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As low-cost sensors have become ubiquitous in air quality measurements, there is a need for more efficient calibration and quantification practices. Here, we deploy stationary low-cost monitors in Colorado and Southern California near oil and gas facilities, focusing our analysis on methane and ozone concentration measurement using metal oxide sensors. In comparing different sensor signal normalization techniques, we propose a z-scoring standardization approach to normalize all sensor signals, making our calibration results more easily transferable among sensor packages. We also attempt several different physical co-location schemes, and explore several calibration models in which only one sensor system needs to be co-located with a reference instrument, and can be used to calibrate the rest of the fleet of sensor systems. This approach greatly reduces the time and effort involved in field normalization without compromising goodness of fit of the calibration model to a significant extent. We also explore other factors affecting the performance of the sensor system quantification method, including the use of different reference instruments, duration of co-location, time averaging, transferability between different physical environments, and the age of metal oxide sensors. Our focus on methane and stationary monitors, in addition to the z-scoring standardization approach, has broad applications in low-cost sensor calibration and utility.

Oil and gas development is occurring in urban, densely populated neighborhoods; however, the impacts of these operations on neighborhood air quality are not well characterized. In this research, we leveraged...

While low-cost air quality sensor quantification has improved tremendously in recent years, speciated hydrocarbons have received little attention beyond total lumped volatile organic compounds (VOCs) or total non-methane hydrocarbons (TNMHCs). In this work, we attempt to use two broad response metal oxide VOC sensors to quantify a host of speciated hydrocarbons as well as smaller groups of hydrocarbons thought to be emanating from the same source or sources. For sensors deployed near oil and gas facilities, we utilize artificial neural networks (ANNs) to calibrate our low-cost sensor signals to regulatory-grade measurements of benzene, toluene, and formaldehyde. We also use positive matrix factorization (PMF) to group these hydrocarbons along with others by source, such as wet and dry components of oil and gas operations. The two locations studied here had different sets of reference hydrocarbon species measurements available, helping us determine which specific hydrocarbons and VOC mixtures are best suited for this approach. Calibration fits on the upper end reach above R2 values of 0.6 despite the parts per billion (ppb) concentration ranges of each, which are magnitudes below the manufacturer’s prescribed detection limits for the sensors. The sensors generally captured the baseline trends in the data, but failed to quantitatively estimate larger spikes that occurred intermittently. While compounds with high variability were not suited for this method, its success with several of the compounds studied represents a crucial first step in low-cost VOC speciation. This work has important implications in improving our understanding of the links between health and environment, as different hydrocarbons will have varied consequences in the human body and atmosphere.

<p>A series of in-situ Carbon Monoxide (CO) observations were recently performed in the Western Pacific region, during summer 2022, in the framework of the ACCLIP project (Asian summer monsoon Chemical and CLimate Impacts Project). During the ACCLIP measurements campaign, located in Osan (South Korea), two different research aircraft were employed with a set of sensors installed onboard. The NASA WB-57 aircraft carried out 15 research flights (reaching a maximum altitude of about 19 km), and the NSF/NCAR Gulfstream (GV) aircraft carried out 14 research flights (reaching a maximum altitude of about 15 km), covering a large region near Korea and Japan.</p> <p>We report on the inter-comparison between five different instruments for in-situ CO mixing ratio measurements: three installed onboard WB-57 (ACOS, COLD2 and COMA), and two installed onboard GV (Aerodyne-CO and Picarro G2401-m). COLD2 (Carbon Oxide Laser Detector 2) <em>[1]</em> and Aerodyne-CO <em>[2]</em> are mid-infrared Quantum Cascade Laser spectrometers, based on direct absorption in combination with a multipass cell. ACOS (Carbonyl Sulfide Analyzer) <em>[3]</em> and COMA (Carbon mOnoxide Measurement from Ames) <em>[4]</em> are mid-infrared absorption spectrometers based on Off-Axis ICOS (Integrated Cavity Output Spectroscopy) technology. The Picarro sensor is a cavity ring down absorption spectrometer <em>[5]</em>.</p> <p>The in-flight CO mixing ratio values measured by the five spectrometers will be compared, with particular attention to both the accuracy of each instrument and the adopted or not-adopted calibration procedures, as, in principle, for many measurement environments the two sensors based on direct absorption do not need in-flight calibration. Laboratory measurements of common primary and secondary calibration standards made by the five CO measurement groups will be presented to increase confidence in method accuracy.</p> <p> </p> <p><em>[1]</em> Viciani S., Montori A., Chiarugi A., and D’Amato F.: "A Portable Quantum Cascade Laser Spectrometer for Atmospheric Measurements of Carbon Monoxide", Sensors, <strong>18</strong>, 2380 -1-18 (2018).</p> <p><em>[2]</em> https://www.eol.ucar.edu/instruments/carbon-monoxide-co-and-nitrous-oxide-n%E2%82%82o-qcl-instrument</p> <p><em>[3]</em> https://ams.confex.com/ams/103ANNUAL/meetingapp.cgi/Paper/421824</p> <p><em>[4]</em> https://espo.nasa.gov/acclip/instrument/COMA</p> <p><em>[5]</em> https://www.eol.ucar.edu/instruments/airborne-carbon-dioxide-co2-methane-ch4-carbon-monoxide-co-and-water-vapor-h2o</p>