Ground-level methane (CH4) concentrations are crucial for improving emission inventories and supporting climate and air quality management efforts. However, the limited spatial coverage of ground-based CH4 monitoring stations across the U.S. constrains detailed spatial and temporal analyses. To address this challenge, we develop artificial neural network (ANN) models to estimate daily surface CH4 concentrations across the U.S. The models use meteorological parameters from the European Centre of Medium-range Weather Forecasts Reanalysis v5 (ERA5) and CH4 column concentrations from the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) as inputs. Surface CH4 measurements from NOAA’s Global Monitoring Laboratory (GML) serve as the reference outputs for model training and evaluation. We implement a feed-forward backpropagation neural network with a hyperbolic tangent sigmoid activation function and evaluate three optimization algorithms, including Bayesian Regularization (BR), Levenberg–Marquardt (LM), and Scaled Conjugate Gradient (SCG), combined with two sets of input parameters. Better results are obtained using the input set that includes 2-m air temperature, soil temperature, u- and v-wind components, total precipitation, net solar radiation, and CH4 column concentrations. With this set, the LM algorithm achieves the highest accuracy (RMSE = 11.31 ppb, R = 0.84), outperforming BR (RMSE = 16.47 ppb, R = 0.79) and SCG (RMSE = 22.57 ppb, R = 0.76). These findings demonstrate the potential of combining satellite-based CH4 data and meteorological variables in machine learning models for surface CH4 estimation and provide guidance for algorithm selection in future applications. Artificial Neural Network (ANN)-Based Estimation of Surface Methane Concentrations over the U.S. This graphical abstract summarizes the application of Artificial Neural Networks (ANNs) to estimate surface methane (CH4) concentrations across the U.S., addressing gaps in sparse ground-based monitoring. ERA5 meteorological data and Sentinel-5P TROPOMI CH4 columns are integrated to capture nonlinear relationships between atmospheric drivers and CH4 distribution. Validation against surface CH4 concentrations from NOAA-GML stations shows that key parameters in Set B, including 2-m temperature, soil temperature, u- and v-wind components, total precipitation, net solar radiation, and CH4 column concentrations, used with the Levenberg–Marquardt (LM) algorithm, achieve the best accuracy (RMSE = 11.31 ppb, R = 0.84), compared with the Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG) algorithms. These findings demonstrate that optimized ANN approaches, trained with satellite and reanalysis data, can reasonably reproduce surface CH4 patterns and enhance greenhouse gas monitoring in under-observed regions.

Landfills are an important component of the environment, serving as major sources of methane (CH4) and reactive trace gases that contribute to radiative forcing and modify atmospheric chemistry, making them a critical target for mitigation. This study employs modeling to investigate the immediate air quality and indirect radiative effects of mitigating landfill CH4 emissions. The analysis is motivated by the potential of a novel, fuel-flexible combustion technology capable of converting all CH4 emissions to carbon dioxide. Three scenarios are assessed: (BASE) a control simulation with unchanged landfill CH4 emissions; (LCC_NAT) a hypothetical national implementation of the combustion system over a summer month; and (LCC_REG) a regional application over Texas. One-month simulations show that, compared to BASE, LCC_NAT yields clear concentration reductions in surface and column-averaged CH4 (XCH4) of -3.03 and -1.60 ppb across the contiguous United States, while LCC_REG shows declines of -2.38 and -1.42 ppb over Texas. CH4 reductions coincide with increases in hydroxyl radical (OH) concentrations under both LCC_NAT and LCC_REG scenarios, indicating an enhanced atmospheric self-cleaning capacity. At the national scale, CH4 reductions in the LCC_NAT scenario also result in improved air quality, with surface concentrations of carbon monoxide (CO), ozone (O3), and fine particulate matter (PM2.5) decreasing by 32.7 ppb, 0.27 ppb, and 0.01 μg m-3, respectively. Atmospheric cooling is induced within the atmosphere, driven primarily by negative longwave radiative forcing under clear-sky conditions in both LCC_REG (-0.38 W m-2) and LCC_NAT (-0.04 W m-2) scenarios. These cooling effects are stronger under all-sky conditions due to cloud radiative effects, as clouds may absorb efficiently thermal radiation emitted from the surface. Furthermore, net radiative forcing under all-sky conditions leads to surface cooling in LCC_NAT but regional warming in LCC_REG, highlighting the spatial variability of climate responses.

