A novel air pollution forecasting, health effects, and economic cost assessment system for environmental management: From a new perspective of the district-level

Abstract

Air pollution prediction and damage assessment are important in environmental management. However, existing research focuses more on the short-term prediction and loss assessment regarding past pollution, ignoring the importance of long-term air pollution prediction and impact pre-assessment at the district level. To address these problems, a novel air pollution forecasting, health effects, and economic cost assessment system is proposed for the first time in this study. The system comprises three subsystems: air pollution forecasting, health effects assessment, and economic cost assessment. The air pollution prediction subsystem adopts the popular damping accumulation operator and data-driven iteration mode, exhibiting excellent performance in air pollution prediction. The health effects and economic costs of PM2.5 were assessed in the health effects and economic cost assessment subsystems using the log-linear exposure response function and willingness-to-pay method, respectively. The following experimental results were obtained: (1) PM2.5 concentrations in downtown Beijing will decrease from 2023 to 2025. (2) Acute bronchial disease accounted for the largest proportion of all health effects. The economic cost of death was the highest. (3) At the C0 = 5 μ/m3 level, the economic cost of Chaoyang District was projected to be relatively the largest in 2023, at approximately 2254.56 million yuan. These findings can be used as a reference for formulating global district-level environmental policies.