigate interactions between land surface changes and
air quality for the Southern California region (106, 113), which is one of the most polluted areas
in the United States (18).
With advances in real-world land surface datasets from satellites, recent modeling studies on land-
atmosphere interactions are able to resolve heterogeneous land surface properties and thus better
capture urban meteorology, enabling modeling studies that more accurately quantify changes in
regional meteorology due to land surface modification. By combining satellite-retrieved high-
resolution land surface data with the Weather Research and Forecasting Model coupled to the
Single-layer Urban Canopy Model (WRF/UCM), simulations reported in Vahmani and Ban-Weiss
(2016a) show improved model performance (i.e. compared to observations) for meteorology in
Southern California compared to the default model, which assumes that urban regions have
homogeneous urban land cover. A follow-up study, Vahmani et al. (2016), suggested that historical
urbanization has altered regional meteorology (e.g., near surface air temperatures and wind flows)
in Southern California mainly because of urban irrigation, and changes in land surface thermal
properties and roughness. While historical urbanization and its associated impacts on meteorology
has the potential to cause important changes in air pollutant concentrations in Southern California,
this is never been investigated in past work.
Therefore, this study aims to characterize the influence of land surface changes via historical
urbanization on urban meteorology and air quality in Southern California by comparing a “Present-
day” scenario with current urban land surface properties and land surface processes to a “Nonurban”
scenario assuming land surface distributions prior to human perturbation. To achieve this goal, we
49
adopt a state-of-the-science regional climate-air quality model, the Weather Research and
Forecasting Model coupled to chemistry and the Single-layer Urban Canopy Model (WRF-UCM-
Chem), and incorporate high-resolution heterogeneity in urban surface properties and processes to
predict regional weather and pollutant concentrations. We assess the response of regional
meteorology and air quality to individual changes in land surface properties and processes to
determine driving factors on atmospheric changes. Note that this study builds on our prior study
Vahmani et al. (2016), but focuses on air quality impacts, whereas our previous research was on
meteorological impacts only. While the influence of land surface changes on regional weather has
been investigated in numerous past studies, its influence on regional air quality has been seldom
studied in past work. In this study, we aim to quantify the importance of historical land cover
change on air pollutant concentrations, and thus the “Nonurban” scenario assumes current
anthropogenic pollutant emissions. This hypothetical scenario cannot exist in reality, since current
anthropogenic emissions would not exist without the city, but our intent is to tease out the relative
importance of land cover change through urbanization (assuming constant emissions) on air
pollutant concentrations.
3.2 Methodology and Data
3.2.1 Model Description and Configuration
WRF-Chem v3.7 is used in this study to simulate meteorological fields and atmospheric chemistry.
WRF-Chem is a state-of-the-science nonhydrostatic mesoscale numerical meteorological model
that facilitates “online” simulation of processes relevant to atmospheric chemistry including
pollutant emissions, gas and particle phase chemistry, transport and mixing, and deposition (26).
In this study, we activate the urban canopy model (UCM) in WRF-Chem that resolves land-
50
atmosphere exchange of water, momentum, and energy for impervious surfaces in urban areas (37,
96). The UCM parameterizes the effects of urban geometry on energy fluxes from urban facets
(i.e., roofs, walls, and roads) and wind profiles within canyons (114). We account for the effect of
anthropogenic heat on urban climate by adopting the default diurnal profile in the UCM. We used
UCM instead of the multilayer canopy layer model (BEP) because BEP would increase
computational costs, but for likely little additional benefit in the quality of simulations (37, 96).
Physics schemes included in our model configuration are the Lin cloud microphysics scheme (28),
the RRTM longwave radiation scheme (29), the Goddard shortwave radiation scheme (30), the
YSU boundary layer scheme (34), the MM5 similarity surface layer scheme (31, 32), the Grell 3D
ensemble cumulus cloud scheme (35), and the unified Noah land surface model (33). Chemistry
schemes include the TUV photolysis scheme (39), RACM-ESRL gas phase chemistry (40, 41),
and MADE/VBS aerosols scheme (10, 43).
All model simulations are carried out from June 28th, 2100 UTC (June 28
th
, 1300 PST) to July 8
th
,
0700 UTC (July 7
th
, 2300 PST), 2012 using the North American Regional Reanalysis (NARR)
dataset as initial and boundary meteorological conditions (45). This simulation period is chosen as
representative of typical summer days in Southern California, which are generally clear or mostly
sunny without precipitation. Hourly model output from July 1
st
, 0000 PST to 7
th
, 2300 PST is used
for analysis, and simulation results prior to July 1
st
, 0000 PST are discarded as spin up. Figure 3-
1a shows the three two-way nested domains with horizontal resolutions of 18 km, 6 km and 2 km,
respectively, centered at 33.9°N, 118.14°W. Only the innermost domain (141×129 grid cells),
which encapsulates the Los Angeles and San Diego metropolitan regions, is used for analysis. All
three domains consist of 29 unequally spaced layers in the vertical from the ground to 100 hPa.
The average depth of the lowest model level is 53 m for all three domains.
51
Figure 3- 1. Maps of (a) the three nested WRF-UCM-Chem domains, and (b,c) land cover types for the innermost
domain (d03) for the (b) Present-day and (c) Nonurban scenarios.
3.2.2 Land Surface Property Characterization and Irrigation Parameterization
One important aspect of accurately simulating meteorology and air quality is to properly
characterize land surface – atmosphere interactions (47, 85). In addition, accurately quantifying
the climate and air quality impacts of historical urbanization requires a realistic portrayal of current
land cover in the urban area (8). For both of these reasons, we update the default WRF-Chem to
include a real-world representation of land surface physical properties and processes.
In this study, we use the (30 m resolution) 33-category National Land Cover Database (NLCD)
for the year 2006 for all three model domains. NLCD differentiates three urban types including
low-intensity residential, high-intensity residential, and industrial/commercial (shown in Figure 3-
1b) (49). In the model (UCM), each of these three types can have unique urban physical properties
such as building morphology, albedo, and thermal properties for each facet. We adopt the grid-cell
specific National Urban Database and Access Portal Tool (NUDAPT) where available in the
innermost domain for building morphology including average building heights, road widths, and
roof widths (50). Where NUDAPT data are unavailable, we use average building and road
52
morphology for three urban categories from the Los Angeles Region Imagery Acquisition
Consortium (LARIAC). Details on the generation of averaged urban morphology parameters from
real-world GIS datasets can be found in Zhang et al. (2018). For the other parameters in the UCM
(e.g., anthropogenic latent heat, surface emissivity), we use default WRF settings documented in
file URBPARM.TBL. Note that the original gaseous dry deposition code based on Wesely (1989)
is only compatible with the default 24-category U.S. Geological Survey (USGS) global land cover
map (115). We therefore modify the code according to Fallmann et al. (2016), which assumes that
the three urban types in the 33-category system have input resistances that are the same as the
urban type for the 24-category system (116). In addition, impervious fractions (i.e., the fraction of
each cell covered by impervious surfaces) for each of the three urban categories in the innermost
domain are from the NLCD impervious surface data (117).
