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The adoption of zero-emission vehicles (ZEVs) offers multiple benefits for the climate, air quality, and public health by reducing tailpipe emissions. However, the environmental justice implications of the nonexhaust emissions from future ZEV fleets for near-roadway communities remain unclear. Here, we model the on-road fine particulate matter (PM2.5) emissions across all California counties and assess the near-roadway exposure disparities at the census block group level in the Los Angeles County in 2050, when almost all passenger vehicles are projected to be ZEVs. We found that promoting zero-emission heavy-duty trucks generates more air quality benefits for disadvantaged communities than light-duty passenger vehicles. Persistent disparities in near-roadway PM2.5 levels, however, exist due to the remaining brake and tire wear emissions and increased truck traffic in disadvantaged communities. We recommend implementing fleet-specific ZEV policies to address brake and tire wear emissions and optimizing freight structures to address these persistent environmental justice issues in California.

In this study, associations between prenatal exposure to fine particulate matter (PM2.5) from 9 sources and development of autism spectrum disorder (ASD) were assessed in a population-based retrospective pregnancy cohort in southern California. The cohort included 318,750 mother-child singleton pairs. ASD cases (N = 4559) were identified by ICD codes. Source-specific PM2.5 concentrations were estimated from a chemical transport model with a 4 × 4 km2 resolution and assigned to maternal pregnancy residential addresses. Cox proportional hazard models were used to estimate the hazard ratios (HR) of ASD development for each individual source. We also adjusted for total PM2.5 mass and in a separate model for all other sources simultaneously. Increased ASD risk was observed with on-road gasoline (HR [CI]: 1.18 [1.13, 1.24]), off-road gasoline (1.15 [1.12, 1.19]), off-road diesel (1.08 [1.05, 1.10]), food cooking (1.05 [1.02, 1.08]), aircraft (1.04 [1.01, 1.06]), and natural gas combustion (1.09 [1.06, 1.11]), each scaled to standard deviation increases in concentration. On-road gasoline and off-road gasoline were robust for other pollutant groups. PM2.5 emitted from different sources may have different impacts on ASD. The results also identify PM source mixtures for toxicological investigations that may provide evidence for future public health policies.

As policymakers increasingly focus on environmental justice, a key question is whether emissions reductions aimed at addressing air quality or climate change can also ameliorate persistent air pollution exposure disparities. We examine evidence from Californias aggressive vehicle emissions control policy from 2000 to 2019. We find a 65% reduction in modeled statewide average exposure to PM2.5 from on-road vehicles, yet for people of color and overburdened community residents, relative exposure disparities increased. Light-duty vehicle emissions are the main driver of the exposure and exposure disparity, although smaller contributions from heavy-duty vehicles especially affect some overburdened groups. Our findings suggest that a continued trend of emissions reductions will likely reduce concentrations and absolute disparity but may not reduce relative disparities without greater attention to the systemic factors leading to this disparity.

BACKGROUND: Diesel exhaust (DE) exposures pose concerns for serious health effects, including asthma and lung cancer, in California communities burdened by multiple stressors. OBJECTIVE: To evaluate DE exposures in disproportionately impacted communities using biomonitoring and compare results for adults and children within and between families. METHODS: We recruited 40 families in the San Francisco East Bay area. Two metabolites of 1-nitropyrene (1-NP), a marker for DE exposures, were measured in urine samples from parent-child pairs. For 25 families, we collected single-day spot urine samples during two sampling rounds separated by an average of four months. For the 15 other families, we collected daily spot urine samples over four consecutive days during the two sampling rounds. We also measured 1-NP in household dust and indoor air. Associations between urinary metabolite levels and participant demographics, season, and 1-NP levels in dust and air were evaluated. RESULTS: At least one 1-NP metabolite was present in 96.6% of the urine samples. Detection frequencies for 1-NP in dust and indoor air were 97% and 74%, respectively. Results from random effect models indicated that levels of the 1-NP metabolite 6-hydroxy-1-nitropyrene (6-OHNP) were significantly higher in parents compared with their children (p-value = 0.005). Urinary 1-NP metabolite levels were generally higher during the fall and winter months. Within-subject variability was higher than between-subject variability (~60% of total variance versus ~40%, respectively), indicating high short-term temporal variability. IMPACT: Biomonitoring, coupled with air monitoring, improves understanding of hyperlocal air pollution impacts. Results from these studies will inform the design of effective exposure mitigation strategies in disproportionately affected communities.

BACKGROUND: National-scale linear regression-based modeling may mischaracterize localized patterns, including hyperlocal peaks and neighborhood- to regional-scale gradients. For studies focused on within-city differences, this mischaracterization poses a risk of exposure misclassification, affecting epidemiological and environmental justice conclusions. OBJECTIVE: Characterize the difference between intraurban pollution patterns predicted by national-scale land use regression modeling and observation-based estimates within a localized domain and examine the relationship between that difference and urban infrastructure and demographics. METHODS: We compare highly resolved (0.01 km2) observations of NO2 mixing ratio and ultrafine particle (UFP) count obtained via mobile monitoring with national model predictions in thirteen neighborhoods in the San Francisco Bay Area. Grid cell-level divergence between modeled and observed concentrations is termed localized difference. We use a flexible machine learning modeling technique, Bayesian Additive Regression Trees, to investigate potentially nonlinear relationships between discrepancy between localized difference and known local emission sources as well as census block group racial/ethnic composition. RESULTS: We find that observed local pollution extremes are not represented by land use regression predictions and that observed UFP count significantly exceeds regression predictions. Machine learning models show significant nonlinear relationships among localized differences between predictions and observations and the density of several types of pollution-related infrastructure (roadways, commercial and industrial operations). In addition, localized difference was greater in areas with higher population density and a lower share of white non-Hispanic residents, indicating that exposure misclassification by national models differs among subpopulations. IMPACT: Comparing national-scale pollution predictions with hyperlocal observations in the San Francisco Bay Area, we find greater discrepancies near major roadways and food service locations and systematic underestimation of concentrations in neighborhoods with a lower share of non-Hispanic white residents. These findings carry implications for using national-scale models in intraurban epidemiological and environmental justice applications and establish the potential utility of supplementing large-scale estimates with publicly available urban infrastructure and pollution source information.

Environmentally persistent free radicals (EPFRs) play an important role in aerosol effects on air quality and public health, but their atmospheric abundance and sources are poorly understood. We measured EPFRs contained in PM2.5 collected in Fairbanks, Alaska, in winter 2022. We find that EPFR concentrations were enhanced during surface-based inversion and correlate strongly with incomplete combustion markers, including carbon monoxide and elemental carbon (R2 > 0.75). EPFRs exhibit moderately good correlations with PAHs, biomass burning organic aerosols, and potassium (R2 > 0.4). We also observe strong correlations of EPFRs with hydrocarbon-like organic aerosols, Fe and Ti (R2 > 0.6), and single-particle mass spectrometry measurements reveal internal mixing of PAHs, with potassium and iron. These results suggest that residential wood burning and vehicle tailpipes are major sources of EPFRs and nontailpipe emissions, such as brake wear and road dust, may contribute to the stabilization of EPFRs. Exposure to the observed EPFR concentrations (18 ± 12 pmol m-3) would be equivalent to smoking ∼0.4-1 cigarette daily. Very strong correlations (R2 > 0.8) of EPFR with hydroxyl radical formation in surrogate lung fluid indicate that exposure to EPFRs may induce oxidative stress in the human respiratory tract.

This study bridges gaps in air pollution research by examining exposure dynamics in disadvantaged communities. Using cutting-edge machine learning and massive data processing, we produced high-resolution (100 meters) daily air pollution maps for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California for 2012-2019. Our findings revealed opposite spatial patterns of NO2 and PM2.5 to that of O3. We also identified consistent, higher pollutant exposure for disadvantaged communities from 2012 to 2019, although the most disadvantaged communities saw the largest NO2 and PM2.5 reductions and the advantaged neighborhoods experienced greatest rising O3 concentrations. Further, day-to-day exposure variations decreased for NO2 and O3. The disparity in NO2 exposure decreased, while it persisted for O3. In addition, PM2.5 showed increased day-to-day variations across all communities due to the increase in wildfire frequency and intensity, particularly affecting advantaged suburban and rural communities.

