Estimated Impacts of Prescribed Fires on Air Quality and Premature Deaths in Georgia and Surrounding Areas in the US, 2015-2020.

Smoke from wildfires poses a substantial threat to health in communities near and far. To mitigate the extent and potential damage of wildfires, prescribed burning techniques are commonly employed as land management tools; however, they introduce their own smoke-related risks. This study investigates the impact of prescribed fires on daily average PM2.5 and maximum daily 8-h averaged O3 (MDA8-O3) concentrations and estimates premature deaths associated with short-term exposure to prescribed fire PM2.5 and MDA8-O3 in Georgia and surrounding areas of the Southeastern US from 2015 to 2020. Our findings indicate that over the study domain, prescribed fire contributes to average daily PM2.5 by 0.94 ± 1.45 µg/m3 (mean ± standard deviation), accounting for 14.0% of year-round ambient PM2.5. Higher average daily contributions were predicted during the extensive burning season (January-April): 1.43 ± 1.97 µg/m3 (20.0% of ambient PM2.5). Additionally, prescribed burning is also responsible for an annual average increase of 0.36 ± 0.61 ppb in MDA8-O3 (approximately 0.8% of ambient MDA8-O3) and 1.3% (0.62 ± 0.88 ppb) during the extensive burning season. We estimate that short-term exposure to prescribed fire PM2.5 and MDA8-O3 could have caused 2665 (95% confidence interval (CI): 2249-3080) and 233 (95% CI: 148-317) excess deaths, respectively. These results suggest that smoke from prescribed burns increases the mortality. However, refraining from such burns may escalate the risk of wildfires; therefore, the trade-offs between the health impacts of wildfires and prescribed fires, including morbidity, need to be taken into consideration in future studies.

Estimating US Background Ozone Using Data Fusion.

US background (US-B) ozone (O3) is the O3 that would be present in the absence of US anthropogenic (US-A) emissions. US-B O3 varies by location and season and can make up a large, sometimes dominant, portion of total O3. Typically, US-B O3 is quantified using a chemical transport model (CTM) though results are uncertain due to potential errors in model process descriptions and inputs, and there are significant differences in various model estimates of US-B O3. We develop and apply a method to fuse observed O3 with US-B O3 simulated by a regional CTM (CMAQ). We apportion the model bias as a function of space and time to US-B and US-A O3. Trends in O3 bias are explored across different simulation years and varying model scales. We found that the CTM US-B O3 estimate was typically biased low in spring and high in fall across years (2016-2017) and model scales. US-A O3 was biased high on average, with bias increasing for coarser resolution simulations. With the application of our data fusion bias adjustment method, we estimate a 28% improvement in the agreement of adjusted US-B O3. Across the four estimates, we found annual mean CTM-simulated US-B O3 ranging from 30 to 37 ppb with the spring mean ranging from 32 to 39 ppb. After applying the bias adjustment, we found annual mean US-B O3 ranging from 32 to 33 ppb with the spring mean ranging from 37 to 39 ppb.

Application and evaluation of a low-cost PM sensor and data fusion with CMAQ simulations to quantify the impacts of prescribed burning on air quality in Southwestern Georgia, USA.

Prescribed burning (PB) is a prominent source of PM2.5 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 PM2.5 concentrations. Here, to quantify the effect of PB on air quality, low-cost sensors are used to measure PM2.5 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 PM2.5 concentrations. Simulated PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 fields to provide better estimates of hourly exposures to smoke from PB.Implications: Prescribed burning emissions are a major cause of elevated PM2.5 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 PM2.5 fields to provide improved estimates of hourly exposures to smoke from prescribed burning.

Satellite Monitoring for Air Quality and Health.

Data from satellite instruments provide estimates of gas and particle levels relevant to human health, even pollutants invisible to the human eye. However, the successful interpretation of satellite data requires an understanding of how satellites relate to other data sources, as well as factors affecting their application to health challenges. Drawing from the expertise and experience of the 2016-2020 NASA HAQAST (Health and Air Quality Applied Sciences Team), we present a review of satellite data for air quality and health applications. We include a discussion of satellite data for epidemiological studies and health impact assessments, as well as the use of satellite data to evaluate air quality trends, support air quality regulation, characterize smoke from wildfires, and quantify emission sources. The primary advantage of satellite data compared to in situ measurements, e.g., from air quality monitoring stations, is their spatial coverage. Satellite data can reveal where pollution levels are highest around the world, how levels have changed over daily to decadal periods, and where pollutants are transported from urban to global scales. To date, air quality and health applications have primarily utilized satellite observations and satellite-derived products relevant to near-surface particulate matter <2.5 µm in diameter (PM2.5) and nitrogen dioxide (NO2). Health and air quality communities have grown increasingly engaged in the use of satellite data, and this trend is expected to continue. From health researchers to air quality managers, and from global applications to community impacts, satellite data are transforming the way air pollution exposure is evaluated.

