Machine Learning for Area-Wide Monitoring of Surface Level Concentration of NO2 Using Remote Sensing Data

Nitrogen dioxide (NO2) is one of the six gaseous air pollutants that need regular monitoring in big cities around the world. It contributes to particle pollution and can trigger chemical reactions that lead to increased concentration of ozone in the troposphere. Lahore, a metropolitan city of Pakistan is among the most polluted cities in the world. Area-wide monitoring of NO2 is necessary in this region to devise a long-term emission control policy. However, it lacks a dense network of ground-based air quality monitoring stations (AQMS), which is need of the hour. The installation of AQMS requires huge financial resources. In this paper, we investigate a machine learning-based approach to estimate surface level concentration of NO2 using remote sensing and modeled meteorological data. We use multiple linear regression (M1) and a polynomial fitted regression (M2) techniques to model ambient NO2, using remotely sensed vertical column density (VCD) of NO2, acquired by tropospheric monitoring instrument (TROPOMI), onboard Sentinel 5P satellite, and modeled meteorological parameters such as surface pressure, dew point temperature, and wind speed. Results show that M2 outperformed M1 with an $\mathbf{R}^{2}$ value of 0.49 and root mean square error (RMSE) value of $\mathbf{19}.\mathbf{27}\ \mu \mathbf{g}/\mathbf{m}^{3}$. There is a moderate positive correlation between in-situ measurements and remotely sensed VCD of NO2, which makes it an interesting problem that needs to be explored further to achieve desirable results.