GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus GmbHGöttingen, Germany10.5194/gmd-8-1775-2015ESP v2.0: enhanced method for exploring emission impacts of future
scenarios in the United States – addressing spatial allocationRanL.LoughlinD. H.loughlin.dan@epa.govYangD.AdelmanZ.BaekB. H.NolteC. G.https://orcid.org/0000-0001-5224-9965University of North Carolina at Chapel Hill, Institute for the
Environment, 100 Europa Dr., Chapel Hill, NC 27517, USAUS Environmental Protection Agency, Office of Research and Development,
109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USAD. H. Loughlin (loughlin.dan@epa.gov)17June201586177517877November201413January201520May201523May2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://gmd.copernicus.org/articles/8/1775/2015/gmd-8-1775-2015.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/8/1775/2015/gmd-8-1775-2015.pdf
The Emission Scenario Projection (ESP) method produces future-year air
pollutant emissions for mesoscale air quality modeling applications. We
present ESP v2.0, which expands upon ESP v1.0 by spatially allocating
future-year non-power sector emissions to account for projected population
and land use changes. In ESP v2.0, US Census division-level emission
growth factors are developed using an energy system model. Regional factors
for population-related emissions are spatially disaggregated to the county
level using population growth and migration projections. The county-level
growth factors are then applied to grow a base-year emission inventory to
the future. Spatial surrogates are updated to account for future population
and land use changes, and these surrogates are used to map projected
county-level emissions to a modeling grid for use within an air quality
model. We evaluate ESP v2.0 by comparing US 12 km emissions for 2005 with
projections for 2050. We also evaluate the individual and combined effects
of county-level disaggregation and of updating spatial surrogates. Results
suggest that the common practice of modeling future emissions without
considering spatial redistribution over-predicts emissions in the urban core
and under-predicts emissions in suburban and exurban areas. In addition to
improving multi-decadal emission projections, a strength of ESP v2.0 is that
it can be applied to assess the emissions and air quality implications of
alternative energy, population and land use scenarios.
Introduction
Emission projections are often the dominant factor influencing the outcome of
future-year air quality modeling studies (e.g., Tagaris et al., 2007; Tao et
al., 2007; Avise et al., 2009). Thus, building plausible emission scenarios
and correctly allocating emissions to modeling grids are critical steps in
conducting those studies. The Emission Scenario Projection v1.0 (ESP v1.0)
method, described by Loughlin et al. (2011), facilitates the development of
future-year air pollutant emission inventories by producing US Census
division level-, source-category- and pollutant-specific emission growth
factors. For most emission categories, multiplicative emission growth factors
are developed using the MARKet ALlocation (MARKAL) energy system model
(Fishbone and Abilock, 1981; Loulou et al., 2004). These factors are applied
to a base-year emissions inventory, such as the United States Environmental
Protection Agency (US EPA) National Emissions Inventory (NEI) (US EPA, 2010),
using the Sparse Matrix Operator Kernel Emission (SMOKE) model (Houyoux et
al., 2000). The resulting future-year emission inventory is then temporally
and spatially allocated to a gridded modeling domain for use by an air
quality model such as the Community Multi-scale Air Quality (CMAQ) model
(Byun and Schere, 2006), typically at 4–36 km grid resolution.
Since the release of ESP v1.0, a number of improvements to the method and its
components have been made. For example, in ESP v1.0, pollutants represented
explicitly in the MARKAL database were carbon dioxide (CO2), nitrogen
oxides (NOx), sulfur dioxide (SO2), and particulate matter with diameter less than 10 µm
(PM10). The pollutant coverage in the ESP
v2.0 MARKAL database has been expanded to include carbon monoxide (CO),
methane (CH4), nitrous oxide (N2O), volatile organic compounds
(VOCs), PM with diameter less than 2.5 µm (PM2.5), black carbon (BC),
and organic carbon (OC). Furthermore, while the ESP v1.0 MARKAL database was
calibrated to the 2006 Annual Energy Outlook (AEO) (US EIA, 2006), the ESP
v2.0 MARKAL database used here is calibrated to AEO 2010 (US EIA, 2010), and
the method accommodates MARKAL databases calibrated to more recent AEO
projections. As a result, developments such as the economic recession of 2008
and the increased availability of natural gas can now be considered.
Additional detail in the electric sector also facilitates consideration of
coal plant retirements and improvements in the cost-effectiveness of
renewables.
