Environmental hazard assessment and monitoring for air pollution using machine learning and remote sensing

Abstract

AbstractThe prediction and study of air pollution is a complex process due to the presence of controlling factors, different land use, and different sources for the elaboration of pollution. In this study, we applied the machine learning technique (Random Forest) with time series of particulate matter pollution records to predict and develop a particulate matter pollution susceptibility map. The applied method is to strict measures and to better manage particulate matter pollution in Ras Garib city, Egypt as a case study. Air pollution data for the period between 2018 and 2021 is collected using five air quality stations. Some of these stations are located near highly urbanized locations and could be dense with the current rates of development in the future. The random forest was applied to verify and visualize the relationships between the particulate matter and different independent variables. Spectral bands of Landsat OLI 8 imaginary and land cover/land use indices were used to prepare independent variables. Analysis of the results reveals that the proper air quality distribution monitoring stations would provide a deep insight into the pollution distribution over the study site. Distance from the roads and the land surface temperature has a significant effect on the distribution of air quality distribution. The obtained probability and classification maps were assessed using the area under the receiver operating characteristic curve. The outcome prediction maps are reasonable and will be helpful for future air quality monitoring and improvements. Furthermore, the applied method of pollutant concentration prediction is able to improve decision-making and provide appropriate solutions.