Search

Fine particulate matter (PM2.5) exposure can induce immune system pathology via epigenetic modification, affecting pregnancy outcomes. Our study investigated the association between PM2.5 exposure and immune response, as well as epigenetic changes using high-dimensional epigenetic landscape profiling using cytometry by time-of-flight (EpiTOF) at the single cell. We found statistically significant associations between PM2.5 exposure and levels of certain cytokines [interleukin-1RA (IL-1RA), IL-8/CXCL8, IL-18, and IL-27)] and histone posttranslational modifications (HPTMs) in immune cells (HPTMs: H3K9ac, H3K23ac, H3K27ac, H2BK120ub, H4K20me1/3, and H3K9me1/2) among pregnant and nonpregnant women. The cord blood of neonates with high maternal PM2.5 exposure showed lower IL-27 than those with low exposure. Furthermore, PM2.5 exposure affects the co-modification profiles of cytokines between pregnant women and their neonates, along with HPTMs in each immune cell type between pregnant and nonpregnant women. These modifications in specific histones and cytokines could indicate the toxicological mechanism of PM2.5 exposure in inflammation, inflammasome pathway, and pregnancy complications.

California's diverse geography and meteorological conditions necessitate models capturing fine-grained patterns of air pollution distribution. This study presents the development of high-resolution (100 m) daily land use regression (LUR) models spanning 1989-2021 for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California. These machine learning LUR algorithms integrated comprehensive data sources, including traffic, land use, land cover, meteorological conditions, vegetation dynamics, and satellite data. The modeling process incorporated historical air quality observations utilizing continuous regulatory, fixed site saturation, and Google Streetcar mobile monitoring data. The model performance (adjusted R2) for NO2, PM2.5, and O3 was 84 %, 65 %, and 92 %, respectively. Over the years, NO2 concentrations showed a consistent decline, attributed to regulatory efforts and reduced human activities on weekends. Traffic density and weather conditions significantly influenced NO2 levels. PM2.5 concentrations also decreased over time, influenced by aerosol optical depth (AOD), traffic density, weather, and land use patterns, such as developed open spaces and vegetation. Industrial activities and residential areas contributed to higher PM2.5 concentrations. O3 concentrations exhibited no significant annual trend, with higher levels observed on weekends and lower levels associated with traffic density due to the scavenger effect. Weather conditions and land use, such as commercial areas and water bodies, influenced O3 concentrations. To extend the prediction of daily NO2, PM2.5, and O3 to 1989, models were developed for predictors such as daily road traffic, normalized difference vegetation index (NDVI), Ozone Monitoring Instrument (OMI)-NO2, monthly AOD, and OMI-O3. These models enabled effective estimation for any period with known daily weather conditions. Longitudinal analysis revealed a consistent NO2 decline, regulatory-driven PM2.5 decreases countered by wildfire impacts, and spatially variable O3 concentrations with no long-term trend. This study enhances understanding of air pollution trends, aiding in identifying lifetime exposure for statewide populations and supporting informed policy decisions and environmental justice advocacy.

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.

BackgroundA growing literature has reported associations between traffic-related air pollution and breast cancer, however there are fewer investigations into specific ambient agents and any putative risk of breast cancer development, particularly studies occurring in populations residing in higher pollution areas such as Los Angeles.ObjectivesTo estimate breast cancer risks related to ambient air toxics exposure at residential addresses.MethodsWe examined the relationships between ambient air toxics and breast cancer risk in the Multiethnic Cohort among 48,665 California female participants followed for cancer from 2003 through 2013. We obtained exposure data on chemicals acting as endocrine disruptors or mammary gland carcinogens from the National-Scale Air Toxics Assessment. Cox proportional hazards models were used to estimate breast cancer risk per one interquartile range (IQR) increase in air toxics exposure lagged by 5-years. Stratified analyses were conducted by race, ethnicity, and hormone receptor types.ResultsAmong all women, increased risks of invasive breast cancer were observed with toxicants related to industries [1,1,2,2-tetrachloroethane (hazard ratio [HR] = 4.22, 95% confidence interval [95% CI] 3.18-5.60), ethylene dichloride (HR = 2.81, 95% CI 2.20-3.59), and vinyl chloride (HR = 2.27, 95% CI 1.81, 2.85); these 3 agents were correlated (r2 = 0.45-0.77)]. Agents related to gasoline production or combustion were related to increased breast cancer risk [benzene (HR = 1.32, 95% CI 1.24, 1.41), ethylbenzene (HR = 1.20, 95% CI 1.13-1.28), toluene (HR = 1.29, 95% CI 1.20-1.38), naphthalene (HR = 1.11, 95% CI 1.02-2.22), acrolein (HR = 2.26, 95% CI 1.92, 2.65)]. Higher hazard ratios were observed in African Americans and Whites compared to other racial and ethnic groups (p-heterogeneity

