Wang Xiaoning, Xu Xiaoqi, Yang Chuanxi, Yang Xuemei, Zhao Xinyan, Wan Ziheng, Xu Yiyong, Guo Qianqian, Sun Haofen, Chen Dong, Zhao Weihua, Xiao Yihua, Dong Wenping, Tang Yizhen, Dai Zhenxue, Liu Changqing, Yun Lexin, Wang Weiliang
School of Environmental and Municipal Engineering, Qingdao University of Technology, Qingdao 266520, China.
School of Business, Qingdao University of Technology, Qingdao 266520, China.
J Hazard Mater. 2025 Mar 15;486:137065. doi: 10.1016/j.jhazmat.2024.137065. Epub 2024 Dec 31.
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), PM, PM, CO, SO and NO showed a seasonal pattern with higher values in winter and lower values in summer, however, O 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.
在过去20年里，山东省的城市化有力地支撑了经济的快速增长和持续转型，然而，由于人类活动密集，该地区遭受了严重的大气污染。识别和量化山东省空气污染的时空变化及其驱动因素，将有助于制定有效的减排政策。深入了解空气质量与社会经济驱动因素之间的耦合关系，对于评估城市化质量和长期可持续性至关重要。因此，本研究调查了2014 - 2022年期间济南和青岛空气质量的时空变化及其驱动因素。空气质量指数（AQI）、PM、PM、CO、SO和NO呈现出季节性模式，冬季值较高，夏季值较低，然而，O冬季值较低，夏季值较高。青岛的空气质量指数质量超过济南，但济南的空气质量指数改善率超过青岛，这意味着青岛的空气质量指数质量较高，而济南的空气质量指数改善速度较快。使用斯皮尔曼相关分析（SCA）、灰色关联分析（GRA）和熵权法（EMW）来评估空气质量指数与污染物排放/经济发展/城市建设指标之间的相互关系。济南的主要驱动因素是工业烟尘（粉尘）排放（SCA，r = 0.94）、第二产业增加值（GRA，r = 0.68）、第二产业增加值（EMW，w = 0.125）和人均公共绿地面积（EMW，w = 0.104）。但青岛的主要驱动因素是第二产业增加值（SCA，r = -0.92）、第一产业增加值（GRA，r = 0.77）、第一产业增加值（EMW，w = 0.147）和城市建成区绿化覆盖率（EMW，w = 0.129）。这些结果可以为未来的空气质量管理和改善提供有价值、有意义且重要的支持和框架。
