The Potential of Low-Cost Tin-Oxide Sensors Combined with Machine Learning for Estimating Atmospheric CH<sub>4</sub> Variations around Background Concentration

The Potential of Low-Cost Tin-Oxide Sensors Combined with Machine Learning for Estimating Atmospheric CH<sub>4</sub> Variations around Background Concentration

Continued developments in instrumentation and modeling have driven progress in monitoring methane (CH<sub>4</sub>) emissions at a range of spatial scales. The sites that emit CH<sub>4</sub> such as landfills, oil and gas extraction or storage infrastructure, intensive livesto...

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Bibliographic Details
Main Authors: Rodrigo Rivera Martinez, Diego Santaren, Olivier Laurent, Ford Cropley, Cécile Mallet, Michel Ramonet, Christopher Caldow, Leonard Rivier, Gregoire Broquet, Caroline Bouchet, Catherine Juery, Philippe Ciais
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Atmosphere
Subjects:
low-cost sensors
artificial neural networks
methane
calibration
Online Access:https://www.mdpi.com/2073-4433/12/1/107

Internet

https://www.mdpi.com/2073-4433/12/1/107

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