Another aspect of the method that has been improved is the spatial
representation of future-year emissions. In ESP v1.0, the application of
multiplicative emission growth factors resulted in emissions being grown (or
shrunk) in place. This approach does not account for any spatial
redistribution of emissions resulting from population shifts or land use
changes. The grow-in-place assumption is common in air quality modeling
applications, most of which project emissions only 5–15 years into the
future (Woo et al., 2008; Zhang et al., 2010). For modeling time horizons
within this range, the grow-in-place assumption may be reasonable in light of
the many other uncertainties associated with predicting future emissions. The
EPA's Office of Research and Development (ORD) is increasingly interested in
air quality modeling applications that extend well beyond 2030, however. In
its Global Change Air Quality Assessment, ORD examined the impacts of climate
change on air quality through 2050 (e.g., Nolte et al., 2008; US EPA, 2009b;
Weaver et al., 2009). Similarly, the GEOS-Chem LIDORT Integrated with MARKAL for the
Purpose of Scenario Exploration (GLIMPSE) framework is being used to examine
climate and air quality management strategies through 2055 (Akhtar et al.,
2013). The rationale for growing emissions in place is weaker when modeling
over multi-decadal time horizons, where trends such as population growth and
migration, as well as urbanization, may result in a very different future
spatial distribution of emissions.
Land use change models are useful tools for investigating alternative
assumptions regarding the spatial distribution of future-year emissions. For
example, the Integrated Climate and Land Use Scenarios (ICLUS) model
(Theobald, 2005; US EPA, 2009a; Bierwagen et al., 2010) was developed to
provide a consistent framework for producing future-year population and land
use change projections. ICLUS outputs have been generated over the US for a
base case scenario, as well as several alternatives that are consistent with
those described in the Intergovernmental Panel on Climate Change (IPCC)
Special Report on Emission Scenarios (Nakicenovic and Swart, 2000).
The key advancement of ESP v2.0 is the integration of ICLUS results to adjust
the spatial allocation of future-year emissions in the residential,
commercial, transportation, and agricultural sectors. ICLUS results are
integrated into ESP v2.0 in three places. First, we use ICLUS population
projections to adjust energy demands in MARKAL, including passenger vehicle
miles traveled, lumens for lighting, and watts per square foot of space
conditioning. Second, county-level population projections also are used to
disaggregate the regional emission growth factors derived from MARKAL into
county-level growth factors. Finally, ICLUS outputs are used to develop new
future-year spatial surrogates that map county-level emissions to an air
quality modeling grid. The incorporation of ICLUS into ESP v2.0 is depicted
in Fig. 1. The two steps associated with spatial allocation of emissions are
listed as 1 and 2 in the figure.
Schematic diagram showing components of the Emission Scenario
Projection v2.0 system. Dashed blue box contains enhancements from ESP v1.0.
The objective of this paper is to describe, demonstrate and evaluate the new
spatial allocation features within ESP v2.0. First, the typical approach for
spatial allocation in emission processing is described. Next, the new
spatial allocation method is presented and evaluated. The method is then
applied using an experimental design that isolates separately the impacts of
using projected spatial surrogates and those of mapping regional growth
factors to the county level. Conclusions and future plans for ESP v3.0 are
presented in the last section.
Background
In most air quality modeling applications with CMAQ, the SMOKE model is used
to transform an emission inventory, such as the NEI, from a textual list of
sources and their respective annual emissions to a gridded, temporally
allocated, and chemically speciated air quality model-ready binary file.
Major steps in the generation of future emissions for an air quality model
include the application of multiplicative emission growth and control
factors to produce a future-year emission inventory, temporal allocation of
emissions by season, day and hour, and spatial allocation of hourly
emissions onto a 2-dimensional grid over the modeling domain. A major
component of the spatial allocation process is the use of other
high-resolution data, such as census block group population or road
networks, as surrogates to map county-level emissions to grid cells.
Spatial surrogate computation for emission allocation is rarely mentioned in
the documentation of air quality modeling studies. In the US, surrogate
shapefiles (a standard file format for representing spatial data) are
released by the US EPA Emissions Modeling Clearinghouse and are used to
compute spatial surrogates to be used in SMOKE. Most of the surrogate
shapefiles used at the time this analysis was conducted were created from
2000 census data (e.g., population and roads), as well as many other spatial
data sets (such as building square footage and agricultural areas) that were
generated around that time period. Note that the spatial surrogate shapefiles
were subsequently updated in the 2011 EPA modeling platform (US EPA, 2011,
2014a, b).
The surrogate shapefiles are processed to create gridded surrogates using the
Surrogate Tools software package (Ran, 2015), a part of the Spatial Allocator
(SA) system (UNC, 2014a). Figure 2 provides an example of the computation of
a population-based spatial surrogate for a 12 km grid cell within Wake
County, North Carolina, which includes the state's capital, Raleigh.
The total population range for each census block group area for Wake County
and some adjacent counties (dark purple boundaries) in North Carolina is
displayed. The surrogate value for any grid cell (i) and county (j) is
computed as
SurrogateValue(i,j)=SurrogateAttribute(i,j)∑iSurrogateAttribute(i,j).
Wake County's total population, found by summing the population of each of
its census block groups, was 627 846 in 2000. A population of 98 681 lived
within the grid cell indicated by the arrow. The population-based spatial
surrogate value for this grid cell and county is calculated as
98 681/627 846, or 0.1572. Thus, 15.72 % of Wake County
population-related emissions are allocated to this grid cell.
