摘要
近年来,为了减少PM_(2.5)和O_(3)污染及其带来的负面环境健康影响,我国实行了一系列的大气污染管控措施.量化气象条件与排放对PM_(2.5)和O_(3)污染变化的贡献,可为检验大气污染治理成效提供科学依据.本研究基于2019—2022年国家环境空气质量监测网络地面观测数据,分析了安徽省5个沿江城市(安庆、池州、铜陵、芜湖和马鞍山)PM_(2.5)和O_(3)浓度的时空变化特征,并利用KZ滤波(Kolmogorov-Zurbenko)耦合逐步多元线性回归评估了气象条件与排放对内陆沿江5市PM_(2.5)和O_(3)污染变化的影响.结果表明,2019—2022年内陆沿江5市PM_(2.5)浓度下降速率为0.25~2.00μg·m^(-3)·a^(-1),O_(3)浓度整体上虽然表现为下降趋势,但在2020—2022年O_(3)浓度呈上升趋势,幅度为0.75~6.00μg·m^(-3)·a^(-1).PM_(2.5)与O_(3)呈现夏季相关性显著,冬季无相关性,而春、秋两季PM_(2.5)和O_(3)相关系数r逐年上升的趋势.排放是影响安徽省5个沿江城市PM_(2.5)与O_(3)浓度变化的主导因素,对PM_(2.5)和O_(3)浓度变化的贡献分别为76%~99%和56%~91%.
In order to reduce particulate matter(PM_(2.5))and ozone(O_(3))pollution and their negative impacts on the environment and human health,China has implemented a series of air pollution control measures in recent years.Quantifying the individual contributions of meteorology and anthropogenic emission to variations in PM_(2.5)and O_(3)will provide scientific support for assessing the effectiveness of the measures.In this study,the PM_(2.5)and O_(3)data during 2019—2022 were obtained from the China National Environmental Monitoring Centre and used to analyze spatiotemporal patterns of PM_(2.5)and O_(3)in the five inland cities(Anqing,Chizhou,Tongling,Wuhu,Ma′anshan)along the Yangtze River(YR)in Anhui Province.The influence of meteorology and anthropogenic emission on PM_(2.5)and O_(3)variations was further evaluated using Kolmogorov-Zurbenko(KZ)filter coupling with multiple linear regression.The results showed that the decline rate of PM_(2.5)concentration in the five inland cities from 2019 to 2022 was0.25~2.00μg·m^(-3)·a^(-1).Although the O_(3)concentration in the five cities decreased overall,its concentration increased by 0.75~6.00μg·m^(-3)·a^(-1)from 2020to 2022.In addition,it was found that the correlation between PM_(2.5)and O_(3)in the five inland cities was significant in summer and not significant in winter and showed an increasing trend of correlation coefficient in spring and autumn from 2019 to 2022.The variations of PM_(2.5)and O_(3)in the five inland cities were mainly influenced by emission,which was responsible for 76%~99%of the PM_(2.5)variation and 56%~91%of the O_(3)variation during the 2019—2022 period.
作者
黄语哲
方华
许红玲
江寒
阮志荣
李凤
吴婷
HUANG Yuzhe;FANG Hua;XU Hongling;JIANG Han;RUAN Zhirong;LI Feng;WU Ting(Center of Cooperative Innovation for Recovery and Reconstruction of Degraded Ecosystem in Wanjiang City Belt,School of Ecology and Environment,Anhui Normal University,Wuhu 241002)
出处
《环境科学学报》
CAS
CSCD
北大核心
2024年第7期317-326,共10页
Acta Scientiae Circumstantiae
基金
国家自然科学基金(No.42207128)
安徽高校自然科学研究重点项目(No.KJ2021A0091)。
