Introduction
Despite extensive efforts to reduce anthropogenic emissions, air pollution
continues to be a public health issue in the United States (US EPA, 2010).
Elevated concentrations of pollutants in the troposphere, such as ozone
(O3) and particulate matter, degrade air quality and have been
associated with, among other things, increasing human respiratory diseases
in urban areas (WHO, 2005), and in the case of particulate matter (PM), with low birth weights
across the world (Dadvand et al., 2012).
High concentrations of tropospheric O3 and PM matter with aerodynamic
diameter smaller than 2.5 µm (PM2.5) are caused by a
combination of adverse meteorological conditions and the atmospheric
emissions of their primary precursors. While regulatory controls are expected
to reduce emissions of many emitted pollutants in the United States (US) in
the future, the negative effects of global climate change may offset the
positive effects of such reductions. Furthermore, global emissions of
greenhouse gases and other pollutant precursors are projected to increase
(IPCC, 2007). Moreover, recent research has provided evidence of increasing
long-range transport of O3 and PM2.5 precursors from Asia and their
influence over the western US (Lelieveld and Dentener, 2009; Wuebbles et al.,
2007; Cooper et al., 2010, 2012; Zhang et al., 2010; Ambrose et al., 2011;
WMO, 2012; Lin et al., 2012).
In the United States, regulations and technological changes in the
transportation and energy sectors are projected to reduce regional
atmospheric pollutants in the future (Loughlin et al., 2011). However, the
interplay between climate change, increasing global emissions, and
intercontinental transport pose challenges that air quality managers will
have to address in order to maintain regional air quality standards
(Ravishankara et al., 2012). To provide a foundation for building effective
management strategies and public policies in a changing global environment,
modeling approaches that link global changes with regional air quality are
required. The general approach has been to use output from general circulation models (GCMs) to drive regional climate models (RCMs) and
regional or global chemical transport models (CTMs/GTMs; Giorgi and Meleux,
2007; Jacob and Winner, 2009).
This downscaling approach has been used in a variety of studies in Europe,
Canada and Asia at different timescales of climate change – e.g., Liao et
al., 2006 (2000 to 2100); Langner et al., 2005 (2000 to 2060); Forkel and
Knoche, 2006 (2990 to 2030); Meleux et al., 2007 (1975 to 1985); Kunkel et
al., 2007 (1990 to 2090); Lin et al., 2008 (2000 to 2100); Spracklen et al.,
2009 (2000 to 2050); Kelly et al., 2012 (2000s to 2050s). These
investigations based the global emissions on future anthropogenic emissions
scenarios developed from the Intergovernmental Panel on Climate Change (IPCC)
assessment reports. Despite the differences in emission scenarios, timescales, modeling frameworks and future climate realizations, increases in
ozone concentrations on the order of 2 to 10 parts per billion (ppb) in polluted regions were
consistently predicted from these studies as a result of climate change
alone. By contrast, there is little consistency among the model predictions
of climate change effects on PM (Jacob et al., 2009; Dawson et al., 2013).
In the US, a combined effort between the Environmental Protection Agency and
the academic community resulted in a set of modeling studies that adopted a
variety of modeling methods (Hogrefe et al., 2004; Leung and Gustafson, 2005;
Liang et al., 2006; Steiner et al., 2006; Tagaris et al., 2007; Liao et al.,
2006; Racherla and Adams, 2006, 2008; Tao et al., 2007; Huang et al., 2007,
2008; Nolte et al., 2008; Wu et al., 2008a, b; Chen et al., 2009b; Avise et
al., 2009; Dawson et al., 2009). These US investigations based their current
and future climate realizations on the results of GCMs using the various IPCC
emissions scenarios (IPCC, 2007) projected to the 2050s. In some of the
studies, the global climate realizations were subsequently downscaled to a
higher resolution using the PSU (Pennsylvania State University)/NCAR
(National Center for Atmospheric Research) Mesoscale Model version 5 (MM5;
Grell et al., 1994) to horizontal resolutions that ranged from 90 to 36 km.
Many of these studies based their analysis on the effects of climate change
on summer air quality in the continental US (CONUS). In summary,
despite the differences in modeling elements, all studies found increases in
the summer average of the daily maximum 8 h average ozone concentrations over
large regions of CONUS on the order of 2 to 8 ppb (Weaver et al., 2009). In
contrast, PM concentrations showed changes between ±0.1 and
±1 µg m-3, with little consistency between studies,
including the sign of the differences (Day and Pandis, 2015; Trail et al.,
2015; Jacob and Winner, 2009).
It is important to note that variations between modeling frameworks did
result in very diverse regional patterns of key weather drivers for ozone and
PM formation. Thus, while most of the studies mentioned above projected an
average increase in ozone concentrations for the simulated domains,
reductions or insignificant changes in certain regions of the domain were
also simulated. Generally, temperature and solar radiation reaching the
surface were the major meteorological drivers for regional ozone
concentrations. For PM concentrations, most of the studies found a direct
link between changes in precipitation and relative humidity and changes in PM
concentrations (Liao et al., 2006; Unger et al., 2006; Racherla and Adams
2006; Tagaris et al., 2007; Avise et al., 2009; Chen et al., 2009b).
Nevertheless, the direct impacts of changes in meteorological conditions are
not the only factors of change for ozone and PM concentrations. Changes in
emissions of biogenic volatile organic compounds (BVOCs), due to climate and
land cover change, and the treatment of isoprene nitrates in the chemical
mechanism were found to be a key factor in the regional variability of ozone
and PM, particularly in areas of the southeastern US (Jacob and Winner, 2009;
Weaver et al., 2009).
In this work, we present a continuation of the work described by Avise et
al. (2009) and Chen et al. (2009a, b), who downscaled the Parallel Climate Model (PCM; Washington et al., 2000) and MOZART (Model for OZone And Related
chemical Tracers; Horowitz, 2006) global model output for the A2 IPCC
scenario using MM5 and the Community Multiscale Air Quality Model (CMAQ;
Byun and Schere, 2006) to simulate current and future air quality in the US.
For this update, we implemented a semi-hemispheric domain for the Weather
Research and Forecasting (WRF) mesoscale meteorological model
(http://www.wrf-model.org) and CMAQ simulations in lieu of using MOZART
output for chemical boundary conditions for our CONUS CMAQ simulations. We
used the ECHAM5 global climate model (Roeckner et al., 1999, 2003) output for
the A1B scenario to drive these simulations for 2 decadal periods: the
current decade 1995–2004 and the future decade 2045–2054. In
presenting our results, we follow the attribution approach described in Avise
et al. (2009), where the separate and combined effects of changes in climate,
US anthropogenic emissions, global anthropogenic emissions and biogenic
emissions due to changes in regional meteorology and land use are
investigated. Ideally, this framework should include feedback from changes in
atmospheric chemistry to the climate system (Raes et al., 2010). However, due
to the computational requirements of an online approach, we did not
incorporate feedback between the atmospheric chemistry and transport
simulations from the CTM to the RCM. Furthermore, despite the observed
sensitivity of tropospheric ozone to regional emissions and global burden of
methane (Zhang et al., 2011; Fiore et al., 2008; Wu et al., 2008a;
Nolte et al., 2008), in this work, we do not address the direct effect of
emissions of methane on the air quality simulations.
In Sect. 2, we provide an overview of the modeling framework and emissions
scenarios. Evaluation of the model performance for the climate simulations
and results of the changes in meteorological fields are also presented in Sect. 2.