First-ever measurements of particulate matter (PM2.5, PM10, and TSP) along with gaseous pollutants (CO, NO2, and SO2) were performed from June 2019 to April 2020 in Faisalabad, Metropolitan, Pakistan, to assess their seasonal variations; Summer 2019, Autumn 2019, Winter 2019-2020, and Spring 2020. Pollutant measurements were carried out at 30 locations with a 3-km grid distance from the Sitara Chemical Industry in District Faisalabad to Bhianwala, Sargodha Road, Tehsil Lalian, District Chiniot. ArcGIS 10.8 was used to interpolate pollutant concentrations using the inverse distance weightage method. PM2.5, PM10, and TSP concentrations were highest in summer, and lowest in autumn or winter. CO, NO2, and SO2 concentrations were highest in summer or spring and lowest in winter. Seasonal average NO2 and SO2 concentrations exceeded WHO annual air quality guide values. For all 4 seasons, some sites had better air quality than others. Even in these cleaner sites air quality index (AQI) was unhealthy for sensitive groups and the less good sites showed Very critical AQI (> 500). Dust-bound carbon and sulfur contents were higher in spring (64 mg g-1) and summer (1.17 mg g-1) and lower in autumn (55 mg g-1) and winter (1.08 mg g-1). Venous blood analysis of 20 individuals showed cadmium and lead concentrations higher than WHO permissible limits. Those individuals exposed to direct roadside pollution for longer periods because of their occupation tended to show higher Pb and Cd blood concentrations. It is concluded that air quality along the roadside is extremely poor and potentially damaging to the health of exposed workers.

Exposure to ambient air pollution has significant adverse health effects; however, whether air pollution is associated with urological cancer is largely unknown. We conduct a systematic review and meta-analysis with epidemiological studies, showing that a 5 μg/m3 increase in PM2.5 exposure is associated with a 6%, 7%, and 9%, increased risk of overall urological, bladder, and kidney cancer, respectively; and a 10 μg/m3 increase in NO2 is linked to a 3%, 4%, and 4% higher risk of overall urological, bladder, and prostate cancer, respectively. Were these associations to reflect causal relationships, lowering PM2.5 levels to 5.8 μg/m3 could reduce the age-standardized rate of urological cancer by 1.5 ~ 27/100,000 across the 15 countries with the highest PM2.5 level from the top 30 countries with the highest urological cancer burden. Implementing global health policies that can improve air quality could potentially reduce the risk of urologic cancer and alleviate its burden.

In California, wildfire risk and severity have grown substantially in the last several decades. Research has characterized extensive adverse health impacts from exposure to wildfire-attributable fine particulate matter (PM2.5), but few studies have quantified long-term outcomes, and none have used a wildfire-specific chronic dose-response mortality coefficient. Here, we quantified the mortality burden for PM2.5 exposure from California fires from 2008 to 2018 using Community Multiscale Air Quality modeling system wildland fire PM2.5 estimates. We used a concentration-response function for PM2.5, applying ZIP code-level mortality data and an estimated wildfire-specific dose-response coefficient accounting for the likely toxicity of wildfire smoke. We estimate a total of 52,480 to 55,710 premature deaths are attributable to wildland fire PM2.5 over the 11-year period with respect to two exposure scenarios, equating to an economic impact of $432 to $456 billion. These findings extend evidence on climate-related health impacts, suggesting that wildfires account for a greater mortality and economic burden than indicated by earlier studies.

Methane is a major contributor to anthropogenic greenhouse gas emissions. Identifying large sources of methane, particularly from the oil and gas sectors, will be essential for mitigating climate change. Aircraft-based methane sensing platforms can rapidly detect and quantify methane point-source emissions across large geographic regions, and play an increasingly important role in industrial methane management and greenhouse gas inventory. We independently evaluate the performance of five major methane-sensing aircraft platforms: Carbon Mapper, GHGSat-AV, Insight M, MethaneAIR, and Scientific Aviation. Over a 6 week period, we released metered gas for over 700 single-blind measurements across all five platforms to evaluate their ability to detect and quantify emissions that range from 1 to over 1,500 kg(CH4)/h. Aircraft consistently quantified releases above 10 kg(CH4)/h, and GHGSat-AV and Insight M detected emissions below 5 kg(CH4)/h. Fully blinded quantification estimates for platforms using downward-facing imaging spectrometers have parity slopes ranging from 0.76 to 1.13, with R2 values of 0.61 to 0.93; the platform using continuous air sampling has a parity slope of 0.5 (R2 = 0.93). Results demonstrate that aircraft-based methane sensing has matured since previous studies and is ready for an increasingly important role in environmental policy and regulation.

Motor vehicles are among the major sources of pollutants and greenhouse gases in urban areas and a transition to "zero emission vehicles" is underway worldwide. However, emissions associated with brake and tire wear will remain. We show here that previously unrecognized volatile and semi-volatile organic compounds, which have a similarity to biomass burning emissions are emitted during braking. These include greenhouse gases or, these classified as Hazardous Air Pollutants, as well as nitrogen-containing organics, nitrogen oxides and ammonia. The distribution and reactivity of these gaseous emissions are such that they can react in air to form ozone and other secondary pollutants with adverse health and climate consequences. Some of the compounds may prove to be unique markers of brake emissions. At higher temperatures, nucleation and growth of nanoparticles is also observed. Regions with high traffic, which are often disadvantaged communities, as well as commuters can be impacted by these emissions even after combustion-powered vehicles are phased out.

Because humans spend about one-third of their time asleep in their bedrooms and are themselves emission sources of volatile organic compounds (VOCs), it is important to specifically characterize the composition of the bedroom air that they experience during sleep. This work uses real-time indoor and outdoor measurements of volatile organic compounds (VOCs) to examine concentration enhancements in bedroom air during sleep and to calculate VOC emission rates associated with sleeping occupants. Gaseous VOCs were measured with proton-transfer reaction time-of-flight mass spectrometry during a multiweek residential monitoring campaign under normal occupancy conditions. Results indicate high emissions of nearly 100 VOCs and other species in the bedroom during sleeping periods as compared to the levels in other rooms of the same residence. Air change rates for the bedroom and, correspondingly, emission rates of sleeping-associated VOCs were determined for two bounding conditions: (1) air exchange between the bedroom and outdoors only and (2) air exchange between the bedroom and other indoor spaces only (as represented by measurements in the kitchen). VOCs from skin oil oxidation and personal care products were present, revealing that many emission pathways can be important occupant-associated emission factors affecting bedroom air composition in addition to direct emissions from building materials and furnishings.

BACKGROUND: Ultrafine particles (UFP) are unregulated air pollutants abundant in aviation exhaust. Emerging evidence suggests that UFPs may impact lung health due to their high surface area-to-mass ratio and deep penetration into airways. This study aimed to assess long-term exposure to airport-related UFPs and lung cancer incidence in a multiethnic population in Los Angeles County. METHODS: Within the California Multiethnic Cohort, we examined the association between long-term exposure to airport-related UFPs and lung cancer incidence. Multivariable Cox proportional hazards regression models were used to estimate the effect of UFP exposure on lung cancer incidence. Subgroup analyses by demographics, histology and smoking status were conducted. RESULTS: Airport-related UFP exposure was not associated with lung cancer risk [per one IGR HR, 1.01; 95% confidence interval (CI), 0.97-1.05] overall and across race/ethnicity. A suggestive positive association was observed between a one IQR increase in UFP exposure and lung squamous cell carcinoma (SCC) risk (HR, 1.08; 95% CI, 1.00-1.17) with a Phet for histology = 0.05. Positive associations were observed in 5-year lag analysis for SCC (HR, 1.12; 95% CI, CI, 1.02-1.22) and large cell carcinoma risk (HR, 1.23; 95% CI, 1.01-1.49) with a Phet for histology = 0.01. CONCLUSIONS: This large prospective cohort analysis suggests a potential association between airport-related UFP exposure and specific lung histologies. The findings align with research indicating that UFPs found in aviation exhaust may induce inflammatory and oxidative injury leading to SCC. IMPACT: These results highlight the potential role of airport-related UFP exposure in the development of lung SCC.

