|关键词: PM2.5 陕西省 空间自相关分析 地理探测器
|英文基金项目:Shaanxi Province Innovation Capability Support Program Progect (2021PT-020); Natural Science Basic Research Plan in Shaanxi Province of China (2015JQ4114); Baoji Science and Technology Project (2018JH-15); Graduate Innovative Research Projects of Baoji University of Arts and Sciences (YJSCX20YB27)
|Regional differences of PM2.5 spatial and temporal distribution in Shaanxi Province
YI Wenli, TIAN Miao, ZHENG Haohao, FENG Shige
1. College of Geography and Environment, Baoji University of Arts and Sciences, Baoji 721013, China
2. Shaanxi Key Laboratory of Disasters Monitoring & Mechanism Simulation, Baoji University of Arts and Sciences, Baoji 721013, China
|Background, aim, and scope More concerns have been paid to atmospheric pollution problem in China in recent years, and the PM2.5 has been considered to be the main component of haze affecting the quality of atmospheric environment. Three areas including Guanzhong, Southern Shaanxi and Northern Shaanxi have been divided by the Qinling Mountains and the Weihe River Basin in Shaanxi Province. Due to the complex natural conditions and human factors, the spatial distribution of PM2.5 pollution intensity in Shaanxi is unbalanced. Therefore, to identify the spatial-temporal variations and influencing factors of PM2.5 is of great significance for providing regional reliable scientific basis of prevention and control in Shaanxi. Materials and methods Based on daily PM2.5 concentration data of counties and districts in Shaanxi in 2019, ArcGIS software and spatial autocorrelation analysis were used to analyze the spatial and temporal distribution characteristics of PM2.5, and geographic detector software was also used to reveal PM2.5 driving forces and their interactions in each region. Results The results showed that PM2.5 pollution in Shaanxi appeared obvious characteristics of “high concentration in winter and low concentration in summer”, as well as “high value in Guanzhong, low value in Northern Shaanxi and Southern Shaanxi”. The spatial distribution of PM2.5 concentration in Shaanxi showed a very significantly positive spatial correlation. Some counties in Southern Shaanxi had low—low concentrations, while some counties in Weinan, Xi’an and Xianyang had high—high concentrations. Social and economic factors displayed the greatest influence on PM2.5 concentration in Shaanxi (in the range of 0.328—0.548). GDP, population density and relative humidity were the dominant driving forces for Northern Shaanxi, Guanzhong and Southern Shaanxi, with the values of 0.932, 0.936 and 0.710, respectively. The interactive detection results showed that the dominant factor in Shaanxi Province was population density∩GDP. Discussion The influences of natural and anthropogenic factors on PM2.5 had significant spatial-temporal differences in Shaanxi. Population density, GDP and relative humidity were the main limiting influencing factors of PM2.5 pollution in different regions. The interaction factors had a greater explanatory power on PM2.5 pollution than that of single factor. The joint action between nature and human activities factor had a significant effect on PM2.5 pollution, which reflected the complexity of the driving force of air pollution in Shaanxi. Conclusions The spatial distribution of PM2.5 in Shaanxi has a stable spatio-temporal pattern and significant spatial autocorrelation characteristics. Obvious regional differences of PM2.5 pollution occur in Northern Shaanxi, Guanzhong and Southern Shaanxi. PM2.5 pollution in Shaanxi is the result of a combination of natural conditions and human activities. Recommendations and perspectives In the future, we should focus on the interaction between various driving forces including natural and anthropogenic factors influencing PM2.5 pollution, and a simple single mode of control is not acceptable. The key to air control in Shaanxi Province is to realize joint prevention and control of air control among different regions and to change the unreasonable way of production and life.
|Key words: PM2.5 Shaanxi Province spatial autocorrelation analysis geographic detector