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The Hartman Park community in Houston, Texas-USA, is in a highly polluted area which poses significant risks to its predominantly Hispanic and lower-income residents. Surrounded by dense clustering of industrial facilities compounds health and safety hazards, exacerbating environmental and social inequalities. Such conditions emphasize the urgent need for environmental measures that focus on investigating ambient air quality. This study estimated benzene, one of the most reported pollutants in Hartman Park, using machine learning-based approaches. Benzene data was collected in residential areas in the neighborhood and analyzed using a combination of five machine-learning algorithms (i.e., XGBR, GBR, LGBMR, CBR, RFR) through a newly developed ensemble learning model. Evaluations on model robustness, overfitting tests, 10-fold cross-validation, internal and stratified validation were performed. We found that the ensemble model depicted about 98.7% spatial variability of benzene (Adj. R2 =0.987). Through rigorous validations, stability of model performance was confirmed. Several predictors that contribute to benzene were identified, including temperature, developed intensity areas, leaking petroleum storage tank, and traffic-related factors. Analyzing spatial patterns, we found high benzene spread over areas near industrial zones as well as in residential areas. Overall, our study area was exposed to high benzene levels and requires extra attention from relevant authorities. [Display omitted] • Estimating spatiotemporal patterns of benzene in environmentally disadvantaged area. • Utilizing ensemble learning model to accurately estimate benzene concentration. • Ensemble model depicted 98.7% benzene spatial variability in Hartman Park community. • Intensively developed areas and industry-related factors contribute to benzene. • High benzene levels were spread over industrial area to residential area nearby. [ABSTRACT FROM AUTHOR]

This study aimed to identify the spatial patterns of lower respiratory tract infections (LRIs) and their association with fine particulate matter (PM2.5). The disability-adjusted life year (DALY) database was used to represent the burden each country experiences as a result of LRIs. PM2.5 data obtained from the Atmosphere Composition Analysis Group was assessed as the source for main exposure. Global Moran's I and Getis-Ord Gi* were applied to identify the spatial patterns and for hotspots analysis of LRIs. A generalized linear mixed model was coupled with a sensitivity test after controlling for covariates to estimate the association between LRIs and PM2.5. Subgroup analyses were performed to determine whether LRIs and PM2.5 are correlated for various ages and geographic regions. A significant spatial auto-correlated pattern was identified for global LRIs with Moran's Index 0.79, and the hotspots of LRIs were clustered in 35 African and 4 Eastern Mediterranean countries. A consistent significant positive association between LRIs and PM2.5 with a coefficient of 0.21 (95% CI 0.06–0.36) was identified. Furthermore, subgroup analysis revealed a significant effect of PM2.5 on LRI for children (0–14 years) and the elderly (≥ 70 years), and this effect was confirmed to be significant in all regions except for those comprised of Eastern Mediterranean countries. [ABSTRACT FROM AUTHOR]

Scientific evidence reported that surrounding greenspace could promote better mental health. Considering bipolar disorder as the health outcome, this study aimed to investigate the association between greenspace and bipolar disorder in Taiwan and quantified the benefits of greenspace on bipolar disorder adjusted for the international greenspace availability standard. By examining datasets across 348 townships, two quantitative measures (i.e., disability-adjusted life year loss and income) were used to represent the benefits. The incidence rate of bipolar disorder was obtained from Taiwan's National Health Insurance Research Database. Normalized different vegetation index (NDVI) was measured as a proxy for the greenspace availability. A generalized additive mixed model coupled with a sensitivity test were applied to evaluate the statistical association. The prevented fraction for the population (PFP) was then applied to develop a scenario for quantifying benefit. The result showed a significant negative association between greenspace and bipolar disorder in Taiwan. Compared to low greenspace, areas with medium and high greenspace may reduce the bipolar risk by 21% (RR = 0.79; 95% CI = 0.76–0.83) and 51% (RR = 0.49; 95% CI = 0.45–0.53). Calculating benefits, we found that the development of a scenario by increasing greenspace adjusted for availability indicator in township categorized as low greenspace could save in DALY loss due to bipolar disorder up to10.97% and increase in income up to 11.04% from the current situation. Lastly, this was the first study in Asia-Pacific to apply a customized greenspace increment scenario to quantify the benefits to a particular health burden such as bipolar disorder. • Greenspace has a significant association with bipolar disorder. • Increased greenspace is related to a reduced risk of bipolar disorder in Taiwan. • 50 of 348 townships in Taiwan did not meet the greenspace availability standard. • Benefits in terms of savings DALY loss and increasing income were estimated. • Greenspace can save DALY loss up to 10.97% and increase in income up to 11.04%. [ABSTRACT FROM AUTHOR]