Quantifying the sources of ozone, fine particulate matter, and regional haze in the Southeastern United States.

A detailed sensitivity analysis was conducted to quantify the contributions of various emission sources to ozone (O3), fine particulate matter (PM2.5), and regional haze in the Southeastern United States. O3 and particulate matter (PM) levels were estimated using the Community Multiscale Air Quality (CMAQ) modeling system and light extinction values were calculated from modeled PM concentrations. First, the base case was established using the emission projections for the year 2009. Then, in each model run, SO2, primary carbon (PC), NH3, NO(x) or VOC emissions from a particular source category in a certain geographic area were reduced by 30% and the responses were determined by calculating the difference between the results of the reduced emission case and the base case. The sensitivity of summertime O3 to VOC emissions is small in the Southeast and ground-level NO(x) controls are generally more beneficial than elevated NO(x) controls (per unit mass of emissions reduced). SO2 emission reduction is the most beneficial control strategy in reducing summertime PM2.5 levels and improving visibility in the Southeast and electric generating utilities are the single largest source of SO2. Controlling PC emissions can be very effective locally, especially in winter. Reducing NH3 emissions is an effective strategy to reduce wintertime ammonium nitrate (NO3NH4) levels and improve visibility; NO(x) emissions reductions are not as effective. The results presented here will help the development of specific emission control strategies for future attainment of the National Ambient Air Quality Standards in the region.

The Impacts of Prescribed Fire on PM2.5 Air Quality and Human Health: Application to Asthma-Related Emergency Room Visits in Georgia, USA.

Short-term exposure to fire smoke, especially particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5), 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 PM2.5 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.

African American Exposure to Prescribed Fire Smoke in Georgia, USA.

Our project examines the association between percent African American and smoke pollution in the form of prescribed burn-sourced, fine particulate matter (PM2.5) 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 PM2.5 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 PM2.5 concentrations at the census tract level for Georgia, and b) identify emitters of PM2.5 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 (PM2.5) 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 PM2.5 concentrations; and government and non-industrial private landowners are the greatest contributors to exceedance levels (4) Conclusions: Reasons for higher PM2.5 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.

Machine Learning-Based Integration of High-Resolution Wildfire Smoke Simulations and Observations for Regional Health Impact Assessment.

Large wildfires are an increasing threat to the western U.S. In the 2017 fire season, extensive wildfires occurred across the Pacific Northwest (PNW). To evaluate public health impacts of wildfire smoke, we integrated numerical simulations and observations for regional fire events during August-September of 2017. A one-way coupled Weather Research and Forecasting and Community Multiscale Air Quality modeling system was used to simulate fire smoke transport and dispersion. To reduce modeling bias in fine particulate matter (PM2.5) and to optimize smoke exposure estimates, we integrated modeling results with the high-resolution Multi-Angle Implementation of Atmospheric Correction satellite aerosol optical depth and the U.S. Environmental Protection Agency AirNow ground-level monitoring PM2.5 concentrations. Three machine learning-based data fusion algorithms were applied: An ordinary multi-linear regression method, a generalized boosting method, and a random forest (RF) method. 10-Fold cross-validation found improved surface PM2.5 estimation after data integration and bias correction, especially with the RF method. Lastly, to assess transient health effects of fire smoke, we applied the optimized high-resolution PM2.5 exposure estimate in a short-term exposure-response function. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 183 (95% confidence interval: 0, 432), with 85% of the PM2.5 pollution and 95% of the consequent multiple-cause mortality contributed by fire emissions. This application demonstrates both the profound health impacts of fire smoke over the PNW and the need for a high-performance fire smoke forecasting and reanalysis system to reduce public health risks of smoke hazards in fire-prone regions.

Recommendations on statistics and benchmarks to assess photochemical model performance.

