Transcript Document
Developing Alternate Anthropogenic Emission Scenarios for Investigating Future Air Quality
William G. Benjey, Daniel H. Loughlin*, Christopher G. Nolte and Robert W. Pinder
U.S. Environmental Protection Agency, National Exposure Research Laboratory, Atmospheric Modeling and Analysis Division
*U.S. Environmental Protection Agency, National Risk Management Research Laboratory, Air Pollution Prevention and Control Division
Abstract: Alternate technologies, emission controls
and carbon policies were used with the MARKAL energy
system model in conjunction with a base inventory and the
SMOKE emission model, to develop alternate anthropogenic
emission scenarios for the year 2050. The emission differences
between scenarios were not as large as anticipated, providing
guidance for future efforts.
Motivation: The U.S. Environmental Protection
Agency’s Climate Impact on Regional Air Quality (CIRAQ)
program initially examined the effect of climate change in
about 2050 with no change in anthropogenic emissions (Nolte
et al, 2008). The second phase of the program (CIRAQ 2)
addresses air quality effects pf plausible future scenarios.
Consequently, an approach to generating scenarios for use
with the EPA Community Multiscale Air Quality (CMAQ) was
developed to produce future instantiations of emissions for the
United States.
3. Make additional changes to examine sensitivities to a wide
range of inputs..
4. Examine different times samples from existing scenarios
(e.g. 2030). Figure 10 shows the variability of change in
time.
5. Examine the response to criteria pollutant response to
increasingly stringent CO2 targets, and the impact of more
stringent criteria pollutant targets on CO2..
Approach: Estimation of future year emissions was
accomplished by using factors developed by the Market
Allocation (MARKAL) energy economic equilibrium model
(Fishbone and Ablilock, 1981). These factors were applied to a
detailed base emission inventory developed for use in air
quality modeling (EPA 2002ae modeling inventory). MARKAL
allows consideration of alternative assumptions about
population growth and migration, economic growth and
transformation, land use change, technology change and
alternative criteria and greenhouse gas policy directions.
Figure 1 summarizes the energy demand sectors used by
MARKAL.
The EPA developed a 9-region (Figure 3) U.S. version of
MARKAL to create technology-specific scenarios and pollutantspecific projection factors at the 3-digit source category code
(SCC) level for energy system sources within the electricity
production, industrial, commercial, residential and
transportation sectors. Regional factors were created for CO2,
NOx, SO2, and PM10. In lieu of pollutant-specific information,
CO2 factors were used for emissions of Volatile Organic
Compounds (VOCs) and carbon monoxide (CO) since CO2
was deemed a reasonable indicator of electric generation and
industrial combustion. For mobile source emissions, NOx
projection factors were used as indicators of CO and VOC
emissions. PM2.5 factors were assumed proportional to PM10
factors.
Factors for emissions from industrial processes and
other sources that are not part of the energy system model
were estimated based on national level energy demand or
industrial shipment projections, in that order of preference, from
an economic model.
The projection factors were mapped to source category codes
(SCCs) and applied to the 2002ae inventory using the Sparse
Matrix Operator Kernel Emission (SMOKE) system (Figure 3)
to estimate six alternative scenario emission inventories
(Houyoux and Adelman, 2004) . Emissions were assumed to
grow in place and were not spatially redistributed.
SMOKE also applied spatial and temporal allocation to the
projected emissions, along with chemical speciation grouping
(SAPRC 99 mechanism) and placed the emissions in a 36km
resolution North American grid for use by the Community
Multiscale Air Quality (CMAQ) model run as a regional climate
model. Figure 4 illustrates the flow of data between MARKAL,
SMOKE and related components
In the intermediate and longer-term (2010 and beyond),
National and regional emission scenarios may be nested in
the IPCC AR5 scenarios
Results and Comparisons:
Examination of all scenarios total annual emissions shows the
expected reductions from the 2002 inventory (Figure 4),
primarily because of existing planned emission limits. The
amounts and patterns of annual total emissions by pollutant
and region between scenarios are similar, with some
variations to reflect differing assumptions The overall
emissions decrease for each scenario (1 – 6) reflecting their
increasing application of controls, carbon policies and hybrid
vehicles. However, the relative amount of pollutants and the
distribution between regions remain nearly the same. Figures
5 (Scenario 1) and 5 (Scenario 6) show the consistent pattern
and range of emissions
Scenarios and Rationale: Six
scenarios were developed using MARKAL with the intent of
exploring the effects of substantially different plausible drivers of
anthropogenic emissions for the year 2050. The scenarios all
reflected the A1 population growth assumptions of the
International Panel on Climate Change (IPCC) as modified at the
county level by the ICLUS-SERGOM (Integrated Climate Land
Use-Spatially Explicit Regional Growth Model) population and
land cover projection models (IPCC,2000). The principal
assumptions varied in MARKAL included: (1) additional controls
applied to large emission sources (e.g., electric generating
plants), (2) carbon limitation policies (3) optimistic assumptions
about the market penetration of high-technology vehicles and (4)
combinations of the above variables. All scenarios assumed
existing laws and regulations, including ongoing efforts to meet
existing standards through State Implementation Plans, the Tier 2
on-road vehicle emission limits and and the Energy Independence
and Security Act (EISA) (Table 1). Table 2 provides a summary
description of the assumptions applied for each scenario.
Quarterly emissions demonstrate a similar pattern, except for
the third quarter for all scenarios. Example Figure 7 shows
that third quarter emissions for the Scenario 6 are relatively
Conclusions: This study successfully
greater, mainly due to increased CO2 emissions that are
mapped to CO and VOC by MARKAL for electric generation
units (EGU). In addition SMOKE applies seasonal factors by
EGU because of seasonal changes in fuels and technologies
(coal, gas, etc). Figure 8 shows the difference factors from
2002 for EGU emissions for the third quarter. However, future
seasonal operations may be very different from current. For
example, increased use of natural gas combined-cycle
technologies may decrease the seasonal variability of natural
gas emissions. This is one example of the complex
interactions that can occur.
.
In the short term
(a year), addition scenarios may be developed by the
following approaches:
1. Examine alternative assumptions about population and
economic growth.
2. Examine alternative time frames for new technology
availability in MARKAL.
Future Approaches:
demonstrated the ability to develop long-range future emission
inventories using tools such as MARKAL and SMOKE. While
the pollutant levels in the scenario inventories did not differ as
much as expected, insights learned in the process will be used
to develop scenarios for the next phase of the analysis.
.
References:
Fishbone, L.G. and H. Abilock, 1981: MARKAL: A linearprogramming model for energy-systems analysis: technical
description of the BNL version. Int. J. Energy Res.,5,4, 353375.
Houyoux, M.R., and Z. Adelman, 2004: Quality assurance
enhancements to the SMOKE modeling system. Preprints,
Tenth Annual Emission Inventory Conference, Denver, CO,
U.S. Environmental Protection Agency, 12 pp.
IPCC, 2000; Special Report on Emission Scenarios. Tech.
Rep., Intergov. Panel on Clim. Change, New York.
Nolte, C.G., A.B. Gilliland, C. Hogrefe, and L.J. Mickley, 2008:
Linking global to regional models to assess future climate
impacts on surface ozone levels in the United States. J.
Geophys. Res. 113, D14307, 14 pp.