Land surface properties including albedo, green vegetation fraction (GVF), and leaf area index
(LAI) are important for accurately predicting absorption and reflection of solar radiation and
evaporative fluxes in urban areas (47). To resolve high-resolution real-world heterogeneity in these
land surface properties, the simulations performed in this study use satellite-retrieved real-time
albedo, GVF, and LAI for the innermost domain. Input data compatible with WRF are regridded
horizontally using albedo, GVF, and LAI maps generated based on MODIS reflectance
(MCD43A4), vegetation indices (MOD13A3), and fraction of photosynthetically active radiation
(MCD15A3) products, respectively. Raw data are available from the USGS National Center for
Earth Resource Observations and Science website at http://earthexplorer.usgs.gov. A detailed
description on the implementation of MODIS-retrieved land surface properties for WRF can be
found in Vahmani and Ban-Weiss (2016a). Our previous research has shown that the model
enhancements described here reduce model biases in surface and near-surface air temperatures
53
(relative to ground and satellite observations) for urban regions in southern California. In particular,
the root-mean-square-error for nighttime near-surface air temperature has been narrowed from 3.8
to 1.9 °C.
Resolving urban irrigation is also of great significance for accurately predicting latent heat fluxes
and temperatures within Los Angeles. Here we use an irrigation module developed by Vahmani
and Hogue (2014), which assumes irrigation occurs three times a week at 2300 PST on the pervious
fraction of urban grid cells. This model was tuned to match observations of evapotranspiration in
the Los Angeles area. Details on the implementation of this irrigation module and its evaluation
with observations can be found in Vahmani and Hogue (2014). Note that we do not use the default
irrigation module available in the single layer canopy model in WRF/UCM v3.7, which assumes
daily irrigation at 2100 PST in summertime, because (1) the irrigation module of Vahmani and
Hogue (2014) was already evaluated and tuned for Southern California, and (2) we strive to
maintain consistency with our previous related studies.
3.2.3 Emission Inventories
Producing accurate air quality predictions also relies on using emission inventories that capture
real-world emissions. We adopt year 2012 anthropogenic emissions from the California Air
Resource Board (CARB) for the two outer domains where data are available (i.e., within
California), and from South Coast Air Quality Management District (SCAQMD) for the innermost
domain. For areas within the two outer domains that are outside California, we use the U.S.
Environmental Protection Agency (EPA) National Emissions Inventory (NEI) for 2011 that is
available with the standard WRF-Chem model. CARB and SCAQMD emission inventories as
provided have 4 km spatial resolution, with 18 and 11 layers in the vertical from the ground to 100
54
hPa, respectively. We regridded these inventories in the horizontal and vertical to match the grids
of our modeling domains. Note that the aforementioned emission inventories use chemical
speciation from the SAPRC chemical mechanism (118), and thus we have converted species to
align with the RACM-ESRL and MADE/VBS mechanisms, both of which use RADM2 (Regional
Acid Deposition Model) speciation (119). The conversion uses species and weighting factors from
the emiss_v04.F script that is distributed with NEI emissions for WRF-Chem modeling. (The
original script is available at: ftp://aftp.fsl.noaa.gov/divisions/taq.) For online calculation of
biogenic volatile organic emissions, we adopt the Model of Emissions of Gases and Aerosols from
Nature (MEGAN) (103). The default LAI in MEGAN is substituted with the satellite-retrieved
LAI for better quantification of biogenic emissions. Note that we have turned on online calculation
of sea salt emissions but turned off that of dust emissions (both available with default WRF).
3.2.4 Meteorology and Air Pollutant Observations
To facilitate model evaluation, we obtain hourly near-surface air temperature observations, hourly
ground-level O3 and daily PM2.5 observations within our simulation period. Near-surface air
temperature data are gathered from 12 stations from the California Irrigation Management
Information System (CIMIS). Air pollutant observations are from the Air Quality System (AQS),
which is maintained by the U.S. EPA. Ozone (PM2.5) data from 33 (27) air quality monitoring
stations are collected representing Los Angeles, Orange, Riverside and San Bernardino Counties.
Among the 27 monitoring stations where PM2.5 observations are available, daily PM2.5
concentrations from gravimetric analysis can be directly obtained from 20 stations, while hourly
observations acquired using a Beta Attenuation Monitoring (BAM) are obtained from 15 stations.
Hourly PM2.5 observations at each station are temporally averaged to obtain daily PM2.5 values.
55
3.2.5 Simulation Scenarios
To investigate the effects of land surface changes via historical urbanization on regional
meteorology and air quality in Southern California, we carry out two simulations, which we refer
to as the “Present-day” scenario and “Nonurban” scenario. The two scenarios differ only by the
assumed land surface properties and processes, which are shown in Figure 3- 2. The Present-day
scenario assumes the land cover (Figure 3- 1b) and irrigation of current for Southern California
(described in Chapter 3.2.2). Urban morphology from NUDAPT and LARIAC, and MODIS-
retrieved albedo, GVF and LAI are used in this scenario. For the Nonurban scenario, we assume
natural land cover prior to human perturbation, and replace all urban grid cells with “shrubs”
(Figure 3- 1c). We modify MODIS-retrieved albedo, GVF and LAI in these areas based on
properties for shrub lands surrounding urban regions in the Present-day scenario. A detailed
explanation on this method (inverse distance weighting approach) can be found in Vahmani et al.
(2016). Note that all three aforementioned scenarios adopt identical anthropogenic emission
inventories described in Chapter 3.2.3. Using current anthropogenic emissions for “Nonurban” is
a hypothetical scenario that cannot exist in reality but allows us to tease out the effects of land
surface changes via urbanization on meteorology and air pollutant concentrations. (Biogenic
emissions do change for the scenarios due to changes in land surface properties (e.g., vegetation
type and LAI) and meteorology (e.g., temperature).) To check whether the influence on regional
meteorology and air quality due to land surface changes are distinguishable from zero, statistical
significance at 95% confidence interval is tested using the paired Student’s t-test with n = 7 days.
56
Figure 3- 2. Spatial patterns of differences (Present-day – Nonurban) in land surface properties for urban grid cells.
Panels (a) to (f) are changes in impervious fraction, albedo, leaf area index (LAI), vegetation fraction (VEGFRA),
surface roughness, and effective soil moisture, respectively. Effective soil moisture is calculated as the product of
pervious fraction for urban grid cells (1 – impervious fraction) and soil moisture for the pervious portion of the grid
cell.
3.2.6 Uncertainties
Note that the results reported in this chapter are based on model simulations and are thus dependent
on how accurately the regional climate/chemistry model characterizes the climate/chemistry
system (e.g., meteorology, surface-atmosphere coupling, and atmospheric chemical reactions).
Results may be dependent on model configuration (e.g., physical and chemical schemes), land
surface characterizations (e.g., satellite data from MODIS, or default dataset available in WRF)
and emission inventories (e.g., anthropogenic emission inventories from CARB, SCAQMD or
NEI). In addition, since irrigation is not included in the Nonurban scenario, simulated meteorology
in the Nonurban scenario is dependent on assumed soil moisture initial conditions. In this study,
we adopt the initial soil moisture conditions from Vahmani et al. (2016) for consistency with our
previous work. Soil moisture initial conditions are based on values from six-month simulations
without irrigation (120).
