Objective: To investigate the effects of pre-pregnancy and prenatal exposure to six major air pollutants (CO, NO2, O3, PM10, PM2.5, SO2) on birth weight in newborns and identify critical exposure windows. Methods: This retrospective cohort study analyzed data from 1 561 mother-infant pairs who delivered at Taiyuan Central Hospital between January 2020 and December 2023, combined with contemporaneous air pollution monitoring records. This study used the geographic information system (GIS) technology to evaluate the individual air pollutants exposure level of pregnant women, and calculated the average pollutant concentrations for four stages: preconception (the 12 weeks before conception), first trimester (weeks 1-13), second trimester (weeks 14-27) and third trimester (weeks 28-37). Multiple linear regression models were used to assess the associations between air pollutant exposure and birth weight at different pregnancy stages. The distributed lag nonlinear model (DLNM) was further constructed to characterize the nonlinear exposure-lag-response relationships, identify sensitive windows, and examine sex differences. Results: Preliminary multiple linear regression showed that third-trimester exposures to PM2.5(β^=-43.00, 95%CI:-79.40- -6.68), PM10(β^=-35.00, 95%CI:-66.00- -4.07), NO2(β^=-35.50, 95%CI:-66.90- -4.08) and SO2(β^=-28.80, 95%CI:-52.70- -4.97) were negatively associated with birth weight (all P<0.05), but these associations disappeared after full adjustment for covariates. DLNM analysis revealed exposure-response relationships for CO, O3, PM10, and SO2 on birth weight, with distinct critical exposure windows: CO (weeks 2-13, 14-20), O3 (weeks 4-13, 14-24), PM10 (weeks 22-27, 28-37), and SO2 (weeks 11-13, 14-27 and 28-37). Among these, the largest effect were observed for CO at gestational week 12, O3 at week 13, and for both PM10 and SO2 at week 37. The effect estimates and their 95% confidence intervals were -37.12(-62.78- -11.45), -3.61(-6.45- -0.77), -5.01(-8.98- -1.04) and -8.31(-12.07- -4.56), all P<0.05. Gender differences in pollutant effects were observed: male newborns were more sensitive to SO2 across multiple stages, to CO in mid-pregnancy, and to PM10 in late-pregnancy; female newborns were more susceptible to PM10 in the preconception and early-to-mid-pregnancy periods, to O3 in early-to-mid pregnancy, and to SO2 in late pregnancy. Conclusion: Exposure to air pollutants during pregnancy is associated with reduced birth weight in newborns. Different pollutants have specific exposure windows, and gender differences exist. This finding provides important scientific evidence for preventing adverse pregnancy outcomes and developing intervention strategies to improve neonatal health.

Cancer therapies have well-documented adverse effects on cardiovascular and respiratory health which could increase cancer survivors' susceptibility to poor air quality. We describe the risk of cardiovascular and respiratory healthcare visits following acute air pollution exposures among adolescent and young adult (AYA) cancer survivors. We identified all Utah AYAs diagnosed at 15-39 years of age with thyroid, melanoma, lymphoma, breast, or testicular cancer from 1998-2016 (N=8,016). AYAs were linked by residential location to daily particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) for 2000-2016. Case-crossover models in a distributed lag non-linear model framework estimated odds ratios (ORs) and 95% confidence intervals (95% CIs) for cardiovascular and respiratory healthcare visits with exposure over the 6 days preceding an event, adjusting for temperature and relative humidity. A total of 3,143 AYAs (39%) experienced ≥1 respiratory/cardiovascular event. O3 was associated with cardiovascular events (ORlag4=1.08, 95% CI: 1.02-1.14; ORlag5=1.05, 95% CI:1.01-1.09) and NO2 was associated with respiratory events (ORlag1=1.05, 95% CI: 1.01-1.10). Breast cancer survivors showed elevated risk for cardiovascular and respiratory visits; melanoma, lymphoma, and testis survivors displayed increased risk for cardiovascular visits. O3 was linked with elevated risk of cardiovascular events while respiratory events were associated with NO2. Associations between air pollutants and healthcare visits varied by primary diagnosis site, indicating that susceptibility to air pollution could differ due to cancer-specific treatment factors. AYAs face increased risk for cardiovascular and respiratory events with exposure to ambient air pollution and may benefit from interventions to reduce exposures.