Spatial surrogate values always range from 0 to 1; 0 indicates that no
emissions are allocated to the grid cell (e.g., the grid cell does not
intersect the county), and 1 indicates that all the county's emissions are
allocated to the grid cell (e.g., the county is completely located within
the grid cell). While the example grid cell lies within just one county,
quite often a grid cell can cross multiple county boundaries. When this
happens, a weighting method (area for polygons, length for lines, or number
of points) is used.
As of April 2014, the EPA has 91 different spatial surrogate shapefiles
(e.g.,
population, housing, urban primary road miles) available via the EPA
Emissions Modeling Clearinghouse (US EPA, 2014b). Since each surrogate has to
be generated for each modeling grid domain and air quality modeling often
includes multiple nested domains, the Surrogate Tools and their associated
quality assurance functions make surrogate computation much easier for
preparing emission input to air quality models.
Population-based spatial surrogate computation for CMAQ 12
km modeling grid (blue cells) over Wake County (dark purple polygon), North
Carolina area, from the 2000 census population at the census block group level
(grey color polygons).
Accurate spatial allocation is particularly important for finer-resolution
modeling (e.g., 12 km or less) when multiple modeling grid cells are located
within a county. While most previous CMAQ studies of future air quality have
been conducted at relatively coarse resolutions (≥ 36 km) (Hogrefe et
al., 2004; Tagaris et al., 2007; Nolte et al., 2008), finer resolutions are
becoming more common with the rapid advancement of computing capabilities
(Zhang et al., 2010; Gao et al., 2013; Trail et al., 2014). Thus, considering
landscape changes due to human activities becomes particularly important in
emission spatial allocation for high-resolution air quality modeling over
long time horizons into the future.
Method
Spatial allocation in ESP v2.0 involves the two-step process displayed in
Fig. 1. The models used in the method are listed and described briefly in
Table 1. For this paper, the method is demonstrated for a 2050 emission
scenario, projecting 2005 base-year emissions using growth factors from
MARKAL. We use ICLUS-produced population and housing density projections that
assume county-level population growth in line with the US Census Bureau
projections and a land use development pattern that follows historical trends
(US EPA, 2009a). Following the business-as-usual (BAU) development
assumption, the method is applied to the conterminous US (CONUS) study area, with additional analysis conducted on the
southeastern US. The MARKAL emission projection regions, CMAQ 12 km modeling
domain, and the Southeast area are depicted in Fig. 3. The grid uses a
Lambert conformal conic projection with 299 rows and 459 columns.
Models used in the ESP v2.0 method.
ModelDescriptionMARKALMARKet ALlocation (MARKAL) is an energy system optimization model (Loulou et al., 2004). We use MARKAL with the ESP v2.0 database to characterize scenarios of the transition of the US energy system from 2005 through 2055 in 5-year increments. ESP v2.0 is an updated version of the EPAUS9r_2010_v1.3 MARKAL database (Lenox et al., 2013). The following major sectors are included: electricity production, refineries, other energy-intensive industries, residential, commercial, and transportation. Spatial coverage is the US, and spatial resolution is the US Census division. Outputs include regional-level, energy-related technology penetrations, fuel use, and emissions of air pollutants and greenhouse gases. The ESP v2.0 baseline scenario is calibrated to approximate the AEO 2010. The primary environmental regulations included in the baseline are the Cross State Air Pollution Rule (CSAPR), Tier II mobile emission requirements, and the corporate average fuel efficiency standard that requires 54.5 miles per gallon by 2025. Regulations that have not been finalized are not included.ICLUSThe Integrated Climate and Land Use Scenarios (ICLUS) model is used to develop US population and land use projections through 2100 (US EPA, 2009a). The demographic model consists of a cohort-component model and a gravity model. Together, these produce future county-level population estimates. A land use change model then computes corresponding housing density at the hectare resolution, or 10 000 m2. Input assumptions regarding household size and travel times can be adjusted to allow different scenarios to be represented. We use a baseline scenario intended to be generally consistent with US Census Bureau projections.SMOKEThe Sparse Matrix Operator Kernel Emissions (SMOKE) modeling system is used to transform an emissions inventory into the emissions format needed for air quality modeling (UNC, 2014b). Specific steps carried out by SMOKE typically include applying growth and control factors, spatially allocating emissions to a modeling grid, temporally allocating emissions to represent seasonal and diurnal patterns, and speciating emissions to provide more detail and account for additional factors such as temperature.Surrogate toolsA set of programs used to develop spatial surrogate files for SMOKE (UNC, 2014a). These surrogates are then used to map emissions to grid cells.CMAQThe Community Scale Air Quality (CMAQ) modeling system is used to characterize meteorology, pollutant transport and chemical transformation, and resulting air pollutant concentrations (UNC, 2012). CMAQ can be applied at a variety of scales, and is commonly used for urban, state, and regional air quality modeling applications within the US and around the world.
