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Wildfires have been increasing in frequency in the western United States (US) with the 2017 and 2018 fire seasons experiencing some of the worst wildfires in terms of suppression costs and air pollution that the western US has seen. Although growing evidence suggests respiratory exacerbations from elevated fine particulate matter (PM2.5) during wildfires, significantly less is known about the impacts on human health of ozone (O3) that may also be increased due to wildfires. Using machine learning, we created daily surface concentration maps for PM2.5 and O3 during an intense wildfire in California in 2008. We then linked these daily exposures to counts of respiratory hospitalizations and emergency department visits at the ZIP code level. We calculated relative risks of respiratory health outcomes using Poisson generalized estimating equations models for each exposure in separate and mutually-adjusted models, additionally adjusted for pertinent covariates. During the active fire periods, PM2.5 was significantly associated with exacerbations of asthma and chronic obstructive pulmonary disease (COPD) and these effects remained after controlling for O3. Effect estimates of O3 during the fire period were non-significant for respiratory hospitalizations but were significant for ED visits for asthma (RR = 1.05 and 95% CI = (1.022, 1.078) for a 10 ppb increase in O3). In mutually-adjusted models, the significant findings for PM2.5 remained whereas the associations with O3 were confounded. Adjusted for O3, the RR for asthma ED visits associated with a 10 μg/m3 increase in PM2.5 was 1.112 and 95% CI = (1.087, 1.138). The significant findings for PM2.5 but not for O3 in mutually-adjusted models is likely due to the fact that PM2.5 levels during these fires exceeded the 24-hour National Ambient Air Quality Standard (NAAQS) of 35 μg/m3 for 4976 ZIP-code days and reached levels up to 6.073 times the NAAQS, whereas our estimated O3 levels during the fire period only occasionally exceeded the NAAQS of 70 ppb with low exceedance levels. Future studies should continue to investigate the combined role of O3 and PM2.5 during wildfires to get a more comprehensive assessment of the cumulative burden on health from wildfire smoke. Keywords: Wildfires, Ozone, Particulate matter, Respiratory disease

A real-time air quality forecasting system was developed using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to provide support for flight planning activities during the NOAA Atmospheric Emissions and Reactions Observed from Megacities to Marine Areas (AEROMMA) and NASA Synergistic TEMPO Air Quality Science (STAQS) 2023 field campaigns. The forecasting system operated on two separate domains centered on Chicago, IL, and New York City, NY, and provided 72-hour predictions of atmospheric composition, aerosols, and clouds. This study evaluates the Chicago-centered forecasting system's 1-, 2-, and 3-day ozone (O 3 ) forecast skill for Chiwaukee Prairie, WI, a rural area downwind of Chicago that often experiences high levels of O 3 pollution. Comparisons to vertical O 3 profiles collected by a Tropospheric Ozone Lidar Network (TOLNet) instrument revealed that forecast skill decreases as forecast lead time increases. When compared to surface measurements, the forecasting system tended to underestimate O 3 concentrations on high O 3 days and overestimate on low O 3 days at Chiwaukee Prairie regardless of forecast lead time. Using July 25, 2023, as a case study, analyses show that the forecasts underestimated peak O 3 levels at Chiwaukee Prairie during this regionwide bad air quality day. Wind speed and direction data indicates that this underestimation can partially be attributed to lake breeze simulation errors. Surface fine particulate matter (PM 2.5 ) measurements, Geostationary Operational Environmental Satellite-16 (GOES-16) aerosol optical depth (AOD) data, and back trajectories from the NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model show that transported Canadian wildfire smoke impacted the Lake Michigan region on this day. Errors in the forecasted chemical composition and transport of the smoke plumes also contributed to underpredictions of O 3 levels at Chiwaukee Prairie on July 25, 2023. The results of this work help identify improvements that can be made for future iterations of the WRF-Chem forecasting system. Implications : Air quality forecasting is an important tool that can be used to inform the public about upcoming high pollution days so that individuals may plan accordingly to limit their exposure to health-damaging air pollutants. Forecasting also helps scientists make decisions about where to make observations during air quality field campaigns. A variety of observational datasets were used to evaluate the accuracy of an air quality forecasting system that was developed for NOAA and NASA field campaigns that occurred in the summer of 2023. These evaluations inform areas of improvement for future development of this air quality forecasting system.