Environmental exposures are increasingly recognized as important drivers of ocular surface inflammation, yet their combined contribution to the onset, exacerbation, and clinical burden of allergic conjunctivitis (AC) has not been comprehensively synthesized. This systematic review evaluated the evidence linking air pollutants, aeroallergens, and indoor or occupational exposures with allergic conjunctivitis. The review was conducted according to PRISMA 2020 and AMSTAR-2 guidelines and registered in PROSPERO (CRD420251162399). PubMed, Web of Science, and Scopus were searched from inception to 18 September 2025. Two independent reviewers screened studies, extracted data, and assessed methodological quality using the MINORS tool. Owing to substantial heterogeneity, findings were synthesized narratively. Twenty-nine studies were included, encompassing more than three million outpatient visits. Consistent associations were observed between particulate matter, nitrogen oxides, sulfur dioxide, carbon monoxide, and ozone with increased AC incidence and symptom severity, with variations by age, sex, and season. Pollen and air pollutants frequently acted synergistically. Indoor exposures were associated with increased risk in children, while occupational settings demonstrated exposure–response relationships. Experimental studies identified mechanisms involving epithelial barrier disruption, NF-κB activation, and thymic stromal lymphopoietin signaling. Overall, environmental exposures substantially contribute to allergic conjunctivitis and may inform improved prevention and personalized clinical management.

Extensive evidence links air pollution exposure to cognitive decline; however, it remains unclear whether cognitive reserve and brain reserve modify this association. We examined the moderating roles of cognitive reserve contributors and brain reserve in the association between air pollution and cognitive function in dementia-free adults. Cross-sectional data were obtained from 650 participants who underwent 3T brain magnetic resonance imaging and completed the Montreal Cognitive Assessment (MoCA). Cognitive reserve contributors were assessed based on education, occupation, and social engagement. Brain reserve was quantified using the ventricle-to-brain ratio derived from brain scans. Five-year average concentrations of particulate matter with diameters ≤10 and≤2.5μm and nitrogen dioxide were estimated based on residential addresses. Partial least squares structural equation modeling was applied to construct latent variables representing the air pollution mixture and composite cognitive reserve (contributors). Analyses examined whether cognitive reserve contributors and brain reserve modified associations of air pollution with MoCA scores and suspected mild cognitive impairment. In individuals with an average level of cognitive reserve, a 1-standard deviation increase in air pollution mixture was associated with a 0.24-point decrease in MoCA scores (95% confidence interval [CI]: -0.31 to -0.16). This association was attenuated in individuals with higher cognitive reserve (β=-0.12; 95% CI: -0.25 to 0.02) and intensified in those with lower cognitive reserve (β=-0.36; 95% CI: -0.37 to -0.35). The moderating effect of brain reserve was not significant. Higher cognitive reserve may mitigate the effects of air pollution on cognitive function.

Prenatal exposure to air pollution and infant cognitive development using an eye-tracking visual paired-comparison task.