We examined associations between prenatal fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) exposures and child respiratory outcomes through age 8-9 years in 1279 ECHO-PATHWAYS Consortium mother-child dyads. We averaged spatiotemporally modeled air pollutant exposures during four fetal lung development phases: pseudoglandular (5-16 weeks), canalicular (16-24 weeks), saccular (24-36 weeks), and alveolar (36+ weeks). We estimated adjusted relative risks (RR) for current asthma at age 8-9 and asthma with recent exacerbation or atopic disease, and odds ratios (OR) for wheezing trajectories using modified Poisson and multinomial logistic regression, respectively. Effect modification by child sex, maternal asthma, and prenatal environmental tobacco smoke was explored. Across all outcomes, 95% confidence intervals (CI) included the null for all estimates of associations between prenatal air pollution exposures and respiratory outcomes. Pseudoglandular PM2.5 exposure modestly increased risk of current asthma (RRadj = 1.15, 95% CI: 0.88-1.51); canalicular PM2.5 exposure modestly increased risk of asthma with recent exacerbation (RRadj = 1.26, 95% CI: 0.86-1.86) and persistent wheezing (ORadj = 1.28, 95% CI: 0.86-1.89). Similar findings were observed for O3, but not NO2, and associations were strengthened among mothers without asthma. While not statistically distinguishable from the null, trends in effect estimates suggest some adverse associations of early pregnancy air pollution exposures with child respiratory conditions, warranting confirmation in larger samples.

Cities represent a significant and growing portion of global carbon dioxide (CO2) emissions. Quantifying urban emissions and trends over time is needed to evaluate the efficacy of policy targeting emission reductions as well as to understand more fundamental questions about the urban biosphere. A number of approaches have been proposed to measure, report, and verify (MRV) changes in urban CO2 emissions. Here we show that a modest capital cost, spatially dense network of sensors, the Berkeley Environmental Air Quality and CO2 Network (BEACO2N), in combination with Bayesian inversions, result in a synthesis of measured CO2 concentrations and meteorology to yield an improved estimate of CO2 emissions and provide a cost-effective and accurate assessment of CO2 emissions trends over time. We describe nearly 5 years of continuous CO2 observations (2018-2022) in a midsized urban region (the San Francisco Bay Area). These observed concentrations constrain a Bayesian inversion that indicates the interannual trend in urban CO2 emissions in the region has been a modest decrease at a rate of 1.8 ± 0.3%/year. We interpret this decrease as primarily due to passenger vehicle electrification, reducing on-road emissions at a rate of 2.6 ± 0.7%/year.

BACKGROUND: Wildfires in the Western United States are a growing and significant source of air pollution that is eroding decades of progress in air pollution reduction. The effects on preterm birth during critical periods of pregnancy are unknown. METHODS: We assessed associations between prenatal exposure to wildland fire smoke and risk of preterm birth (gestational age

BACKGROUND: Epidemiological findings regarding the association of particulate matter ≤2.5 μm (PM2.5) exposure with hypertensive disorders in pregnancy (HDP) are inconsistent; evidence for HDP risk related to PM2.5 components, mixture effects, and windows of susceptibility is limited. We aimed to investigate the relationships between HDP and exposure to PM2.5 during pregnancy. METHODS AND FINDINGS: A large retrospective cohort study was conducted among mothers with singleton pregnancies in Kaiser Permanente Southern California from 2008 to 2017. HDP were defined by International Classification of Diseases-9/10 (ICD-9/10) diagnostic codes and were classified into 2 subcategories based on the severity of HDP: gestational hypertension (GH) and preeclampsia and eclampsia (PE-E). Monthly averages of PM2.5 total mass and its constituents (i.e., sulfate, nitrate, ammonium, organic matter, and black carbon) were estimated using outputs from a fine-resolution geoscience-derived model. Multilevel Cox proportional hazard models were used to fit single-pollutant models; quantile g-computation approach was applied to estimate the joint effect of PM2.5 constituents. The distributed lag model was applied to estimate the association between monthly PM2.5 exposure and HDP risk. This study included 386,361 participants (30.3 ± 6.1 years) with 4.8% (17,977/373,905) GH and 5.0% (19,381/386,361) PE-E cases, respectively. In single-pollutant models, we observed increased relative risks for PE-E associated with exposures to PM2.5 total mass [adjusted hazard ratio (HR) per interquartile range: 1.07, 95% confidence interval (CI) [1.04, 1.10] p < 0.001], black carbon [HR = 1.12 (95% CI [1.08, 1.16] p < 0.001)] and organic matter [HR = 1.06 (95% CI [1.03, 1.09] p < 0.001)], but not for GH. The population attributable fraction for PE-E corresponding to the standards of the US Environmental Protection Agency (9 μg/m3) was 6.37%. In multi-pollutant models, the PM2.5 mixture was associated with an increased relative risk of PE-E ([HR = 1.05 (95% CI [1.03, 1.07] p < 0.001)], simultaneous increase in PM2.5 constituents of interest by a quartile) and PM2.5 black carbon gave the greatest contribution of the overall mixture effects (71%) among all individual constituents. The susceptible window is the late first trimester and second trimester. Furthermore, the risks of PE-E associated with PM2.5 exposure were significantly higher among Hispanic and African American mothers and mothers who live in low- to middle-income neighborhoods (p < 0.05 for Cochrans Q test). Study limitations include potential exposure misclassification solely based on residential outdoor air pollution, misclassification of disease status defined by ICD codes, the date of diagnosis not reflecting the actual time of onset, and lack of information on potential covariates and unmeasured factors for HDP. CONCLUSIONS: Our findings add to the literature on associations between air pollution exposure and HDP. To our knowledge, this is the first study reporting that specific air pollution components, mixture effects, and susceptible windows of PM2.5 may affect GH and PE-E differently.

Wildfires are a significant threat to human health, in part through degraded air quality. Prescribed burning can reduce wildfire severity but can also lead to an increase in air pollution. The complexities of fires and atmospheric processes lead to uncertainties when predicting the air quality impacts of fire and make it difficult to fully assess the costs and benefits of an expansion of prescribed fire. By modeling differences in emissions, surface conditions, and meteorology between wildfire and prescribed burns, we present a novel comparison of the air quality impacts of these fire types under specific scenarios. One wildfire and two prescribed burn scenarios were considered, with one prescribed burn scenario optimized for potential smoke exposure. We found that PM2.5 emissions were reduced by 52%, from 0.27 to 0.14 Tg, when fires burned under prescribed burn conditions, considerably reducing PM2.5 concentrations. Excess short-term mortality from PM2.5 exposure was 40 deaths for fires under wildfire conditions and 39 and 15 deaths for fires under the default and optimized prescribed burn scenarios, respectively. Our findings suggest prescribed burns, particularly when planned during conditions that minimize smoke exposure, could be a net benefit for the impacts of wildfires on air quality and health.

Wildfires have become more frequent and intense due to climate change and outdoor wildfire fine particulate matter (PM2.5) concentrations differ from relatively smoothly varying total PM2.5. Thus, we introduced a conceptual model for computing long-term wildfire PM2.5 and assessed disproportionate exposures among marginalized communities. We used monitoring data and statistical techniques to characterize annual wildfire PM2.5 exposure based on intermittent and extreme daily wildfire PM2.5 concentrations in California census tracts (2006 to 2020). Metrics included: 1) weeks with wildfire PM2.5 < 5 μg/m3; 2) days with non-zero wildfire PM2.5; 3) mean wildfire PM2.5 during peak exposure week; 4) smoke waves (≥2 consecutive days with

Future climate change can cause more days with poor air quality. This could trigger more alerts telling people to stay inside to protect themselves, with potential consequences for health and health equity. Here, we study the change in US air quality alerts over this century due to fine particulate matter (PM2.5), who they may affect, and how they may respond. We find air quality alerts increase by over 1 mo per year in the eastern United States by 2100 and quadruple on average. They predominantly affect areas with high Black populations and leakier homes, exacerbating existing inequalities and impacting those less able to adapt. Reducing emissions can offer significant annual health benefits ($5,400 per person) by mitigating the effect of climate change on air pollution and its associated risks of early death. Relying on people to adapt, instead, would require them to stay inside, with doors and windows closed, for an extra 142 d per year, at an average cost of $11,000 per person. It appears likelier, however, that people will achieve minimal protection without policy to increase adaptation rates. Boosting adaptation can offer net benefits, even alongside deep emission cuts. New adaptation policies could, for example: reduce adaptation costs; reduce infiltration and improve indoor air quality; increase awareness of alerts and adaptation; and provide measures for those working or living outdoors. Reducing emissions, conversely, lowers everyones need to adapt, and protects those who cannot adapt. Equitably protecting human health from air pollution under climate change requires both mitigation and adaptation.

