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RESEARCH ARTICLE
Air-quality-related health impacts from
climate change and from adaptation of
cooling demand for buildings in the eastern
United States: An interdisciplinary modeling
study
David W. Abel 1*, Tracey Holloway1,2, Monica Harkey1, Paul Meier3,4,5, Doug Ahl6, Vijay
S. Limaye 1,7
, Jonathan A. Patz 1,7
1 Center for Sustainability and the Global Environment (SAGE), Nelson Institute for Environmental Studies,
University of Wisconsin–Madison, Madison, Wisconsin, United States of America, 2 Department of
Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, Wisconsin, United States of
America, 3 Wisconsin Energy Institute (WEI), University of Wisconsin–Madison, Madison, Wisconsin, United
States of America, 4 Great Lakes Bioenergy Research Center (GLBRC), University of Wisconsin–Madison,
Madison, Wisconsin, United States of America, 5 Meier Engineering Research LLC, Stoughton, Wisconsin,
United States of America, 6 Seventhwave, Madison, Wisconsin, United States of America, 7 Global Health
Institute, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
Abstract
Background
Climate change negatively impacts human health through heat stress and exposure to wors-
ened air pollution, amongst other pathways. Indoor use of air conditioning can be an effec-
tive strategy to reduce heat exposure. However, increased air conditioning use increases
emissions of air pollutants from power plants, in turn worsening air quality and human health
impacts. We used an interdisciplinary linked model system to quantify the impacts of heat-
driven adaptation through building cooling demand on air-quality-related health outcomes in
a representative mid-century climate scenario.
Methods and findings
We used a modeling system that included downscaling historical and future climate data
with the Weather Research and Forecasting (WRF) model, simulating building electricity
demand using the Regional Building Energy Simulation System (RBESS), simulating power
sector production and emissions using MyPower, simulating ambient air quality using the
Community Multiscale Air Quality (CMAQ) model, and calculating the incidence of adverse
health outcomes using the Environmental Benefits Mapping and Analysis Program (Ben-
MAP). We performed simulations for a representative present-day climate scenario and 2
representative mid-century climate scenarios, with and without exacerbated power sector
emissions from adaptation in building energy use. We find that by mid-century, climate
change alone can increase fine particulate matter (PM2.5) concentrations by 58.6%
PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002599 July 3, 2018 1 / 27
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Citation: Abel DW, Holloway T, Harkey M, Meier P,
Ahl D, Limaye VS, et al. (2018) Air-quality-related
health impacts from climate change and from
adaptation of cooling demand for buildings in the
eastern United States: An interdisciplinary
modeling study. PLoS Med 15(7): e1002599.
https://doi.org/10.1371/journal.pmed.1002599
Academic Editor: Madeleine Thomson, Africa
Program, UNITED STATES
Received: February 9, 2018
Accepted: May 30, 2018
Published: July 3, 2018
Copyright: © 2018 Abel et al. This is an open access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All data are available
through Mary Sternitsky ([email protected])
of the University of Wisconsin - Madison, Nelson
Institute Center for Sustainability and the Global
Environment.
Funding: This study was conducted with support
from the National Institutes of Health Grant
1R21ES020232-01 received by TH, MH, PM, DA,
VSL, and JAP. This study was also supported by
the George Bunn Wisconsin Distinguished
(2.50 μg/m3) and ozone (O3) by 14.9% (8.06 parts per billion by volume [ppbv]) for the month of July. A larger change is found when comparing the present day to the combined
impact of climate change and increased building energy use, where PM2.5 increases 61.1%
(2.60 μg/m3) and O3 increases 15.9% (8.64 ppbv). Therefore, 3.8% of the total increase in PM2.5 and 6.7% of the total increase in O3 is attributable to adaptive behavior (extra air con-
ditioning use). Health impacts assessment finds that for a mid-century climate change sce-
nario (with adaptation), annual PM2.5-related adult mortality increases by 13,547 deaths (14
concentration–response functions with mean incidence range of 1,320 to 26,481, approxi-
mately US$126 billion cost) and annual O3-related adult mortality increases by 3,514 deaths
(3 functions with mean incidence range of 2,175 to 4,920, approximately US$32.5 billion
cost), calculated as a 3-month summer estimate based on July modeling. Air conditioning
adaptation accounts for 654 (range of 87 to 1,245) of the PM2.5-related deaths (approxi-
mately US$6 billion cost, a 4.8% increase above climate change impacts alone) and 315
(range of 198 to 438) of the O3-related deaths (approximately US$3 billion cost, an 8.7%
increase above climate change impacts alone). Limitations of this study include modeling
only a single month, based on 1 model-year of future climate simulations. As a result, we do
not project the future, but rather describe the potential damages from interactions arising
between climate, energy use, and air quality.
Conclusions
This study examines the contribution of future air-pollution-related health damages that are
caused by the power sector through heat-driven air conditioning adaptation in buildings.
Results show that without intervention, approximately 5%–9% of exacerbated air-pollution-
related mortality will be due to increases in power sector emissions from heat-driven building
electricity demand. This analysis highlights the need for cleaner energy sources, energy effi-
ciency, and energy conservation to meet our growing dependence on building cooling sys-
tems and simultaneously mitigate climate change.
Author summary
Why was this study done?
• As temperature rises, the adaptive response of using air conditioning increases electric-
ity demand and subsequent emissions of harmful pollutants from electric power plants.
• Independent of emissions from the electric power sector, climate change is known to
worsen air quality through changes in atmospheric chemistry and natural biogenic
emissions.
• We examine the effects of climate change on air quality and human health through the
combined impacts of increased emissions from power plants due to altered air condi-
tioning demand and from direct effects on atmospheric chemistry.
Air-quality-related health impacts from climate change adaptation of cooling demand
PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002599 July 3, 2018 2 / 27
Graduate Fellowship in Energy Analysis and Policy
(DA) and the Wes and Ankie Foell Graduate Award
in Energy Analysis and Policy (DA). The funders
had no role in study design, data collection and
analysis, decision to publish, or preparation of the
manuscript.
Competing interests: I have read the journal’s
policy and the authors of this manuscript have the
following competing interests: PM has an
ownership interest in the MyPower model used to
generate power plant emissions estimates for this
study. The data from this study are publicly
available. JAP served as a Guest Editor on PLOS
Medicine’s Special Issue on Climate Change and
Health.
Abbreviations: BenMAP, Environmental Benefits
Mapping and Analysis Program; CB05, Carbon
Bond 5; CCSM, Community Climate System Model;
CMAQ, Community Multiscale Air Quality; C-R,
concentration–response; EGU, electricity
generating unit; EIA, Energy Information
Administration; EPA, Environmental Protection
Agency; MDA8, maximum daily 8-hour average;
MEGAN, Model of Emissions of Gases and
Aerosols from Nature; NAAQS, National Ambient
Air Quality Standards; NARCCAP, North American
Regional Climate Change Assessment Program;
NARR, North American Regional Reanalysis;
NEEDS, National Electric Energy Data System; NEI,
National Emissions Inventory; NOX, nitrogen oxide
(s); ppbv, parts per billion by volume; RBESS,
Regional Building Energy Simulation System; WRF,
Weather Research and Forecasting.
What did the researchers do and find?
• We used computer models to calculate the air pollution and health impacts of a warmer
climate with and without greater use of air conditioning and subsequent increases in
harmful emissions from power plants.
• We found that concentrations of fine particulate matter (PM2.5) and ozone (O3) increase
in a warmer climate and that 3.8% of the total increase in PM2.5 and 6.7% of the total
increase in ozone (O3) are attributable to extra air conditioning use.