Photochemical grid models are addressing an increasing variety of air quality related issues, yet procedures and metrics used to evaluate their performance remain inconsistent. This impacts the ability to place results in quantitative context relative to other models and applications, and to inform the user and affected community of model uncertainties and weaknesses. More consistent evaluations can serve to drive improvements in the modeling process as major weaknesses are identified and addressed. The large number of North American photochemical modeling studies published in the peer-reviewed literature over the past decade affords a rich data set from which to update previously established quantitative performance "benchmarks" for ozone and particulate matter (PM) concentrations. Here we exploit this information to develop new ozone and PM benchmarks (goals and criteria) for three well-established statistical metrics over spatial scales ranging from urban to regional and over temporal scales ranging from episodic to seasonal. We also recommend additional evaluation procedures, statistical metrics, and graphical methods for good practice. While we primarily address modeling and regulatory settings in the United States, these recommendations are relevant to any such applications of state-of-the-science photochemical models. Our primary objective is to promote quantitatively consistent evaluations across different applications, scales, models, model inputs, and configurations. The purpose of benchmarks is to understand how good or poor the results are relative to historical model applications of similar nature and to guide model performance improvements prior to using results for policy assessments. To that end, it also remains critical to evaluate all aspects of the model via diagnostic and dynamic methods. A second objective is to establish a means to assess model performance changes in the future. Statistical metrics and benchmarks need to be revisited periodically as model performance and the characteristics of air quality change in the future. IMPLICATIONS: We address inconsistent procedures and metrics used to evaluate photochemical model performance, recommend a specific set of statistical metrics, and develop updated quantitative performance benchmarks for those metrics. We promote quantitatively consistent evaluations across different applications, scales, models, inputs, and configurations, thereby (1) improving the user's ability to quantitatively place results in context and guide model improvements, and (2) better informing users, regulators, and stakeholders of model uncertainties and weaknesses prior to using results for policy assessments. While we primarily address U.S. modeling and regulatory settings, these recommendations are relevant to any such applications of state-of-the-science photochemical models.

Analysis of surface ozone and nitrogen oxides at urban, semi-rural and rural sites in Istanbul, Turkey.

Ozone (O(3)) mixing ratios were measured at three different sites (urban/traffic, semi-rural and rural/island) in Istanbul from September 2007 to December 2009 in order to determine the diurnal, monthly and seasonal variations of O(3) and nitrogen oxides (NO(x)) and to study the local and regional impacts. This is the first study that evaluates the O(3) levels in semi-rural and rural sites in Istanbul in addition to the urban sites. The diurnal O(3) variations are generally characterized by afternoon maxima (64 ppb at the urban, 80 ppb at the semi-rural and 100 ppb at the rural site) and the nighttime minimum being more pronounced at the polluted urban site. The monthly mean O(3) mixing ratios start to increase in March, reaching their maximum values in August for the urban (~25 ppb) and semi-rural sites (30 ppb). However, at the rural site, the monthly mean O(3) levels reach their maximum value in June (35 ppb). The O(3) mixing ratios for weekends were higher than those on weekdays at each site by up to 28%, possibly due to changes in VOC sensitivity and reduction in NO(x) levels. In order to better understand and characterize the relationship between air masses and O(3) levels, cluster analysis was applied to the back-trajectories calculated by the HYSPLIT model for the semi-rural site. The analyses clearly showed that major transport is characterized by northern and western clusters, particularly from the Eastern Europe and the Mediterranean region, as well as recirculation over Istanbul due to high pressure systems leading to accumulated levels of O(3). The results clearly suggest that extended measurement networks from urban to rural sites should be considered for a more comprehensive evaluation of O(3) levels.

The impact of anthropogenic and biogenic emissions on surface ozone concentrations in Istanbul.

Surface ozone concentrations at Istanbul during a summer episode in June 2008 were simulated using a high resolution and urban scale modeling system coupling MM5 and CMAQ models with a recently developed anthropogenic emission inventory for the region. Two sets of base runs were performed in order to investigate for the first time the impact of biogenic emissions on ozone concentrations in the Greater Istanbul Area (GIA). The first simulation was performed using only the anthropogenic emissions whereas the second simulation was performed using both anthropogenic and biogenic emissions. Biogenic NMVOC emissions were comparable with anthropogenic NMVOC emissions in terms of magnitude. The inclusion of biogenic emissions significantly improved the performance of the model, particularly in reproducing the low night time values as well as the temporal variation of ozone concentrations. Terpene emissions contributed significantly to the destruction of the ozone during nighttime. Biogenic NMVOCs emissions enhanced ozone concentrations in the downwind regions of GIA up to 25ppb. The VOC/NO(x) ratio almost doubled due to the addition of biogenic NMVOCs. Anthropogenic NO(x) and NMVOCs were perturbed by ±30% in another set of simulations to quantify the sensitivity of ozone concentrations to the precursor emissions in the region. The sensitivity runs, as along with the model-calculated ozone-to-reactive nitrogen ratios, pointed NO(x)-sensitive chemistry, particularly in the downwind areas. On the other hand, urban parts of the city responded more to changes in NO(x) due to very high anthropogenic emissions.

