摘要
随着未来空气质量标准持续加严和污染物浓度进一步下降,研究不同站点类型、站点间大气污染物浓度的差异和站点代表性问题,对于精准制定环境治理政策和保护公共健康具有重要意义。通过分析临沂市2015-2024年监测站点分布变化、不同站点类型大气污染物浓度及其组分差异,并以PM_(2.5)为例,采用浓度相似频率方法评估了临沂市PM_(2.5)国控站点的空间代表性。结果表明:①2015-2024年,临沂市国控站点由4个增至7个。2024年,除O_(3)日最大8小时平均值第90百分位数浓度均为182μg/m^(3)外,7个国控站点PM_(2.5)、PM10、SO_(2)、NO_(2)和CO年平均浓度分别为51.3μg/m^(3)、97.3μg/m^(3)、16.4μg/m^(3)、35.5μg/m^(3)和0.9 mg/m^(3),而同年原4个国控站点PM_(2.5)、PM10、SO_(2)、NO_(2)和CO年平均浓度分别为39.4μg/m^(3)、69.7μg/m^(3)、9.2μg/m^(3)、27.6μg/m^(3)和0.6 mg/m^(3)。②2022-2024年临沂市国控站点、城区站点、城区周边站点、农村站点、交通站点和背景站点的PM_(2.5)年平均浓度分别为39.3、42.6、38.6、39.3、42.0和23.5μg/m^(3),呈城区站点>交通站点>农村站点和国控站点>城区周边站点>背景站点的特征,其他污染物浓度分布与其基本一致;而背景站点O_(3)年平均浓度高于国控站点、城区站点、城区周边站点、农村站点、交通站点和背景站点,其余不同点位O_(3)浓度空间分布差异性并不显著。③国控站点大气污染物浓度平均值已不足以代表城市整体污染情况,2018年国控站点PM_(2.5)浓度的空间代表性区域面积仅占整个城市面积的1.95%,2022年增至2.27%。研究显示:2015-2024年临沂市逐步增设不同类型监测站点,主要大气污染物浓度呈现显著的空间异质性特征,其中新增3个国控站点后,其覆盖区域污染情况较原4个国控站点大气复合污染特征更明显;同时,不同类型监测站点VOCs和PM_(2.5)排放源贡献存在显著差异,2022年临沂市国控站点PM_(2.5)浓度空间代表性范围相对较小。
As air quality standards become increasingly stringent and pollutant concentrations continue to decrease,studying the differences in atmospheric pollutant concentrations across different monitoring site types,and the representativeness of these sites is essential for formulating precise environmental governance policies and protecting public health.This study analyzes the changes in the distribution of monitoring sites in Linyi City from 2015 to 2024,the differences in atmospheric pollutant concentrations and their components across various site types,and evaluates the spatial representativeness of national control sites for PM_(2.5)using the concentration similarity frequency method.The results show that:(1)From 2015 to 2024,the number of national control sites in Linyi City increased from four to seven.In 2024,except for the 90th percentile of the daily maximum 8-hour moving average of O_(3)(182μg/m^(3)),the annual average concentrations of PM_(2.5),PM10,SO_(2),NO_(2)and CO at the seven national control sites were 51.3μg/m^(3),97.3μg/m^(3),16.4μg/m^(3),35.5μg/m^(3)and 0.9 mg/m^(3),respectively.At the four original national sites,the annual average concentrations of PM_(2.5),PM10,SO_(2),NO_(2)and CO in the same year were 39.4μg/m^(3),69.7μg/m^(3),9.2μg/m^(3),27.6μg/m^(3)and 0.6 mg/m^(3)respectively.(2)From 2021 to 2024,the annual average concentrations of PM_(2.5)at national control,urban,suburban,rural,traffic,and background stations in Linyi City were 39.3,42.6,38.6,39.3,42.0,and 23.5μg/m³,respectively,following the characteristics of urban area stations>traffic stations>rural stations>national control stations>stations around the urban area>background stations.The concentration distribution of other pollutants showed a similar pattern.However,the annual average concentration of O_(3)at background stations was higher than at all other site types,and differences among the remaining different sites were not statistically significant.(3)The average values of air pollutant concentrations at national controlled stations are no longer sufficient to represent the overall air quality in Linyi City.For example,the spatially representative range of PM_(2.5)concentrations at national controlled stations covered only 1.95%of the city′s area in 2018,increasing slightly to 2.27%in 2022.The study reveals that from 2015 to 2024,Linyi City gradually expanded its monitoring network to include more diverse station types,and the spatial distribution of major air pollutants displayed significant heterogeneity characteristics.The newly added national control stations were located in areas with more pronounced compound pollution characteristics compared to the original sites.Additionally,there were significant differences in PM_(2.5)and VOC emission source contributions across different types of monitoring stations.In 2022,the spatial representativeness of PM_(2.5)concentrations at national control stations remained limited.
作者
刘一伊
高文康
王帅
孔凡华
白鹤鸣
马占云
田嘉菁
张井贝
LIU Yiyi;GAO Wenkang;WANG Shuai;KONG Fanhua;BAI Heming;MA Zhanyun;TIAN Jiajing;ZHANG Jingbei(Atmosphere Sub-Center of Chinese Ecosystem Research Network(SCAS-CERN),Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China;China National Environmental Monitoring Centre,Beijing 100012,China;Linyi Eco-Environment Monitoring Center of Shandong Province,Linyi 276000,China;Research Center for Intelligent Information Technology,Nantong University,Nantong 226019,China;Chinese Research Academy of Environmental Sciences,Beijing 100012,China;Chifeng Branch of the General Environmental Monitoring Station of Inner Mongolia Autonomous Region,Chifeng 024000,China;Huainan Academy of Atmospheric Sciences,Huainan 232000,China;University of Chinese Academy of Sciences,Beijing 101408,China)
出处
《环境科学研究》
北大核心
2025年第8期1743-1752,共10页
Research of Environmental Sciences
基金
国家重点研发计划项目(No.2022YFC3703003)
中国科学院基础与交叉前沿科研先导专项(No.XDB0760400)。
