Atmospheric 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

With Special Thanks to: NIEHS, US EPA, FHWA, Southern Company, SAMI –Financial assistance JGSEE of Thailand And more…

Issues 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

Ozone Formation h (sunlight) O3O3 NO x oxides of nitrogen (NO + NO 2 ) VOCs Volatile organic compounds E VOC E NOx Low O 3 High O 3 Ozone Isopleth

PM Formation h (sunlight) PM NO x VOCs, OC & EC SO 2 Sulfur dioxide

Particulate Matter Complex mixture of solid and liquid particles suspended in the ambient air Size classifications –“super-coarse” > 10μm –“coarse” (PM 10 ) < 10μm –“fine” (PM 2.5 ) < 2.5μm –“ultrafine” < 0.1μm Many sources Many chemical species:

Outline Air quality modeling –Basics –Approaches Advanced approaches Applications –Source impacts –Climate impacts on air quality

Emissions-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 Air Quality Model 200 species x hor. grids x 20 layers= 40 million coupled, stiff non-linear differential equations Atmospheric Diffusion Equation Discretize Operator splitting Emissions Chemistry Meteorology Numerics C=AxB+E

Air Quality Model Air Quality Temperature Radiation Cloud Cover Wind Emissions 1. Anthropogenic 2. Geogenic 3. Biogenic Sources Transported Pollutants Sources (E,S, BCs) Geographical Features Transport (U, K, V d ) Turbulence Numerical Solution Techniques Surface Deposition Sink Processes Topography & Land use Photochemical Reactions Thermochemical Reactions Homogeneous Processes Heterogeneous Processes Computed Concentrations Meteorology Aerosol Dynamics Chemical Processes (R) Chemistry and Aerosol Dynamics

Atmospheric Modeling Process Foundation Pollutant Distributions Evolving: Sensitivities Uncertainties Emissions Model Meteorological Inputs Historical 2- or 3-D winds; Ground level T, RH; Mixing height, Land use Evolving: 3-D Winds, Diffusivities, Temp., RH, D, Solar Insolation (UV & total solar)... Chemical Mechanism Historical: Specified Evolving: Compiler Numerical Routines Historical: Advection Chem. Kinet. Evolving Sens. Anal. Proc. Integ. Unc. Anal. Air Quality Model Emissions Inputs Historical: NO, NO 2, HONO Lumped VOCs CO, SO 2 Evolving: PM, NH 3, Detailed VOCs, Adv. Biogenics Inputs: Emissions Inventory Population Roads Land Use Industry Meteorology Model Parameter Calculation Temperature, Solar Insolation Chemical Mechanism Specification Chemical Mechanism Specification Air Quality Data Analysis and Processing Meteorological Model (Diagnostic or Prognostic) Meteorological Model (Diagnostic or Prognostic) Model Evaluation Air Quality Observations Air Quality Observations Meteorological Observations Meteorological Observations Emissions, Industry and Human Activity Data Emissions, Industry and Human Activity Data Topographical Data Topographical Data Emissions Inventory Development Emissions Inventory Development

Grids Nested Multiscale (Odman et al.) Adaptive (Odman et al.) About 15 vertical Layers up to 15 km (many in first 1 km)

How 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

What’s 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

Emissions reductions lead to about a 12 ppb ozone reduction: Atlanta and Macon do not attain ozone standard (Macon by 6ppb) Example Results : Impact of Planned Controls: 2000 vs. 2007

Sensitivity analysis Given a system, find how the state (concentrations) responds to incremental changes in the input and model parameters: Inputs (P) Model Parameters (P) Model Sensitivity Parameters: State Variables: If P j are emissions, S ij are the sensitivities/responses to emission changes, e.g.., the sensitivity of ozone to Atlanta NOx emissions

Define first order sensitivities as Take derivatives of Solve sensitivity equations simultaneously Sensitivity Analysis with Decoupled Direct Method (DDM): The Power of the Derivative AdvectionDiffusionChemistryEmissions

