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SPATIAL AUTOCORRELATION ANALYSIS OF CITY PM2.5 CONCENTRATION IN HENAN

Journal: INTERNATIONAL JOURNAL OF RESEARCH -GRANTHAALAYAH (Vol.7, No. 8)

Publication Date:

Authors : ;

Page : 454-462

Keywords : PM 2.5 Concentration; Seasonal Evolution; Global Spatial Autocorrelation Analysis; Local Spatial Autocorrelation Analysis; Spatial Aggregation; Henan Province.;

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Abstract

PM2.5 has become the main pollutant of air pollution in China, and PM2.5 pollution control is one of the important means of atmospheric environmental governance. Aiming on the spatial and temporal distribution characteristics of regional PM2.5 concentration, this paper bases on the monitoring data of city PM2.5 concentration in Henan Province from 2015 to 2018 to study the spatial autocorrelation characteristics of city PM2.5 concentration and explore the city PM2.5 concentration spatial relationship by using the geo-statistical analysis method. The results showed that: the PM2.5 concentration in Henan shows obvious seasonal variation characteristics, the PM2.5 concentrations in the northern cities are significantly higher than that in the southern cities. The PM2.5 concentrations in the northern cities vary significantly with the seasonal transition, PM2.5 pollution is mainly moderately and above polluted, the PM2.5 concentrations in southern cities vary little with the seasonal transition, PM2.5 pollution is mainly lightly polluted. The city PM2.5 concentration shows a trend of regional integration with the significant spatial autocorrelation, the global autocorrelation characteristic is independent of PM2.5 concentration. The city PM2.5 concentration also presents the characteristics of local instability, forms a HighHigh aggregation region centered on Xinxiang and Zhengzhou, the aggregation degree tends to be significant with the increase of PM2.5 concentration, the aggregation area gradually expands to northward. The PM2.5 concentration in Anyang is no significant autocorrelation with the PM2.5 concentrations in other cities of the province.

Last modified: 2019-09-07 12:08:56