| 机器学习在空气污染研究方面的应用进展 |
| 摘要点击 2870 全文点击 441 投稿时间：2024-05-21 修订日期：2024-08-05 |
| 查看HTML全文
查看全文 查看/发表评论 下载PDF阅读器 |
| 中文关键词 空气污染 机器学习 数值模式 卫星遥感 污染成因 多源数据融合 |
| 英文关键词 air pollution machine learning numerical model remote sensing pollution causes multi-source data fusion |
|
| 中文摘要 |
| 空气污染是当前全球面临的严峻挑战之一，研究和改善空气质量具有重要的现实意义. 传统的研究方法多依赖于多源观测数据和基于大气物理与化学理论构建的数值模式，然而以上方法在准确性、时空覆盖范围和计算资源等方面受到限制. 机器学习作为一种强大的数据处理和信息挖掘的工具，已开始被研究者应用于空气污染研究领域，旨在通过大量数据揭示空气污染的变化规律及影响因素，并预测未来空气质量的变化趋势. 综述了近几年机器学习在空气污染研究中的典型应用，主要涉及以下4个方面：基于卫星遥感的大气成分反演与估算、监测与预测；空气质量模拟与预报准确性的提高；空气污染成因分析和多源数据融合. 此外，进一步探讨了当前研究中存在的科学问题和技术难点. 未来研究应重点关注如何将机器学习与传统数值模式相结合，例如开发智能参数化方案和学习模式参数等. 同时，还应探索机器学习在污染源解析、空气质量健康影响评估，以及多源数据融合技术中的应用，可实现更精准的空气质量管理和政策制定. |
| 英文摘要 |
| Air pollution is one of the most serious global challenges at present， and it has great practical importance to study and improve air quality. Traditional research methods mostly rely on multi-source observations and numerical models constructed based on atmospheric physics and chemistry theories， although these methods are limited in terms of accuracy， spatial and temporal coverage， and computational resources. As a powerful data processing and information mining tool， machine learning has begun to be applied by researchers in the field of air pollution research， aiming to reveal the changing patterns and influencing factors of air pollution through analyzing large amounts of data and predict future trends in air quality. This study reviews the typical applications of machine learning in air pollution research in recent years， mainly involving the following four aspects： inverting and estimation， monitoring， and prediction of atmospheric composition based on satellite remote sensing； improvement of air quality simulation and forecast accuracy； analysis of air pollution causes； and fusion of multi-source data. In addition， the scientific problems and technical difficulties in the current research are further discussed. Future research should focus on how to combine machine learning with traditional numerical models， such as developing intelligent parameterization schemes and learning model parameters. The application of machine learning in pollution source analysis， air quality health impact assessment， and multi-source data fusion techniques should also be explored to achieve more accurate air quality management and policy making. |