• We calculated that climate change alone increases summer air-pollution-related prema-
ture mortality by about 13,000 deaths due to PM2.5 and 3,000 deaths due to O3 (consis-
tent with other studies).
• Increased air conditioning, specifically, accounts for 654 future summer PM2.5-related
deaths (approximately $6 billion cost—based on a value of statistical life calculated from
26 studies—and a 4.8% increase above climate change impacts alone) and 315 O3-
related deaths (approximately $3 billion cost and an 8.7% increase above climate change
impacts alone).
What do these findings mean?
• This is the first study to our knowledge to examine future air-pollution-related health
damages of power plant emissions driven by increased electricity demand for air condi-
tioning, a primary adaptation to warmer temperatures.
• Quantifying the extent to which air conditioning adaptation exacerbates climate-
change-related air quality can improve decision-making, especially in both the health
and electric power sectors.
• This analysis highlights the need for cleaner energy sources, energy efficiency, and
energy conservation to meet our growing dependence on buildings’ cooling systems,
while simultaneously mitigating the extent of climate change.
Introduction
Climate change poses many health risks, from elevated risk of heat stroke to the broadening
reach of vector-borne disease, food insecurity, and air pollution [1]. According to the Lancet
Countdown on health and climate change, climate change “is affecting the health of popula-
tions around the world, today” [2]. Climate change has direct impacts on health and well-
being from exacerbated extreme weather, extremes of the hydrologic cycle, and heat waves, as
well as indirect effects such as increases in the burden of infectious disease, sea-level rise,
ocean acidification, and climate-induced population displacement or conflict. Ultimately,
these changes threaten access to clean air, water, and food, while potentially creating new
health disparities and exacerbating existing ones. However, climate mitigation and adaptation
strategies have the potential to address these issues and improve public health broadly. This
study focuses on ambient air pollution, and the potential increase in adverse air-pollution-
Air-quality-related health impacts from climate change adaptation of cooling demand
PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002599 July 3, 2018 3 / 27
related health impacts associated with building air conditioning use, in response to warmer
temperatures, highlighting the need for clean energy solutions as tools for improving public
health.
Relationships between meteorological conditions and air quality have been established in
past literature. For example, warmer temperatures and sunlight enhance production of bio-
genic, or natural, volatile organic carbons (VOCs) from certain plant species, which are pre-
cursors to both ozone and fine particulate matter [3,4]. Warm temperatures and sunlight also
enhance ozone-forming reactions [5,6]. Pollutant concentrations decrease with increased air
mixing [7,8] and precipitation [9,10], while increased humidity can increase formation of par-
ticulate matter [7,9]. Additional work has explored the impact of a warming climate on wild-
fire emissions [11–14], soil emissions of nitrogen oxides (NOX) [15], and NOX from lightning
[16–18]. Using these relationships, a number of studies have investigated the potential impact
of climate change on air quality, particularly the response of ozone and particulate matter con-
centrations to warming temperatures [7,19–21]. Past studies assessing climate change impacts
on air pollution often focused on the impact of climate change and meteorological variables
(as well as biogenic, natural emissions) [7,19–21], the impact of future anthropogenic emission
scenarios [22], or the combined impact of climate change and anthropogenic emission scenar-
ios [10,22–27].
Air conditioning in buildings is a form of adaptation to warmer temperatures that could
increase population health risks, by increasing power plant emissions on hot days. As air con-
ditioning use increases to cool buildings, the increased demand for electricity is supplied by a
mix of generation sources including fossil fuels, thus increasing harmful emissions. In this
work, we deploy a novel interdisciplinary modeling effort to quantify the air pollution and
health impacts of this climate change adaptation mechanism.
Few studies have explored the impact of climate change on health-damaging emissions
from electricity generating units (EGUs), specifically emissions of nitrogen dioxide (NO2) and
sulfur dioxide (SO2), but we know there is a relationship between power plant emissions and
temperature through electricity demand in buildings. Buildings are the largest source of US
electricity demand, responsible for more than 60% of demand in most states in the eastern US
(https://www.eia.gov/electricity/data/state/). Electricity for cooling is a large component of this
demand, with direct correlation to rising temperatures. Abel et al. showed that historical east-
ern US EGU emissions of NOX, SO2, and carbon dioxide (CO2) increase 3.3%–3.6% per 1 ˚C
increase in daily temperature regionally over the summer [28], consistent with the findings of
He et al. [29], who found an increase of 2.5%–4.0% per 1 ˚C increase in the eastern US states,
and Dreschler et al. [30], who found an increase of 5.8% per 1 ˚C increase in California.
Additional emissions from increased air conditioning demand have been shown to have a
significant impact on fine particulate matter (PM2.5), responsible for up to 87% of concentra-
tions in the Pennsylvania–New Jersey–Maryland electricity grid interconnection during July
2006 heat wave conditions [31]. The hourly variability of EGU emissions due to temperature
can increase PM2.5 mass, sulfate, and elemental carbon concentrations by 83%, 103%, and
310%, respectively, but the increase in emissions from anticipated heat-driven adaptation
response is typically not included in air quality modeling studies [32]. Power plants have been
extensively evaluated as a controllable source of pollution [33–35]. However, without action,
residential and commercial buildings are expected to see an increase in cooling load and subse-
quent emissions [36,37]. Recent research has demonstrated the air-quality-related health bene-
fit of the green building movement and reducing energy demand in buildings. MacNaughton
et al. quantified the health benefits of US Leadership in Energy and Environmental Design
(LEED)–certified buildings built from 2000 to 2016 as 172–405 avoided premature mortalities
[38].
Air-quality-related health impacts from climate change adaptation of cooling demand
PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002599 July 3, 2018 4 / 27
This is the first study to our knowledge to compare the impact of potential mid-century cli-
mate change on air quality with and without associated heat-driven changes in emissions from
the electricity sector. This work advances the line of research characterizing health co-benefits
from mitigation strategies [39–52] and the direct quantification of health damages from air
pollution in a future climate [1,2,30,53–59]. This study builds upon a large body of epidemio-
logical work relating air pollution and human health, including the studies utilized in the Envi-
ronmental Protection Agency’s (EPA’s) Benefits Mapping and Analysis Program (BenMAP)
[60].
Methods
Overview
We apply a system of linked numerical models to assess changes in building energy demand,
electricity production, power sector emissions, air quality, and human health outcomes
based on meteorology consistent with present-day conditions and a warm mid-century
summer climate. We focus on the eastern US, where electricity production and use are
connected through a regional power grid. This region also experiences levels of ground-
level O3 and PM2.5 in exceedance of EPA health-based standards [61], and demographic
trends in this area suggest continuing and increasing vulnerabilities to air pollution expo-
sures [62–65].
Fig 1 provides a visual representation of the modeling system, which includes simulating
present and future meteorology, electricity demand in buildings, electricity production and
EGU emissions, air quality, and health impacts. For information on how to access the software
used, please see S1 Text. We performed simulations for 3 scenarios using this linked model
Fig 1. A visual representation of the methods used in this study.
https://doi.org/10.1371/journal.pmed.1002599.g001
Air-quality-related health impacts from climate change adaptation of cooling demand
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system, and a fourth simulation is used for validation of results, following standard practice for
chemical transport modeling, for which uncertainty estimates are ill-suited and model evalua-
tion is preferred (see S1 Text) [66–68]. Satellite-derived NO2 and previous studies are also
used to validate results. Scenarios are shown below and outlined in Fig 1. Additionally, Table 1
shows the major data inputs for each model in the system.