CMAQ 12 km modeling domain (blue box) showing nine MARKAL emission
projection regions (dark purple) and the Southeast area (black box).
Figure 4 shows county-level population growth factors over the CONUS as well
as 2005 and 2050 housing densities in the North Carolina, South Carolina, and
Georgia areas. In the ICLUS projection, there is a distinct trend of
population shifts towards big cities (e.g., Atlanta, Georgia and Charlotte,
North Carolina) and a resulting increase in housing density around those
urban areas. In general, county populations increase in most southern and
coastal counties but decrease in northern and inland rural counties. The
approaches for using these ICLUS projections to disaggregate regional
emission growth factors and create future-year spatial surrogates are
presented below.
County-level population growth factors (2050/2005) (top) and
ICLUS housing densities for 2005 and 2050 (bottom) for the Southeast area
shown in Fig. 3. Areas in white are designated as undevelopable.
Developing county-level emission growth factors
MARKAL outputs include regional growth factors for energy-related source
category codes (SCCs). SMOKE projection packets with growth factors for each
species and source category of interest were generated, as described by
Loughlin et al. (2011). The six emission source sectors (US EPA, 2011)
included in this projection were
point sources from the electric generating utility (EGU) sector
non-EGU point sources (e.g., airports)
remaining non-point sources (area sources not in agriculture and fugitive dust sectors)
on-road mobile sources (e.g., light duty vehicles)
non-road mobile sources (e.g., construction equipment)
mobile emissions from aircraft, locomotives, and commercial marine
vessels.
Though MARKAL-generated regional growth factors capture large-scale emission
growth patterns, they do not capture variation in growth from one state to
another or from one county to another within the region. To capture this
spatial variation while maintaining the overall regional growth pattern from
MARKAL, we introduce an adjustment calculation.
Let Fp denote the regional population growth factor and
fp denote the county-level population growth factor. The ratio of
fp over Fp captures the relative population growth rate
of a county in comparison to its region (e.g., fp/Fp= 1
means the same growth rate, and fp/Fp> 1 means the
county population growth is faster than the regional average growth). The
regional emission growth factor Fe is adjusted by this ratio in
computing the initial county emission growth factor fe′:
fe′(r,j,SCC,s)=Fe(r,SCC,s)⋅fp(r,j)Fp(r),
where r is the region,
j is a county within r, and s is the species. To ensure that the total
regional projected emission is preserved after applying the county-level
growth factors, the projected county emissions are re-normalized as
e2050(r,j,SCC,s)=fe′(r,j,SCC,s)⋅e2005(r,j,SCC,s)⋅Rre(r,SCC,s),
where e2005 and e2050 are county-level emissions for 2005 and 2050
and Rre is the ratio of regional emissions computed using regional
growth factors to regional emissions derived from county growth factors:
Rre(r,SCC,s)=Fe(r,SCC,s)⋅∑je2005(r,j,SCC,s)∑jfe′(r,j,SCC,s)⋅e2005(r,j,SCC,s).
The final county emission growth factors (fe) are then computed as
fe(r,j,SCC,S)=e2050(r,j,SCC,s)e2005(r,j,SCC,s).
For source categories expected to have emission changes correlated with
population changes, the resulting set of fe(r,j,SCC,s)
factors are then used to grow the matching county-level emissions into the
future. A spreadsheet with example calculations is included in the
Supplement that accompanies this manuscript.
Changes in the spatial distribution of some emissions will not necessarily be
correlated with population shifts, however. For example, we use regional
emission growth factors, Fe(r,SCC,s), for electric
utilities, large external combustion boilers, and petroleum refining.
We applied ESP v2.0 to grow the 2005 NEI (US EPA, 2010) inventory to 2050.
Figure 5 displays representative county-level emission growth factors. The
two plots on the left are the MARKAL regional growth factors for NOx
from highway light duty gasoline vehicles (LDGVs) and for SO2 from
residential stationary source fuel combustion, both of which would be
expected to be correlated with population. The overall regional emission
trends are driven by population growth, fuel switching and regulations that
limit emissions. The county-level growth factors illustrate the effects of
projected county-by-county population changes on these overall trends. Using
county-level emission growth factors, we then generated SMOKE projection
packets and used SMOKE to grow the emission inventory to 2050.
Light duty gasoline vehicle (LDGV) regional NOx growth
factors, generated by MARKAL, are shown in the top left panel. The top right
panel shows corresponding county-level growth factors after adjustments are
made to account for ICLUS county-level population changes. Similarly, the
bottom two panels show regional- and county-level SO2 growth factors
for residential combustion, before and after population-based adjustments
have been made.
