Quantifying O3 Impacts in Urban Areas Due to Wildfires Using a Generalized Additive Model

Wildfire Impacts on O3 in the Continental United States Using PM2.5 and a Generalized Additive Model (2018-2023)

We examined PM2.5 and Hazard Mapping System smoke plume satellite data at ∼600 United States (US) air monitoring stations to identify surface smoke on 14.0% of all May-September days for 2018-2023, with large influences in 2020 and 2021, due to California fires, and 2023, due to Canadian fires. Days with smoke have an average of 11 μg m-3 more PM2.5 and 8 ppb higher maximum daily 8 h average (MDA8) O3 concentrations than nonsmoke days, and they also account for 94% of all days that exceed the daily PM2.5 health standard (35 μg m-3) and 36% of all days that exceed the O3 health standard (70 ppb). To estimate the smoke contributions to the O3 MDA8, Generalized Additive Models (GAMs) were built for each site using the nonsmoke day data and up to 8 predictors. The mean and standard deviation of the residuals from the GAMs were 0 ± 6.1 ppb for the nonsmoke day data and 4.3 ± 7.9 ppb for the smoke day data, indicating a significant enhancement in the MDA8 O3 on smoke days. We found positive residuals on 72% of the smoke days and for these days, we calculate an average smoke contribution to the O3 MDA8 of 7.8 ± 6.0 ppb. Over the 6 year period, the percentage of exceedance days due to smoke in the continental US was 25% of all exceedance days, and the highest was in 2023 (38%). In 2023, the Central US experienced an unusually high number of exceedance days, 1522, with 52% of these impacted by smoke, while the Eastern US had fewer exceedance days, 288, with 78% of these impacted by smoke. Our results demonstrate the importance of wildland fires as contributors to exceedances of the health-based national air quality standards for PM2.5 and O3.

Key results from the salt lake regional smoke, ozone, and aerosol study (SAMOZA)

The Northern Wasatch Front area is one of ~ 50 metropolitan regions in the U.S. that do not meet the 2015 O3 standard. To better understand the causes of high O3 days in this region we conducted the Salt Lake regional Smoke, Ozone and Aerosol Study (SAMOZA) in the summer of 2022. The primary goals of SAMOZA were: Measure a suite of VOCs, by Proton Transfer Reaction Mass Spectrometry (PTR-MS) and the 2,4-dinitrophenylhydrazine (DNPH) cartridge method.Evaluate whether the standard UV O3 measurements made in SLC show a positive bias during smoke events, as has been suggested in some recent studies.Use the observations to conduct photochemical modeling and statistical/machine learning analyses to understand photochemistry on both smoke-influenced and non-smoke days.Implications: The Northern Wasatch Front area is one of ~50 metropolitan regions in the U.S. that do not meet the 2015 O3 standard. To better understand the causes of high O3 days in this region we conducted the Salt Lake regional Smoke, Ozone and Aerosol Study (SAMOZA) in the summer of 2022. A number of policy relevant findings are identified in the manuscript including role of smoke and NOx vs VOC sensitivity.

Impact of wildfire smoke on ozone concentrations using a Generalized Additive model in Salt Lake City, Utah, USA, 2006-2022

We investigated the impact of wildfires on maximum daily 8-hr average ozone concentrations (MDA8 O3) at four sites in Salt Lake City (SLC), Utah for May to September for 2006-2022. Smoke days, which were identified by a combination of overhead satellite smoke detection and surface PM2.5 data and accounted for approximately 9% of the total number of days, exhibited O3 levels 6.8 to 8.9 ppb higher than no-smoke days and were predominantly characterized by high daily maximum temperatures and low relative humidity. A Generalized Additive Model (GAM) was developed to quantify the impact of wildfire contributions to O3. The GAM, which provides smooth functions that make the interpretation of relationships more intuitive, employed 17 predictors and demonstrated reliable performance in various evaluation metrics. The mean of the residuals for all sites was approximately zero for the training and cross-validation data and 5.1 ppb for smoke days. We developed three approaches to estimate the contribution of smoke to O3 from the model residuals. These generate a minimum and maximum contribution for each smoke day. The average of the minimum and maximum wildfire contributions to O3 for the SLC sites was 5.1 and 8.5 ppb, respectively. Between 2006 and 2022, an increasing trend in the wildfire contributions to O3 was observed in SLC. Moreover, trends of the fourth-highest MDA8 O3 before and after removing the wildfire contributions to O3 at the SLC Hawthorne site in 2006-2022 were quite different. Whereas the unadjusted data do not meet the current O3 standard, after removing the contributions from wildfires the SLC region is close to achieving levels that are consistent with meeting the O3 standard. We also found that the wildfire contribution during smoke days was particularly high under conditions of high temperature, high PM2.5 concentration, and low cloud fraction.Implications: In this study, we quantified the impact of wildfires on maximum daily 8-hr average ozone concentrations (MDA8 O3) in Salt Lake City, Utah, using a Generalized Additive Model (GAM). The GAM results demonstrate the importance of wildfires as contributors to O3 air pollution. Our results suggest that states could use the GAM approach to assist in quantifying the wildfire contribution to MDA8 O3 under the U.S. EPA exceptional events rule. These findings also highlight the need for strategies to manage wildfires and their subsequent impacts on air quality in an era of climate warming.