Three scenarios are simulated: the present-day climate, a mid-century climate with present-
day emissions, and a mid-century climate with emissions from adaptation. Each of these sce-
narios utilizes meteorology from the WRF model for present-day and NARCCAP CCSM ver-
sion 3 for mid-century. The RBESS is used to assess building energy demand, and MyPower is
used to simulate electricity dispatch (production) and associated power sector emissions.
CMAQ is used to simulate air quality, and BenMAP assesses the health outcomes from air
quality changes.
Present-day (PD) scenario. This scenario represents present-day conditions for climate.
Building energy demand and power sector (EGU) emissions are simulated for present-day
conditions.
Mid-century climate-only (MCCO) scenario. This scenario represents warm mid-cen-
tury conditions for climate, selected as described in detail below. Building energy demand and
power sector (EGU) emissions remain constant as simulated for present-day conditions. This
scenario represents the impact of climate change alone on air quality and health. There is no
change in building activity or associated anthropogenic emissions from electricity demand.
Table 1. Major data inputs and sources for each step of the modeling framework.
Model Input data needed Input data source
WRF Present-day meteorology North American Regional Reanalysis (NARR): meteorological dataset
(includes assimilated observations)
Future meteorology North American Regional Climate Change Assessment Program
(NARCCAP) (Community Climate System Model [CCSM]):
meteorological dataset selected from a suite of climate models
RBESS Meteorology WRF
Representative building
types
Built for this study based on Department of Energy DOE-2 platform
Building stock US Energy Information Administration (EIA) Commercial Buildings
Energy Consumption Survey (CBECS), Residential Energy Consumption
Survey (RECS), Manufacturing Energy Consumption Survey (MECS)
MyPower Electricity demand RBESS
Power plants and
characteristics
EPA’s National Electric Energy Data System (NEEDS), EPA’s Clean Air
Markets Database
CMAQ Power plant emissions MyPower
Other anthropogenic
emissions
EPA’s National Emissions Inventory (NEI)
Biogenic emissions Model of Emissions of Gases and Aerosols from Nature (MEGAN)
Meteorology WRF
BenMAP Population US Census
Baseline incidence Many data sources; see [69]
Concentration–response
functions
Many studies; see health impact tables or BenMAP documentation for
references [60,69]
Air quality data CMAQ
BenMAP, Environmental Benefits Mapping and Analysis Program; CMAQ, Community Multiscale Air Quality;
EPA, Environmental Protection Agency; RBESS, Regional Building Energy Simulation System; WRF, Weather
Research and Forecasting.
https://doi.org/10.1371/journal.pmed.1002599.t001
Air-quality-related health impacts from climate change adaptation of cooling demand
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Mid-century adaptation (MCA) scenario. This scenario represents warm mid-century
conditions for climate. Building energy demand and power sector emissions are simulated
using mid-century representative meteorology with inventory and performance held constant
(modern natural gas power plants are assumed to provide the additional capacity needed to
meet increased electricity demand). This scenario represents the impact of climate change and
increased EGU emissions due to greater building air conditioning demand in response to
warmer temperatures.
Climate and meteorological modeling
Warm-climate simulations of air quality use meteorology downscaled from the NARCCAP
[70] archive per Harkey and Holloway [71]. NARCCAP is a suite of climate data from several
Global Climate Model–Regional Climate Model pairs built on the A2 emissions scenario of
the Intergovernmental Panel on Climate Change (IPCC), a trajectory that most closely mirrors
current global greenhouse gas emissions trends [72,73]. This emissions scenario assumes that
little or no action is taken to mitigate climate change, which makes it appropriate for the goals
of this study, to isolate the potential impact of increased power sector emissions on air quality.
Thus, any successful future action to mitigate climate change would alleviate some of the dam-
ages calculated here.
Due to the computationally demanding simulations of this study, it is not feasible to con-
sider a 30-year subset of mid-century years, as is recommended for climate impact studies.
Rather, July of a single year from a single model in NARCCAP was selected to represent a
warm, realistic mid-century summer, as discussed in detail in Harkey and Holloway [71]. We
selected the year 2069 from CCSM version 3 [74], downscaled with the WRF model in NARC-
CAP, and further downscaled with WRF for this study to our 12 km by 12 km study domain.
This year was chosen as the warmest year from the mean model in the suite, as shown in S1
Fig, adapted from Meier et al. [36]. To isolate the impact of climate change on air quality, we
used the same 2011 emissions data and lateral boundary conditions for all simulations. Climate
processes are considered to affect biogenic emissions, power plant emissions, and the transport
of point-source anthropogenic emissions.
Present-day meteorology is downscaled in WRF from NARR for 2011 conditions, as
described in Harkey and Holloway [71]. The NARR model assimilates measured meteorologi-
cal data to produce a gridded, continuous dataset [75]. We focus on July 2011 as representative
of peak summertime electricity demand and the summer high O3 season, and consistent with
the latest NEI at the time of modeling.
Therefore, findings are separated into the impacts of meteorology representative of 2 sum-
mer climate scenarios, the present-day climate and a warm mid-century climate representative
of climate change mitigation inaction (July 2011 and July 2069). Meteorological conditions for
July in the warm mid-century climate used in the MCCO and MCA scenarios are on average
approximately 3.5 ˚C (29.1 ˚C versus 25.6 ˚C, 13.7%) warmer in the eastern US region than in
the present-day.
Building energy demand modeling
Present-day and warm mid-century meteorology were input to the RBESS, a modeling process
developed following Schuetter et al. [76] and used here to determine the response of building
energy demand to meteorology. This process merges industry-standard building energy
modeling techniques using the DOE-2 software (developed by James J. Hirsch & Associates
and Lawrence Berkeley National Laboratory) and regional building stock data with the meteo-
rology discussed above following Meier et al. [36], which describes the methodology used here
Air-quality-related health impacts from climate change adaptation of cooling demand
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in detail. Building stock data were provided by the US EIA through the Commercial Buildings
Energy Consumption Survey (CBECS), the Manufacturing Energy Consumption Survey
(MECS), and the Residential Energy Consumption Survey (RECS). The building stock was
held static under both the present-day and warm-climate scenarios. The simulation was cali-
brated using historical 2007 electricity data from a US EPA compilation of Federal Energy Reg-
ulatory Commission (FERC) data. Use of the present-day building stock was not meant to be
predictive but was chosen to bound the potential damages of climate inaction.
Electricity sector dispatch modeling
Building energy demand was input to the MyPower model, a load duration curve (LDC) elec-
tricity dispatch model, used to simulate plant-level electricity production and emissions of
NOX, SO2, and CO2. Detailed methodology for MyPower is described in Meier et al. [36]. Data
for power plant characteristics including heat rates and emissions rates were derived from
NEEDS, a part of the US EPA’s Power Sector Modeling Platform, and modified to reflect data
reported in the US EPA’s Clean Air Markets Database through 2013. Present-day conditions
reflect electricity sector characteristics through 2011. Warm-climate conditions reflect planned
changes to the electricity grid. Existing renewable energy portfolio standards are met through
a combination of technologies reported in the Database of State Incentives for Renewables &
Efficiency (DSIRE) database [77]. Nuclear power plants are retired as specified by existing
operating licenses, and applications for new constructions are as reported by the Nuclear Reg-
ulatory Commission [78]. In the warm-climate scenarios, power plants are assumed to main-
tain “resource adequacy” such that generating capacity exceeds the highest single hour of
demand by 15%. The additional required power is supplied through new construction of natu-
ral gas power plants (70% combined-cycle, 30% single-cycle) with characteristics based on the
Annual Energy Outlook from the US EIA [79]. All existing plants not retired are not modified.
