Comprehensive evaluation method of urban air quality statistics based on environmental monitoring data and its application  

作　　者：Linglu Qu Fahe Chai Shijie Liu Jingchun Duan Fan Meng Miaomiao Cheng 

机构地区：[1]Atmospheric Environment Institute,Chinese Research Academy of Environmental Sciences,Beijing 100012,China [2]Asia Center for Air Pollution Research,Niigata 950-2144,Japan

出　　处：《Journal of Environmental Sciences》2023年第1期500-509,共10页环境科学学报（英文版）

基　　金：supported by the National Key Research and Development Program of China(No.2019YFC0214800)。

摘　　要：Air quality monitoring is effective for timely understanding of the current air quality status of a region or city.Currently,the huge volume of environmental monitoring data,which has reasonable real-time performance,provides strong support for in-depth analysis of air pollution characteristics and causes.However,in the era of big data,to meet current demands for fine management of the atmospheric environment,it is important to explore the characteristics and causes of air pollution from multiple aspects for comprehensive and scientific evaluation of air quality.This study reviewed and summarized air quality evaluation methods on the basis of environmental monitoring data statistics during the 13th Five-Year Plan period,and evaluated the level of air pollution in the Beijing-Tianjin-Hebei region and its surrounding areas(i.e.,the“2+26”region)during the period of the three-year action plan to fight air pollution.We suggest that air quality should be comprehensively,deeply,and scientifically evaluated from the aspects of air pollution characteristics,causes,and influences of meteorological conditions and anthropogenic emissions.It is also suggested that a threeyear moving average be introduced as one of the evaluation indexes of long-term change of pollutants.Additionally,both temporal and spatial differences should be considered when removing confounding meteorological factors.

关 键 词：Air quality assessment Air pollution Statistical analysis Environmental monitoring data Big data 

分 类 号：X823[环境科学与工程—环境工程]