<p class="first" id="d4151302e101">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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