摘 要
随着城市化进程的加速，大气污染尤其是PM2.5污染成为影响居民健康和城市可持续发展的关键问题。本研究旨在深入解析城市大气中PM2.5的主要来源，并提出针对性的防控策略。通过综合运用受体模型、源清单分析及数值模拟等技术手段，对典型城市区域进行为期一年的连续监测，获取了高时间分辨率的PM2.5浓度数据及其化学组分特征。研究表明，交通排放、工业生产、建筑施工和生物质燃烧是主要贡献源，其中机动车尾气排放占比最高可达40%，且不同季节源贡献率存在显著差异。创新性地引入了基于机器学习算法的源解析模型，提高了源识别精度，实现了对复杂污染过程的动态追踪。此外，结合气象条件与地理信息系统的空间分析，揭示了PM2.5传输路径及其在城市内部的分布规律。
关键词：PM2.5污染源解析 机动车尾气排放 源清单分析
Abstract
With the acceleration of urbanization, air pollution, especially PM2.5 pollution, has become a key issue affecting the health of residents and the sustainable urban development. This study aims to deeply analyze the main sources of PM2.5 in the urban atmosphere, and propose targeted prevention and control strategies. Through the comprehensive use of receptor model, source list analysis and numerical simulation, the PM2.5 concentration data and its chemical component characteristics with high time resolution were obtained. The study shows that traffic emissions, industrial production, construction and biomass combustion are the main contributing sources, among which the vehicle exhaust emissions can account for up to 40%, and the contribution rate of different seasons is significantly different. The source analysis model based on machine learning algorithm, which improves the source identification accuracy is innovatively introduced and realizes the dynamic tracking of complex pollution process. Moreover, the spatial analysis of meteorological conditions and geographic information system reveals the PM2.5 transmission path and its distribution law within the city.
Keyword:Pm2.5 Source Apportionment Vehicle Exhaust Emissions Source Inventory Analysis
目 录
1绪论 1
1.1城市 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 2
2PM2.5来源解析 2
2.1主要污染源识别 2
2.2源清单编制方法 3
2.3化学组成特征分析 3
3PM2.5形成机制 4
3.1一次污染物排放 4
3.2二次颗粒物生成 4
3.3气象条件影响 5
4PM2.5防控策略 5
4.1源头减排措施 5
4.2过程控制技术 6
4.3综合治理方案 7
结论 7
参考文献 9
致谢 10
随着城市化进程的加速，大气污染尤其是PM2.5污染成为影响居民健康和城市可持续发展的关键问题。本研究旨在深入解析城市大气中PM2.5的主要来源，并提出针对性的防控策略。通过综合运用受体模型、源清单分析及数值模拟等技术手段，对典型城市区域进行为期一年的连续监测，获取了高时间分辨率的PM2.5浓度数据及其化学组分特征。研究表明，交通排放、工业生产、建筑施工和生物质燃烧是主要贡献源，其中机动车尾气排放占比最高可达40%，且不同季节源贡献率存在显著差异。创新性地引入了基于机器学习算法的源解析模型，提高了源识别精度，实现了对复杂污染过程的动态追踪。此外，结合气象条件与地理信息系统的空间分析，揭示了PM2.5传输路径及其在城市内部的分布规律。
关键词：PM2.5污染源解析 机动车尾气排放 源清单分析
Abstract
With the acceleration of urbanization, air pollution, especially PM2.5 pollution, has become a key issue affecting the health of residents and the sustainable urban development. This study aims to deeply analyze the main sources of PM2.5 in the urban atmosphere, and propose targeted prevention and control strategies. Through the comprehensive use of receptor model, source list analysis and numerical simulation, the PM2.5 concentration data and its chemical component characteristics with high time resolution were obtained. The study shows that traffic emissions, industrial production, construction and biomass combustion are the main contributing sources, among which the vehicle exhaust emissions can account for up to 40%, and the contribution rate of different seasons is significantly different. The source analysis model based on machine learning algorithm, which improves the source identification accuracy is innovatively introduced and realizes the dynamic tracking of complex pollution process. Moreover, the spatial analysis of meteorological conditions and geographic information system reveals the PM2.5 transmission path and its distribution law within the city.
Keyword:Pm2.5 Source Apportionment Vehicle Exhaust Emissions Source Inventory Analysis
目 录
1绪论 1
1.1城市 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 2
2PM2.5来源解析 2
2.1主要污染源识别 2
2.2源清单编制方法 3
2.3化学组成特征分析 3
3PM2.5形成机制 4
3.1一次污染物排放 4
3.2二次颗粒物生成 4
3.3气象条件影响 5
4PM2.5防控策略 5
4.1源头减排措施 5
4.2过程控制技术 6
4.3综合治理方案 7
结论 7
参考文献 9
致谢 10
