The high-resolution estimation of sulfur dioxide (SO₂) concentration, health effect and monetary costs in Beijing

Severe air pollution episodes with high SO₂ loading have been frequently observed during the last decades in Beijing and have caused a noticeable damage to human health. To advance the spatiotemporal prediction of SO₂ exposure in Beijing, we developed the monthly land use regression (LUR) models using daily SO₂ concentration data collected from 34 monitoring stations during 2016 and 7 categories of potential independent variables (socio-economic factors, traffic and transport, emission source, land use, meteorological data, building morphology and Geographic location) in Beijing. The average adjusted R² of 12 final LUR models was 0.62, and the root-mean-squared error (RMSE) was 4.12 μg/m³. The LOOCV R² and RMSE of LUR models reached 0.56 and 5.43 μg/m³, respectively, suggesting that the LUR models achieved the satisfactory performance. The prediction results suggested that the average SO₂ level in Beijing was 11.06 μg/m³ with the highest one up to 22.49 μg/m³ but the lowest one down to 3.86 μg/m³. The SO₂ exposure showed strong spatial heterogeneity, which was much higher in the southern area than that in the northern in Beijing. The mortality and morbidity due to the excessive SO₂ concentration were estimated to be 73 (95% CI:(38–125)) and 27854 (95% CI:(13852-41659)) cases per year in Beijing, leading to economic cost of 35.76 (95% CI:(16.45–54.06)) and 441.47 (95% CI:(318.31–562.04)) million RMB Yuan in 2016, respectively. This study clarified the intra- and inter-regional transport modeling of the SO₂ pollution in Beijing and supplied an important support for the future air-quality and public health management strategies.