The diel drawdown of CO2 provides a direct measure of light use efficiency and of the capacity of terrestrial ecosystems to mitigate climate change, as these processes influence how much carbon is absorbed by vegetation through photosynthesis. This study investigates CO2 drawdown in Northeastern U.S. forests by analyzing five years of column-averaged CO2 measurements (XCO2) from the EM27 instrument at the Harvard Forest and evaluating the performance of two vegetation models, including CarbonTracker and the Vegetation Photosynthesis and Respiration Model (VPRM), in representing CO₂ fluxes. EM27 XCO2 data reveal clear seasonal patterns with peaks in late spring and lows in late summer, reflecting an annual increase of approximately 3 ppm. We model XCO2 enhancements by convolving simulated footprints from the Stochastic Time-Inverted Lagrangian Transport (STILT) model with biological CO2 fluxes from both vegetation models. Observed and modeled CO2 drawdown during the daytime all peak in the warmer months. VPRM drawdown rates closely align with observed data, showing a slight underestimation of peak values (-0.17 ppm hr-1 in July compared to -0.18 ppm hr-1 observed) in the average annual variation of daily XCO2 slopes. In contrast, CarbonTracker simulates weaker CO2 drawdown. The study highlights stable interannual variability in CO2 drawdown, with no indication of saturation in the ecosystems' CO2 drawdown capacity. In the context of climate change, this work underscores the value of long-term monitoring and modeling of XCO2 to track changes in CO2 drawdown and to identify environmental stresses affecting terrestrial carbon sinks.

California wildfires have grown increasingly frequent and intense over recent decades, raising serious public health concerns. In response, the California Air Resources Board (CARB) 2022 Scoping Plan outlines land management strategies to reduce wildfire risk and associated emissions under various climate change scenarios. This study evaluates the health benefits of CARB's official mitigation pathway, the S3 scenario, compared to a business-as-usual approach, using three global climate models (GCMs) and three future time slices. We apply the GEOS-Chem model to estimate fire-induced PM2.5 concentrations and use the U.S. EPA's BenMAP-CE tool, along with a wildfire-specific chronic mortality dose-response function, to assess associated morbidity and mortality. Results suggest that S3 can significantly reduce fire-related PM2.5 exposure, particularly in northern and central California where concentrations are typically highest-and where S3 treatments are most effective. In 2035 under the second generation Canadian Earth System Model GCM, for instance, S3 is associated with 1,927 fewer premature deaths and substantial reductions in asthma- and respiratory-related emergency room visits. However, health benefits vary by GCM and year, underscoring the influence of meteorological conditions on fire activity and health outcomes. These findings point to the importance of strategically timed and located land management actions and integrating climate variability into future mitigation planning.

Wildfires in North America, particularly in western states, have caused widespread environmental, economic, social, and health impacts. Smoke from these fires travels long distances, spreading pollutants and worsening the air quality across continents. Vulnerable groups, such as children, the elderly, and those with preexisting conditions, face heightened health risks, as do firefighters working in extreme conditions. Wildfire firefighters are of particular concern as they are fighting fires in extreme conditions with minimal protective equipment. This study examined wildfire smoke during July–August 2021, when intense fires in Canada and the western U.S. led to cross-continental smoke transport and caused significant impacts on the air quality across North America. Using the GEOS-Chem model, we simulated the transport and distribution of PM2.5 (particulate matter with a diameter of 2.5 μm or smaller), identifying significant carcinogenic risks for adults, children, and firefighters using dosimetry risk methodologies established by the U.S. EPA. Significant carcinogenic risks for adult, child, and firefighter populations due to exposure to PM2.5 were identified over the two-month period of evaluation. The findings emphasize the need for future studies to assess the toxic chemical mixtures in wildfire smoke and consider the risks to underrepresented communities.

