摘要
基于2017年逐时空气质量监测数据、归一化植被指数(NDVI) 16 d合成数据以及社会经济数据,对京津冀大气污染特征进行了系统分析,并利用线性回归、地理加权回归模型等探讨了其变化规律与NDVI的关系,及其受社会经济因素的影响.结果表明:①京津冀地区大气污染整体表现为南高北低、平原高山区低的分布特征,由北向南递次升高,大气污染呈现出显著的空间异质性;②从季节变化看,呈现冬季>秋季>春季>夏季的总体规律,京津冀地区大气污染呈现出显著的时间异质性;③SO_2、NO_2、CO、PM_(2.5)、PM_(10)等污染物浓度均与NDVI值呈负相关关系;在气候、地形等自然条件较为一致的前提下,NDVI值越低人类活动干扰越明显、产业经济布局越集中,进而污染排放量越大,对空气质量产生显著负面影响;④NDVI指数一定程度上反映了土地利用、人口分布以及产业布局状况,而这些因素直接或间接决定着大气污染排放水平,进而能够指示区域的污染分布特征;⑤地理加权回归模型(GWR)计算结果表明,经济发展水平越高的地区NDVI与社会经济因子、PM_(2.5)等污染物浓度相关性越好. NDVI的分布可以大体反映社会经济发展水平.对PM_(2.5)的分布也有一定的指示作用.
Based on 2017 hourly air quality monitoring data, NDVI 16 d synthetic data, and socio-economic data, the air pollution characteristics of Beijing-Tianjin-Hebei were systematically analyzed, and its variation, normalized vegetation index, and the relationship between the index ( NDVI) and its impact on socio-economic factors, were analyzed by linear regression analysis and a geographically weighted regression model. The conclusions are as follows:① The overall air pollution in the Beijing-Tianjin-Hebei region is characterized by high-level pollution over the southern plain areas and low-level pollution over the northern mountainous areas. The air pollution increases from north to south, and shows significant spatial heterogeneity.② From the perspective of seasonal changes, the overall order winter > autumn > spring > summer is observed, and atmospheric pollution in the Beijing-Tianjin-Hebei region shows significant temporal heterogeneity.③The concentrations of pollutants such as SO2, NO2, CO, PM2.5 , and PM10 all have a negative correlation with the NDVI value. Assuming that natural conditions such as climate and topography are relatively consistent, the lower the NDVI value, the more obvious the interference of human activities, the more concentrated the industrial economy layout, and the greater the pollution emissions, the more significant the negative impact on air quality.④The NDVI reflects the land use, population distribution, and industrial layout to a certain extent, and these factors directly or indirectly determine the level of air pollution emissions and thus indicate the pollution distribution characteristics of the region.⑤The results of the GWR model calculation show that the higher the level of economic development, the better the correlation between the NDVI and socioeconomic factors, PM2.5 , and other pollutant concentrations. The distribution of the NDVI can generally reflect the level of social and economic development. The distribution of the NDVI also correlates to the distribution of PM2.5 to a certain extent.
作者
孙爽
李令军
赵文吉
齐梦溪
田欣
李珊珊
SUN Shuang;LI Ling-jun;ZHAO Wen-ji;QI Meng-xi;TIAN Xin;LI Shan-shan(College of Resources Environment and Tourism, Capital Normal University,Beijing 100048,China;Beijing Municipal Environmental Monitoring Center, Beijing 100048,China;Beijing Municipal Research Institute of Environmental Protection, Beijing 100037,China)
出处
《环境科学》
EI
CAS
CSCD
北大核心
2019年第4期1585-1593,共9页
Environmental Science
基金
国家重点研发计划项目(2018YFC0706004
2018YFC0706000)
关键词
大气污染
空气质量指数(AQI)
归一化植被指数
相关关系
地理加权回归模型(GWR)
atmospheric pollution
air quality index (AQI)
normalized vegetation index (NDVI)
correlation coefficient
geographically weighted regression model (GWR)
