Study on Characteristics, Influencing Factors and Health Benefits of Atmospheric Multi-Pollutants in Southern Xinjiang
Wang, Jinyang1,2; Ju, Tianzhen1,2; Lei, Shengtong1,2; Li, Bingnan3; Niu, Xiaowen1,2
2023-11
发表期刊ATMOSPHERE
EISSN2073-4433
卷号14期号:11
摘要In recent years, air pollution in Xinjiang, the core region of the Silk Road Economic Belt, has become increasingly severe, posing a more significant threat to human health. This paper selects the tropospheric ozone, nitrogen dioxide, and formaldehyde datasets under OMI remote-sensing monitoring and the PM2.5 dataset in China High Air Pollutants (CHAP) for 2018-2021. The spatial and temporal distribution of multi-pollutants, the spatial autocorrelation of Moran's I index pollutants, and the correlation between pollutants in the warm period were studied in southern Xinjiang. Meanwhile, the geographical and temporal weighted regression (GTWR) model was used for influencing factor analysis, and the BenMap-CE model was used for health benefit analysis. The results showed that the spatial distribution of ozone concentration values in southern Xinjiang shows a decreasing distribution pattern from the east-central region to the western and southern regions. The spatial distribution of formaldehyde concentration values is opposite to that of ozone. There is a clear high-value area in the ozone concentration value in April-September. The NO2 column concentration values were in the range of 0.55 similar to 1.09 x 10(15) molec/cm(2) in most parts of southern Xinjiang. The area of high concentration values is located in the northeast of the study area; PM2.5 concentration values are higher in the middle area of southern Xinjiang. The spatial autocorrelation characteristics showed that the spatial aggregation of O-3 and NO2 displayed a slow increasing trend year by year. The spatial aggregation of HCHO and PM2.5 fluctuated slightly in four years. The overall trend of HCHO is slowly decreasing, while PM2.5 is fluctuating and increasing. In the GTWR model analysis, overall, the atmospheric pressure has a strong influence on all pollutants. The effect of NO2 on O-3 was higher than that of HCHO among the four pollutants. The correlation between O-3 and PM2.5 was as high as -0.7872. The BenMap-CE health benefits assessment concluded that the number of premature deaths caused by ozone pollution was much higher than that of premature deaths caused by PM2.5. The highest number of premature deaths for both pollutants occurred in Kashgar.
关键词O-3 PM2.5 GTWR BenMap-CE Moran's I index oasis
DOI10.3390/atmos14111681
收录类别SCIE ; EI
语种英语
WOS研究方向Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS类目Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS记录号WOS:001119566100001
出版者MDPI
EI入藏号20234815120895
EI主题词Spatial distribution
EI分类号405.3 Surveying ; 443.1 Atmospheric Properties ; 451 Air Pollution ; 461.7 Health Care ; 804 Chemical Products Generally ; 804.1 Organic Compounds ; 804.2 Inorganic Compounds ; 902.1 Engineering Graphics ; 921 Mathematics
原始文献类型Article
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符https://ir.nwnu.edu.cn/handle/39RV6HYL/94114
专题实体学院_地理与环境科学学院
直属单位_科教服务中心(政府采购办公室)
通讯作者Ju, Tianzhen
作者单位1.Northwest Normal Univ, Coll Geog & Environm Sci, Lanzhou 730070, Peoples R China;
2.Key Lab Resource Environm & Sustainable Dev Oasis, Lanzhou 730000, Peoples R China;
3.Shaanxi Normal Univ, Fac Atmospher Remote Sensing, Xian 710062, Peoples R China
第一作者单位地理与环境科学学院
通讯作者单位地理与环境科学学院
第一作者的第一单位地理与环境科学学院
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GB/T 7714
Wang, Jinyang,Ju, Tianzhen,Lei, Shengtong,et al. Study on Characteristics, Influencing Factors and Health Benefits of Atmospheric Multi-Pollutants in Southern Xinjiang[J]. ATMOSPHERE,2023,14(11).
APA Wang, Jinyang,Ju, Tianzhen,Lei, Shengtong,Li, Bingnan,&Niu, Xiaowen.(2023).Study on Characteristics, Influencing Factors and Health Benefits of Atmospheric Multi-Pollutants in Southern Xinjiang.ATMOSPHERE,14(11).
MLA Wang, Jinyang,et al."Study on Characteristics, Influencing Factors and Health Benefits of Atmospheric Multi-Pollutants in Southern Xinjiang".ATMOSPHERE 14.11(2023).
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