Three-Dimensional Air Quality System (3D-AQS)

THE THREE DIMENSIONAL AIR QUALITY SYSTEM (3D-AQS) Raymond M. Hoff1, Jill A. Engel-Cox2 1

University of Maryland, Baltimore County, Baltimore, MD, 21228 2 Battelle Memorial Institute, Arlington, VA 22201 1.

INTRODUCTION

Monitoring of air quality for regulatory compliance has been a two-dimensional effort for many years. Thousands of in-situ surface stations have been operated in the US in order to determine whether priority pollutants (SO2, O3, NO2, PM, lead, CO) exceed regulatory limits set by the US Environmental Protection Agency (EPA). It is now widely recognized that regional and long-range transport of these pollutants and their secondary photochemical products are significant in exceedences of these limits, sometimes thousands of kilometers from their source. The Clean Air Interstate Rule [1] takes a new view of such pollutants and requires States to reduce pollutants that may cross State boundaries. This places a larger obligation on models and the measurements of transport of the pollutants aloft. In 2006, we initiated a study, in cooperation with NASA, NOAA and the EPA to provide satellite derived measurements of column and profile measurements of significant pollutants and to use these measurements for analysis of compliance and also for real-time air quality forecasting. To date, the system has made significant progress in incorporating major satellite data sets into EPA and NOAA operations and this paper will describe that progress.

2.

SOURCES OF 3D AIR QUALITY DATA

3D-AQS was proposed and funded from a NASA Applied Sciences Division cooperative agreement. The proposal recognized that significant progress had been made in incorporating aerosol optical depth data from the MODIS sensors on the Terra and Aqua satellites into a system called IDEA (Incorporating Satellite Data into Environmental Applications) which has been distributed from the University of Wisconsin [2]. In that system, MODIS AOD data are mapped daily. At locations with high levels of AOD, trajectories run in the forward direction to help air quality forecasters predict following day behavior of the aerosols. In addition, an empirical relationship was developed which compared AOD columns to ground-based measures of PM2.5. The strength of this relationship depends on a multitude of factors, including aerosol type, mixing height, aerosol extinction efficiency, aerosol humidification, surface reflectance, etc. Despite the potential variability in these factors, a simple scaling of 62
g m-3/unit AOD has significant predictive skill in mapping AOD onto particulate mass. We have published a series of papers which have refined this relationship [3-7] and the conclusion is that one can fill-in between ground based PM2.5 monitors with satellite data. We have also shown that utilization of satellite AOD can be used to predict the proportion of long-range transported aerosol to locally produced pollution [8]. The IDEA product was designed to be a real-time system for forecaster use with some archival capability. It seemed appropriate, however, that the ultimate repository for such data be with the agencies with a mandate for their gathering. We proposed to integrate the AOD data from the NASA satellites into an EPA's archival multi-data system, called AIRQuest. The IDEA product itself was designed as a research tool but ultimately should reside at an agency with operation capability. To that end, IDEA is being migrated to NOAA, with support from the National Environmental Satellite, Data, and Information Service (NESDIS). IDEA's inputs are being broadened to include AOD data from the NOAA geostationary satellites with the addition of the GOES Aerosol and Smoke Product (GASP). Early in 2008, a new IDEA website (http://www.orbit.nesdis.noaa.gov/smcd/spb/aq/) has been initiated and the GASP data can be viewed alongside the MODIS data by switching between the products. The products above are a significant aid to air quality forecasters and analysts but they still have one shortcoming: if there is pollution aloft which is uncorrelated with the surface PM2.5, the AOD images can be misleading. We have incorporated ground-based lidar from sites in the US Northeast (UMBC, CCNY: New York, MPLNet sites at GSFC and Cove, VA) into the system since the profiles of extinction from the lidars determine where in the vertical transport is taking place. The UMBC data are now available for the years 2004-2008 at the AIRQuest site. In 2006, NASA launched the Cloud and Aerosol Lidar for Pathfinder Spaceborne Observation (CALIPSO) satellite. A downward pointing 532/1064 nm lidar has been measuring profiles of aerosol globally since that time and in 2008, extinction profiles will become available. We are including CALIPSO data over North America into the 3D-AQS scheme. Additional products which are being utilized are the NO2 column content from the Ozone Measurement Instrument and potentially the tropospheric ozone product from that instrument.

