Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants

Effective and precise monitoring is a prerequisite to control human emissions and slow disruptive climate change. To obtain the near-real-time status of power plant emissions, we built machine learning models and trained them on satellite observations (Sentinel 5), ground observed data (EPA eGRID), and meteorological observations (MERRA) to directly predict the NO2 emission rate of coal-fired power plants. A novel approach to preprocessing multiple data sources, coupled with multiple neural network models (RNN, LSTM), provided an automated way of predicting the number of emissions (NO2, SO2, CO, and others) produced by a single power plant. There are many challenges on overfitting and generalization to achieve a consistently accurate model simply depending on remote sensing data. This paper addresses the challenges using a combination of techniques, such as data washing, column shifting, feature sensitivity filtering, etc. It presents a groundbreaking case study on remotely monitoring global power plants from space in a cost-wise and timely manner to assist in tackling the worsening global climate.

Automated summary

This study tackles the challenges of overfitting and generalization by building machine learning models and using satellite observations, ground observed data, and meteorological observations to predict the emissions of coal-fired power plants. Techniques such as data washing, column shifting, and feature sensitivity filtering are employed. It presents a groundbreaking case study on remotely monitoring global power plants from space.