Search

Air pollution is inextricable from human activity patterns. This is especially true for nitrogen oxide (NO x ), a pollutant that exists naturally and also as a result of anthropogenic factors. Assessing exposure by considering diurnal variation is a challenge that has not been widely studied. Incorporating 27 years of data, we attempted to estimate diurnal variations in NO x across Taiwan. We developed a machine learning-based ensemble model that integrated hybrid kriging-LUR, machine-learning, and an ensemble learning approach. Hybrid kriging-LUR was performed to select the most influential predictors, and machine-learning algorithms were applied to improve model performance. The three best machine-learning algorithms were suited and reassessed to develop ensemble learning that was designed to improve model performance. Our ensemble model resulted in estimates of daytime, nighttime, and daily NO x with high explanatory powers (Adj-R 2 ) of 0.93, 0.98, and 0.94, respectively. These explanatory powers increased from the initial model that used only hybrid kriging-LUR. Additionally, the results depicted the temporal variation of NO x , with concentrations higher during the daytime than the nighttime. Regarding spatial variation, the highest NO x concentrations were identified in northern and western Taiwan. Model evaluations confirmed the reliability of the models. This study could serve as a reference for regional planning supporting emission control for environmental and human health., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Inc. All rights reserved.)