Application of an advanced spatiotemporal model for PM_2.5 prediction in Jiangsu Province, China

Either long- or short-term of fine particle (PM_2.5) exposure is associated with adverse health effects especially for children. Primary school students spend much time in schools whereas PM_2.5 prediction for such fine-scale places remains a demanding task, let alone a combined prediction with high temporal resolution. The study aimed to estimate PM_2.5 levels of different time scales for primary schools in Jiangsu Province, China. Hourly PM_2.5 measurements within the academic year (Sept. 2016–June 2017) were collected from 72 routine monitoring sites. Together with PM_2.5 emission inventory and dozens of geographic variables, an advanced spatiotemporal land use regression (LUR) model was employed to estimate PM_2.5 concentrations of biweekly, seasonal and academic year levels in Jiangsu Province at 2457 primary school locations. 10-fold cross-validation verified high prediction ability with squared correlations R_CV² of 0.72 for temporal and 0.71 for spatial changes. PM_2.5 levels in primary schools in Nanjing and Nantong were >10% higher than that of the corresponding cities while pollution levels in primary schools in Xuzhou were >20% lower. For 10 out of the 13 cities in Jiangsu, PM_2.5 levels for primary schools surpassed 70 μg/m³ in winter. Schools in Lianyungang, Zhenjiang and Huai'an suffered the most. This study demonstrated the fine-scale prediction ability of the novel spatiotemporal LUR model, as well as the potential and necessity to apply it in epidemiological studies. It also verified the emergency of pollution control for primary schools from cities such as Lianyungang and Zhenjiang.