Assessment of air quality changes and the individual and combined effects
from changes in model components are presented in Sect. 3. Finally, we
present a summary of the results and conclusions in Sect. 4.
Methodology
General framework
Results from the global climate model ECHAM5 under the IPCC Special Report on Emissions Scenarios (SRES) A1B scenario (Nakicenovic et al., 2000) were
downscaled using the WRF model separately to a semi-hemispheric (S-HEM)
220 km domain and nested CONUS domains of 108 km (not shown) and 36 km
(Fig. 1). Although, it has been suggested that periods of 10 to 30 years are
required to fully determine climatological conditions (Andersson and Engardt,
2010), the fact that emission inventories can substantially change from
one
decade to the next suggests that using 5 to 10 year periods for air
quality assessment is more appropriate. Thus, five representative summers
(June–July–August; with May as a spin-up period) for the present (1995
to 2004) and the future (2045 to 2054) decades were selected. Ranked in terms
of their CONUS-mean maximum temperature of the year, the summers of the
warmest and coldest years, as well as the second, fifth and seventh warmest
years in each decade were selected for CMAQ simulations. Comparison of the
meteorological conditions of these five selected summers to those of the full
decades is presented in Sect. 3.1. These five representative summers for the
present and future periods were processed with the meteorology–chemistry interface processor v3.4.1 (MCIP; Otte and Pleim, 2010) for the S-HEM and
36 km CONUS domains. Meteorological fields generated from MCIP for both
domains were used to estimate biogenic emissions using the Model of Emissions
of Gases and Aerosols from Nature v2.04 (MEGANv2.04; Guenther et al., 2006)
and to calculate the temporal profiles within the Sparse Matrix Operator
Kernel Emissions (SMOKE) v2.7 (http://www.smoke-model.org). With the
elements described above, a framework to perform air quality simulations
using the CMAQ4.7.1 (Foley et al.,
2010) was created. The overall schematic for the modeling system is shown in
Fig. 2.
Projected future DM8O concentrations with future anthropogenic and
biogenic emissions for the 220 and 36 km CMAQ modeling domains.
![]()
Schematic of the modeling framework.
![]()
Climate and meteorology
The regional weather model WRF includes advanced representations of
land-surface dynamics and cloud microphysics to simulate complex interactions
between atmospheric processes and the land surface characteristics. Detailed
descriptions of WRF can be found at http://wrf-model.org and a
discussion of its range of regional climate modeling applications is detailed
by Leung et al. (2006). In this experiment, WRF was used to downscale the
ECHAM5 output for both the S-HEM and 108/36 km CONUS domains. The model was
applied with 31 vertical levels and a vertical resolution of
∼ 40–100 m throughout the boundary layer with the model top fixed at
50 mb. Details of the model setup and a discussion of the results are
reported by Salathé et al. (2010), Zhang et al. (2009, 2012) and
Duliére (2011, 2013).
Current and future biogenic emissions and<?xmltex \hack{\break}?> land use changes
The MEGANv2.04 biogenic emission model (Guenther et al., 2006;
Sakulyanontvittaya et al., 2008) was used to estimate current and future
BVOC and soil NOx emissions based on the WRF meteorology with
current and future estimates of land use and land cover. For the current
decade, the default MEGANv2.04 land cover and emission factor data (Guenther
et al., 2006) were used. For the future decade, cropland distributions were
estimated by combining three data sets: the IMAGE 2100 global cropland extent
data set, (Zuidema et al., 1994), the SAGE maximum cultivable land data set
(Ramankutty et al., 2002) and the MODIS-derived current cropland data (as
used in MEGANv2.04 and described in Guenther et al., 2006). The IMAGE 2100
data set was created from the output of a land cover model, which forms part
of a sub-system of the IMAGE 2.0 model of global climate change (Alcamo,
1994). The SAGE cultivable data set was created using a 1992 global cropland
data set (Ramankutty and Foley, 1998) modified by characterizing limitations
to crop growth based on both climatic and soil properties. The future global
cropland extent distribution was generated by analyzing predicted changes in
agriculture on a continent-by-continent basis (using the IMAGE data). These
changes were then applied to the MODIS based cropland map (used for present-day MEGAN simulations) using the SAGE maximum cultivable data set as an upper
limit to cropland extent. The resulting land cover data have considerably
lower cropland fraction than the original IMAGE data, which likely
overestimates future cropland area by not considering whether a location is
cultivable.
In addition to generating a future crop cover data set to simulate potential
biogenic VOC emissions using MEGAN, future data sets representing several
other MEGAN driving variables were developed. These included geo-gridded
potential future plant functional type (PFT)-specific emission factor (EF)
maps for isoprene and terpene compounds, as well as future-extent maps of
four non-crop PFTs: broadleaf trees, needle-leaf trees, shrubs and grasses.
For regions outside of the US, the non-crop PFT distributions were generated
by reducing the current extent of each non-crop PFT map by an amount that
would appropriately offset the predicted cropland expansion for a given
continent. For the US, future non-crop PFT maps were generated using the
Mapped Atmosphere–Plant-Soil System (MAPSS) model output
(http://www.fs.fed.us/pnw/mdr/mapss/; Neilson, 1995), based on three GCM future scenarios. Present-day MAPSS
physiognomic vegetation classes were associated with current PFT fractional
coverage estimates by dividing the US into sub-regions and by averaging
existing (MODIS-derived) geospatially explicit PFT data within each
sub-region as a function of MAPSS class. Sub-regions were created based on
Ecological Regions of North America
(http://www.epa.gov/wed/pages/ecoregions.htm). After every current
MAPSS class had been assigned PFT-specific fractional coverage estimates,
future PFT cover was determined by re-classifying future distribution maps
for the three MAPSS data sets using the fractional PFT cover estimates for
each MAPSS class (within each ecological region), and averaging the three
resultant future data sets into a single estimate of future cover for each
PFT.
For the eastern US, future isoprene and monoterpene PFT-specific EF maps were
constructed using changes in tree species composition predicted by the US
Department of Agriculture “Climate Change Tree Atlas” (CCTA;
http://nrs.fs.fed.us/atlas/tree/). The CCTA data are based on ecosystem
changes driven by the average of three GCMs that represented the most
conservative emissions scenarios available.
Using existing speciated EF data (Guenther, 2013), we applied anticipated
changes in the average species composition of each PFT to generate
species-weighted PFT-specific EF maps on a state-by-state basis (the CCTA
data are organized by state). As data were lacking on predicted species-level
changes for areas outside the eastern US, we did not attempt to alter EF
maps outside the eastern US.
Anthropogenic emissions
For S-HEM domain CMAQ simulations, global emissions of ozone precursors from
anthropogenic, natural and biomass burning sources were estimated for the
period 1990–2000 (applied to 1995–2004) using the POET emission inventory
(Granier et al., 2005). Non-US anthropogenic emissions (containing
15 sectors) were projected based on national activity data and emission
factors. Gridded maps (e.g., population maps) were applied to spatially
distribute the emissions within a country. The global emission inventory for
black and organic carbon (BC and OC, respectively) was obtained from Bond et
al. (2004), which applies emission factors based on fuel type and economic
sectors alone. The Bond et al. (2004) inventory includes emissions from
fossil fuels, biofuels, open burning of biomass, and urban waste. Emissions
are varied by combustion practices, which consider combinations of fuel,
combustion type and emission controls, as well as their prevalence on a
regional basis.