Electrifying freight trucks will be key to alleviating air pollution burdens on disadvantaged communities and mitigating climate change. The United States plans to pursue this aim by adding vehicle charging infrastructure along specific freight corridors. This study explores the coevolution of the electricity grid and freight trucking landscape using an integrated assessment framework to identify when each interstate and drayage corridor becomes advantageous to electrify from a climate and human health standpoint. Nearly all corridors achieve greenhouse gas emission reductions if electrified now. Most can reduce health impacts from air pollution if electrified by 2040 although some corridors in the Midwest, South, and Mid-Atlantic regions remain unfavorable to electrify from a human health standpoint, absent policy support. Recent policy, namely, the Inflation Reduction Act, accelerates this timeline to 2030 for most corridors and results in net human health benefits on all corridors by 2050, suggesting that near-term investments in truck electrification, particularly drayage corridors, can meaningfully reduce climate and health burdens.

INTRODUCTION: Air pollution is widely acknowledged as a significant factor in respiratory outcomes, including coughing, wheezing, emergency department (ED) visits, and even death. Although several literature reviews have confirmed the association between air pollution and respiratory outcomes, they often did not standardize associations across different studies and overlooked other increasingly impactful pollutants such as trace metals. Recognizing the importance of consistent comparison and emissions of non-exhaust particles from road traffic, this study aims to comprehensively evaluate the standardized effects of various criteria pollutants and trace metals on respiratory health. METHODS: We conducted a comprehensive meta-analysis of peer-reviewed journal articles on air pollution and respiratory outcomes published between 1 January 2000, and 1 June 2024. The study included children (age

BackgroundPrior studies suggested that air pollution exposure may increase the risk of Parkinson's Disease (PD). We investigated the long-term impacts of traffic-related and multiple sources of particulate air pollution on PD in central California.MethodsOur case-control analysis included 761 PD patients and 910 population controls. We assessed exposure at residential and occupational locations from 1981 to 2016, estimating annual average carbon monoxide (CO) concentrations - a traffic pollution marker - based on the California Line Source Dispersion Model, version 4. Additionally, particulate matter (PM2.5) concentrations were based on a nationwide geospatial chemical transport model. Exposures were assessed as 10-year averages with a 5-year lag time prior to a PD diagnosis for cases and an interview date for controls, subsequently categorized into tertiles. Logistic regression models were used, adjusting for various factors.ResultsTraffic-related CO was associated with an increased odds ratio for PD at residences (OR for T3 vs. T1: 1.58; 95% CI: 1.20, 2.10; p-trend = 0.02) and workplaces (OR for T3 vs. T1: 1.91; 95% CI: 1.22, 3.00; p-trend

Occupational exposure to charcoal smoke and dust is a threat to workers' respiratory systems. What is the prevalence of COPD in charcoal workers compared with that of farmers in rural areas of Democratic Republic of Congo (DRC)? This cross-sectional, comparative, and multisite study was performed in the charcoal-producing provinces of southwestern DRC. We randomly included charcoal workers and compared them with farmers (age range, 18-70 years). Air quality indexes, anthropometric features, physical activity, sociodemographic characteristics, and related medical events data were recorded. A lung function questionnaire was used to assess respiratory symptoms and spirometry was performed. COPD was defined as the presence of respiratory symptoms for > 3 months with an FEV 1 to FVC ratio less than the lower limit of normal. The prevalence of COPD was calculated, and logistic regression was used to identify COPD-associated factors. We included 485 participants between August 2020 and July 2021. Charcoal producers (CPs; n = 229) were compared with farmers (n = 118), and charcoal saleswomen (n = 72) were compared with vegetable saleswomen (n = 66). Respective groups were similar in age, job seniority, height, and weight. The air was more polluted at charcoal workplaces. The prevalence of COPD was higher in CPs than in farmers (39.7% vs 14.4%; P <.0001) and higher in charcoal saleswomens compared with vegetable saleswomen (40.3% vs 13.6%; P <.0001). Being a charcoal worker was associated independently with COPD in the CP and farmers groups (adjusted OR, 3.54; 95% CI, 1.94-6.46) and in the saleswomen group (adjusted OR, 7.85; 95% CI, 2.85-21.5), where it was also associated independently with young age (adjusted OR, 0.85; 95% CI, 0.80-0.93) and monthly income (adjusted OR, 0.88; 95% CI, 0.83-0.96). In rural areas of DRC, producing or selling charcoal is associated with a higher risk of COPD. [ABSTRACT FROM AUTHOR]

The prevalence of type 2 diabetes (T2D) poses a significant health challenge, yet the contribution of air pollutants to T2D epidemics remains under-studied. Several studies demonstrated a correlation between exposure to volatile organic compounds (VOCs) in indoor/outdoor environments and T2D. Here, we conducted the first meta-analysis, establishing a robust association between exposure to benzene, a prevalent airborne VOC, and insulin resistance in humans across all ages. We used a controlled benzene exposure system, continuous glucose monitoring approach, and indirect calorimetry in mice, to investigate the underlying mechanisms. Following exposure, disruptions in energy homeostasis, accompanied by modifications in the hypothalamic transcriptome and alterations in insulin and immune signaling, were observed exclusively in males, leading to a surge in blood glucose levels. In agreement, RNA sequencing of microglia revealed increased expression of genes associated with immune response and NF-κB signaling. Selective ablation of IKKβ in immune cells (Cx3cr1GFPΔIKK) or exclusively in microglia (Tmem119ERΔIKK) in adult mice alleviated benzene-induced gliosis, restored energy homeostasis and hypothalamic gene expression, and protected against hyperglycemia. We conclude that the microglial NF-κB pathway plays a critical role in chemical-induced metabolic disturbances, revealing a vital pathophysiological mechanism linking exposure to airborne toxicants and the onset of metabolic diseases. Article Highlights: The first meta-analysis, establishing a robust association between exposure to benzene, a prevalent airborne volatile organic compound, and insulin resistance in humans across all ages. Short-term benzene exposure in male mice results in hyperglycemia and disruptions in energy balance. Acute benzene exposure triggers hypothalamic insulin resistance and provokes an inflammatory shift in the microglial transcriptome. The microglial NF-κB pathway is critical in mediating hyperglycemia and metabolic dysregulation induced by benzene exposure. [ABSTRACT FROM AUTHOR]

Microplastics are contaminating air, water, soils, both in populated megacities and in remote areas. Here we review analytical methodologies and occurrence of suspended airborne microplastics in Asia. Forty-three studies on suspended airborne microplastics were examined in thirteen countries across Asia. Abundance of suspended airborne microplastics ranged from 0.93 to 8,865 particles/m3 in indoor locations, 0.017 to 18,880 particles/m3 in outdoor areas, and 0.39 to 19 particles per 100 m3 in the oceanic environment. Suspended airborne microplastics mostly had the shape of fibers and fragments. Polyethylene, polypropylene, polyethylene terephthalate, polyvinyl chloride, and polystyrene polymers were commonly found. The size of plastic particles ranged from 0.43 to 9,555 µm, and the strategies used in sampling and analytical methods can influence the size of suspended airborne microplastics. Occurrence of suspended airborne microplastics in Asia demonstrates a critical pollution issue in the region. [ABSTRACT FROM AUTHOR]