57
3.3 Results and Discussion
3.3.1 Evaluation of Simulated Meteorology and Air Pollutant Concentrations
In this section, we focus on the predicative capability of the model for simulated near-surface air
temperature, O3 and total PM2.5 concentrations (including sea salt, but excluding dust) for the
Present-day scenario. Note that for the evaluation of PM2.5 concentrations we include only
observations from daily (gravimetric) measurements here. In addition, we only include
observations from monitoring sites that are located in urban grid cells in the Present-day scenario.
Figure 3- 3 shows the comparison between observed and modeled hourly near surface air
temperature, O3 concentrations, and daily PM2.5 concentrations. As shown in Figure 3- 3, the
model simulations better capture higher air temperatures during the daytime relative to lower
values during nighttime. By contrast, predictions of O3 and PM2.5 concentrations show good fit
with observations at low values that occur with high occurrence frequency. However, observed O3
and PM2.5 concentrations are underestimated by the model at higher values that occur with lower
frequency of occurrence. The underestimation of PM2.5 concentrations may be occurring mainly
due to the following factors: 1) not including dust emissions in the simulation, which makes up an
appreciable fraction of real-world total PM2.5, and 2) while the observations measure values for
one single point near the surface, model values represent a grid cell average with a larger spatial
“footprint”. Note that the focus of this study is on the changes in pollutant concentrations, and thus
relative differences are of increased interest relative to absolute values. Table 3- 1 shows four
statistical metrics for model evaluation, including mean bias (MB) and normalized mean bias
(NMB) for the quantification of bias, and mean error (ME) and root mean square error (RMSE)
58
for the quantification of error. The statistical results indicate that model simulations underestimate
near-surface air temperature, O3 and PM2.5 concentrations by 1.0 K, 22% and 31%, respectively.
Figure 3- 3. Comparison between modeled and observed (a) hourly near-surface air temperature (K), (b) hourly O 3
concentrations (ppb), and (c) daily PM 2.5 concentrations ( μg/𝑚 3
). Note that daily PM 2.5 concentrations from
simulations include sea salt, but exclude dust. Darker hexagonal bins correspond to higher point densities in the scatter
plots. Histograms of both observations and modeled values are also shown at the edges of each panel.
Table 3- 1. Summary statistics (mean bias (MB), normalized mean bias (NMB), mean error (ME), and root mean
square error (RMSE)) for model evaluation, which compares simulated hourly near-surface air temperature (T2),
hourly O 3 and daily PM 2.5 concentrations to observations.
Variable N
a
Mean
MB
b
NMB
c
ME
d
RMSE
e
Observations Simulations
T2 1944 293.0 K 292.0 K -1.0 K -0.3% 1.9 K 2.2 K
O 3 5171 38.7 ppb 30.0 ppb -8.7 ppb -22% 11.8 ppb 14.6 ppb
PM 2.5 81 12.9 μg/𝑚 3
9.2 μg/𝑚 3
-4.0 μg/𝑚 3
-31% 6.2 μg/𝑚 3
9.5 μg/𝑚 3
a.
Total number of data points comparing modeled versus observed values across all measurement station locations
over the simulation period.
b.
MB =
1
N
∑(𝑚𝑜𝑙 𝑖 − 𝑜𝑏𝑠 𝑖 )
c.
NMB =
∑(𝑚𝑜𝑙 𝑖 −𝑜𝑏𝑠 𝑖 )
∑ 𝑜𝑏𝑠 𝑖
d.
ME =
1
N
∑ |𝑚𝑜𝑙 𝑖 − 𝑜𝑏𝑠 𝑖 |
e.
RMSE = [
1
N
∑(𝑚𝑜𝑙 𝑖 − 𝑜𝑏𝑠 𝑖 )
2
]
1
2
59
3.3.2 Effects of Urbanization on Air Temperature and Ventilation Coefficient
The effects of land surface changes via urbanization in Southern California on air temperature and
ventilation coefficient are discussed in this section. Air temperatures are reported for the lowest
atmosphere model layer rather than the default diagnostic 2m (near-surface) air temperature
variable to be consistent with reported air pollutant concentrations shown in later sections. (The
chemistry code makes use of grid cell air temperature and does not use 2 m air temperature.)
Ventilation coefficient is calculated as the product of PBL height and the average wind speed
within the PBL, and thus considers the combined effects of vertical and horizontal mixing, and
indicates the ability of the atmosphere to disperse air pollutants (121). This calculation can be
written as (Eq3-1).
Ventilation Coefficient = ∑ 𝑈 (𝑧 𝑖 ) × ∆𝑧 𝑖 𝑚 𝑖 =1
(Eq3-1)
where 𝑈 (𝑧 𝑖 ) stands for horizontal wind speed within the i
th
model layer (m/s), ∆𝑧 𝑖 is the depth of
the ith model layer that is within the PBL (m), and m is the number of vertical layers up to PBL
height.
3.3.2.1 Spatial average temperature change
As shown in Figure 3- 4, urbanization in Southern California has in general led to urban
temperature reductions during daytime from 7 PST to 16 PST, and urban temperature increases
during other times of day. The largest spatially averaged temperature reduction occurs at 10 PST
(∆T = –1.4 K), whereas the largest temperature increase occurs at 20 PST (+1.7 K). Additionally,
urbanization led to spatially averaged reduction in diurnal temperature range by 1.5 K. Spatially
averaged urban temperature reductions during morning (i.e., defined here and in the following
sections as 7:00 – 12:00 PST) and afternoon (i.e., 12:00 – 19:00 PST) are –0.9 K and –0.3 K,
60
respectively. At nighttime (i.e., 19:00 – 7:00 PST), the spatially averaged temperature increase is
+1.1 K. The spatially averaged changes significantly differ from zero at the 95% confidence level
for all three times of the day using the paired Student’s t-test with n=7 days.
Figure 3- 4. Diurnal cycles for present-day (red), nonurban (blue), and present-day – nonurban (black) for (a) air
temperature in the lowest atmospheric layer (K) and (b) ventilation coefficient (m
2
/s). Values are obtained by averaging
over urban grid cells and the entire simulation period for each hour of day. The solid and dashed curves give the
median values, while the shaded bands show 25
th
and 75
th
percentiles. Dots indicate mean values for differences
between Present-day and nonurban. The horizontal dotted line in light grey shows ∆= 0 as an indicator of positive or
negative change by land surface changes via urbanization.
3.3.2.2 Spatial distributions of temperature change
During the morning, temperature reductions are larger in regions further away from the sea (e.g.,
San Fernando Valley and Riverside County) than coastal regions (e.g., west Los Angeles and
Orange County) (Figure 3- 5a). (Note that regions that are frequently mentioned in this study are
61
in Figure 3- 2.) Spatial patterns in the afternoon are similar to morning, with the exception that
coastal regions experience temperature increases (as opposed to decreases) of up to +0.82 K
(Figure 3- 5b). During nighttime, temperature increases spread throughout urban regions, and are
generally larger in the inland regions of the basin relative to coastal regions Figure 3- 5c).