Obstructive Sleep Apnea (OSA) impairs children's quality of life (QoL), but the impact of air pollution and heat wave on pediatric OSA-related QoL remains unclear. This retrospective cross-sectional study investigated associations between short-term air pollutant (PM2.5, PM10, SO2, and NO2) exposure, heat wave, and QoL in children with OSA, including pollutant lag effects. A retrospective cross-sectional study was conducted at Henan Provincial Children's Hospital (2021-2023). Eligible participants were ≤ 14-year-old Henan residents with confirmed OSA (OAHI > 1) via polysomnography. QoL was assessed by the OSA-18 Scale. Air pollutant and heat wave data were from national monitoring networks. Generalized Linear Regression Models (GLMs) analyzed continuous QoL outcomes, with sensitivity analyses performed. 1943 children were included. The statistical analysis results showed that PM2.5 lag03-lag07; PM10 lag0-lag6, lag01-lag07; SO2 lag0-lag2, lag01-lag07; NO2 lag7, lag05-lag07 were statistically correlated with the impact on the QoL of pediatric patients. The maximum effect values OR were 1.042 (95% CI 1.006, 1.079), 1.030 (95% CI 1.016, 1.045), 1.774 (95% CI 1.279, 2.462), and 1.101 (95% CI 1.013, 1.197), respectively. Additionally, heat wave was associated with worse QoL (OR:1.537, 95% CI 1.021, 2.313). Sensitivity analyses confirmed result stability. Air pollutants (especially SO2 and NO2) and heat waves reduce QoL in children with OSA, highlighting the need for targeted environmental interventions.

Association of ambient air pollution with risk of anxiety disorders: an updated systematic review and meta-analysis.

Predicting Atherogenic index of plasma risk from long-term spatiotemporal air pollution exposure using an attention-based deep learning

Prenatal exposure to air pollutants and neonatal necrotizing enterocolitis: Identifying critical gestational windows in Southern China.

Association of ambient air pollutants with risk of lung cancer subtypes and survival after diagnosis.

Ambient air pollution and hospitalization of wide-spectrum digestive diseases: A nationwide case-crossover study in China.

To address the increasing concern regarding woodsmoke (WS) exposure and better understand its effects on human health, a WS generation system was built in the Air Pollution Exposure Laboratory to facilitate future controlled human exposure studies. Ground lodgepole pine was burned to generate WS, with PM2.5 concentrations of approximately 500 µg/m3 obtained. The WS produced by this system was characterized and directly compared with diesel exhaust (DE) generated and collected at the same facility. For gases, WS showed slight increases in CO and CO2 compared with filtered air (FA), whereas DE had significantly higher levels of NOx, CO, CO2, and total volatile organic compounds than FA. The non-refractory composition of WS aerosols was approximately 98% organics, 0.2% ammonium, 1.3% nitrate, and 0.2% sulfate. Among the organic species, the fraction of oxygenated species was much higher in WS aerosols than in DE aerosols. Moreover, WS aerosols had higher concentrations of Cd compared with DE aerosols. Greater oxidative potential was also observed for WS compared with DE, with dithiothreitol consumption rates of 0.0090 nmol/min/µg. This study established a controlled human exposure platform for WS and described the methods used for analyzing and comparing the concentrations, particulate morphologies, chemical compositions, and oxidative potentials of different lab-generated pollutants. The observed differences between WS and DE in oxidative potential and amounts of gases, organic species, and metals provide a foundation for investigating how specific air pollution components differentially impact human health.

Accurately assessing personal exposure to air pollution has long posed a challenge due to limitations in conventional monitoring approaches. Most studies still rely on sparse, stationary regulatory monitors, assigning identical exposure values to individuals regardless of their movements. This approach neglects the dynamic nature of human mobility patterns and activity locations, leading to inferential errors. This method developed an approach by integrating high-resolution global positioning system (GPS) trajectory data from 100 participants with air quality data from 213 PurpleAir low-cost stationary monitors across Eastern North Carolina. Using geostatistical modelling, which is an automated kriging (ordinary kriging) algorithm developed in Python, the method estimates individualised PM2.5 exposure every minute over a 3-day window (two weekdays and one weekend day), which encompasses 129,600-minute points. The study offers an innovative fusion of spatial and temporal data that bridges the gap between environmental sensing and actual human experience, and the result is a transformative methodology that significantly enhances the precision of personal air pollution exposure assessments from stationary air quality sensors.