Updating surrogate shapefiles and emission surrogates
The next step in spatial allocation is to create surrogate shapefiles using
ICLUS-projected population and housing density. Standard EPA population and
housing surrogate shapefiles are slightly different from 2005 ICLUS data. To
avoid this discrepancy and ensure that surrogate shapefiles are generated
consistently for comparison, ICLUS data are used to develop both the 2005
base and the 2050 shapefiles.
Surrogate shapefiles
Using ICLUS data, we created four new surrogate shapefiles for both 2005 and
2050. The first shapefile contains census block group polygons with
associated population, housing units, urban, and level of development (e.g.,
no, low or high). The census polygon boundaries are based on the EPA 2002
population surrogate shapefiles. For each census block group, ICLUS housing
units are spatially allocated to the census polygon using the area weighted
method. Then, ICLUS county population is allocated to each census block group
within a county according to the fraction of the county's housing units
within that block group. Using ICLUS outputs for 2000, 2005, 2040, and 2050,
we computed housing unit changes from 2000 to 2005 and from 2040 to 2050,
which are needed for housing unit change surrogate computations for 2005 and
2050. For both 2005 and 2050, we classified census block groups as urban if
their ICLUS-produced population density per square mile is ≥ 1000. This
criterion is partially consistent with the US Census Bureau's definition of
an urban area although, for simplicity, we did not use the Census Bureau's
requirement of the surrounding area having a total population of 50 000 or
more. In addition, census block groups were classified into no, low, or high
development areas based on housing density.
Figure 6 shows the change in population and urban surrogate shapefile data
over the Southeast region between 2005 and 2050. The figure indicates
expansion of urban areas, including Atlanta, Charlotte, Greensboro, and
Raleigh. However, some rural areas, particularly in the north and south of
this region, display slightly decreasing population densities.
ICLUS population density and urban shapefiles
for 2005 are shown on the left. Difference plots indicating ICLUS-predicted
changes to these metrics from 2005 to 2050 are shown to the right.
The second surrogate shapefile we generated contains road networks. Though
road networks are likely to expand in the future, it is very difficult to
project future road networks. We use existing current road surrogate
shapefiles with the ICLUS-identified urban areas to classify roads into four
categories: rural and urban primary roads and rural and urban secondary
roads. These categories are required for surrogate computation for mobile
emission allocations. The third surrogate shapefile we generated contains
rural land classification. We created this shapefile from the EPA 2002 rural
land surrogate shapefile using urban and non-urban areas identified in the
first shapefile. The last surrogate shapefile we created contains
agricultural land classes. This shapefile was created from the EPA 2002
agricultural land surrogate file by excluding urban areas identified in the
first shapefile.
Surrogates computation
With the ICLUS-based surrogate shapefiles, we computed 2005 and 2050
surrogates using the Surrogate Tools. As noted previously, EPA employs a set
of 65 spatial surrogates to allocate emissions from various source sectors to
a gridded modeling domain. The 17 surrogates listed in Table 2 were computed
using the four ICLUS-based shapefiles. We assumed that the other 48
surrogates remain unchanged from current EPA surrogates.
ICLUS-based surrogates generated for 2005 and 2050.
Surrogate nameSurrogate codePopulation100Urban population110Rural population120Housing change130Housing change and population137Urban primary road miles140Rural primary road miles200Urban secondary road miles210Rural secondary road miles220Total road miles230Urban primary plus rural primary road miles2400.75 total roadway miles plus 0.25 population255Low intensity residential300Total agriculture310Rural land area400Residential – high density500
The percentage change of ICLUS population-based surrogates from 2005 to 2050
is shown in Fig. 7. As expected, population-based surrogate changes on the
12 km grid follow the trends shown in Fig. 4. Since surrogates for the grid
cells intersecting a county necessarily sum to 1, large surrogate increases
(red colors) in some grid cells are often accompanied by large decreases
(blue colors) in other grid cells within the same county. Large percentage
changes are particularly obvious in sparsely populated areas, such as parts
of California, Nevada, Arizona, New Mexico, Texas, and Florida. The mean
change of population-based surrogates from 2005 to 2050 is 6.23 %,
although a standard deviation of 46.96 % indicates a wide range across
the grid cells.
Application
We applied ESP v2.0 to generate 2005 and 2050 CMAQ-ready gridded emission
files. Only the six sectors listed above from the 2005 NEI were used in the
2050 projection. Emissions from any SCCs not included in the projection
packets were held constant from 2005.We used the Emission Modeling Framework
(Houyoux et al., 2006) to conduct SMOKE modeling tasks.
Next, two additional 2050 inventories were created, one using the regional
growth factors from MARKAL and one using the surrogates based upon 2005 ICLUS
results. The four resulting gridded inventories that were developed are
listed in Table 3.
Standard and sensitivity runs for ESP v2.0 demonstration and
evaluation.
Population-based surrogate change (%) for CMAQ 12 km
modeling grids.
Future represents the result of the full ESP v2.0 projection method.
