Source-Specific Forecasting of Air Quality Impacts with Dynamic Emissions Updating & Source Impact Reanalysis Georgia Institute of Technology Yongtao Hu 1, Cesunica Ivey 1, M. Talat Odman 1, Peter Vasilakos, Michael E. Chang 2 and Armistead G. Russell 1 1 School of Civil & Environmental Engineering, 2 Brook Byers Institute of Sustainable Systems Georgia Institute of Technology With thanks to Pius Lee and the NOAA ARL Forecasting Team AQAST 9 th Semi-Annual Meeting, June 3 rd, 2015

Motivation Air Quality Forecasting –Source specific air quality impacts used in air quality management Both for “static” and, in the future, targeted “dynamic” air quality management, and for health related public and individual exposure management. –Current source specific air quality impact forecasting/hindcasting accuracy call for improvement. Prediction with 3-D models relies on accuracy of emissions –Uncertain (particularly for smaller sources) –Change with time Source impact assessment with chemical reanalysis –Desire to integrate advanced, hybrid source impact assessment as part of chemical re-analysis and emissions updates Improving model performance –Most CTMs currently are biased low when simulating organic aerosol (OA) during the summer –Linked to secondary (SOA) formation Experiments suggest biogenic SOA formation underestimated Georgia Institute of Technology

Objective Provide information that can assist air quality management and exposure assessment –Source impact forecasting in addition to air quality forecasting Critical for dynamic air quality management –Improve air quality and source impact forecasting accuracy using near real time measurements through dynamic adjustment of emissions –Improve source impact hindcasting Utilize measurements (gases, PM, AOD and PM composition) Work with Georgia EPD, Forest Service, Georgia Forestry Commission, Atlanta Regional Commission, and EPA on use of the products and information Provide source-specific impacts as part of Tiger Team reanalysis Georgia Institute of Technology

Source-Specific Air Quality Impact Forecasting Georgia Institute of Technology

Hi-Res2 Forecasting System Georgia Institute of Technology Forecasting Air Quality for CONUS ( open since November 28 th,2014) Updated base emissions to 2011NEI WRF3.6.1 and CMAQv5.02 used 72-hour forecasts at 4-km resolution for Georgia and surrounding states, 12-km for most of Eastern states and 36-km for the rest of CONUS

Georgia Institute of Technology Forecasting Source Impacts at 4-km for Georgia ( open since November 28 th,2014)

Currently working with measurements at ~20 sites in Georgia and Soon with MODIS C6 AOD An emissions and air quality auto-correction system utilizing near real-time satellite and surface observations Minimizes the differences between forecasted and observed concentrations Minimal adjustment to source emissions Uses impacts of emission sources calculated by CMAQ- DDM-3D –Source impacts can be used for dynamic air quality management.(e.g., traffic and fires) Georgia Institute of Technology Hi-Res2: Online Auto-Adjustment Inverse Modeling Approach for Adjusting Emissions

Solve for the Adjustment Factors, R j, that minimize  2 Georgia Institute of Technology DDM-3D calculated sensitivity of concentration i to source j emissions emission adjustment ratio weight Inverse Model Formulation L-BFGS algorithm is used for the optimization (R package nloptr) Remaining Error Amount of Change in Source Strengths

Hindcasting Air Quality Impacts from Specific Sources: 2010 Nationwide Source Apportionment Effort within the Tiger Team Project of Operational Chemical Reanalysis Georgia Institute of Technology

1. CMAQ-DDM Source Impacts (~30 sources, daily) 4. Temporal Interpolation of Adjustment Factors 2. Hybrid Analysis at Monitors to find Adjustment Factors (Rj’s) 3. Spatial Interpolation of Adjustment Factors (Kriging) 5. Adjust CMAQ-DDM Spatial Fields (Daily, Spatially Dense) Woodstove Adjustment Factors Adjusted Woodstove Impact Original Woodstove Impact January 4, 2004 Spatial Hybrid Approach (Ivey et al, GMDD, 2015)

2010 Annual PM 2.5 Source Apportionment WRF-CMAQ model physics and chemistry options 42 vertical layers 12 km nested to 4 km

CMAQ-DDM PM 2.5 Biomass Burning Impact Adjusted PM 2.5 Biomass Burning Impact Biomass Impact: Before/After Assimilation of Observations Many sources are highly variable leading to significant differences between observations and simulations –Biomass –Dust –Agriculture Seasonal results in better agreement –Suggests inventories reasonable “on average” 04/01/04

2006 Seasonally-Averaged PM2.5 Impacts (ug/m3): Biomass Burning (2006) CMAQ-DDM Spatial Hybrid CMAQ - Spatial Hybrid WINTER SUMMER

Updated isoprene OA Mechanism Comparison of CMAQ-simulated SOA from isoprene to observations from AMS factors led to updates Assimilated PBL measurements Improved IEPOX physics and chemistry: Dry deposition resistance reduced Updated reaction rate constants Modified Henry’s law for IEPOX Not yet in forecasting system

Summary PM and ozone forecasting system (Hi-Res2) operational with source impact forecasting and dynamic emission adjustments –Supports dynamic air quality management through providing source specific information –Currently for traffic, power plant and prescribed-burn (Talat’s talk) emissions –Expansion to include other species measurements underway –Improved approach to assimilating AOD and PM measurements underway (Utilizing data-fused fields) Hybrid approach for source apportionment –Utilizes gases concentration and PM composition measurements –Application nationwide for 2010: Tiger Team Chemical Reanalysis project –Results for air quality management (EPD, ARC) and health (CDC) Extension and update of biogenic SOA formation improves SOA results –Isoprene oxidation and NO 3 +terpene reactions

Georgia Institute of Technology Acknowledgements NASA Georgia EPD Georgia Forestry Commission US Forest Service – Scott Goodrick, Yongqiang Liu, Gary Achtemeier Environmental Protection Agency (EPA) Atlanta Regional Commission (ARC) RD