Zhou Enlai School of Government, Nankai University, Tianjin 300071, China.
College of Management and Economy, Tianjin University, Tianjin 300072, China.
Int J Environ Res Public Health. 2022 Oct 5;19(19):12723. doi: 10.3390/ijerph191912723.
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.
本研究运用文献计量学技术和社会网络分析方法，对 2001 年至 2021 年期间发表的 14955 篇关于空气污染与健康的论文进行了评估。为了跟踪研究热点，本研究应用机器学习原理，通过 OmniViz 软件将 10212 条关键词记录分为 96 个聚类。我们的研究结果突出了控制空气污染以改善人类健康的强烈研究兴趣和实际需求，相关出版物的年增长率超过 15.8%。聚类分析表明，聚类 C22（暴露、模型、死亡率）和 C8（健康、环境、风险）是该研究领域最热门的话题。此外，我们还根据聚类分析结果开发了共现网络，从而获得了更具体的关键词分类。这些重点领域包括：“空气污染物源”、“暴露-反应关系”、“公众和职业健康”等。通过聚类组的特征分析未来的研究热点，包括健康风险评估技术的进步、量化人类暴露于空气污染的跨学科方法以及健康风险评估策略。
