Georgia Institute of Technology SUPPORTING INTEX THROUGH INTEGRATED ANALYSIS OF SATELLITE AND SUB-ORBITAL MEASUREMENTS WITH GLOBAL AND REGIONAL 3-D MODELS: Bottom-up Emissions Inventory Development and Inverse Modeling Armistead (Ted) Russell Air Resources Engineering Center Environmental Engineering Georgia Institute of Technology

Objectives Use satellite, in-situ and aircraft observations to evaluate chemical transport model (CTM) results to identify likely emissions biases using inverse modeling –Oxidized nitrogen species (NO2, NO, HNO3, PAN, PM-nitrate) –HCHO –CO –SO2-sulfate –PM Evaluate satellite observations –Consistency with well-characterized emissions and “analyzed” air quality fields Examine spatial variability and ground/aircraft-based monitor spatial representativeness

Georgia Institute of Technology Approach Develop “accurate” emissions inventories for –Model processes (mobile, area, biogenic) (10-50+% unc.) –CEM for major point sources (<15% unc.) Simulate August 2003 air quality –Use inverse modeling to identify likely inventory biases/timing issues –Identify conditions where model works better/worse Simulate INTEX study periods –Evaluate model –Compare results to satellite observations Assess mass consistency between observations and model simulations –Use model results to address objectives

Georgia Institute of Technology Emissions: Nationwide NO X SO 2 NO X Anthropogenic VOC PM 10 EPA National Air Quality and Emissions Trends Report, 2003

Georgia Institute of Technology Emissions Inventory: Northeast 2003 Emission Inventory, Fall Line Air Quality Study (FAQS) NO X PM 2.5 SO 2 VOC

Georgia Institute of Technology Emissions Inventory: Northeast States Percent Contribution StateAreaBiogenicsEGUMobileNon-EGUNonroad NO X Maine New Hampshire Vermont Massachusetts PM 2.5 Maine New Hampshire Vermont Massachusetts SO 2 Maine New Hampshire Vermont Massachusetts VOC Maine New Hampshire Vermont Massachusetts

Georgia Institute of Technology Top SO 2 Emitters (Nationwide)

Georgia Institute of Technology Top NO X Emitters (Nationwide)

Georgia Institute of Technology Chemical Transport Modeling Use MM5/SMOKE/CMAQ-DDM3D –CMAQ-DDM3D SAPRC99 (more detailed chemical species, part. HCHO) DDM3D provides sensitivity fields directly Conduct inverse modeling to identify likely emissions biases –Use source-air quality sensitivities and observations to modify emissions estimates Modifications viewed as suggestive, not absolute.

Georgia Institute of Technology horizontal domain vertical structure 36km grid over US, southern Canada and northern Mexico corresponds to the RPO (Regional Planning Organization) unified grid Air quality Model Domain

Georgia Institute of Technology 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) Mode l Sensitivity Parameters: State Variables: If P j are emission, S ij are the sensitivities/responses to emission changes: This is done automatically using DDM-3D

Georgia Institute of Technology Sensitivity Analysis Calculate sensitivity of gas and aerosol phase concentrations and wet deposition fluxes to input and system parameters – s ij (t)=  c i (t)/  p j Brute-Force method –Must run the model a number of different times –Inaccurate sensitivities may result due to numerical noise propagating in the model DDM - Decoupled Direct Method –Use direct derivatives of governing equations –Initial and boundary conditions, horizontal transport, vertical advection and diffusion, emissions, chemical transformation, aerosol formation, and scavenging processes

Georgia Institute of Technology Atmospheric Advection-Diffusion Equation and corresponding sensitivity equation ADE equation (IC/BCs not shown) Sensitivity equation (semi-normalized), P j is unperturbed field

Georgia Institute of Technology 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

Georgia Institute of Technology Inverse Modeling and Sensitivity Analysis Inverse modeling involves using observations along with a physical model (e.g., traditional air quality) model to estimate model parameters and inputs, e.g., emissions InputsModelOutput ~ Observations Need how model responds: Sensitivity

Georgia Institute of Technology Emissions Inventory Assessment using Inverse Modeling/Four Dimensional Data Assimilation (FDDA) Emissions inventory (Mobile, area, biogenic, point sources) Pollutant distribution (spatial & temporal) (e.g. Ozone, NO x, NO y, SO 2, CO, VOCs); and sensitivity fields Air Quality Model + DDM-3D Ridge regression Module Observations taken from routine measurement networks or special field studies New emissions distribution by source that minimize the difference between observations and simulations Other inputs that remain as defined in the base case scenario INPUTS Main assumption in the formulation: A driving source for the discrepancy between predictions and observations is the emission estimates

Georgia Institute of Technology Estimated emission adjustments for Southeast emissions using FDDA * * * Includes mobile and area sources Using only IMPROVE measurements

Georgia Institute of Technology Plan Applying approach to August 2003 –Identify initial inventory and model performance issues Look at impact of blackout Extend inverse method to use satellite observations Apply to INTEX period –Further assess inventory –Reconcile bottom-up and top-down emissions estimates

Georgia Institute of Technology Considerations SO2 emissions estimates most accurately quantified –Good ability to simulate sulfate (dominant PM species in east) NOx emissions estimates quantified better where major point sources dominate: –Ohio River Valley (e.g., West VA) –Southeast (TN-NC) Interesting experiments over time –Plants applying NOx and SO2 controls 25-85% reductions Seasonal variation (summer season application) –Blackout