| 淮河流域细颗粒物化学组分时空特征及驱动因素分析 |
| 摘要点击 1657 全文点击 324 投稿时间：2023-12-14 修订日期：2024-02-25 |
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| 中文关键词 PM2.5 化学组分 KZ滤波 时空分布 气象影响 |
| 英文关键词 PM2.5 chemical composition Kolmogorov-Zurbenko（KZ） filter spatial-temporal distribution meteorological influence |
| DOI 10.13227/j.hjkx.20241007 |
| 作者 | 单位 | E-mail | | 刘晓咏 | 信阳师范大学地理科学学院, 信阳 464000
信阳师范大学河南省水土环境污染协同防治重点实验室, 信阳 464000 | xyliu_liuxy@163.com | | 牛继强 | 信阳师范大学地理科学学院, 信阳 464000
信阳师范大学河南省水土环境污染协同防治重点实验室, 信阳 464000 | | | 刘航 | 中国科学院大气物理研究所大气边界层物理和大气化学国家重点实验室, 北京 100029 | | | 张一丹 | 信阳师范大学地理科学学院, 信阳 464000 | | | 颜俊 | 信阳师范大学地理科学学院, 信阳 464000 | | | 闫军辉 | 信阳师范大学地理科学学院, 信阳 464000
信阳师范大学河南省水土环境污染协同防治重点实验室, 信阳 464000 | | | 苏方成 | 郑州大学化学学院, 郑州 450001 | |
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| 中文摘要 |
| 基于淮河流域35个城市2015~2021年的细颗粒物（PM2.5）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PM2.5浓度的影响.采用KZ（Kolmogorov-Zurbenko）滤波和多元线性回归（MLR）对PM2.5、硫酸盐（SO42-）、硝酸盐（NO3-）、铵盐（NH4+）、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PM2.5、SO42-、NO3-、NH4+、OM和BC的变化速率分别为-4.71、-0.99、-1.05、-0.77、-1.01和-0.19 μg·（m3·a）-1. PM2.5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. PM2.5短期、季节和长期分量对35个城市PM2.5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PM2.5浓度的降低,2019~2021年气象条件有利于PM2.5浓度的降低. 2015~2021年气象条件对淮河流域PM2.5、SO42-、NO3-、NH4+、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PM2.5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PM2.5浓度降低,硫氧化率（SOR）明显上升,而氮氧化率（NOR）变化不大. |
| 英文摘要 |
| According to the data sets of fine particulate matter （PM2.5） 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 PM2.5 concentrations was examined using a random forest model. The original series of PM2.5, sulfate （SO42-）, nitrate （NO3-）, ammonium salt （NH4+）, 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 PM2.5, SO42-, NO3-, NH4+, OM, and BC in the Huaihe River Basin were 4.71, 0.99, 1.05, 0.77, 1.01, and 0.19 μg·（m3·a）-1, respectively. The high mass concentrations of PM2.5 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 PM2.5 to the original PM2.5 sequences in 35 cities were 51.6%, 35.9%, and 7.0%, respectively. The PM2.5 in coastal cities were more affected by the short-term components. The meteorological conditions were unfavorable for PM2.5 reduction in the HRB from 2015 to 2018, whereas the meteorological conditions supported the PM2.5 decrease from 2019 to 2021. From 2015 to 2021, the contribution rates of meteorological conditions to the long-term component reductions of PM2.5, SO42-, NO3-, NH4+, 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 PM2.5 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 PM2.5 concentration in the HRB, the sulfur oxidation rate （SOR） increased significantly, while the nitrogen oxide oxidation rate （NOR） changed little. |
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