Estimation of Surface-Level NO2 Using Satellite Remote Sensing and Machine Learning: A review
Muhammad Adnan Siddique, Ehtasham Naseer, Muhamamd Usama, Abdul Basit
Abstract
Nitrogen dioxide (NO<sub>2</sub>) is one among several constituents of air pollution. To restrict its surface-level concentration to within the limits prescribed by regulatory authorities, dedicated monitoring of its spatiotemporal spread is needed. Satellite-based remote sensing of the tropospheric composition can be used to estimate NO<sub>2</sub> concentration. However, this is not a direct measurement of the surface-level NO<sub>2</sub> concentration, though several studies have shown that the tropospheric vertical column density (VCD) estimated by the satellite sensor is correlated with surface-level concentration. This review article covers various aspects related to the estimation of surface-level NO<sub>2</sub> using remotely sensed data. It provides detailed literature, tracing the evolution of the various methods developed for the estimation, from scaling methods to the initial linear regression (LR) models onward to the more recent deep learning (DL) architectures. The performance of these models is critically reviewed.