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2016~2020年山东省空气质量时空分布特征及影响因素分析
摘要点击 878  全文点击 169  投稿时间:2021-09-02  修订日期:2021-11-08
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中文关键词  空气质量  时空分布  多尺度地理加权回归  小波分析  山东省
英文关键词  air quality  temporal and spatial distribution  multiscale geographically weighted regression  wavelet analysis  Shandong province
作者单位E-mail
周梦鸽 中国科学院地理科学与资源研究所 陆地表层格局与模拟重点实验室, 北京 100101
中国科学院大学, 北京 100049 
zhoumg.20b@igsnrr.ac.cn 
杨依 中国科学院地理科学与资源研究所 陆地表层格局与模拟重点实验室, 北京 100101
中国科学院大学, 北京 100049 
 
孙媛 中国环境监测总站, 北京 100012  
张凤英 中国环境监测总站, 北京 100012  
李永华 中国科学院地理科学与资源研究所 陆地表层格局与模拟重点实验室, 北京 100101 yhli@igsnrr.ac.cn 
中文摘要
      基于2016~2020年山东省城市空气质量监测结果,结合人口密度和城镇化率等社会经济数据以及风速、气温和相对湿度等气象要素,综合应用地理加权回归模型(GWR)、多尺度地理加权回归模型(MGWR)和小波分析等方法,探究山东省空气污染物的时空分布特征及其与社会经济和气象要素的关系.结果表明:①近5年来山东省空气质量整体呈好转趋势,除O3外,SO2、NO2、PM2.5和PM10等污染物浓度逐年降低.空气污染物分布存在明显的空间差异,沿海地区污染物浓度更低.②山东省PM2.5与人口密度和第二产业占比具有极显著的正相关关系(P<0.01),而与城镇化率呈极显著负相关关系(P<0.01).同时,这种相关关系在空间上存在尺度差异,人口密度、民用汽车量和工业用电量与PM2.5空间关系较平稳,而城镇化率和第二产业占比对PM2.5的影响的空间异质性较高.③气象要素对菏泽和威海两市PM2.5的影响程度不同,菏泽市PM2.5与气温、相对湿度和日照时数相关性更强,而威海市海陆风盛行,PM2.5与风速相关性更高.④小波分析表明每年冬季菏泽市比威海市空气污染频繁,频率约为每次1~2周;年周期上,菏泽市PM2.5滞后于风速,而威海市PM2.5和风速表现为同相位.山东省空气质量存在明显的时空差异,并受社会经济和气象因素的综合影响.
英文摘要
      Based on the daily monitoring data of urban air quality in Shandong province from 2016 to 2020, combined with socio-economic data such as population density and urbanization rate, as well as meteorological data such as wind speed, temperature, and relative humidity, the methods of geographic weighted regression (GWR), multiscale geographically weighted regression (MGWR), and wavelet analysis were comprehensively applied to explore the temporal and spatial distribution characteristics of air pollutants and their relationship with socio-economic and meteorological elements. The results showed that:① In the past five years, the air quality in Shandong province has shown an overall improvement trend. Except for ozone, the concentrations of SO2, NO2, PM2.5, and PM10 decreased annually. Additionally, their distribution had obvious spatial differences, which was reflected in the lower concentration of air pollutants in coastal areas. ② PM2.5 in Shandong province had an extremely significant positive correlation with population density and the proportion of secondary industry (P<0.01) but had a very negative correlation with urbanization rate (P<0.01). Moreover, there were scale differences in the spatial relationship. The spatial relationship between population density, civil vehicle volume, industrial power consumption, and PM2.5 was relatively stable, whereas the spatial heterogeneity of the impact of urbanization rate and the proportion of secondary industry on PM2.5 concentration was high. ③ Meteorological factors had different effects on PM2.5 in Heze and Weihai. PM2.5 in Heze had a stronger correlation with air temperature, relative humidity, and sunshine hours, whereas sea land breeze prevailed in Weihai, resulting in a higher correlation between PM2.5 and wind speed. ④ Wavelet analysis showed that the frequency of air pollution in Heze was higher than that in Weihai, approximately one-two weeks/time in winter. In the annual cycle, the PM2.5 in Heze lagged behind the wind speed, whereas the PM2.5 and wind speed in Weihai were in the same phase. To summarize, there were obvious temporal and spatial differences in air quality in Shandong province, which was comprehensively affected by socio-economic and meteorological factors.

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