Title: Atmospheric Modeling and its Application to Energy and the Environment: From Local Impacts to Climate Change
1Atmospheric Modeling and its Application to
Energy and the Environment From Local Impacts to
Climate Change
- Amit Marmur, , K. Manomaiphiboon , , and
- Armistead (Ted) Russell
- Georgia Power Professor
- Georgia Institute of Technology
2With Special Thanks to
- NIEHS, US EPA, FHWA, Southern Company, SAMI
- Financial assistance
- JGSEE of Thailand
- And more
3Issues
- Energy sources contribute to local, regional and
global air pollution problems - Local CO, Particulate matter, air toxics
- Regional Particulate matter, acid deposition,
ozone - Global CO2 and climate change
- Primary and secondary pollutants impact health
- Approximately 799,000 premature deaths yearly
- Mainly due to particulate matter and ozone
- Emissions from energy sources undergo complex
atmospheric processing - Turbulent atmospheric transport
- Non-linear chemical reactions (e.g., produce
ozone) - Deposition
- Feedbacks between meteorology and air pollution
- Climate change
4Ozone Formation
h? (sunlight)
Ozone Isopleth
ENOx
NOx oxides of nitrogen (NO NO2)
High O3
O3
Low O3
EVOC
VOCs Volatile organic compounds
5PM Formation
h? (sunlight)
SO2 Sulfur dioxide
PM
NOx
VOCs, OC EC
6Particulate Matter
- Complex mixture of solid and liquid particles
suspended in the ambient air - Size classifications
- super-coarse gt 10µm
- coarse (PM10) lt 10µm
- fine (PM2.5) lt 2.5µm
- ultrafine lt 0.1µm
- Many sources
- Many chemical species
7Outline
- Air quality modeling
- Basics
- Approaches
- Advanced approaches
- Applications
- Source impacts
- Climate impacts on air quality
8Emissions-based Air Quality Model
- Representation of physical and chemical processes
- Numerical integration routines
- Scientifically most sound method to link future
emissions changes to air quality
Computational Planes
5-20
50-100
50-200
Air Quality Model
Atmospheric Diffusion Equation
Numerics CAxBE
Discretize
Emissions
Meteorology
Operator splitting
200 species x 10000 hor. grids x 20 layers 40
million coupled, stiff non-linear differential
equations
9Air Quality Model
10Atmospheric Modeling Process
11Grids
Adaptive (Odman et al.)
Nested
About 15 vertical Layers up to 15 km (many in
first 1 km)
Multiscale (Odman et al.)
12How well do they work?
Performance relies on quality of inputs. US has
spent decades on emissions inventory development.
Meteorological modeling also contributes
significantly to errors
13Whats next?
- Emissions-based air quality models work pretty
well, how might we use them - Identify, quantitatively, how specific sources
impact air quality. - Develop and test control strategies
- Decoupled direct method (implemented in CIT, URM,
MAQSIP, CMAQ, CAMX) - Dunker initial applications
- Yang et al. large scale application, comp.
efficient (CIT, URM) - Hakami et al. ,Cohan et al Higher order, with
applications (MAQSIP, CMAQ) - Napelenok et al., PM
- Control strategy assessment
- Least cost approach to attainment for Macon, GA
(Cohan et al.) - Assessing impacts of individual sources
- Climate impacts on air quality
14Example Results Impact of Planned Controls
2000 vs. 2007
Emissions reductions lead to about a 12 ppb ozone
reduction Atlanta and Macon do not attain ozone
standard (Macon by 6ppb)
15Sensitivity analysis
- Given a system, find how the state
(concentrations) responds to incremental changes
in the input and model parameters
If Pj are emissions, Sij are the
sensitivities/responses to emission changes,
e.g.., the sensitivity of ozone to Atlanta NOx
emissions
16Sensitivity Analysis with Decoupled Direct
Method (DDM) The Power of the Derivative
- Define first order sensitivities as
- Take derivatives of
- Solve sensitivity equations simultaneously
17DDM-3D
NOo NO2o VOCio ... T K u, v, w Ei ki BCi ...