Simulation of air quality impacts from prescribed fires on an urban area.

On February 28, 2007, a severe smoke event caused by prescribed forest fires occurred in Atlanta, GA. Later smoke events in the southeastern metropolitan areas of the United States caused by the Georgia-Florida wild forest fires further magnified the significance of forest fire emissions and the benefits of being able to accurately predict such occurrences. By using preburning information, we utilize an operational forecasting system to simulate the potential air quality impacts from two large February 28th fires. Our "forecast" predicts that the scheduled prescribed fires would have resulted in over 1 million Atlanta residents being potentially exposed to fine particle matter (PM2.5) levels of 35 microg m(-3) or higher from 4 p.m. to midnight. The simulated peak 1 h PM2.5 concentration is about 121 microg m(-3). Our study suggests that the current air quality forecasting technology can be a useful tool for helping the management of fire activities to protect public health. With postburning information, our "hindcast" predictions improved significantly on timing and location and slightly on peak values. "Hindcast" simulations also indicated that additional isoprenoid emissions from pine species temporarily triggered by the fire could induce rapid ozone and secondary organic aerosol formation during late winter. Results from this study suggest that fire induced biogenic volatile organic compounds emissions missing from current fire emissions estimate should be included in the future.

Regional air quality: local and interstate impacts of NO(x) and SO2 emissions on ozone and fine particulate matter in the eastern United States.

While the U.S. air quality management system is largely designed and managed on a state level, many critical air quality problems are now recognized as regional. In particular, concentrations of two secondary pollutants, ozone and particulate matter, are often above regulated levels and can be dependent on emissions from upwind states. Here, impacts of statewide emissions on concentrations of local and downwind states' ozone and fine particulate matter are simulated for three seasonal periods in the eastern United States using a regional Eulerian photochemical model. Impacts of ground level NO(x) (e.g., mobile and area sources), elevated NO(x) (e.g., power plants and large industrial sources), and SO2 emissions are examined. An average of 77% of each state's ozone and PM(2.5) concentrations that are sensitive to the emissions evaluated here are found to be caused by emissions from other states. Delaware, Maryland, New Jersey, Virginia, Kentucky, and West Virginia are shown to have high concentrations of ozone and PM(2.5) caused by interstate emissions. When weighted by population, New York receives increased interstate contributions to these pollutants and contributions to ozone from local emissions are generally higher. When accounting for emission rates, combined states from the western side of the modeling domain and individual states such as Illinois, Tennessee, Indiana, Kentucky, and Georgia are major contributors to interstate ozone. Ohio, Indiana, Tennessee, Kentucky, and Illinois are the major contributors to interstate PM(2.5). When accounting for an equivalent mass of emissions, Tennessee, Kentucky, West Virginia, Virginia, and Alabama contribute large fractions of these pollutants to other states.

High-order, direct sensitivity analysis of multidimensional air quality models.

A direct sensitivity analysis technique is extended to calculate higher-order sensitivity coefficients in three-dimensional air quality models. The time evolution of sensitivity coefficients of different order is followed alongside that of the concentrations. Calculation of higher-order sensitivity coefficients requires few modifications to the original (first-order) sensitivity modules and is carried out efficiently and with minimal computational overhead. The modeling results (first-, second-, and third-order sensitivity coefficients) for an ozone episode in central California are shown and discussed. Second-order sensitivity coefficients of ozone concentration with respect to domain-wide NO emissions show reasonable agreement with brute-force results and exhibit less noisy behavior. By using second-order sensitivity coefficients the nonlinear responses are better captured and described. For a Taylor series projection from the base case, including the second-order term improves the accuracy. In general, higher-order sensitivity analysis shows a noticeable improvement in terms of accuracy over the conventional first-order analysis. Of particular interest, second-order sensitivity analysis is better equipped to address the nonlinear behavior around the peak ozone in NO(x)-rich plumes.