Investigation of Ozone Formation Chemistry during the Salt Lake Regional Smoke, Ozone, and Aerosol Study (SAMOZA)

Salt Lake City (SLC), UT, is an urban area where ozone (O3) concentrations frequently exceed health standards. This study uses an observationally constrained photochemical box model to investigate the drivers of O3 production during the Salt Lake Regional Smoke, Ozone, and Aerosol Study (SAMOZA), which took place from August to September 2022 in SLC. During SAMOZA, a suite of volatile organic compounds (VOCs), oxides of nitrogen (NOx), and other parameters were measured at the Utah Technical Center, a high-NOx site in the urban core. We examined four high-O3 cases: 4 August and 3, 11, and 12 September, which were classified as a nonsmoky weekday, a weekend day with minimal smoke influence, a smoky weekend day, and a smoky weekday, respectively. The modeled O3 production on 4 August and 3 September was highly sensitive to VOCs and insensitive to NOx reductions of ≤50%. Box model results suggest that the directly emitted formaldehyde contributed to the rapid increase in morning O3 concentrations on 3 September. Model sensitivity tests for September 11-12 indicated that smoke-emitted VOCs, especially aldehydes, had a much larger impact on O3 production than NOx and/or anthropogenic VOCs. On 11 and 12 September, smoke-emitted VOCs enhanced model-predicted maximum daily 8 h average O3 concentrations by 21 and 13 parts per billion (ppb), respectively. Overall, our results suggest that regionwide VOC reductions of at least 30-50% or NOx reductions of at least 60% are needed to bring SLC into compliance with the national O3 standard of 70 ppb.

Variation trend prediction of ground-level ozone concentrations with high-resolution using landscape pattern data

Scientifically configuring landscape patterns based on their relationship with ground-level ozone concentrations (GOCs) is an effective way to prevent and control ground-level ozone pollution. In this paper, a GOC variation trend prediction model (hybrid model) combining a generalized linear model (GLM) and a logistic regression model (LRM) was established to analyze the spatiotemporal variation patterns in GOCs as well as their responses to landscape patterns. The model exhibited satisfactory performance, with percent of samples correctly predicted (PCP) value of 82.33% and area under receiver operating characteristics curve (AUC) value of 0.70. Using the hybrid model, the per-pixel rise probability of annual average GOCs at a spatial resolution of 1 km in Shenzhen were generated. The results showed that (1) annual average GOCs were increasing in Shenzhen from 2015 to 2020, and had obvious spatial differences, with a higher value in the west and a lower value in the east; (2) variation trend in GOCs was significant positively correlated with landscape heterogeneity (HET), while significant negatively correlated with dominance (DMG) and contagion (CON); (3) GOCs in Shenzhen has a great risk of rising, especially in GuangMing, PingShan, LongGang, LuoHu and BaoAn. The results provide not only a preliminary index for estimating the GOC variation trend in the absence of air quality monitoring data but also guidance for landscape optimizing design from the perspective of controlling ground-level ozone pollution.

The Marginal Ice Zone as a dominant source region of atmospheric mercury during central Arctic summertime

Atmospheric gaseous elemental mercury (GEM) concentrations in the Arctic exhibit a clear summertime maximum, while the origin of this peak is still a matter of debate in the community. Based on summertime observations during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition and a modeling approach, we further investigate the sources of atmospheric Hg in the central Arctic. Simulations with a generalized additive model (GAM) show that long-range transport of anthropogenic and terrestrial Hg from lower latitudes is a minor contribution (~2%), and more than 50% of the explained GEM variability is caused by oceanic evasion. A potential source contribution function (PSCF) analysis further shows that oceanic evasion is not significant throughout the ice-covered central Arctic Ocean but mainly occurs in the Marginal Ice Zone (MIZ) due to the specific environmental conditions in that region. Our results suggest that this regional process could be the leading contributor to the observed summertime GEM maximum. In the context of rapid Arctic warming and the observed increase in width of the MIZ, oceanic Hg evasion may become more significant and strengthen the role of the central Arctic Ocean as a summertime source of atmospheric Hg.