Figure 3- 5. Spatial patterns of differences (Present-day – nonurban) in temporally averaged values during morning,
afternoon and nighttime for (a,b,c) air temperature in the lowest atmospheric layer, and (d,e,f) ventilation coefficient.
Morning is defined as 7 PST to 12 PST, afternoon as 12 PST to 19 PST, and nighttime as 19 PST to 7 PST. We refer
to morning and afternoon as daytime. Note that values are shown only for urban grid cells. Black dots indicate grid
cells where changes are not significantly different from zero at 95% confidence level using the paired Student’s t-test
with n=7 days.
3.3.2.3 Processes driving daytime changes
The temporal and spatial patterns of air temperature changes suggest that the climate response to
urbanization during daytime is mainly associated with the competition between (a) temperature
reductions from increased evapotranspiration and thermal inertia from urban irrigation, and (b)
temperature increases from decreased onshore sea breezes. Decreases in the onshore sea breeze
are primarily caused by increased roughness lengths from urbanization. (Note that the onshore sea
breeze decreases in strength despite higher temperatures in the coastal region of Los Angeles
62
during the afternoon, which would tend to increase the land-sea temperature contrast and thus be
expected to increase the sea breeze strength.) Inland regions show larger temperature reductions
relative to coastal because they have lower urban fractions, and thus higher pervious fractions.
Since irrigation increases soil moisture in the pervious fraction of the grid cell in this model,
irrigation will have a larger influence on grid cell averaged latent heat fluxes and thermal inertia
when pervious fractions are higher. The inland regions are also less affected by changes in the sea
breeze relative to coastal regions since they are (a) farther from the ocean, and (b) experience
smaller increases in roughness length. Roughness length effects on the sea breeze are especially
important in the afternoon when baseline wind speeds are generally highest in the Los Angeles
basin. Thus, the afternoon temperature increases simulated in the coastal region occur because
temperature increases from reductions in the afternoon onshore flows dominate over temperature
decreases from increased evapotranspiration. In addition, increases in thermal inertia caused by
use of manmade materials (e.g., pavements and buildings) can contribute to simulated temperature
reductions during the morning.
Note that changes in air temperature during daytime shown here disagree with Vahmani et al.
(2016). While our study detects daytime temperature reductions due to urbanization, Vahmani et
al. (2016) suggests daytime warming. After detailed comparison of the simulations in our study
versus Vahmani et al. (2016), we find that the differences are mainly associated with UCM
configuration. First, our study uses model default calculations of surface temperature for the
impervious portion of urban grid cells, whereas Vahmani et al. (2016) applied an alternative
calculation proposed by Li and Bou-Zeid, 2014. Li and Bou-Zeid, 2014 intended the alternate
surface temperature calculations to be performed as a post-processing step rather than during
runtime. After a careful comparison among different model set-ups, we found that the
63
parameterization of surface temperature is an important factor that affects simulated daytime air
temperature. Second, our study accounts for shadow effects in urban canopies, whereas Vahmani
et al. (2016) assumes no shadow effects. (We note here that the default version of the UCM has
the shadow model turned off. The boolean SHADOW variable in module_sf_urban.F needs to be
manually switched to true to enable the shadow model calculations. With the shadow model turned
off, all shortwave radiation within the urban canopy is assumed diffuse.) We suggest that it is
important to include the effects of building morphology on shadows within the canopy, and to
track direct and diffuse radiation separately, and therefore perform simulations in this study with
the shadow model on. Note that the effect of shadows is not as significant as the parameterization
of surface temperature for most of the domain in our study because the ratio between building
height and road width is small.
3.3.2.4 Processes driving nighttime changes
The climate response to urbanization during nighttime is driven by the combined effects of (a)
temperature increases from increasing upward ground heat fluxes, and (b) temperature increases
from increasing PBL heights. Increased soil moisture (from irrigation) and use of man-made
materials leads to higher thermal inertia of the ground; this in turn leads to increased heat storage
during the day and higher upward ground heat fluxes and thus surface temperatures at night.
Increasing PBL heights can also lead to warming because of lower air cooling rates during
nighttime. Changes in PBL heights are associated with surface roughness changes since shear
production dominants TKE at night. Coastal (inland) regions show larger (smaller) variation in
roughness length (Figure 3- 2e), which leads to larger (smaller) increases in PBL heights. Despite
larger increases in PBL heights in coastal versus inland regions, smaller air temperature increases
64
occur in coastal versus inland regions, likely due to accumulative effects from coastal to inland
regions with onshore wind flows.
3.3.2.5 Temporal and spatial patterns of ventilation changes and process drivers
Changes in ventilation coefficient show a similar temporal pattern as air temperature (Figure 3-
4b); values decrease by up to –36.6% (equivalent to –826 m
2
/s, at 10 PST) during daytime, and
increase up to +27.0% (equivalent to +77 m
2
/s, at 23 PST) during nighttime, due to urbanization.
Absolute reductions in ventilation coefficient are more noticeable in the afternoon than in the
morning; the spatially averaged decreases are –726 m
2
/s (–23%) and –560 m
2
/s (–34%),
respectively. These reductions significantly differ from zero at 95% confidence level using the
paired Student’s t-test with n=7 days. Reductions during daytime are also generally greater in
inland regions than in coastal regions as shown is Figure 3- 5d and Figure 3- 5e. Daytime
reductions in ventilation occur due to the combined effect of weakened wind speeds due to higher
surface roughness and changes (mostly decreases) in PBL heights. Changes in PBL heights during
daytime are mainly associated with air temperature changes because buoyancy production
dominants TKE during the day. Where there are larger air temperature decreases (increases), there
is reduced (increased) buoyancy production of TKE, which results in shallower (deeper) PBLs.
At night, spatially averaged ventilation coefficient increases by +8.2% (+24.3 m
2
/s). This increase
significantly differs from zero at 95% confidence level. As shown in Figure 3- 5f, statistically
significant ventilation growth occurs in most parts of coastal Los Angeles and Orange County,
likely due to higher PBL height increases (i.e., stemming from higher surface roughness increases
from urbanization). By contrast, in Riverside County, the effect of reductions in wind speed
surpasses changes in PBL heights, leading to slight but not statistically significant reductions in
atmospheric ventilation.
65
3.3.3 Effects of Urbanization on NOx and O3 Concentrations due to Meteorological Changes
Concentrations of pollutants are profoundly impacted by meteorological conditions including air
temperature and the ventilation capability of the atmosphere (122). This section discusses how
meteorological changes due to land surface changes via urbanization in Southern California affect
gaseous pollutant concentrations (i.e., NOx and O3).
