摘要
基于淮河流域35个城市2015~2021年的细颗粒物(PM_(2.5))及其组分数据集,分析了污染物的时空分布格局,利用随机森林模型考察了气象因子对PM_(2.5)浓度的影响.采用KZ(Kolmogorov-Zurbenko)滤波和多元线性回归(MLR)对PM_(2.5)、硫酸盐(SO_(4)^(2-))、硝酸盐(NO_(3)^(-))、铵盐(NH_(4)^(+))、有机物(OM)和黑炭(BC)的原始序列进行气象调整,定量气象条件的影响.结果表明,2015~2021年淮河流域PM_(2.5)、SO_(4)^(2−)、NO_(3)^(-)、NH_(4)^(+)、OM和BC的变化速率分别为-4.71、-0.99、-1.05、-0.77、-1.01和-0.19μg·(m^(3)·a)^(-1).PM_(2.5)及其组分浓度高值集中在淮河流域中部和西部区域,而沿海及南部城市的浓度较低.PM_(2.5)短期、季节和长期分量对35个城市PM_(2.5)原始序列总方差的贡献率分别为51.6%、35.9%和7.0%,沿海城市更受短期分量影响.2015~2018年气象条件不利于淮河流域PM_(2.5)浓度的降低,2019~2021年气象条件有利于PM_(2.5)浓度的降低.2015~2021年气象条件对淮河流域PM_(2.5)、SO_(4)^(2-)、NO_(3)^(-)、NH_(4)^(+)、OM和BC长期分量下降的贡献率分别为28.3%、29.1%、31.0%、29.3%、27.8%和28.6%.气象条件对安徽、山东、江苏和河南省淮河流域城市PM_(2.5)长期分量降低的贡献率分别为43.4%、25.6%、25.5%和20.6%.随着淮河流域PM_(2.5)浓度降低,硫氧化率(SOR)明显上升,而氮氧化率(NOR)变化不大.
According to the data sets of fine particulate matter(PM_(2.5))and its components in 35 cities in the Huaihe River Basin from 2015 to 2021,the temporal and spatial distribution patterns of pollutants were analyzed.The influence of meteorological factors on PM_(2.5)concentrations was examined using a random forest model.The original series of PM_(2.5),sulfate(SO_(4)^(2-)),nitrate(NO_(3)^(-)),ammonium salt(NH_(4)^(+)),organic matter(OM),and black carbon(BC)were rebuilt using KZ(Kolmogorov-Zurbenko)filtering and multiple linear regression(MLR)to quantify the effects of meteorological conditions.The results demonstrated that from 2015 to 2021,the declining rates of PM_(2.5),SO_(4)^(2-),NO_(3)^(-),NH_(4)^(+),OM,and BC in the Huaihe River Basin were 4.71,0.99,1.05,0.77,1.01,and 0.19μg·(m^(3)·a)^(-1),respectively.The high mass concentrations of PM_(2.5)and its components were concentrated in the central and western regions of the HRB,whereas those in coastal and southern cities were lower.The variance contributions of the short-term,seasonal,and long-term components of PM_(2.5)to the original PM_(2.5)sequences in 35 cities were 51.6%,35.9%,and 7.0%,respectively.The PM_(2.5)in coastal cities were more affected by the short-term components.The meteorological conditions were unfavorable for PM_(2.5)reduction in the HRB from 2015 to 2018,whereas the meteorological conditions supported the PM_(2.5)decrease from 2019 to 2021.From 2015 to 2021,the contribution rates of meteorological conditions to the long-term component reductions of PM_(2.5),SO_(4)^(2-),NO_(3)^(-),NH_(4)^(+),OM,and BC were 28.3%,29.1%,31.0%,29.3%,27.8%,and 28.6%,respectively.The contribution rates of meteorological conditions to the long-term PM_(2.5)reduction were 43.4%,25.6%,25.5%,and 20.6% in the HRB cities in Anhui,Shandong,Jiangsu,and Henan Provinces,respectively.With the decrease in PM_(2.5)concentration in the HRB,the sulfur oxidation rate(SOR)increased significantly,while the nitrogen oxide oxidation rate(NOR)changed little.
作者
刘晓咏
牛继强
刘航
张一丹
颜俊
闫军辉
苏方成
LIU Xiao-yong;NIU Ji-qiang;LIU Hang;ZHANG Yi-dan;YAN Jun;YAN Jun-hui;SU Fang-cheng(School of Geographic Sciences,Xinyang Normal University,Xinyang 464000,China;Henan Key Laboratory for Synergistic Prevention of Water and Soil Environmental Pollution,Xinyang Normal University,Xinyang 464000,China;State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China;College of Chemistry,Zhengzhou University,Zhengzhou 450001,China)
出处
《环境科学》
EI
CAS
CSCD
北大核心
2024年第10期5650-5660,共11页
Environmental Science
基金
国家自然科学基金项目(42371255,41771438)
河南省科技攻关计划项目(232102320130)
河南省高校科技创新团队支持计划项目(22IRTSTHN010)
信阳师范大学“南湖学者奖励计划”青年项目。
关键词
PM_(2.5)
化学组分
KZ滤波
时空分布
气象影响
PM_(2.5)
chemical composition
Kolmogorov-Zurbenko(KZ)filter
spatial-temporal distribution
meteorological influence