In observational studies, a possible correlation between atmospheric environmental factors and the number of daily outpatient visits by Otitis media with effusion(OME) patients has been observed. However, the causal relationship is not clear.To study the relationship between the incidence of OME and meteorological factors and air pollutants in the main urban areas of Lanzhou, it is helpful to further understand the health effects of meteorological and environmental factors on OME and to prevent and treat the disease, it is of great academic and practical significance to the prevention, treatment and prognosis of diseases. The levels of AQI、PM2.5、PM10、NO2、O3、SO2、CO、AP、RH、W and T were obtained from local monitor stations. Data of patients with OME were collected from two Grade A Level hospitals in Lanzhou from January 1, 2014 to December 31, 2016. Descriptive analysis of data was carried out for the study subjects. Spearman correlation coefficients between atmospheric environmental factors and daily visits of patients with OME were calculated by SPSS statistical software. Lag effects, relative risks(RR) and exposure-response curves were calculated by generalized additive model (GAM) with R software. (1) The incidence of OME in winter and spring was more than that in summer and autumn, which was consistent with the seasonal variation of meteorological environmental factors of Lanzhou. That was, the meteorological conditions and air quality in winter and spring were poor, while in summer and autumn they were relatively good. (2) The number of male outpatients were 1.05, 1.08 and 1.09 times of female outpatients during the period 2014–2016, respectively. And aged 0–10 years old outpatients accounted for 31% of the total OME outpatients. (3) Exposure-response curve showed that PM2.5, PM10, NO2 and SO2 were positively correlated with OME, T was negatively correlated with OME. When concentration < 1mg/m3, CO was positively correlated with OME. When concentration>1mg/m3, CO was negatively correlated with OME. When concentration<30 ug/m3, O3 was positively correlated with OME. When concentration>30ug/m3, O3 was negatively correlated with OME. (4) The factors we studied could significantly affect the number of OME outpatients within 2–3 days of single lag effects, and 3–4 days of cumulative lag effects.(5) The influential factors on OME were as follows: PM2.5、NO2、SO2、O3、 CO and T. The daily average number of OME patients in different seasons was different in major region of Lanzhou city, with more in winter、spring and fewer in summer、autumn. Age and sex were the important factors affecting the daily average number of OME patients. Males were more susceptible to OME than females and children awere moresusceptible to OME than adults. The change of OME patients was related to air quality, air pressure and temperature. The worse the air quality, the higher the air pressure, the lower the temperature, the more the average daily number of OME patients. Meteorological environmental factors affected the visits of OME, and the lagging effect time of different factors were different. Most of the research factors within 3–4 days had a significant impact on the number of patients of OME. 1.The number of OME visits in the Lanzhou was more seasonal in winter and spring than in summer and fall. 2.Age and sex were the most important factors affecting the number of patients with OME. According to the prevalence of OME in Lanzhou, children were more likely to have OME than adults and men were more likely to have OME than women. 3.The number of OME patients was related to air quality, air pressure and temperature. 4.The meteorological factors have a delayed effect on the onset of OME, and the time of delayed effect is different for different factors. The single delayed effect of 2–3 days and the cumulative delayed effect of 3–4 days have a significant effect on the change of the number of patients with OME. To study the relationship between the incidence of OME and meteorological factors and air pollutants in the main urban areas of Lanzhou, it is helpful to further understand the health effects of meteorological and environmental factors on OME and to prevent and treat the disease, it is of great academic and practical significance to the prevention, treatment and prognosis of diseases. [ABSTRACT FROM AUTHOR]

Climate change, environmental degradation, and biodiversity loss are adversely affecting human health and exacerbating existing inequities, intensifying pressures on already strained health systems. Paradoxically, healthcare is a high-polluting industry, responsible for 4.6% of global greenhouse gas emissions and a similar proportion of air pollutants. Perioperative services are among the most resource-intensive healthcare services and are responsible for some unique pollutants. Opportunities exist to mitigate pollution throughout the entire continuum of perioperative care, including those that occur upstream of the operating room in the process of patient selection and optimisation, delivery of anaesthesia and surgery, and the postoperative recovery period. Within a patient-centred, holistic approach, clinicians can advocate for healthy public policies that modify the determinants of surgical illness, can engage in shared decision-making to ensure appropriate clinical decisions, and can be stewards of healthcare resources. Innovation and collaboration are required to redesign clinical care pathways and processes, optimise logistical systems, and address facility emissions. The results will extend beyond the reduction of public health damages from healthcare pollution to the provision of higher value, higher quality, patient-centred care. [ABSTRACT FROM AUTHOR]

Ozone (O3) is one of the most harmful and widespread air pollutants, affecting crop yield and plant health worldwide. There is evidence that O3 reduces the major limiting factor of photosynthesis, namely CO2 mesophyll conductance (gm), but there is little quantitative information of O3‐caused changes in key leaf anatomical traits and their impact on gm. We exposed two O3‐responsive clones of the economically important tree species Populus × canadensis Moench to 120 ppb O3 for 21 days. An anatomical diffusion model within the leaf was used to analyse the entire CO2 diffusion pathway from substomatal cavities to carboxylation sites and determine the importance of each structural and subcellular component as a limiting factor. gm decreased substantially under O3 and was found to be the most important limitation of photosynthesis. This decrease was mostly driven by an increased cell wall thickness and length of subcellular diffusion pathway caused by altered interchloroplast spacing and chloroplast positioning. By contrast, the prominent leaf integrative trait leaf dry mass per area was neither affected nor related to gm under O3. The observed relationship between gm and anatomy, however, was clone‐dependent, suggesting that mechanisms regulating gm may differ considerably between closely related plant lines. Our results confirm the need for further studies on factors constraining gm under stress conditions. Summary statement: O3‐caused decrease of gm in two Populus × canadensis Moench clones is mainly driven by altered subcellular traits, notably cell wall and cytoplasm thickening, inter‐chloroplast spacing and the ratio of exposed chloroplasts to mesophyll surface area (Sc/Sm). However, the observed gm‐anatomy relationship is clone‐dependent. [ABSTRACT FROM AUTHOR]

Fluoride is one of the most highly toxic soil and air pollutant prevalent in aquatic as well as terrestrial ecosystems. It seriously affects crop plant quality and productivity worldwide. Although a crop with great nutritional value; oats are susceptible to a variety of abiotic stresses. In the current study, the responses of six oat cultivars (JHO-99-1, JHO-2009, BJ-2012, KANT, JHO-822, and JHO-2000) to fluoride stress on growth measurements, photosynthetic pigments, and photosynthetic parameters have been studied. The fluoride (200 and 400 mg/kg) was added to the soil artificially, seeds were sown and the pots were kept under controlled conditions. Another set of plants without the fluoride treatment (0 mg/kg) were labeled as control. The experiment was conducted in a plant growth chamber and fluoride was artificially added to the soil prior to seeding. All six oat cultivars subjected to fluoride stress exhibited an overall decrease in plant growth when compared to the control. Further under fluoride stress, while the oat cultivars also showed a decrease in chlorophyll content and different photosynthetic parameters but on the contrary the antioxidant activities were increased. While the cultivar JHO-822 was most affected under fluoride stress, JHO-99-1 showed tolerance toward fluoride toxicity in comparison to other cultivars JHO-2009, BJ-2012, KANT, JHO-822, and JHO-2000. It is concluded that the tolerance or susceptibility of a cultivar to fluoride can be well correlated with various parameters studied in the present study. [ABSTRACT FROM AUTHOR]

Background: Gestational diabetes mellitus (GDM) can have negative effects on both the pregnancy and perinatal outcomes, as well as the long-term health of the mother and the child. It has been suggested that exposure to air pollution may increase the risk of developing GDM. this study investigated the relationship between exposure to air pollutants with gestational diabetes. Methods: the present study is a retrospective cohort study. We used data from a randomised community trial conducted between September 2016 and January 2019 in Iran. During this period, data on air pollutant levels of five cities investigated in the original study, including 6090 pregnant women, were available. concentrations of ozone (O3), nitric oxide (NO), nitrogen dioxide (NO2), nitrogen oxides (NOx), sulphur dioxide (SO2), carbon monoxide (CO), particulate matter < 2.5 (PM2.5) or <10 µm (PM10) were obtained from air pollution monitoring stations. Exposure to air pollutants during the three months preceding pregnancy and the first, second and third trimesters of pregnancy for each participant was estimated. The odds ratio was calculated based on logistic regression in three adjusted models considering different confounders. Only results that had a p < .05 were considered statistically significant. Results: None of the logistic regression models showed any statistically significant relationship between the exposure to any of the pollutants and GDM at different time points (before pregnancy, in the first, second and third trimesters of pregnancy and 12 months in total) (p > .05). Also, none of the adjusted logistic regression models showed any significant association between PM10 exposure and GDM risk at all different time points after adjusting for various confounders (p > .05). Conclusions: This study found no association between GDM risk and exposure to various air pollutants before and during the different trimesters of pregnancy. This result should be interpreted cautiously due to the lack of considering all of the potential confounders. [ABSTRACT FROM AUTHOR]

