摘要
以PM2.5污染物为主的大气污染对社会的可持续发展及人类健康带来了严峻的挑战,厘清我国PM2.5污染物的空间分布特征及演变规律,对于PM2.5污染物的区域联防联控具有重要的意义。基于Aqua/MODIS气溶胶产品、气象基础数据以及PM2.5污染物实测站点监测数据,构建地理加权回归模型,对2015年中国PM2.5污染物浓度进行了模拟估算,对PM2.5污染物浓度的空间分异格局及季节演化特征进行分析。结果表明:①2015年全国PM2.5浓度整体表现出明显的空间地带性分异特征。北方PM2.5污染物浓度明显高于南方,中部明显高于东部与西部;②4个季度PM2.5浓度表现出明显的季节适应性演化特征。第四季度PM2.5污染最重,第三季度和第一季度次之,第二季度最低,最大值出现在第四季度(165μg/m3),最小值出现在第二季度(4.3μg/m3)。③通过多因子构建的地理加权回归模型估算的PM2.5浓度具有较高的模拟精度,第一至第四季度的相对误差分别为10.2%、7.0%、9.3%和8.6%。
Air pollution characterized by PM2.5 pollutants poses severe challenges to the sustainable development of society and human health.Therefore,it is of great significance to clarify the spatial-temporal distribution and evolution of PM2.5 pollutants in China for regional joint prevention and control of PM2.5 pollutants.Based on the MODIS satellite aerosol products,meteorological basic data and PM2.5 pollutant monitoring site monitoring data,a geographically weighted regression model was established to simulate and estimate PM2.5 pollutant concentration in China in 2015 on the basis of aerosol and meteorological data pre-processing.In addition,the spatial distribution pattern,the seasonal evolution characteristics of PM2.5 pollutant concentration were analyzed.The results showed that:(1)the PM2.5 concentration values in China in 2015 as a whole showed obvious spatial zonal differentiation characteristics.The concentration of pollutants in the north is significantly higher than that in the south,and the areas with high PM2.5 concentrations are mainly concentrated in the Beijing-Tianjin-Hebei region,the Jianghuai plain,the Sichuan basin,and the Takaramalkan desert.The area has a wide spatial distribution and significant continuity;(2)The PM2.5 concentration in the fourth quarter showed obvious seasonal adaptive evolution characteristics.The PM2.5 concentration changed significantly in the season.PM2.5 pollution was the heaviest in the fourth quarter,followed by the first quarter of the third quarter and the lowest in the second quarter.The maximum occurred in the fourth quarter(165μg/m^3).The minimum appeared in the second quarter(4.3μg/m^3).Seasonal changes in PM2.5 concentrations were influenced by meteorological factors and human social activities;and(3)The accuracy of the inversion of PM2.5 concentration by a multi-factorial,geographically weighted regression model was higher,with relative errors in the four quarters being 10.2%,7.0%,9.3%,and 8.6%,respectively.
作者
魏石梅
潘竟虎
妥文亮
Wei Shimei;Pan Jinghu;Tuo Wenliang(College of Geography and Environmental Sciences,Northwest Normal University,Lanzhou 730070,China)
出处
《遥感技术与应用》
CSCD
北大核心
2020年第4期845-854,共10页
Remote Sensing Technology and Application
基金
国家自然科学基金项目(41661025)
西北师范大学青年教师科研能力提升计划(NWNU-LKQN-16-7)。
关键词
PM2.5
MODIS
遥感估算
时空分布
中国
PM2.5
MODIS
Remote sensing estimation
Spatio-temporal distribution
China