3-D Air Quality Model
O3(t,x,y,z) NO(t,x,y,z) NO2(t,x,y,z) VOCi(t,x,y,z)
...
decoupled
DDM-3D Sensitivity Analysis
J
18DDM compared to Brute Force
Sulfate
Emissions of SO2
19Consistency of first-order sensitivities
Brute Force (20 change) DDM-3D
R2 gt 0.99 Low bias error
20Advantages of DDM-3D
- Computes sensitivities of all modeled species to
many different parameters in one simulation - Can tell model to give sensitivities to 10s of
parameters in the same run - Captures small perturbations in input parameters
- Strangely wonderful
- Avoids numerical errors sometimes present in
sensitivities calculated with Brute Force - Lowers the requirement for computational
resources
21Evidence of Numerical Errors in BF
NH4 sensitivity to domain-wide SO2 reductions
NOx reductions at a point
22Efficiency of DDM-3D
23Complication Nonlinearity
- Often, only a handful of sensitivities are
modeled (e.g., 30 NOx reduction) - Assumptions of scaling and additivity not
necessarily accurate - But it may be impractical to model all
combinations
24Calculation of higher-order derivativesIf
taking the derivative once is good, twice must be
better
- High-order Decoupled Direct Method HDDM, (Hakami
et al., 2003)
(n.b. gt third order derivatives numerically
sensitive)
25Brute Force and HDDM-3D
Ozone
A
CA
?C
a1
B
CB
EVOC
-DeEA
EB
EA
26Control Strategy Development
- Macon out of attainment by 6 ppb in 2007
- Want to identify least cost control strategy
- Process
- Identify possible controls and costs (/ton of
VOC or NOx) - Simulate response to controls (DO3/ton VOC or
NOx) - Calculate control effectiveness(DO3/)
- Choose most effective controls until get 6 ppb
- Test strategy
27 Macon Scherer
Sources of Macons ozone
A
B
S
M
Atlanta Branch
8-hr ozone, Aug. 17, 2000 (2007 emissions)
28Sensitivity of 8-hr Ozone in Macon
292007 Emissions and Sensitivities
NOx emission rates (tpd)
Macon ozone sensitivity (ppt/tpd)
30Marginal Abatement Costs by Region
Cost
Cost-optimization Choose options with
least marginal /impact until (1) attain a.q.
goal, or (2) reach budget constraint
Impact
Source-Receptor Response
31Strategies for Macon attainment (need 6.5 ppb)
- Key Measures
- Zero-cost options (PRB coal, burning ban, ...)
1.72 ppb, 0 - Bibb industrial NOx 0.82 ppb, 2.6 million
- Locomotive controls 0.77 ppb, 7.3 million
- SCRs at Scherer 1.63 ppb, 20.9 million
- Vehicle IM in Bibb
- 0.25 ppb, 4.9 million
32Single-Source Impact Analysis (Bergin et al.)
Provide a technique to evaluate the impacts from
a single large emissions source on regional air
quality, incorporating non-linear processes and
multi-day effects in estimating pollutant
responses to relatively small emissions
perturbations.
33Motivation and Application
- The ability to evaluate regional secondary
pollution impacts from large single sources would
provide a valuable tool for more effective air
quality management practices, such as refining
programs (e.g. emissions trading, regional
planning), and supporting more effective
compliance enforcement. -
- Typical modeling approach (removing the emissions
from a single source) has numerical errors. - Court case led to need to assess impact of a
single power plant (Sammis) in Ohio on downwind
areas (a distance of up to about 1000 km)
34 Average Day Elevated NOx Emissions
2500
2000
1500
NOx Emissions (avg tons/day)
1000
excess
500
allowable
0
Ohio Elevated EGU Jul-95 Model Inventory
May-95
Jul-95
Aug-00
W. H. Sammis Power Plant(court estimated
emissions)
Court Estimated from W.H. Sammis Plant
35Approach
- Two air quality models and grids, three ozone
episodes, and three sensitivity techniques
(brute-force, DDM, higher order DDM)
36Maximum increase in 1-hr avg O3
- Comparison of the maximum increase in
hourly-averaged ozone concentrations due to
excess NOx emissions from the Sammis plant.