Wildfire smoke (WFS) increases the risk of respiratory hospitalizations. We evaluated the association between WFS and asthma healthcare utilization (AHCU) during the 2013 wildfire season in Oregon. WFS particulate matter ≤ 2.5 μm in diameter (PM 2.5 ) was estimated using a blended model of in situ monitoring, chemical transport models, and satellite-based data. Asthma claims and place of service were identified from Oregon All Payer All Claims data from 1 May 2013 to 30 September 2013. The association with WFS PM 2.5 was evaluated using time-stratified case-crossover designs. The maximum WFS PM 2.5 concentration during the study period was 172 µg/m 3 . A 10 µg/m 3 increase in WFS increased risk in asthma diagnosis at emergency departments (odds ratio [OR]: 1.089, 95% confidence interval [CI]: 1.043-1.136), office visit (OR: 1.050, 95% CI: 1.038-1.063), and outpatient visits (OR: 1.065, 95% CI: 1.029-1.103); an association was observed with asthma rescue inhaler medication fills (OR: 1.077, 95% CI: 1.065-1.088). WFS increased the risk for asthma morbidity during the 2013 wildfire season in Oregon. Communities impacted by WFS could see increases in AHCU for tertiary, secondary, and primary care.

NASA's Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ, conducted in 2011-2014) campaign in the United States and the joint NASA and National Institute of Environmental Research (NIER) Korea-United States Air Quality Study (KORUS-AQ, conducted in 2016) in South Korea were two field study programs that provided comprehensive, integrated datasets of airborne and surface observations of atmospheric constituents, including nitrogen dioxide (NO 2 ), with the goal of improving the interpretation of spaceborne remote sensing data. Various types of NO 2 measurements were made, including in situ concentrations and column amounts of NO 2 using ground- and aircraft-based instruments, while NO 2 column amounts were being derived from the Ozone Monitoring Instrument (OMI) on the Aura satellite. This study takes advantage of these unique datasets by first evaluating in situ data taken from two different instruments on the same aircraft platform, comparing coincidently sampled profile-integrated columns from aircraft spirals with remotely sensed column observations from ground-based Pandora spectrometers, intercomparing column observations from the ground (Pandora), aircraft (in situ vertical spirals), and space (OMI), and evaluating NO 2 simulations from coarse Global Modeling Initiative (GMI) and high-resolution regional models. We then use these data to interpret observed discrepancies due to differences in sampling and deficiencies in the data reduction process. Finally, we assess satellite retrieval sensitivity to observed and modeled a priori NO 2 profiles. Contemporaneous measurements from two aircraft instruments that likely sample similar air masses generally agree very well but are also found to differ in integrated columns by up to 31.9 %. These show even larger differences with Pandora, reaching up to 53.9 %, potentially due to a combination of strong gradients in NO 2 fields that could be missed by aircraft spirals and errors in the Pandora retrievals. OMI NO 2 values are about a factor of 2 lower in these highly polluted environments due in part to inaccurate retrieval assumptions (e.g., a priori profiles) but mostly to OMI's large footprint (> 312 km 2 )., Competing Interests: Competing interests. The authors declare that they have no conflict of interest.

Due to their enhanced fuel economy, the market share of gasoline direct injection (GDI) vehicles has increased significantly over the past decade. However, GDI engines emit higher levels of black carbon (BC) aerosols compared to traditional port fuel injection (PFI) engines. Here, we performed coupled chemical transport and radiative transfer simulations to estimate the aerosol-induced public health and direct radiative effects of shifting the U.S. fleet from PFI to GDI technology. By comparing simulations with current emission profiles and emission profiles modified to reflect a shift from PFI to GDI, we calculated the change in aerosol (mostly BC) concentrations associated with the fleet change. Standard concentration-response calculations indicated that the total annual deaths in the U.S. attributed to particulate gasoline-vehicle emissions would increase from 855 to 1599 due to shifting from PFI to GDI. Furthermore, the increase in BC associated with the shift would lead to an annual average positive radiative effect over the U.S. of approximately +0.075 W/m 2 , with values as large as +0.45 W/m 2 over urban regions. On the other hand, the reduction in CO 2 emissions associated with the enhanced fuel economy of GDI vehicles would yield a globally uniform negative radiative effect, estimated to be -0.013 W/m 2 over a 20 year time horizon. Therefore, the climate burden of the increase in BC emissions dominates over the U.S., especially over source regions.