In June 2023, an elevated smoke layer from record-breaking Canadian wildfires was transported across the eastern half of the United States, impacting air quality for millions of people. Houston, TX experienced a notable biomass burning (BB) event associated with this wildfire smoke from Jun 4 to 9, 2023. The vertical transport of this smoke layer down to the surface followed afternoon convective activity in the Houston urban area on Jun 6–8. Our monitoring sites at urban, rural, and coastal locations around Houston experienced different levels of wildfire smoke. Carbon monoxide, aerosol absorption, and the Absorption Ångström Exponent (AAE) revealed stronger smoke incursions overnight at the urban site. The average nighttime AAE during the BB event period was 1.28 with 384 ppbv of CO; by comparison, the monthly nighttime averages for Jun 2023 were 1.03 and 172 ppbv, respectively. Enhanced PM2.5 and NO2 coincided with BB tracers while higher ozone concentrations were observed the following day at downwind sites relative to the peak observed BB smoke sites. The nighttime NO2 for the BB event was also significantly higher than the monthly average with standard deviation for Jun 2023 (14.5 ppbv versus 5.19 ± 4.61 ppbv, respectively). Ozone concentrations peaked over 100 ppbv on Jun 9 driven by clear skies after the overnight high BB. Understanding the role of convective activity in enhancing the downdraft of BB plumes to the surface will improve assessment of the long-range impacts of wildfire smoke on urban populations.

Existing projections of wetland methane emissions usually neglect feedbacks from global biogeochemical cycles. Using data-driven approaches, we estimate wetland methane emissions from 2000 to 2100, considering effects of meteorological changes and biogeochemical feedbacks from atmospheric sulfate deposition and CO 2 fertilization. In low-CO 2 scenarios (1.5° and 2°C warming pathways), the suppressive effect of sulfate deposition on wetland methane emissions largely diminishes by 2100 due to clean air policies, with resulting emission increases (7 ± 2 Tg a −1 ) being 35 and 22% of total wetland emission changes. In mid-CO 2 scenarios (2.4° to 3.6°C warming pathways), sulfate deposition changes modestly, and CO 2 fertilization contributes >30% of wetland emission increases. Across all scenarios, biogeochemical feedbacks can stimulate 30 to 45% of future wetland emission rises. Under 1.5° and 2°C pathways, wetland methane emissions will likely increase by 20 to 34 Tg a −1 by 2100, representing 8 to 15% of the allowable space for anthropogenic methane emissions, a factor not yet considered by current assessments.

The high degree of urbanization and economic development in the United States has intensified competition for land resources, leading to land use conflicts and environmental challenges. Addressing these issues first requires an exploration of high-resolution, sector-specific land use patterns. However, existing land classification studies rarely provide spatially explicit data on urban land use across all sectors at a national level. This study aims to fill this gap by analyzing land use areas across various economic sectors in the United States, particularly focusing on urban impervious surfaces. Using a bottom-up approach, we created a full-sector land use inventory, integrating Points of Interest (POI) data, road networks, building characteristics, and geographic information processing techniques. Our primary innovation lies in the development of a spatially explicit database that details sector-specific land use areas nationwide. On this robust foundation, we further explored the heterogeneity of sectoral land use eco-efficiency (SLUEE) among different sectors and states, providing supplementary insights into spatial disparities in urban land use. Our findings reveal significant spatial heterogeneity, with service sectors dominating land use and exhibiting higher SLUEE compared to goods-producing industries. Key sectors such as Professional, Scientific, and Technical Services (441064.58 ha), and Administrative and Support Services (420148.34 ha) are prominent across states according to land use area, reflecting the country's service-oriented economy. Additionally, regional disparities in SLUEE are evident, with jurisdictions like Washington D.C., New York, and California demonstrating more efficient land use. Compared with traditional land use evaluations, our model effectively offers higher spatial resolution, down to the census block level, to sectoral land use evaluation. Building on this robust database, our SLUEE analysis has uncovered notable heterogeneity between sectors and states, offering new insights into cross-sectoral and inter-state land use dynamics that can inform and promote sustainable urban planning.

