Reconstruction of high-frequency methane atmospheric concentration peaks from measurements using metal oxide low-cost sensors

摘要: Detecting and quantifying CH gas emissions at industrial facilities is an important goal for being able to reduce these emissions. The nature of CH emissions through “leaks” is episodic and spatially variable, making their monitoring a complex task; this is partly being addressed by atmospheric surveys with various types of instruments. Continuous records are preferable to snapshot surveys for monitoring a site, and one solution would be to deploy a permanent network of sensors. Deploying such a network with research-level instruments is expensive, so low-cost and low-power sensors could be a good alternative. However, low cost usually entails lower accuracy and the existence of sensor drifts and cross-sensitivity to other gases and environmental parameters. Here we present four tests conducted with two types of Figaro® Taguchi gas sensors (TGSs) in a laboratory experiment. The sensors were exposed to ambient air and peaks of CH concentrations. We assembled four chambers, each containing one TGS sensor of each type. The first test consisted in comparing parametric and non-parametric models to reconstruct the CH peak signal from observations of the voltage variations of TGS sensors. The obtained relative accuracy is better than 10 % to reconstruct the maximum amplitude of peaks (RMSE  ppm). Polynomial regression and multilayer perceptron (MLP) models gave the highest performances for one type of sensor (TGS 2611C, RMSE  ppm) and for the combination of two sensors (TGS 2611C TGS 2611E, RMSE  ppm), with a training set size of 70 % of the total observations. In the second test, we …