摘要
为研究三门峡市大气颗粒物污染状况,根据2017年-2022年PM_(2.5)和PM_(10)监测数据,利用地理空间分布和Mann-Kendall趋势检验法分析其时空变化特征,并基于时间序列模型对2023年PM_(2.5)和PM_(10)浓度进行预测,结果表明:三门峡下辖义马市污染程度高于其它辖区,卢氏县污染程度最低;PM_(2.5)和PM_(10)浓度呈显著正相关,均存在季节性变化规律,冬春季浓度较高,夏季浓度低;2017年-2022年间,PM_(2.5)和PM_(10)浓度整体呈下降趋势;建立的AR(1)模型拟合较好,该模型预测2023年1月PM_(2.5)浓度较高,为87.710μg/m^(3)(95%置信区间:61.202~114.219μg/m^(3))。研究结果可为环境部门针对重点区域开展秋冬季综合治理提供科学依据。
In order to study the atmospheric air pollution with particulate matters in Sanmenxia,geospatial distribution and Mann-Kendall trend test method were introduced to analyze its temporal and spatial changes based on the PM_(2.5)and PM_(10) data from 2017 to 2022,and time series model was used to predict the concentrations of PM_(2.5)and PM_(10) in 2023.The results showed that:over the last few years,Yima has higher pollution level than other districts,Lushi was the county has the lowest level of PM_(2.5)and PM_(10).PM_(2.5)and PM_(10) concentrations showed seasonal trends with higher values in winter and lower in summer.from 2017 to 2022,PM_(2.5)and PM_(10) concentrations showed a reduced tendency.the time series models were established and used to forecast PM_(2.5)and PM_(10) concentrations.The highest level of PM_(2.5)is registered in January 2023,with an average concentration of 87.710μg/m^(3)(95%CI:61.202~114.219μg/m^(3)).These results can provide scientific support for environmental departments to carry out comprehensive management in different regions.
作者
张晶
王小国
孔玉华
胡三宁
杨喜田
Zhang Jing;Wang Xiaoguo;Kong Yuhua;Hu Sanning;Yang Xitian(College of Applied Engineering,Henan University of Science and Technology,Sanmenxia 472000,China;Sanmenxia Polytechnic,Sanmenxia 472000,China;College of Forestry,Henan Agriculture University,Zhengzhou 450002,China)
出处
《环境科学与管理》
CAS
2023年第5期97-101,共5页
Environmental Science and Management
基金
河南省高等学校重点科研项目(22A610011)
三门峡市科技攻关计划项目(2022002097)
三门峡市软科学计划项目(2022003031)
河南省重点研发与推广专项项目(232102110061)。
