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

ABSTRACT: 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_2.5 (fine inhalable particles with diameters ≤ 2.5 μm) concentrations from two regions in Korea, Incheon and Jeju, and one in Singapore were used as training data to build a visibility-based calibration mode

SUBMITTER: Won WS 

PROVIDER: S-EPMC10423292 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature