# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 6 Conclusions and Future Work  
This paper utilized ML to simulate the non-linear relationships and memorize the hidden patterns between remote sensing data and EPA ground observation to estimate emissions from power plants. Several representative ML algorithms have been tested. 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. Future iterations of this study can better use ML models rather than linear regression as their baseline models to achieve higher accuracy and better reliability. This study collected data from multiple data sources including TROPOMI NRT NO\({}_{2}\) products, EPA eGRID ground monitoring network data, and NASA MERRA weather data. The training data used TROPOMI and MERRA variables as inputs and EPA emission data as outputs. It covered more than two hundred power plants across the United States. However, due to the relatively coarse spatial resolution of remote sensing data at present (TROPOMI has 7 \(\times\) 3.5 km before August 2019 and a higher resolution of 3.5 \(\times\) 5.5 km currently), the NO\({}_{2}\) reflected in remote sensing data possibly did not exclusively come from power plants, especially in urban regions. To exclude impacts from other emission sources, we initially chose those power plants located in the rural regions for initial tests. The results showed that ML is capable of correctly detecting the changing trends of NO\({}_{2}\) emission by power plants. They confirm that ML is very promising in identifying a reliable value range for specific ground emission sources simply based on remotely sensed daily data (if the remote sensing data has no gaps, such as clouds).  
In the future, we will continue to explore different combinations of input variables and tune hyperparameters to improve the accuracy of the trained ML models. More data sources, such as new satellite observations, will enable experiments to increase the spatiotemporal coverage of remote sensing to refine prediction. Right now, the model is not spatially reusable, and we will explore the options to transfer and reuse the models in new power plants in other regions or countries. Another important focus of research is to deploy the model into cloud servers and to start to provide operational services to stakeholders as an alternative information source to monitor coal-fired power plants.  
Conceptualization, Z.S. and D.T.; methodology, Z.S.; software, Z.S.; validation, A.A. and Z.S.; formal analysis, A.A. and Z.S.; investigation, A.A. and Z.S.; resources, Z.S. and D.T.; data curation, Z.S., D.T. and A.A.; writing--original draft preparation, A.A. and Z.S.; writing--review and editing, A.A., Z.S.; visualization, A.A.; supervision, Z.S.; project administration, Z.S.; funding acquisition, Z.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by NASA ACCESS (#80 NSSC21M0028), NASA Applied Science (#17-HAQ17-0044), NSF Geoinformatics (#EAR-1947893).  
Thanks to Microsoft for providing the Azure computing resources to carry out the initial data processing. Thanks, [PERSON], for initially collecting and processing the data. Thanks to all the authors of the open-source software we have used during this research.  
The authors declare no conflict of interest.