| 基于后向轨迹模式的豫南地区冬季PM2.5来源分布及传输分析 |
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| 引用本文： | 高阳,韩永贵,黄晓宇,韩磊.基于后向轨迹模式的豫南地区冬季PM2.5来源分布及传输分析[J].环境科学研究,2021,34(3):538-548. |
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| 作者姓名： | 高阳 韩永贵 黄晓宇 韩磊 |
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| 作者单位： | 1.宁夏大学资源环境学院, 宁夏 银川 750021 |
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| 基金项目： | 国家自然科学基金项目（No.31760236，31460220）；宁夏自然科学基金项目（No.2019AAC03043） |
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| 摘 要： | 为研究2017年12月—2018年2月冬季不同来源区域对豫南地区ρ（PM2.5）的贡献影响及污染特征，利用HYSPLIT-4后向轨迹模式模拟了豫南地区冬季24 h的气团后向轨迹，结合ρ（PM2.5）在线监测数据进行了聚类分析，研究了以豫南地区为受点的各月份PM2.5不同轨迹的输送特征，并使用潜在源贡献（PSCF）分析法和浓度权重轨迹（CWT）分析法识别了豫南地区冬季PM2.5的潜在贡献源区及贡献大小.结果表明:①信阳市空气质量最好，其次为驻马店市，南阳市空气质量最差；南阳市、信阳市和驻马店市ρ（PM2.5）分别超过GB 3095—2012《环境空气质量标准》二级标准限值（75 μg/m3）的1.5、1.2和1.2倍，ρ（PM2.5）日变化均呈双峰特征.②后向轨迹聚类分析表明，豫南地区主要受到来自西北和东北方向长距离传输和正南方向较短距离输送的影响.③潜在源区分析表明，除豫南地区及周边市县本地污染贡献外，冀鲁豫交界区域、陕鄂交界区域、陕西省中西部、湖北省东北部和西部、河南省中北部、山东省南部是影响豫南地区ρ（PM2.5）的主要潜在源区.研究显示，豫南地区PM2.5污染过程除了与地形条件、本地污染源排放有关外，来自东北、西北传输通道城市的远距离输送和南部的近距离传输也不容忽视.
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| 关 键 词： | PM2.5 后向轨迹模式 聚类分析 潜在源贡献(PSCF)分析法 浓度权重轨迹(CWT)分析法 豫南地区 |
| 收稿时间： | 2020/2/8 0:00:00 |
| 修稿时间： | 2020/9/17 0:00:00 |
PM2.5 Source Distribution and Transmission in Winter in Southern Henan Province Based on Backward Trajectory Model |
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| GAO Yang,HAN Yonggui,HUANG Xiaoyu,HAN Lei.PM2.5 Source Distribution and Transmission in Winter in Southern Henan Province Based on Backward Trajectory Model[J].Research of Environmental Sciences,2021,34(3):538-548. |
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| Authors: | GAO Yang HAN Yonggui HUANG Xiaoyu HAN Lei |
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| Affiliation: | 1.School of Recourses and Environment, Ningxia University, Yinchuan 750021, China2.Ningxia Key Laboratory of Resource Assessment and Environment Regulation in Arid Region, Yinchuan 750021, China3.Joint International Research Laboratory of China-Arab Featured Resources and Environmental Governance in Arid Region, Yinchuan 750021, China4.Institute of Environmental Engineering, Ningxia University, Yinchuan 750021, China |
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| Abstract: | In order to study the contribution of different source regions to ρ(PM2.5) and pollution characteristics in southern Henan Province from December 2017 to February 2018, the HYSPLIT-4 backward trajectory model was used to simulate the 24-hour air mass in southern Henan Province in winter backward trajectory, combined with online monitoring data of ρ(PM2.5), and cluster analysis were performed at the same time. The transport characteristics of different trajectories of PM2.5 with southern Henan Province as the receiving point were studied each month. The potential source contribution function (PSCF) analysis method and concentration-weighted trajectory (CWT) analysis method identified the potential contribution source area and contribution size of PM2.5. The results showed that: (1) The air quality in Xinyang was the best, followed by Zhumadian and Nanyang. ρ(PM2.5) in Nanyang, Xinyang and Zhumadian exceeded the National Ambient Air Quality Standards (GB 3095-2012, grade Ⅱ) by 1.5, 1.2 and 1.2 times, respectively. ρ(PM2.5) showed double-peak characteristics. (2) The backward trajectory cluster analysis showed that southern Henan Province was mainly affected by the long-distance transmission from the northwest and northeast directions and the short-distance transmission from the due south direction. (3) The analysis of potential source areas showed that in addition to the local pollution contributions of southern Henan Province and surrounding cities and counties, the border areas between Hebei Province, Shandong Province and Henan Province, the border areas between Shaanxi Province and Hubei Province, the central and western parts of Shaanxi Province, the northeast and western parts of Hubei Province, the central and northern parts of Henan Province, and the southern part of Shandong Province were the main potential source areas of ρ(PM2.5). The PM2.5 pollution process in southern Henan Province was not only related to topographical conditions and local pollution source emissions, but also related to the long-distance transportation from the northeast and northwest and short-distance transmission in the south. |
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| Keywords: | PM2 5 backward trajectory model cluster analysis potential source contribution function (PSCF) concentration-weighted trajectory (CWT) southern Henan Province |
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