1. Analysis of the spatial and temporal evolution of PM2.5 pollution in China during COVID -19 epidemic
- Author
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ChuanLin Li and Fenghua Huang
- Subjects
Pollution ,geography ,Pearl river delta ,Plateau ,geography.geographical_feature_category ,business.industry ,Computer science ,media_common.quotation_subject ,Outbreak ,Climate change ,Distribution (economics) ,Pollution in China ,Physical geography ,China ,business ,Spatial analysis ,Mountain range ,media_common - Abstract
Environmental health has recently become more and more important in the discussion of international health issues. Due to the impact of haze pollution on human health, ecological environment and climate change, it has become the first problem to be solved in the process of protecting environmental health. PM2.5 is the main cause of haze pollution. Based on the observation data of PM2.5 concentration in 368 cities in China from February to march in 2020, this paper uses the statistical model of spatial data to reveal the spatial and temporal pattern distribution of PM2.5 in China during the period of severe outbreak and spread (February 3 to March 29) of 8 weeks and 56 days. The analysis results show that 1) during the epidemic period, the average concentration of PM2.5 in China ranged from 32.876 to 42.173, which generally showed a trend of rapid decrease and then rebound and continued decline after the outbreak of the epidemic. 2) in terms of space, the concentration of PM2.5 is the highest in the central region, higher in the north than in the south, and higher in the eastern bohai rim region and northern China than in the western qinghai-tibet plateau and Himalayan mountain belt. The high-value areas were mainly distributed in the north China plain, the beijing-tianjin-hebei region, urumqi and hotan regions of xinjiang. 3) the hot spot analysis shows that the hot spots of pollution distribution are the beijing-tianjin-hebei region and the north China plain. The cold spots are the pearl river delta and the yunnan-guizhou region. 4) spatial autocorrelation analysis shows that the average weekly concentration of PM2.5 shows a strong local spatial autocorrelation.
- Published
- 2020
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