1. Using the MODIS Rapid Response Fire Products to Refine the Wildland Fire Emission Estimates Biswadev Roy1, Alice Gilliland1*, Thomas Pierce1*, Steve Howard1*, and William Benjey1* 1Atmospheric Modeling Division, National Exposure Research Laboratory (NERL), USEPA, Research Triangle Park, North Carolina *On assignment from the Air Resources Laboratory, NOAA
Introduction
Wildland fires are sources of PM2.5, gaseous pollutants, and trace gases (Andrae and Merlet, 2001). The U.S. Environmental Protection Agency (EPA) Models-3 Community Multi-scale Air Quality model (CMAQ) uses emission inputs from the EPA 2001 National Emission Inventory (NEI). The NEI includes fire-related emission estimates at the State level, and in monthly time scale. Hence, emissions could not be distributed at a higher resolution. As a first attempt to refine these NEI emission rates we have used the NASA/MODIS Rapid Response (RR) 1 km x 1 km fire pixel count product (Collection 4) based on an absolute detection technique developed at the University of Maryland (UMD), Geography Department (Justice et al., 2002). We have used the collection-4 data and reprocessed them to get hourly wildland fire pixel count maps in CMAQ grid cells (36 km x 36 km). For this study we have selected the months of May, and August In May 2001 State of Florida alone accounted for about 60% of the total PM2.5 emissions for the contiguous United States (CONUS). During the month of August, 2001 considerable emissions due to wildland fire were from the Northwestern states Washington, Oregon, and California. In this study we have attempted to determine qualitative differences between the model outputs when the emission input to the model is done once using the NEI emissions, and another time using the reallocated NEI emissions based on satellite detected fire pixel fire pixel counts.
Data
The UMD RR collection-4 active fire pixel data is based on an absolute detection criteria using the brightness temperature (apparent observed temperature assuming emissivity equal to 1) information from MODIS sensor at 4 micron (Tb4) wavelength and at 11 micron (Tb11) wavelength (Justice et al., 2002). This method bases fire detection on either of the two conditions: 1) Tb4 > 360 K (330 K at night and, 2) Tb4 > 330 K (315 K at night), and Tb4 - Tb11 > 25 K (10 K at night). If neither is satisfied then a relative fire detection algorithm is applied where the fire is distinguished from the background values in the manner:
False detections due to sun-glint are rejected using MODIS 250 m red and near infra-red channel reflectance values (i.e., If they are above 30% and lies within 400 of specular reflection position). The MODIS instrument was on the Terra satellite which is sun-synchronous. Data are continuously available for the daytime and nighttime passes over the CONUS. There are 2 possible observations over the same scene and over the entire CONUS for the same day.
Air Quality Model
The air quality modeling simulations that will be presented in the paper were generated using the Models-3 Community Multiscale Air Quality (CMAQ) model (Byun and Ching, 1999).
● Pre-release version for CMAQ 4.3
● Simulations performed using the Carbon Bond IV (CB4)
● Domains - CONUS, portions of Canada, and Northern Mexico
● Modal aerosol model (Binkowski and Roselle, 2003) and the ISORROPIA thermodynamic equilibrium model (Nenes et al., 1998).
● Chemical boundary prepared from global GEOS-CHEM model (Bey et al., 2001).
● Meteorological inputs based on the Penn State/NCAR Mesoscale Model or MM5 (Grell et al., 1994) version model-ready meteorology using MCIP v2.2; vertical layers collapsed to 14 σ-layers from surface to 100 mb with first being 36 m thick
● Both the MM5 and CMAQ simulations at 36 km x 36 km.
● Mobile emissions: MOBILE6 module (U.S. EPA, 2003) and BEIS3.12 model for biogenic emissions. The emission inventory for other sources : USEPA NEI for year 2001.
● Seasonality of NH3 emissions for prediction of PM2.5 -based on seasonal information: Gilliland et al. (2003) and Pinder et al. (2004)
Emission Reallocation
● MODIS-derived fire pixels used for redistributing the monthly-averaged, State-level NEI inventory fluxes.
● 90% of the NEI emissions reallocated using hourly pixel counts
● 10% of the emissions left intact, to account for MODIS missed fires
● redistributed monthly emission rates increased over major fire locations as detected by the MODIS, and smoothed out over the rest.
--Lafayette County, FL PM2.5 for May increased ~ 4752 g/s
--PM2.5 emissions in Oregon Aug increased ~ 5800 g/s.
Impact of Emission Refinement
May 24-25, Florida Case
August Case (Northwest)
Summary
State-reported wildland fire emissions in the NEI only provide information at the State and monthly level. We have used MODIS derived fire pixel data to reallocate these NEI emissions at a higher spatial and temporal resolution, since it was possible to produce hourly fire maps (in CMAQ projection) using the RR datasets. We have shown that the MODIS fire pixel count and location data can be used to redistribute the PM2.5 flux for wildland fire emissions to more accurately represent the location of major fire events. We have seen sharp changes on the PM2.5 distribution over the active wild fire regions. The results are as expected. From the Florida Lafayette County fire (May 24-25) and for the Northwestern study (August 18-19) we have seen that the MODIS based NEI reallocation resulted in our ability to localize PM2.5, EC, and OC emissions in vicinity of the active fires. Using the IMPROVE network data it was also possible to check on the range dependent variability of the base-modeled, emission reallocated modeled, and observed carbon (OC + EC) concentrations. It is seen that at the 14.8 Km site the reallocated run produce carbon concentration which is much higher than the observed, and the base model run value. However, on other days, and at larger distances from fire location, the reallocated run carbon concentrations closely match with those obtained from IMPROVE. However, the overall scatter between the base run and the reallocated run produced PM2.5 concentrations for the entire CONUS did not show much difference. Similar scatter plots over the region of multiple fire that occurred in close temporal separation might be useful for model sensitivity studies. For future studies it will be useful to present more case studies using satellite sub-pixel fire size and by actually using the fire extent with fuel proper fuel loading to produce emission rates required for CMAQ runs.
Acknowledgment
We wish to thank Dr. Chris Justice and Diane Davies of the University of Maryland Geography Department for providing MODIS Rapid Response product which is used in the present study. We also thank Dr. Prakash Bhave of NOAA/ARL at NERL for his valuable suggestions.
Disclaimer: The research presented here was performed under the Memorandum of Understanding between the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Commerce’s Natioanal Oceanic & Atmospheric Administration (NOAA) and under agreement number DW Although it has been reviewed by EPA and NOAA and approved for publication, it does not necessarily reflect policies or views.
5449 samples for May 8155 samples for Aug
IMPROVE SITES
Courtesy: NASA/MODIS Rapid Response, UMD, Geography #4
Courtesy: NASA/MODIS Rapid Response, UMD, Geography
CONUS
Since the retrievals are scattered in time across the CONUS we have averaged MODIS RR pixel counts for 0600 UTC and 1800 UTC for each day of May, and August Each of these maps have CMAQ grid cell dimensions and are linearly interpolated to obtain hourly maps of fire pixel counts. MODIS RR data
also provide detection confidence in order to gauge the fire pixel data quality. The pixel confidence values were found to lie between 60-88% for May 2001, and between % for August The confidence level remains reasonably stable over a large range of total 1 km x 1 km fire pixel counts as observed by the instrument.
IMPROVE: Interagency Monitoring of Protected Visual Environment