Effect modification of the association between fine particulate air pollution during a wildfire event and respiratory health by area-level measures of socio-economic status, race/ethnicity, and smoking prevalence

Fine particulate air pollution (PM2.5) is decreasing in most areas of the United States, except for areas most affected by wildfires, where increasing trends in PM2.5 can be attributed to wildfire smoke. The frequency and duration of large wildfires and the length of the wildfire season have all increased in recent decades, partially due to climate change, and wildfire risk is projected to increase further in many regions including the western United States. Increasingly, empirical evidence suggests differential health effects from air pollution by class and race; however, few studies have investigated such differential health impacts from air pollution during a wildfire event. We investigated differential risk of respiratory health impacts during the 2008 northern California wildfires by a comprehensive list of socio-economic status (SES), race/ethnicity, and smoking prevalence variables. Regardless of SES level across nine measures of SES, we found significant associations between PM2.5 and asthma hospitalizations and emergency department (ED) visits during these wildfires. Differential respiratory health risk was found by SES for ED visits for chronic obstructive pulmonary disease where the highest risks were in ZIP codes with the lowest SES levels. Findings for differential effects by race/ethnicity were less consistent across health outcomes. We found that ZIP codes with higher prevalence of smokers had greater risk of ED visits for asthma and pneumonia. Our study suggests that public health efforts to decrease exposures to high levels of air pollution during wildfires should focus on lower SES communities.

Evaluation on leachability of heavy metals from tailings: risk factor identification and cumulative influence

The leachability of heavy metals (HMs) in tailings is significantly affected by multivariate factors associated with environmental conditions. However, the leaching patterns of HMs in molybdenum (Mo) tailings due to environmental change and cumulative influences of multi-leaching factors remain unclear. The leaching behaviors of HMs in Mo tailings were studied through static leaching tests. The key leaching factors were discussed via simulating acid rain leaching scenario in terms of global and local environmental conditions. The potential risk factors were identified, and their cumulative influences on the leachability of HMs were evaluated with boosted regression trees (BRT) and generalized additive model (GAM) analyses. Environmental factors showed interactive effects on the leachability of HMs in tailings. The leachability of HMs in tailings decreased significantly with the interaction of increasing liquid/solid (L/S) ratio and pH. Rebound of leachability was observed with high L/S ratio (> 60) and long-time leaching (> 30 h). L/S ratio and pH were the most sensitive factors to the leachability of HMs with the corresponding contribution of 40.8% and 27.1%, respectively, followed by leaching time and temperature (~ 16%). The total contribution of global climate-associated factors, i.e., L/S ratio, leaching time, and temperature to the leachability of HMs was up to 70%, while leachate pH shared the other 30%. With the increase of persistent heavy rain in summer globally, As and Cd were found to having higher leaching risks than the other HMs in tailings, although an obvious decrease in their leachability was obtained due to the improvement of acid rain pollution in China. The study provides a valuable method for the identification of potential risk factors and their associations with the leaching behaviors of HMs in tailings under the background of obvious improvement on acid rain pollution in China and global climate change.

Influence of Wildfire on Urban Ozone: An Observationally Constrained Box Modeling Study at a Site in the Colorado Front Range

Increasing trends in biomass burning emissions significantly impact air quality in North America. Enhanced mixing ratios of ozone (O3) in urban areas during smoke-impacted periods occur through transport of O3 produced within the smoke or through mixing of pyrogenic volatile organic compounds (PVOCs) with urban nitrogen oxides (NOx = NO + NO2) to enhance local O3 production. Here, we analyze a set of detailed chemical measurements, including carbon monoxide (CO), NOx, and speciated volatile organic compounds (VOCs), to evaluate the effects of smoke transported from relatively local and long-range fires on O3 measured at a site in Boulder, Colorado, during summer 2020. Relative to the smoke-free period, CO, background O3, OH reactivity, and total VOCs increased during both the local and long-range smoke periods, but NOx mixing ratios remained approximately constant. These observations are consistent with transport of PVOCs (comprised primarily of oxygenates) but not NOx with the smoke and with the influence of O3 produced within the smoke upwind of the urban area. Box-model calculations show that local O3 production during all three periods was in the NOx-sensitive regime. Consequently, this locally produced O3 was similar in all three periods and was relatively insensitive to the increase in PVOCs. However, calculated NOx sensitivities show that PVOCs substantially increase O3 production in the transition and NOx-saturated (VOC-sensitive) regimes. These results suggest that (1) O3 produced during smoke transport is the main driver for O3 increases in NOx-sensitive urban areas and (2) smoke may cause an additional increase in local O3 production in NOx-saturated (VOC-sensitive) urban areas. Additional detailed VOC and NOx measurements in smoke impacted urban areas are necessary to broadly quantify the effects of wildfire smoke on urban O3 and develop effective mitigation strategies.

