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<p>Air pollution forecasting has emerged as a crucial response to Asia’s escalating environmental and public health challenges. With increased urbanisation, industrialisation, and emissions from vehicles, most parts of Asia often exceed WHO air quality guidelines. This research provides a systematic bibliometric review of air pollution forecasting studies in Asia from 2015 to 2024 based on 1118 documents from the Web of Science Core Collection. Tools like Bibliometrix, VOSviewer, and logistic S-curve modelling were employed to map the intellectual structure of the field. Key insights include rising research momentum since 2019 with an 18.56% annual growth rate, strong contributions from China, India, South Korea, and Japan, and a focus on themes such as “machine learning”, “air pollution”, and “air quality”. The study highlights top contributors, leading journals, and collaboration networks, offering a clear picture of research evolution and pointing to future directions in machine learning-based forecasting in the region.</p>

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Air pollution forecasting trends and regional patterns in the Asia Pacific through bibliometric analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月27日

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  • Ritesh Rana,
  • Naveen Kumar

摘要

Air pollution forecasting has emerged as a crucial response to Asia’s escalating environmental and public health challenges. With increased urbanisation, industrialisation, and emissions from vehicles, most parts of Asia often exceed WHO air quality guidelines. This research provides a systematic bibliometric review of air pollution forecasting studies in Asia from 2015 to 2024 based on 1118 documents from the Web of Science Core Collection. Tools like Bibliometrix, VOSviewer, and logistic S-curve modelling were employed to map the intellectual structure of the field. Key insights include rising research momentum since 2019 with an 18.56% annual growth rate, strong contributions from China, India, South Korea, and Japan, and a focus on themes such as “machine learning”, “air pollution”, and “air quality”. The study highlights top contributors, leading journals, and collaboration networks, offering a clear picture of research evolution and pointing to future directions in machine learning-based forecasting in the region.