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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]