Bin Han

Particulate black carbon (BC) is a critical traffic-related air pollutant associated with adverse health effects in susceptible populations, but exposure modeling remains challenging because BC is not routinely monitored in regulatory networks. This study developed a mobile monitoring-based spatiotemporal exposure modeling framework for estimating ambient BC exposure in a pregnancy cohort in Beijing, China. Equivalent black carbon (eBC) was measured using a vehicle platform during three seasonal campaigns from 2023 to 2024 in urban residential areas, generating a spatially rich but temporally fragmented real-time data set that was aggregated to the hourly level. To fill temporal gaps at locations with mobile BC measurements, we integrated mobile measurements with external temporal predictors using tree-based machine learning models and interpreted feature contributions using SHapley Additive exPlanations (SHAP) values. The resulting site-level daily BC estimates were subsequently used to develop a hierarchical geostatistical model based on geographic covariates, which was applied directly to estimate daily BC exposure at unmonitored cohort locations. The eXtreme Gradient Boosting (XGBoost) model achieved a 10-fold cross-validation mean squared error-based R2 (Rmse2) of 0.69 against measured site-hour eBC, while the subsequent spatiotemporal model showed strong internal interpolation performance for the XGBoost-derived daily BC estimates at the mobile monitoring locations. This study provides a practical framework for integrating temporally fragmented mobile monitoring data with external data sets to support BC exposure assessment at high spatial resolution in subsequent epidemiological studies and health risk assessment.

Gestational exposure to ambient air pollution has been linked to adverse birth outcomes. Folic acid (FA) supplementation supports fetal development, but the optimal timing for protective effects remains uncertain. This study aims to determine whether maternal FA supplementation prior to conception modifies the association between gestational air pollution exposure and birthweight-related outcomes in a population where most women also took FA during pregnancy. We analyzed data from 10,579 mother–child pairs from a preconception cohort in Shanghai, China. Ambient concentrations of PM2.5, PM10, NO2, and ozone were estimated at a residential level using spatiotemporal models for the preconception period and each trimester. We assessed whether preconception FA supplementation modified the association between air pollution exposure and birth outcomes, including birthweight, birthweight Z-score, small-for-gestational age (SGA), and low birthweight (LBW). Linear and logistic regression models with interaction terms, as well as stratified analyses by FA supplementation status, were applied. Children’s sex-specific associations were also explored. Significant interactions between preconception FA supplementation and air pollution exposures were observed for birthweight and SGA. A 10 μg/m3 increase in PM2.5 exposure during pregnancy was associated with a −62.8 g (95% CI: −97.0, −28.6) change in birthweight and a 1.78 (95% CI: 1.16, 2.73) odds ratio for SGA among women without preconception FA supplementation. No such significant associations were found among women receiving preconception FA supplementation, with an estimated change of −1.7 g in birthweight (95% CI: −65.7 to 62.4) and an odds ratio of 0.70 for SGA (95% CI: 0.32–1.44). The protective effect appeared more pronounced for male infants than females. Maternal preconception FA supplementation can attenuate the adverse effects of gestational air pollution exposure on birthweight and SGA risk in women who also received FA supplementation during pregnancy. These findings support the importance of incorporating preconception FA supplementation as a protective measure against gestational exposure to air pollution.

Particulate black carbon (BC) is an important air pollutant linked to adverse health effects in susceptible populations. This study developed a spatiotemporal exposure model for BC for a pregnancy cohort in Beijing, China, addressing the challenge of a temporally and spatially fragmented and unbalanced dataset obtained from a mobile monitoring campaign. Ambient particulate BC was measured using a mobile vehicle platform from 2023 through 2024 in urban residential areas. The resulting dataset, characterized by high spatial but limited temporal coverage at an hourly resolution, was processed using an interpretable tree-based machine learning model (XGBoost) with SHapley Additive exPlanations (SHAP) to generate a temporally continuous daily dataset. This dataset was subsequently integrated into a hierarchical geostatistical model incorporating multiple geographic covariates. Ten-fold cross-validation demonstrated good predictive performance, with an R²ₘₛₑ of 0.90 for the final spatiotemporal exposure model. The quartile misclassification of the BC exposure assessments, as estimated by different exposure models, was evaluated. By integrating an interpretable machine learning approach, this work provides a robust framework for managing a fragmented and unbalanced mobile monitoring dataset and supports high-resolution spatiotemporal exposure assessments for a pregnant cohort study.