Scenario selection, specifically using the present-day building stock and power plants, is not
meant to be predictive, but to quantify the portion of future damages that could be alleviated
by changes to the building sector and electricity sector. The scenario was chosen to describe
the potential damages of interactions between climate, energy use, and air quality through this
previously unstudied mechanism.
Air quality modeling
Air quality simulations were performed using the CMAQ model version 5.0.1 [67,80]. Anthro-
pogenic emissions were input from the EPA 2011 NEI [81], and biogenic emissions were simu-
lated using MEGAN version 2.1 [82]. We focus on July 2011 conditions for the present-day as
representative of peak summertime electricity demand and production within the high O3 sea-
son, consistent with past literature and the latest available NEI emissions data at the time of
modeling [31]. S1 Text includes validation of results and discussion of model performance.
EGU emissions from MyPower were gridded for use in CMAQ and substituted for NOX
and SO2 emissions in the NEI. Emissions of NOX were assigned constant partitioning of 85%
NO and 15% NO2. Chemical species that are contained in the NEI but not directly calculated
by MyPower are listed in S1 Table, with associated discussion.
All CMAQ simulations were configured with “AERO6” aerosol chemistry [67], in-line pho-
tolysis, and the Carbon Bond 5 (CB05) chemical mechanism with updated toluene and chlo-
rine chemistry [83,84]. Simulations do not include estimates of emissions from fires but do
include in-line estimates of lightning-generated NOX. CMAQ was run with 25 vertical layers, a
12 km by 12 km horizontal resolution over the eastern US, and boundary conditions taken
from a month-averaged run of present-day conditions with NEI emissions estimates over the
Air-quality-related health impacts from climate change adaptation of cooling demand
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continental US, which in turn used boundary conditions from the Model for Ozone and
Related Chemical Tracers, version 4 [85].
We chose to run simulations through CMAQ for only July as these simulations were the
most computationally expensive part of our linked model system. As results represent esti-
mates based on only a single year of climate simulations, findings are meant as exploratory
and illustrative, and as such the marginal limitation of extrapolating July results as representa-
tive of summer (and summer as representative of annual impacts) is small. Future research
could utilize less computationally expensive methods to run more scenarios over longer and
more representative timescales, but the complex mechanisms included in CMAQ are neces-
sary to explore the impacts of power sector emissions on air quality in a changing climate
through the new relationship described here. We also chose July based on 3 additional simula-
tions that were run (baseline, baseline with fires, mid-century baseline) and 2 others that were
prepared but not run (present and future emissions approximated through temperature versus
emissions relationships defined as in Abel et al. [28]). These additional simulations influenced
the decision to simulate July only, but did not contribute to the objectives of this paper and
were therefore disregarded.
Health impacts assessment
We assessed increased incidence of premature mortality and morbidity associated with expo-
sure to higher daily mean PM2.5, maximum daily 8-hour average (MDA8) O3, and maximum
daily 1-hour O3 using the EPA’s BenMAP–Community Edition version 1.3 [60]. BenMAP
calculates the incidence of adverse health outcomes given a change in air quality. Expert-
derived PM2.5 exposure–response (or concentration–response [C-R]) functions and pooling
methods used for the US EPA 2012 Regulatory Impact Analysis and O3 C-R functions used
for the 2008 National Ambient Air Quality Standards (NAAQS) evaluations are applied in
this analysis [86–88]. These standard EPA configurations are available with the BenMAP
software. Population is held constant for 2011 in all scenarios. Comparative analysis of the
benefits of air conditioning in buildings for reducing direct heat-related mortality versus air
pollution effects from air-conditioning-related electricity demand is beyond the scope of this
study.
BenMAP combines population data from the US Census, baseline health outcome inci-
dence data provided from several sources but primarily the Centers for Disease Control and
Prevention (CDC) (outlined in Appendix D of [69]), and an effect estimate from the chosen
C-R function with specified changes in gridded air quality data to quantify health impacts.
Each exposure–response function and pooling of incidence and valuation was run in a
5,000-member Monte Carlo ensemble to calculate mean impacts and associated uncertainty.
Pooling methods are used to combine results for similar health endpoints across C-R functions
as an alternative to meta-analysis. The techniques used here follow standard EPA methods
including user-assigned weighting, random effects, fixed effects, addition, and subtraction to
combine results of studies as described in Appendix K of [69]. Here we focus on mortality,
which is not pooled as standard practice in the EPA methodology. Amongst mortality results,
the American Cancer Society’s Cancer Prevention Study II, used for PM2.5-related mortality
estimates, is especially relevant because the study data include the most representative expo-
sure sites in the US and a follow-up period of 18 years [89]. Health impacts based on maxi-
mum daily 1-hour O3 are simulated but not pooled, as there is no standard EPA methodology
based on maximum daily 1-hour metrics, and these results are used primarily for comparison.
Valuation to monetize the costs of exacerbated air pollution is performed according to stan-
dard EPA configurations by assigning a value to each health effect through a combination of
Air-quality-related health impacts from climate change adaptation of cooling demand
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willingness to pay and cost of illness (e.g., value of a statistical life) methods, then applying that
to calculated incidences [60]. All costs are presented in US dollars.
Impact estimates are based on exacerbated pollution in July alone. Annual impacts are cal-
culated as a 3-month summer average based on July modeling. Thus, we take July as represen-
tative of the entire summer and triple our calculated results to arrive at a summer estimate.
This is a reasonable assumption for the changes in air pollution and health impacts analyzed
here, especially given the focus on the incremental impact of adaptation. Values presented in
tables are for July exposure alone and have not been tripled. Average baseline scenario concen-
trations of PM2.5 and O3 from July modeling are applied outside of July in all calculations to
isolate changes. Summer results are a good estimate for annual impacts although they are likely
conservative as we would also expect spring and fall to exhibit some increased air pollution
and adverse health outcomes. Winter air quality conditions are less influenced by the electric-
ity sector. Impacts for estimated annual/summer exposure are provided in the text while July
exposure impacts are presented in the tables.
All health impact functions for PM2.5-related mortality apply an annual average air pollu-
tion metric, calculated from daily mean values with changes only in July. The daily mean is
used directly for many morbidity functions. All impacts calculated by BenMAP at any time-
scale are summed and reported annually by the model as standard practice. Therefore, values
provided in the tables are annual impacts based on July exposure, while values provided in the
text are annual impacts based on estimated annual exposure calculated as a 3-month summer
average based on July modeling, as discussed above. O3-related premature mortality functions
are based on metrics of MDA8 or maximum daily 1-hour O3, with Jerrett et al. [90] the only
study based on an annual average metric. Justification for modeling only July is discussed in
detail above but centers on balancing computational demands with the exploratory and repre-
sentative (rather than predictive) nature of this study. We present in the main text primarily
the results for mortality, which by standard methods are not pooled. Please see S2–S4 Tables
for morbidity results.
Results
Emissions and air quality
Changes in energy demand associated with warmer temperatures are driven by the distribu-
tion of temperatures at hourly or even sub-hourly scales. Fig 2 shows a histogram of regional
(eastern US) average hourly temperatures over the month of July for current and mid-century
conditions. Results show a shift in the maximum ambient temperature from 32.4 ˚C (present)
to 38.5 ˚C (future), an 18.8% increase. The mid-century scenario exhibits a decrease in the
frequency of colder temperatures and an increase in the frequency of warmer temperatures.
The higher temperatures seen in the mid-century scenarios drive changes in electricity
demand, production, and associated emissions. Fig 3 shows the hourly distribution of electric-
ity production and emissions for current and future climates. These results show the response
of electricity production to ambient temperature through demand for air conditioning. Under
the future climate assumptions, regionally summed average hourly electricity demand
increases from 213 to 274 GWh (28.6%), and regionally summed average hourly eastern US
CO2 emissions increase from 169,000 to 200,000 metric tonnes (18.3%). Thus, adaptation
through air conditioning use also constitutes a positive climate feedback.
