Monitoring and Ensuring Worker Health in Controlled Environments Using Economical Particle Sensors

Sensors (Basel). 2024 Aug 14;24(16):5267. doi: 10.3390/s24165267.

Abstract

Nowadays, indoor air quality monitoring has become an issue of great importance, especially in industrial spaces and laboratories where materials are handled that may release particles into the air that are harmful to health. This study focuses on the monitoring of air quality and particle concentration using low-cost sensors (LCSs). To carry out this work, particulate matter (PM) monitoring sensors were used, in controlled conditions, specifically focusing on particle classifications with PM2.5 and PM10 diameters: the Nova SDS011, the Sensirion SEN54, the DFRobot SEN0460, and the Sensirion SPS30, for which an adapted environmental chamber was built, and gaged using the Temtop M2000 2nd as a reference sensor (SRef). The main objective was to preliminarily assess the performance of the sensors, to select the most suitable ones for future research and their possible use in different work environments. The monitoring of PM2.5 and PM10 particles is essential to ensure the health of workers and avoid possible illnesses. This study is based on the comparison of the selected LCS with the SRef and the results of the comparison based on statistics. The results showed variations in the precision and accuracy of the LCS as opposed to the SRef. Additionally, it was found that the Sensirion SEN54 was the most suitable and valuable tool to be used to maintain a safe working environment and would contribute significantly to the protection of the workers' health.

Keywords: air quality; health; low-cost sensors; monitoring; particulate matter.

MeSH terms

  • Air Pollution, Indoor* / analysis
  • Environment, Controlled
  • Environmental Monitoring* / methods
  • Humans
  • Occupational Health
  • Particle Size
  • Particulate Matter* / analysis

Substances

  • Particulate Matter

Grants and funding

This research was funded by the Cepsa Foundation Chair of the ETSIME-UPM within the framework of the III program of grants for research projects awarded in the 2022–2023 academic year and by the EELISA-ESCE community thanks to the 2022–2023 project support funds.