To assess the impact of different heights of the viaduct on air pollutant dispersion and the prediction accuracy of pollutant concentration in urban street canyons, simulation results based on LES and RANS models are analyzed. The presence of a viaduct generated a poorly ventilated region underneath it, and RANS significantly underestimated the wind speed and grossly overestimated the pollutant concentration. LES gives better results for the flow pattern, distribution of turbulent kinetic energy and mean pollutant concentration. With a fluctuation of less than 15% of the pollutant concentration, both RANS and LES cases show that an increase in the viaduct height has a weak impact on the concentration of pollutants in most areas of the canyon except windward, and cases with a viaduct height of 0.75H had the lowest predicted pollutant concentration relative to other cases with a viaduct as a result of better ventilation. In addition, LES found a subregion of pollutant accumulation above the ground, but RANS did not. [ABSTRACT FROM AUTHOR]

The severe lockdown imposed to prevent the spread of COVID-19 decreased the emissions of air pollutants in large cities. A comparative approach was adopted to analyze the effect of the COVID-19 lockdown on ambient air pollution concentrations and the impacts of meteorological parameters on them using data from air quality monitoring stations (AQMS) in Tabriz, Iran. Air quality improvement was significant for all pollutants, except for O3, in the first phase of the lockdown compared to other phases. The lockdown (restricted social contact, closing of shops, schools, universities, restaurants, and many administrative centers and companies, etc.) temporarily reduced air pollutants. Comparing meteorological parameters between lockdown periods and the same period in previous years showed no statistically significant variations (P-value < 0.05). Therefore, the meteorological parameters did not intervene in reducing air pollutants during the lockdown. The effects of lockdown on the concentration of air pollutants could provide a special way to understand the extent of quarantine compliance by citizens, evaluate additional air quality policies, and assess the impacts of reducing various emission sources.Highlights: A comparative approach to analyze the effect of the COVID-19 lockdown on air pollutants concentrations and the impacts of meteorological parameters on them. Reductions in air pollutants concentrations during COVID-19 lockdown periods compared to the same intervals in the previous years. Lack intervene the meteorological parameters in reducing air pollutants, and mainly due to reduction emissions during COVID-19 lockdown periods. [ABSTRACT FROM AUTHOR]

We consider a manufacturer that produces in response to a stochastic demand and emits pollution during the production process. Industrial pollutants released into the air are characterised by spatial variability and heterogeneity, and the precision of instruments for measuring stochastic pollution stocks varies widely across pollutants. Consequently, commitment (open-loop) strategies to control production and associated emissions are considered more practical than contingent (feedback) approaches when manufacturers are concerned about environmental consequences. In this article, we derive an optimal, open-loop control policy over an infinite time horizon and show a condition that ensures that such a policy induces asymptotic convergence of expected inventory stock and pollution stock trajectories to unique steady states. Moreover, we find that the variance in the pollution stock also converges asymptotically to a unique steady state, which is critical for prevention of irreversible environmental consequences. We show that environmental uncertainty affects inventories, leading, in the long run, to lower expected steady-state inventory stocks. We compare feedback and open-loop production policies and show that though there are losses associated with the inability to track inventory and pollution stocks accurately and thus applying the open-loop control instead of feedback, the expected long-term inventory and pollution stocks are not affected. [ABSTRACT FROM AUTHOR]

The prior empirical studies indicate torusim sector as an engine of economic growth worldwide. However, the activities linked with the tourism industries create several environmental problems as they require massive energy consumption. This study empirically explores the influence of tourism sub-sectors, i.e., accommodation (ACC), travel (TSP), food and drinks services (FDS), and shopping and entertainment (SAE) on the environmental quality in China measured by different greenhouse gasses emission and air pollutants. The bound testing ARDL approach has been employed to analyze the relationships for the data from 2001 to 2019. We also account for globalization (GLBN), economic growth (RGDP), and energy consumption (ENG), to normalize the tourism-linked sub-sectors' environmental effects. The findings revealed that greenhouse gas emissions, especially N2O and CH4, are highly caused by FDS activities related to tourism, whereas CO2 emission is mostly caused by the TSP sector in long-term. Likewise, FDS is the highest causing factor of air pollutants except for PM2.5, and the SAE sector is highly responsible for N2O and CH4 in long-term. Furthermore, all the tourism-related sub-sectors are positively affecting economic growth and energy consumption in long-term, while TSP and SAE sectors contribute higher to the energy consumption and economic growth, respectively. Surprisingly, globalization has been seen to be a significant cause of decreasing greenhouse gases and air pollutants. Finally, the causality analysis outcomes revealed that most of the tourism sub-sectors are significantly causing air pollutants and greenhouse gases. As evidenced by the empirical results, numerous policy measures are proposed for policy initiatives for China to promote its environmental quality. [ABSTRACT FROM AUTHOR]

This study examines the relationship between air pollution and daily mobility, focusing on daily bus and subway usage in Seoul using panel data with the fixed effects model. This study empirically examines the effect of air pollutant exposure on the daily moving population volume, including daily bus and subway usage in Seoul over 1271 days. The results show that people's sensitivity to particulate matter is complex. The most remarkable aspect of the results is that people are not sensitive to continuous pollutant levels, but to the comprehensive air-quality index (CAI). In addition, people's sensitivity is different depending on the moving distance and the means of transportation. Considering that CAI is defined by adopting the first air quality guidelines proposed by the World Health Organization (WHO) in 2005, these results explain how sensitive people are to the human health risks of particulate matter exposure recommended by the WHO. Our findings can be used as evidence to establish the new particulate matter standards and policies that can be strengthened by reflecting the recently updated WHO's air quality guidelines in 2021. [ABSTRACT FROM AUTHOR]

The study delves into how vehicle engine characteristics impact the release of air pollutants from various vehicle fleets in Lagos, Nigeria. It involved the direct measurement of emissions from the exhaust pipes of 88 vehicles using gas analyzers. The vehicle fleets encompassed motorcycles, tricycles, private cars, minibuses, large buses, and trucks. A statistical analysis was conducted on carbon monoxide (CO) and nitrogen oxide (NOx) emissions to develop a model equation based on vehicle type, engine type, vehicle age, and purchase status. Results indicate that personal cars and minibuses predominantly emit CO from gasoline engines, whereas large buses and trucks significantly contribute to NOx emissions from diesel engines. Further scrutiny revealed that 66% of the vehicles examined were over 10 years old, resulting in a 65% increase in emission levels. Approximately 60% of gasoline and 75% of diesel vehicles exceeded the permissible emission limits, leading to air quality deterioration and heightened health risks. The study underscores the risks associated with ageing vehicles and different engine types, emphasizing the imperative for a gradual transition to low-carbon or electric vehicles in developing African cities to combat air pollution and mitigate health hazards. [ABSTRACT FROM AUTHOR]

The study examined the core, buffer, and transition zones within the Cat Ba Biosphere Reserve in Vietnam to assess changes in five land cover categories (water surface, cropland, vacant land, residential land, and forestland) between 2018 and 2022. Additionally, we aimed to evaluate water quality using three indicators (normalized difference turbidity index, normalized difference chlorophyll index, total suspended solids) and air quality using four indicators (nitrogen dioxide, sulfur dioxide, carbon monoxide, ozone) for the 2022 data. Land cover change analysis and monitoring of water and air quality indicators were conducted using the Google Earth Engine platform, employing Sentinel-2 imagery for land cover and water quality assessment and Sentinel-5P images for air quality assessment. The findings revealed correlations between changes in land cover and water and air quality indicators within the Cat Ba Biosphere Reserve. While occasional instances of values surpassing permissible limits were noted, the average values over the 12-month period in 2022 indicated that water and air quality indicators remained within acceptable thresholds. These results suggest effective preservation efforts in the Cat Ba Biosphere Reserve, indicating no significant signs of degradation. [ABSTRACT FROM AUTHOR]

