Li Yunzhe, Sha Zhipeng, Tang Aohan, Goulding Keith, Liu Xuejun
Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China.
Beijing Key Laboratory of Farmland Soil Pollution Prevention and Remediation, College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China.
Ecotoxicol Environ Saf. 2023 Jun 1;257:114911. doi: 10.1016/j.ecoenv.2023.114911. Epub 2023 Apr 15.
Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future.
机器学习（ML）是一种先进的计算机算法，它模拟人类学习过程来解决问题。随着监测数据的爆炸式增长以及对快速准确预测的需求不断增加，机器学习模型在空气污染研究中得到了迅速发展和应用。为了探究机器学习在空气污染研究中的应用现状，基于1990年至2021年发表的2962篇文章进行了文献计量分析。2017年之后出版物数量急剧增加，约占总数的75%。中国和美国的机构贡献了所有出版物的一半，大多数研究是由单个团队进行的，而非全球合作。聚类分析揭示了机器学习应用的四个主要研究主题：污染物的化学表征、短期预测、检测改进和优化排放控制。机器学习算法的快速发展提高了探索多种污染物化学特性、分析化学反应及其驱动因素以及模拟情景的能力。结合多领域数据，机器学习模型是分析大气化学过程和评估空气质量管理的有力工具，在未来值得更多关注。