3.3.3.1 Temporal and spatial patterns of NOx concentration changes and process drivers
As shown in Figure 3- 6a, changes in meteorological fields due to urbanization have led to
increases in hourly NOx concentrations during the day (7 PST to 18 PST) and decreases at all other
times of day. Peak increases in NOx of +2.7 ppb occur at 10 PST (i.e., for spatial mean values),
while peak decreases of –4.7 ppb occur at 21 PST. Spatial mean changes in NOx concentrations
are +2.1 ppb and +1.2 ppb in the morning and afternoon, respectively, and –2.8 ppb at night. The
spatially averaged changes are significantly different from zero at 95% confidence level for all
three times of the day. In addition, daily 1-hour maximum NOx concentrations change only
slightly: from 17.8 ppb at 6 PST in the Nonurban scenario to 17.9 ppb at 7 PST in the Present-day
scenario.
66
Figure 3- 6. Diurnal cycles for present-day (red), nonurban (blue), and present-day – nonurban (black) for (a) NOx
(ppb) and (b) O 3 concentrations (ppb). Values are obtained by averaging over urban grid cells and the entire simulation
period for each hour of day. The solid and dashed curves give the median values, while the shaded bands show 25
th
and 75
th
percentiles. Dots indicate mean values for differences between Present-day and nonurban. The horizontal
dotted line in light gray shows ∆= 0 as an indicator of positive or negative change by land surface changes via
urbanization.
Figure 3- 7a-c show the spatial patterns of NOx concentration changes due to urbanization. In the
morning (afternoon), most inland urban regions show statistically significant increases in NOx
concentrations (Figure 3- 7a, b), with larger NOx concentration increases of up to +13.8 ppb (+5.5
ppb) occurring in inland regions compared to coastal regions. By contrast, NOx concentrations
decrease at night across the region, with the largest decreases reaching –20.8 ppb. In general,
greater decreases are shown in inland regions compared to coastal regions.
67
The spatial patterns of changes in NOx concentrations are similar to those for CO concentrations
(Figure 3- 7d-f). CO is an inert species and can be used as a tracer for determining the effect of
ventilation on air pollutant dispersion since it includes accumulation effects of ventilation changes
both spatially and temporally. Thus, the similarity in changes to NOx and CO spatial patterns
suggests that NOx changes are driven by ventilation changes. For example, at night, Riverside
County shows decreases of up to –20.8 ppb in NOx concentrations (with corresponding decreases
in CO of –119 ppb) despite suppressed ventilation at this location because of accumulative effects
from coastal to inland regions.
Figure 3- 7. Spatial patterns in differences (Present-day – nonurban) of temporally averaged values during morning,
afternoon and nighttime for (a,b,c) NOx, (d,e,f) CO, and (g,h,i) O
3
concentrations. Morning is defined as 7 PST to 12
PST, afternoon as 12 PST to 19 PST, and nighttime as 19 PST to 7 PST. Black dots indicate grid cells where changes
are not significantly different from zero at 95% confidence level using the paired Student’s t-test with n=7 days.
68
3.3.3.2 Temporal and spatial patterns of O3 concentration changes
As indicated by Figure 3- 6b, O3 concentrations in the lowest atmospheric layer decrease from 7
PST to 11 PST but increase during other times of day. The largest decrease of –0.94 ppb occurs at
10 PST, while the largest increase of +5.6 ppb occurs at 19 PST. Spatially averaged hourly O3
concentrations undergo a –0.6 ppb decrease, +1.7 ppb increase, and +2.1 ppb increase in the
morning, afternoon, and night, respectively. The spatially averaged changes significantly differ
from zero at 95% confidence level for all three times of the day. Additionally, daily 1-hour
maximum O3 concentrations, which occurs at 14 PST in both scenarios, increases by +3.4%, from
41.3 ppb in the Nonurban scenario to 42.7 ppb in the Present-day scenario. The daily 8-hour
maximum O3 concentration increases from 38.0 ppb to 39.3 ppb (averaged over 11 PST to 19 PST
in both scenarios).
Figure 3- 7g-i show the spatial patterns of surface O3 concentration changes. In the morning
(Figure 3- 7g), while most regions show reductions in O3 concentrations, the reductions are in
general statistically insignificant. During the afternoon, most inland urban regions show increases
in O3 concentrations (Figure 3- 7h), with the largest increase of +5.7 ppb occurring in Riverside
County. Increases in O3 concentrations are larger during night than the afternoon (Figure 3- 7i),
especially in the Riverside County, with the largest increase in O3 concentrations reaching +12.8
ppb.
3.3.3.3 Processes driving daytime and nighttime changes in O3
The temporal and spatial patterns of changes in O3 concentrations during the day suggest that these
changes are mainly driven by the competition between (a) decreases in ventilation, which would
tend to cause increases in O3, and (b) the nonlinear response of O3 to NOx changes. In the VOC-
69
limited regime, increases in NOx tend to decrease O3 concentrations, and vice versa. (This explains
why decreases in NOx emissions over weekends can cause increases in O3 concentrations, a
phenomenon termed the “weekend effect” (123).) The underlying cause of the weekend effect has
to do with titration of O3 by NO, as shown in R3-1.
NO + O
3
→ NO
2
+ O
2
(R3-1)
When NOx is high relative to VOC, R1 dominates NO to NO2 conversion, which involves
consuming O3. In addition, increases in NO2 can reduce OH lifetime due to increased rates of the
OH + NO2 reaction (R3-2), which is chain terminating.
NO
2
+ OH + M → HNO
3
+ M (R3-2)
In addition to these two aforementioned processes, changes in air temperature can also affect the
production rate of O3, with higher temperatures generally leading to higher O3 (124).
In the morning when ventilation is relatively weak (shallow PBL and weak sea breeze), changes
in NOx concentrations play an important role in driving surface O3 concentrations. Regions with
greater increases in NOx concentrations in general show greater decreases in O3 concentrations
(Figure 3- 7g). Decreases in air temperature would also contribute to decreases in O3
concentrations due to reductions in O3 production rates. In the afternoon when ventilation is
strengthened (deep PBL, and stronger sea breeze), changes in both NOx concentrations and
ventilation play important roles in determining O3 concentrations (Figure 3- 7h). Regions with
higher increases in NOx concentrations tend to have lower increases in O3 concentrations; this
indicates that NOx increases (that would tend to decrease O3) are counteracting decreases in
ventilation (that would tend to increase O3). In regions with relatively lower increases in NOx
70
concentrations and greater decreases in ventilation, such as Riverside County, increases in O3
concentrations are larger.
At night, changes in O3 concentrations are dominated by its titration by NO2 as shown in (R3-3).
𝑁𝑂
2
+ 𝑂 3
→ 𝑁𝑂
3
+ 𝑂 2
(R3-3)
Where there are larger decreases in NOx concentrations (Figure 3- 7c), there are greater increases
in O3 concentrations (Figure 3- 7i), regardless of the magnitude of increases in atmospheric
dilution (Figure 3- 5f).
3.3.4 Effects of Urbanization on Total and Speciated PM2.5 Concentrations due to
Meteorological Changes
In this section, we discuss changes in total and speciated PM2.5 mass concentrations due to
urbanization. Total mass concentrations reported here only consider PM2.5 generated from
anthropogenic and biogenic sources mentioned in Chapter 3.2.3, and exclude sea salt and dust.