The change in maximum CO2 is not as large as the change in electricity production because
additional capacity in mid-century (necessary to meet increased demand) is generated by nat-
ural gas power plants based on the US EIA’s Annual Energy Outlook, which emit less carbon
than the current mix of generation sources [36]. We find that electricity production and
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emissions in the present-day exhibit a more uniform distribution than does temperature
(Fig 2). This difference is due to the changing sensitivity of electricity generation as a function
of temperature, with responsiveness increasing at higher temperatures and decreasing at
cooler temperatures, when building cooling is less important. The distribution becomes less
uniform in the mid-century climate as temperature dependence plays a greater role compared
to other end uses of electricity.
Trends in the distribution of hourly electricity production and CO2 emissions more closely
follow changes in temperature than do emissions of NOX and SO2, as shown in Fig 3. Overall,
emissions in the future climate scenario increase 13.7% for NOX and 17.2% for SO2, but the
maximum hourly emissions rate does not increase for either NOX or SO2. Rather, the increase
in average hourly emissions of NOX and SO2 occurs from greater frequency of emissions on
the higher end of the present-day emissions distribution. Even as electricity demand increases,
new peak electricity demand in the model is met by natural gas power plants that have little
impact on NOX and SO2 emissions during peak conditions. Simulating likely retirements of
coal-fired power plants and market-driven renewable energy investments would also result in
lower emissions than found here, where we maintain the existing power plant inventory to
explore the arising interactions between climate, energy production, and air quality without
being predictive. This highlights the importance of considering cleaner energy sources in
reducing future harmful emissions.
Fig 2. A histogram of regional average hourly temperatures. A histogram of regional average hourly temperatures is
presented for July in the present-day and in the warm mid-century climate. Present-day mean: 25.6 ˚C; minimum: 19.1
˚C; maximum: 32.4 ˚C. Mid-century mean: 29.1 ˚C; minimum: 18.3 ˚C; maximum: 38.5 ˚C.
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Overall, a 3.5 ˚C warmer summer is responsible for an increase in hourly average building
energy demand of 28.6%. The air conditioning adaptation response to climate change in the
eastern US is thus responsible for hourly average emissions increases of 13.7% for NOX, 17.2%
for SO2, and 18.5% for CO2.
We analyzed air quality in the PD (present-day climate, present-day EGU emissions),
MCCO (mid-century climate only), and MCA (mid-century adaptation) scenarios as
described in the Methods. On a regional average, we find that climate change alone (MCCO
versus PD) increases PM2.5 by 58.6% (2.50 μg/m 3 ) and O3 by 14.9% (8.06 parts per billion by
volume [ppbv]). A larger change is found when comparing the present day to the mid-century
adaptation scenario, which includes building air conditioning (MCA versus PD). In that case,
PM2.5 increases 61.1% (2.60 μg/m 3 ) and O3 increases 15.9% (8.64 ppbv). Overall, 2.5% of the
61.1% increase in PM2.5 and 1.0% of the 15.9% increase in O3 are attributable to adaptive
behavior (extra air conditioning use).
The July average change in each pollutant due to building energy use is shown in Fig 4 for
PM2.5 (Fig 4a) and MDA8 O3 (Fig 4b). Increases in PM2.5 from the MCCO to the MCA sce-
nario (Fig 4a) are highest (as high as >5%) in and downwind of the Ohio River Valley, coinci-
dent with the highest concentration of fossil fuel, especially coal-fired, power plants and the
Fig 3. Histograms of hourly electricity production and emissions. Histograms are provided for regionally summed hourly
electricity production, CO2 emissions, nitrogen oxide (NOX) emissions, and SO2 emissions for July in the present-day and warm
mid-century warm climate scenarios. For electricity production: present-day mean: 212.9 GWh; minimum: 120.4; maximum: 320.3.
Mid-century mean: 274.2 GWh; minimum: 172.0; maximum: 438.0. For CO2 emissions: present-day mean: 168,800 tonnes;
minimum: 99,800; maximum: 238,800. Mid-century mean: 200,100 tonnes; minimum: 132,700; maximum: 276,500. For NOX emissions: present-day mean: 140 tonnes; minimum: 80; maximum: 210. Mid-century mean: 160 tonnes; minimum: 100; maximum:
210. For SO2 emissions: present-day mean: 430 tonnes; minimum: 250; maximum: 610. Mid-century mean: 500 tonnes; minimum:
300; maximum: 590.
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greatest increase in EGU emissions. A small decrease (<2.5%) in concentrations is observed in
the southeast, centered over South Carolina and the Chesapeake Bay. This is primarily due to a
decrease in emissions in these regions (as seen in Fig 5) associated with power plant dispatch
changes (see Meier et al. [36]).
We examined the distribution of regional average concentrations as a function of air pollu-
tion level in Fig 6. The number of hours with pollution at the highest levels increases due to
Fig 4. Change in ambient air pollution concentrations. Maps of the percentage change in (a) PM2.5 and (b) O3 from the warm
mid-century climate-only (MCCO) scenario to the warm mid-century adaptation (MCA) scenario. Red shows concentrations that
are greater in the MCA scenario compared to MCCO, while blue shows a decrease in concentrations compared to MCCO. Axes
show latitude and longitude.
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climate change alone, and further rises given greater emissions of NOX and SO2 associated
with higher climate-induced electricity demand. For PM2.5, the minimum regional average
concentration simulated under a future climate (4.37 μg/m3 for MCCO) is above the average value for present-day (4.26 μg/m3). Present-day values range from a minimum of 2.91 μg/m3
to a maximum 5.98 μg/m3. The highest regional average concentrations modeled under a future climate (8.75 μg/m3 for MCCO) are higher than we see at any time in the present-day simulation. The additional consideration of adaptation through air conditioning use further
increases the minimum and maximum values to 4.48 μg/m3 and 8.87 μg/m3, respectively. Biogenic emissions, enhanced under a warmer climate, are the dominant contributor to the
MCCO increase in PM2.5. This impact is sensitive to the choice of chemical mechanism in the
atmospheric model and details regarding the formation of secondary organic aerosol as a func-
tion of volatile organic compounds. Past studies have suggested that the CB05 mechanism in
CMAQ may have errors in the representation of this atmospheric chemical process [91–93].
Thus, while the direct impact of climate on PM2.5 is notable, we focus our discussion on the
changes due to building energy use (i.e., MCCO versus MCA).
Modeled EGU emissions of SO2 increase by 17.2%, and NOX by 13.7%, due to building
energy use in the future climate (state-by-state variation shown in Fig 5). This increase in
EGU emissions results in increases in sulfate particulate matter (SO4 2−
, 5.8% as compared to
MCCO, or 0.09 μg/m3) and nitrate PM (NO3 − , 3.1% as compared to MCCO, or 0.7 × 10−3 μg/
m 3 ).
Ozone exhibits many of the same patterns as exhibited by PM2.5. However, the increase in
hourly O3 is not as pronounced from the present-day to mid-century scenarios as seen for
PM2.5. In the case of O3, adaptive behavior is responsible for an approximately 1% increase in
O3. Like PM2.5, O3 increases across most of the region (Fig 4), with the greatest increases in
Fig 5. Change in emissions by state. The state by state changes in nitrogen oxide (NOX) and SO2 emissions from the present-day (PD) to mid-
century (MC) as an absolute value (designated by the bars) and as a percentage (as listed).
