Abstract
With the advancement of air pollution management, low-cost sensors are increasingly being used in air quality monitoring, but the data quality of these sensors is still a major source of concern. In this paper, data from five air monitoring stations in Sofia were compared to data from fixed low-cost PM sensors. The values of atmospheric pressure from low-cost sensors and the effects of relative humidity were investigated. A two-step model was created to refine the calibration process for low-cost PM sensors. At first, we calibrated the sensors with five separate supervised machine learning models and then the ANN-final model with anomaly detection completed the results. The ANN-final model improved the R2 values of the PM10 determined by low-cost sensors from 0.62 to 0.95 as compared to standard instruments. In conclusion, the two-step calibration model proved to be a positive solution to addressing low-cost sensor efficiency issues.
Highlights
A IR POLLUTION is a significant public health problem that has long been a source of anxiety for citizens.An air pollutant is described as any substance that can affect humans, animals, plants, or materials
The mean value of R squared between PM sensors and standard instruments without calibration was 0.62
The best correlation for PM10 came from the Artificial Neural Network (ANN) model
Summary
A IR POLLUTION is a significant public health problem that has long been a source of anxiety for citizens.An air pollutant is described as any substance that can affect humans, animals, plants, or materials. In the case of humans, an air pollutant may cause or lead to an increase in mortality or serious illness, as well as pose a current or potential health risk [1]. PM (Particulate Matter) air pollution is a suspended combination of solid and liquid particles that vary in quantity, size, shape, surface area, chemical composition, solubility, and origin. Total suspended particles (TSPs) have a trimodal size distribution in the ambient air, including coarse particles, fine particles, and ultrafine particles [3]. PM size-selective sampling refers to the collection of particles that are below, above, or within a defined aerodynamic range of sizes, which is commonly chosen to be relevant to inhalation and deposition, causes, or toxicity [4]
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