Speciated PM2.5 is classified into three categories: (secondary) inorganic aerosols including nitrate
(NO3
-
), sulfate (SO4
2-
) and ammonium (NH4
+
); primary carbonaceous aerosols including elemental
carbon (EC), and primary organic carbon (POC); and secondary organic aerosol (SOA) including
SOA formed from anthropogenic VOC precursors (ASOA) and biogenic VOC precursors (BSOA).
3.3.4.1 Temporal patterns of total and speciated PM2.5 concentration changes
Figure 3- 8 illustrates diurnal changes in total and speciated PM2.5 concentrations due to
meteorological changes attributable to urbanization. As suggested by Figure 3- 8a, urbanization is
simulated to cause slight spatially averaged increases in total PM2.5 concentrations from 9 PST to
16 PST (up to +0.62 μg/m
3
occurring at 12 PST), and decreases during other times of day (up to –
71
3.1 μg/m
3
at 0 PST). Increases in total PM2.5 during 9 PST to 16 PST come from increases in
primary carbonaceous aerosols, and nitrate; these species show hourly averaged concentration
increases of up to +0.21, +0.14 μg/m
3
, respectively. By contrast, BSOA decreases slightly during
these hours. During other times of day, concentrations of all PM2.5 species decrease dramatically.
Inorganic aerosols, primary carbonaceous aerosols, and SOA show decreases of up to –1.7, –0.5
and –0.3 μg/m
3
, respectively.
During morning hours, averaged hourly total PM2.5 concentrations decrease by –0.20 μg/m
3
but
are not statistically significant. In the afternoon, spatially averaged total PM2.5 concentrations
increase by +0.24 μg/m
3
. Primary carbonaceous aerosols contribute to half of the increase (+0.12
μg/m
3
). For nighttime, total PM2.5 concentrations undergo a decrease of –2.5 μg/m
3
, with 54% of
the decrease attributed to changes in inorganic aerosols and 17% by primary carbonaceous aerosols.
Both afternoon and nighttime changes are significantly different from zero at 95% confidence
interval.
72
Figure 3- 8. Diurnal cycles for spatially averaged PM 2.5 concentrations. Panel (a) shows Present-day, nonurban, and
present-day – nonurban for total PM 2.5 (excluding sea salt and dust). The lower row shows differences (Present-day –
nonurban) in speciated PM 2.5 including (b) inorganic aerosols (NO 3
-
, SO 4
2-
, NH 4
+
), (c) primary carbonaceous aerosols
(EC, POC), and (d) secondary organic aerosols (ASOA, BSOA). The horizontal dotted line in light grey is shown for
∆= 0 as an indicator of positive or negative change by urbanization.
3.3.4.2 Spatial patterns of total and speciated PM2.5 concentration changes
Figure 3- 9 presents spatial patterns of changes in total and speciated PM2.5 due to urbanization.
Decreases in concentrations prevail in urban regions during morning and night, whereas increases
in concentrations are dominant during the afternoon.
In the morning, changes in total PM2.5 and speciated PM2.5 are not statistically distinguishable from
zero at 95% confidence level. In the afternoon, increases in total PM2.5 are statistically significant
in only some inland regions, driven mostly by increases in primary carbonaceous aerosols (up to
+0.5 μg/m
3
, Figure 3- 9h). At night, most regions within the Los Angeles metropolitan area show
decreases in total PM2.5 of –3.0 to –6.0 μg/m
3
(Figure 3- 9c) with contributions from all three
categories of speciated PM2.5.
3.3.4.3 Processes driving daytime and nighttime changes in PM2.5
During the day, changes in speciated PM2.5 concentrations are dictated by the relative importance
of various competing pathways, including (a) reductions in ventilation causing increases in PM2.5,
(b) changes in gas-particle phase partitioning causing increases (decreases) in PM2.5 from
decreases (increases) in temperature, and (c) increases (decreases) in atmospheric oxidation from
increases (decreases) in temperature. Changes in ventilation appear to dominate the changes in
primary carbonaceous aerosols, as indicated by the similarity in spatial pattern to changes in CO,
which can be considered a conservative tracer (Figure 3- 7d and 7e). As for semi-volatile
compounds such as nitrate aerosols (red dotted curve in Figure 3- 8b) and some SOA species,
73
concentrations increase during daytime hours. This is because both decreased ventilation and gas-
particle phase partitioning effects favoring the particle phase (from temperature decreases)
outweigh reductions in atmospheric oxidation. Concentrations of sulfate and ammonium slightly
increase due to urbanization (blue dotted curve in Figure 3- 8b). Since sulfate is nonvolatile, gas-
particle phase partitioning does not affect sulfate concentrations; lowered atmospheric oxidation
rates due to reduced temperatures (which would tend to decrease sulfate) nearly offset the effect
of weakened ventilation (which would tend to increase sulfate). In addition, BSOA concentrations
are simulated to decrease (blue dotted curve in Figure 3- 8d) due to reduced biogenic VOC
emissions, which occur due to reductions in both vegetation coverage and air temperature from
urbanization.
At night, decreases in PM2.5 across urban regions are due to (1) enhanced ventilation owing to
deeper PBLs (relevant for all PM species), and (2) gas-particle phase partitioning effects that favor
the gas phase for semi-volatile compounds (i.e., nitrate aerosols and some SOA species) because
of higher air temperatures.
74
Figure 3- 9. Spatial patterns in differences (Present-day – nonurban) of temporally averaged values during morning,
afternoon, and nighttime for PM 2.5. Panels (a)–(c) show total PM 2.5; (d)–(f) inorganic aerosol; (g)–(i) primary
carbonaceous aerosol; and (j)–(l) secondary organic aerosol. Morning is defined as 7 PST to 12 PST, afternoon as 12
PST to 19 PST, and nighttime as 19 PST to 7 PST. Black dots indicate grid cells where changes are not significantly
different from zero at 95% confidence level using the paired Student’s t-test with n=7 days.
3.4 Conclusion and Discussion
In this study, we have characterized the impact of land surface changes via urbanization on regional
meteorology and air quality in Southern California using an enhanced version of WRF-UCM-
75
Chem. We use satellite data for the characterization of land surface properties, and include a
Southern California-specific irrigation parameterization. The two main simulations of focus in this
study are the real-world “Present-day” and the hypothetical “Nonurban” scenarios; the former
assumes current land cover distributions and irrigation of vegetative areas, while the latter assumes
land cover distributions prior to widespread urbanization and no irrigation. We assume identical
anthropogenic emissions in these two simulations to allow for focusing on the effects of land cover
change on air pollutant concentrations.