The rapid growth of the vehicle population is a major factor in heavy air pollution and public health issues. Traffic-related air pollutants (TRAPs) on roads are often much higher than ambient values, leading to high exposure levels in vehicles. This research proposes a hybrid forecasting model for early detection and early warning systems (EWS) of road networks during real-world travels. Data is collected from Kannur, Calicut, Palakkad, and Coimbatore using real-time sensors, including surrounding discussion information, activity information, vehicle speed, and stopping events. The study predicts ambient air quality (AAQ) levels on the road network using the Gaussian Dispersion model (GDM) and measures the risk sensitivity of PM10 and PM2.5 in selected regions. This helps formulate powerful prevention strategies and prevent negative health impacts. The air pollution module for predicting concentration has an innovative hybridization model that combines an improved cuckoo search (CS) and differential evolution (DE) algorithm with a stacked LSTM model to increase forecasting accuracy of six major environmental pollution levels. This model predicts the AAQ level and is effective and robust for warning one day before the pollutant event occurs based on the risk level of an ambient air pollutant from the RN. [ABSTRACT FROM AUTHOR]

Chemical transport models (CTM) have wide and profound applications in air quality simulations and managements. However, its applications are often constrained by high computational burdens. In this study, we developed a neural network based CTM model (FastCTM) to efficiently simulate ten air pollutant composition variables, including major PM2.5 species of SO42-, NO3-, NH4+, organic matters and other inorganic components, coarse part of PM10, SO2, NO2, CO and O3. The FastCTM has a principle-informed structure by explicitly encoding atmospheric physical and chemical processes in a basic simulator. Specifically, in the simulator, five neural network modules are proposed to respectively represent five major atmospheric processes of primary emissions, transport, diffusion, chemical reactions and depositions. Given 1-hour initial condition data, the FastCTM is able to simulate future 24-hour concentrations of the ten air pollutants with corresponding meteorology fields and emissions as input. The FastCTM is trained with operational forecast data from a numerical CTM model named Community Multiscale Air Quality (CMAQ) in 2018–2022. The well-trained FastCTM is evaluated comparing to the long-term CMAQ forecast in an independent year 2023, and achieves high agreements with mean RMSE values of 9.1, 11.9, 4.4, 4.0, 48.9 and 10.9 μg/m3 and R2 values of 0.8, 0.81, 0.8, 0.83, 0.9 and 0.7 for PM2.5, PM10, SO2, NO2, CO, and O3. Besides, assessed against hourly site observations of six criteria pollutants, the RMSE values of FastCTM have small relative differences of 4.3 %, 4.2 %, -2.8 %, -1.7 %, -0.3 % and -3.2 % compared to that of CMAQ. The FastCTM model also exhibited reasonable responses of air quality to meteorological variables of air temperature, wind speed and planetary boundary layer height, as well as to input pollutant emissions. Furthermore, due to the principles-oriented structure, internal process analysis could be performed by FastCTM to quantify the specific contribution from each of the five processes for hourly air pollutant concentration changes. In a nutshell, FastCTM has multi-functional advantages in air pollutant concentration simulations, sensitivity analysis and internal process analysis with high computation efficiencies on GPU and accuracy. [ABSTRACT FROM AUTHOR]

Direct evidence on the impact of air pollution on preterm birth (PTB) among advanced maternal age (AMA) primiparas is limited. This study examined the association between air pollution and PTB among 6,295 AMA primiparas in rural Henan Province. Logistic regression and distributed lag nonlinear models showed that prenatal exposure to carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and fine particulate matter (PM2.5) was associated with an increased PTB risk among AMA primiparas, while ozone (O3) exposure was inversely related to PTB risk (all P < 0.05). Moreover, the vulnerable periods were identified as gestational weeks 1–6 and 22–28. The education level, smoking status, and season of conception significantly modified the association between PTB and air pollution (P interaction < 0.10) among AMA primiparas. These findings emphasize the impact of air pollution on PTB risk in AMA primiparas, highlighting the need for targeted interventions in vulnerable populations. [ABSTRACT FROM AUTHOR]

Background: Air pollution is considered one of the risk factors for stroke prevalence in the long term and incidence in the short term. Tabriz is one of the most important industrial cities in Iran. Hence, air pollution has always been one of the main concerns in environmental health in the region. Method: The patient data were retrieved from electronic health records of the primary tertiary hospital of the city (Imam Reza Hospital). Air pollution data was obtained from the Environmental Protection Agency and is generated by 8 sensor stations spread across the city. Average daily values were calculated for CO, NO, NO, NOx, O3, SO2, PM2.5, and PM10 from hourly measurement data. Autoregressive integrated moving average (ARIMA-X) model with 3 lag days was developed to assess the correlation. Results: Air pollutants and hospital admission data were collected for 1821 day and includes 4865 stroke cases. our analysis showed no statistically significant association between the daily concentrations of CO (p = 0.41), NOx (p = 0.96), O3 (p = 0.65), SO2 (p = 0.91), PM2.5 (p = 0.44), and PM10 (p = 0.36). Only the binary COVID variable which was used to distinguish between COVID-19 era and other days, was significant (p value = 0.042). The goodness of fit measures, Root Mean Squared Error (RMSE), and Median Absolute Error (MAE) were 1.81 and 1.19, respectively. Conclusion: In contrast to previous reports on the subject, we did not find any pollutant significantly associated with an increased number of stroke patients. [ABSTRACT FROM AUTHOR]

Transport of exogenous anthropogenic mercury (Hg) is an important source of Hg pollution in the Tibetan Plateau (TP) and its downstream water ecosystems, but the origins and contributions of Hg sources remain uncertain. Here, we investigate the concentrations and isotopic compositions of gaseous elemental mercury (GEM) at four rural sites in the TP and three urban sites surrounding the TP to quantify the sources of GEM in the TP. GEM concentrations in the surrounding cities (site‐specific means: 2.36–9.12 ng m−3) were highly elevated mainly due to strong local anthropogenic emissions as indicated by their negative δ202Hg and near zero Δ199Hg and Δ200Hg signatures. GEM isotopes indicate that GEM pollution in the TP, typically observed during the summer monsoon and the pre‐monsoon, were mainly caused by trans‐boundary transport of anthropogenic Hg from surroundings. Using an Hg isotope mixing model, we estimate that exogenous anthropogenic emissions on average contributed 26 ± 5% (1sd) to the GEM in the TP. Further analysis of the transport of anthropogenic Hg emissions based on the backward trajectory and gridded anthropogenic Hg emissions suggests that 16 ± 9% and 6 ± 13% of the GEM in the TP were derived from anthropogenic sources in South Asia and China, respectively. Our study suggests that anthropogenic Hg emissions in South Asia could be effectively transported to the TP across the Himalayan range. Future studies are needed to better assess the role of rapidly increasing anthropogenic Hg emissions in South Asia on the regional to global scale atmospheric Hg cycling. Plain Language Summary: Long‐range transport of air pollutants to the Tibetan Plateau (TP) are of particular concern for ecological and human health. Analysis of atmospheric gaseous elemental mercury (GEM) isotopes at multiple sites in the rural TP and surrounding urban sites revealed that transport of exogenous anthropogenic mercury (Hg) to the TP represents an important source of atmospheric GEM in the TP. Anthropogenic Hg emissions from South Asia could be effectively transported to the TP across the Himalayan range and subsequently represented the largest anthropogenic sources of GEM in the TP. This work highlights the need for assessing the role of rapidly increasing anthropogenic mercury emissions in South Asia on the atmospheric mercury cycle at regional and global scales. Key Points: Exogenous anthropogenic emissions on average contribute 26% of the atmospheric gaseous elemental mercury in the Tibetan Plateau (TP)Transport of anthropogenic mercury emissions to the TP can be intensified by Indian and East Asian summer monsoonAnthropogenic mercury emissions in South Asia can be effectively transported to the TP across the Himalayan range [ABSTRACT FROM AUTHOR]

