Spatial and temporal changes of air quality in Shandong Province from 2016 to 2022 and model prediction

被引:6
作者
Zhang, Xu [1 ]
Zhang, Xinrui [1 ]
Yang, Huanhuan [2 ]
Cheng, Xu [5 ]
Zhu, Yong Guan [3 ]
Ma, Jun [4 ]
Cui, Dayong [2 ]
Zhang, Zhibin [1 ,4 ]
机构
[1] Shandong Jianzhu Univ, Sch Municipal & Environm Engn, Jinan 250101, Peoples R China
[2] Qilu Normal Univ, Sch Life Sci, Jinan 250200, Peoples R China
[3] Chinese Acad Sci, Res Ctr Ecoenvironm Sci, Beijing 100085, Peoples R China
[4] Harbin Inst Technol, Sch Environm, Harbin 150090, Peoples R China
[5] Shandong Univ, Inst Adv Technol, Jinan 250061, Peoples R China
基金
中国国家自然科学基金;
关键词
Air Quality; Seasonal Variations; Pollutant Analysis; Predictive Modeling; Environmental Management; NITROGEN-DIOXIDE; RELATIVE RISK; CHINA; POLLUTION; POLLUTANTS; EMISSIONS; FRAMEWORK; EXPOSURE; PLATFORM; CLIMATE;
D O I
10.1016/j.jhazmat.2024.135408
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This study investigates the spatial and temporal dynamics of air quality in Shandong Province from 2016 to 2022. The Air Quality Index (AQI) showed a seasonal pattern, with higher values in winter due to temperature inversions and heating emissions, and lower values in summer aided by favorable dispersion conditions. The AQI improved significantly, decreasing by approximately 39.4 % from 6.44 to 3.90. Coastal cities exhibited better air quality than inland areas, influenced by industrial activities and geographical features. For instance, Zibo's geography restricts pollutant dispersion, resulting in poor air quality. CO levels remained stable, while O3 increased seasonally due to photochemical reactions in summer, with correlation coefficients indicating a strong positive correlation with temperature (r = 0.65). Winter saw elevated NO2 levels linked to heating and vehicular emissions, with an observed increase in correlation with AQI (r = 0.78). PM2.5 and PM10 concentrations were higher in colder months due to heating and atmospheric dust, showing a significant decrease of 45 % and 40 %, respectively, over the study period. Predictive modeling forecasts continued air quality improvements, contingent on sustained policy enforcement and technological advancements. This approach provides a comprehensive framework for future air quality management and improvement.
引用
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页数:13
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