Evidence suggests maternal exposure to ambient air pollution increases the risk of stillbirth, but few studies conducted in the United States have evaluated temporally varying exposures or susceptibility across gestational windows. Moreover, the generalizability of existing findings is often limited by restricted geographic coverage or reliance on selected study populations. Using Georgia vital records from 2005 to 2014, we conducted a matched case-control study including 8,384 stillbirths and 33,459 live birth controls matched on maternal county of residence and conception month. We used stratified Cox proportional hazards models with time-varying covariates to estimate hazard ratios (HRs) for ten air pollutants across five exposure windows (first month, weekly, and first, second, and third trimester). Our primary analysis included all stillbirths combined, with subgroup analyses separating second and third trimester losses. Stillbirths had a median gestational age of 27 weeks (IQR: 6.67) compared with 38 weeks for live births (IQR: 2.13). Particulate matter showed strong associations in the second trimester exposure window for all stillbirths (PM 10 : HR = 1.07; 95% CI: 1.04, 1.11; PM 2.5 : HR = 1.05; 95% CI: 1.01, 1.09). This pattern was consistent for NO 2 and NH 4 , which also exhibited positive associations across early and entire pregnancy exposure windows (first month, first trimester, weekly), with the strongest associations for the second trimester exposures. Associations were larger for second trimester stillbirths, whereas estimates for third trimester stillbirths were largely null or negative. In this population-based study in Georgia, time-varying ambient air pollution exposures during pregnancy were associated with increased risk of stillbirth, particularly for second trimester exposures and for stillbirths occurring earlier in pregnancy. These findings highlight the importance of considering gestational timing when evaluating environmental risk factors for stillbirth. This study is the first to evaluate maternal ambient air pollution exposure and stillbirth using time-varying exposures on vital records in the state of Georgia. By examining ten air pollutants across multiple gestational windows and subset analyses by timing of stillbirth, we identified second trimester susceptibility to NO 2 , PM 10 , PM 2.5 , and NH 4 . These findings highlight periods of vulnerability to ambient air pollution during pregnancy.

Cardiometabolic multimorbidity (CMM) has emerged as a major threat to health among older adults, yet the effects of air pollution and physical activity on CMM remain insufficiently investigated. This study aimed to examine the associations between air pollutant exposure, physical activity, and CMM risk, and to analyze the potential moderating and mediating roles of physical activity in the relationship between pollutants and CMM. This study utilized data from the China Health and Retirement Longitudinal Study (CHARLS) from 2015-2020, including 17,718 participants. We assessed exposure levels to PM2.5, PM10, NO2, SO2, CO, and O3, and categorized physical activity into four levels (Q1-Q4) based on metabolic equivalent quartiles. Cox regression models were employed to analyze the effects of pollutants and physical activity metabolic equivalents on CMM, with restricted cubic splines for dose-response analysis. Additionally, Baron & Kenny's method was applied to evaluate the mediating effect of physical activity. During the follow-up period, 741 participants developed CMM. After multivariate adjustment, all pollutants were significantly associated with increased CMM risk, with SO2 (HR=2.768, 95% CI: 2.526-3.033) showing the most significant impact. Higher levels of physical activity demonstrated pronounced protective effects. Dose-response analyses revealed non-linear relationships between pollutants and CMM (P for nonlinear < 0.001), while physical activity exhibited a protective non-linear relationship with CMM (P for nonlinear = 0.037). Mediation analysis identified significant partial mediating effects of physical activity in the relationships between pollutants (PM2.5, PM10, NO2, and O3) and CMM, with mediation proportions ranging from 5.71% to 19.88%, with NO2 showing the highest mediation proportion (19.88%). Long-term exposure to air pollutants is significantly associated with increased CMM risk, while higher levels of physical activity confer substantial protective effects. Pollutants may partially increase CMM risk indirectly by inhibiting physical activity.

