A hybrid satellite and land use regression model of source-specific PM2.5 and PM2.5 constituents

High-precision long time series simulation of toxic metallic and metalloid composition of PM2.5 in the Beijing-Tianjin-Hebei Region and surrounding areas of China: 0.01°× 0.01° spatial resolution

Metals and metalloids in PM2.5 are harmful to human health. However, long-term monitoring data are scarce due to high costs and logistical challenges. This study addresses the scientific problem of the scarcity of continuous long-term spatiotemporal PM2.5 metals and metalloids data. This study integrated multi-source data to eatablish the random forest models of daily concentrations of 10 metals and metalloids (Al, As, Cd, Cr, Mn, Ni, Pb, Sb, Se, Tl), with high spatial resolution of 0.01°× 0.01°. The test-R² of the models ranged 0.87-0.91. We predicted metals and metalloids in PM2.5 across the Beijing-Tianjin-Hebei region and its surrounding areas from 2015 to 2020. The concentrations of all 10 components showed a declining trend year by year, with particularly in Al, Pb, and Mn. Seasonally, the highest concentrations occurred in winter, while the lowest were observed in summer. Spatially, higher concentrations were found in Shanxi, southern Hebei, northern Henan, and western Shandong. The high spatialtemporal resolution models constructed and the dataset produced in this study could provide important reference for source control of metal pollution and further study on health effects. SYNOPSIS: This study established high spatiotemporal resolution random forest models and predicted long time series data of 10 metals and metalloids in PM2.5, thereby solving the scientific problem of limited of big data for monitoring metallic and metalloid components in PM2.5.

Toxic effects of trace metal(loid) mixtures on aquatic organisms

The co-occurrence of metal (loid)s in realistic aquatic environments necessitates the evaluation of their combined effects. However, the generality of the additive effect hypothesis is contentious, particularly due to metal(loid)-metal(loid) interactions. The absence of systematic evaluation approaches restricts our ability to draw overall conclusions and make reliable predictions. In this study, we reviewed 1473 effect sizes from 38 publications, and classified all responses into seven main categories (from molecular to individual levels) according to their toxicological significance. Our meta-analysis revealed that metal(loid) mixtures had significant effects on aquatic organisms (33 %, 95 % CI 28 %-39 %, P < 0.05), along with significant response heterogeneity (Qt = 690,319.62, P < 0.0001; I2 = 99.95 %). Concurrently, we developed a Random Forest machine learning model to predict adverse effects and identify key variables. These two methods demonstrated that the toxicity of metal(loid) mixtures is primarily linked to the choice of toxicity endpoints, and the characteristics of metal(loid) mixtures. Our findings underscore the potential of combining meta-analysis with machine learning, a more systematic approach, to enhance the understanding and prediction of the adverse effects of metal(loid) mixtures, and they offer guidance for risk assessment and policy-making in complex environmental scenarios.

Associations between Fine Particulate Matter Components, Their Sources, and Cognitive Outcomes in Children Ages 9-10 Years Old from the United States

BACKGROUND

Emerging literature suggests that fine particulate matter [with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 )] air pollution and its components are linked to various neurodevelopmental outcomes. However, few studies have evaluated how PM 2.5 component mixtures from distinct sources relate to cognitive outcomes in children.

OBJECTIVES

This cross-sectional study investigated how ambient concentrations of PM 2.5 component mixtures relate to neurocognitive performance in 9- to 10-year-old children, as well as explored potential source-specific effects of these associations, across the US.

METHODS

Using spatiotemporal hybrid models, annual concentrations of 15 chemical components of PM 2.5 were estimated based on the residential address of child participants from the Adolescent Brain Cognitive Development (ABCD) Study. General cognitive ability, executive function, and learning/memory scores were derived from the NIH Toolbox. We applied positive matrix factorization to identify six major PM 2.5 sources based on the 15 components, which included crustal, ammonium sulfate, biomass burning, traffic, ammonium nitrate, and industrial/residual fuel burning. We then utilized weighted quantile sum (WQS) and linear regression models to investigate associations between PM 2.5 components' mixture, their potential sources, and children's cognitive scores.

