Performance Evaluation of Low-Cost PM Sensor Based on Light Scattering According to Particle Size: Focusing On Coincidence Effect

KEUN TAEK KIM, Horim Kim, Xin Zhao, Hyeri Jo, Sangjae Jeong, Jae Young Kim, Seoul National University

     Abstract Number: 615
     Working Group: Urban Aerosols

Abstract
The accurate measurement of particulate matter (PM) concentrations is crucial for assessing air quality and understanding its impact on human health. However, low-cost particulate matter sensors often suffer from limitations due to factors such as temperature, relative humidity, and the coincidence effect. This study aims to investigate the coincidence effect, a phenomenon where small particles pass the detecting zone simultaneously and are erroneously classified as larger particles. We examined this effect, caused by particles under 1 μm, on the measurement of PM_2.5 and PM₁₀ concentrations. The PM concentrations reported by the Plantower PMS7003, low-cost sensor, was compared to those reported by the Grimm 11-D, reference sensor. To assess the coincidence effect, a controlled chamber tests were conducted by utilizing atomized KCl particles under 1 μm. The experimental setup allowed for precise control over the concentration of particles in the chamber. By adjusting the flow rate entering the atomizer, we managed to modify the concentration of fine dust in the chamber. We then observed an increasing concentration of PM₁ and quantified their impact on the PM_2.5 and PM₁₀ concentrations. The results of the chamber tests revealed a clear relationship between PM₁ concentration and the coincidental misclassification of particles as PM_2.5 and PM₁₀. As the concentration of PM₁ particles increased, a higher number of coincident events occurred, leading to a significant overestimation of PM_2.5 and PM₁₀ concentrations by PMS7003. The R² values correlating PM_1.0 with the overestimated PM_1.0-2.5, and PM_1.0 with the overestimated PM_2.5-10, were 0.99 and 0.95, respectively. These findings highlight the importance of understanding and addressing the coincidence effect when using low-cost PM sensors for accurate air quality monitoring. By quantifying the coincidence effect caused by particles under 1 μm, this study emphasizes the need for improved sensor design and calibration methods to minimize measurement errors.