Global emissions for the year 2000 from the POET, MEGAN and Bond et
al. (2004) inventories were combined, and the 16 gas-phase POET and MEGAN
species, along with the OC and BC species were adapted to the Statewide Air Pollution Research Center (SAPRC)-99
(Carter, 1990, 2009) chemical mechanism. Diurnal patterns were developed and
applied to the gridded emission inventories and processed using SMOKE. For
the future decade hemispheric domain simulations, current decade emissions
were projected to the year 2050 based on the IPCC A1B emission scenario. The
percent change in emissions was summarized according to the regions and
countries in the S-HEM domain that surround the CONUS domain (Fig. 3).
US anthropogenic emissions for the 36 km CONUS current decade CMAQ
simulations were developed using the 2002 National Emission Inventory. The
Emission Scenario Projection (ESP) methodology, version 1.0 (Loughlin et
al., 2011), was applied to project future decade US anthropogenic emissions.
A primary component of ESP v1.0 is the MARKet Allocation (MARKAL) energy
system model (Loulou et al., 2004). MARKAL is an energy system optimization
model that characterizes scenarios of the evolution of an energy system over
time. In this context, the energy system extends from obtaining primary
energy sources, through their transformation to useful forms, to the variety
of technologies (e.g., classes of light-duty personal vehicles, heat pumps,
or gas furnaces) that meet “end-use” energy demands (e.g., projected
vehicle miles traveled, space heating). Within ESP 1.0, MARKAL is used to
develop multiplicative factors that grow energy-related emissions from a
base year to a future year. Surrogates, such as projected population growth
or industrial growth, are used to develop non-energy-related growth factors.
The resulting factors were applied within SMOKE to develop a future decade
inventory from the 2002 National Emissions Inventory (NEI).
Summary of regional changes in global anthropogenic and biogenic
emissions.
![]()
For the work presented here, the EPAUS9r06v1.3 database (Shay et al., 2006)
was used with MARKAL to develop growth factors for CO2, NOx, SO2
and PM with aerodynamic diameter smaller than 10 µm (PM10).
The PM10 growth factors were also applied to PM2.5, and the
CO2 growth factors were used as a surrogate for energy system CO,
NH3, VOC, HCl and chlorine. For mobile sources, NOx growth factors
were used for CO, VOC and NH3. Non-combustion industrial emission
growth factors were developed from projections of economic growth. Growth
factors for non-combustion emissions from the residential and commercial
sectors are linked to population growth. The resulting energy and non-energy
factors were then used within SMOKE to multiply emissions from the 2002
NEI to 2050.
EPAUS9r06v1.3 originally was calibrated to mimic the fuel use projections of
the US Energy Information Administration's 2006 Annual Energy Outlook (AEO06;
US DOE, 2008). Energy demands were adjusted to account for population growth
consistent with the A1B storyline. The results reflect business as usual
assumptions about future environmental and energy regulations as of 2006.
Thus, while electric sector emissions are capped to capture the effects of
the Clean Air Interstate Rule (CAIR; US EPA, 2005), the impacts of increases
in natural gas availability, the 2007 economic downturn, and the relatively
new 54.5 Corporate Average Vehicle Efficiency (CAFÉ) standard (US CFR,
2011) are not reflected. More recent versions of the MARKAL database reflect
these factors with expanded pollutant growth coverage and refined emission
factors (US EPA, 2013). The ESP v1.0, including the MARKAL database
EPAUS9rv1.3, was selected here to maintain compatibility with previous and
ongoing activities.
The differences between the base year and future-year US inventories were
summarized at the pollutant and regional level (Fig. 4). Using the ESP v1.0
methodology, emissions of NOx and SO2 are projected to decrease
between 16 % in the South and Southwest to 35 % in the Northeast and
Northwest. On the other hand, emissions of pollutants that were not captured
endogenously by MARKAL, such as carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs) and ammonia (NH3) are projected to increase
in nearly all regions across the CONUS domain. The use of surrogates for
growth factors as described above means the projected changes in CO and VOC
emissions are likely too high. The largest increase (70 %) in emissions
of CO is projected in the Midwest; this is co-located with an increase of
about 20 % of NMVOC. The smallest increase of CO is projected for the
South; however, the same region was projected to increase NMVOC by about
12 %. The largest increase in PM2.5 emissions is projected in the
Northwest (< 20 %) and the smallest increase (3 %) of PM2.5
is projected in the Central region, which also has a projected 34 %
increase in NMVOC.
Air quality simulations
The CMAQ model version 4.7.1 was employed to simulate the potential impact of
climate change on surface ozone and PM2.5 over the CONUS at 36 km
horizontal grid spacing and covering 18 vertical layers from the surface up
to 100 mb. The model configuration included the use of the SAPRC-99 chemical
mechanism and version 5 of the aerosol module, with secondary organic aerosol
(SOA)
parameters of Carlton et al. (2010). Methane concentration is fixed at 1.85
parts per million (ppm) for all CMAQ simulations. Stratospheric intrusion
(STE) was not included in the CMAQ simulations; high STE events are mostly
relevant during the spring season; thus, lack of STE in our summer
simulations is not expected to have a significant effect in our results.
List of simulations to assess the effect of global climate changes
upon air quality in the United States.
Climate
Biogenic emissions
Anthropogenic emissions
climate
land use
USA
global
0
Current
Current
Current
Current
Current
1
Future
Current
Current
Current
Current
2
Future
Future
Current
Current
Current
3
Future
Future
Future
Current
Current
4
Current
Current
Current
Future
Current
5
Current
Current
Current
Current
Future
6
Future
Future
Future
Future
Future
Summary of regional changes in US anthropogenic and biogenic
emissions from future decade land use.
![]()
Using the framework components described above, a matrix of CMAQ simulations
that included changes in predicted meteorological conditions and potential
emission scenarios was constructed (Table 1). For each set of simulations
shown in Table 1, five representative summers were modeled. Simulation 0
represents the base case simulation, where all model inputs are set to
current decade conditions. Simulation 1 is used to investigate the impact of
climate change alone: all model inputs are set to current decade conditions
except for meteorology (biogenic emissions are not allowed to change with the
future climate for this case). Simulation 2 is the same as Simulation 1,
except that biogenic emissions are allowed to change with the future climate,
and in Simulation 3, future land use is also incorporated into the biogenic
emission estimates. Simulation 4 is used to investigate the impact of future
decade US anthropogenic emissions, where all inputs are set to current decade
levels except for US anthropogenic emissions. The impact of future global
emissions is investigated in Simulation 5. Finally, Simulation 6 represents
the combined impacts of Simulations 1–5.
Comparison of modeled and observed seasonal-mean meteorological
variables by region: maximum daily temperatures (top); and precipitation
rates (bottom). Each box-and-whisker indicates median, 25 and 75 %
quartiles, maximums and minimums of the values across all sites within each
region.
![]()
Evaluation of model performance
To aid in summarizing model results, the 36 km domain was divided
geographically into seven regions (Fig. 4, lower right). Since the WRF
simulations used to drive CMAQ are based on a climate realization rather than
reanalysis data, a direct comparison between the modeled output and
observations cannot be made. Instead, the frequency distributions of
simulated and observed values are compared. For the simulated meteorological
fields, daily maximum temperature and daily precipitation are compared
against a decade of summer observations (1995–2004) from the United States
Historical Climatological Network (US-HCN;
http://cdiac.ornl.gov/ftp/ushcn_daily/; Karl et al., 1990) in Fig. 5.
The model distributions of temperature and precipitation agree reasonably
well with the observations, and provide a good representation of the regional
variability of precipitation and temperature. Except for the Northwest and
Southwest regions, the observed mean and maximum temperatures are over
predicted, with the largest overprediction in the Midwest. For all analyzed
regions the model successfully simulates the seasonal trend of summer
temperatures, showing the observed increase in mean temperature from June to
July and subsequent decrease in mean temperature from July to August (not
shown).