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

This study aimed to biomonitor air pollution by measuring heavy metals (HMs) accumulation levels in the leaves of common urban trees, Cupressus arizonica Greene, Melia azedarach L., Morus alba L. and Buxus colchica in different regions of Isfahan. Their association with the levels of PM2.5 and PM2.5-bond HMs was also investigated. PM2.5 were collected on a glass-fiber filter and measured by gravimetric method. The HM contents of the PM2.5 and tree leaves were extracted and analyzed by ICP-OES. The average PM2.5 concentrations in ambient air of all areas varied from 52.34 to 103.96 μg/m3. The mean HMs levels in the leaves were in the following orderZn(31.2) > Cu(11.04) > Pb(4.38) > Ni(4.01) > Cr(3.03) > Co(0.61) > Cd(0.04) (μg/g). The highest level of HMs was detected in the leaves of Morus alba L., followed by Buxus colchica, Melia azedarach L. and Cupressus arizonica Greene. There was a significant correlation between the amounts of Pb and Cu in tree leaves and those in ambient PM2.5 (p value ≤ 0.05). In conclusion, tree leaves can be used as a suitable bioindicator in the evaluation of air pollution. Morus alba L. compared to the other species can be confidently used for green space development. NOVELTY STATEMENT: Isfahan is one of the most populated, industrialized cities in Iran experiencing serious air pollution. The currently operated air pollution monitoring stations in the city do not measure atmospheric heavy metals, a hazardous component of PM2.5. To assess air pollution and level of exposure, biomonitor species such as tree leaves are the best tool due to their availability and low cost. Tree leaves can absorb and retain air pollutants. In this study, we found a good correlation between the concentration of heavy metals especially Pb and Cu in the leaves of commonly used tree species and their concentration in the airborne PM2.5. Also, the results revealed that among the tree species, Morus alba L. retains more heavy metals followed by Buxus colchica, Melia azedarach L., and Cupressus arizonica Greene, which can be used for green space development. [ABSTRACT FROM AUTHOR]

Previous observational studies have reported inconsistent associations between air pollution and autoimmune eye diseases (AEDs). The primary objective of this Mendelian randomization (MR) study was to investigate the causal link of air pollution with AEDs risk. The instrumental variables were selected based on genome-wide association study data. Univariable and multivariable MR analyses were conducted to disentangle the causality of air pollutants with AEDs. The estimates of univariable MR analysis revealed a suggestively causal link between NO2 or NOx exposure and diabetic retinopathy (OR=1.29, 95% CI=1.05-1.58, P=0.015; OR=1.33, 95% CI=1.05-1.69, P=0.019, respectively). A suggestive association was observed between PM2.5 exposure and age-related macular degeneration (OR=1.46, 95% CI=1.09-1.97, P=0.013). In addition, multivariable MR indicated that the observed association was remained consistent and robust. Rigorous sensitivity analyses confirmed the robustness and consistency of these findings. Our study firstly provided the genetic evidence linking air pollution, specially NO2, NOx and PM2.5, to AEDs susceptibility. [ABSTRACT FROM AUTHOR]

Achieving synergistic effects in pollution reduction and carbon mitigation is a major national strategy for China. Given the common origins and processes of air pollutants and greenhouse gases, this study constructs a theoretical framework for the study of the synergistic effects of air pollution and carbon emissions. Based on the coupling coordination degree model and the geographically and temporally weighted regression model, it identifies significant factors influencing the synergistic effects of air pollution and carbon emissions and their varying mechanisms of action. Results are as follows: 1) The spatial and temporal trends of PM2.5 pollution and carbon emissions in the Wuhan metropolitan area exhibit homogeneity. The coupling coordination degree between air pollution and carbon emissions shows an initial increase followed by a decrease over time and a spatial pattern of "local clustering of areas with medium–high-level coupling coordination". 2) Twelve factors significantly impact the synergistic effects of air pollution and carbon emissions at the county level in the Wuhan metropolitan area: number of inversion days, precipitation, temperature, vegetation coverage, number of green patents, total population, regional GDP, per capita regional GDP, proportion of secondary industry, total nighttime light, energy consumption efficiency and built-up area. 3) The impact intensity of these factors on the synergistic effects of air pollution and carbon emissions varies not only over time but also across different regions within the same year. Regions with strong impact forces shift over time. This manuscript provides a solid foundation for theoretical research on and practical strategies for advancing differentiated pollution reduction and carbon mitigation coordination. [ABSTRACT FROM AUTHOR]

