Topic: Application of low-cost sensors, internet of things, and artificial intelligence algorithms to perform environmental monitoring and personal exposure assessment of particles and noise

Speaker: Ta-Yuan Chang (Professor of Department of Occupational Safety and Health, China Medical University)

Time: November 6, 2025 1:30-3:00 pm

Venue: Lecture Room 602 at College of Medicine

Abstract: The low-cost sensor (LCS) and Internet of Things (IoT) technologies recently have been applied to establish LCS networks for monitoring single pollutants in the environment. Few studies have developed a mixed-sensor system for simultaneous measurements of particles and noise, but the influences of meteorology and other pollutants are not taken into account. Additionally, the LCS and IoT technologies are commonly used for environmental exposure monitoring. Their application in measuring respirable dust (RD) in the workplace remains limited. Artificial intelligence (AI) algorithms are becoming popular to develop predictive models of environmental pollutants. No study has been conducted to apply the LCS, IoT, and AI algorithms for developing the predictive model of occupational noise. This speech includes (1) the development of a mixed-sensor system for fine particles and noise with LCS technologies for considering effects of temperature, relative humidity, and carbon dioxide; (2) the establishment of a predictive model for RD using LCS and AI algorithms and subsequently assessment of its validity using a standard sampling approach; and (3) the application of LCS, IoT, and AI algorithms to develop a predictive model of occupational noise in comparison with traditional noise measurements for the feasibility and validation. The presentation not only shows up estimated noise levels as a contour map for identifying the hot-zone during working periods, but also demonstrates how to apply for personal exposure assessment in the workplace.