Spatio-Temporal Variations of Satellite-Based PM 2.5 Concentrations and Its Determinants in Xinjiang, Northwest of China

Wei Wang, Alim Samat, Jilili Abuduwaili and Yongxiao Ge
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Wei Wang: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
Alim Samat: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
Jilili Abuduwaili: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
Yongxiao Ge: State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China

IJERPH, 2020, vol. 17, issue 6, 1-24

Abstract: With the aggravation of air pollution in recent years, a great deal of research on haze episodes is mainly concentrated on the east-central China. However, fine particulate matter (PM 2.5 ) pollution in northwest China has rarely been discussed. To fill this gap, based on the standard deviational ellipse analysis and spatial autocorrelation statistics method, we explored the spatio-temporal variation and aggregation characteristics of PM 2.5 concentrations in Xinjiang from 2001 to 2016. The result showed that annual average PM 2.5 concentration was high both in the north slope of Tianshan Mountain and the western Tarim Basin. Furthermore, PM 2.5 concentrations on the northern slope of the Tianshan Mountain increased significantly, while showing an obviously decrease in the western Tarim Basin during the period of 2001–2016. Based on the result of the geographical detector method (GDM), population density was the most dominant factor of the spatial distribution of PM 2.5 concentrations ( q = 0.550), followed by road network density ( q = 0.423) and GDP density ( q = 0.413). During the study period (2001–2016), the driving force of population density on the distribution of PM 2.5 concentrations showed a gradual downward trend. However, other determinants, like DEM (Digital elevation model), NSL (Nighttime stable light), LCT (Land cover type), and NDVI (Normalized Difference Vegetation Index), show significant increased trends. Therefore, further effort is required to reveal the role of landform and vegetation in the spatio-temporal variations of PM 2.5 concentrations. Moreover, the local government should take effective measures to control urban sprawl while accelerating economic development.

Keywords: PM 2.5; spatio-temporal; geographical detector method; Xinjiang (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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