Air Quality Impacts from Prescribed Forest Fires under Different Management Practices

Abstract

Large amounts of air pollutants are emitted during prescribed forest fires. Such emissions and corresponding air quality impacts can be modulated by different forest management practices. The impacts of changing burning seasons and frequencies and of controlling emissions during smoldering on regional air quality in Georgia are quantified using source-oriented air quality modeling, with modified emissions from prescribed fires reflecting effects of each practice. Equivalent fires in the spring and winter are found to have a greater impact on PM 2.5 than those in summer, though ozone impacts are larger from spring and summer fires. If prescribed fires are less frequent, more biofuel is burnt in each fire, leading to larger emissions and air quality impacts per fire. For example, emissions from a fire with a 5-year fire return interval (FRI) are 72% larger than those from a fire of the same acreage with a 2-year FRI. However, corresponding long-term regional impacts are reduced with the longer FRI since the annual burned area is reduced. Total emissions for fires in Georgia with a 5-year FRI are 32% less than those with a 2-year FRI. Smoldering emissions can lead to approximately 1.0 or 1.9 µg/m 3 of PM 2.5 in the Atlanta PM 2.5 nonattainment area during March 2002.

Wildfire smoke exposure and human health: Significant gaps in research for a growing public health issue

“…Wildfires also have a long smoldering phase, as wildfire containment strategies focus on extinguishing the flame phase while the smoldering phase is left to burn itself out, sometimes for months after a fire is considered contained (Graham et al, 2004). The smoldering phase of wood burning is associated with higher output of particulate, and can account for a large proportion of the total wildfire air pollutant emissions (Radke et al, 1991; Tian et al, 2008; Urbanski, 2013)…”

Vegetation burning for game management in the UK uplands is increasing and overlaps spatially with soil carbon and protected areas

“…It can reduce wildfire risk (Boer et al, 2009), manage forest succession (Sah et al, 2006), enhance grazing pasture (Augustine and Milchunas, 2009), manage game (Kilburg et al, 2015) and deliver nature conservation by mimicking natural fire events to maintain desired habitat conditions (Williams et al, 2012). However, across a range of systems, there is increasing evidence of negative environmental impacts of burning, including soil erosion (Cawson et al, 2012), alteration of soil processes including nutrient cycling and soil hydrology (Neary et al, 1999), water quality (Battle and Gollday, 2003) air pollution (Tian et al, 2008) and habitat condition and biodiversity impacts (Suárez and Medina, 2001). These impacts must in some cases be evaluated against purported benefits of burning, for example wildfire risk reduction, with the benefits and disbenefits debated (Altangerel and Kulla, 2013).…”

Using Machine Learning to Estimate Global PM2.5 for Environmental Health Studies

“…Rather, it is determined by a multivariate function of a large number of parameters, including humidity, temperature, boundary layer height, surface pressure, population density, topography, wind speed, surface type, surface reflectivity, season, land use, normalized variance of rainfall events, size spectrum and phase of cloud particles, cloud cover, cloud optical depth, cloud top pressure, and the proximity to particulate sources. 7172,75,78,[80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95][96][97][98][99] The picture is further complicated by the biases present in the satellite AOD products, [100][101][102][103][104] the difference in spatial scales of the in situ point PM 2.5 observations and the remote sensing data (several kilometers per pixel), and, finally, the sharp PM 2.5 gradients that can exist in and around cities.…”