Separating emissions and meteorological impacts on peak ozone concentrations in Southern California using generalized additive modeling

Ozone levels have been declining in the Los Angeles, CA, USA area for the last four decades, but there was a recent uptick in the 4th highest daily maximum 8-h (MDA8) ozone concentrations from 2014 to 2018 despite continued reductions in the estimated precursor emissions. In this study, we assess the emissions and meteorological impacts on the 4th highest MDA8 ozone concentrations to better understand the factors affecting the observed MDA8 ozone using a two-step generalized additive model (GAM)/least squares approach applied to the South Coast Air Basin (SoCAB) for the 1990 to 2019 period. The GAM model includes emissions, meteorological factors, large-scale climate variables, date, and the interactions between meteorology and emissions. A least squares method was applied to the GAM output to better capture the 4th highest MDA8 ozone. The resulting two-step model had an R² of 0.98 and a slope of 1 between the observed and predicted 4th highest MDA8 ozone. Emissions and the interactions between the maximum temperature and emissions explain most of the variation in the peak MDA8 ozone concentrations. Declining emissions have lowered the 4th highest MDA8 ozone concentration. Meteorology explains the higher than expected 4th-high, ozone levels observed in 2014-2018, indicating that meteorology was a stronger forcer than the continued reductions in emissions during that time period. The model was applied to estimate future ozone levels. Meteorology developed from climate modeling of the representative concentration pathway (RCP) scenarios, and two sets of emissions were used in the application. The modeling results indicated climate trends will push ozone levels slightly higher if no further emissions reductions are realized and that of two emissions trajectories modeled, the more stringent is required to reliably meet the federal ozone standard given annual meteorological variability.

NOx and O3 Trends at U.S. Non-Attainment Areas for 1995-2020: Influence of COVID-19 Reductions and Wildland Fires on Policy-Relevant Concentrations

We analyzed NO2 and O3 data from 32 U.S. non-attainment areas (NAAs) for 1995-2020. Since 1995, all regions have shown steady reductions in NO2 and the weekend-weekday pattern indicates that the O3 production regime in most NAAs has transitioned to a NOx-limited regime, while a few NAAs remain NOx-saturated. In the eastern U.S., all NAAs have made steady progress toward meeting the current (70 ppb) O3 standard, but this is less true in midwestern and western NAAs, with most showing little improvement in peak O3 concentrations since about 2010. Due to COVID-19 restrictions, NO2 concentrations were substantially reduced in 2020. In the eastern NAAs, we see significant reductions in both NO2 and peak O3 concentrations. In the midwestern U.S., results were more variable, with both higher and lower O3 values in 2020. In the western U.S. (WUS), we see variable reductions in NO2 but substantial increases in O3 at most sites, due to the influence from huge wildland fires. The recent pattern over the past decade shows that the large amount of wildland fires has a strong influence on the policy-relevant O3 metric in the WUS, and this is making it more difficult for these regions to meet the O3 standard.

A potential controlling approach on surface ozone pollution based upon power big data

Surface ozone pollution has attracted extensive attention with the decreasing of haze pollution, especially in China. However, it is still difficult to efficiently control the pollution in time despite numbers of reports on mechanism of ozone pollution. Here we report a method for implementing effective control of ozone pollution through power big data. Combining the observation of surface ozone, NO2, meteorological parameters together with hourly electricity consumption data from volatile organic compounds (VOCs) emitting companies, a generalized additive model (GAM) is established for quantifying the influencing factors on the temporal and spatial distribution of surface ozone pollution from 2020 to 2021 in Anhui province, central China. The average R2 value for the modelling results of 16 cities is 0.82, indicating that the GAM model effectively captures the characteristics of ozone. The model quantifies the contribution of input variables to ozone, with both NO2 and industrial VOCs being the main contributors to ozone, contributing 33.72% and 21.12% to ozone formation respectively. Further analysis suggested the negative correlation between ozone and NO2, revealing VOCs primarily control the increase in ozone. Under scenarios controlling for a 10% and 20% reduction in electricity use in VOC-electricity sensitive industries that can be identified by power big data, ozone concentrations decreased by 9.7% and 19.1% during the pollution period. This study suggests a huge potential for controlling ozone pollution through power big data and offers specific control pathways.

