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

Abstract

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.

Utilization of a Low-Cost Sensor Array for Mobile Methane Monitoring

“…Alternatively, over the past decade, studies have pioneered the use of low-cost sensors (LCS) to accurately quantify methane concentrations in lab studies [ 14 , 15 , 16 ], stationary deployments in urban areas [ 17 , 18 , 19 ], and fence line monitoring [ 20 ]. Other studies have leveraged the combination of low-cost sensors with machine learning methods to specify and predict individual VOC concentrations in stationary laboratory and field experiments with high fidelity [ 21 , 22 ]. Similarly, researchers have demonstrated the ability of machine learning algorithms for the classification and quantification of individual VOCs [ 22 , 23 ] and CH 4 [ 24 ].…”