Our results indicate that land surface modifications from historical urbanization have had a
profound influence on regional meteorology. Urbanization has led to daytime reductions in air
temperature for the lowest model layer and reductions in ventilation within urban areas. The impact
of urbanization at nighttime shows the opposite effect, with air temperatures and ventilation
coefficients increasing. Spatially averaged reductions in air temperature and ventilation during the
day are –0.6 K and –650 m
2
/s respectively, whereas increases at night are +1.1 K and 24.3 m
2
/s
respectively. Changes in meteorology are spatially heterogeneous; greater changes are simulated
in inland regions for (a) air temperatures decrease during day and increases during night, and (b)
ventilation reductions during daytime. Ventilation at night shows increases in coastal areas and
decreases in inland areas. Changes in meteorology are mainly attributable to (a) increased surface
roughness from buildings, (b) higher evaporative fluxes from irrigation, and (c) higher thermal
inertia from building materials and increased soil moisture (from irrigation).
Changes in regional meteorology in turn affect concentrations of gaseous and particulate pollutants.
NOx concentrations in the lowest model layer increase by +1.6 ppb during the day, and decrease
by –2.8 ppb at night, due to changes in atmospheric ventilation. O3 concentrations decrease by –
0.6 ppb in the morning, and increase by +1.7 (2.2) ppb in the afternoon (night). Decreases in the
76
morning and increases during other times of day are more noticeable in inland regions. Changes
in O3 concentrations are mainly attributable to the competition between (a) changes in atmospheric
ventilation, and (b) changes in NOx concentrations that alter O3 titration. Note that while changes
in air temperature can also influence O3 concentrations during the day, this effect is overwhelmed
by changes in ventilation and concentrations of NOx in our study. As for PM2.5, total mass
concentrations increase by +0.24 μg/m
3
in the afternoon, and decrease by –2.5 μg/m
3
at night.
Changes during the morning are not statistically significant. The major driving processes of
changes in PM2.5 concentrations are (a) changes in atmospheric ventilation, (b) changes in gas-
particle phase partitioning for semi-volatile compounds due to air temperature changes, and (c)
changes in atmospheric chemical reaction rates from air temperature changes.
This study highlights the role that land cover properties can have on regional meteorology and air
quality. We find that increases in evapotranspiration, thermal inertia, and surface roughness due
to historical urbanization are the main drivers of regional meteorology and air quality changes in
Southern California. During the day, our simulations suggest that increases in evapotranspiration
and thermal inertial from urbanization lead to regional air temperature reductions. Temperature
reductions together with increases in surface roughness contribute to decreases in ventilation and
consequent increases in ozone and PM2.5 concentrations. During nighttime, increases in thermal
inertial from urbanization lead to increases in regional air temperatures. Increases in temperatures
together with increase in surface roughness lead to decreases in NOx and PM2.5 concentrations. O3
concentrations increase because of decreased titration by NOx. Our findings indicate that air
pollutant concentrations have been impacted by land cover changes since pre-settlement times (i.e.,
urbanization), even assuming constant anthropogenic emissions. These air pollutant changes are
driven by urbanization-induced changes in meteorology. This suggests that policies that impact
77
land surface properties (e.g., urban heat mitigations strategies) can have impacts on air pollutant
concentrations (in addition to meteorological impacts). For example, a study that the author has
collaborated on investigated the impacts of adopting cool roofs and walls (i.e., increasing albedo)
on O3 and PM2.5 concentrations) in Southern California using a similar WRF-Chem set-ups. The
study finds that increasing roof and wall albedo can lead to reductions in summertime daily
maximum 8-hour average O3 concentration but increases in daily average PM2.5 concentrations
due to air temperature reductions. Thus, to the extent possible, all environmental systems should
be taken into account when studying the benefits or potential penalties of policies that impact the
land surface in cities.
78
Chapter 4
Conclusion and Future Work
Air pollution is an environmental issue that is of great concern for urban areas because of its
adverse impact on the climate and human society. Yet, it is important to note that air pollution is
affected by a variety of factors, from human activity to the natural environment itself. First, air
pollution is tightly associated with energy generation and demand because fossil fuel combustion
is a primary cause of air pollutant emissions. Second, there are profound interdependencies among
land cover, regional meteorology, and air quality, in which each one affects the others. Thus, this
dissertation investigates the role of energy transition pathways and the land cover properties on
urban air quality in Southern California.
Chapter 2 analyzes how energy decarbonization pathways affect urban air quality and public health
in the city of Los Angeles. Results suggest that adopting 100% renewable electricity generation
together with electrifying the transportation sector, commercial and residential buildings, and the
Ports of Los Angeles and Long Beach can reduce air pollutant emissions, including NOx and
primary PM2.5, in Los Angeles in a future year 2045 compared to the current time reference year
2012. The largest emission reductions result from the increased electrification of light-duty
vehicles. These citywide emission reductions result in citywide reductions in annual average daily
PM2.5 concentrations but increases in summertime daily maximum 8-hour average ozone
concentrations in most parts of Los Angeles. Increases in ozone concentrations can be thought of
as a temporary “growing pain” that Los Angeles will likely go through to ultimately reduce ozone
from decreasing emissions. The combination of these concentration changes could result in net
79
monetized public health benefits (driven by avoided deaths) of up to $1.4 billion in 2045 in Los
Angeles. This is the first local-scale study, initiated from actual local planning, that quantifies the
air quality and public health co-benefits resulting from renewable energy adoption scenarios based
on sector-specific grid modeling and state-of-the-science methods. It provided the scientific
foundation for the 2021 decision by the city of Los Angeles to adopt a 100% clean electricity target
by 2035, which is a decade ahead of its prior goal.
Chapter 3 focuses on the interaction among land cover, meteorology, and air quality. It looks at
how land surface property changes caused by urbanization affect urban air quality in Southern
California by comparing a present-day real-world land surface scenario with a hypothetical non-
urban scenario that assumes land cover conditions prior to any human perturbation. This chapter
shows that land cover changes via historical urbanization have led to decreases in daytime air
temperature and atmospheric ventilation, but increases in air temperatures and atmospheric
ventilation at night. These changes in meteorology are mainly attributable to higher evaporative
fluxes and increased thermal inertia due to increased soil water content from irrigation, and
increased surface roughness and thermal inertia from buildings. Hourly O3 concentrations decrease
in the morning and increase at other times of day. Changes in O3 concentrations are driven by the
competing effects of changes in ventilation and changes in concentrations of precursor NOx. PM2.5
concentrations show slight increases during the day and large decreases at night, which are affected
by modifications in atmospheric ventilation and temperature that impact gas–particle phase
partitioning for semi-volatile compounds and chemical reactions. These results reveal the
important role of land cover properties on regional meteorology and air quality, and they suggest
that urban planning decisions that modify land cover (e.g., climate adaption strategies that alter
urban land surface properties to make cities more resilient to climate change consequences) in
80
megapolitan areas should carefully evaluate their impact on regional air quality in addition to their
impact on regional meteorology.
Aside from the factors in this dissertation that affect regional air quality, it is also worth noting the
impact of climate change itself on regional air quality and air pollution mitigation, which warrants
future investigation. Climate change alters regional meteorological conditions, which in turn
influences concentrations of air pollutants including O3 and PM2.5 via atmospheric transport and
chemical reactions, etc. Thus, climate change can affect regional air quality given the same level
of air pollutant emissions, renewable energy adoption, and air pollution mitigation strategies.
