The impact of US wildland fires on ozone and particulate matter: a comparison of measurements and CMAQ model predictions from 2008 to 2012

Wilkins, Joseph L.; Pouliot, George; Foley, Kristen M.; Appel, Wyat; Pierce, T.E.

doi:10.1071/wf18053

Cited by 38 publications

(51 citation statements)

References 55 publications

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“…As the three inventories used in the simulations differ only in the wildfire emissions, the occurrence of the maximum MFE in winter indicates that those emissions are not the major contributor to the ozone biases for any of the cases. This is consistent with the findings of Wilkins et al (2018), whose brute-force zero-out analyses of wildfire emissions impacts on air quality showed only a 1% increase in ozone due to 25 wildfire from 2008 -2012 over the CONUS.…”

Section: Discussionsupporting

confidence: 91%

“…The large negative biases in OC predictions, which are seen across both rural and urban networks, are likely to be a result of 30 underprediction in the CMAQ v5.0.2 SOA mechanism rather than in the primary wildfire OC emissions. Some of the underestimation could come from the assumed NEI temporal profiles for emissions from smaller wildfires that are less than a full day in duration (Wilkins et al, 2018). Residential wood combustion has also been shown to be as a source of Atmos.…”

Section: Discussionmentioning

confidence: 99%

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Evaluating wildfire emissions projection methods in comparisons of simulated and observed air quality

Shankar¹,

McKenzie²,

Prestemon³

et al. 2019

Preprint

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Climate warming has been implicated as a major driver of recent catastrophic wildfires world-wide but analyses of regional differences in U.S. wildfires show that socioeconomic factors also have a large role. We previously leveraged statistical projections of annual areas burned (AAB) over the fast-growing Southeastern U.S. that include both climate and 15 socioeconomic changes from 2011 to 2060, and developed wildfire emissions estimates over the region at 12-km x 12-km resolution to enable air quality (AQ) impact assessments for 2010 and selected future years. These estimates employed two AAB datasets, one using statistical downscaling ("statistical d-s"), and another using dynamical downscaling ("dynamical d-s") of climate inputs from the same climate realization. This paper evaluates these wildfire emissions estimates against the U.S. National Emissions Inventory (NEI) as a benchmark in contemporary (2010) simulations with the Community 20Multiscale Air Quality (CMAQ) model, and against network observations for ozone and particulate matter below 2.5 µm in diameter (PM2.5). We hypothesize that our emissions estimates will yield model results that meet acceptable performance criteria, and are comparable to those using the NEI. The three simulations, which differ only in wildfire emissions, compare closely, with differences in ozone and PM2.5 below 1% and 8% respectively, but have much larger maximum mean fractional biases (MFBs) against observations (25% and 51% respectively). The largest biases for ozone are in the fire-free winter, 25 indicating that modeling uncertainties other than wildfire emissions are mainly responsible. Statistical d-s, with the largest AAB domain-wide, is 7% more positively biased and 4% less negatively biased in PM2.5 on average than the other two cases, while dynamical d-s and the NEI differ only by 2% -3% partly because of their equally large summertime PM2.5 underpredictions. Primary species (elemental carbon, and ammonium from ammonia) have good-to-acceptable results, especially for the downscaling cases, providing confidence in our emissions estimation methodology. Compensating biases 30 in sulfate (positive), and in organic carbon and dust (negative) lead to acceptable PM2.5 performance. As these species are driven by secondary chemistry or non-wildfire sources, their production pathways can be fruitful avenues for CMAQ Atmos. Chem. Phys. Discuss., https://doi.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Evaluating wildfire emissions projection methods in comparisons of simulated and observed air quality

Shankar¹,

McKenzie²,

Prestemon³

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…As the three inventories used in the simulations differ only in the wildfire emissions, the occurrence of the maximum MFE in winter indicates that those emissions are not the major contributor to the ozone biases for any of the cases. This is consistent with the findings of Wilkins et al (2018), whose brute-force zero-out analyses of wildfire emissions impacts on air quality showed only a 1 % increase in ozone due to wildfire from 2008 to 2012 over the CONUS.…”

Section: Discussionsupporting

confidence: 91%

“…The large negative biases in OC predictions, which are seen across both rural and urban networks, are likely to be a result of underprediction in the CMAQ v5.0.2 secondary organic aerosol (SOA) mechanism rather than in the primary wildfire OC emissions. Some of the underestimation could come from the assumed NEI temporal profiles for emissions from smaller wildfires that are less than a full day in duration (Wilkins et al, 2018). Residential wood combustion has also been shown to be as a source of underestimation of carbonaceous PM in the NEI in a 2007 study over the southeastern US (Napelenok et al, 2014), and this possibility is supported by the better model performance for OC at CSN (urban) sites compared to IMPROVE (rural) sites.…”

