# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 2 Related Work  
Machine learning and remote sensing have attracted a lot of attention in air quality research. [PERSON] [8] describes the meteorological drivers of CH4 by linking remote sensed TROPOMI CH4 data. [PERSON] et al. [9] consider the use of TROPOMI NO2 for observations over Paris as having superior accuracy to numerical models based on inventories that rely on indirect data. [PERSON] et al. [10] used TROPOMI to map NOx emissions from power plants near high urban pollution areas in Riyadh, KSA. [PERSON] et al. [11] and [PERSON] et al. [12] compared TROPOMI NO2 and ground observations and found that TROPOMI underestimated NO2. [PERSON] et al. [13] constructed a long short-term memory (LSTM) neural network to model the relationship between operational parameters and the NOx emissionsof a 660 MW boiler. [PERSON] et al. [14] utilized an LSTM model to forecast PM2.5, PM10, SO\({}_{2}\), NO\({}_{2}\), CO\({}_{2}\), and O\({}_{3}\) emissions produced in Dhaka, India, between 2013 and 2020. [PERSON] et al. [15] forecast PM2.5 emissions using an LSTM model. [PERSON] et al. [16] forecast air pollution using an LSTM multivariate model to predict precise PM2.5 measurements during summer and cold seasons. [PERSON] et al. [17] used remote sensing data to analyze NO\({}_{2}\) plume emissions by ships in the ocean.  
[PERSON] et al. [18] used XGBoost to predict NOx emissions from a boiler located in Alberta, Canada. They showed XGBoost achieved better accuracy than an ANN. [PERSON] et al. [19] used random forest to estimate daily ambient NO\({}_{2}\) across China based on satellite images. The land use regression model (LUR) is a commonly used algorithm to monitor pollutants in populated regions. It couples regression algorithms to describe the relationship between environmental variables and pollutants. [PERSON] et al. [20] used an LUR model to assess spatial-temporal variation of NO\({}_{2}\) in Taiwan and captured 90% and 87% of NO\({}_{2}\) variations in annual and monthly datasets, respectively. [PERSON] et al. [21] used an LUR model for monitoring outdoor NO\({}_{2}\) pollution in the U.S. [PERSON] et al. [22] used several machine learning algorithms to estimate long-term daily NO\({}_{2}\) data and concluded that XGBoost outperforms random forest and DNN (deep neural network) models.  
[PERSON] et al. [23] and [PERSON] et al. [24] discuss the correlation existing between NO\({}_{2}\), SO\({}_{2}\) with aerosol optical depth (AOD) and examined their effects on pollutant dispersion and tropospheric data retrieval. [PERSON] et al. [25] compared the aerosol optical thickness (AOT) from MAIAC against data from the Visible Infrared Imaging Radiometer Suite (VIIRS) for accuracy. [PERSON] et al. [26] used Pandora instruments in Toronto, CA, to measure NO\({}_{2}\) and evaluate TROPOMI NO\({}_{2}\) accuracy. Using a wind-based validation technique, the study showed both Pandora and TROPOMI instruments could reveal detailed spatial patterns and regional air quality changes. [PERSON] et al. [27] presented a validation method for TROPOMI NO\({}_{2}\) against consolidated ground-based results. [PERSON] et al. [28] compared and validated NO\({}_{2}\) data measured by TROPOMI and its predecessor, the Ozone Monitoring Instrument (OMI), over China.  
Overall, most existing research has focused on studying the spatial-temporal distribution of NO\({}_{2}\) and monitoring the general trends of variances at a large scale. However, the regulatory agencies need detailed and specific reports about individual emission sources, such as power plants. Therefore, this study changed the focus and re-examined the capabilities of remote sensing and ML, using them for estimating single specific sources. Below are our methods and findings.