摘要
大气颗粒物污染已成为全球关注的环境问题，其对人类健康、生态系统及气候变化的影响日益显著。本研究旨在通过源解析技术揭示大气颗粒物的主要来源，并提出针对性的防控策略。研究采用受体模型与化学质量平衡法相结合的方法，结合长期监测数据和气象参数，分析了典型城市区域颗粒物的时空分布特征及其来源构成。结果表明，工业排放、交通尾气、建筑扬尘和生物质燃烧是主要污染源，且不同季节和区域的贡献比例存在显著差异。此外，研究创新性地引入机器学习算法优化源解析模型，提高了源识别的准确性和可靠性。基于源解析结果，提出了分级分类的防控措施，包括强化重点行业排放控制、优化能源结构以及加强区域联防联控机制。研究表明，精准源解析对于制定科学有效的颗粒物防控政策具有重要意义，同时为其他地区的污染治理提供了可借鉴的技术路径和管理经验。
关键词：大气颗粒物；源解析；防控策略；机器学习；时空分布特征
Abstract
Atmospheric particulate pollution has become a globally recognized environmental issue, with its impacts on human health, ecosystems, and climate change becoming increasingly significant. This study aims to reveal the major sources of atmospheric particulate matter through source apportionment techniques and proposes targeted prevention and control strategies. By integrating receptor models with chemical mass balance methods, long-term monitoring data and meteorological parameters were analyzed to examine the spatiotemporal distribution characteristics and source composition of particulate matter in typical urban areas. The results indicate that industrial emissions, vehicle exhaust, construction dust, and biomass burning are the primary pollution sources, with notable seasonal and regional variations in their contribution ratios. Additionally, this study innovatively incorporates machine learning algorithms to optimize the source apportionment model, thereby enhancing the accuracy and reliability of source identification. Based on the source apportionment results, tiered and categorized control measures were proposed, including strengthening emission controls in key industries, optimizing energy structures, and reinforcing regional joint prevention and control mechanisms. This study demonstrates that precise source apportionment is crucial for formulating scientifically effective particulate matter control policies and provides other regions with replicable technical approaches and management experiences for pollution governance.
Keywords：Atmospheric Particles; Source Apportionment; Prevention And Control Strategies; Machine Learning; Spatiotemporal Distribution Characteristics
目 录
摘要 I
Abstract II
一、绪论 1
(一) 大气颗粒物污染的研究背景 1
(二) 源解析与防控策略的研究意义 1
(三) 国内外研究现状分析 1
(四) 本文研究方法与技术路线 2
二、大气颗粒物源解析方法研究 2
(一) 源解析的基本原理与框架 2
(二) 常用源解析技术的对比分析 3
(三) 数据采集与处理方法优化 3
(四) 源解析结果的验证与评估 4
三、大气颗粒物来源特征分析 4
(一) 不同区域颗粒物来源差异 4
(二) 主要污染源的贡献率分析 5
(三) 季节性变化对源解析的影响 5
(四) 特殊事件下的颗粒物来源特征 6
四、大气颗粒物防控策略研究 6
(一) 防控策略的制定原则与目标 6
(二) 区域联防联控机制设计 7
(三) 典型污染源的治理措施分析 7
(四) 长期防控效果的模拟与预测 8
结 论 9
参考文献 10
