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Abstract Existing motor vehicle pollutant measurement techniques, including those that employ ground-based and multirotor small uncrewed aircraft system (sUAS) methods, can accurately measure traffic-related air pollution (TRAP) concentrations at a single location. However, these techniques often lack the mobility to assess pollutant trends across a large horizontal area. Fixed-wing sUAS represents an alternative instrument platform compared to ground-based systems and multirotor sUAS, as fixed-wing sUASs are able to carry air pollutant monitor payloads, have extended endurance, and offer expansive three-dimensional ranges across a field site. To demonstrate the utility of fixed-wing sUAS for urban TRAP assessment, we conducted two flights using a Super Robust Autonomous Aerial Vehicle–Endurant Nimble (RAAVEN) sUAS [University of Colorado (CU) Boulder] at a large field site adjacent to a major highway in Erie, Colorado. Concentrations of solid particulate matter (PM₁₀) and gas-phase (carbon monoxide) pollutants displayed decay as a function of altitude. During the morning flight, PM₁₀concentrations decreased from 19.0μg m⁻³at ground level to a minimum concentration of 14.3μg m⁻³at 90 m above ground level. During the afternoon flight, concentrations of PM₁₀displayed minimal vertical stratification, ranging from 8.9 at ground level to 10.0μg m⁻³at 45 m above ground level. Similarly, pollutants displayed decreasing concentrations as the horizontal distance from the roadway increased. Concentrations of TRAP may be significantly elevated in the area both above and beyond roadways, which contribute to additional pollutant exposure from on-road pollution sources. This study demonstrated that the general behavior of TRAP in near-road environments and that the use of fixed-wing sUAS are viable option for urban air quality measurements. Significance StatementThis study represents one of the first uses of a fixed-wing small uncrewed aircraft system (sUAS) to assess near-roadside concentrations of traffic-related air pollution (TRAP) in urbanized areas. We found that local meteorology, including local wind and solar radiation, had a substantial influence on the concentrations of common air pollutants, including particulate matter, black carbon, carbon monoxide, and carbon dioxide. Furthermore, we found large-scale spatiotemporal variation in pollutant concentrations as a function of the vertical and horizontal distance from the highway, indicating that diminished spatial variation employed in multirotor sUAS studies may not be sufficient to fully assess TRAP in roadside environments. 

Researchers can improve the ecological validity of brain research by studying humans moving in real-world settings. Recent work shows that dual-layer EEG can improve the fidelity of electrocortical recordings during gait, but it is unclear whether these positive results extrapolate to non-locomotor paradigms. For our study, we recorded brain activity with dual-layer EEG while participants played table tennis, a whole-body, responsive sport that could help investigate visuomotor feedback, object interception, and performance monitoring. We characterized artifacts with time-frequency analyses and correlated scalp and reference noise data to determine how well different sensors captured artifacts. As expected, individual scalp channels correlated more with noise-matched channel time series than with head and body acceleration. We then compared artifact removal methods with and without the use of the dual-layer noise electrodes. Independent Component Analysis separated channels into components, and we counted the number of high-quality brain components based on the fit of a dipole model and using an automated labeling algorithm. We found that using noise electrodes for data processing provided cleaner brain components. These results advance technological approaches for recording high fidelity brain dynamics in human behaviors requiring whole body movement, which will be useful for brain science research.