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Fig 6. Histograms of ambient air pollutant concentrations. Histograms of regional average hourly concentrations of PM2.5 (μg/ m
3 ) and O3 (parts per billion by volume [ppbv]) for July in the present-day (PD) scenario, the warm mid-century climate-only
(MCCO) scenario, and the warm mid-century adaptation (MCA) scenario. For PM2.5 concentrations: PD mean: 4.19 μg/m 3 ;
minimum: 2.91; maximum: 5.98. MCCO mean: 6.57 μg/m3; minimum: 4.37; maximum: 8.75. MCA mean: 6.67 μg/m3; minimum: 4.48; maximum: 8.87. For O3 concentrations: PD mean: 43.4 ppbv; minimum: 23.6; maximum: 61.7. MCCO mean: 48.0 ppbv;
minimum: 26.4; maximum: 70.2. MCA mean: 48.4 ppbv; minimum: 26.6; maximum: 70.9.
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and downwind of the Ohio River Valley (as high as >5%) due to increases in EGU NOX emis-
sions. Small decreases due to localized emissions decreases from changes in electricity dispatch
are also evident over South Carolina and Chesapeake Bay, as well as a highly localized decrease
in Maine and a very small decrease along the Texas domain boundary.
Building energy use also results in a greater frequency of high O3 days (Fig 6). Note that the
highest regional (eastern US) average hourly concentrations exceed the current NAAQS for
MDA8 O3 of 70 ppbv [87]. These metrics are not directly comparable as standards are met or
achieved at the county or state level and are based on the fourth highest annual MDA8,
whereas we present regional average hourly concentrations. Additionally, the standards may
be lowered by mid-century, but this comparison highlights the relevance of results to attain-
ment of regulatory standards. Overall, adaptation causes a 4.5% increase in the number of high
O3 hours (defined as when regional average hourly O3 exceeds 60 ppbv) and a 22% increase in
the number of high PM2.5 hours (defined as when regional average hourly PM2.5 exceeds 8 μg/ m
3 ). Note that the NAAQS for PM2.5 is an annual average concentration of 12 μg/m
3 . How-
ever, our analysis is limited to a sample size of the 744 hours of July and not directly compara-
ble to the NAAQS.
Health impacts
Increased exposure to PM2.5 and O3 increases risk of premature mortality, which we quantify
using BenMAP. Health impact functions are based on EPA-selected epidemiological studies
and expert elicitation used in the US EPA 2012 Regulatory Impact Analysis for revisions to the
NAAQS for particulate matter. Tables 2 and 3 summarize the changes to premature mortality
from increased July exposure to PM2.5 and O3 under each scenario (negative numbers indicate
adverse health outcomes and monetary costs). Morbidity impacts are summarized in S2–S4
Tables.
As discussed in the Methods, we present annual impacts (estimated as a 3-month summer
average based on July modeling) in the text, while results in tables are annual impacts based on
changes to July exposure only. We include 14 C-R functions for PM2.5-related adult mortality,
with each function reported separately. The change in mortality incidence and the economic
valuation of this loss of life are shown in Table 2 with 95% confidence intervals based on the
reported uncertainty underlying each relative risk point estimate simulated in 5,000-member
Monte Carlo ensembles. Morbidity impacts are reported in S2 Table, and validation of air
quality results is provided in S1 Text. For O3, we calculated mortality based on MDA8 concen-
trations as well as maximum daily 1-hour concentrations as shown in Table 3 (morbidity
impacts are reported in S3 and S4 Tables).
For the impact of adaptation alone (MCA–MCCO), the 14 functions for PM2.5 exhibit a
range of mean increases in mortality from 87 to 1,245 deaths ($0 to $12 billion in costs) annu-
ally and an average of 654 deaths ($6 billion); see Table 2 for individual study confidence inter-
vals. The average 95% CI across studies is 131 to 1,251 deaths. Adapting to climate change as
calculated here accounts for a 4.8% increase over the impacts from climate change alone
(MCCO–PD), which on average causes 12,906 additional premature deaths (mean estimate
range across studies: 1,254 to 25,227) with mean costs of $120 billion (mean estimate range
across studies: $12 billion to $234 billion). The average 95% CI across studies is 2,558 to 24,978
deaths. The total impact of climate and adaptation (MCA–PD) causes a mean of 13,547 prema-
ture deaths (mean estimate range across studies: 1,320 to 26,481) based on the average of all
functions (roughly the sum of climate alone and adaptation alone), with mean costs of $126
billion (mean estimate range across studies: $12 billion to $246 billion). The average 95% CI
across studies is 2,685 to 26,213 deaths.
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Considering the main focus of these results, the health impact of projected mid-century
building energy use on PM2.5 (MCA–MCCO), we find, as stated above, a range of mean esti-
mates of 87 to 1,245 excess deaths annually ($1 billion to $12 billion in costs), with an average
of 654 deaths ($6 billion). For comparison, application of the C-R function from the most rep-
resentative epidemiological study, the American Cancer Society’s Cancer Prevention Study II
[89], finds a mean estimate of 366 (95% CI: 249 to 486) deaths annually, slightly on the lower
end of all study estimates.
For O3, the results are similar to the findings for PM2.5, but additional functions address
mortality from specific causes. The health impacts of projected mid-century building energy
use on O3 (MCA–MCCO) include an average of 315 deaths ($3 billion) based on 3 standard
configuration studies with a range of 198 to 438; ($2 billion to $4 billion). The average 95% CI
Table 2. PM2.5-related mortality results summed regionally for July exposure and displayed for each scenario comparison.
PM2.5 (24-hour mean)
C-R function source
MCA–MCCO MCCO–PD MCA–PD
Mortality
incidence (95% CI)
Valuation (95% CI)
[billions of dollars]
Mortality
incidence (95% CI)
Valuation (95% CI)
[billions of dollars]
Mortality
incidence (95% CI)
Valuation (95% CI)
[billions of dollars]
Expert A −319 −3 −6,459 −60 −6,779 −63 (−657, −37) (−10, 0) (−13,348, −749) (−202, −3) (−14,010, −786) (−212, −3)
Expert B −261 −2 −4,956 −46 −5,201 −48 (−561, −17) (−9, 0) (−10,980, −115) (−184, −1) (−11,522, −122) (−193, −1)
Expert C −251 −2 −5,073 −47 −5,325 −49 (−446, −61) (−7, 0) (−9,037, −1,223) (−146, −3) (−9,486, −1,284) (−153, −4)
Expert D −176 −2 −3,565 −33 −3,742 −35 (−302, 0) (−5, 0) (−6,117, 0) (−104, 0) (−6,420, 0) (−109, 0)
Expert E −415 −4 −8,409 −78 −8,827 −82 (−658, −149) (−11, 0) (−13,373, −3,020) (−229, −6) (−14,037, −3,169) (−240, −7)
Expert F −239 −2 −4,281 −40 −4,497 −42 (−353, −107) (−6, 0) (−6,660, −2,027) (−114, −3) (−6,992, −2,128) (−120, −4)
Expert G −147 −1 −2,966 −27 −3,113 −29 (−278, 0) (−5, 0) (−5,622, 0) (−100, 0) (−5,901, 0) (−105, 0)
Expert H −183 −2 −3,707 −34 −3,891 −36 (−521, 0) (−7, 0) (−10,562, 0) (−142, 0) (−11,086, 0) (−149, 0)
Expert I −248 −2 −5,028 −46 −5,277 −49 (−442, 0) (−7, 0) (−8,954, 0) (−149, 0) (−9,398, 0) (−156, 0)
Expert J −202 −2 −4,085 −38 −4,288 −40 (−468, −16) (−7, 0) (−9,485, −314) (−136, −2) (−9,956, −330) (−143, −2)
Expert K −29 0 −418 −4 −440 −4 (−135, 0) (−2, 0) (−2,394, 14) (−29, 0) (−2,513, 13) (−31, 0)
Expert L −183 −2 −3,121 −29 −3,276 −30 (−433, −1) (−6, 0) (−8,218, −2) (−119, 0) (−8,625, −2) (−125, 0)
Krewski et al. [89] −122 −1 −2,476 −23 −2,599 −24 (−162, −83) (−3, 0) (−3,279, −1,673) (−62, −2) (−3,442, −1,755) (−65, −2)
Lepeule et al. [94]� −280 −3 −5,682 −52 −5,962 −55 (−420, −140) (−7, 0) (−8,537, −2,828) (−150, −5) (−8,959, −2,968) (−157, −5)
Expert functions were used for the Environmental Protection Agency 2012 Regulatory Impact Analysis [69,88]. Elicitation was performed to help characterize
uncertainty of PM2.5-related mortality estimates.