Both greenness and air pollution have widely been linked with asthma. However, the potential mechanism has rarely been investigated. This study aimed to identify the association between residential greenness and air pollution (fine particulate matter [PM 2.5 ]; nitrogen dioxide [NO 2 ]; ozone [O 3 ]) with nasal microbiota among asthmatic children during the recovery phase. The normalized difference vegetation index was used to assess the extent of residential greenness. Spatiotemporal air pollution variation was estimated using an integrated hybrid kriging-LUR with the XG-Boost algorithm. These exposures were measured in 250-m intervals for four incremental buffer ranges. Nasal microbiota was collected from 47 children during the recovery phase. A generalized additive model controlled for various covariates was applied to evaluate the exposure-outcome association. The lag-time effect of greenness and air pollution related to the nasal microbiota also was examined. A significant negative association was observed between short-term exposure to air pollution and nasal bacterial diversity, as a one-unit increment in PM 2.5 or O 3 significantly decreased the observed species (PM 2.5 : −0.59, 95%CI -1.13, −0.05 and O 3 : −0.93, 95%CI -1.54, −0.32) and species richness (PM 2.5 : −0.64, 95%CI -1.25, −0.02 and O 3 : −0.68, 95%CI -1.43, −0.07). Considering the lag-time effect, we found a significant positive association between greenness and both the observed species and species richness. In addition, we identified a significant negative association for all pollutants with the observed species richness. These findings add to the evidence base of the links between nasal microbiota and air pollution and greenness. This study establishes a foundation for future studies of how environmental exposure plays a role in nasal microbiota, which in turn may affect the development of asthma. • A scarce study assesses links between greenness, air pollution, and nasal microbiota. • Short-term air pollution exposure significantly decreases nasal bacterial diversity. • Lag-time greenness has a significant association with nasal bacterial diversity. • Air pollution and greenness influence nasal microbiota in asthmatic children. [ABSTRACT FROM AUTHOR]

It is generally established that PCDD/Fs is harmful to human health and therefore extensive field research is necessary. This study is the first to use a novel geospatial-artificial intelligence (Geo-AI) based ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and geographic predictor variables selected using SHapley Additive exPlanations (SHAP) values to predict spatial-temporal fluctuations in PCDD/Fs concentrations across the entire island of Taiwan. Daily PCDD/F I-TEQ levels from 2006 to 2016 were used for model construction, while external data was used for validating model dependability. We utilized Geo-AI, incorporating kriging, five machine learning, and ensemble methods (combinations of the aforementioned five models) to develop EMSMs. The EMSMs were used to estimate long-term spatiotemporal variations in PCDD/F I-TEQ levels, considering in-situ measurements, meteorological factors, geospatial predictors, social and seasonal influences over a 10-year period. The findings demonstrated that the EMSM was superior to all other models, with an increase in explanatory power reaching 87 %. The results of spatial-temporal resolution show that the temporal fluctuation of PCDD/F concentrations can be a result of weather circumstances, while geographical variance can be the result of urbanization and industrialization. These results provide accurate estimates that support pollution control measures and epidemiological studies. [Display omitted] • A novel Geo-AI with 10-year data was used to predict daily PCDD/Fs across Taiwan. • The Shapley Additive exPlanations (SHAP) was used for predictor selection. • The long-term PCDD/Fs variations across Taiwan main island were examined. • The ensemble mixed spatial model can refine air pollution modelling techniques. • The EMSMs demonstrated strong performance (R2 87 %) and robustness in validations. [ABSTRACT FROM AUTHOR]