Spatial–Temporal Distribution Characteristics of PM2.5 in China in 2016

被引：58
作者：
Huang Y. [1 ]
Yan Q. [1 ]
Zhang C. [2 ]
机构：
[1] China University of Mining and Technology, No1, Daxue Road, Xuzhou, 221116, Jiangsu
[2] University of Connecticut, Storrs, 06269, CT
来源：
Journal of Geovisualization and Spatial Analysis | 2018年 / 2卷 / 2期
关键词：
Air monitoring; Impacting factors; PM[!sub]2.5[!/sub; Spatial distribution; Spatial statistical model;
D O I：
10.1007/s41651-018-0019-5
中图分类号：
学科分类号：
摘要：
Air pollution in China, mainly caused by PM2.5, has become worse in recent years. Large-scale studies on spatial–temporal distribution characteristics of PM2.5 in China have been carried out. Little research was focused on nationwide China, and most previous studies have paid little attention to western China. Based on the observed PM2.5 concentration sample data in 2016 from 1445 air monitoring sites in 367 cities, our research reveals the spatial–temporal variations of PM2.5 concentrations and the possible impacting factors in China using spatial statistical and regression models. The results show that (1) PM2.5 concentrations varied hourly, daily, monthly, and seasonally. The hourly averaged values have two peaks and two valleys every day; the daily and monthly averaged values show U-shaped patterns; and the seasonal averaged values are high in winter but low in summer, with spring and autumn somewhere in between. (2) The Huanyong Line is the E–W dividing boundary between high and low pollution values in China; the Yangtze River and the second ladder dividing line in the east and west of Hu Huanyong Line are the respective S–N dividing boundaries. (3) The concentration of PM2.5 shows a significant clustering pattern in Xinjiang and the North China Plain, as the centers of the dual core of the “hot spots”. The “cold spots” consist of the southeast coastal areas, Tibet, and several other cities with good air quality. The warm temperate zones and subtropical regions are places for hot spots and cold spots, whose rankings and locations are both very sensitive to temperature. (4) China’s air quality is good in the first half of the year and becomes worse in the second half. The spatial distribution of PM2.5 concentrations mainly follows a NE–SW direction, and the air quality in North China is significantly worse than that in the south. (5) The regression relation and spatial variability of PM2.5 pattern and its impacting factors are discussed from both global and local dimensions. Overall, if the aridity index, average annual temperature, industrial GDP, and road network density increased by 1%, PM2.5 concentrations would increase by 66.9, 35.7, 29.1, and 17.4%, respectively. From a local perspective, the positive and negative influences of each impacting factor exist, and the influence differs in different geographical locations. © 2018, Springer Nature Switzerland AG.
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