�Lepeule et al. [94] is based on an age range of 25–99 years while all others are based on an age range of 30–99 years.
MCCO, mid-century climate-only; MCA, mid-century adaptation; PD, present-day; C-R, concentration–response.
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across studies is 184 to 447 deaths. Using maximum daily 1-hour O3 concentrations to assess
this same scenario (MCA–MCCO), one study calculates mortality from all causes, finding 369
additional deaths. Analyzing the studies with common health endpoints, we find that using
maximum daily 1-hour O3 rather than MDA8 O3 concentrations results in higher mortality
from all causes (369 versus 315 deaths annually) and more non-accidental deaths (189 versus
162 deaths annually).
Table 3. O3-related mortality results summed regionally for July exposure and displayed for each scenario comparison.
O3 MCA–MCCO MCCO–PD MCA–PD
Health outcome C-R function
source
Incidence
(95% CI)
Valuation (95% CI)
[millions of dollars]
Incidence
(95% CI)
Valuation (95% CI)
[millions of dollars]
Incidence
(95% CI)
Valuation (95% CI)
[millions of dollars]
Mortality all cause Bell et al. [96] −103 −955 −1,057 −9,760 −1,149 −10,600 (−158, −49) (−2,752, −84) (−1,634,
−493) (−28,323, −848) (−1,775,
−536) (−30,775, −922)
Mortality all cause Levy et al. [97] −146 −1,350 −1,509 −14,000 −1,640 −15,200 (−192, −100) (−3,678, −126) (−2,010,
−1,019) (−37,984, −1,297) (−2,182,
−1,107) (−41,264, −1,410)
Mortality all cause Zanobetti &
Schwartz [98]
−66 −609 −667 −6,160 −725 −6,690 (−97, −35) (−1,730, −54) (−984, −353) (−17,474, −546) (−1,069,
−385) (−18,999, −593)
Mortality
cardiopulmonary
Huang et al. [95] −38 −353 −407 −3,760 −440 −4,060 (−62, −14) (−1,045, −29) (−672, −149) (−11,203, −309) (−727, −161) (−12,111, −334)
Mortality non-
accidental
Bell et al. [99] −29 −270 −300 −2,770 −325 −3,000 (−49, −10) (−813, −22) (−502, −99) (−8,353, −223) (−545, −107) (−9,054, −242)
Mortality non-
accidental
Ito et al. [100] −132 −1,220 −1,398 −12,900 −1,513 −14,000 (−184, −79) (−3,392, −112) (−1,992,
−820) (−36,202, −1,174) (−2,155,
−888) (−39,190, −1,272)
Mortality non-
accidental
Schwartz [101] −44 −411 −458 −4,230 −496 −4,580 (−75, −14) (−1,248, −33) (−780, −140) (−12,914, −335) (−845, −152) (−13,996, −364)
Mortality non-
accidental
Smith et al.
[102]
−29 −266 −296 −2,730 −321 −2,960 (−66, 8) (−978, 63) (−679, 79) (−10,097, 640) (−736, 86) (−10,942, 695)
Mortality non-
accidental
Smith et al. (2)
[102]
−36 −333 −370 −3,420 −401 −3,710 (−55, −18) (−956, −30) (−563, −179) (−9,833, −304) (−610, −194) (−10,658, −330)
Mortality all cause Levy et al. [97] a −123 −886 −1,477 −10,600 −1,603 −11,507
(−162, −85) (−1,576, −308) (−1,965, −998)
(−18,985, −3,653) (−2,132, −1,084)
(−20,605, −3,966)
Mortality non-
accidental
Ito & Thurston
[103] a,b
−87 −623 −1,037 −7,440 −1,125 −8,075 (−154, −19) (−1,369, −125) (−1,879,
−224) (−16,571, −1,455) (−2,039,
−243) (−17,976, −1,580)
Mortality non-
accidental
Ito et al. [100] a −55 −395 −647 −4,645 −703 −5,044
(−73, −37) (−706, −136) (−863, −434) (−8,334, −1,598) (−937, −471) (−9,049, −1,735) Mortality non-
accidental
Schwartz [101] a −47 −341 −557 −3,999 −605 −4,342
(−80, −15) (−721, −85) (−950, −170) (−8,507, −987) (−1,032, −185)
(−9,236, −1,073)
Mortality respiratory Jerrett et al.
[90] a,b
−55 −397 −623 −4,473 −679 −4,872 (−92, −19) (−830, −104) (−1,039,
−209) (−9,360, −1,165) (−1,131,
−228) (−10,196, −1,269)
a These functions are based on maximum daily 1-hour O3 concentrations.
b These functions have age ranges other than 0–99 years: 30–99 years for Jerrett et al. [90] and 18–99 years for Ito and Thurston [103].
MCCO, mid-century climate-only; MCA, mid-century adaptation; PD, present-day; C-R, concentration–response.
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For comparison of these building-related impacts with the health impacts associated with
climate change alone (MCCO–PD), we calculated premature mortality from all causes and
MDA8 O3 exposure as 3,234 deaths (range of 2,001 to 4,527) based on three studies and non-
accidental mortality from MDA8 O3 exposure as 1,692 deaths (range of 888 to 4,194) based on
5 studies. Using maximum daily 1-hour concentrations, we find 4,431 all-cause deaths (range
of 2,994 to 5,895) and 2,241 non-accidental deaths (range of 1,671 to 3,111). For MCA-PD, we
calculated 3,514 deaths on average (range of 2,175 to 4,920), with a cost of $32.5 billion. The
average 95% CI across studies is 2,028 to 5,026 deaths. Using MDA8 O3 and considering pre-
mature mortality from all causes, we find that 8.0% of additional deaths in the MCA scenario
are from adaptation and 92.0% are from climate alone, i.e., adaptation yields an 8.7% increase
above climate change impacts alone.
Morbidity impacts are summarized in S2–S4 Tables. Health impacts are assessed for end-
points including hospital admissions, respiratory symptoms (including asthma), minor
restricted activity days, work loss days, and school loss days. Mean estimates of the costs of
morbidity impacts vary from $0 to $45 million annually for PM2.5, $0 to $39 million annually
for MDA8 O3, and $6 million to $18 million for maximum daily 1-hour O3.
The independent health impact estimates from exposure to PM2.5 and O3 cannot be directly
summed because BenMAP does not account for interaction effects between the 2 pollutants,
and exposures often occur in the same location at the same time. The spatial distributions of
mortality are shown by county in Fig 7 for PM2.5 and O3 (maximum daily 1-hour and MDA8).
