Applications of artificial intelligence in the field of air pollution: A bibliometric analysis

Qiangqiang Guo; Mengjuan Ren; Shouyuan Wu; Yajia Sun; Jianjian Wang; Qi Wang; Yanfang Ma; Xuping Song; Yaolong Chen

doi:10.3389/fpubh.2022.933665

Applications of artificial intelligence in the field of air pollution: A bibliometric analysis

Front Public Health. 2022 Sep 7:10:933665. doi: 10.3389/fpubh.2022.933665. eCollection 2022.

Authors

Qiangqiang Guo¹, Mengjuan Ren¹, Shouyuan Wu¹, Yajia Sun¹, Jianjian Wang¹, Qi Wang^{2

3}, Yanfang Ma⁴, Xuping Song^{1

5

6

7}, Yaolong Chen^{1

5

6

7}

Affiliations

¹ School of Public Health, Lanzhou University, Lanzhou, China.
² Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada.
³ McMaster Health Forum, McMaster University, Hamilton, ON, Canada.
⁴ School of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China.
⁵ Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences (2021RU017), School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
⁶ Lanzhou University Institute of Health Data Science, Lanzhou, China.
⁷ World Health Organization Collaborating Center for Guideline Implementation and Knowledge Translation, Lanzhou, China.

Abstract

Background: Artificial intelligence (AI) has become widely used in a variety of fields, including disease prediction, environmental monitoring, and pollutant prediction. In recent years, there has also been an increase in the volume of research into the application of AI to air pollution. This study aims to explore the latest trends in the application of AI in the field of air pollution.

Methods: All literature on the application of AI to air pollution was searched from the Web of Science database. CiteSpace 5.8.R1 was used to analyze countries/regions, institutions, authors, keywords and references cited, and to reveal hot spots and frontiers of AI in atmospheric pollution.

Results: Beginning in 1994, publications on AI in air pollution have increased in number, with a surge in research since 2017. The leading country and institution were China (N = 524) and the Chinese Academy of Sciences (N = 58), followed by the United States (N = 455) and Tsinghua University (N = 33), respectively. In addition, the United States (0.24) and the England (0.27) showed a high degree of centrality. Most of the identified articles were published in journals related to environmental science; the most cited journal was Atmospheric Environment, which reached nearly 1,000 citations. There were few collaborations among authors, institutions and countries. The hot topics were machine learning, air pollution and deep learning. The majority of the researchers concentrated on air pollutant concentration prediction, particularly the combined use of AI and environmental science methods, low-cost air quality sensors, indoor air quality, and thermal comfort.

Conclusion: Researches in the field of AI and air pollution are expanding rapidly in recent years. The majority of scholars are from China and the United States, and the Chinese Academy of Sciences is the dominant research institution. The United States and the England contribute greatly to the development of the cooperation network. Cooperation among research institutions appears to be suboptimal, and strengthening cooperation could greatly benefit this field of research. The prediction of air pollutant concentrations, particularly PM_2.5, low-cost air quality sensors, and thermal comfort are the current research hotspot.

Keywords: CiteSpace; air pollution; artificial intelligence; bibliometric analysis (BA); public health.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Air Pollutants*
Air Pollution*
Artificial Intelligence
Bibliometrics
Humans
Particulate Matter
United States

Substances

Air Pollutants
Particulate Matter