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Background: Although a number of studies have reported on the health effects of fine particulate matter (PM2.5) exposure, particularly in North American and European countries as well as China, the evidence about intermediate to high levels of PM2.5 exposures is still limited. We aimed to investigate the associations between long-term exposure to PM2.5 and risk of cardiopulmonary disease incidence in Taiwan with intermediate levels of PM2.5 exposure.Methods: A cohort of Taiwanese adults, who participated in the 2001, 2005, 2009 and 2013 National Health Interview Surveys, was followed through 2016 to identify cardiopulmonary disease onset. Exposure to PM2.5 was estimated by incorporating a widespread monitoring network of air quality monitoring stations and microsensors. We used time-dependent Cox regression models to examine the associations between the PM2.5 exposures and health outcomes, adjusting for individual characteristics and ecological covariates. The natural cubic spline functions were used to explore the non-linear effects of the PM2.5 exposure.Results: A total of 62 694 adults from 353 towns were enrolled. Each 10-μg/m3 increase in 5-year average exposure to PM2.5 was associated with a 4.8% increased risk of incident ischaemic heart disease (95% CI: -3.3, 13.6), 3.9% increased risk of incident stroke (95% CI: -2.9, 11.1), 6.7% increased risk of incident diabetes (95% CI: 1.1, 12.7), 15.7% increased risk of incident lung cancer (95% CI: -0.9, 35.1) and 11.5% increased risk of incident chronic obstructive pulmonary disease (95% CI: -0.8, 25.2). The concentration-response curve showed that there was no statistical evidence of non-linearity for most of the disease outcomes except for ischaemic heart disease (P for non-linearity = 0.014).Conclusions: Long-term exposure to intermediate levels of ambient PM2.5 was associated with cardiopulmonary health outcomes. Our study adds value to future application and national burden of disease estimation in evaluating the health co-benefits from ambient air pollution reduction policy in Asian countries. [ABSTRACT FROM AUTHOR]

Air pollution, particularly ambient fine particulate matter (PM 2.5) pollution, poses a significant risk to public health, underscoring the importance of comprehending the long-term impact on health burden and expenditure at national and subnational levels. Therefore, this study aims to quantify the disease burden and healthcare expenditure associated with PM 2.5 exposure in Taiwan and assess the potential benefits of reducing pollution levels. Using a comparative risk assessment framework that integrates an auto-aggressive integrated moving average model, we evaluated the avoidable burden of cardiopulmonary diseases (including ischemic heart disease, stroke, chronic obstructive pulmonary disease, lung cancer, and diabetes mellitus) and related healthcare expenditure under different air quality target scenarios, including status quo and target scenarios of 15, 10, and 5 μg/m3 reduction in PM 2.5 concentration. Our findings indicate that reducing PM 2.5 exposure has the potential to significantly alleviate the burden of multiple diseases. Comparing the estimated attributable disease burden and healthcare expenditure between reference and target scenarios from 2022 to 2050, the avoidable disability-adjusted life years were 0.61, 1.83, and 3.19 million for the 15, 10, and 5 μg/m3 target scenarios, respectively. Correspondingly, avoidable healthcare expenditure ranged from US$ 0.63 to 3.67 billion. We also highlighted the unequal allocation of resources and the need for policy interventions to address health disparities due to air pollution. Notably, in the 5 μg/m3 target scenario, Kaohsiung City stands to benefit the most, with 527,368 disability-adjusted life years avoided and US$ 0.53 billion saved from 2022 to 2050. Our findings suggest that adopting stricter emission targets can effectively reduce the health burden and associated healthcare expenditure in Taiwan. Overall, this study provides policymakers in Taiwan with valuable insights for mitigating the negative effects of air pollution by establishing a comprehensive framework for evaluating the co-benefits of air pollution reduction on healthcare expenditure and disease burden. [Display omitted] • By 2050, a total of 11.1 million DALYs and $13.2 billion health costs due to PM 2.5 • Achieving the air quality Target 5 scenario could potentially avoid 3.2 million DALYs • Health cost savings range from $0.6 to $3.7 billion in different target scenarios • Kaohsiung City would experience the greatest health benefits in the Target 5 scenario [ABSTRACT FROM AUTHOR]

