About the webinar:
Fine particulate matter, also known as PM2.5, poses significant health risks to humans, such as heart disease, lung-disease, and cancer. Occupational environments are especially prone to high concentrations of PM2.5, which makes monitoring air quality essential to the health and safety of workers. This webinar will present the results of a study assessing the calibration precision of low-cost PM2.5 sensors, using Linear Regression (LR), Random Forest (RF), and Polynomial Regression (PR) models across various concentration ranges of fine particulate matter.