Evaluation of different factors on metal leaching from nickel tailings using generalized additive model (GAM)

Compared with sulfide tailings, the oxidation and transformation of certain substances in oxidized tailings into more soluble forms may affect the bioaccumulation and biomagnification properties and enhance the risk of toxic effects in the ecosystem. This study aimed to apply the generalized additive model (GAM) to evaluate factors affecting heavy metal leaching from nickel (Ni) tailings. We created an orthogonal experiment table (L₁₈(3⁷)) to evenly distribute the different combinations of factor values. The Ni tailings were immersed in solutions with different combinations of factor values for 16 d, and samples were taken on days 1, 2, 4, 7, 11, and 16 to measure the pH and heavy metal concentration of the leachate. The GAM was used to fit the concentration of heavy metals of the leachate and the initial factors of the leaching solution. The results showed that the pH and Cr concentration of the leachate increased with time and stabilized after 1 d (pH of approximately 7), while the Mn, Ni, and Tl concentrations gradually decreased and stabilized after peaking on the first day. An analysis of the GAM results showed that the Cr concentration was highly sensitive to the solid-liquid ratio (F = 127.8) and tailing particle size (F = 10.7). The Cr concentration of the leachate was significantly higher when exposed to a high solid-liquid ratio or a fine particle size, whereas the Mn, Ni, and Tl concentrations were highly sensitive to the KCl concentration and solid-liquid ratio (F = 77.4, 146.9, and 315.9 respectively). The GAM identified interactions between key factors, which have complex and strong effects on the leaching of tailings and the migration of heavy metals, either promotional or antagonistic. The prediction of the minimum Cr leaching concentration shows that GAM can be used to determine the conditions associated with minimum leaching concentrations of heavy metals and to effectively predict the metal concentrations of leachate. As such, the results of this study can be applied to the management of nickel tailings.

Aging of Volatile Organic Compounds in October 2017 Northern California Wildfire Plumes

In the western United States, the number and severity of large wildfires have been growing for decades. Biomass burning (BB) is a major source of volatile organic compounds (VOCs) to the atmosphere both globally and regionally. Following emission, BB VOCs are oxidized while being transported downwind, producing ozone, secondary organic aerosols, and secondary hazardous VOCs. In this research, we measured VOCs using proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) in an urban area 55-65 km downwind of the October 2017 Northern California wildfires. Nonaromatic oxygenated compounds were the dominant component of BB VOCs measured. In the smoke plumes, the VOCs account for 70-75% of the total observed organic carbon, with the remainder being particulate matter (with a diameter of <2.5 μm, PM_2.5). We show that the correlation of VOCs with furan (primary BB VOC) and maleic anhydride (secondary BB VOC) can indicate the origin of the VOCs. This was further confirmed by the diurnal variations of the VOCs and their concentration-weighted trajectories. Oxidation during transport consumed highly reactive compounds including benzenoids, furanoids, and terpenoids and produced more oxygenated VOCs. Furthermore, wildfire VOCs altered the ozone formation regime and raised the O₃ levels in the San Francisco Bay Area.

Increasing co-occurrence of fine particulate matter and ground-level ozone extremes in the western United States

Wildfires and meteorological conditions influence the co-occurrence of multiple harmful air pollutants including fine particulate matter (PM_2.5) and ground-level ozone. We examine the spatiotemporal characteristics of PM_2.5/ozone co-occurrences and associated population exposure in the western United States (US). The frequency, spatial extent, and temporal persistence of extreme PM_2.5/ozone co-occurrences have increased significantly between 2001 and 2020, increasing annual population exposure to multiple harmful air pollutants by ~25 million person-days/year. Using a clustering methodology to characterize daily weather patterns, we identify significant increases in atmospheric ridging patterns conducive to widespread PM_2.5/ozone co-occurrences and population exposure. We further link the spatial extent of co-occurrence to the extent of extreme heat and wildfires. Our results suggest an increasing potential for co-occurring air pollution episodes in the western US with continued climate change.

When a Generalized Linear Model Meets Bayesian Maximum Entropy: A Novel Spatiotemporal Ground-Level Ozone Concentration Retrieval Method

In China, ground-level ozone has shown an increasing trend and has become a serious ambient pollutant. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) is urgently needed. Generalized linear models (GLMs) and Bayesian maximum entropy (BME) models are practical for predicting GOCs. However, GLMs have limited capacity to capture temporal variations and can miss some short-term and regional patterns, while the performance of BME models may degrade in cases of sparse or imperfect monitoring networks. Thus, to predict nationwide 1 km monthly average GOCs for China, we designed a novel hybrid model containing three modules. (1) A GLM was established to accurately describe the variability in GOCs in the space domain. (2) A BME model incorporating GLM residuals was employed to capture the temporal variability of GOCs in detail. (3) A combination of GLM and BME models was developed based on the specific broad range of each submodel. According to the cross-validation results, the hybrid model exhibited superior performance, with coefficient of determination (R2) values of 0.67. The predictive performance of the large-scale and high-resolution hybrid model is superior to that in previous studies. The nationwide spatiotemporal variability of the GOCs derived from the hybrid model shows that they are valuable indicators for ground-level ozone pollution control and prevention in China.