3-D Air Quality Model NO o NO 2 o VOC i o... T K u, v, w E i k i BC i... O 3 (t,x,y,z) NO(t,x,y,z) NO 2 (t,x,y,z) VOC i (t,x,y,z)... DDM-3D Sensitivity Analysis DDM-3D J decoupled

DDM compared to Brute Force Emissions of SO 2 Sulfate EBEB EAEA CBCB CACA

Consistency of first-order sensitivities Brute Force (20% change) DDM-3D R 2 > 0.99 Low bias & error

Advantages 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

Evidence of Numerical Errors in BF NH 4 sensitivity to domain-wide SO 2 reductions NOx reductions at a point

Efficiency of DDM-3D

Complication: Nonlinearity Often, only a handful of sensitivities are modeled (e.g., 30% NO x reduction) Assumptions of scaling and additivity not necessarily accurate But it may be impractical to model all combinations E VOC E NOx Low O 3 High O 3 Ozone Isopleth

Calculation of higher-order derivatives: If taking the derivative once is good, twice must be better High-order Decoupled Direct Method [HDDM, (Hakami et al., 2003)]: (n.b.: > third order derivatives numerically sensitive)

E VOC Ozone -EA-EA  ∆C∆C EAEAEAEA CACACACA Brute Force and HDDM-3D CBCBCBCB EBEBEBEB B A + +

Control Strategy Development Macon out of attainment by 6 ppb in 2007 Want to identify least cost control strategy Process: –Identify possible controls and costs ($/ton of VOC or NOx) –Simulate response to controls (  [O 3 ]/ton VOC or NOx) –Calculate control effectiveness(  [O 3 ]/$) –Choose most effective controls until get 6 ppb –Test strategy

Sources of Macon’s ozone Macon Scherer Atlanta Branch 8-hr ozone, Aug. 17, 2000 (2007 emissions) M A S B

Sensitivity of 8-hr Ozone in Macon

NO x emission rates (tpd) Macon ozone sensitivity (ppt/tpd) 2007 Emissions and Sensitivities

Source-Receptor Response Marginal Abatement Costs by Region Cost-optimization Choose options with least marginal $/impact until: (1) attain a.q. goal, or (2) reach budget constraint Cost Impact

Strategies for Macon attainment (need 6.5 ppb) Key Measures Zero-cost options (PRB coal, burning ban,...): 1.72 ppb, $0 Bibb industrial NO x : 0.82 ppb, $2.6 million Locomotive controls: 0.77 ppb, $7.3 million SCRs at Scherer: 1.63 ppb, $20.9 million Vehicle I&M in Bibb: 0.25 ppb, $4.9 million

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. Single-Source Impact Analysis (Bergin et al.)

Motivation 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)

Average Day Elevated NO x Emissions W. H. Sammis Power Plant (court estimated emissions) May-95Jul-95Aug-00 Ohio Elevated EGU Jul-95 Model Inventory NOx Emissions (avg tons/day) excess allowable Court Estimated from W.H. Sammis Plant

Approach Two air quality models and grids, three ozone episodes, and three sensitivity techniques (brute-force, DDM, higher order DDM) CMAQ, 36x36 km Aug , ord. DDM URM, multiscale from 24x24 km 2 July and May 24-29, 1995 DDM

Maximum increase in 1-hr avg O 3 Comparison of the maximum increase in hourly-averaged ozone concentrations due to excess NO x emissions from the Sammis plant. (a)July 11-19, 1995(b) May 24-29, 1995(c) August 12-20, 2000 URM with DDM CMAQ with 2 nd order DDM

When O 3 > ppm 1-hr O 3 cell responses to excess emissions All hours Max. increases Max. decreases maximum = 2.2 minimum = -3.6 minimum = -1.2 CMAQ, 2 nd ord DDM, August

Conclusions Single-source simulation results agree with past field experiments, indicating that appropriate modeling techniques are available for quantifying single-source regional air quality impacts.

Climate 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 PM 2.5 (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.