Ambient air pollution is associated with heart failure (HF), but underlying biological mechanisms remain unclear. We aimed to elucidate metabolic pathways linking air pollution exposure with HF. This prospective cohort study analysed 229 812 UK Biobank participants with nuclear magnetic resonance metabolomics data. Air pollution score was constructed by fine particulate matter, coarse particulate matter, nitrogen dioxide and nitrogen oxides. Air pollution-associated metabolic signatures were identified using elastic net regression among 251 circulating metabolites. Cox regression evaluated associations between metabolic signatures and incident HF risk. Mediation analysis quantified metabolic signatures' role in air pollution-HF relationships. During median 13.1-year follow-up, 8986 participants (3.9%) developed HF. We identified 53 metabolic metabolites reflecting air pollution exposure, comprising lipoprotein metabolism markers (22.6%), fatty acids (17.0%) and amino acids (13.2%), which were used to construct the air pollution-related metabolic signatures score. After adjustment for confounding factors, each SD increase in the metabolic signatures was associated with 8% elevated HF risk (HR 1.08, 95% CI 1.06 to 1.11). Participants in the highest quantile showed a 24% increased HF risk compared with those in the lowest quantile (HR 1.24, 95% CI 1.16 to 1.3). The metabolic signatures mediated 13.08% (95% CI 12.15% to 15.71%) of air pollution-HF associations, with lipoprotein metabolism and fatty acid signatures as primary mediators. Air pollution was associated with increased HF risk, with metabolic perturbations appearing to play a mediating role. These metabolic signatures provide insights into potential mechanisms linking air pollution to cardiovascular outcomes.

The temporal pattern of air pollution exposure may affect cognitive aging. We characterized 10-year trajectories of particulate matter (PM2.5) and nitrogen dioxide (NO2) exposure at the United States census tract level using sequence and cluster analysis and evaluated their association with memory among 6750 National Health and Aging Trends Study (NHATS) participants aged 65 or older. For each year between 2000 and 2010, we trichotomized PM2.5 and NO2 concentrations in each census tract using the national 2010 25th and 75th percentiles of PM2.5 (7.9 and 10.8 µg/m3) and NO2 (4.8 and 10.8 ppb), creating nine mutually exclusive air pollution categories for each census tract each year (e.g., high PM2.5 and high NO2, high PM2.5 and medium NO2, etc.). We quantified the dissimilarity between each census tract’s 10-year PM2.5 and NO2 trajectory and all other trajectories in NHATS using sequence analysis, and combined similar air pollution trajectories using hierarchical clustering. We assigned a 10-year air pollution trajectory cluster to each participant based on their residential census tract location in 2011. Episodic memory score in 2011 was the mean of immediate and delayed word recall scores. We estimated the association of air pollution trajectory with this episodic memory score using linear models adjusted for age, gender, education level, race/ethnicity, neighborhood socioeconomic status, census region, and urbanicity. We clustered 1080 unique air pollution trajectories from 2000 to 2010 into nine distinct groups. In general, participants belonging to clusters with higher pollutant concentrations in 2000 had lower memory scores in 2011, even when pollutant concentrations in the cluster later declined. For example, those exposed to high PM2.5 and high NO2 concentrations in 2000, with decreasing NO2 concentration (and consistently high PM2.5) starting in 2006, had an adjusted mean memory score that was 0.20 units lower (95% confidence interval: −0.35, −0.046) than those in clusters with consistently low concentrations of both pollutants. This application of sequence analysis offers a new approach for characterizing temporal patterns of PM2.5 and NO2 exposure, simultaneously incorporating pollutant type, timing, duration, and concentration.