Methane (CH4) uptake in alpine ecosystems is an important component of the global CH4 sink. However, large uncertainties remain regarding the magnitude and spatial patterns of CH4 uptake, owing to its extensive spatial variability, diverse controlling factors, and limited regional-scale observations. Here, we investigated field ecosystem CH4 uptake along a 3200-km transect across various alpine grasslands on the Qinghai-Tibetan Plateau (QTP). We found a substantial spatial variation in in situ CH4 uptake among alpine grasslands, with the highest rates in drier regions of the mid-western QTP. Soil moisture was the most important factor controlling CH4 uptake, exhibiting a remarkably low threshold of 6.2 ± 0.1 v/v %. Below this threshold, CH4 uptake was constrained by soil moisture, moisture-induced nitrogen limitation, and high temperatures. Above this threshold, CH4 uptake was mainly limited by gas diffusion and low temperatures. By integrating grid predictors with a random forest model trained on 1851 field measurements encompassing both our observations and a regional synthesis across the QTP, we estimated a regional CH4 uptake of 0.88 ± 0.020 Tg CH4 year-1 from all alpine grasslands on the QTP. This higher estimate, primarily driven by alpine steppes, was significantly greater than current regional estimates from global CH4 models. Our findings highlight the importance of CH4 sink in dry alpine ecosystems characterized by low soil moisture, suggesting that the contribution of CH4 sink in drylands may have been substantially underestimated in the current global CH4 budget.

Background: Particulate matter (PM) based air pollution closely linked to lower respiratory infections (LRIs). PM2.5 air pollution is identified as the leading risk factor for LRIs. This study examined deaths and disability-adjusted life years (DALYs) associated with PM2.5-related LRIs to understand and prevent their impact. Methods: Using Global Burden of Disease Study 2021 data, we analyzed PM2.5's effects on LRIs globally, regionally, and in China across different socio-demographic index (SDI) regions. Autoregressive integrated moving average (ARIMA) modeling predicted 15-year trends in age-standardized mortality rates (ASMR) and disability-adjusted life year rates (ASDR). Results: PM2.5 contributed to 0.65 million deaths and 29.1 million DALYs from LRIs in 2021. An inverse relationship existed between PM2.5-related LRIs and SDI, with males showing higher vulnerability. Age-specific rates followed a V-shaped distribution in global and lower SDI regions. Mortality peaked in those over 95, while DALYs were highest in children under 5. ASMR and ASDR projections show slight decreases over 15 years. China's burden approaches high-middle SDI levels. Conclusions: Higher SDI areas show lower disease burden, while PM2.5-related LRIs remain substantial in lower SDI regions. This necessitates targeted preventive strategies for vulnerable populations, particularly in less developed regions.

Recent studies highlight the role of climate change in creating conditions that are increasingly conducive to wildfire activity across large areas of North America. This trend raises significant concerns about the growing impacts of wildfires on regional air quality. In particular, Alaska is warming at an accelerated rate, making it critical to understand how its wildfire seasons are evolving and how these changes influence fire activity and air quality. Fire seasons mark the period each year when wildfires are most likely to occur, and their length and intensity have important implications for management and mitigation efforts aimed at protecting human health in this vulnerable region. By applying temperature thresholds, we find that Alaska's fire season has lengthened by approximately 13.8 days from the early 2000s to recent years, with an average annual increase of 0.81 days. Using fire emissions from the Global Fire Assimilation System (GFAS), we estimate an even greater increase in fire season length-up to 42.33 days-based on recorded fire start and end dates, though this may include nonnatural fires. The extension of fire season, as derived from temperature thresholds, has led to an additional 2.09 x 108 kg of PM2.5 emissions, representing a 3.6% overall increase. To assess the air quality impacts of this lengthened fire season, we use the GEOS-Chem chemical transport model with GFAS fire emissions. Our results reveal that the Alaskan population has been experiencing prolonged exposure to elevated fire-driven PM2.5 levels each year, with local peaks reaching extreme levels, such as 2036.14 mu g/m3 in 2010. The peak fire-driven PM2.5 concentrations during the extended fire season far exceed the U.S. Environmental Protection Agency (EPA)'s 24h standard (35 mu g/m3), underscoring the escalating air quality and public health risks in Alaska. With global implications for climate, air quality, and public health, this study provides a valuable reference for future wildfire research in fire-prone regions.