CMAQ with 2nd order DDM
URM with DDM
- July 11-19, 1995
(b) May 24-29, 1995
(c) August 12-20, 2000
371-hr O3 cell responses to excess emissions
When O3 gt 0.060 ppm
All hours
Max. increases
Max.decreases
CMAQ, 2nd ord DDM, August
38Conclusions
- Single-source simulation results agree with past
field experiments, indicating that appropriate
modeling techniques are available for quantifying
single-source regional air quality impacts.
39Climate Change Impacts on Air Quality
- Climate change is forecast to affect air
temperature, absolute humidity, precipitation
frequency, etc. - Increases in ground-level ozone concentrations
are expected in the future due to higher
temperatures and more frequent stagnation events.
- Ozone-related health effects are also anticipated
to be more significant. - Both ozone and PM2.5 (particulate matter with
aerodynamic diameter less than 2.5 micron meters)
are also found to impact climate via direct and
indirect effects on radiative forcing.
http//www.nature.com/news/2004/040913/images/clim
ate.jpg
40Potential Climate Changes in 2050
- IPCC SRES, A1B scenario using GISS
41Issues
- How will climate change affect air quality with
non-projected and projected emissions? - How well currently planned control strategies
will work if climate changes in the future ?
- Above questions can be answered by quantifying
- sensitivities of air pollutants (e.g., ozone and
PM2.5) - to their precursors (e.g., NOx, NH3, VOCs and
SO2) - and associated uncertainties.
42Modeling Procedure
Leung and Gustafson (2005)
Leung and Gustafson (2005), Geophys. Res. Lett.,
32, L16711
43Global and Regional Climate Models
GISS GCM grid spacing 4º x 5º 9
levels output every 6 hours
MM5 Domain 1 dx 108 km 67x109 points output
hourly MM5 Domain 2 dx 36 km 115x169
points output hourly
Leung and Gustafson (2005), Geophys. Res. Lett.,
32, L16711
44Air Quality Simulation Domain
- 147 x 111 grid cells
- 36-km by 36-km grid size
- 9 vertical layers
- U.S. regions
- West (ws)
- Plains (pl)
- Midwest (mw)
- Northeast (ne)
- Southeast (se)
- Also investigating Mexico and Canada
45Emission Inventory Projection
- Accurate projection of emissions key to comparing
relative impacts on future air quality - Step 1. Use latest projection data available for
the near future - - Use EPA CAIR Modeling EI (Point/Area/Nonroad
, from 2001 to 2020) - - Use RPO SIP Modeling EI (Mobile, from 2002
to 2018) - Step 2. Get growth data for the distant future
- - Use IMAGE model (IPCC SRES, A1B)
- - From 2020(2018 for mobile activity) to 2050
- - Use SMOKE/Mobile6 for Mobile source control
Woo et. al, 2006
46Emission Inventory Projection
47Regional Emissions
Year 2001
Year 2020
Year 2050
Present and future years NOx emissions by state
and by source types
48Emission Changes
49Summary of Air Quality Simulations
Scenario Emission Inventory (E.I.) Climatic Conditions Future Air Quality Impacting Factors
2001 Historic (2001) Historic (2001 whole year) N.A.
2000-2002 summers Historic (2000-2002) Historic (2000-2002 summers) N.A.
2050_np (non-projected emissions, but meteorologically influenced for consistency) Historic (2001) Future (2050 whole year) Potential future climate changes
2049-2051_np summers Historic (2001) Future (2049-2051 summers) Potential future climate changes
2050 Future (2050) Future (2050 whole year) Potential future climate changes projected E.I.
2049-2051 summers Future (2049-2051) Future (2049-2051 summers) Potential future climate changes projected E.I.