Background: Substantial inequalities in the overall prevalence and patterns of multimorbidity have been widely reported, but the causal mechanisms are complex and not well understood. This study aimed to identify common patterns of multimorbidity in Serbia and assess their relationship with air pollutant concentrations and water quality indicators. Methods: This ecological study was conducted on a nationally representative sample of the Serbian population. Data were obtained from the European Health Interview (EHIS) Survey, a periodic study designed to assess population health using widely recognized standardized instruments. The study included 13,069 participants aged 15 and older, randomly selected through a multistage stratified sampling design. Multimorbidity was defined as having two or more self-reported diagnoses of chronic non-communicable diseases. Latent class analysis (LCA) was performed to identify clusters of multimorbidity. Concentrations of particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3), as well as water quality indicators, were obtained from the Serbian Environmental Protection Agency. Results: The overall prevalence of multimorbidity was 33.4% [32.6%—34.2%]. Six latent classes of multimorbidity were identified: Healthy, Multicondition, Cardiovascular, Metabolic syndrome, Respiratory, and Musculoskeletal. Annual increases in PM10 and SO2 concentrations, as well as daily increases in O3 concentrations, significantly raised the odds of having multimorbidity (OR = 1.02, 95% CI 1.02–1.03; OR = 1.01, 95% CI 1.00–1.02 and OR = 1.03, 95% CI 1.02–1.03, respectively). A pattern of increased risk was observed with rising levels of water contamination. Exposure to physico-chemical, microbiological and combined contamination was associated with a 3.92%, 5.17% and 5.54% higher probability, respectively, of having multiple chronic conditions. There was strong evidence that air pollutants, as well as chemical and microbial water contamination, were significantly associated with higher odds of the most common clusters of multimorbidity identified by LCA. Conclusion: There is compelling evidence of an association between multimorbidity and environmental pollution, suggesting that exposure to air pollutants and water contaminants may contribute to disease accumulation and help explain geographically and socioeconomically patterned inequalities. These findings underscore the need for extensive studies that simultaneously measure both multimorbidity and pollution to explore their complex interrelationships. [ABSTRACT FROM AUTHOR]

Abstract\nNOVELTY STATEMENTUrbanization and industrialization are exponentially deteriorating air quality, ecosystems, and human health. Phytoremediation is cost cost-effective, sustainable, and nature-based solution against air pollution. This study is designed to evaluate four species, Chlorophytum comosum, Rhapis excelsa, Spathiphyllum wallisii, and Ficus benjamina for their phytoremediation potential. The experimental setup consisted of a sealed chamber to place potted plants and equipment, it was also connected to the vehicular exhaust pipe. The Air Pollution Tolerance Index was highest for F. benjamina (12.19) and lowest for Rhapis excels (8.58). C. comosum has the highest VOC removal efficiency (90%, 0.172 ppm h−1). NOx remediation was highest by F. benjamina with 0.057 ppm h−1 (77%) removal efficiency. SOx and CO were remediated more efficiently by C. comosum, as 89%, (0.18 ppm h−1) and 80% (0.23 ppm h−1), respectively. R. excelsa reduced a higher concentration of NH3 (77%, 0.06 ppm h−1) compared to other species. R. excelsa and S. wallisii may serve as bio-indicator species. These findings provide a sustainable, natural, economical, and eco-friendly way to mitigate air pollution. F. benjamina and C. comosum are suitable species for urban landscapes, green spaces, urban plantations, and green walls to curb air pollutants due to traffic and industries.Based on the air pollution tolerance index, this study concludes that Rhapis excels (APTI = 8.58) can be used as a bio-indicator species for pollution monitoring and evaluation. Whereas, Ficus benjamina (APTI = 12.2) and Chlorophytum comosum (APTI = 10.3) are the best options for urban landscapes, green belts, or green walls for air pollution combat and mitigation. [ABSTRACT FROM AUTHOR]

The article discusses the health impacts of climate change, focusing on extreme precipitation events, wildfires, and heat waves. It highlights the challenges faced by health services researchers in understanding and measuring the effects of these climate-related phenomena on human health. The commentary emphasizes the need for precise measurement of exposures at individual and local levels, as well as the development of comprehensive definitions of health impacts. The growing body of research in this area aims to address the increasing intensity and frequency of extreme weather events due to climate change, urging investigators and policymakers to be aware of these environmental determinants of health for future research and strategies. [Extracted from the article]

Background: Growing epidemiological evidence suggests an association between exposure to air pollutants and breast cancer. Yet, the underlying mechanisms remain poorly understood. This study explored the mediating role of thirteen metabolic health biomarkers in the relationship between exposure to three air pollutants, i.e. nitrogen dioxide (NO2), polychlorinated biphenyls 153 (PCB153), and benzo[a]pyrene (BaP), and breast cancer risk. Methods: We used data from a nested case–control study within the French national prospective E3N-Generations cohort, involving 523 breast cancer cases and 523 matched controls. The four-way decomposition mediation of total effects for thirteen biomarkers was applied to estimate interaction and mediation effects (controlled direct, reference interaction, mediated interaction, and pure indirect effects). Results: The analyses indicated a significant increase in breast cancer risk associated with BaP exposure (odds ratio (OR)Q4 vs Q1 = 2.32, 95% confidence intervals (CI): 1.00–5.37). PCB153 exposure showed a positive association only in the third quartile (ORQ3 vs Q1 = 2.25, CI 1.13–4.57), but it appeared to be non-significant in the highest quartile (ORQ4 vs Q1 = 2.07, CI 0.93–4.61). No association was observed between NO2 exposure and breast cancer risk. Estradiol was associated with an increased risk of breast cancer (OR per one standard deviation (SD) increment = 1.22, CI 1.05–1.42), while thyroid-stimulating hormone was inversely related to breast cancer risk (OR per 1SD increase = 0.87, CI 0.75–1.00). We observed a suggestive mediated effect of the association between the three pollutants and breast cancer risk, through albumin, high-density lipoproteins cholesterol, low-density lipoprotein cholesterol, parathormone, and estradiol. Conclusion: Although limited by a lack of statistical power, this study provides relevant insights into the potential mediating role of certain biomarkers in the association between air pollutant exposure and breast cancer risk, highlighting the need for further in-depth studies in large populations. [ABSTRACT FROM AUTHOR]

Background: During the COVID-19 pandemic, non-therapeutic interventions (NPIs), such as traffic restrictions, work stoppages, and school suspensions, have led to a sharp decline in the concentration of air pollutants in the epidemic sites. However, few studies focused on the impact of air pollutant changes on the risk of nonaccidental death. Method: We selected Yancheng City, China, as the study site and applied a Generalized Additive Model (GAM) based on the quasi-Poisson distribution to evaluate the impact of atmospheric pollutants exposure on the nonaccidental death of local residents. The time span of this study was set from January 1, 2013, to December 21, 2022, that is, before and after the outbreak of COVID-19. Results: The concentration of some air pollutants has greatly varied after the outbreak of COVID-19, with a significant decline for PM2.5 (− 43.4%), PM10 (− 38.5%), SO2 (− 62.9%), and NO2 (− 22.6%), but an increase for O3 (+ 4.3%). Comparative analysis showed that PM2.5 contributed to an increased risk of nonaccidental death after the outbreak of COVID-19. With an increase in PM2.5 by 10 µg/m³, the excess relative risks (ER) of nonaccidental death of residents increased by 1.01% (95%CI: 0.19%,1.84%). The stratified analysis revealed that air pollutants impacted nonaccidental deaths in both men and women before the outbreak of COVID-19. After the outbreak of COVID-19, PM10 had a significant effect on male nonaccidental deaths. The concentrations of PM2.5, PM10, and SO2 increased by 10 µg/m³, the ER of PM2.5, PM10, and SO2 on female nonaccidental death increased by 1.52% (0.38%,2.67%), 0.58% (0.02%,1.13%), and 15.09% (5.73%,25.28%), respectively. Before the outbreak of COVID-19, five air pollutants had an impact on the death of residents from cardiovascular disease (CVD). After the outbreak of COVID-19, only PM10 significantly affected the death risk of CVD. In addition, we discovered that PM2.5, PM10, and SO2 significantly impacted the risk of death due to respiratory diseases before and after the outbreak of COVID-19. Conclusions: Air pollutants have different effects on nonaccidental deaths before and after the COVID-19 outbreak. A decrease in air pollutant concentration due to the NPIs for COVID-19 had a significant effect on the reduction of the risk of nonaccidental death. [ABSTRACT FROM AUTHOR]