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<p>The increasing concern over air pollution has intensified the need for efficient air quality monitoring solutions. Low-cost sensors (LCSs) have emerged as a promising tool to complement traditional monitoring systems due to their cost-effectiveness and scalability, enabling detailed spatial and temporal data collection, albeit often requiring calibration to ensure data reliability. This study applies bibliometric and content analysis on 986 publications (1970–2023) to identify research trends, collaborations, and emerging themes in LCSs technology. Four key themes were found: Community-Based Monitoring, IoT Integration, Indoor Air Quality Assessment, and Advanced Calibration Techniques. Recent trends highlight the growing integration of IoT, AI, and machine learning to improve sensor calibration, real-time monitoring, and spatial analysis. While LCSs play a vital role in addressing air pollution challenges, enhancing sensor accuracy and integrating LCSs data with regulatory systems, UAVs, and satellites remains crucial for improved air quality management.</p>

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Mapping trends and analyzing key themes in low-cost sensors for air quality monitoring

  • 期刊论文

  • 发表日期：

    2025年6月3日

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  • Kemal Maulana Alhasa,
  • Hernani Yulinawati,
  • Deni Kurnia,
  • Heru Dwi Wahyono,
  • Satmoko Yudo,
  • Irwan Kustianto,
  • Dodi Rusjadi Tatang Endi

摘要

The increasing concern over air pollution has intensified the need for efficient air quality monitoring solutions. Low-cost sensors (LCSs) have emerged as a promising tool to complement traditional monitoring systems due to their cost-effectiveness and scalability, enabling detailed spatial and temporal data collection, albeit often requiring calibration to ensure data reliability. This study applies bibliometric and content analysis on 986 publications (1970–2023) to identify research trends, collaborations, and emerging themes in LCSs technology. Four key themes were found: Community-Based Monitoring, IoT Integration, Indoor Air Quality Assessment, and Advanced Calibration Techniques. Recent trends highlight the growing integration of IoT, AI, and machine learning to improve sensor calibration, real-time monitoring, and spatial analysis. While LCSs play a vital role in addressing air pollution challenges, enhancing sensor accuracy and integrating LCSs data with regulatory systems, UAVs, and satellites remains crucial for improved air quality management.