Estimating air methane and total hydrocarbon concentrations in Alberta, Canada using machine learning

Abstract

Fugitive emission sources are significant contributors to methane emissions, and time series data on reported emissions from such sources remain underutilized. The Alberta Energy Regulator (AER) has been collecting air quality data since 1986, including methane and total hydrocarbons concentration data. However, this data has not been thoroughly analyzed to forecast air quality trends. Our analysis of the data shows that average methane concentrations measured at most Alberta airshed stations exceed the global average, and the data exhibits increasing and decreasing trends depending on the station. We compared the predictive performance of three machine learning methods: Long Short-Term Memory (LSTM) recurrent neural network, Fully-Connected Neural Network (FC-NN), and Autoregressive Integrated Moving Average (ARIMA), using the AER methane concentration data. Our results indicate that the LSTM neural network model outperforms the other two methods. Also, our findings suggest that the AER methane concentration data can be effectively analyzed and utilized to forecast air quality trends in the region.