Visualization and Analysis of Air Pollution and Human Health Based on Cluster Analysis: A Bibliometric Review from 2001 to 2021

Diyi Liu, Kun Cheng, Kevin Huang, Hui Ding, Tiantong Xu, Zhenni Chen and Yanqi Sun ()
Additional contact information
Diyi Liu: Zhou Enlai School of Government, Nankai University, Tianjin 300071, China
Kun Cheng: College of Management and Economy, Tianjin University, Tianjin 300072, China
Kevin Huang: School of Accounting, Economics and Finance, University of Wollongong, Sydney, NSW 2522, Australia
Hui Ding: School of Marxism, Hangzhou Medical College, Hangzhou 310053, China
Tiantong Xu: School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China
Zhenni Chen: School of Economics and Finance, Xi’an Jiaotong University, Xi’an 710061, China
Yanqi Sun: School of Economics and Management, Beijing Institute of Petrochemical Technology, Beijing 102617, China

IJERPH, 2022, vol. 19, issue 19, 1-15

Abstract: Bibliometric techniques and social network analysis are employed in this study to evaluate 14,955 papers on air pollution and health that were published from 2001 to 2021. To track the research hotspots, the principle of machine learning is applied in this study to divide 10,212 records of keywords into 96 clusters through OmniViz software. Our findings highlight strong research interests and the practical need to control air pollution to improve human health, as evidenced by an annual growth rate of over 15.8% in the related publications. The cluster analysis showed that clusters C22 (exposure, model, mortality) and C8 (health, environment, risk) are the most popular topics in this field of research. Furthermore, we develop co-occurrence networks based on the cluster analysis results in which a more specific keyword classification was obtained. These key areas include: “Air pollutant source”, “Exposure-Response relationship”, “Public & Occupational Health”, and so on. Future research hotspots are analyzed through characteristics of the cluster groups, including the advancement of health risk assessment techniques, an interdisciplinary approach to quantifying human exposure to air pollution, and strategies in health risk assessment.

Keywords: air pollution; health; bibliometric; machine learning; social network analysis; co-occurrence network (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/19/12723/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/19/12723/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:19:p:12723-:d:933679

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().