Enhancing the reliability of particulate matter sensing by multivariate Tobit model using weather and air quality data.

Low-cost particulate matter (PM) sensors have been widely used following recent sensor-technology advancements; however, inherent limitations of low-cost monitors (LCMs), which operate based on light scattering without an air-conditioning function, still restrict their applicability. We propose a regional calibration of LCMs using a multivariate Tobit model with historical weather and air quality data to improve the accuracy of ambient air monitoring, which is highly dependent on meteorological conditions, local climate, and regional PM properties. Weather observations and PM (fine inhalable particles with diameters ≤ 2.

Hygroscopic properties of particulate matter and effects of their interactions with weather on visibility.

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; PM) and the effects of their interactions with weather factors on visibility. A censored regression model was built to investigate the relationships between CPM and PM concentrations and weather observations.