@article{TEXTUAL,
      recid = {10616},
      author = {Silberstein, Jonathan and Wellbrook, Matthew and Hannigan,  Michael},
      title = {Utilization of a Low-Cost Sensor Array for Mobile Methane  Monitoring},
      journal = {Sensors},
      address = {2024-01-14},
      number = {TEXTUAL},
      abstract = {The use of low-cost sensors (LCSs) for the mobile  monitoring of oil and gas emissions is an understudied  application of low-cost air quality monitoring devices. To  assess the efficacy of low-cost sensors as a screening tool  for the mobile monitoring of fugitive methane emissions  stemming from well sites in eastern Colorado, we colocated  an array of low-cost sensors (XPOD) with a reference grade  methane monitor (Aeris Ultra) on a mobile monitoring  vehicle from 15 August through 27 September 2023. Fitting  our low-cost sensor data with a bootstrap and aggregated  random forest model, we found a high correlation between  the reference and XPOD CH<sub>4</sub> concentrations (r =  0.719) and a low experimental error (RMSD = 0.3673 ppm).  Other calibration models, including multilinear regression  and artificial neural networks (ANN), were either unable to  distinguish individual methane spikes above baseline or had  a significantly elevated error (RMSD<sub>ANN</sub> = 0.4669  ppm) when compared to the random forest model. Using  out-of-bag predictor permutations, we found that sensors  that showed the highest correlation with methane displayed  the greatest significance in our random forest model. As we  reduced the percentage of colocation data employed in the  random forest model, errors did not significantly increase  until a specific threshold (50 percent of total calibration  data). Using a peakfinding algorithm, we found that our  model was able to predict 80 percent of methane spikes  above 2.5 ppm throughout the duration of our field  campaign, with a false response rate of 35 percent.},
      url = {http://knowledge.uchicago.edu/record/10616},
}