Understanding the trends and drivers of greenhouse gases (GHGs) is vital to making effective climate mitigation strategies and benefiting human health. In this study, we investigate carbon dioxide (CO2) trends in the top three emitting states in the U.S. (i.e., Texas, California, and Florida) using column-averaged CO2 concentrations (XCO2) from the Greenhouse Gases Observing Satellite (GOSAT) from 2010 to 2022. Annual XCO2 enhancements are derived by removing regional background values (XCO2, enhancement), and their interannual changes (ΔXCO2, enhancement) are analyzed against key influencing factors, including population, gross domestic product (GDP), nonrenewable and renewable energy consumption, and normalized vegetation difference index (NDVI). Overall, interannual changes in socioeconomic factors, particularly GDP and energy consumption, are more strongly correlated with ΔXCO2, enhancement in Florida. In contrast, NDVI and state-specific environmental policies appear to play a more influential role in shaping XCO2 trends in California and Texas. These differences underscore the importance of regionally tailored approaches to emissions monitoring and mitigation. Although renewable energy use is increasing, CO2 trends remain primarily influenced by nonrenewable sources, limiting progress toward atmospheric CO2 reduction.

Natural wetlands account for one-third of global methane (CH4) emissions and so profoundly influence climate. However, existing estimates of future changes in CH4 usually neglect feedbacks associated with global biogeochemical cycles. Here, we employ data-driven approaches to estimate both current and future wetland emissions that consider the effects of changing meteorology and biogeochemical feedbacks arising from sulfate deposition and CO2 fertilization. We report intensified wetland emissions from 2000-2100, with biogeochemical effects explaining 30% of emissions growth by 2100. Our results suggest that 8-15% more aggressive cuts to anthropogenic methane emissions are needed if we are to stay within the Paris Agreement guardrails of 1.5°C warming.

Wildfire activity has increased dramatically in the western United States over the last three decades, having a significant impact on air quality and human health. However, quantifying the drivers of trends in wildfires and subsequent smoke concentrations is challenging, as both natural variability (NV) and anthropogenic climate change (ACC) play important roles. Here, we devise an approach involving observed meteorology and vegetation and a range of models to determine the relative roles of ACC and NV in driving burned area across the western United States. We also examine the influence of ACC on smoke concentrations. We estimate that ACC accounts for 33 to 82% of observed total burned area, depending on the ecoregion, yielding 65% of total fire emissions on average across the western United States from 1992 to 2020. In all ecoregions except Mediterranean California, ACC contributes to a greater percentage of burned area in lightning-ignited wildfires than in human-ignited wildfires. On average, ACC contributes 49% to smoke PM2.5 concentrations in the western United States from 1997 to 2020, and explains 58% of the increasing trend in smoke PM2.5 from 2010 to 2020. Northern California and areas in Oregon, Washington, and Idaho experience the greatest smoke concentrations attributable to ACC, averaging 40 to 66% of total PM2.5 over 2010-2020. Our work highlights the significant role of ACC in degrading air quality in the western United States and identifies those regions most vulnerable to wildfire smoke and thus adverse health impacts.