Section: Discussionmentioning

confidence: 99%

Evaluating wildfire emissions projection methods in comparisons of simulated and observed air quality

et al. 2019

View full text Add to dashboard Cite

Climate warming has been implicated as a major driver of recent catastrophic wildfires worldwide but analyses of regional differences in US wildfires show that socioeconomic factors also play a large role. We previously leveraged statistical projections of annual areas burned (AAB) over the fast-growing southeastern US that include both climate and socioeconomic changes from 2011 to 2060 and developed wildfire emissions estimates over the region at 12 km × 12 km resolution to enable air quality (AQ) impact assessments for 2010 and selected future years. These estimates employed two AAB datasets, one using statistical downscaling ("statistical d-s") and another using dynamical downscaling ("dynamical d-s") of climate inputs from the same climate realization. This paper evaluates these wildfire emissions estimates against the U.S. National Emissions Inventory (NEI) as a benchmark in contemporary (2010) simulations with the Community Multiscale Air Quality (CMAQ) model and against network observations for ozone and particulate matter below 2.5 µm in diameter (PM 2.5 ). We hypothesize that our emissions estimates will yield model results that meet acceptable performance criteria and are comparable to those using the NEI. The three simulations, which differ only in wildfire emissions, compare closely, with differences in ozone and PM 2.5 below 1 % and 8 %, respectively, but have much larger maximum mean fractional biases (MFBs) against observations (25 % and 51 %, respectively). The largest biases for ozone are in the fire-free winter, indi-cating that modeling uncertainties other than wildfire emissions are mainly responsible. Statistical d-s, with the largest AAB domain-wide, is 7 % more positively biased and 4 % less negatively biased in PM 2.5 on average than the other two cases, while dynamical d-s and the NEI differ only by 2 %-3 % partly because of their equally large summertime PM 2.5 underpredictions. Primary species (elemental carbon and ammonium from ammonia) have good-to-acceptable results, especially for the downscaling cases, providing confidence in our emissions estimation methodology. Compensating biases in sulfate (positive) and in organic carbon and dust (negative) lead to acceptable PM 2.5 performance to varying degrees (MFB between −14 % and 51 %) in all simulations. As these species are driven by secondary chemistry or nonwildfire sources, their production pathways can be fruitful avenues for CMAQ improvements. Overall, the downscaling methods match and sometimes exceed the NEI in simulating current wildfire AQ impacts, while enabling such assessments much farther into the future.

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“…Extreme heat can affect cardiovascular and respiratory health (Gronlund et al, 2018;Mora et al, 2017), but these impacts are not included in our analysis. Wildfire impacts were characterized only for PM 2.5 exposures, not for wildfire-linked ozone air pollution (Baker et al, 2016;Wilkins et al, 2018). Other effects on well-being, such as the toll of displacement and uncertainty stemming from adverse exposures, are difficult to quantify but nonetheless important (Afifi et al, 2012;Berry et al, 2018;Tschakert et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

Estimating the Health‐Related Costs of 10 Climate‐Sensitive U.S. Events During 2012

et al. 2019

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Climate change threatens human health, but there remains a lack of evidence on the economic toll of climate‐sensitive public health impacts. We characterize human mortality and morbidity costs associated with 10 climate‐sensitive case study events spanning 11 US states in 2012: wildfires in Colorado and Washington, ozone air pollution in Nevada, extreme heat in Wisconsin, infectious disease outbreaks of tick‐borne Lyme disease in Michigan and mosquito‐borne West Nile virus in Texas, extreme weather in Ohio, impacts of Hurricane Sandy in New Jersey and New York, allergenic oak pollen in North Carolina, and harmful algal blooms on the Florida coast. Applying a consistent economic valuation approach to published studies and state estimates, we estimate total health‐related costs from 917 deaths, 20,568 hospitalizations, and 17,857 emergency department visits of $10.0 billion in 2018 dollars, with a sensitivity range of $2.7–24.6 billion. Our estimates indicate that the financial burden of deaths, hospitalizations, emergency department visits, and associated medical care is a key dimension of the overall economic impact of climate‐sensitive events. We found that mortality costs (i.e., the value of a statistical life) of $8.4 billion exceeded morbidity costs and lost wages ($1.6 billion combined). By better characterizing health damages in economic terms, this work helps to shed light on the burden climate‐sensitive events already place on U.S. public health each year. In doing so, we provide a conceptual framework for broader estimation of climate‐sensitive health‐related costs. The high health‐related costs associated with climate‐sensitive events highlight the importance of actions to mitigate climate change and adapt to its unavoidable impacts.

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The impact of US wildland fires on ozone and particulate matter: a comparison of measurements and CMAQ model predictions from 2008 to 2012

Cited by 38 publications

References 55 publications

Evaluating wildfire emissions projection methods in comparisons of simulated and observed air quality

Evaluating wildfire emissions projection methods in comparisons of simulated and observed air quality

Evaluating wildfire emissions projection methods in comparisons of simulated and observed air quality

Estimating the Health‐Related Costs of 10 Climate‐Sensitive U.S. Events During 2012

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