Observed Relationship between Ozone and Temperature for Urban Nonattainment Areas in the United States

This study examined the observed relationship between ozone (O3) and temperature using data from 1995 to 2020 at 20 cities across the United States (U.S.) that exceed the O3 National Ambient Air Quality Standard (NAAQS). The median slope of the O3 versus temperature relationship decreased from 2.8 to 1.5 parts per billion per degrees Celsius (ppb °C−1) in the eastern U.S., 2.2 to 1.3 ppb °C−1 in the midwestern U.S., and 1.7 to 1.1 ppb °C−1 in the western U.S. O3 in the eastern and midwestern U.S. has become less correlated with temperature due to emission controls. In the western U.S., O3 concentrations have declined more slowly and the correlation between O3 and temperature has changed negligibly due to the effects of high background O3 and wildfire smoke. This implies that meeting the O3 NAAQS in the western U.S. will be more challenging compared with other parts of the country.

Investigating the biophysical and socioeconomic determinants of China tropospheric O3 pollution based on a multilevel analysis approach

Severe tropospheric O₃ pollution has swept across China in recent years. Consequently, investigation of tropospheric O₃ concentration influencing mechanism is of significance for O₃ pollution control in China. Previous studies have rarely detected combined impacts of natural factors and anthropogenic activities behind tropospheric O₃ concentration in China at a national scale. Moreover, there is significant spatiotemporal heterogeneity of O₃ pollution distribution in China due to the temporal and regional differences of socioeconomic and natural environmental condition in the vast territory. The targeted O₃ control recommendations for different regions and seasons should be put forward in terms of the spatiotemporal heterogeneity of O₃ concentration determinants. In this context, a three-level regression model integrating multi-scale biophysical and socioeconomic variables was proposed to explore the determinants of O₃ pollution in China. The results showed that the tropospheric O₃ concentration in the eastern and southeastern regions of China was strongly affected by meteorological conditions. In contrast, tropospheric O₃ pollution concentrated in inland areas mainly depended on the emission intensity from anthropogenic sources.

Impact of emission reductions and meteorology changes on atmospheric mercury concentrations during the COVID-19 lockdown

Controlling anthropogenic mercury emissions is an ongoing effort and the effect of atmospheric mercury mitigation is expected to be impacted by accelerating climate change. The lockdown measures to restrict the spread of Coronavirus Disease 2019 (COVID-19) and the following unfavorable meteorology in Beijing provided a natural experiment to examine how air mercury responds to strict control measures when the climate becomes humid and warm. Based on a high-time resolution emission inventory and generalized additive model, we found that air mercury concentration responded almost linearly to the changes in mercury emissions when excluding the impact of other factors. Existing pollution control and additional lockdown measures reduced mercury emissions by 16.7 and 12.5 kg/d during lockdown, respectively, which correspondingly reduced the concentrations of atmospheric mercury by 0.10 and 0.07 ng/m³. Emission reductions from cement clinker production contributed to the largest decrease in atmospheric mercury, implying potential mitigation effects in this sector since it is currently the number one emitter in China. However, changes in meteorology raised atmospheric mercury by 0.41 ng/m³. The increases in relative humidity (9.5%) and temperature (1.2 °C) significantly offset the effect of emission reduction by 0.17 and 0.09 ng/m³, respectively, which highlights the challenge of air mercury control in humid and warm weather and the significance of understanding mercury behavior in the atmosphere and at atmospheric interfaces, especially the impact from relative humidity.

Impacts of the COVID-19 event on the NOx emissions of key polluting enterprises in China

The unprecedented cessation of human activities during the COVID-19 pandemic has affected China's industrial production and NOx emissions. Quantifying the changes in NOx emissions resulting from COVID-19 and associated governmental control measures is crucial to understanding its impacts on the environment. Here, we divided the research timeframe into three periods: the normal operation period (P1), the Spring Festival period (P2), and the epidemic period following the Spring Festival (P3). We then calculated the NOx operating vent numbers and emission concentrations of key polluting enterprises in 29 provinces and 20 industrial sectors and compared the data for the same periods in 2020 and 2019 to obtain the impacts of COVID-19 on industrial NOx emissions. We found that spatially, from P1 to P2 in 2020, the operating NOx vent numbers in North China changed the most, with a relative change rate of -33.84%. Comparing the operating vent numbers in P1 and P3, East China experienced the largest decrease, approximately -32.72%. Among all industrial sectors, the mining industry, manufacturing industry, power, heat, gas, and water production and supply industry, and the wholesale and retail industry, were the most heavily influenced. In general, the operating vent numbers of key polluting enterprises in China decreased by 24.68%, and the standardized NOx (w)_5-day decreased by an average of -9.54 ± -6.00 due to the COVID-19 pandemic. The results suggest that COVID-19 significantly reduced the NOx emission levels of the key polluting enterprises in China.

