Machine Learning-Based Estimation of PM2.5 Concentration Using Ground Surface DoFP Polarimeters

In this paper, we propose a machine learning system for the estimation of atmospheric particulate matter (PM) concentration, specifically, particles with a maximum diameter of
$2.5{\mu }\text{m}$
. These very fine particles, also known as PM
_2.5
particles, are very dangerous to the human body as they are small enough to penetrate deep areas of the vital organs. The proposed system uses a combination of features from both polarimetric and spectral imaging modalities in training and developing a machine learning model that provides high accuracy PM
_2.5
estimates. Furthermore, acquisition of the polarimetric images is done near the ground surface with a horizontal field of view aiming at standard targets which enables higher accuracy at the surface level. The accuracy of the approach was verified through a study conducted during the summer months of the United Arab Emirates (UAE). The proposed system employs different machine learning techniques such as Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Bagging Ensemble Trees (BET), to provide high accuracy PM
_2.5
estimates. Our proposed system achieves the best performance within the red wavelength with accuracy up to 93.8627% and an R
²
score up to 0.9420.