In the southwestern United States, the frequency of summer wildfires has elevated ambient PM2.5 concentrations and rates of adverse birth outcomes. Notably, hypertensive disorders in pregnancy (HDP) constitute a significant determinant associated with maternal mortality and adverse birth outcomes. Despite the accumulating body of evidence, scant research has delved into the correlation between chemical components of wildfire PM2.5 and the risk of HDP. Derived from data provided by the National Center for Health Statistics, singleton births from >2.68 million pregnant women were selected across 8 states (Arizona, AZ; California, CA, Idaho, ID, Montana, MT; Nevada, NV; Oregon, OR; Utah, UT, and Wyoming, WY) in the southwestern US from 2001 to 2004. A spatiotemporal model and a Goddard Earth Observing System chemical transport model were employed to forecast daily concentrations of total and wildfire PM2.5-derived exposure. Various modeling techniques including unadjusted analyses, covariate-adjusted models, propensity-score matching, and double robust typical logit models were applied to assess the relationship between wildfire PM2.5 exposure and gestational hypertension and eclampsia. Exposure to fire PM2.5, fire-sourced black carbon (BC) and organic carbon (OC) were associated with an augmented risk of gestational hypertension (ORPM2.5 = 1.125, 95 % CI: 1.109,1.141; ORBC = 1.247, 95 % CI: 1.214,1.281; OROC = 1.153, 95 % CI: 1.132, 1.174) and eclampsia (ORPM2.5 = 1.217, 95 % CI: 1.145,1.293; ORBC = 1.458, 95 % CI: 1.291,1.646; OROC = 1.309, 95 % CI: 1.208,1.418) during the pregnancy exposure window with the strongest effect. The associations were stronger that the observed effects of ambient PM2.5 in which the sources primarily came from urban emissions. Social vulnerability index (SVI), education years, pre-pregnancy diabetes, and hypertension acted as effect modifiers. Gestational exposure to wildfire PM2.5 and specific chemical components (BC and OC) increased gestational hypertension and eclampsia risk in the southwestern United States.

This review summarizes studies on the relationships between climate change and Valley Fever (VF), also termed Coccidioidomycosis, a potentially fatal upper-respiratory fungal infection caused by the pathogenic fungi, C. immitis or C. posadasii. The intensified onset of climate change has caused frequencies and possibly intensities of natural hazard events like dust storms and drought to increase, which has been correlated with greater prevalence of VF. These events, followed by changes in patterns of precipitation, not only pick up dust and spread it throughout the air, but also boost the growth and spread of Coccidioides. In California alone, cases of VF have increased fivefold from 2001 to 2021, and are expected to continue to increase. From 1999 to 2019, there was an average of 200 deaths per year caused by VF in the United States. The number of deaths caused by VF fluctuates year to year, but because more infections are predicted to occur due to a changing climate, deaths are expected to rise; thus, the rising prevalence of the disease is becoming a larger focus of the scientific community and poses an increased threat to public health. By reviewing recent and past studies on Coccidioidomycosis and its relationships with climate factors, we categorize future impacts of this disease on the United States, and highlight areas that need more study. Factors affecting the incidence of VF, such as modes of dispersal and the optimum environment for Coccidioides growth, that could potentially increase its prevalence as weather patterns change are discussed and how the endemic regions could be affected are assessed. In general, regions of the United States, including California and Arizona, where VF is endemic, are expanding and incidences of VF are increasing in those areas. The surrounding southern states, including Nevada, New Mexico, Utah, and Texas, are experiencing similar changes. In addition, the entire endemic region of the United States is predicted to spread northward as drought is prolonged and temperatures steadily increase. The findings from the keyword search from eight databases indicate that more studies on VF and its relation to dust and climate are needed especially for endemic states like Nevada that are currently not adequately studied. Overall, results of this survey summarize mechanisms and climate factors that might drive spread of VF and describes trends of incidence of VF in endemic states and predicted likely trends that might occur under a changing climate. Through reviewing recent and past studies of Coccidioidomycosis and its relationships with climate factors, future impacts of this disease have been categorized and speculated on effects it might have on the United States. Better understanding of how climate factors affect VF as well as identifying regions that require more research could inform both environmental managers and medical professionals with the resources needed to make more accurate predictions, design better mitigation strategies, send timely warnings, and protect public health.Shortened versionThis review explores how climate change affects Valley Fever (VF), a dangerous fungal infection caused by C. immitis or C. posadasii. Climate change has increased natural hazard events such as dust storms and droughts, which have caused the spread of VF. Cases of the disease have increased fivefold between 2001 and 2021 in California alone, and it poses an increasing threat to public health. The review summarizes mechanisms that drive the spread of VF and highlights trends in endemic states under a changing climate. It recommends more studies on VF and its relation to dust and climate, especially for states like Nevada. Identifying regions that require more research can help make more accurate predictions, design better mitigation strategies, send timely warnings, and protect public health.