Comparing Future with Base thus reveals the projected
changes in both magnitude and location of emissions over the 45-year period.
Comparing Future with FutureRegGF isolates the effects of
disaggregating regional growth factors to the county level. Similarly,
comparing Future with Future05Surr identifies spatial
changes resulting from updating the future spatial surrogates.
The fractional difference (FD) metric is used to evaluate grid-level
differences among the inventories. For a model grid cell (i) and species
(s), the FD is calculated as
fractional difference (FD)=2⋅eA(i,s)-eB(i,s)eA(i,s)+eB(i,s)⋅100,
where eA(i,s) and eB(i,s) are the emissions of species
s
in grid cell i for the gridded inventories, A and B, that are being
compared. FD is generally called fractional bias when it is used to evaluate
errors of modeling results against observations (e.g., Morris et al., 2006).
FD is a symmetric metric ranging from -200 to +200 %. A value of
67 % for FD represents that eA is larger than eB by a
factor of 2, while an FD of 0 means that values are the same. The mean and
standard deviation of FD values across groups of grid cells provide
information about the magnitude and variability of differences between two
gridded inventories. Other statistical metrics can be used to evaluate
differences from one gridded inventory to another. Several such metrics are
described and applied in the Supplement.
Base and future emission differences
Figure 8 shows FDs between annual emissions in the Base and
Future for each of the six projected pollutant species. These plots
reflect the combined effects of population growth and migration, economic
growth and transformation, fuel switching, technological improvements, land
use change, and various regulations limiting emissions (Loughlin et al.,
2011). Most of the US has more than a 30 % reduction (green and blue
colors) in modeled NOx, SO2, CO, VOCs, PM2.5 and PM10.
Grids with emission increases for these six species are mainly located in
areas projected to have high population growth (e.g., Los Angeles and
Atlanta). Among the six species, NOx and SO2 show reductions of
more than a factor of 2 in many areas because of control requirements on
electricity production, transportation, and many industrial sources.
Emissions of CO, VOCs, PM2.5 and PM10 also fall across most of the
domain.
FD (%) of annual emissions,
Future minus Base, over the 12 km CONUS domain. (Future: 2050 inventory, 2050 surrogates,
county growth factors; Base: 2005 inventory, 2005 surrogates).
Region-to-county growth disaggregation
Next, we evaluate the effect of disaggregating regional growth factors to the
county level by examining the differences between Future and
FutureRegGF. Grid-cell-level FD values are shown in Fig. 9 for the
six projected pollutants. The spatial distribution of FD indicates that
regional-to-county disaggregation results in increased emissions around urban
areas (e.g., Los Angeles, Las Vegas and Dallas in the west and Atlanta in the
Southeast) as those areas expand into surrounding counties. Many grid cells
at the fringe of large urban areas have FD values exceeding 30 %,
indicating a large increase in emissions as a result of using county-level
growth factors. Large reductions in emissions, indicated by FD values ≤-20 %, are particularly obvious in rural areas in the west and south
regions. Using county growth factors has high impacts on emission
allocations in the regions of the west and south, particularly for SO2.
FD (%) of annual 2050 emissions,
Future minus FutureRegGF, for grid cells in the CONUS 12 km domain. (Future: 2050 inventory, 2050
surrogates, county growth factors; FutureRegGF: 2050 inventory, 2050 surrogates,
regional growth factors).
Another way to analyze FD results is to calculate mean FD (MFD) values
across grid cells with common characteristics. For example, in Fig. 10, we
provide MFDs for each pollutant over grid cells that are in the same
population density range.
For areas with greater density, the trend is that emission differences become
increasingly positive, reflecting that ICLUS population algorithm typically
results in migration of people to more dense areas. However, as described
above, the ICLUS predicts continued urban sprawl such that the positive MFD
in the urban cores (population density
>= 200 k grid-cell-1,
about 1400 km-2) is
slightly less than in the more moderately dense areas, where density is
between 130 and 200 k grid-cell-1. Thus, projecting emission changes by
region without using the county growth allocation method significantly
underestimates the future emissions in the more populated areas.
Updating emission surrogates
Next, we evaluate the effects of adjusting future surrogates by comparing
Future and Future05Surr. The two gridded emission files
were generated from the same 2050 county-level emission growth factors but
using ICLUS-derived surrogates for 2050 and 2005, respectively. Thus,
emission differences are introduced only from different spatial surrogates.
Figure 11 presents the resulting FD values for the six projected pollutants.
MFD (%) of 2050 annual
emissions, Future minus FutureRegGF, stratified by grid-cell population for 2050. (Future: 2050
inventory, 2050 surrogates, county growth factors; FutureRegGF: 2050 inventory, 2050
surrogates, regional growth factors).