In the western U.S., smoke from wild and prescribed fires can severely degrade air quality. Due to changes in climate and land management, wildfires have increased in frequency and severity, and this trend is expected to continue. Consequently, wildfires are expected to become an increasingly important source of air pollutants in the western U.S. Hence, there is a need to develop a quantitative understanding of wildfire-smoke-specific health effects. A necessary step in this process is to determine who was exposed to wildfire smoke, the concentration of the smoke during exposure, and the duration of the exposure. Three different tools have been used in past studies to assess exposure to wildfire smoke: in situ measurements, satellite-based observations, and chemical-transport model (CTM) simulations. Each of these exposure-estimation tools has associated strengths and weakness. We investigate the utility of blending these tools together to produce estimates of PM 2.5 exposure from wildfire smoke during the Washington 2012 fire season. For blending, we use a ridge-regression model and a geographically weighted ridge-regression model. We evaluate the performance of the three individual exposure-estimate techniques and the two blended techniques by using leave-one-out cross validation. We find that predictions based on in situ monitors are more accurate for this particular fire season than the CTM simulations and satellite-based observations because of the large number of monitors present; therefore, blending provides only marginal improvements above the in situ observations. However, we show that in hypothetical cases with fewer surface monitors, the two blending techniques can produce substantial improvement over any of the individual tools., (©2017. The Authors.)

Climate forecasts predict an increase in frequency and intensity of wildfires. Associations between health outcomes and population exposure to smoke from Washington 2012 wildfires were compared using surface monitors, chemical-weather models, and a novel method blending three exposure information sources. The association between smoke particulate matter ≤2.5 μm in diameter (PM 2.5 ) and cardiopulmonary hospital admissions occurring in Washington from 1 July to 31 October 2012 was evaluated using a time-stratified case-crossover design. Hospital admissions aggregated by ZIP code were linked with population-weighted daily average concentrations of smoke PM 2.5 estimated using three distinct methods: a simulation with the Weather Research and Forecasting with Chemistry (WRF-Chem) model, a kriged interpolation of PM 2.5 measurements from surface monitors, and a geographically weighted ridge regression (GWR) that blended inputs from WRF-Chem, satellite observations of aerosol optical depth, and kriged PM 2.5 . A 10 μg/m 3 increase in GWR smoke PM 2.5 was associated with an 8% increased risk in asthma-related hospital admissions (odds ratio (OR): 1.076, 95% confidence interval (CI): 1.019-1.136); other smoke estimation methods yielded similar results. However, point estimates for chronic obstructive pulmonary disease (COPD) differed by smoke PM 2.5 exposure method: a 10 μg/m 3 increase using GWR was significantly associated with increased risk of COPD (OR: 1.084, 95%CI: 1.026-1.145) and not significant using WRF-Chem (OR: 0.986, 95%CI: 0.931-1.045). The magnitude (OR) and uncertainty (95%CI) of associations between smoke PM 2.5 and hospital admissions were dependent on estimation method used and outcome evaluated. Choice of smoke exposure estimation method used can impact the overall conclusion of the study.

Background: In 2012, Colorado experienced one of its worst wildfire seasons of the past decade. The goal of this study was to investigate the relationship of local PM2.5 levels, modeled using the Weather Research and Forecasting Model with Chemistry, with emergency department visits and acute hospitalizations for respiratory and cardiovascular outcomes during the 2012 Colorado wildfires., Methods: Conditional logistic regression was used to assess the relationship between both continuous and categorical PM2.5 and emergency department visits during the wildfire period, from June 5(th) to July 6(th) 2012., Results: For respiratory outcomes, we observed positive relationships between lag 0 PM2.5 and asthma/wheeze (1 h max OR 1.01, 95 % CI (1.00, 1.01) per 10 μg/m(3); 24 h mean OR 1.04 95 % CI (1.02, 1.06) per 5 μg/m(3)), and COPD (1 h max OR 1.01 95 % CI (1.00, 1.02) per 10 μg/m(3); 24 h mean OR 1.05 95 % CI (1.02, 1.08) per 5 μg/m(3)). These associations were also positive for 2-day and 3-day moving average lag periods. When PM2.5 was modeled as a categorical variable, bronchitis also showed elevated effect estimates over the referent groups for lag 0 24 h average concentration. Cardiovascular results were consistent with no association., Conclusions: We observed positive associations between PM2.5 from wildfire and respiratory diseases, supporting evidence from previous research that wildfire PM2.5 is an important source for adverse respiratory health outcomes.

