Comparing Short-Term Volatile Organic Compound Measurements in Fenceline Environments Using Multiple Mobile Air Monitoring Platforms and Methods

Abstract

The presence of volatile organic compounds (VOCs) can increase the air pollution burden in fenceline communities. Technological advancements have made mobile air toxic monitoring a useful and attractive approach to spatially quantify VOC concentrations in real-time, but there is a need to evaluate the accuracy of these measurements in real-world applications using intercomparison techniques. Here, we conducted a two-week field campaign near different VOC-emitting facilities using three different mobile monitoring platforms and four different ambient VOC-measurement technologies. Our primary focus was the intercomparison of a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS) and canister samples analyzed by an offline gas chromatograph mass spectrometer (GC-MS), but we also collected measurements using a closed-path ultraviolet differential optical absorption spectrometer (UV-DOAS) and a selected ion flow tube mass spectrometer (SIFT-MS). The PTR-ToF-MS and offline GC-MS analyses show strong agreement in stationary settings (all targeted VOCs R² = 0.92, slope = 1.1) for aromatic compounds including benzene (R² = 0.95, p < 0.001), naphthalene (R² = 0.84, p < 0.01), and xylenes + ethylbenzene (R² = 0.93, p < 0.01). PTR-ToF-MS and UV-DOAS comparisons have varied results. The UV-DOAS compared well at some monitoring locations but had poor agreement in ambient air matrices containing naphthalene, which caused uncorrectable interferences for measurements of benzene, toluene, and xylenes. Lastly, the PTR-ToF-MS and SIFT-MS showed strong agreement (all targeted VOCs R² = 0.68, slope = 0.85) in mobile format comparisons but only when aldehyde compounds with high background noise were removed. Our results highlight some potential interferences that should be accounted for when performing monitoring of mobile air toxics and demonstrate multi-instrument comparison techniques that can be used to ensure robust data collection.