ObjectiveTo investigate metabolic pathways linking air pollution exposure to osteoarthritis (OA) development and quantify their mediating role in disease pathogenesis.MethodsThis prospective cohort study utilized UK Biobank data from 220,872 participants. Air pollution exposure was assessed using land use regression models for PM₂.₅, PM₁₀, PM₂.₅ ₋ ₁₀, NO₂, and NOₓ, with composite scores constructed. Nuclear magnetic resonance metabolomics profiling quantified 251 circulating metabolites. Elastic net regression identified air pollution-related metabolic signatures. Cox proportional hazards models assessed associations of air pollution related metabolic profiles with incident OA. Causal mediation analysis quantified metabolic pathway mediation using counterfactual methods.ResultsDuring follow-up, 40,399 participants (18.3%) developed incident OA. Elastic net regression identified 50 metabolites associated with air pollution scores, encompassing lipoprotein subclasses (26%), fatty acids (16%), amino acids (12%), and inflammatory biomarkers. Air pollution-related metabolic signatures showed stronger associations with OA risk (HR 1.095, 95% CI: 1.082–1.108 per IQR increase) than air pollution scores alone (HR 1.030, 95% CI: 1.018–1.042). Effects were most pronounced for knee OA (HR 1.140, 95% CI: 1.118–1.162). Causal mediation analysis revealed that metabolic signatures mediated 21.04% (95% CI: 16.52%−41.95%) of the air pollution-OA association.ConclusionMetabolic pathways significantly mediate air pollution-OA associations, providing novel insights into environmental contributions to musculoskeletal health and identifying potential therapeutic targets for prevention strategies.

Wildfire events are increasing in frequency and intensity, and it is well-known that aging individuals are more susceptible to air pollution exposures, and that air pollution exposures result in neurological sequelae. Despite this, it is unclear how declining levels of ovarian hormones that naturally occur in aging females influence brain vulnerability to air pollution. Menopause and the menopausal transition represent a period of profound physiological change that affects cardiovascular, neurological, and immune health. We tested whether perimenopausal-like hormonal status amplifies hippocampal responses to acute wood smoke (WS) using an ovary-intact, 4-vinylcyclohexene diepoxide (VCD) model of moderate accelerated ovarian failure (AOF) in female C57BL/6 mice. Animals were exposed to HEPA-filtered air (FA) or WS for 4 h/day over 2 consecutive days (∼0.5 mg/m³). Exposure characterization confirmed a complex mixture of combustion products with significant levels of both trace metals and gas release during WS exposure. Spatial transcriptomics (10x Visium; (n=4 sections/group) with automated cell-type annotation identified astrocytes, GABAergic and glutamatergic neurons, oligodendrocytes, revealed cell type-specific transcriptional alterations following WS exposure. Distinct transcriptional patterns were observed across all identified neuronal and glial cell populations. Together, these findings define a cell type-resolved transcriptional framework linking WS exposure and ovarian hormone decline and identify potential cellular pathways relevant to hippocampal vulnerability.

There are extant studies on the knowledge, attitudes and practices of air pollution in sub-Saharan Africa and Ghana, however, studies among commercial drivers and traders in transport stations in Ghana are sparse. This study examined the mediation effect of attitude on the association between knowledge and practice toward air pollution among two high-risk workers in the Greater Accra Metropolitan Area, Ghana. A population-based cross-sectional study was conducted among commercial drivers and traders between November 2023 and June 2024. A consecutive sampling technique was used to select 1011 participants (drivers [n = 619, 61.2%] and traders [n = 392, 38.8%]). A structured questionnaire was used, including questions on knowledge (9 items), attitude (7 items) and practices (5 items) toward air pollution. Statistical analysis (descriptive, hierarchical linear regression and partial least squares structural equation modelling) was performed using Stata 17/MP. The primary sources of air pollution information for drivers and traders were social media and radio. The PLS-SEM revealed a significant direct effect of knowledge for both drivers/traders (β = 0.23/0.26, p < 0.001). Moreover, attitude partially mediated the relationship between knowledge and practices, with a direct significant effect observed for drivers/traders (β = 0.10/0.02, p < 0.001). HLR further confirmed that knowledge strongly predicted protective practices for both drivers (β = 0.23, p < 0.001) and traders (β = 0.26, p < .001. Higher educational level consistently predicted better knowledge and protective practices but old age had an inverse relationship with protective behaviour toward air pollution. Although a cross-sectional design precludes causality, attitude partially mediated the association between knowledge and practices, but the effect was more substantial among drivers than traders. The findings have implications for social and traditional media education and attitudinal change campaigns to effectively reduce air pollution exposure risk among these high-risk occupational groups.