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

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.