Near-Surface NO₂ Concentration Estimation by Random Forest Modeling and Sentinel-5P and Ancillary Data

Abstract

In the present study, a daily model is proposed for estimating the near-surface NO2 concentration in China, combining for the first time the Random Forest (RF) machine learning algorithm with the tropospheric NO2 columns from the TROPOspheric Monitoring Instrument (TropOMI) satellite and meteorological and NO2 data of surface sites in China for the year 2019. Furthermore, near-surface NO2 concentration data of ground sites during the COVID-19 outbreak from 1–5 February 2020 were used to verify the developed model. The daily model was verified by the ten-fold cross-validation method, revealing a coefficient of determination (R2) of 0.78 and root-mean-square error (RMSE) of 7.04 μg/m3, which are reasonable and also comparable to other published studies. In addition, our model showed that near-surface NO2 in China during the COVID-19 pandemic was significantly reduced compared with 2019, and these predictions were in good agreement with reference ground data. Our proposed model can also provide NO2 estimates for areas in western China where there are few ground monitoring sites. Therefore, all in all, our study findings suggest that the model established herein is suitable for estimating the daily NO2 concentration near the surface in China and, as such, can be used if there is a lack of surface sites and/or missing observations in some areas.

Keywords

• Random Forest
• NO₂
• TropOMI
• near-surface pollution
• remote sensing