RESULTS

Mixture modeling revealed associations between cumulative exposure and worse cognitive performance across all three outcome domains, including shared overlap in detrimental effects driven by ammonium nitrates, silicon, and calcium. Using the identified six sources of exposure, source-specific negative associations were identified between ammonium nitrates and learning & memory, traffic and executive function, and crustal and industrial mixtures and general cognitive ability. Unexpected positive associations were also seen between traffic and general ability as well as biomass burning and executive function.

DISCUSSION

This work suggests nuanced associations between outdoor PM 2.5 exposure and childhood cognitive performance, including important differences in cognition related both to individual chemicals as well as to specific sources of these exposures. https://doi.org/10.1289/EHP14418.

Air pollution from biomass burning disrupts early adolescent cortical microarchitecture development

Exposure to outdoor particulate matter (PM2.5) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM2.5 from multiple sources may disrupt neurodevelopment. Studies addressing neurodevelopmental implications of PM exposure have been limited by small, geographically limited samples and largely focus either on macroscale cortical morphology or postmortem histological staining and total PM mass. Here, we leverage residentially assigned exposure to six, data-driven sources of PM2.5 and neuroimaging data from the longitudinal Adolescent Brain Cognitive Development Study (ABCD Study®), collected from 21 different recruitment sites across the United States. To contribute an interpretable and actionable assessment of the role of air pollution in the developing brain, we identified alterations in cortical microstructure development associated with exposure to specific sources of PM2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8-10 years) to PM2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age.

Air pollution from biomass burning disrupts early adolescent cortical microarchitecture development

Exposure to outdoor particulate matter (PM 2.5 ) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM 2.5 from multiple sources may disrupt neurodevelopment. Studies addressing neurodevelopmental implications of PM exposure have been limited by small, geographically limited samples and largely focus either on macroscale cortical morphology or postmortem histological staining and total PM mass. Here, we leverage residentially assigned exposure to six, data-driven sources of PM 2.5 and neuroimaging data from the longitudinal Adolescent Brain Cognitive Development Study (ABCD Study®), collected from 21 different recruitment sites across the United States. To contribute an interpretable and actionable assessment of the role of air pollution in the developing brain, we identified alterations in cortical microstructure development associated with exposure to specific sources of PM 2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8-10 years) to PM 2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age.

High spatial resolution estimates of major PM2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment approach

Few studies have focused on the spatial distribution of the typical components and source tracers of PM2.5 and their associated health risks, despite the fact that the chemical components of PM2.5 pose potentially significant and independent risks to human health. The main objective of this study was to evaluate the spatial distribution of major PM2.5 components and their associated health risks in Hong Kong using a coupled land use regression and health risk assessment modeling approach. The established land use regression models of the major PM2.5 components and source tracers achieved a relatively high statistical performance, with training and leave-one-out cross-validation R2 values of 0.85-0.96 and 0.62-0.88, respectively. The high spatial resolution (500 m × 500 m) distribution patterns of the chemical components of PM2.5 showed the heterogeneity of population exposure to different components and the related potential health risks, as evidenced by the weak spatial correlations between the mass of PM2.5 and some components. Elemental carbon, nickel, arsenic, and chromium from PM2.5 made major contributions to the total health risk and should therefore be reduced further. Our results will enable researchers to determine independent associations between exposure to the various components of PM2.5 and health endpoints in epidemiological studies.

High-spatiotemporal-resolution mapping of PM2.5 traffic source impacts integrating machine learning and source-specific multipollutant indicator

Traffic sources are a major contributor to fine particulate matter (PM2.5) pollution, with their emissions and diffusion exhibiting complex spatiotemporal patterns. Receptor models have limitations in estimating high-resolution source contributions due to insufficient observation networks of PM2.5 compositions. This study developed a source apportionment method that integrates machine learning and emission-based integrated mobile source indicator (IMSI) to rapidly and accurately estimate PM2.5 traffic source impacts with high spatiotemporal resolution in the Beijing-Tianjin-Hebei region. Firstly, we utilized multisource data and developed various machine learning models to optimize the traffic-related pollutant concentration fields simulated by a chemical transport model. Results demonstrated that the Extreme Gradient Boosting (XGBoost) model exhibited excellent prediction accuracy of nitrogen oxide (NO2), carbon oxide (CO), and elemental carbon (EC), with the cross-validated R values increasing to 0.87-0.92 and error indices decreasing by 50-67%. Furthermore, we estimated and predicted daily mappings of PM2.5 traffic source impacts using the IMSI method based on optimized concentration fields, which improved spatially resolved source contributions to PM2.5. Our findings reveal that PM2.5 traffic source impacts display significant spatial heterogeneity, and these hotspots can be precisely identified during the pollution processes with sharp changes. The evaluation results indicated that there is a good correlation (R of 0.79) between PM2.5 traffic source impacts by IMSI method and traffic source contributions apportioned by a receptor model at Beijing site. Our study provides deeper insights of estimating the spatiotemporal distribution of PM2.5 source-specific impacts especially in regions without PM2.5 compositions, which can provide more complete and timely guidance to implement precise air pollution management strategies.

