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Project Team

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 User’s Conference, Chapel Hill, NC.

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Project Team

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  1. An Integrated Measurement-Modeling Approach to Quantify Contribution of Washington Dulles Airport Emissions to Local Air QualitySaravanan ArunachalamInstitute of the EnvironmentUniversity of North Carolina at Chapel HillOctober 11-13, 20109th Annual CMAS User’s 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

  3. Motivation • Aviation activities release emissions of CO, NOx, VOC, SOx, PM2.5, 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., NOx from aviation contributes < 1% in 77% of counties, PM2.5 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

  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 O3 and PM25) • 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)

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

  7. 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

  8. Airport Operations Data Departures Arrivals • Data derived from PASSUR/Radar • Daily runway use varies

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

  10. Multiscale Modeling System CMAQ Modeling Domains 12-km 4-km

  11. 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

  12. Evaluation against AIRNOW data: Apr 2009 Max 8h O3 12-km 4-km MB: -3.6 NMB: -6.6 NME: 10.1 MB: -4.5 NMB: -7.9 NME: 10.4 24-hr Ave PM2.5 MB: 1.6 NMB: 17.7 NME: 30.1 MB: 0.05 NMB: 2.0 NME: 26.4

  13. CMAQ Model Performance – April 2009 CMAQ evaluated against other gas-phase species (AQS) and STN - High error for SO2, and ASO4, ANO3 and TC

  14. Comparison of CMAQ to Dulles Apr-2009 Data EC NOx SO4 OC PM2.5 O3

  15. Incremental AQ Contribution from Dulles Airport – Apr 2009 Abs Diff Aerosol EC Abs Diff PM2.5 % Diff Aerosol EC % Diff PM2.5 Dulles airport contributes upto 40% of EC and 4% of PM2.5, compared to background

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

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

  18. 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

  19. 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

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

  21. CMAQ Model Performance – January 2010 CMAQ evaluated against other gas-phase species (AQS) and STN - High error for SO2, OC and TC

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