Shown are the 2nd, 25th, 50th, 75th, 98th percentiles of observed vs. modeled
values of DM8O for each region.
![]()
The modeled daily maximum 8 h ozone concentrations (DM8O) from the five
representative summers (Fig. 6) from the current decade CMAQ simulations
(Simulation 0) were compared to the range of observations from the AIRNow
network (http://airnow.gov/). As seen in Fig. 6, DM8O is
overestimated in regions where temperature maxima are also over predicted,
most noticeably in the Midwest, the South and the Southeast but also in the
Northeast. Except for the less populated Central region, DM8O shows a bias
that ranges between +10 ppb (+15 %) and +25 ppb (+37 %)
across the domain. This is consistent with previous climate downscaled
results by Tagaris et al. (2007), who found a bias of +15 %, and with
Avise et al. (2009), who found regional biases as high as +39 %.
Despite the bias, results from the modeling framework presented here have
been shown to accurately represent the correlation between ozone and
temperature at rural Clean Air Status and Trends Network (CASTNET) sites
throughout the US (Avise et al., 2012), suggesting that the bias in
temperature is the main cause of the bias in DM8O. This implies that the
CTM is responding to the meteorological driver of ozone
production and thus can predict the impact of climate change on DM8O.
Shown are the 2nd, 25th, 50th, 75th, 98th percentiles of observed vs. modeled
values of 24 h average PM2.5 for each region.
![]()
(Top panel) mean regional temperature for the five chosen summers
(red) and ten summers (blue) of the current (C) and future (F) decades.
(Bottom panel) total regional precipitation per day for the five chosen
summers (red) and ten summers (blue) of the current (C) and future (F)
decades. Each box-and-whisker indicates median, 5, 25, 75 and 95 %
quartiles within each region.
![]()
Simulations for the current decade show a domain-average DM8O of 66 ± 20 ppb (standard deviation between simulated DM8O for the five summers),
while the observed average at the AIRNow sites was 56 ± 18 ppb. The
simulations successfully captured the enhanced DM8O concentrations over the
major urban areas and regions with high biogenic sources (Fig. 12a).
Interannual variability of the simulated summer DM8O concentrations is on the
order of 10 % (not shown) in highly populated areas and as little as
1 % in less populated areas, with the greatest variability found in the
Northeast region.
Simulated concentrations of current decade PM2.5 (PM2.5 with no
water content, unless otherwise specified) show a five summer average of
12.05 ± 10.8 µg m-3, compared to
14.3 ± 9.2 µg m-3 observed at the Speciation Trends Network (STN; US EPA, 2000). In general, the model slightly overestimates
PM2.5 in the Midwest, the Southeast and the Northeast and significantly
underestimates PM2.5 in the western half of the US (Fig. 7). Several
factors contribute to the underestimation of PM2.5 in the western US,
including a lack of windblown dust and fire smoke emissions, and an
underestimation of SOA formation (Carlton et al.,
2010; Foley et al., 2010; Appel et al., 2012; Luo et al., 2011). Another
important factor that influenced the underestimation of PM2.5 is the
overprediction of precipitation as shown in Fig. 5
Results and discussion
Projected changes in meteorology
For these types of climate change simulations, it is important to consider
whether the five selected summers represent the climatological conditions for
the 1995–2004 and 2045–2054 periods. To address this, we compared the
regional mean temperature and total precipitation (Fig. 8) as well as maximum
daily insolation and mean relative humidity (not shown) for all ten summers
versus the five selected summers. Based on the two sample t test, except
for the Northwest region, we found no statistical difference in the overall
regional average conditions between the five and ten summer samples
(p > 0.01). For the purposes of this air quality assessment, this
comparison of the meteorological conditions for the five selected summers to
the full ten summer set of data suggest that the five summers provide a
reasonable representation of decadal summer meteorological conditions. While
no statistical difference was found between the five and ten summer samples,
some distinct features should be highlighted: (1) for the current decade,
except for the Southeast, the chosen set of five summers on average is
slightly warmer than the average of the ten summers; (2) the five summers
chosen for current and future decades led to a projection of cooling in the
Northwest. The effects of the higher average temperature as result of the
five summer sample, and the projected decrease in future temperature in the
Northwest are discussed below.
Similar to the 30 year meteorological variability assessment carried out by
Andersson and Engardt (2010), the differences between current and future
summer meteorological conditions, based on the five representative periods,
were found to be significant at the 99 % confidence level for all regions
except for the Northwest. This further supports the use of five
representative summers as the basis for the air quality assessment of current
and future conditions.
Projected changes in summertime meteorological fields (future
decade–current decade): (a) changes in 2 m temperature
(∘C); (b) percent change in solar radiation reaching the
ground; (c) percent change in precipitation; (d) change in
relative humidity.
![]()
Projected changes in selected meteorological parameters are shown in Fig. 9.
Except for some minor cooling along the Pacific coast, mean summer
temperature across the continental US is projected to increase between 0.5
and 4 ∘C (Fig. 9a). This increase falls within the lower bound of
the warming predicted by the ensemble of 20 GCMs under the A1B emission
scenario described by Christensen et al. (2007), but differs in the regional
variability due to the higher resolution of our simulations. When compared to
similar studies of equal resolution using a GCM (e.g., Goddard Institute for
Space Studies (GISS) GCM II) driven by the A1B IPCC emission scenario and
downscaled with MM5 to 36 km resolution, our simulated temperatures show
higher temperature differences between future and current decades (Leung and
Gustafson, 2005; Tagaris et al., 2007). Tagaris et al. (2007) and Leung and
Gustafson (2005) predicted an average increase of between 1 and 3 ∘C
for most of CONUS, and temperature reductions in the border states of the
Central and South regions. Nevertheless, despite the differences in physical
parameterizations contained in the GCMs and the driving IPCC emission
scenarios that were used, similar temperature differences (2 to
4 ∘C) between our study and previous investigations were simulated
for the Northeast and Southeast regions (Leung and Gustafson, 2005; Tagaris
et al., 2007; Avise et al., 2009).
(a) Current decade summertime isoprene emissions, and
(b) percent change induced by climate on future summertime isoprene
emissions with current decade land use.
![]()
Percent change between future and current decade summertime
emissions for future climate and land use for (a) isoprene and
(b) monoterpene.
![]()
Projected increases in solar radiation reaching the ground vary by region. A
decrease in solar radiation in the Northwest that extends to the northern
boundaries of the Central regions is simulated. Small changes in the
Southwest, South and Midwest are also predicted, with the largest increase
experienced in the Northeast and Southeast regions (Fig. 9b). Similar results
for the Northeast regions are reported by previous investigations (Leung and
Gustafson, 2005; Tagaris et al., 2007; Avise et al., 2009); however, these
same investigations had higher reductions in solar radiation at the border
states between the Central and South regions.
Projected changes in precipitation across the US also vary depending on the
region. With the exception of the Northwest and the northern boundary of the
Central region, summertime precipitation is projected to decrease between
-10 and -80 %. The largest decrease is projected in the Southwest
region. Our results show greater precipitation reductions than those
presented in Christensen et al. (2007), who projected between a 5 to 15 %
decreases in the South and Southwest regions. Also, previous investigations
agreed with our projected mean precipitation reductions across the domain
(Fig. 9c). In the Northwest, the modeled increase in precipitation is
consistent with Leung and Gustafson (2005), who projected an increase in
precipitation throughout the Northwest region. In contrast, the Southeast and
Northeast regions show disparities in the magnitude and the sign of the
change in precipitation. While our simulations show a reduction in
precipitation between 10 to 20 %, the ensemble of 20 GCMs in Christensen
et al. (2007) resulted in an increase between 5 to 10 % across these
regions. The disparities may be a result of the differences in resolution and
parameterization schemes between our study and those used for the 20 GCMs.
