1. Page 1 An Integrated Measurement-Modeling Approach to Quantify Contribution of Washington Dulles Airport Emissions to Local Air Quality Saravanan Arunachalam Institute of the Environment University of North Carolina at Chapel Hill October 11-13, 2010 9 th Annual CMAS Users Conference, Chapel Hill, NC

2. Project Team N. Davis, B.H. Baek, D. Yang, U. Shankar, M. Omary, K. Talgo, G. Arora, A. Hanna, UNC Chapel Hill Brian Kim, ESA Jawad Rachami, Wyle Labs Roger Wayson, U.S. DOT Volpe Center Steven Cliff and Yongjing Zhao, University of California at Davis Phil Hopke, Clarkson University Page 2

3. Motivation Aviation activities release emissions of CO, NO x, VOC, SO x, PM 2.5, and numerous hazardous air pollutants Aviation emissions vary in 4-D (in space and time) and undergo complex chemical transformation in atmosphere –Need to properly characterize emissions, their transformation and atmospheric impacts Compared to all other sources that impact air quality, aviation emissions are usually small –For e.g. in the U.S., NO x from aviation contributes < 1% in 77% of counties, PM 2.5 contributes < 1% in 94% of counties –However, in some counties, the airport contribution could be significant Limited research on relative contribution of airport emissions to ambient air quality Page 3

4. Page 4 Statement of Objectives Provide guidance for airport operators on effective tools and techniques for measuring airport contributions to ambient AQ –Evaluate existing and potential monitoring strategies and forecasting techniques that airports can use to measure airport-related AQ impacts on local jurisdictions –Identify gaps in existing models and model inputs, and identify research needed to fill gaps and improve the predictive capabilities of available models –Provide detailed recommendations for implementing an optimal emissions monitoring and forecasting strategy, and guidance to airport operators on how to select and carry out that strategy.

5. Project Overview Washington Dulles International Airport (IAD) chosen after extensive screening process –428,482 operations in 2007 (TAF, 2007) –Located in non-attainment area (8h O 3 and PM 25 ) –Willingness of airport authority to work with us –Strong seasonality –Less interference issues from non-airport sources, and Easy Access Conduct measurement campaigns for three seasons –Apr 2009 (Spring), Jan 2010 (Winter), July 2010 (Summer) Air quality modeling using –Source-oriented models (CMAQ and AERMOD) –Receptor-oriented models (PMF) Page 5

6. Page 6 PollutantIntensive Period (1 week) Less-intensive Period (1 week) Base station gas analyzers: SO 2, NO/NO 2 /NO x, CO, O 3 Continuous measurements MiniVol Tedlar bag samples: SO 2, NO/NO 2 /NO x, CO, O 3 1-hr samples, 4/day at 5 locations MiniVol PM filters: elemental composition, BC, NO 3, SO 4 24-hr integrated samples, once/day at 5 locations 24-hr integrated samples, once/day at 5 locations RDI: size-segregated PM and elemental composition 3-hr integrated samples at 3 locations Mobile lab gas analyzers: SO 2, NO/NO 2 /NO x, CO, O 3 Continuous measurements, approx. 2-3 sites Continuous measurements, approx. 2-3 sites Mobile lab SMPS: PM size distributions Continuous measurements, approx. 2-3 sites Continuous measurements, approx. 2-3 sites Mobile lab Summa canisters and cartridges: VOCs and Aldehydes 1-3-hr integrated samples, 3/day at 2-3 sites

7. Monitoring Locations Page 7 Spring: April 2009Winter and Summer: Jan, July 2010

8. Other Important Data Collected Meteorology Wind speed, direction, temperature, pressure and RH Downloaded National Weather Service Data Additional Needs Extensive Field Notes Pictures, Maps, Coordinates Airline Services Quality Performance (AQSP) Data Detailed Operations Data Enhanced Traffic Management System (ETMS) Data Data for Runway Usage / Flight Paths Background Concentrations From AIRNOW/AQS Page 8