3. STATUS OF AIRQUEST, DATA ACCESS AND EXAMPLE PRODUCTS AIRQuest has a beta-test public portal (http://epa.gov/oar/umbc) which allows users to request a comma delimited dataset to be drawn out of the database which gives time series of PM2.5 and AOD from any of EPA's monitoring stations. These can be used to determine correlations at a given site between AOD and PM [7]. More useful, however, is the two dimensional AOD retrievals which have been

remapped to the Community Multiscale Air Quality (CMAQ) forecast grid. This allows modelers to do direct comparison with model output of PM and AOD fields from the NASA satellites without having to retrieve the data from NASA archival sources. Figure 1a,b shows a map of a 12x12 km MODIS product which has been mapped onto the CMAQ grid for August 1, 2004.

Figure 1: MODIS AOD for the eastern US (left) and the regridded 12x12 km CMAQ data which resides in AIRQuest (right). GASP, MODIS and MISR AOD data matched to the EPA sites, CMAQ AOD grids of MODIS, CMAQ AOD grids of GASP, lidar profiles from the REALM network, and NO2 column data matched to EPA sites are currently available in AIRQUEST. CALIPSO extinction data will be mapped onto the CMAQ grid in the latter half of 2008. To date, access to the AIRQuest data is not complete for users outside of EPA. For access to the entire data set, interested parties are encouraged to contact the authors who can help enable access. A final goal of the project is to enable comparison of these air quality parameters with health outcomes which may be related to particulate matter. We are working with the Center for Disease Control to comparatively map the PM measures on a national scale with CDC health indicators.

4. REFERENCES [1]

US Environmental Protection Agency, Federal Register/Vol. 70, No. 91/Thursday, May 12, 2005.

[2]

Al-Saadi, J.; J. Szykman, B. Pierce, C. Kittaka, D. Neil, A. Chu, L. Remer, L. Gumley, E. Prins, L. Weinstock, C. MacDonald, R. Wayland, F. Dimmick, and J. Fishman (2005). Improving National Air Quality Forecasts with Satellite Aerosol Observations. Bulletin of the American Meteorological Society 86, 1249-1261.

[3]

Engel-Cox, J., Holloman, C., Coutant, B., and Hoff, R. (2004). Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality. Atmospheric Environment 38, pp 2495-2509.

[4]

Engel-Cox, J., Hoff, R.M., and Haymet, A.D.J. (2004). Recommendations on the Use of Satellite Remote-Sensing Data for Urban Air Quality. Journal of the Air and Waste Management Association 54, pp 1360-1371.

[5]

Engel-Cox, Jill A., Raymond M. Hoff, Raymond Rogers, Fred Dimmick, Alan C. Rush, James J. Szykman, Jassim Al-Saadi, D. Allen Chu, and Erica R. Zell, 2006. Integrating Lidar and Satellite Optical Depth with Ambient Monitoring for 3-Dimensional Particulate Characterization, Atmos. Environ. 40, 8056-8067.

[6]

Weber, S. A. , J. A. Engel-Cox, R. Rogers, R. M. Hoff, A. Prados, and H. Zhang, Evaluation of 3-D Air Quality System RemotelySensed Aerosol Optical Depth and Surface PM2.5 for the Baltimore/Washington Metropolitan Air Shed, Rem. Sens. Environment, (in review)

[7]

Zhang, H., R. M. Hoff , K. McCann, J. A. Engel-Cox, D. A. Chu, A. Prados, and A. Wimmers, The relation between MODIS aerosol optical depth and PM2.5 over the United States : a comparison on MODIS AOD versions, Rem. Sens. Environ (in review).

[8] Dimmick, F., R. Scheffe, J. Tikvart, J. A. Engel-Cox, S. A. Weber, and T. D. Fairlie, 2008. 3D Air Quality and the Clean Air Interstate Rule: Advanced Monitoring Initiative Final Report (in review).