In the United States (US), neonatal intensive care units (NICUs) monitor and treat newborns for a variety of adverse health concerns including preterm status, respiratory distress and restricted growth. As such, NICU admission is an integrated measure of neonatal risk. We linked 2018 US national birth registry NICU admission data among singleton births with satellite and modelled air pollution levels for the month prior to birth to examine whether late-pregnancy exposure to ambient air pollutants is associated with adverse neonatal health outcomes. Regardless of season, higher ambient levels of nitrogen dioxide (NO2) and fine particulate matter < 2.5 microns (PM2.5) increased the likelihood of NICU admission 30–35% for NO2 and 11–22% for PM2.5 even after adjustment for parental characteristics. Results for ozone exposure were inconsistent with largely null or reduced risk except for summer months. Despite the relatively low-moderate US exposure levels, traffic-related pollutants near the end of pregnancy appear to increase overall adverse health risks for newborns, underscoring the need to reduce prenatal exposure to ambient pollutants. [ABSTRACT FROM AUTHOR]

As a significant global concern, air pollution triggers enormous challenges in public health and ecological sustainability, necessitating the development of precise algorithms to forecast and mitigate its impacts, which has led to the development of many machine learning (ML)-based models for predicting air quality. Meanwhile, overfitting is a prevalent issue with ML algorithms that decreases their efficacy and generalizability. The present investigation, using an extensive collection of data from 16 sensors in Tehran, Iran, from 2013 to 2023, focuses on applying the Least Absolute Shrinkage and Selection Operator (Lasso) regularisation technique to enhance the forecasting precision of ambient air pollutants concentration models, including particulate matter (PM2.5 and PM10), CO, NO2, SO2, and O3 while decreasing overfitting. The outputs were compared using the R-squared (R2), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and normalised mean square error (NMSE) indices. Despite the preliminary findings revealing that Lasso dramatically enhances model reliability by decreasing overfitting and determining key attributes, the model's performance in predicting gaseous pollutants against PM remained unsatisfactory (R2PM2.5 = 0.80, R2PM10 = 0.75, R2CO = 0.45, R2NO2 = 0.55, R2SO2 = 0.65, and R2O3 = 0.35). The minimal degree of missing data presumably explained the strong performance of the PM model, while the high dynamism of gases and their chemical interactions, in conjunction with the inherent characteristics of the model, were the primary factors contributing to the poor performance of the model. Simultaneously, the successful implementation of the Lasso regularisation approach in mitigating overfitting and selecting more important features makes it highly suggested for application in air quality forecasting models. [ABSTRACT FROM AUTHOR]