Climate change is known to lead to higher temperature, more stagnant weather conditions, and
changes in relative humidity and precipitation at global scale. However, how these changes impact
regional air quality has not been well studied (125, 126). Thus, there is an urgent need to apply
climate downscaling methods, which will incorporate coarse-resolution global climate projections
into high-resolution regional atmospheric physics and chemistry models, to reveal the dominant
impact of future warming on air pollution.
In addition, while regional air pollution levels are primarily determined by the abundance of local
emissions, the contribution of background pollutant concentrations from trans-regional and trans-
national atmospheric transport to total pollutant concentrations should not be neglected. With rapid
renewable energy adoption and more stringent air pollutant regulations implemented in Los
Angeles and the entire Southern California region, remote emission sources are likely to represent
a higher fraction of total air pollution in the studied region. Thus, future studies should look at how
local air pollution is affected by remote emission sources, and if feasible regional collaborations
on air pollutant mitigation and climate change mitigation can be carried out to achieve greater
mutual benefits.
81
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Appendix A: Supplementary Information for Chapter 2: Air Quality
and Public Health Co-benefits of 100% Renewable Electricity
Adoption and Electrification Pathways in Los Angeles
Table A- 1. Emission factors (kg/MMBTU) for the LADWP-owned power plants applied in the SB100 scenario.
Values in black are based on LADWP emission reporting system (year 2019), values in red are calculated based on
RULE 1135. CT is natural gas combustion turbine power plant, CC is natural gas combined cycle power plant.
Power Plant Category CO NOx SOx VOC PM NH 3
Harbor CT 0.0056 0.0092 0.00025 0.0022 0.0030 0.0069
Haynes CT 0.0042 0.0085 0.00027 0.00070 0.0014 0.0026
Scattergood CC 0.0022 0.0058 0.00028 0.00066 0.0016 0.0014
Valley CC 0.00066 0.0074 0.00040 0.00070 0.00071 0.0028
Valley CT 0.0037 0.0092 0.00039 0.00092 0.00023 0.0035
Table A- 2. Emission factors (kg/MMBTU) for the LADWP-owned power plants applied in the Early & No Biofuels
Scenario. Values in red are calculated based on RULE 1135, others are assumed to be zero. H 2 is hydrogen storage
combustion power plant.
Power Plant Category CO NOx SOx VOC PM NH 3
Harbor H 2 0 0.0063 0 0 0 0.0047
Haynes H 2 0 0.0063 0 0 0 0.0047
Scattergood H 2 0 0.0063 0 0 0 0.0047
Victorville H 2 0 0.0063 0 0 0 0.0047
Table A- 3. Scaling factors of fuel consumption in commercial buildings from 2020 to 2045 under moderate and high
electrification load assumptions
Month Moderate High
1 125.8% 3.5%
2 126.7% 3.5%
3 125.3% 3.7%
4 135.4% 2.6%
5 142.3% 2.2%
6 142.9% 2.2%
7 142.5% 2.2%
90
8 144.1% 2.2%
9 145.5% 2.1%
10 141.9% 2.2%
11 134.0% 2.6%
12 113.2% 5.1%
Table A- 4. Scaling factors of fuel consumption in residential buildings from 2020 to 2045 under moderate and high
electrification load assumptions
Month Moderate High
1 77.6% 13.4%
2 77.5% 13.5%
3 77.7% 13.6%
4 74.6% 13.0%
5 75.0% 13.7%
6 77.3% 16.9%
7 77.7% 17.4%
8 78.7% 17.8%
9 79.1% 17.3%
10 77.2% 13.8%
11 75.2% 12.5%
12 78.0% 13.9%
91
Figure A- 1. Annually averaged daily (a) CO, (b) SOx, (c) VOCs and (d) NH 3 emissions from all anthropogenic
sources in the city of Los Angeles for all scenarios. Emissions for future scenarios (SB100 – Moderate, SB100 – High,
Early & No Biofuels – Moderate and Early & No Biofuels – High) are projected to year 2045. Emissions that are
directly influenced by LA100 include those from buses, LDVs, commercial buildings, residential buildings, the ports
of Los Angeles and Long Beach, and LADWP-owned power plants. Note that ‘LADWP-owned Power Plants’ include
only those located in SoCAB. The sources labeled as “All Other Categories” are all anthropogenic emissions not
directly influenced by LA100.
92
Figure A- 2. Change in incidences of cardiovascular hospital admissions (row 1) and heart attacks (row 2) in the city
of Los Angeles for the scenarios compared
Abstract (if available)
Abstract
Air pollution is among the biggest environmental concerns of our time. Exposure to air pollutants including fine particulate matter (PM2.5) and ozone (O3) cause respiratory and other diseases, making it a major concern for public health as well. Air pollution is caused primarily by fossil fuel combustion from energy use and is affected by land—climate—air quality interactions via chemical reactions, deposition and atmospheric transport. This dissertation investigates how energy system decarbonization and land cover property changes affect air quality in Southern California, which includes Los Angeles, one of the most polluted cities in the US. Exposure to air pollution in Los Angeles has resulted in hundreds to thousands of hospitalizations and deaths annually and billions of US dollars lost.
First, this dissertation analyzes the air quality impacts of the city of LA’s renewable energy adoption plan, which aims to achieve 100% clean electricity generation in the Los Angeles Department of Water and Power’s service territory by the mid-21st century, along with a push to electrify the city’s buildings and transportation sectors. Using a detailed bottom-up approach and a state-of-the-art regional meteorology-chemistry model (WRF-Chem), this work projects air pollutant emission inventories for future renewable energy adoption scenarios and simulates concentrations of major air pollutants including PM2.5 and O3 for Los Angeles in 2045. Overall changes in air pollution from adopting clean energy in Los Angeles are found to avoid as many as 150 premature deaths annually, which is equivalent to $1.4 billion in savings. This work helped form the scientific foundation for the Los Angeles City Council’s approval of its 100% clean energy target by 2035, which is a decade ahead of its prior goal.
Second, this dissertation investigates how land surface property changes via historical urbanization influence regional meteorology and air quality in Southern California. While the impact of land cover changes on regional meteorology has been widely researched, studies that quantify its impact on regional air quality have been limited and typically fail to resolve the wide spatial heterogeneity of the urban land surface Thus, this work fills this knowledge gap by incorporating satellite-derived high-resolution real-world land cover representations into WRF-Chem and comparing the simulated air pollutant concentrations between a present-day land cover scenario and a land cover scenario representing conditions prior to any human perturbation (assuming identical anthropogenic air pollutant emissions). Results from this work show the important role of urban morphology, building materials, and urban irrigation on regional air quality, in addition to affecting local meteorology.
The body of work summarized in this dissertation reveals the importance of the connection between energy and air quality, and the interactions among land cover, climate, and air quality. It suggests that policy makers should seek sustainable solutions that address air pollution, the energy crisis, and climate change problems synergistically.
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Li, Yun
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Evaluating the role of energy system decarbonization and land cover properties on urban air quality in southern California
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Viterbi School of Engineering
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Doctor of Philosophy
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Environmental Engineering
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2022-08
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