# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 1 Introduction
It is widely used in monitoring many aspects of our planet, ranging from forests to urban expansion, and provides consistent and continuous information to decision-makers. Ground observations are point-based and cannot provide full spatial coverage, which is where satellite data can play a role. Multi and hyperspectral sensors carried on many satellites are capable of remotely measuring common air pollutants and greenhouse gases. The selection of sensors and algorithm development are largely dependent on the spatiotemporal resolution, sun height angle, spectral range, and the reflectance characteristics of NO\({}_{2}\). Remote sensing for NO\({}_{2}\) is currently a very active area of research with many studies highlighting its usefulness for accurately monitoring NOx emission using satellite instruments, such as OMI (Ozone Monitoring Instrument) on NASA Aura satellite and TROPOMI (TROPO-spheric Monitoring Instrument) on ESA Sentinel-5 Precursor (S5P) satellite. Additionally, The Geostationary Air Quality (Geo-AQ) mission constellation consists of geostationary satellites focused on air quality. The missions GEMS, Sentinel-4, and TEMPO are capable of capturing spectrally resolved radiances in the ultraviolet (UV) and near infrared spectral domains that are key for observing short-lived tropospheric trace gases and aerosols. These missions are planned to launch within the 2019-2023 timeframe. The data from these satellites might help in identifying specific sources of emissions from relatively small-scale sources, such as power plants, through its key validation changes in sampling of the diurnal cycle of atmospheric constituents in comparison to the validation used by OMI, S5P, and OMPS that face large variability of short-lived species [4]. TROPOMI satellite instrument has yet to reach a micro-spatial resolution able to distinguish power plant pollution from other sources accurately. Under ideal weather conditions, there are examples of TROPOMI accurately identifying relatively small-scale isolated sources with the help of spatial oversampling, temporal averaging [5], or enhanced surface reflectance conditions [6]. Machine learning (ML) has emerged as a promising method for building connections between remotely sensed data and ground-based observations, without requiring explicit performance of computationally expensive atmospheric chemistry and atmospheric dynamics calculations. ML algorithms are capable of recognizing patterns in multi-dimensionaldatasets. Geoscience is a field that may be readily applied, with large data sets available to train these models and many complex physical interactions that are, in some cases, not fully defined. It has already been successfully applied to complete tasks, such as land cover classification and automatically producing counterpart maps, with as high accuracy as conventional mapping techniques. Recently, ML has been widely introduced to the physics-based modeling community to help understand model uncertainty and biases and how to correct them [7]. Considering the requirements of monitoring power plants from space and the availability of remote sensing data and the ML analytic framework, this study sought to use only remote sensing data to monitor NO2 emissions by coal-fired power plants. It attempted to utilize the massive power of ML for simulating the non-linear relationships and memorizing the hidden patterns in training datasets to estimate emissions from power plants based only on remotely sensed data once the model is well trained. We tested several typical ML algorithms, including deep learning (DL) neural networks, such as long short-term memory (LSTM). Compared to traditional techniques, such as numeric modeling and rule-based workflow processing systems, ML has fewer restrictions, such as requiring no initial field conditions, equation coefficient adjustments, expensive computation costs, and no unrealistic assumptions.