The growing fetus is very sensitive to environmental conditions. There is limited and conflicting evidence about the short-term effects of exposure to air pollutants on the pregnancy outcome. In this time-stratified case-crossover study, the effect of several air pollutants (i.e. O3, CO, NO2, SO2, and PM2.5) on the occurrence of stillbirth was evaluated in Tehran (the capital of Iran) between December 2018 and March 2023. Using a quasi-Poisson regression model and distributed lag nonlinear models (DLNM), we estimated the effect of exposure to air pollutants measured as lags (0 to 7 days) and cumulative average days (0–2, 0–6, and 0–14-day lag) before delivery on stillbirth. The association was adjusted for potential confounding factors including meteorological factors. During the study period in Tehran, 5311 stillbirths were reported. In single-pollutant models, during the entire year, SO2 (lag 1 day) and NO2 (lag 2 days) were found to have a direct and significant relationship with stillbirth. In the warm seasons, we found direct and inverse relationships between NO2 (lag 2 days) and PM2.5 (lag 4 days), respectively. In cold seasons, PM 2.5 (lag 1 day) and cumulative lag (0–2 days), SO2 (lag 1 day), and cumulative lag (0–2 and 0–6 days) were found to have direct and significant relationships with stillbirth. In two-pollutant models, SO2 & CO, and SO2 & O3, direct and significant associations were observed between SO2 exposures and stillbirth for the entire year. A similar pattern was observed for PM2.5 in combinations with NO2, and O3 and for NO2 in the two-pollutant model (O3 & NO2). However, in warm seasons, inverse associations were observed between PM2.5 and stillbirth in combinations with O3, NO2, and SO2, while NO2 showed a direct association in combinations with PM2.5, CO, and O3. In the cold seasons, direct and significant associations were observed between SO2 and stillbirth in model combinations with CO, O3, and NO2. This relationship was observed for PM2.5 in combination with CO, and NO2. Also, for CO in the two-pollutant model of CO & NO2. As a result, this study showed evidence of a relationship between short-term exposure to ambient air pollution before birth, especially SO2, NO2, PM2.5, and CO with increased risk of stillbirth. [ABSTRACT FROM AUTHOR]

Exposure to air pollutants is linked to an increased risk of obesity, and socioeconomic status (SES) could modulate this risk. We employed the "Jinchang Cohort" as a platform to investigate the influence of SES (education level, monthly income per household, and marital status) on the obesity risk associated with PM2.5 and its constituents. Study has demonstrated that air pollutant exposure enhances the likelihood of overweight/obesity, with a risk ratio (HR) of 1.229 for each quartile increase in PM2.5 concentration (95% CI: 1.137–1.328, P < 0.05). The risk of overweight/obesity rises with pollutant levels across various SES strata, with the effect being most marked among those with higher SES. For instance, the HRs and 95% CIs for overweight/obesity with each quartile increase in SO42− concentration were 1.338 (1.207–1.484), 1.311 (1.121–1.533), and 2.224 (1.823, 2.714) at low, medium, and high SES levels, respectively (all P < 0.05). An interaction between air pollutants and SES was observed in the context of obesity risk, with RERIs of 0.723 (0.473–0.973) and 0.562 (0.268–0.856) for medium-high SES levels and high NO3− exposure, respectively (both P < 0.05). These findings have practical implications. Public health campaigns could be launched to raise awareness among higher SES individuals about the obesity risk associated with air pollutants and encourage them to adopt preventive measures such as using air purifiers and increasing physical activity. [ABSTRACT FROM AUTHOR]

Air pollution is strongly associated with autoimmune diseases (ADs), however, the genetic causality between them remains poorly understood. Therefore, the aim of this study is to determine the relationship between common air pollutants and ADs through Mendelian randomization (MR) analysis. We conducted a MR study using aggregated data from publicly available genome-wide association studies (GWAS). Single nucleotide polymorphisms (SNPs) associated with 5 common air pollutants are used as instrumental variables. Random-effects inverse variance weighted (IVW) is used as the primary method to assess causal relationships, with results reported in terms of odds ratios (OR). In addition, we used a two-step MR to assess the mediating role of common risk factors for ADs in the effects of air pollution on ADs. Our analysis revealed causal associations between selected air pollutants and specific ADs. Exposure to nitrogen oxides was positively associated with the risk of rheumatoid arthritis (RA) (OR = 1.47,95% CI 1.01–2.14, P = 0.043), Sjogren's syndrome (SS) (OR = 2.29,95% CI 1.08–4.89, P = 0.032), and systemic lupus erythematosus (OR = 7.26,95% CI 2.25–23.40, P = 9.10E−04). Genetically predicted PM2.5 and PM10 were risk factors for ulcerative colitis (OR = 1.68,95% CI 1.05–2.68, P = 0.032) and psoriasis (OR = 1.34,95% CI 1.02–1.76, P = 0.037), respectively. Our results also suggest a negative causal relationship between PM2.5–10 and SS (OR = 0.29, 95% CI 0.10–0.90, P = 0.032). In risk factor-related mediation analyses, BMI and smoking mediated 6% (95% CI 1–10%) and 9% (95% CI 2–17%) of the effect of nitrogen oxides on RA, respectively. This study provides evidence of a causal relationship between air pollutants and specific ADs risks, suggesting that improving air pollution may be important in preventing ADs. [ABSTRACT FROM AUTHOR]