Work

Analyzing the Contribution of Bare Soil Surfaces to Resuspended Particulate Matter in Urban Areas via Machine Learning

Year: 2025

Type: article

Abstract: Particulate matter (PM) pollution is high in most Bulgarian regions, especially large urban areas. In a previous study covering one year of data collection and analysis by source apportionment techniques such as positive matrix factorization we show that the main source of high PM10 (PM with a diameter of 10 μm or less) concentration in the city of Sofia is soil and road dust resuspension into the surface layer of the air. Resuspension has seasonal variations, with a relatively large impact (25%) associated with drying periods. In the present paper we combine classical indices (NDVI, BSI, NDMI) derived from Sentinel-2 imagery with meteorological and air quality data, as well as other related variables regarding yearly average traffic and inventory estimates, transportation infrastructure and demographic data, including motorized inhabitants and wood/coal stoves in use, by area. We apply statistical and machine learning methods to analyze the contribution of bare soil surfaces to the overall PM resuspension. Based on a series of stack ensemble meta-models with coefficient of determination R2≈0.9 we conclude that the contribution of bare soil surfaces to the overall PM10 resuspension is around 10% (between 5% and 15%), by our preliminary rough estimates.

Source: Applied Sciences

DOI: 10.3390/app152312783