Spatiotemporal evolution of the remotely sensed global continental PM2.5 concentration from 2000-2014 based on Bayesian statistics. (July 2018) - Li, Junming; Wang, Nannan; Wang, Jinfeng; Li, Honglin
Abstract: PM2.5 pollution is threatening human health and quality of life, especially in some densely populated regions of Asia and Africa. This paper used remotely sensed annual mean PM2.5 concentrations to explore the spatiotemporal evolution of global continental PM2.5 pollution from 2000 to 2014. The work employed an improved Bayesian space-time hierarchy model combined with a multiscale homogeneous subdivision method. The statistical results quantitatively demonstrated a 'high-value increasing and low-value decreasing' trend. Areas with annual PM2.5 concentrations of more than 70 μg / m 3 and less than 10 μg / m 3 expanded, while areas with of an annual PM2.5 concentrations of 10 – 25 μg / m 3 shrank. The most heavily PM2.5 -polluted areas were located in northwest Africa, where the PM2.5 pollution level was 12.0 times higher than the average global continental level; parts of China represented the second most PM2.5 -polluted areas, followed by northern India and Saudi Arabia and Iraq in the Middle East region. Nearly all (96.50%) of the highly PM2.5 -polluted area (hot spots) had an increasing local trend, while 68.98% of the lightly PM2.5 -polluted areas (cold spots) had a decreasing local trend. In contrast, 22.82% of the cold spot areas exhibited an increasing local trend. Moreover, the spatiotemporal variation in the health risk from exposure to PM 2.5 over the global continents was also investigated. Four areas, India, eastern and southern China, western Africa and central Europe, had high health risks from PM 2.5 exposure. Northern India, northeastern Pakistan, and mid-eastern China had not only the highest risk but also a significant increasing trend; the areas of high PM2.5 pollution risk are thus expanding, and the number of affected people is increasing. Northern and central Africa, the Arabian Peninsula, the Middle East, western Russia and central Europe also exhibited increasing PM2.5 pollution health risks. Graphical abstract: Image 1 Highlights: Firstly Bayesian statistical method applied in analysing global PM2.5 pollution. Estimated quantitatively spatial relative magnitude of global PM2.5 pollution. Demonstrated the local trends of 'high value increasing and low value reducing'. Investigated spatiotemporal variation of global PM2.5 pollution health risk.