| Summary: | The hygroscopic property of particulate matter (PM) influencing light scattering and absorption is vital for determining visibility and accurate sensing of PM using a low-cost sensor. In this study, we examined the hygroscopic properties of coarse PM (CPM) and fine PM (FPM; PM₂.₅) and the effects of their interactions with weather factors on visibility. A censored regression model was built to investigate the relationships between CPM and PM₂.₅ concentrations and weather observations. Based on the observed and modeled visibility, we computed the optical hygroscopic growth factor, f(RH), and the hygroscopic mass growth, GMᵥᵢₛ, which were applied to PM₂.₅ field measurement using a low-cost PM sensor in two different regions. The results revealed that the CPM and PM₂.₅ concentrations negatively affect visibility according to the weather type, with substantial modulation of the interaction between the relative humidity (RH) and PM₂.₅. The modeled f(RH) agreed well with the observed f(RH) in the RH range of the haze and mist. Finally, the RH-adjusted PM₂.₅ concentrations based on the visibility-derived hygroscopic mass growth showed the accuracy of the low-cost PM sensor improved. These findings demonstrate that in addition to visibility prediction, relationships between PMs and meteorological variables influence light scattering PM sensing.
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