50Emission Changes
51- Impact of Future Climate Change on
Ground-level Ozone and PM2.5 Concentrations
52Daily maximum 8 hour ozone concentration CDF
plots in 2001, 2050 and 2050_np
NOx limitation sharpening S, reducing peak
Small increase in O3 due to climate
Substantial decrease in O3 due to climate
Reduced NOx scavenging
Peaks (ppb) 2001 141 (actual 146) 2050_NP
152 2050 120
53Summer Average Max 8hr O3
54Annual PM2.5
PM2.5_2050
PM2.5_2001
PM2.5_2050 - PM2.5_2001
PM2.5_2050 - PM2.5_2050np
np Emission Inventory 2001, Climate 2050
55Impact of Potential Climate Change on Average
Max8hrO3
All grid averages (not just monitor locations)
- 3-8 ppbV lower in 2050
- Only /- 1ppbV difference without considering
future emission controls (2050_np) - More significant reductions in summers.
56Impact of Potential Climate Change on PM2.5
- about 0.3-3.8 µg/m3 lower in 2050
- maximum 0.6 µg/m3 difference without
considering future emission controls (2050_np) - Usually np is slower in summer, though can be
higher on average
57Annual Averaged Changes in Averaged Max8hrO3
PM2.5
Max8hrO3 () Max8hrO3 () PM2.5 () PM2.5 ()
2050 2050np 2050 2050np
West -6.5 0.2 -9.2 2.9
Plains -7.9 1.4 -22.0 -0.8
Midwest -10.5 -0.2 -22.7 4.2
Northeast -10.0 -0.5 -28.5 6.5
Southeast -14.8 2.3 -31.4 -2.4
US -9.2 0.9 -23.4 1.1
58Regional Predicted Max8hrO3 Characteristics
Unit of 99.5 and peak ppbV
2000-2002 summers 2000-2002 summers 2000-2002 summers 2000-2002 summers 2049-2051 summers 2049-2051 summers 2049-2051 summers 2049-2051 summers 2049-2051 summers 2049-2051 summers 2049-2051_np summers 2049-2051_np summers 2049-2051_np summers 2049-2051_np summers
of days over 80 ppb of days over 80 ppb of days over 85 ppb (sim/act) Peak of days over 80 ppb of days over 80 ppb of days over 85 ppb Peak Peak of days over 80 ppb of days over 80 ppb of days over 80 ppb of days over 85 ppb Peak
West / Los Angeles 149 95/85 95/85 119 31 6 6 6 97 97 221 186 186 146
Plains / Houston 127 107/87 107/87 127 29 10 10 10 94 94 165 146 146 143
Midwest / Chicago 78 66/32 66/32 138 19 12 12 12 106 106 59 44 44 152
Northeast / New York 51 38/46 38/46 112 1 0 0 0 81 81 82 60 60 121
Southeast / Atlanta 199 182/54 182/54 124/ 139 0 0 0 0 78 78 195 177 177 131
1998-2000 137
59Conclusions
- Climate change, alone, with no emissions growth
or controls has a mixed effects on the ozone and
PM2.5 levels as well as their sensitivities to
precursor emissions. - Ozone generally up some, PM mixed
- The impact of changes in precursor emissions due
to planned controls and anticipated changes in
activity levels have a much greater effect than
the impact of climate change for ozone and PM2.5
levels. - Carefully forecasting emissions is critical to
result relevancy - Spatial distribution and annual variations in the
contribution of precursors to ozone and PM2.5
formation remain quite similar. - Sensitivities of ozone to NOx increase on a per
ton basis mostly due to reduced NOx levels, a bit
due to climate - Sensitivities of PM2.5 to precursors similar on
per ton basis - Lower NOx and higher NH3 emissions increase
sensitivity of NO3 to NOx in 2050 projected
emissions case
60Thanks Questions?
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