Assessing household fine particulate matter (PM2.5) through measurement and modeling in the Bangladesh cook stove pregnancy cohort study (CSPCS)

Biomass fuel burning is a significant contributor of household fine particulate matter (PM2.5) in the low to middle income countries (LMIC) and assessing PM2.5 levels is essential to investigate exposure-related health effects such as pregnancy outcomes and acute lower respiratory infection in infants. However, measuring household PM2.5 requires significant investments of labor, resources, and time, which limits the ability to conduct health effects studies. It is therefore imperative to leverage lower-cost measurement techniques to develop exposure models coupled with survey information about housing characteristics. Between April 2017 and March 2018, we continuously sampled PM2.5 in three seasonal waves for approximately 48-h (range 46 to 52-h) in 74 rural and semi-urban households among the participants of the Bangladesh Cook Stove Pregnancy Cohort Study (CSPCS). Measurements were taken simultaneously in the kitchen, bedroom, and open space within the household. Structured questionnaires captured household-level information related to the sources of air pollution. With data from two waves, we fit multivariate mixed effect models to estimate 24-h average, cooking time average, daytime and nighttime average PM2.5 in each of the household locations. Households using biomass cookstoves had significantly higher PM2.5 concentrations than those using electricity/liquefied petroleum gas (626 μg/m3 vs. 213 μg/m3). Exposure model performances showed 10-fold cross validated R2 ranging from 0.52 to 0.76 with excellent agreement in independent tests against measured PM2.5 from the third wave of monitoring and ambient PM2.5 from a separate satellite-based model (correlation coefficient, r = 0.82). Significant predictors of household PM2.5 included ambient PM2.5, season, and types of fuel used for cooking. This study demonstrates that we can predict household PM2.5 with moderate to high confidence using ambient PM2.5 and household characteristics. Our results present a framework for estimating household PM2.5 exposures in LMICs, which are often understudied and underrepresented due to resource limitations.

Gaps and future directions in research on health effects of air pollution

Despite progress in many countries, air pollution, and especially fine particulate matter air pollution (PM2.5) remains a global health threat: over 6 million premature cardiovascular and respiratory deaths/yr. have been attributed to household and outdoor air pollution. In this viewpoint, we identify present gaps in air pollution monitoring and regulation, and how they could be strengthened in future mitigation policies to more optimally reduce health impacts. We conclude that there is a need to move beyond simply regulating PM2.5 particulate matter mass concentrations at central site stations. A greater emphasis is needed on: new portable and affordable technologies to measure personal exposures to particle mass; the consideration of a submicron (PM1) mass air quality standard; and further evaluations of effects by particle composition and source. We emphasize the need to enable further studies on exposure-health relationships in underserved populations that are disproportionately impacted by air pollution, but not sufficiently represented in current studies.

Spatial association network of PM2.5 and its influencing factors in the Beijing-Tianjin-Hebei urban agglomeration

In this paper, we empirically study the spatial association network of PM2.5 and the factors influencing those correlations using the gravity model, social network analysis (SNA), and the quadratic assignment procedure (QAP) based on data from the Beijing-Tianjin-Hebei urban agglomeration (BTHUA) in China from 2005 to 2018. We draw the following conclusions. First, the spatial association network of PM2.5 exhibits relatively typical network structure characteristics: the network density and network correlations are highly sensitive to efforts to control air pollution, and there are obvious spatial correlations within the network. Second, cities in the center of the BTHUA have large network centrality values, while cities in the peripheral region have small centrality values. Tianjin is a core city in the network, and the spillover effect of PM2.5 pollution in Shijiazhuang and Hengshui is the most noticeable. Third, the 14 cities can be divided into four plates, with each plate having obvious geographical location characteristics and linkage effects. The cities in the association network are divided into three tiers. Beijing, Tianjin, and Shijiazhuang are located in the first tier, and a considerable number of PM2.5 connections are completed through these cities. Fourth, differences in geographical distance and urbanization are the main drivers of the spatial correlations of PM2.5. The greater the urbanization differences, the more likely the generation of PM2.5 links is, while the opposite is true for differences in geographical distance.

