The application of machine learning to air pollution research: A bibliometric analysis

A - Papers appearing in refereed journals

Li, Y., Sha, Z., Tang, A., Goulding, K. W. T. and Liu, X. 2023. The application of machine learning to air pollution research: A bibliometric analysis. Ecotoxicology and Environmental Safety. 257 (1 June), p. 114911. https://doi.org/10.1016/j.ecoenv.2023.114911

AuthorsLi, Y., Sha, Z., Tang, A., Goulding, K. W. T. and Liu, X.
Abstract

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.

KeywordsAir pollution; Machine learning; Bibliometric analysis; Research focuses; Development
Year of Publication2023
JournalEcotoxicology and Environmental Safety
Journal citation257 (1 June), p. 114911
Digital Object Identifier (DOI)https://doi.org/10.1016/j.ecoenv.2023.114911
Open accessPublished as ‘gold’ (paid) open access
FunderBiotechnology and Biological Sciences Research Council
Funder project or codeS2N - Soil to Nutrition [ISPG]
Publisher's version
Output statusPublished
Publication dates
Online15 Apr 2023
Publication process dates
Accepted10 Apr 2023
ISSN0147-6513
PublisherAcademic Press Inc Elsevier Science

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