Over the past 20 years, urbanization of Shandong Province has strongly supported the rapid growth and sustained transformation of economy, however, this region has suffered from serious atmospheric pollution due to intense human activity. Identifying and qualifying the spatio-temporal variation of air pollution and its driving forces of Shandong Province would help in the formulation of effective mitigation policies. A deep understanding of the coupling relationship between air quality and socioeconomic drivers was essential for evaluating the quality of urbanization and long term sustainability. Hence, this study investigates the spatio-temporal variation and its driving factors of air quality in Jinan and Qingdao during 2014-2022. The air quality index (AQI), PM2.5, PM10, CO, SO2 and NO2 showed a seasonal pattern with higher values in winter and lower values in summer, however, O3 showed lower values in winter and higher value in summer. AQI quality for Qingdao surpassed Jinan, but AQI improvement rates of Jinan surpassed Qingdao, which means higher AQI quality in Qingdao and faster AQI improvement in Jinan. Spearman correlation analysis (SCA), gray relational analysis (GRA) and entropy weight method (EMW) were used to evaluated the interrelations between AQI and pollutant-emission / economic-development / urban-construction index. The primary driving factors were industrial smoke (dust) emissions (SCA, r = 0.94), value-added of secondary industry (GRA, r = 0.68), value-added of secondary industry (EWM, w = 0.125) and per capita public green space area (EWM, w = 0.104) for Jinan. But the primary driving factors were value-added of secondary industry (SCA, r = -0.92), value-added of primary industry (GRA, r = 0.77), value-added of primary industry (EWM, w = 0.147) and green coverage rate of urban built-up areas (EWM, w = 0.129) for Qingdao. These results could provide valueable, meaningful and significant supporting and framework for future air quality management and improvement.
Keywords: Air quality; Correlation analysis; Driving factors; Environmental management; Spatio-temporal variations.
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