Potential Climate Changes in 2050 Temperature ( o C)Absolute Humidity (%) oCoC - IPCC SRES, A1B scenario using GISS

Issues  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.

Modeling Procedure With 2050 climate With 2001 & 2050 climate SMOKE (w/ 2001 EI) SMOKE (w/ 2050 EI) NASA GISS IPCC A1B MCIP CMAQ-DDM MM5 Leung and Gustafson (2005) *Leung and Gustafson (2005), Geophys. Res. Lett., 32, L16711

Global 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

Air 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

Emission 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 Use SMOKE/Mobile6 for Mobile source control Woo et. al, 2006

Emission Inventory Projection Update cross-references Use EPA 2020 CAIR-case inventory SMOKE/M6- ready activity data for 2050 RPO 2018 Activity data (On-road mobile)

Regional Emissions Year 2001Year 2020 Year 2050 Present and future years NOx emissions by state and by source types

Emission Changes

Summary of Air Quality Simulations ScenarioEmission Inventory (E.I.) Climatic ConditionsFuture Air Quality Impacting Factors 2001Historic (2001) Historic (2001 whole year) N.A summersHistoric ( ) Historic ( summers) N.A. 2050_ np (non-projected emissions, but meteorologically influenced for consistency) Historic (2001) Future (2050 whole year) Potential future climate changes _np summersHistoric (2001) Future ( summers) Potential future climate changes 2050Future (2050) Future (2050 whole year) Potential future climate changes & projected E.I summersFuture ( ) Future ( summers) Potential future climate changes & projected E.I.

Emission Changes

Impact of Future Climate Change on Ground-level Ozone and PM 2.5 Concentrations Impact of Future Climate Change on Ground-level Ozone and PM 2.5 Concentrations

Daily maximum 8 hour ozone concentration CDF plots in 2001, 2050 and 2050_np Reduced NOx scavenging NOx limitation sharpening “S”, reducing peak Small increase in O3 due to climate Substantial decrease in O3 due to climate Peaks (ppb) 2001: 141 (actual= 146) 2050_NP: : 120

O 3 _ summers O 3 _ summers O 3 _ FutureSummers - O 3 _ HistoricSummers O 3 _ FutureSummers - O 3 _ FutureSummers_np np: Emission Inventory 2001, Climate 2050 Summer Average Max 8hr O 3

PM 2.5 _ 2050 PM 2.5 _ 2001 PM 2.5 _ PM 2.5 _ 2001 PM 2.5 _ PM 2.5 _ 2050np np: Emission Inventory 2001, Climate 2050 Annual PM 2.5

Impact of Potential Climate Change on Average Max8hrO3 ( ppbV lower in Only +/- 1ppbV difference without considering future emission controls (2050_np) - More significant reductions in summers. All grid averages (not just monitor locations)

Impact of Potential Climate Change on PM 2.5 ( - about µg/m 3 lower in maximum 0.6 µg/m 3 difference without considering future emission controls (2050_np) -Usually np is slower in summer, though can be higher on average

Annual Averaged Changes in Averaged Max8hrO3 & PM 2.5 Max8hrO3 (%)PM 2.5 (%) np np West Plains Midwest Northeas t Southeas t US

Regional Predicted Max8hrO3 Characteristics summers summers _np summers # of days over 80 ppb # of days over 85 ppb (sim/act) Peak# of days over 80 ppb # of days over 85 ppb Peak# of days over 80 ppb # of days over 85 ppb Peak West / Los Angeles / Plains / Houston / Midwest / Chicago 78 66/ Northeast / New York 51 38/ Southeast / Atlanta /54* 124/ Unit of 99.5% and peak: ppbV Significant improvement Stagnation events Increase in some areas * : 137

Conclusions Climate change, alone, with no emissions growth or controls has a mixed effects on the ozone and PM 2.5 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 PM 2.5 levels. –Carefully forecasting emissions is critical to result relevancy Spatial distribution and annual variations in the contribution of precursors to ozone and PM 2.5 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

Thanks… Questions?