Abstract. Highly reactive volatile organic compounds (HRVOCs) from mobile and petrochemical sources are important players in atmospheric photochemistry that contribute to the formation of ozone (O3). In a typical elevated O3 episode, we applied a high-resolution large eddy simulation (LES), coupled with the Weather Research and Forecasting model with chemistry (WRF-LES-Chem) to understand the mechanism of high O3 production over the Houston area. Our modeling was constrained and evaluated using field measurements from the NASA Tracking Aerosol Convection Interactions ExpeRiment – Air Quality (TRACER-AQ) project, Texas Commission on Environmental Quality (TCEQ), and vertical column density observations from Pandora spectrometers. The modeling results show enhanced performance in the LES domain, compared to the mesoscale models in simulating key chemicals. O3 sensitivity in the Houston urban area demonstrates a nearly homogenous early morning VOC-limited regime and transits to a noontime NOX-limited regime. As the day progresses into the afternoon, the atmospheric oxidative capacity (AOC) increases with major contribution from hydroxyl (OH) radical (90 %). High concentrations of alkenes also increased O3 (8–10 %) contribution to AOC in the late afternoon. The OH reactivity (KOH) is dominated by isoprene (35.76 %), carbon monoxide (CO; 12.98 %), formaldehyde (HCHO; 12.21 %), and alkanes with C > 3 (6.29 %), thus accelerating the production of hydroperoxyl (HO2) and peroxy (RO2) radicals. The concentrations of short-lived VOCs such as HCHO and acetaldehyde from the oxidation of HRVOCs, increased in the afternoon, which elevated O3 production rates under a NOX-limited regime. The oxidation of isoprene also accelerated the production of HCHO and contributed to the production of HO2 radicals, thus leading to a high O3 production rate. This study suggests the possible impacts of NOX-O3-VOC sensitivity on O3 production rates in polluted urban areas with high emission of HRVOCs, and also provides insights on radical chemistry that drives the photochemical processes of O3 formation. Ultimately, the study underlines the need to control anthropogenic emissions such as alkenes and HCHO and also highlights the role of naturally emitted isoprene species in elevated urban O3 levels.

Objectives: Wildfire air pollution is a growing concern on human health. The study aims to assess the associations between wildfire air pollution and pregnancy outcomes in the Southwestern United States. Study design: This was a retrospective cohort study. Methods: Birth records of 627,404 singleton deliveries in 2018 were obtained in eight states of the Southwestern United States and were linked to wildfire-sourced fine particulate matter (PM2.5) and their constituents (black carbon [BC] and organic carbon [OC]) during the entire gestational period. A doublerobust logistic regression model was used to assess the associations of wildfire-sourced PM2.5 exposures and preterm birth and term low birth weight, adjusting for non-fire-sourced PM2.5 exposure and individual- and area-level confounder variables. Results: Wildfire-sourced PM2.5 contributed on average 15% of the ambient total PM2.5 concentrations. For preterm birth, the strongest association was observed in the second trimester (odds ratio [OR]: 1.06, 95% confidence interval [CI]: 1.05-1.07 for PM2.5; 1.06, 95% CI: 1.05-1.07 for BC; 1.04, 95% CI: 1.03-1.05 for OC, per interquartile range increment of exposure), with higher risks identified among non-smokers or those with low socio-economic status. For term low birth weight, the associations with wildfiresourced PM2.5 exposures were consistently elevated for all trimesters except for the exposure averaged over the entire gestational period. Overall, the associations between wildfire-sourced PM2.5 and pregnancy outcomes were stronger than those with total PM2.5. Conclusions: Wildfire-sourced PM2.5 and its constituents are linked to higher risks of preterm birth and term low birth weight among a significant US population than the effects of ambient total PM2.5. (c) 2024 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.