In Fig. 11, it is apparent that large increases (FD ≥ 20 %) often
occur in the grid cells surrounding large cities. Furthermore, FD percentage increases
are particularly obvious in the west and southwest regions, where urban
expansion moves into previously low density grid cells. The counties in these
regions tend to be large; thus, changes in spatial surrogates affect a larger
number of grid cells. In contrast, changes in gridded emissions tend to be
less pronounced in areas with small counties that are closer in size to the
12 × 12 km grid cells. Updating the spatial surrogates has a small
or negligible impact in rural areas with limited urbanization. Among the six
species compared, SO2 has the least changes. SO2 emissions from
mobile sources would have been reduced considerably by regulations limiting
sulfur content in fuels. Most of the remaining SO2 emissions originate
from electricity production and industrial sources. In the ESP v2.0 method,
we do not adjust the spatial surrogates for either category, assuming that
they are not correlated with population. In contrast, incorporating the 2050
surrogates has particularly high impacts on CO and VOCs. Major sources for
these pollutants are the transportation, residential and commercial sectors,
all of which are linked to population- and land-use-based surrogates.
Figure 12 also provides an indication of how updating surrogates affects
emissions by land use class. MFDs for each of the six
pollutants by 2050 population density ranges are shown in Fig. 12. This
figure indicates a complicated relationship. There is a small decrease in
emissions in rural areas and a larger decrease in the densest areas.
Conversely, there is an increase in emissions from categories ranging in
density from 5 to 80 k per cell. Thus, emission modeling using 2050 surrogates
allocates more emissions to the suburban areas as they densify, while
emissions allocated to the high density urban core grid cells are reduced.
This does not mean that populations in cities are projected to decline but
rather that the projected urban emissions are partially redistributed to the
fringe areas since county emission totals are the same for both scenarios.
This analysis demonstrates that the common practice of projecting future
emissions without projecting future surrogates can lead to over-prediction of
urban core emissions and under-prediction of suburban/exurban emissions.
FD (%) of annual 2050 emissions,
Future minus Future05Surr, for grid cells in the CONUS 12 km domain. (Future: 2050 inventory, 2050
surrogates, county growth factors; Future05Surr: 2050 inventory, 2005 surrogates, county
growth factors).
Conclusions
Gridded emission data are key inputs to air quality models. Pollutant growth
factors play a dominant role in determining regional emission and air quality
patterns (Tao et al., 2007; Avise et al., 2012). It is commonplace in such
applications to apply these growth factors such that emissions grow in
place. In this paper, we demonstrate that the region-to-county growth factor
disaggregation and county-to-grid allocation approaches included in ESP v2.0
yield a different spatial pattern of emissions. For a given population and
land use change scenario, the region-to-county growth disaggregation enables
the distinction of different growth levels among counties, and updating
spatial surrogates provides a more realistic mapping of emissions to grid
cells.
Conversely, growing residential emissions in place and applying current
spatial surrogates to future-year emissions may result in an over-prediction
of urban core emissions and under-prediction of suburban emissions. Thus,
ignoring these shifts may overstate future improvements in human exposure
and health risk due to air pollution mitigation as more dense urban cores
yield greater opportunities for human exposures (e.g., Post et al., 2012;
West et al., 2013; Silva et al., 2013).
MFD (%) of 2050 annual
emissions, Future minus Future05Surr, stratified by 2050 grid-cell population. (Future: 2050
inventory, 2050 surrogates, county growth factors; Future05Surr: 2050 inventory, 2005
surrogates, county growth factors).
There are many uncertainties in future air quality studies associated with
emissions, climate, and changes of landscape. Improving emission allocation
in SMOKE will help reduce uncertainties in outcomes (e.g., O3 and
PM2.5 concentrations and climate forcing from gases and aerosols) from
regional climate and air quality modeling systems such as the coupled
WRF/CMAQ (Wong et al., 2012) and help improve confidence in making air
quality policies related to human health and environment. Another important
aspect of the approach presented here is that it could be applied to examine
alternative development scenarios. For example, a smart growth scenario would
project greater growth factors in cities and less in suburban/exurban areas
than the BAU scenario on which ICLUS was based. Furthermore, within the
larger ESP v2.0 framework, emissions and resulting impacts could be examined
for wide ranging scenarios that differ in assumptions about population growth
and migration, economic growth and transformation, technology change, land
use change, and various energy, environmental and land use policies.
While ESP v2.0 represents a state-of-the-art method for generating
multi-decadal air pollutant emission projections for non-power sector
sources, there are a number of limitations that must be considered in
evaluating its utility for specific applications. One such limitation is the
current omission of a mechanism to change the spatial distribution of power
sector and large industrial emission sources. Spatial re-allocation of these
“point” source emissions requires a siting algorithm, the development or
application of which is beyond the scope of ESP v2.0. We acknowledge that
this is a desirable capability, however, and that considerable research has
been conducted in this area (e.g., Cohon et al., 1980; Hobbs et al., 2010;
Kraucunas et al., 2015).