Estimating ground-level PM2.5 levels in Taiwan using data from air quality monitoring stations and high coverage of microsensors

A widespread monitoring network of Airbox microsensors was implemented since 2016 to provide high-resolution spatial distributions of ground-level PM_2.5 data in Taiwan. We developed models for estimating ground-level PM_2.5 concentrations for all the 3 km × 3 km grids in Taiwan by combining the data from air quality monitoring stations and the Airbox sensors. The PM_2.5 data from the Airbox sensors (AB-PM_2.5) was used to predict daily mean PM_2.5 levels at the grids in 2017 using a semiparametric additive model. The estimated PM_2.5 level at the grids was further applied as a predictor variable in the models to predict the monthly mean concentration of PM_2.5 at all the grids in the previous year. The modeling-predicting procedures were repeated backward for the years from 2016 to 2006. The model results revealed that the model R² increased from 0.40 to 0.87 when the AB-PM_2.5 data were included as a nonlinear component in the model, indicating that AB-PM_2.5 is a significant predictor of ground-level PM_2.5 concentration. The cross-validation (CV) results demonstrated that the root of mean squared prediction errors of the estimated monthly mean PM_2.5 concentrations were smaller than 5 μg/m³ and the R² of the CV models of 0.79-0.88 during 2006-2017. We concluded that Airbox sensors can be used with monitoring data to more accurately estimate long-term exposure to PM_2.5 for cohorts of small areas in health impact assessment studies.

Wildfire and prescribed burning impacts on air quality in the United States

Air quality impacts from wildfires have been dramatic in recent years, with millions of people exposed to elevated and sometimes hazardous fine particulate matter (PM _2.5 ) concentrations for extended periods. Fires emit particulate matter (PM) and gaseous compounds that can negatively impact human health and reduce visibility. While the overall trend in U.S. air quality has been improving for decades, largely due to implementation of the Clean Air Act, seasonal wildfires threaten to undo this in some regions of the United States. Our understanding of the health effects of smoke is growing with regard to respiratory and cardiovascular consequences and mortality. The costs of these health outcomes can exceed the billions already spent on wildfire suppression. In this critical review, we examine each of the processes that influence wildland fires and the effects of fires, including the natural role of wildland fire, forest management, ignitions, emissions, transport, chemistry, and human health impacts. We highlight key data gaps and examine the complexity and scope and scale of fire occurrence, estimated emissions, and resulting effects on regional air quality across the United States. The goal is to clarify which areas are well understood and which need more study. We conclude with a set of recommendations for future research.

IMPLICATIONS

In the recent decade the area of wildfires in the United States has increased dramatically and the resulting smoke has exposed millions of people to unhealthy air quality. In this critical review we examine the key factors and impacts from fires including natural role of wildland fire, forest management, ignitions, emissions, transport, chemistry and human health.

Relationships between Particulate Matter, Ozone, and Nitrogen Oxides during Urban Smoke Events in the Western US

Urban ozone (O₃) pollution is influenced by the transport of wildfire smoke but observed impacts are highly variable. We investigate O₃ impacts from smoke in 18 western US cities during July-September, 2013-2017, with ground-based monitoring data from air quality system sites, using satellite-based hazard mapping system (HMS) fire and smoke product to identify overhead smoke. We present four key findings. First, O₃ and PM_2.5 (particulate matter <2.5 μm in diameter) are elevated at nearly all sites on days influenced by smoke, with the greatest mean enhancement occurring during multiday smoke events; nitrogen oxides (NO_x) are not consistently elevated across all sites. Second, PM_2.5 and O₃ exhibit a nonlinear relationship such that O₃ increases with PM_2.5 at low to moderate 24 h PM_2.5, peaks around 30-50 μg m^-3, and declines at higher PM_2.5. Third, the rate of increase of morning O₃ is higher and NO/NO₂ ratios are lower on smoke-influenced days, which could result from additional atmospheric oxidants in smoke. Fourth, while the HMS product is a useful tool for identifying smoke, O₃ and PM_2.5 are elevated on days before and after HMS-identified smoke events implying that a significant fraction of smoke events is not detected.

