摘要
为分析长沙市PM_(2.5)浓度时间变化特征、空间分布特征及其影响因子,利用数据统计分析、克里金空间插值技术、地理探测器等方法与Arc GIS平台表达,选取长沙市中心城区10个监测点2013—2019年PM_(2.5)日变化数据。结果显示:在PM_(2.5)浓度时间变化特征方面,不同季节中,PM_(2.5)浓度表现出冬季>秋季>春季>夏季的季节特征,不同时段中,各季节PM_(2.5)浓度日均小时变化曲线均大致呈双峰形态;在PM_(2.5)浓度空间变化特征方面,PM_(2.5)浓度的高值区主要分布在中部芙蓉区,整体呈城区向郊区逐渐递减的变化规律。根据地理探测器研究结果发现,2017年长沙主城区PM_(2.5)浓度主要受气温、降雨和风速因子影响,其次是道路、相对湿度、气压和人口密度,高程、植被和餐饮因子影响较小;且任意两个影响因子共同作用均会对PM_(2.5)浓度影响增强。
Based on data statistical analysis,Kriging spatial interpolation technology,geographic detector,and the Arc GIS platform expression,the daily variation data of PM_(2.5)at 10 monitoring points from 2013 to 2019 in the central part of Changsha City,Hunan,China,were selected to analyze the temporal variation characteristics,spatial distribution characteristics,and influencing factors of PM_(2.5)concentration in the city.The results show that first,in different seasons,the concentration of PM_(2.5)showed seasonal characteristics of Winter>Autumn>Spring>Summer.In different periods,the daily and hourly variation curves of PM_(2.5)concentration in different seasons presented roughly double peaks.Secondly,in terms of the spatial variation characteristics of PM_(2.5)concentration,the high value areas of PM_(2.5)concentration were mainly distributed in the central Furong District,showing a gradually decreasing trend from urban area to suburban area.Thirdly,the geographical detector showed that,the PM_(2.5)concentration in the main urban area of the city in 2017 was mainly affected by temperature,rainfall,and wind speed,followed by roads,relative humidity,air pressure,and population density,while influence by the factors of elevation,vegetation,and the catering factors was relatively small,and the combined effect of any influencing factors could increase the PM_(2.5)concentration.
作者
王长梅
万大娟
王开心
田倩倩
魏力辉
WANG Chang-mei;WAN Da-juan;WANG Kai-xin;TIAN Qian-qian;WEI Li-hui(School of Resources and Environmental Sciences,Hunan Normal University,Changsha 410081,China)
出处
《科学技术与工程》
北大核心
2021年第12期5157-5165,共9页
Science Technology and Engineering