Changes in relative humidity are shown in Fig. 9d. Relative humidity is shown
to decrease in most of the domain except for the regions where decreases in
solar radiation were projected. The greater decrease in relative humidity
occurs in the Southwest and Central regions of the domain, and the largest
increase is observed in the Northwest region.
Changes in biogenic emissions
Average summertime isoprene emissions over five summers of simulation for
each decade are shown in Fig. 10a. Isoprene emissions occur at relatively
high rates (> 50 metric tons day-1) in the eastern US and at much lower rates in the
western US (< 10 metric tons day-1). Under future climate conditions
and current land use, isoprene and monoterpene emissions are projected to
increase in all regions except for the Northwest (Figs. 4 and 10b); this
follows the spatial pattern of projected temperature changes (Fig. 9a). The
most noticeable increases occur in the Northeast and Southeast regions. The
model projects a larger percentage increase in monoterpenes than isoprene
across the domain; however, total isoprene emission is an order of magnitude
higher and thus dominates the changes in total BVOC. The increase in total
BVOC ranges between 17 and 45 %. The only region that is projected to
have reduced total BVOC emissions is the Northwest, where the model simulates
a 7 % reduction in isoprene emissions (Fig. 4) that in absolute amounts is
greater than the 20 % increase in simulated monoterpene emissions. The
reduction in isoprene emissions in the Northwest is a result of the decrease
in temperatures in the coastal area where the higher isoprene emissions are
encountered (Fig. 9a). Previous investigations (Liao et al., 2006; Nolte et
al., 2008) show the greatest increase in BVOC emissions in the Southeast
region (10–50 %). Similarly, Nolte et al. (2008) predicted the
greatest increase in BVOC in the Southeast, but did not show any significant
changes in the Northwest region.
When future climate is combined with future land use to project biogenic
emissions, the spatial extent of isoprene emission increase is reduced,
reflecting the expansion of low isoprene-emitting croplands into regions of
high isoprene-emitting deciduous forests. In this case, the domain-average
increase was approximately 12 % of current decade emissions, compared
with a 25 % increase when changes in land use are not included
(Fig. 11a). Thus, future expansion of cropland and subsequent reduction of
broadleaf forested lands are projected to lessen the overall increase in US
isoprene emissions that result from a warmer climate. When the future decade
meteorology is combined with future land use, an increase of over 100 %
of current decade monoterpene emissions is predicted (Fig. 11b). The growth
is most noticeable in the Central, South and Midwest regions. Also, an
overall increase between 25 and 50 % for the western and eastern regions
is simulated. This limited increase is primarily driven by the projected
changes in land use predicted for those regions.
Since the version of MEGAN used in this work does not include the suppression
of isoprene emissions due to elevated concentrations of CO2 (Rosenstiel
et al., 2003; Heald et al., 2009), the future estimates in this study are
likely to be an upper bound on isoprene emissions, and it is likely that
future isoprene emissions will be lower than predicted by this work.
Monoterpene emissions from US landscapes are not expected to be suppressed by
increasing CO2 and so are not impacted by omitting this process.
Percent change in DM8O between each future scenario and the current
decade base case.
Region
Climate
Climate
Climate,
US
Boundary
Combined
(1)
& BVOC
BVOC,
anthropogenic
conditions
(6)
(2)
land
emissions
(5)
use (3)
(4)
DM8O
Northwest
0.4
-1.0
-0.8
-0.6
8.1
4.5
Southwest
2.0
0.4
0.0
-3.5
9.1
4.2
Central
5.6
4.5
4.9
-0.1
8.9
12.3
South
6.2
4.3
6.1
-0.9
9.6
13.0
Midwest
7.6
7.2
8.5
0.0
2.6
10.0
Northeast
8.2
6.6
7.6
-2.3
1.4
5.3
Southeast
8.6
6.1
7.7
-3.0
3.3
6.1
Effects of global changes upon ozone concentrations
Results for how the various global changes affect summertime DM8O are
summarized in Table 2 and Fig. 12. Simulations for the future decade
(Simulation 6) show a domain average of 48 ± 11 ppb with higher DM8O
in the Northwest, Central and South regions than the current decade
simulation (Simulation 0). In general, increases in DM8O are due to growing
global anthropogenic emissions and climate change, while decreasing US
emissions reduce DM8O. Changes in biogenic emissions as a result of a
changing climate and land use have less of an influence on DM8O than an increase
of global anthropogenic emissions. Factors that influence future DMO3 are
discussed in the following sections.
(a) Current decade base case daily maximum 8 h ozone average concentrations for five summers in the 2000s; spatial distribution;
and regional effect on maximum 8 h ozone due to (b) changes in
meteorology (Simulation 1–Simulation 0); (c) changes in meteorology
and biogenic emissions (Simulation 2–Simulation 0); (d) changes in
meteorology, biogenic emissions, and land use (Simulation 3–Simulation 0);
(e) changes in US anthropogenic emissions
(Simulation 4–Simulation 0); (f) changes in global anthropogenic
emissions (Simulation 5–Simulation 0); and (g) all the changes
above combined (Simulation 6–Simulation 0).
![]()
Contributions from changes in global and US anthropogenic
emissions
The effects of increased long-range transport of global emissions are shown
in Fig. 12f. The changes in chemical boundary conditions (the difference
between Simulations 0 and 5) increase DM8O between 2 to 6 ppb across the
CONUS domain. The general west-to-east and south-to-north gradients of the
change in DM8O reflects intercontinental and regional transport of ozone and
its precursors from the west and from Mexico at the south. The greatest
impact occurs in the South (6 ppb) and Southwest (5 ppb) regions. These
results show a smaller influence in DM8O from the intercontinental transport
than the simulations presented in Avise et al. (2009), who report increases
between 3 and 6 ppb of DM8O across the domain, with the greatest increase in
the Southwest and South regions. The higher effect from intercontinental
transport presented in Avise et al. (2009) is due to larger increases NOx
emissions from global anthropogenic sources under the SRES A2 emission
scenario. The effects of future global emissions and intercontinental
transport of ozone precursors in the continental US have also been
investigated by Hogrefe et al. (2004), who predicted an increase of 5 ppb in
the Northeast region under the SRES A2 emission scenario.
Changes in regional US emissions of ozone precursors (difference between
Simulations 0 and 4) reduce DM8O concentrations between 2 and 15 ppb in most
of eastern US, most of western US and Texas. Projected increases in ozone in
urban areas near the coasts are mainly due to the limited representation of
the heavy-duty, shipping and rail sectors on the ESPv1.0 (Loughlin et al.,
2011) by which local steady or increase in emissions of NOx and VOCs in
ports are the main cause of increase in ozone in those urban areas.