Associations between respiratory health and ozone and fine particulate matter during a wildfire event

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 (PM_2.5) 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 PM_2.5 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, PM_2.5 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 PM_2.5 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 PM_2.5 was 1.112 and 95% CI = (1.087, 1.138). The significant findings for PM_2.5 but not for O₃ in mutually-adjusted models is likely due to the fact that PM_2.5 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 PM_2.5 during wildfires to get a more comprehensive assessment of the cumulative burden on health from wildfire smoke.

Roles of N-Vacancies over Porous g-C3N4 Microtubes during Photocatalytic NO x Removal

The development of catalysts that effectively activate target pollutants and promote their complete conversion is an admirable objective in the environmental photocatalysis field. In this work, graphitic carbon nitride (g-C₃N₄) microtubes with tunable N-vacancy concentrations were controllably fabricated using an in situ soft-chemical method. The morphological evolution of g-C₃N₄, from the bulk to the porous tubular architecture, is discussed in detail with the aid of time-resolved hydrothermal experiments. We found that the NO removal ratio and apparent reaction rate constant of the g-C₃N₄ microtubes were 1.8 and 2.6 times higher than those of pristine g-C₃N₄, respectively. By combining detailed experimental characterization and density functional theory calculations, the effects of N-vacancies in the g-C₃N₄ microtubes on O₂ and NO adsorption activation, electron capture, and electronic structure were systematically investigated. These results demonstrate that surface N-vacancies act as specific sites for the adsorption activation of reactants and photoinduced electron capture, while enhancing the light-absorbing capability of g-C₃N₄. Moreover, the porous wall structures of the as-prepared g-C₃N₄ microtubes facilitate the diffusion of reactants, and their tubular architectures favor the oriented transfer of charge carriers. The intermediates formed during photocatalytic NO removal processes were identified by in situ diffuse reflectance infrared Fourier transform spectroscopy, and different reaction pathways over pristine and N-deficient g-C₃N₄ are proposed. This study provides a feasible strategy for air pollution control over g-C₃N₄ by introducing N-vacancy and porous tubular architecture simultaneously.

Effects of BTEX on the Removal of Acetone in a Coaxial Non-Thermal Plasma Reactor: Role Analysis of the Methyl Group

The removal of acetone and benzene series (BTEX) under individual and concurrent conditions is carried out in a coaxial nonthermal plasma (NTP) reactor. The results show that the benzene series has a significant negative impact on acetone conversion and CO₂ selectivity under NTP treatment. Furthermore, it is found that p-xylene significantly promotes CO_x selectivity under co-treatment with acetone because of greater CO generation. Based on the results of transient FTIR, MS, and GC-MS, it is seen that quantities of formic acid, formaldehyde, and ring-opening byproducts from benzene series decomposition are reduced, while quantities of aromatic byproducts with carboxyl, phenolic, and aldehyde groups on the benzene ring increase under coexistence conditions. With the help of theoretical calculation and kinetic analysis, hydrogen abstraction from the methyl group and active hydroxyl radical consumption are proposed as critical factors in the BTEX inhibition effect on acetone degradation.

Scientific assessment of background ozone over the U.S.: Implications for air quality management

Ozone (O3) is a key air pollutant that is produced from precursor emissions and has adverse impacts on human health and ecosystems. In the U.S., the Clean Air Act (CAA) regulates O3 levels to protect public health and welfare, but unraveling the origins of surface O3 is complicated by the presence of contributions from multiple sources including background sources like stratospheric transport, wildfies, biogenic precursors, and international anthropogenic pollution, in addition to U.S. anthropogenic sources. In this report, we consider more than 100 published studies and assess current knowledge on the spatial and temporal distribution, trends, and sources of background O3 over the continental U.S., and evaluate how it inflattainment of the air quality standards. We conclude that spring and summer seasonal mean U.S. background O3 (USB O3), or O3 formed from natural sources plus anthropogenic sources in countries outside the U.S., is greatest at high elevation locations in the western U.S., with monthly mean maximum daily 8-hour average (MDA8) mole fractions approaching 50 parts per billion (ppb) and annual 4th highest MDA8s exceeding 60 ppb, at some locations. At lower elevation sites, e.g., along the West and East Coasts, seasonal mean MDA8 USB O3 is in the range of 20-40 ppb, with generally smaller contributions on the highest O3 days. The uncertainty in U.S. background O3 is around ±10 ppb for seasonal mean values and higher for individual days. Noncontrollable O3 sources, such as stratospheric intrusions or precursors from wildfires, can make significant contributions to O3 on some days, but it is challenging to quantify accurately these contributions. We recommend enhanced routine observations, focused fi studies, process-oriented modeling studies, and greater emphasis on the complex photochemistry in smoke plumes as key steps to reduce the uncertainty associated with background O3 in the U.S.