新型污染物大区域污染扩散及其浓度预测的研究：从机理模型到智能算法
Regional-Scale Dispersion and Concentration Prediction of Emerging Pollutants: From Mechanistic Models to Intelligent Algorithms
DOI: 10.12677/aep.2025.159135, PDF,    国家自然科学基金支持
作者: 陈美凤, 王 恒, 王 涵, 张千峰*：安徽工业大学分子工程与应用化学研究所，安徽 马鞍山；袁 静*：铜陵学院建筑工程学院，安徽 铜陵
关键词: 新型污染物；统计模型；深度学习；健康暴露；Emerging Pollutants； Statistical Models； Deep Learning； Health Exposure
摘要: 新型污染物(如多环芳烃)因其持久性和潜在健康风险，已经成为当前研究的热点。在区域乃至全球尺度上准确模拟其扩散行为与预测浓度变化，对于制定污染防控政策和开展健康风险评估具有重要意义。本文系统回顾了新型污染物在大气中扩散和迁移的基本理论基础，梳理了当前统计模型和机器学习以及深度学习预测模型的适用场景，重点分析了不同模型在处理复杂污染过程的表现差异，探讨了多种暴露–反应模型(如分布式滞后非线性模型)在污染物浓度与呼吸系统、心血管系统疾病以及慢性阻塞性肺疾病(COPD)发病率和死亡率分析中的应用现状与挑战。研究指出，个体暴露估计不确定性、区域差异性及多污染物交互作用仍是当前健康风险评估的难点。未来，构建多源数据驱动的高分辨率预测体系，建立污染物浓度–健康影响的一体化模型方法，将是提高大气污染治理效能的关键方向。
Abstract: Emerging pollutants, such as polycyclic aromatic hydrocarbons (PAHs), have been identified as a focal point of current research due to their persistence in the environment and potential health risks. Accurate simulation of their atmospheric dispersion and concentration dynamics on regional and global scales is considered essential for the formulation of pollution control policies and the conduction of health risk assessments. In this review, the fundamental theoretical basis of pollutant dispersion and transport in the atmosphere is systematically outlined, and the applicable scenarios of current statistical models, machine learning, and deep learning-based prediction approaches are examined. Particular emphasis is placed on the performance differences of various models in addressing complex pollution processes. Additionally, the application status and challenges of multiple exposure-response models (e.g., Distributed Lag Non-linear Models, DLNMs) are discussed with regard to their use in analyzing associations between pollutant concentrations and respiratory diseases, cardiovascular diseases, and chronic obstructive pulmonary disease (COPD) incidence and mortality. Persistent challenges such as uncertainties in individual-level exposure estimation, regional heterogeneity, and multi-pollutant interactions are highlighted in current health risk evaluations. Looking forward, the development of high-resolution, multi-source data-driven prediction frameworks and the establishment of integrated models linking pollutant concentrations with health outcomes are expected to be key in enhancing the effectiveness of air pollution control strategies.
文章引用：陈美凤, 王恒, 王涵, 袁静, 张千峰. 新型污染物大区域污染扩散及其浓度预测的研究：从机理模型到智能算法[J]. 环境保护前沿, 2025, 15(9): 1202-1214. https://doi.org/10.12677/aep.2025.159135

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