Use of Machine Learning in Air Pollution Research: A Bibliographic Perspective

Abstract

This research is an attempt to examine the recent status and development of scientific studies on the use of machine learning algorithms to model air pollution challenges. This study uses the Web of Science database as a primary search engine and covers over 900 highly peer-reviewed articles in the period 1990–2022. Papers published on these topics were evaluated using the VOSViewer and biblioshiny software to identify and visualize significant authors, key trends, nations, research publications, and journals working on these issues. The findings show that research grew exponentially after 2012. Based on the survey, “particulate matter” is the highly occurring keyword, followed by “prediction”. Papers published by Chinese researchers have garnered the most citations (2421), followed by papers published in the United States of America (2256), and England (722). This study assists scholars, professionals, and global policymakers in understanding the current status of the research contribution on “air pollution and machine learning” as well as identifying the relevant areas for future research.

Prediction of air pollution from power generation using machine learning

“…Research on the use of AI technology to forecast air pollution using various data sets has been published in the literature. A systematic review and a bibliographic perspective on air pollution detection are detailed in [7,8]. Additionally, machine learning algorithms-based air pollution detection and forecasting methods have been suggested in [9,10].…”