Regionally, larger reductions are observed in the Southeast (-3 %) and
Southwest (-3.5 %) regions with a reduction of 5 ppb and the Northeast
(-2.3 %) and South (-0.9 %) regions with a reduction of 3 ppb
(Fig. 12e, Table 2). Similar results are shown in Nolte et al. (2008) and
Tagaris et al. (2007) despite a difference in the magnitude of projected
emissions reductions. Tagaris et al. (2007) simulated similar ozone
reductions (about 9 %), with a higher nationwide reduction of 51 % in
NOx emissions and a slight increase (about 2 %) in VOC emissions
from projections based on the CAIR emission
inventory. Nolte et al. (2008) showed a decrease in ozone across the domain
(-12 to -16 ppb) as a result of projected reductions of 45 % for
NOx and 21 % for VOC emissions from the NEI 2002, following the SRES
A1B emission scenario. In contrast, our future simulations included a
38 % reduction in NOx emissions and a slight increase (about
2 %) in VOC emissions. Avise et al. (2009) predicted an average
contribution of +3 ppb across the domain as a result of projecting the NEI
1999 (NEI-1999) with the Economic Growth Analysis System (EGAS) and the SRES
A2 emission scenario, increasing emissions by 5 % for NOx and
50 % for VOCs in the future. The smaller reduction in ozone
concentrations between the future and the current decade in comparison to
Nolte et al. (2008) is likely to be a consequence of the increase in VOC and
CO emissions from the business-as-usual scenario of MARKAL, which, as
explained in Sect. 2.4, uses diverse surrogates for growth factors for CO and
VOC (Loughlin et al. 2011).
Contributions from changes in meteorological fields
Figure 12b shows the difference between simulations that include changes in
meteorological conditions (without the effect of biogenic emissions or land
use) and the current decade base case (Simulations 0 and 1). The local
reductions in DM8O concentrations in the Northwest resulted from an increase
in cloud cover and lower solar radiation reaching the ground, which cause a
reduction in photochemistry (Fig. 9b). For other regions, increases in DM8O
concentrations were projected (+5 ppb) because of increases in temperature
and solar insolation; this is particularly evident in the eastern half of the
US.
Contributions from changes in biogenic emissions and future land use
When biogenic emissions are allowed to change with the future meteorology, an
average increase of DM8O with respect to the current decade base case
simulations is predicted (Simulations 0 and 2). Increases of as much as
7 ppb in DM8O concentrations are mainly predicted in areas with substantial
biogenic sources (Fig. 12c). Similar results are shown by Nolte et al. (2008)
and Tagaris et al. (2007); both predicted an increase of DM8O above 5 ppb in
the east coast. Simulated reductions between 2 and 4 ppb of DM8O in the
coastal areas of the western regions are probably due to cooler temperatures
and reduced solar insolation (Fig. 9a, b). Minor changes in DM8O
concentrations are shown over the Southwest and Northwest regions. This is in
agreement with Avise et al. (2009) and Nolte et al. (2008) who predicted
reductions in DM8O concentrations from 1 to 4 ppb in the western regions,
while Tagaris et al. (2007) also predicted similar reductions in ozone in the
Central and Midwest regions. The disparities between this investigation and
Avise et al. (2009) are reasonable due to the different climate realizations
used (A2 vs. A1B; storyline in scenario A2 considers higher emissions of
CO2 by 2050 than the scenario A1B). However, the difference in
geographical features of DM8O changes with Nolte et al. (2008) and Tagaris et
al. (2007) suggests that the source of disparities reside in the simulated
regional meteorological fields resulting from different global climate
models, RCMs and the methods used to estimate emissions
from biogenic sources. We used the ECHAM5 global climate model results while
both Nolte et al. (2008) and Tagaris et al. (2007) used results from the GISS
global climate model. For regional climate simulations, we used WRF; both
Nolte et al. (2008) and Tagaris et al. (2007) used MM5. In contrast with
Nolte et al. (2008) and Tagaris et al. (2007) who use the BEIS/BELD3 (Hanna
et al., 2005; http://www3.epa.gov/ttn/chief/emch/biogenic/) tool to compute biogenic emissions, this investigation estimates
the biogenic emissions with MEGAN v2.04, which generally predicts higher
isoprene emissions than BEIS (Hogrefe et al., 2011; Sakulyanontvittaya et
al., 2012). Hogrefe et al. (2011) showed that MEGAN leads to higher DM8O in
the Northeast by upwards of 7 ppb under the scenario of 2005 anthropogenic
emissions; however, for a scenario by which anthropogenic NOx emissions
were reduced by ∼ 60 %, the difference in DM8O was generally 3 ppb due
to greater sensitivity to NOx emissions when MEGAN was used.
When the results from Simulation 2 (Fig. 12c) are compared to the
climate-only simulations (Simulation 1, Fig. 12b), our results suggest that
changes in the meteorological fields are the main driver of DM8O enhancement
in Simulations 2 and 3 (Fig. 12c and d) across the domain. Even though BVOC
emissions are higher in Simulation 2 relative to Simulation 1, Simulation 2
resulted in 2–4 ppb lower DM8O in the Southeast. This decrease is
associated with a reduction in NOx concentrations (Fig. 14a). This
decrease in NOx suggests that the effect of sequestration of NOx by the
biogenic VOCs as organic nitrates (RNO3) is predominant over the effect
of recycling of NOx considered in SAPRC-99, which lumps all
non-peroxyacetyl (non-PAN)
organic nitrates as one compound that has a NOx recycling efficiency of
about 30 %. The simulated reduction in ozone is consistent with the
results of Xie et al. (2013), who reported increases in NOx and ozone in
the Southeast when sequestration by isoprene nitrates was reduced relative to
the base SAPRC-07T mechanism that has the same RNO3 treatment as
SAPRC-99. Evidence of the predominant effect of sequestration over the
recycling of NOx in the eastern US is seen in Fig. 14, which shows an
increase in RNO3 and reduction in the NOx concentrations in most of
the eastern US for Simulation 2 relative to Simulation 1. When land use
changes are included along with biogenic emissions, the increase in BVOC
emissions is projected to be less while NO emission is projected to increase
in areas where natural vegetation is converted to cropland. This combination
leads to higher DM8O in Simulation 3 than Simulation 2 (Table 2, Fig. 12d).
Contributions from combined global change to future changes in
DM8O concentrations
When the combined global changes are considered (Simulation 6), DM8O is
projected to increase in all regions, except along the western coastlines.
Increases of DM8O between 1 to 3 ppb in the Northwest, Southwest and
Northeast regions are shown along with a local increase of 1 to 6 ppb in
parts of the South, Midwest and Central regions (Fig. 12g). The increase in
DM8O is mostly due to an increase in global emissions of ozone precursors
from the semi-hemispheric domain, which contributes to an increase of
2–6 ppb under current climate conditions (Fig. 12f). The other contributing
factors to increasing DM8O are a combination of meteorological changes
(Fig. 12b) and higher BVOC emissions (with current and future land use;
Fig. 12c, d). Reductions in DM8O in the urban areas resulted generally from
reductions in ozone precursors from regional anthropogenic sources
(Fig. 12e). However, in the western regions, lower DM8O are the result of a
combination of favorable meteorological conditions (e.g., reduction in
temperature and solar radiation reaching the ground) and reductions in
regional ozone precursors.
Percent change in the aerosol NH4+, SO42- and
NO3- between each future scenario and the current decade base case.
The corresponding simulation number for each sensitivity simulation is shown
in parenthesis.
