Abstract
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, fleft( {RH} right), and the hygroscopic mass growth, GM_{VIS}, 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 fleft( {RH} right) agreed well with the observed fleft( {RH} right) 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.
Highlights
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
We focus on determining the characteristics of coarse PM (CPM) and PM2.5 that interact with meteorological factors and quantifying the relationship between PM concentration and visibility
Increasing wind speed is associated with decreasing PM2.5 concentration and visibility recovery, whereas the CPM exhibits a relationship in which its concentration increases with wind s peed[46]
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. 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. Due to increasing particulate matter (PM) concentrations from air pollutants in industrial areas and automobile exhaust in urban areas, visibility impairment, characterized by heavy haze or a haze-fog mixture, has increased c ontinually[6,7] These anthropogenic emissions interacting with meteorological variables complicate visibility prediction. Hygroscopic PM scatters more light under humid weather conditions, resulting in lower visibility at high RH levels; predicting visibility under PM effects while considering their complicated interaction with meteorological factors is still c hallenging[13,24]. While understanding the hygroscopic properties of the CPM and P M2.5 and their relationship with weather is essential for predicting visibility, it can contribute to enhancing sensor technology for more accurate measurements of PM concentrations
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