RESUMO
Air pollution, particularly ambient fine particulate matter (PM2.5) 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 PM2.5 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 PM2.5 concentration. Our findings indicate that reducing PM2.5 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.
RESUMO
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.