9. Airport Operations Data Page 9 Data derived from PASSUR/Radar Daily runway use varies Departures Arrivals

10. Comparison of PM from on-site measurements to AQS Page 10 Direct Comparison Average Comparison

11. Multiscale Modeling System Page 11 12-km 4-km CMAQ Modeling Domains

12. Modeling Tools Weather Research Forecast (WRF) Model Version 3.1 –Used NCEP 40-km NAM analysis data for initialization, boundary conditions and FDDA –Run for 2.5 day durations starting each day, to obtain 12-hour and 36-hour forecasts Emissions Dispersion and Modeling System (EDMS) Version 5.0 –Radar data used as primary inputs for commercial flight activity –Average statistics and/or general use assumptions for other airport sources SMOKE Version 2.6 –Anthropogenic Emissions from NEI-2005 projected to 2009 CMAQ Version 4.7 –IC/BC from NCEP CMAQ simulations for ConUS at 12-km AERMOD Page 12

13. Evaluation against AIRNOW data: Apr 2009 Page 13 Max 8h O3 24-hr Ave PM2.5 12-km4-km MB: -3.6 NMB: -6.6 NME: 10.1 MB: -4.5 NMB: -7.9 NME: 10.4 MB: 0.05 NMB: 2.0 NME: 26.4 MB: 1.6 NMB: 17.7 NME: 30.1

14. CMAQ Model Performance – April 2009 Page 14 CMAQ evaluated against other gas-phase species (AQS) and STN - High error for SO 2, and ASO4, ANO3 and TC

15. Comparison of CMAQ to Dulles Apr-2009 Data Page 15 NO x O3O3 EC PM 2.5 OC SO 4

16. Incremental AQ Contribution from Dulles Airport – Apr 2009 Page 16 % Diff Aerosol EC Abs Diff PM 2.5 Abs Diff Aerosol EC % Diff PM 2.5 Dulles airport contributes upto 40% of EC and 4% of PM 2.5, compared to background

17. Average Elemental Size Distribution of RDI Samples Page 17 178 RDI Samples from 3 sites 27 Chemical Elements by XRF 8 Size Fractions

18. Size resolved PM measurements from RDI Page 18 CMAQ predicts PM chemical components in 3 modes Tools being developed to convert CMAQs modal size distribution to compare with 8 size bins measured Ref: Liu and Bowman (2004)

19. Discussion Successful measurement campaign conducted for three different seasons at Washington Dulles airport –Air quality, meteorological and on-site flight activity data collected Near Real-time Meteorological and Air Quality forecast system developed at multiple resolutions of 12-km and 4-km for IAD –Model performance evaluated against both routine measurements from AIRNOW/AQS and STN, and from on-site field measurements at Dulles –CMAQ performance for Apr-2009 marginally better than for Jan-2010 –Additional evaluation ongoing using on-site measurements Airport contribution to local AQ being assessed using 3 approaches, and corroborated by on-site measurements –CMAQ and AERMOD modeling –Receptor modeling Page 19

20. Acknowledgements This project was conducted with funding from the Transportation Research Board (TRB) and developed under the Airport Cooperative Research Program (ACRP) Project 02-08 We would like to thank the ACRP 02-08 panel for guidance and directions Page 20

21. Evaluation against AIRNOW data: Jan 2010 Page 21 Max 8h O3 24-hr Ave PM2.5 12-km 4-km MB: 3.8 NMB: 13.9 NME: 17.9 MB: 5.9 NMB: 20.2 NME: 21.0 MB: 1.9 NMB: 17.9 NME: 31.8 MB: 5.9 NMB: 20.2 NME: 21.0

22. CMAQ Model Performance – January 2010 Page 22 CMAQ evaluated against other gas-phase species (AQS) and STN - High error for SO 2, OC and TC