Spatiotemporal variation and determinants of population’s PM2.5 exposure risk in China, 1998–2017: a case study of the Beijing-Tianjin-Hebei region

Jin, Ning; Li, Junming; Jin, Meijun; Zhang, Xiaoyan

Spatiotemporal variation and determinants of population’s PM2.5 exposure risk in China, 1998–2017: a case study of the Beijing-Tianjin-Hebei region

2020

Jin, Ning | Li, Junming | Jin, Meijun | Zhang, Xiaoyan

PM₂.₅ pollution has emerged as a global human health risk. The best measure of its impact is a population’s PM₂.₅ exposure (PPM₂.₅E), an index that simultaneously considers PM₂.₅ concentrations and population spatial density. The spatiotemporal variation of PPM₂.₅E over the Beijing-Tianjin-Hebei (BTH) region, which is the national capital region of China, was investigated using a Bayesian space-time model, and the influence patterns of the anthropic and geographical factors were identified using the GeoDetector model and Pearson correlation analysis. The spatial pattern of PPM₂.₅E maintained a stable structure over the BTH region’s distinct terrain, which has been described as “high in the northwest, low in the southeast”. The spatial difference of PPM₂.₅E intensified annually. An overall increase of 6.192 (95% CI 6.186, 6.203) ×10³ μg/m³ ∙ persons/km² per year occurred over the BTH region from 1998 to 2017. The evolution of PPM₂.₅E in the region can be described as “high value, high increase” and “low value, low increase”, since human activities related to gross domestic product (GDP) and energy consumption (EC) were the main factors in its occurrence. GDP had the strongest explanatory power of 76% (P < 0.01), followed by EC and elevation (EL), which accounted for 61% (P < 0.01) and 40% (P < 0.01), respectively. There were four factors, proportion of secondary industry (PSI), normalized differential vegetation index (NDVI), relief amplitude (RA), and EL, associated negatively with PPM₂.₅E and four factors, GDP, EC, annual precipitation (AP), and annual average temperature (AAT), associated positively with PPM₂.₅E. Remarkably, the interaction of GDP and NDVI, which was 90%, had the greatest explanatory power for PPM₂.₅E ′ s diffusion and impact on the BTH region.

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