摘要
随着全球环境问题日益严峻，大气污染物排放清单作为评估和管理空气质量的重要工具，其科学性和准确性受到广泛关注。本研究旨在建立一套系统化、精细化的大气污染物排放清单编制方法，并探讨其在环境政策制定与污染控制中的应用价值。研究基于多源数据整合技术，结合高分辨率地理信息与行业活动水平数据，构建了覆盖主要污染物的动态排放清单模型。通过引入机器学习算法优化排放因子估算，显著提高了清单的空间分辨率和时间动态性。结果表明，该方法能够准确反映区域污染物排放的空间分布特征及季节变化规律，为污染源解析提供了可靠依据。此外，研究将编制的排放清单应用于空气质量模拟，验证了其对污染物浓度预测的改进效果。本研究的创新点在于首次实现了机器学习与传统清单编制方法的深度融合，提升了排放清单的精度和适用性，为精准治污和科学决策提供了重要支撑。研究成果可为区域性大气污染防控策略的制定提供理论依据和技术支持。
关键词：大气污染物排放清单；机器学习；空间分布特征；污染源解析；空气质量模拟
Abstract
With the increasing severity of global environmental issues, emission inventories of atmospheric pollutants, as crucial tools for assessing and managing air quality, have drawn significant attention regarding their scientific validity and accuracy. This study aims to develop a systematic and refined methodology for compiling atmospheric pollutant emission inventories and to explore its application value in environmental policy formulation and pollution control. Based on multi-source data integration techniques, combining high-resolution geographic information with industry activity level data, a dynamic emission inventory model covering major pollutants was constructed. By incorporating machine learning algorithms to optimize emission factor estimation, the spatial resolution and temporal dynamics of the inventory were significantly enhanced. The results indicate that this method can accurately reflect the spatial distribution characteristics and seasonal variation patterns of regional pollutant emissions, providing a reliable basis for source apportionment. Furthermore, the compiled emission inventory was applied to air quality modeling, verifying its improvement in predicting pollutant concentrations. The innovation of this study lies in the first successful integration of machine learning with traditional inventory compilation methods, which improves the accuracy and applicability of emission inventories, offering critical support for targeted pollution control and scientific decision-making. The research findings provide theoretical foundations and technical support for the development of regional atmospheric pollution prevention and control strategies.
Keywords：Emission Inventory Of Atmospheric Pollutants; Machine Learning; Spatial Distribution Characteristics; Source Apportionment; Air Quality Simulation
目 录
摘要 I
Abstract II
一、绪论 1
(一) 大气污染物排放清单的研究背景 1
(二) 编制方法与应用的研究意义 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、排放清单编制方法体系 2
(一) 数据收集与处理方法 2
(二) 活动水平数据的获取与优化 3
(三) 排放因子选取与校准方法 3
(四) 清单编制模型与算法选择 4
三、排放清单的空间与时间分配 4
(一) 空间分布特征及影响因素 4
(二) 时间分辨率的确定与优化 5
(三) 不同源类的分配方法比较 5
(四) 分配结果的验证与不确定性分析 6
四、排放清单的应用与政策支持 6
(一) 在空气质量模拟中的应用 6
(二) 对污染控制策略的支撑作用 7
(三) 清单在区域协同治理中的应用 8
(四) 政策制定中的清单改进需求 8
结 论 10
参考文献 11
