Modeling Fire and ecosystems: Why Bambi and Smokey the Bear deceived us
Citation:
Wilkins, J. Modeling Fire and ecosystems: Why Bambi and Smokey the Bear deceived us. University of Louisville Depart of Physics & Astronomy Seminar, Louisville, KY, March 23, 2018.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Computational Exposure Division (CED) develops and evaluates data, decision-support tools, and models to be applied to media-specific or receptor-specific problem areas. CED uses modeling-based approaches to characterize exposures, evaluate fate and transport, and support environmental diagnostics/forensics with input from multiple data sources. It also develops media- and receptor-specific models, process models, and decision support tools for use both within and outside of EPA.
Description:
The area burned by wildland fires (prescribed and wild) across the contiguous United States (U.S.) has expanded by nearly 50% and now averages 2 million hectares per year. Such fires are estimated to cause 8,000 deaths per year and are monetized as having a ~$450 billion impact to the U.S. economy. Air quality simulation models, like the Community Multiscale Air Quality (CMAQ) modeling system, are extensively used by environmental decision-makers to both examine the impact of air pollution on human health and devise strategies for reducing or mitigating exposure of humans to harmful levels of air pollution. With fires now occurring more frequently and burning more intensely, the exposure of humans to fine particulate matter (PM2.5) and ozone (O3) is projected to grow. Understanding how the contaminated plumes from these fire emissions move vertically through the atmosphere is important for estimating these exposures. Many air quality models rely on complex algorithms to determine transportation of air pollution, such as plume rise algorithms, which determine the vertical allocation of emissions. Recent findings have shown that by adding fires in CMAQ, it can be estimated that fires contributed 11% to mean PM2.5 and less than 1% to mean O3 concentrations during 2008-2012. During that same time frame the model indicated increases to number of “grid cell days” with PM2.5 above 35 µg m-3 by a factor of 4 and the number of grid cell days with maximum daily 8-hour average O3 above 70 ppb by 14%. Furthermore, we explore current work looking to improve modeling efforts associated with fire emissions.