Short-term exposures to temperature and fine particulate matter (PM_2.5) have been associated with metabolomic perturbations. However, their combined effect on the metabolome has not been evaluated. We investigated the effect of short-term coexposure to temperature and PM_2.5 on metabolomic signatures and the potential roles of serum lipids and biomarkers using a repeated-measures study among Chinese women of childbearing age. We performed untargeted metabolomic profiling to quantify plasma metabolites. Data on temperature and PM_2.5 exposures were estimated using a fused estimator. Serum lipids and biomarkers of oxidative stress and inflammation were detected. The independent and combined effects of temperature and PM_2.5 were estimated using a linear mixed-effect model and quantile-based g-computation, respectively. Pathway analysis was conducted to identify perturbed metabolic pathways. Ten women provided 46 blood samples, from which 139 metabolites were quantified. Temperature and PM_2.5 had independent effects on several metabolites and pathways. The largest positive and negative combined effects were observed for benzyl sulfate [β = 1.460, 95% confidence interval (CI): 0.438, 2.481] and chenodeoxycholic acid glycine conjugate (β = −1.933, 95% CI: −3.473, −0.392), respectively. Co-exposure to temperature and PM_2.5 perturbed four pathways, including biosynthesis of unsaturated fatty acids; phenylalanine, tyrosine and tryptophan biosynthesis; linoleic acid metabolism; and phenylalanine metabolism. The metabolomic perturbations were mainly related to oxidative stress and the inflammatory response. We observed a positive combined effect of temperature and PM_2.5 on interleukin-8. Our findings demonstrate that the metabolic mechanisms induced by temperature and PM_2.5 involve oxidative stress and the inflammatory response and suggest that these metabolomic perturbations might promote an inflammatory response via release of interleukin-8.

Xiamen, a rapidly developing coastal metropolis and tourist hub in southeastern China, faces air quality challenges due to its dense population and tourism reliance. This study investigates PM_2.5 sources and temporal variations during autumn 2013–2017 via chemical characterization, mass reconstruction, and receptor modeling. The Positive Matrix Factorization (PMF) model identified five sources: secondary sulfate (31%), coal/vehicle emissions (28%), industrial emissions with secondary organic aerosols (SOA, 20%), ship emissions (14%), and fugitive dust (7%). Interannual variations in source contributions highlighted impacts of anthropogenic activities, meteorology, power plant upgrades, and stricter vehicle standards. PM_2.5 declined 19% (2013–2017), driven by emission controls, while SOA surged 42% (2015–2017) due to VOC oxidation and lower temperatures. Backward trajectory and Potential Source Contribution Function (PSCF) analyses revealed significant regional transport from northern industrial zones (32% contribution) and maritime activities. Ship emissions, which have remained relatively stable over the years, underscore the need for stricter marine regulations. Fugitive dust peaked in 2015 (25.8% of PM_2.5), linked to urban construction. The findings emphasize the interplay of local emissions and regional transport in shaping PM_2.5 pollution, providing a scientific basis for targeted control strategies in coastal cities with similar socioeconomic and geographic contexts.

In recent years, air pollution exposure assessment models have experienced significant advancements, particularly in integrating advanced technologies. However, the intrinsic deficiency of the geostatistical model in existing studies restricted further development of the air pollution exposure model. In this perspective, we summarized several emerging technologies that can overcome the limitations and estimate air pollution exposures with high spatial and temporal resolutions. As these technologies evolve, they are expected to play an increasingly significant role in improving public health and managing environmental challenges.