Estimations of the Ground-Level NO2 Concentrations Based on the Sentinel-5P NO2 Tropospheric Column Number Density Product

Abstract

The main objective of the presented study was to verify the potential of the Sentinel-5 Precursor (S-5P) Tropospheric NO2 Column Number Density (NO2 TVCD) to support air pollution monitoring in Poland. The secondary objective of this project was to establish a relationship between air pollution and meteorological conditions. The ERA-5 data together with the NO2 TVCD product and auxiliary data were further assimilated into an artificial intelligence model in order to estimate surface NO2 concentrations. The results revealed that the random forest method was the most accurate method for estimating the surface NO2. The random forest model demonstrated MAE values of 3.4 μg/m3 (MAPE~37%) and 3.2 μg/m3 (MAPE~31%) for the hourly and weekly estimates, respectively. It was observed that the proposed model could be used for at least 120 days per year due to the cloud-free conditions. Further, it was found that the S-5P NO2 TVCD was the most important variable, which explained more than 50% of the predictions. Other important variables were the nightlights, solar radiation flux, road density, population, and planetary boundary layer height. The predictions obtained with the proposed model were better fitted to the actual surface NO2 concentrations than the CAMS median ensemble estimations (~15% better accuracy).