Region
Climate
Climate
Climate,
US
Boundary
Combined
(1)
& BVOC
BVOC,
anthropogenic
conditions
(6)
(2)
land
emissions
(5)
use (3)
(4)
NH4+
Northwest
15.7
-0.6
-0.9
12.8
-0.2
12.2
Southwest
3.4
-8.8
-7.9
4.2
8.2
4.8
Central
12.5
2.1
2.7
6.9
3.3
14.8
South
9.1
4.3
5.8
7.5
4.8
22.9
Midwest
5.1
0.6
3.3
12.2
0.4
18.1
Northeast
1.8
-5.0
-4.2
17.5
-0.3
12.7
Southeast
10.0
5.0
4.8
12.4
0.5
21.3
SO42-
Northwest
10.0
-5.4
-5.3
6.3
0.9
1.6
Southwest
5.4
-4.6
-4.0
0.7
6.2
2.8
Central
10.9
1.5
2.0
3.4
3.2
10.1
South
7.3
3.7
4.7
1.5
4.8
14.5
Midwest
7.2
1.8
4.1
2.7
0.4
10.9
Northeast
3.5
-4.0
-3.2
3.2
-0.2
2.3
Southeast
8.8
3.5
2.9
1.9
0.8
9.3
NO3-
Northwest
-0.3
2.3
0.9
20.3
6.4
27.4
Southwest
-10.1
-8.0
-7.3
11.8
8.2
12.7
Central
-34.4
-17.1
-12.0
87.6
2.6
68.4
South
-7.0
-18.7
-11.5
38.5
-2.0
17.0
Midwest
-38.4
-31.1
-23.6
96.6
2.6
56.4
Northeast
-43.9
-43.2
-42.1
74.0
2.0
4.8
Southeast
-29.4
-28.7
-28.7
54.6
7.5
19.6
Effects of global changes upon PM<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn>2.5</mml:mn></mml:msub></mml:math></inline-formula> concentrations
Results for how the various global changes affect PM2.5 concentrations
and composition are summarized in Tables 3–5 and Fig. 13. Overall, projected
increase in US anthropogenic emissions have the largest impact on PM2.5,
leading to an increase in concentrations in all regions. Changes in global
emissions do not have a significant impact on PM2.5 concentrations,
while changes in the climate and biogenic emissions can lead to both
increases and decreases in PM2.5 depending on the region.
(a) Current decade base case PM2.5 average
concentrations for five summers in the 2000s; and spatial distribution and
regional effect on PM2.5 due to (b) changes in meteorology
(Simulation 1–Simulation 0); (c) changes in meteorology and
biogenic emissions (Simulation 2–Simulation 0); (d) changes in
meteorology, biogenic emissions, and land use (Simulation 3–Simulation 0);
(e) changes in US anthropogenic emissions (Simulation 4–Simulation
0); (f) changes in global anthropogenic emissions (Simulation
5–Simulation 0); and (g) all the changes above combined (Simulation
6–Simulation 0).
![]()
Contribution to PM<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn>2.5</mml:mn></mml:msub></mml:math></inline-formula> concentrations
from changes in global and regional anthropogenic emissions
The results from this study are similar to those reported by Avise et
al. (2009), who predicted a change in PM2.5 of less than
1 µg m-3 as a result of changes in future chemical boundary
conditions. In our simulation, the highest increase in PM2.5
concentrations is found in the South region (< 1 %). This increase in
the South region is indicative of the effects of increased emissions from
Mexico (Fig. 13f). When the chemical composition is analyzed, Table 3 shows
an increase in aerosol nitrate (NO3-) in nearly all regions except
for the South; these increases are less than 0.1 µg m-3,
similar to the results of Avise et al. (2009). In our simulation, increases
between 3 and 8 % in SO42 and NH4+ in the Southwest,
Central and South regions are mostly a result of increase in emissions of
SO2 and NH3 from Mexico. Similarly, Avise et al. (2009) showed
higher future concentrations (by 7 to 25 %) of SO42- for the same
regions resulting from higher global SO2 emissions. In our simulations,
changes in global anthropogenic emissions cause reductions in SOA in the
Southwest, Central and South regions and increases in the Northwest, Midwest,
Southeast and Northeast regions (Table 4); the simulated changes in SOA are
very small (< 1.3 % and < 0.05 µg m-3) and the
variation may be due to small differences in modeled OH radical
concentrations.
In the US, reductions in regional SO2 and NOx emissions from
regulatory curtailment on electricity generation are offset by the projected
increase in emissions of PM2.5 and NH3 from other sources, thus
resulting in an overall increase in PM2.5 concentrations between 1 and
4 µg m-3 across CONUS. Similarly, Avise et al. (2009)
predicted an average increase of 3 µg m-3 across the domain
but as a result of increasing NOx and SO2 from anthropogenic
sources. The greatest increase, between 2 and 4 µg m-3, is
found in the urban areas across the Northwest, Northeast, Midwest and
Southeast region (Fig. 13e) as a result of increase in primary emissions of
PM2.5. Similarly, Trail et al. (2015) find an increase in PM2.5
concentrations between 1 and 2 µg m-3 as a result of a
scenario that consider changes in fuel use. In contrast, Tagaris et
al. (2007) predicted a decrease of 23 % as a result of decreasing
emissions. Increase in SOA concentrations resulted from higher emissions of
NMVOC and an increase in primary organic aerosol from anthropogenic sources
in the US (Table 4).
Differences in (a) RNO3 and (b) NOx
concentrations between Simulation 2 and Simulation 1.
![]()
In terms of the inorganic PM2.5 components, reductions in SO2 and
NOx emissions in the US are offset by higher emissions of primary
sulfate and nitrate and ammonia, leading to an increase in both sulfate and
ammonium. Compared to Tagaris et al. (2007), our investigation shows no
reduction in sulfate concentrations as a result of smaller reduction in
SO2 emissions from anthropogenic sources. Furthermore, similar to
Shimadera et al. (2014) the increase in nitrate concentrations in the form of
ammonium nitrate is highly dependent on the increase in NH3 emissions
and less sensitive to changes in emissions of NOx.
Contribution to PM<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn>2.5</mml:mn></mml:msub></mml:math></inline-formula> concentrations from global climate change
alone
Despite the effect of precipitation on PM loading, as it washes out the
precursors and the existing PM from the atmosphere (Seinfeld and Pandis,
2006), the effect of climate change alone (with no change to biogenic
emissions) on total PM2.5 concentrations over land is a change of less
than 1 µg m-3 (Fig. 13b). However, the change in PM2.5
composition due to climate change is highly variable and depends on changes
in temperature, relative humidity and precipitation. Increases in reaction
rate constants of SO2 and higher oxidant concentrations from increased
temperature and solar insolation lead to an increase in aerosol sulfate
formed (Dawson et al., 2007). Relative humidity and temperature affect the
thermodynamic equilibrium of SO42-–NH4+–NO3-,
especially the partitioning of HNO3 between the gas and particulate
phases.
Percent change of SOA between each future scenario and the current
decade base case. The corresponding simulation number for each sensitivity
simulation is shown in parenthesis,
Region
Climate
Climate
Climate
US
Boundary
Combined
(1)
& BVOC
BVOC
anthropogenic
conditions
(6)
(2)
& land
emissions
(5)
use (3)
(4)
SOA
Northwest
11.6
17.5
40.7
17.4
1.3
61.1
Southwest
2.2
20.3
31.9
10.2
-0.2
41.0
Central
16.2
43.9
118.6
7.1
-0.2
126.4
South
4.7
57.0
113.2
7.4
-0.4
121.3
Midwest
16.0
48.6
121.2
7.9
0.1
131.0
Northeast
17.9
59.5
108.8
9.8
0.2
119.1
Southeast
14.2
73.2
135.1
8.1
0.3
143.5
For all regions, sulfate concentrations are predicted to increase by
3–10 %. Except for the Northwest regions, this change in concentrations
is consistent with decreased precipitation, which reduces wet deposition, and
increases in temperature and solar insolation, which increase radical
production rates and increase the oxidation of SO2 to produce aerosol
sulfate. The same increase in temperature leads to nitrate being more
volatile and thus decreases aerosol nitrate concentrations in regions where
sulfate concentrations are predicted to increase. For the same regions where
sulfate is projected to increase, higher concentrations of radicals also lead
to higher oxidation of VOC, thus increasing SOA concentrations in the same
regions.