Exploring the effect of ecological land structure on PM2.5: A panel data study based on 277 prefecture-level cities in China

In the context of serious urban air pollution and limited land resources, it is important to understand the environmental value of ecological land. Previous studies focused mostly on the effectiveness of a particular type of green space or the total amount of ecological land on PM_2.5 and have rarely analyzed the association between ecological land structure and PM_2.5 systematically and quantitatively. Therefore, we took 277 cities in China as an example, comprehensively compared the results of different models, and selected a spatial Durbin model using time-fixed effects to dissect the degree of influence of ecological land and different land types within it on PM_2.5. The urban ecological land use structure was closely related to PM_2.5, and the higher the proportion of ecological land use was, the lower the PM_2.5. The degree and direction of influence of different types of land functions within ecological land on PM_2.5 differed, with forests, shrubs, and grasslands causing a weakening impact on PM_2.5, while wetlands and waters did not have a weakening role. The degree of reduction of PM_2.5 by a single type of ecological land was significantly smaller than that by a composite type of ecological land. Green space should be comprehensively considered, designed and adjusted in urban planning to continuously optimize the ecological spatial structure, increase landscape diversity and maximize ecological benefits. The findings of this study help with exploring the effects of land use structure under the goal-oriented control of air pollution and provide theoretical reference and decision-making support for formulating precise air pollution control policies and optimizing the spatial development of national land.

Fine Particulate Air Pollution and the "No-Multiple-Versions-of-Treatment" Assumption: Does Particle Composition Matter for Causal Inference?

Here we discuss possible violations of the "no-multiple-versions-of-treatment" assumption in studies of outdoor fine particulate air pollution (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5)) owing to differences in particle composition, which in turn influence health. This assumption is part of the potential outcomes framework for causal inference, and it is needed for well-defined potential outcomes, as multiple versions of the same treatment could lead to different health risks for the same level of treatment. Since 2 locations can have the same outdoor PM2.5 mass concentration (i.e., treatment) but different chemical compositions (i.e., versions of treatment), violations of the "no-multiple-versions-of-treatment" assumption seem likely. Importantly, violations of this assumption will not bias health risk estimates for PM2.5 mass concentrations if there are no unmeasured confounders of the "version of treatment"-outcome relationship. However, confounding can occur if these factors are not identified and controlled for in the analysis. We describe situations in which this may occur and provide simulations to estimate the magnitude and direction of this possible bias. In general, violations of the "no-multiple-versions-of-treatment" assumption could be an underappreciated source of bias in studies of outdoor PM2.5. Analysis of the health impacts of outdoor PM2.5 mass concentrations across spatial domains with similar composition could help to address this issue.

Prenatal exposure to tailpipe and non-tailpipe tracers of particulate matter pollution and autism spectrum disorders

BACKGROUND

Traffic-related air pollution exposure is associated with increased risk of autism spectrum disorder (ASD). It is unknown whether carbonaceous material from vehicular tailpipe emissions or redox-active non-tailpipe metals, eg. from tire and brake wear, are responsible. We assessed ASD associations with fine particulate matter (PM_2.5) tracers of tailpipe (elemental carbon [EC] and organic carbon [OC]) and non-tailpipe (copper [Cu]; iron [Fe] and manganese [Mn]) sources during pregnancy in a large cohort.

