Short-term exposure to fire smoke, especially particulate matter with an aerodynamic diameter less than 2.5 µm (PM_2.5), is associated with adverse health effects. In order to quantify the impact of prescribed burning on human health, a general health impact function was used with exposure fields of PM_2.5 from prescribed burning in Georgia, USA, during the burn seasons of 2015 to 2018, generated using a data fusion method. A method was developed to identify the days and areas when and where the prescribed burning had a major impact on local air quality to explore the relationship between prescribed burning and acute health effects. The results showed strong spatial and temporal variations in prescribed burning impacts. April 2018 exhibited a larger estimated daily health impact with more burned areas compared to Aprils in previous years, likely due to an extended burn season resulting from the need to burn more areas in Georgia. There were an estimated 145 emergency room (ER) visits in Georgia for asthma due to prescribed burning impacts in 2015 during the burn season, and this number increased by about 18% in 2018. Although southwestern, central, and east-central Georgia had large fire impacts on air quality, the absolute number of estimated ER asthma visits resulting from burn impacts was small in these regions compared to metropolitan areas where the population density is higher. Metro-Atlanta had the largest estimated prescribed burn-related asthma ER visits in Georgia, with an average of about 66 during the reporting years.

Prescribed burning (PB) is one of the most prominent sources of PM_2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) in the southeastern United States. The PB emissions estimates may have significant uncertainty because they are based on the burned areas reported to the state agencies when burners apply for burn permits. When no permit records are available, satellite‐derived products could be used as a substitute tool to provide burned area data. In order to evaluate burned areas from satellite‐derived products, we conducted a comparison between PB permit records and two satellite‐derived products, Blended Polar Geo Biomass Burning Emissions Product and Global Fire Emissions Database, in Georgia and Florida. The comparison results indicate that both satellite‐derived products underestimate seriously the burned areas compared to permit record data. They can capture a cluster of fires better than isolated fires but may misinterpret those small fires together as one big fire. Overall, current satellite‐derived products have limitations in estimating the burned areas of small fires and still need improvements.

Our project examines the association between percent African American and smoke pollution in the form of prescribed burn-sourced, fine particulate matter (PM_2.5) in the U.S. state of Georgia for 2018. (1) Background: African Americans constitute 32.4% of Georgia’s population, making it the largest racial/ethnic minority group in the state followed by Hispanic Americans at 9.8%. African Americans, Hispanic Americans, and lower wealth groups are more likely than most middle and upper income White Americans to be exposed to environmental pollutants. This is true because racial and ethnic minorities are more likely to live in urban areas where pollution is more concentrated. As a point of departure, we examine PM_2.5 concentrations specific to prescribed fire smoke, which typically emanates from fires occurring in rural or peri-urban areas. Two objectives are specified: a) examine the association between percent African American and PM_2.5 concentrations at the census tract level for Georgia, and b) identify emitters of PM_2.5 concentrations that exceed National Ambient Air Quality Standards (NAAQS) for the 24-h average, i. e., >35 µg/m³. (2) Methods: For the first objective, we estimate a spatial Durbin error model (SDEM) where pollution concentration (PM_2.5) estimates for 1683 census tracts are regressed on percent of the human population that is African American or Hispanic; lives in mobile homes; and is employed in agriculture and related occupations. Also included as controls are percent evergreen forest, percent mixed evergreen/deciduous forest, and variables denoting lagged explanatory and error variables, respectively. For the second objective, we merge parcel and prescribed burn permit data to identify landowners who conduct prescribed fires that produce smoke exceeding the NAAQS. (3) Results: Percent African American and mobile home dweller are positively related to PM_2.5 concentrations; and government and non-industrial private landowners are the greatest contributors to exceedance levels (4) Conclusions: Reasons for higher PM_2.5 concentrations in areas with higher African American and mobile home percent are not clear, although we suspect that neither group is a primary contributor to prescribed burn smoke but rather tend to live proximate to entities, both public and private, that are. Also, non-industrial private landowners who generated prescribed burn smoke exceeding NAAQS are wealthier than others, which suggests that African American and other environmental justice populations are less likely to contribute to exceedance levels in the state.

Prescribed burning (PB) is a prominent source of PM_2.5 in the southeastern US and exposure to PB smoke is a health risk. As demand for burning increases and stricter controls are implemented for other anthropogenic sources, PB emissions tend to be responsible for an increasing fraction of PM_2.5 concentrations. Here, to quantify the effect of PB on air quality, low-cost sensors are used to measure PM_2.5 concentrations in Southwestern Georgia. The feasibility of using low-cost sensors as a supplemental measurement tool is evaluated by comparing them with reference instruments. A chemical transport model, CMAQ, is also used to simulate the contribution of PB to PM_2.5 concentrations. Simulated PM_2.5 concentrations are compared to observations from both low-cost sensors and reference monitors. Finally, a data fusion method is applied to generate hourly spatiotemporal exposure fields by fusing PM_2.5 concentrations from the CMAQ model and all observations. The results show that the severe impact of PB on local air quality and public health may be missed due to the dearth of regulatory monitoring sites. In Southwestern Georgia PM_2.5 concentrations are highly non-homogeneous and the spatial variation is not captured even with a 4-km horizontal resolution in model simulations. Low-cost PM sensors can improve the detection of PB impacts and provide useful spatial and temporal information for integration with air quality models. R² of regression with observations increases from an average of 0.09 to 0.40 when data fusion is applied to model simulation withholding the observations at the evaluation site. Adding observations from low-cost sensors reduces the underestimation of nighttime PM_2.5 concentrations and reproduces the peaks that are missed by the simulations. In the future, observations from a dense network of low-cost sensors could be fused with the model simulated PM_2.5 fields to provide better estimates of hourly exposures to smoke from PB. Implications: Prescribed burning emissions are a major cause of elevated PM_2.5 concentrations, posing a risk to human health. However, their impact cannot be quantified properly due to a dearth of regulatory monitoring sites in certain regions of the United States such as Southwestern Georgia. Low-cost PM sensors can be used as a supplemental measurement tool and provide useful spatial and temporal information for integration with air quality model simulations. In the future, data from a dense network of low-cost sensors could be fused with model simulated PM_2.5 fields to provide improved estimates of hourly exposures to smoke from prescribed burning.

Prescribed burning (PB) is practiced throughout the USA, most extensively in the southeast, for the purpose of maintaining and improving the ecosystem and reducing wildfire risk. However, PB emissions contribute significantly to trace gas and particulate matter loads in the atmosphere. In places where air quality is already stressed by other anthropogenic emissions, PB can lead to major health and environmental problems. We developed a PB impact forecasting system to facilitate the dynamic management of air quality by modulating PB activity. In our system, a new decision tree model predicts burn activity based on the weather forecast and historic burning patterns. Emission estimates for the forecast burn activity are input into an air quality model, and simulations are performed to forecast the air quality impacts of the burns on trace gas and particulate matter concentrations. An evaluation of the forecasts for two consecutive burn seasons (2015 and 2016) showed that the modeling system has promising forecasting skills that can be further improved with refinements in burn area and plume rise estimates. Since 2017, air quality and burn impact forecasts are being produced daily with the ultimate goal of incorporating them into the management of PB operations.