Identifying factors that affect environmental air quality using geographical detectors in the NKEFAs of China

Abstract

The establishment of the National Key Ecological Function Areas (NKEFAs) is an important measure for national ecological security, but the current ecological and environmental evaluation of NKEFAs lacks research on the air quality in the NKEFAs. This study presented the current status of the air quality in the NKEFAs and its driving factors using the geographic detector q-statistic method. The air quality in the NKEFAs was overall better than individual cities and urban agglomeration in eastern coast provinces of China, accounting for 9.21% of the days with air quality at Level III or above. The primary air pollutant was PM₁₀, followed by PM_2.5, with lower concentrations of the remaining pollutants. Pollution was more severe in the sand fixation areas, where air pollution was worst in spring and best in autumn, contrasting with other NKEFAs and individual cities and urban agglomerations. The main influencing factors of air quality index (AQI) in the NKEFAs were land use type, wind speed, and relative humidity also weighted more heavily than factors such as industrial pollution and anthropogenic emissions, and most of these influence factors have two types of interactive effects: binary and nonlinear enhancements. These results indicated that air pollution in the NKEFAs was not related with the emission by intensive economic development. Thus, the policies taking the NKEFAs as restricted development zones were effective, but the air pollution caused by PM₁₀ also showed the ecological status in the NKEFAs, especially at sand fixation areas was not quite optimistic, and more strict environmental protection measures should be taken to improve the ecological status in these NKEFAs.

Keywords

air environmental quality / geographical detectors / air auality index / spatiotemporal analysis

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Acknowledgments

The monitoring data provided by the China National Environmental Monitoring Center (available at China National Environmental Monitoring Center website). The geographic data set was supported from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences (available at Resource and Environmental Science Data Center website) and National Earth System Science Data Center, National Science & Technology Infrastructure of China (available at National Earth System Science Data Center website). This work was supported by the National Key Research and Development Plan of China (Grant No. 2016YFC0500205) and the Research on Multi_Level Complex Spatial Data Model and the Consistency (No. 41571391).

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