A widespread monitoring network of Airbox microsensors was implemented since 2016 to provide high-resolution spatial distributions of ground-level PM 2.5 data in Taiwan. We developed models for estimating ground-level PM 2.5 concentrations for all the 3 km × 3 km grids in Taiwan by combining the data from air quality monitoring stations and the Airbox sensors. The PM 2.5 data from the Airbox sensors (AB-PM 2.5) was used to predict daily mean PM 2.5 levels at the grids in 2017 using a semiparametric additive model. The estimated PM 2.5 level at the grids was further applied as a predictor variable in the models to predict the monthly mean concentration of PM 2.5 at all the grids in the previous year. The modeling–predicting procedures were repeated backward for the years from 2016 to 2006. The model results revealed that the model R2 increased from 0.40 to 0.87 when the AB-PM 2.5 data were included as a nonlinear component in the model, indicating that AB-PM 2.5 is a significant predictor of ground-level PM 2.5 concentration. The cross-validation (CV) results demonstrated that the root of mean squared prediction errors of the estimated monthly mean PM 2.5 concentrations were smaller than 5 μg/m3 and the R2 of the CV models of 0.79–0.88 during 2006–2017. We concluded that Airbox sensors can be used with monitoring data to more accurately estimate long-term exposure to PM 2.5 for cohorts of small areas in health impact assessment studies. Image 1 • Microsensors can significantly improve PM 2.5 estimation accuracy. • Microsensors improved historical PM 2.5 estimation in grids in Taiwan. • Annual average PM 2.5 estimates decreased during 2006–2017 in Taiwan. • Our results provide long-term PM 2.5 exposure estimates for cohort studies. The study results indicated that a wide-spread network of micro sensors is helpful to improving estimates of PM 2.5 concentrations in small areas for health impact assessment studies. Long-term exposure to PM 2.5 for residents can be accurately estimated by using a two-step predicting procedure. [ABSTRACT FROM AUTHOR]

A widespread monitoring network of Airbox microsensors was implemented since 2016 to provide high-resolution spatial distributions of ground-level PM 2.5 data in Taiwan. We developed models for estimating ground-level PM 2.5 concentrations for all the 3 km × 3 km grids in Taiwan by combining the data from air quality monitoring stations and the Airbox sensors. The PM 2.5 data from the Airbox sensors (AB-PM 2.5 ) was used to predict daily mean PM 2.5 levels at the grids in 2017 using a semiparametric additive model. The estimated PM 2.5 level at the grids was further applied as a predictor variable in the models to predict the monthly mean concentration of PM 2.5 at all the grids in the previous year. The modeling-predicting procedures were repeated backward for the years from 2016 to 2006. The model results revealed that the model R 2 increased from 0.40 to 0.87 when the AB-PM 2.5 data were included as a nonlinear component in the model, indicating that AB-PM 2.5 is a significant predictor of ground-level PM 2.5 concentration. The cross-validation (CV) results demonstrated that the root of mean squared prediction errors of the estimated monthly mean PM 2.5 concentrations were smaller than 5 μg/m 3 and the R 2 of the CV models of 0.79-0.88 during 2006-2017. We concluded that Airbox sensors can be used with monitoring data to more accurately estimate long-term exposure to PM 2.5 for cohorts of small areas in health impact assessment studies., 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 © 2020 Elsevier Ltd. All rights reserved.)

Land use regression (LUR) models are increasingly used to evaluate intraurban variability in population exposure to fine particulate matter (PM2.5). However, most of these models lack information on PM2.5 elemental compositions and vertically distributed samples. The purpose of this study was to evaluate intraurban exposure to PM2.5 concentrations and compositions for populations in an Asian city using LUR models, with special emphasis on examining the effects of having measurements on different building stories. PM2.5 samples were collected at 20 sampling sites below the third story (low-level sites). Additional vertically stratified sampling sites were set up on the fourth to sixth (mid-level sites, n=5) and seventh to ninth (high-level sites, n=5) stories. LUR models were built for PM2.5, copper (Cu), iron (Fe), potassium (K), manganese (Mn), nickel (Ni), sulfur (S), silicon (Si), and zinc (Zn). The explained concentration variance (R(2)) of the PM2.5 model was 65%. R(2) values were >69% in the Cu, Fe, Mn, Ni, Si, and Zn models and <44% in the K and S models. Sampling height from ground level was a significant predictor in the PM2.5 and Si models. This finding stresses the importance of collecting vertically stratified information on PM2.5 mass concentrations to reduce potential exposure misclassification in future health studies. In addition to traffic variables, some models identified gravel-plant, industrial, and port variables with large buffer zones as important predictors, indicating that PM from these sources had significant effects at distant places., (Copyright © 2014 Elsevier Inc. All rights reserved.)