Another limitation of ESP v2.0 is that temporal re-allocation of emissions is
not included at this time. Our research suggests that the changing role of
technologies and fuels in electricity production may affect seasonal and
diurnal emission patterns. For example, natural gas historically has been
used within combustion turbines to generate electricity for meeting summer
afternoon air conditioning demands. With expanded access to natural gas
resources, however, electric utilities are incrementally shifting gas to
baseload electricity production. Thus, over the coming decades, the temporal
profile of gas-related emissions will change both seasonally and diurnally.
ESP will always be limited by the limitations of its components. The MARKAL
energy modeling system, for example, does not account for economic feedbacks
associated with changes in energy prices. Also, real-world electric sector
decisions are influenced by many factors, some of which act at a much finer
resolution than the spatial and temporal resolution of MARKAL. For example,
on hot summer days, electric utility dispatch decisions must factor in
meteorological conditions that both increase energy demands and tropospheric
ozone formation (Chen et al., 2015). Dispatch decisions thus might result in
temporal and spatial changes that could not be captured by MARKAL. ESP v2.0
is more suited to longer-range projections with the intent on capturing
long-term trends and the multi-decadal effects of transformations in energy,
economy and land use. Alternatively, there may be approaches for using ESP
in conjunction with a more detailed dispatch model.
Another current limitation is the inability to evaluate the effects of
climate change on energy demands. Climate-related changes currently would
need to be evaluated outside of ESP v2.0. However, exogenous estimates of
increased energy demands could be input into MARKAL to evaluate how they
would affect energy system emissions.
These various limitations are driving our current ESP v3.0 development
process. For example, we are working towards generating scenario-specific
temporal adjustment factors, and we plan to explore the inclusion of point
source siting algorithms. Furthermore, future ESP iterations will
incorporate more recent versions of ICLUS and MARKAL and thus utilize
updated population, land use, economic, and energy projections, as well as
recent emission regulations.
Other possible updates are being considered. To improve compatibility with
other long-term projections, it may be advantageous to harmonize the
population, land use and energy assumptions with the IPCC's representative
concentration pathways (RCPs) (Van Vuuren et al., 2011) and shared
socioeconomic scenarios (Van Vuuren et al., 2012). Also, while the baseline
spatial surrogates used here were developed in 2000, these could be updated
to the 2010 surrogate files that are now used within the EPA's 2011 modeling
platform.
Model and data availability
Most of the modeling components that comprise this methodology are publically
available. SMOKE and the Spatial Allocator can be downloaded from the
Community Modeling & Analysis System Center
(http://www.cmascenter.org). ICLUS modeling tools and land use
projections can be obtained from the US EPA
(http://www.epa.gov/ncea/global/iclus/). The MARKAL model is
distributed by the Energy Technology Systems Analysis Program of the
International Energy Agency (http://www.iea-etsap.org). Executing
MARKAL requires licensing and additional software. Please contact Dan
Loughlin (loughlin.dan@epa.gov) for information about obtaining the US EPA's
database, which allows MARKAL to be applied to the US energy system. The
EPA's MARKAL nine-region database used in this study, as well as more recent
versions, are available upon request at no cost. Regional- and county-level
emission growth factors and surrogate shapefiles for 2005 and 2050 are
available for download in the Supplement.
The Supplement related to this article is available online at doi:10.5194/gmd-8-1775-2015-supplement.
Limei Ran was the lead author and the lead in designing, implementing and
demonstrating the spatial allocation component of ESP 2.0. Dan Loughlin
conceived of the project and was instrumental in developing the spatial
allocation method. Furthermore, he provided the emission growth and control
factors used to develop the future-year inventory. Dongmei Yang, Zach Adelman
and B. H. Baek assisted with the development and implementation of the
method, including applications of the various emissions modeling components.
Chris Nolte was instrumental in developing ESP 1.0 and contributed to this
effort through a thorough review and constructive comments on this
manuscript.
Acknowledgements
Much of the effort of developing, implementing and demonstrating the spatial
allocation method embodied in ESP 2.0 was funded by the US Environmental
Protection Agency Office of Research and Development. Alison Eyth, of the
US EPA's Office of Air Quality Planning and Standards, contributed to a
previous implementation of the spatial allocation method. ICLUS-related land
use projections were provided by Phil Morefield and Britta Bierwagen of the
US EPA's National Center for Environmental Assessment (NCEA). William Benjey
helped develop ESP v1.0 and reviewed this manuscript. Others contributing the
emission growth factor projections are current and past members of the Office
of Research and Development Energy and Climate Assessment Team, including
Carol Lenox, Rebecca Dodder, Ozge Kaplan and
William Yelverton.Disclaimer. While this work has been reviewed and
cleared for publication by the US EPA, the views expressed here are those of
the authors and do not necessarily represent the official views or policies
of the Agency. Mention of software and organizations does not constitute an
endorsement. Edited by: J. Williams
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