Characterizing methane and total non-methane hydrocarbon levels in Los Angeles communities with oil and gas facilities using air quality monitors

Okorn, Kristen; Jimenez, Amanda; Collier-Oxandale, Ashley; Johnston, Jill; Hannigan, Michael P.

doi:10.1016/j.scitotenv.2021.146194

Cited by 22 publications

(30 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This suggests that the term may not be needed in general. This has been demonstrated in other studies [2,36], but may be useful in cases such as this where the desired concentration is much lower than the sensors are designed to quantify.…”

Section: Calibration Best-fitting Ann Parametersmentioning

confidence: 89%

“…One previous study used the same two generalized VOC sensors-the Figaro TGS 2600 and 2602-to quantify speciated VOC data [21]. As multivariate linear regression has been frequently used to match low-cost sensor signals to reference concentrations [1][2][3]18,[21][22][23][24][25], Collier-Oxandale and co-workers used this method to quantify benzene as an individual hydrocarbon as well as aromatics as a group (benzene, toluene, C 8 , and C 9 ). Using one of the pod datasets explored in this work, R 2 values between the reference and sensor benzene signals reached 0.72 for calibration and 0.67 for validation, explaining nearly three-fourths of the variability in the species.…”

Section: Voc Quantification Using Low-cost Sensorsmentioning

confidence: 99%

“…Based on previous success in using ANNs to quantify pollutants with low-cost sensors [26], we focused our efforts on this machine learning technique, which has not been used previously for this specific hydrocarbon application. When using multivariate linear regression to quantify pollutants such as methane and generalized VOCs previously, interaction terms among sensors proved beneficial [1,2,21]. For instance, the ratio or product of the two VOC sensors has been added as an additional term to better quantify methane.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Note that several different species use the exact same ANN input data, similarly to how the same linear multivariable equation has been used for both methane and TNMHCs in the past [1,2]. However, the same pollutants or groups of pollutants at the two different sites typically had different inputs and parameters that produced the best results, likely due to the different sets of compounds comprising each group at the two sites.…”

Section: Calibration Best-fitting Ann Parametersmentioning

confidence: 99%

“…While numerous studies have utilized low-cost sensors to study generalized VOCs or TNMHCs in addition to methane [1][2][3][4], few have attempted to characterize specific hydrocarbons other than methane in outdoor monitoring experiments. These current characterizations have limited utility; it is helpful to know when and where hydrocarbon emissions are significant, but not knowing the exact chemical composition leaves lingering questions about source attribution as well as impacts on climate and human health, since some hydrocarbons are more damaging to people and the environment than others [5,6].…”

Section: Introduction 1the Need For Low-cost Speciated Hydrocarbon Datamentioning

confidence: 99%

See 4 more Smart Citations

Applications and Limitations of Quantifying Speciated and Source-Apportioned VOCs with Metal Oxide Sensors

Okorn

Hannigan

2021

Atmosphere

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Calibration Best-fitting Ann Parametersmentioning

confidence: 89%