Influence of uncertainties in burned area estimates on modeled wildland fire PM2.5 and ozone pollution in the contiguous U.S.

Abstract

Wildland fires are a major source of fine particulate matter (PM 2.5), one of the most harmful ambient pollutants for human health globally. To represent the influence of wildland fire emissions on atmospheric composition, regional and global chemical transport models rely on emission inventories developed from estimates of burned area (i.e. fire size and location). While different methods of estimating annual burned area agree reasonably well in the western U.S. (within 20-30% for most years during 2002-2014), estimates for the southern U.S. can vary by more than a factor of 5. These differences in burned area lead to significant variability in the spatial and temporal allocation of emissions across fire emission inventory platforms. In this work, we implement wildland fire emission estimates for 2011 from three different products-the USEPA National Emission Inventory (NEI), the Fire Inventory of NCAR (FINN), and the Global Fire Emission Database (GFED4s)-into the Community Multiscale Air Quality (CMAQ) model to quantify and characterize differences in simulated PM and ozone concentrations across the contiguous U.S. (CONUS) due to the fire emission inventory used. The NEI is developed specifically for the U.S., while both FINN and GFED4s are available globally. We find that NEI emissions lead to the largest increases in modeled annual average PM 2.5 (0.85 μg m −3) and April-September maximum daily 8-h ozone (0.28 ppb) nationally compared to a "no fire" baseline, followed by FINN (0.33 μg m −3 and 0.22 ppb) and GFED4s (0.12 μg m −3 and 0.17 ppb). Annual mean enhancements in wildland fire pollution are highest in the southern U.S. across all three inventories (over 4 μg m −3 and 2 ppb in some areas), but show considerable spatial variability within these regions. We also examine the representation of five individual fire events during 2011 and find that of the two global inventories, FINN reproduces more of the acute changes in pollutant concentrations modeled with NEI and shown in surface observations during each of the episodes investigated compared to GFED4s. Understanding the sensitivity of modeling fire-related PM 2.5 and ozone in the U.S. to burned area estimation approaches will inform future efforts to

The changing risk and burden of wildfire in the United States

“…However, generating accurate exposure estimates from CTMs requires surmounting several major uncertainties in the pathway between source and receptor. First, large uncertainties in wildfire emissions inventories have been shown to lead to many-fold differences in wildfire-attributed (particulate matter with diameter <2.5 μm) concentrations across the United States (and 20× regional differences in high fire years) when different inventories are used as input to the same CTM ( 15 , 16 ), and integration of satellite observations only slightly improves performance ( 17 ). Second, the detailed conditions surrounding emissions such as the height of emissions injections and very localized meteorology and their transport may not be captured by models and can dramatically impact downstream exposure estimates ( 18 , 19 ).…”

Incorporating Sentinel-1 SAR imagery with the MODIS MCD64A1 burned area product to improve burn date estimates and reduce burn date uncertainty in wildland fire mapping

“…The improvement in burn date and reduction in associated uncertainty resulting from this study could prove useful for a variety of applications including those related to multisensor biomass burning inventories, associated air quality impact studies, temporallyconsistent emissions comparisons, fire early-warning systems, post-impact fire assessments, timing of drought impacts on fire, changes to fire regime patterns over time, and others (Prasad et al 2002;Palandjian et al 2009;Singh et al 2009;Kanabkaew and Oanh 2011;Reid et al 2013;Hayasaka et al 2014;Gibe and Cayetano 2017;Shi and Matsunaga 2017;van der Werf et al 2017;Z uñiga-V asquez et al 2017;Hayasaka and Sepriando 2018;Itahashi et al 2018;Koplitz et al 2018;Nguyen et al 2018). Some of these example studies rely upon dynamic atmospheric conditions which change from day-to-day such as wind speed and direction.…”

Characterizing grassland fire activity in the Flint Hills region and air quality using satellite and routine surface monitor data

“…3). The NEI estimates of annual total area burned are higher than FINN because of the sometimes much larger burn area assignments and also because FINN only uses fire detections from MODIS whereas NEI uses fire detections from both geostationary and polar orbiting satellites (Koplitz et al, 2018;U.S. Environmental Protection Agency, 2018;Wiedinmyer et al, 2011).…”

“…Due to its reliance on daily fire detections, FINN is sensitive to missed detections due to cloud cover or other data quality issues which sometimes leads to underestimated burned area during large fires (Paton-Walsh et al, 2012). Conversely, GFED has difficulty capturing small fires, even with the small fire correction in version 4.1s (Koplitz et al, 2018;Reddington et al, 2016). The NEI likely better represents fire activity in the U.S. overall compared to either FINN or GFED since it includes information not used by the global products.…”

“…The NEI likely better represents fire activity in the U.S. overall compared to either FINN or GFED since it includes information not used by the global products. Issues related to land cover assumptions and other factors influencing burned area distributions may introduce errors in each of these products (Koplitz et al, 2018;Larkin et al, 2014).…”