Testing the performance of field calibration techniques for low-cost gas sensors in new deployment locations: across a county line and across Colorado Journal Article uri icon

Overview

abstract

  • <p><strong>Abstract.</strong> We assessed the performance of ambient ozone (<span class="inline-formula">O<sub>3</sub></span>) and carbon dioxide; (<span class="inline-formula">CO<sub>2</sub></span>) sensor field calibration techniques when they were generated using; data from one location and then applied to data collected at a new location.; This was motivated by a previous study (Casey et al., 2018), which highlighted; the importance of determining the extent to which field calibration; regression models could be aided by relationships among atmospheric trace; gases at a given training location, which may not hold if a model is applied; to data collected in a new location. We also explored the sensitivity of; these methods in response to the timing of field calibrations relative to; deployment periods. Employing data from a number of field deployments in; Colorado and New Mexico that spanned several years, we tested and compared; the performance of field-calibrated sensors using both linear models (LMs); and artificial neural networks (ANNs) for regression. Sampling sites covered; urban and rural–peri-urban areas and environments influenced by oil and gas production.; We found that the best-performing model inputs and model type depended on; circumstances associated with individual case studies, such as differing; characteristics of local dominant emissions sources, relative timing of model; training and application, and the extent of extrapolation outside of; parameter space encompassed by model training. In agreement with findings; from our previous study that was focused on data from a single location; (Casey et al., 2018), ANNs remained more effective than LMs; for a number of these case studies but there were some exceptions. For; <span class="inline-formula">CO<sub>2</sub></span> models, exceptions included case studies in which training; data collection took place more than several months subsequent to the test; data period. For <span class="inline-formula">O<sub>3</sub></span> models, exceptions included case studies in; which the characteristics of dominant local emissions sources (oil and gas; vs. urban) were significantly different at model training and testing; locations. Among models that were tailored to case studies on an individual; basis, <span class="inline-formula">O<sub>3</sub></span> ANNs performed better than <span class="inline-formula">O<sub>3</sub></span> LMs in six out of; seven; case studies, while <span class="inline-formula">CO<sub>2</sub></span> ANNs performed better than <span class="inline-formula">CO<sub>2</sub></span>; LMs in three out of five case studies. The performance of <span class="inline-formula">O<sub>3</sub></span> models tended; to be more sensitive to deployment location than to extrapolation in time,; while the performance of <span class="inline-formula">CO<sub>2</sub></span> models tended to be more sensitive to; extrapolation in time than to deployment location. The performance of; <span class="inline-formula">O<sub>3</sub></span> ANN models benefited from the inclusion of several secondary; metal-oxide-type sensors as inputs in five of seven case studies.</p>;

publication date

  • November 28, 2018

Full Author List

  • Casey JG; Hannigan MP

Other Profiles

Additional Document Info

start page

  • 6351

end page

  • 6378

volume

  • 11

issue

  • 11