Fig 7. The mortality impacts of adaptation due to air pollution. Shown is the air-pollution-related mortality increase due to
adaptation (the mid-century adaptation scenario minus the mid-century climate-only scenario) for (a) PM2.5 as taken from the
Expert F concentration–response function (the median function), (b) O3 based on Levy et al. [97] using maximum daily 1-hour
concentrations, and (c) O3 based on Levy et al. [97] using maximum daily 8-hour average concentrations.
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The spatial distribution of impacts follows the patterns seen for air pollution in Fig 5. Regions
near the Ohio River Valley and urban areas see the greatest mortality damages. In South Caro-
lina, the Chesapeake Bay, and small portions of Maine and Texas, there is a slight decrease in
mortality associated with a small, localized decrease in modeled emissions associated with
modeled building energy demand and electricity dispatch.
Discussion
Simulating adaptive behavior to a warmer mid-century climate shows that increased air condi-
tioning use leads to higher emissions, degraded air quality, and adverse health outcomes. We
find that the increase in air-pollution-related health outcomes attributable to climate change
alone is 92%–95% of the overall health burden (depending on air pollutant), while changes in
human behavior to adapt to climate change through increased air conditioning in buildings
comprises 5%–8% of the health burden.
While our adaptation-related results are novel, our climate-only results are comparable
to existing findings. Weaver et al. find that substantial regions of the US show increases in
MDA8 O3 of 2–8 ppbv in a future climate [21], and Jacob and Winner find increases in O3
of 1–10 ppbv [20]. Fiore et al. find that previous studies show O3 increases of up to 9 ppbv
[19]. For PM2.5, Jacob and Winner find an increase of 0.1 to 1 μg/m 3
[20], and Fiore et al.
find a greater variability of results across studies dependent upon meteorology, ranging
from −2 to +3 μg/m3 [19]. Tai et al. find that PM2.5 likely will not increase by more than 0.5 μg/m3 [104]. Our findings fit within the high end of previous estimates, and this is expected as we consider a particularly warm July, when large increases in PM2.5 would be
expected.
Quantifying the role of air conditioning adaptation in future air quality bears relevance to
decision-making, as power sector emissions are controllable by technology and policy in a way
that other climate-driven air quality mechanisms are not (i.e., chemical reaction rates, biogenic
emissions, NOX from lightning, and wildfire emissions). The scenario chosen here highlights
the role of interactive effects amongst climate, energy production, and air quality. Interven-
tions would, and likely will, reduce the damages calculated here. Control options include
stack-level technological controls, such as SO2 scrubbers and NOX selective catalytic reduction,
which have been the traditional approach employed by US air quality management agencies
and power sector utilities to meet health-based standards. Although this technological
approach would serve to reduce pollution exposure, such strategies do not modulate cost,
energy use, or carbon emissions. In fact, end-of-pipe controls increase energy requirements to
balance the decrease in plant efficiency associated with effluent treatment methods; this is
often called the capacity or heat rate penalty.
An alternative to end-of-pipe controls is the use of building energy efficiency measures
(e.g., increasing insulation or installing more efficient cooling equipment [105,106]) that
reduce building energy demand in a manner that directly responds to the increased utilization
of air conditioning. Efficiency measures would reduce demand on the electricity system, as
well as associated carbon emissions, air quality impacts, and adverse health outcomes. Another
option to reduce both carbon emissions and air-pollution-related health impacts would be to
increase the portion of electricity generated by renewable sources like solar and wind. Studies
show that the use of solar energy would reduce and has reduced fine particulates in the eastern
US, especially on the highest concentration days [39,107]. Other options include demand
response programs, building codes and standards, and conservation education. All of these
alternatives would mitigate climate change and reduce the air-pollution-related health burden
from adaptation measures.
Air-quality-related health impacts from climate change adaptation of cooling demand
PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002599 July 3, 2018 20 / 27
This study explores power plants and heat-driven electricity demand in buildings as an
insufficiently understood mechanism of future air-quality-related health damages in a warmer
climate. Here we parse the contribution of this adaptation, but the study limitations include
modeling only a single representative month from 1 year in future climate projections. Typi-
cally, studies of climate would be based on a 30-year average of results, which is not computa-
tionally feasible for this type of study. Additionally, our results do not project future changes
to population, air pollution exposure patterns in humans, building stock, and the electric
power sector, but rather highlight the interactions amongst climate, electricity production, air
quality, and health. With less computationally demanding methods, more simulations could
be run over longer timeframes to test the sensitivity of results to potential changes. Future
directions could also include assessing the impact of interventions for climate change mitiga-
tion and air pollution control. Lastly, health impacts assessment relies on C-R functions for O3
and PM2.5, and these relationships continue to be improved through epidemiological and toxi-
cological research.
Supporting information
S1 Fig. The average summer temperatures of NARCCAP models and the present-day.
(TIF)
S2 Fig. Comparison of MyPower and NEI CMAQ results.
(TIF)
S3 Fig. Evaluation of the present-day simulations’ NO2 column amounts with satellite
Ozone Monitoring Instrument NO2.
(TIF)
S1 Table. List of chemical species included in NEI emissions estimates from electricity gen-
erating units (EGUs).
(DOCX)
S2 Table. PM2.5-related morbidity results for standard configuration functions.
(DOCX)
S3 Table. MDA8 O3-related morbidity results for standard configuration functions.
(DOCX)
S4 Table. Maximum daily 1-hour O3-related morbidity results for included BenMAP func-
tions.
(DOCX)
S5 Table. Validation of MyPower and CMAQ results.
(DOCX)
S6 Table. Measurement, model, and satellite correlations.
(DOCX)
S7 Table. Comparison of CMAQ NO2 results with DOMINO satellite NO2 estimates.
(DOCX)
S1 Text. A graphical depiction of temperatures from NARCCAP models shown in S1 Fig
and referenced in the main text.
(DOCX)
Air-quality-related health impacts from climate change adaptation of cooling demand
PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002599 July 3, 2018 21 / 27
Acknowledgments
Thank you to others who have contributed to this work including Scott Schuetter and Scott
Hackel for work to develop the RBESS. We wish to thank the publicly funded organizations
that have provided data and models used in this study, in particular, the US EPA for develop-
ing the CMAQ model, BenMAP, and associated data, and NARCCAP for providing the data
used in this paper. NARCCAP is funded by the National Science Foundation, the US Depart-
ment of Energy, the National Oceanic and Atmospheric Administration, and the US EPA
Office of Research and Development.
Author Contributions
Conceptualization: Tracey Holloway, Paul Meier, Doug Ahl, Jonathan A. Patz.
Data curation: David W. Abel, Monica Harkey, Paul Meier.
Formal analysis: David W. Abel, Monica Harkey.
Funding acquisition: Tracey Holloway, Paul Meier, Jonathan A. Patz.
Investigation: Tracey Holloway, Doug Ahl, Vijay S. Limaye, Jonathan A. Patz.
Methodology: David W. Abel, Tracey Holloway, Monica Harkey, Paul Meier, Doug Ahl, Vijay
S. Limaye.
Project administration: Tracey Holloway, Jonathan A. Patz.
Software: David W. Abel, Monica Harkey, Paul Meier, Doug Ahl.
Supervision: Tracey Holloway, Jonathan A. Patz.
Validation: David W. Abel, Monica Harkey, Paul Meier, Doug Ahl.
Visualization: David W. Abel, Monica Harkey.
Writing – original draft: David W. Abel, Monica Harkey.
Writing – review & editing: David W. Abel, Tracey Holloway, Monica Harkey, Paul Meier,
Doug Ahl, Vijay S. Limaye, Jonathan A. Patz.
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