While increasing precipitation is generally associated with decreasing
PM2.5, results here for the Northwest region showed an increase in
PM2.5 despite an increase in precipitation (Fig. 13b). This suggests the
effects of slightly colder temperature and higher relative humidity in this
region, leading to an enhanced formation of ammonium nitrate (Table 3).
Furthermore, the increase in relative humidity in the Northwest and the
coastal areas of the Southwest regions leads to the increase in production of
sulfate aerosol via aqueous reaction (Luo et al., 2011). Higher
concentrations of ammonium nitrate and higher concentrations of SOA (Table 4)
indicate increased aerosol formation dominate over the effect of
precipitation.
Contribution to PM<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn>2.5</mml:mn></mml:msub></mml:math></inline-formula> concentrations from
changes in biogenic emissions and future land use
Simulations that consider projected climate change as well as the associated
change in biogenic emissions (Simulation 2) show an increase in PM2.5
between 0.5 and 3 µg m-3; these changes are mainly reflected
in areas with high biogenic sources (Fig. 13c). When the effects of future
land use are considered (Simulation 3), an increase in the geographical
extent of PM2.5 is observed in comparison to the climate and biogenic
emissions case, and higher increases (up to 6 µg m-3) of
PM2.5 are predicted in parts of the South, Southeast, Midwest and
Northeast regions (Fig. 13d). This is primarily due to the increase in
emissions of sesquiterpenes (not shown) and monoterpenes
(Fig. 11b), leading to more SOA being formed.
In terms of the inorganic components of PM2.5, the effect of climate
change is still the predominant factor for the change in sulfate
concentrations for the Central, South, Midwest and Southeast regions
(Table 3). The smaller increase or absolute reduction in sulfate in
comparison to the climate-only case is due to the competition between BVOC
and SO2 for the availability of OH, which is an oxidant for both.
Additionally, a smaller decrease in NO3- in most of the domain and
increase in the Northwest is predicted due to changes in the availability of
OH as a result of the changes in emissions of BVOC and soil NO. The increase
in availability of OH and increase in soil NO emissions lead to a higher
formation of ammonium nitrate in Simulations 2 and 3 than in Simulation 1.
Percent change of PM2.5 between each future scenario and the
current decade base case. The corresponding simulation number is shown is
parenthesis.
Region
Climate
Climate
Climate,
US
Boundary
Combined
(1)
& BVOC
BVOC,
anthropogenic
conditions
(6)
(2)
land
emissions
(5)
use (3)
(4)
PM2.5
Northwest
7.0
2.1
7.3
43.2
-0.8
51.7
Southwest
3.3
3.3
7.1
20.7
0.7
27.8
Central
10.5
12.6
31.0
14.5
0.0
46.5
South
5.4
21.3
40.5
17.6
1.0
60.8
Midwest
7.8
15.2
37.6
22.4
0.1
61.2
Northeast
7.8
16.0
30.4
28.5
0.0
58.3
Southeast
10.6
29.8
52.4
24.3
0.4
78.5
SOA concentrations are predicted to increase as a result of higher emissions
of BVOC across the domain (Table 4). Furthermore, when climate change and
biogenic emissions are combined with future land use, concentrations of SOA
are predicted to increase up to 121 % in the Central region and up to
188 % in the Southeast due to increased biogenic monoterpene and
sesquiterpene emissions (not shown).
Changes in precursors and
PM<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn>2.5</mml:mn></mml:msub></mml:math></inline-formula> concentrations from the combined global changes
Table 5 shows the summary of changes to PM2.5 for all Simulations. The
differences in PM2.5 between the future decade and current decade base
case are greater in the eastern half of the US compared to the western half.
In the eastern half of the US, the largest increases in PM2.5 occur in
the Southeast. Our results show that the 2 to 10 µg m-3
increase in PM2.5 in the Southeast region is dominated by higher
concentrations of SOA due to increased biogenic emissions as a result of
climate change (Fig. 13c), changes in land use (Fig. 13d; Table 4) and
increase in anthropogenic emissions (Fig. 13e). Table 5 indicates that the
combination of climate change, biogenic emissions and land use, and increase
in anthropogenic emissions increases the concentrations of PM2.5 between
27 and 78 % depending on the region.
Conclusions
We have investigated the individual and combined contributions of factors
that impact US air quality by dynamically downscaling future climate
projections using the WRF model and using the regional chemical transport
model CMAQ version 4.7.1. Decreases in future US anthropogenic ozone
precursor emissions are the only consistently beneficial influence that
improves the air quality in the US and updated assumptions to generate
scenarios of future US anthropogenic emissions may show even more positive
influence. However that positive influence is offset by (1) increasing global
emissions and changes in long-range transport, which have a negative impact
on air quality across the domain; (2) climate changes (namely, increased
temperatures and solar radiation), which increase ozone concentrations in the
Central, South, Midwest, Northeast and Southeast regions of the domain; and
(3) increases in US BVOC emissions, which also increase ozone concentrations
in regions with high biogenic emissions such as the South, Midwest, Northeast
and Southeast.
In the case of the overall concentrations of PM2.5, our results indicate
that the effects of increasing biogenic emissions in addition to
increased
primary PM from anthropogenic sources have an overall negative impact on air
quality by increasing PM2.5 concentrations between 27 and 78 %. In
terms of the PM2.5 composition, we show a regionally dependent mixture
of inorganic aerosols and SOA. For the case of the Southeast, our findings
indicate that increases in BVOC may result in higher concentrations of
PM2.5. This effect extends to the Midwest and Northeast regions due to
changes in land use. Furthermore, meteorological changes or regulatory
curtailment, as incorporated in these simulations do not offset the
increasing concentrations of primary PM and BVOC. In addition, synergistic
effects of changes in meteorological parameters and changes in emission may
shift the composition of the inorganic fraction of PM2.5 in the US. The
synergistic effects of increase of sulfate and SOA in the urban areas of the
coastal regions of the Northwest and Southwest lead to an increase in
PM2.5 in those regions, off-setting decreases due to increased
precipitation and temperature, and reduced primary anthropogenic emissions of
SO2 and NOx.
In conclusion, the results of this study suggest that the efforts to improve
air quality through low emission technologies and public policy directed to
the electricity generation sector may not have a major effect, if future
emissions from other sectors are allowed to increase. In addition, higher
global anthropogenic emissions, a warmer future world and the effects of
these changes on emissions from biogenic sources may increasingly undermine
all regulatory efforts. Consequently, additional measures may be necessary to
improve air quality in the US.
Much of the modeling components used for this research carry different levels
of complexity and have reached diverse stages of development; thus,
subsequent research intended to assess the effect of climate change and
future regional emissions upon air quality would benefit from newer versions
of the emission inventories (e.g., 2011), updated assumptions on the US
emission projections (e.g., New versions of MARKAL with the use of the ESP 2.0
methodology), newer versions of MEGAN that take into account the isoprene
emission suppression due to CO2 concentrations and more realistic
estimates of land use change, and the inclusion of emissions from wildfires
and the consequent effect upon air quality.