METHODS

This retrospective cohort study included 318,750 children born in Kaiser Permanente Southern California (KPSC) hospitals during 2001-2014, followed until age 5. ASD cases were identified by ICD codes. Monthly estimates of PM_2.5 and PM_2.5 constituents EC, OC, Cu, Fe, and Mn with 4 km spatial resolution were obtained from a source-oriented chemical transport model. These exposures and NO₂ were assigned to each maternal address during pregnancy, and associations with ASD were assessed using Cox regression models adjusted for covariates. PM constituent effect estimates were adjusted for PM_2.5 and NO₂ to assess independent effects. To distinguish ASD risk associated with non-tailpipe from tailpipe sources, the associations with Cu, Fe, and Mn were adjusted for EC and OC, and vice versa.

RESULTS

There were 4559 children diagnosed with ASD. In single-pollutant models, increased ASD risk was associated with gestational exposures to tracers of both tailpipe and non-tailpipe emissions. The ASD hazard ratios (HRs) per inter-quartile increment of exposure) for EC, OC, Cu, Fe, and Mn were 1.11 (95% CI: 1.06-1.16), 1.09 (95% CI: 1.04-1.15), 1.09 (95% CI: 1.04-1.13), 1.14 (95% CI: 1.09-1.20), and 1.17 (95% CI: 1.12-1.22), respectively. Estimated effects of Cu, Fe, and Mn (reflecting non-tailpipe sources) were largely unchanged in two-pollutant models adjusting for PM_2.5, NO₂, EC or OC. In contrast, ASD associations with EC and OC were markedly attenuated by adjustment for non-tailpipe sources.

CONCLUSION

Results suggest that non-tailpipe emissions may contribute to ASD. Implications are that reducing tailpipe emissions, especially from vehicles with internal combustion engines, may not eliminate ASD associations with traffic-related air pollution.

Random forest algorithm-based classification model of pesticide aquatic toxicity to fishes

Aquatic toxicity of pesticides can result in poisoning of many non-target organisms, of which various fishes are the most prominent one. It is a challenge to predict the toxicity (LC₅₀) classes of organic pesticides to various fish species from global QSAR models with a larger applicability domain. In this paper, by applying the random forest (RF) algorithm for a two-class problem, only eight molecular descriptors were used to develop a quantitative structure-activity relationship (QSAR) model for 1106 toxicity data (96 h, LC₅₀) of organic pesticides to various fish species including Oncorhynchus mykiss, Lepomis macrochirus, Pimephales promelas, Brachydanio rerio, Cyprinodon, Cyprinus carpio, etc. By the prediction of the optimal RF Model I (ntree =280, mtry = 3 and nodesize = 5), the training set (885 organic pesticides) has the prediction accuracies of 99.6% for Class 1 (LC₅₀ ≤ 10) and 96.7% for Class 2 (LC₅₀ > 10); the test set (221 organic pesticides) has the accuracies being 90.8% for Class 1 and 91.2% for Class 2. The optimal RF Model I is satisfactory compared with other QSAR model reported in the literature, although its descriptor subset is small.

Influencing factors and trend prediction of PM2.5 concentration based on STRIPAT-Scenario analysis in Zhejiang Province, China

The government's development of eco-environmental policies can have a scientific foundation thanks to the fine particulate matter (PM2.5) medium- and long-term change forecast. This study develops a STRIPAT-Scenario analysis framework employing panel data from 11 cities in Zhejiang Province between 2006 and 2020 to predict the changing trend of PM2.5 concentrations under five alternative scenarios. The results reveal that: (1) urbanization development (P), economic development (A), technological innovation investment (T) and environmental regulation intensity have a significant inhibitory effect on PM2.5 concentration in Zhejiang Province, while industrial structure, industrial energy consumption and the number of motor vehicles (TR) have a significant increase on PM2.5 concentration. (2) Under any scenario, the PM2.5 concentration of 11 cities in Zhejiang Province can reach the constraint target set in the 14th Five-Year plan. The improvement in urban PM2.5 quality is most obviously impacted by the high-quality development scenario (S4). (3) Toward 2035, PM2.5 concentrations of 11 cities in Zhejiang Province can reach the National Class I level standard in most scenario models, among which Hangzhou, Jiaxing and Shaoxing are under high pressure to reduce emissions and are the key areas for PM2.5 management in Zhejiang Province. However, most cities cannot reach the 10 μg/m3 limit of WHO's AQG2005 version. Finally, this study makes recommendations for reducing PM2.5 in terms of enhancing industrial structure and funding science and technology innovation.