Wildfires generate substantial emissions of nitrogen oxides (NOx) and volatile organic compounds (VOCs). As such, wildfires contribute to elevated ozone (O3) in the atmosphere. However, there is a large amount of variability in the emissions of O3 precursors and the amount of O3 produced between fires. There is also significant interannual variability as seen in median O3, organic carbon and satellite derived carbon monoxide mixing ratios in the western U.S. To better understand O3 produced from wildfires, we developed a statistical model that estimates the maximum daily 8 h average (MDA8) O3 as a function of several meteorological and temporal variables for three urban areas in the western U.S.: Salt Lake City, UT; Boise, ID; and Reno, NV. The model is developed using data from June-September 2000-2012. For these three locations, the statistical model can explain 60, 52, and 27% of the variability in daily MDA8. The Statistical Model Residual (SMR) can give information on additional sources of O3 that are not explained by the usual meteorological pattern. Several possible O3 sources can explain high SMR values on any given day. We examine several cases with high SMR that are due to wildfire influence. The first case considered is for Reno in June 2008 when the MDA8 reached 82 ppbv. The wildfire influence for this episode is supported by PM concentrations, the known location of wildfires at the time and simulations with the Weather and Research Forecasting Model with Chemistry (WRF-Chem) which indicates transport to Reno from large fires burning in California. The contribution to the MDA8 in Reno from the California wildfires is estimated to be 26 ppbv, based on the SMR, and 60 ppbv, based on WRF-Chem. The WRF-Chem model also indicates an important role for peroxyacetyl nitrate (PAN) in producing O3 during transport from the California wildfires. We hypothesize that enhancements in PAN due to wildfire emissions may lead to regional enhancements in O3 during high fire years. The second case is for the Salt Lake City (SLC) region for August 2012. During this period the MDA8 reached 83 ppbv and the SMR suggests a wildfire contribution of 19 ppbv to the MDA8. The wildfire influence is supported by PM2.5 data, the known location of wildfires at the time, HYSPLIT dispersion modeling that indicates transport from fires in Idaho, and results from the CMAQ model that confirm the fire impacts. Concentrations of PM2.5 and O3 are enhanced during this period, but overall there is a poor relationship between them, which is consistent with the complexities in the secondary production of O3. A third case looks at high MDA8 in Boise, ID, during July 2012 and reaches similar conclusions. These results support the use of statistical modeling as a tool to quantify the influence from wildfires on urban O3 concentrations.

We present a modeling study investigating the influence of climate conditions and solar radiation intensity on gas-phase trichloroacetic acid (TCA) formation. As part of the ECCA-project (Ecotoxicological Risk in the Caspian Catchment Area), this modeling study uses climate data specific for the two individual climate regimes, namely "Kalmykia" and "Kola Peninsula". A third regime has also been included in this study, namely "Central Europe", which serves as a reference to somehow more moderate climate conditions. The simulations have been performed with a box modeling package (SBOX, photoRACM), which uses Regional Atmospheric Chemistry Mechanism (RACM) as its chemistry scheme. For this model a mechanism supplement has been developed including the reaction pathways of methyl chloroform photooxidation. The investigations are completed by a detailed sensitivity study addressing the impact of temperature and relative humidity. Atmospheric OH and HO2 concentrations and the NOx/HO2 ratio were identified as the governing quantities controlling the TCA formation trough methyl chloroform oxidation in the gas phase. Model calculations show a TCA production rate ranging between almost zero and 6.5 x 10(3) molecules cm(-3) day(-1) depending on location and season. In the Kalmykia regime the model predicts mean TCA production rates of 1.3 x 10(-4) and 5.4 x 10(-5) microg m(-3) year(-1) for the urban and rural environment, respectively. From the comparison of model calculations with measured TCA burdens in the soil ranging between 130 g m(-3) and 1750 g m(-3) we conclude that TCA formation through methyl chloroform photooxidation in the gas-phase is probably not the principal atmospheric TCA source in this region.