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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.
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Spatiotemporal air quality prediction using stochastic advection–diffusion model for multimodal data fusion
Abstract Particulate matter poses significant risks to respiratory and cardiovascular health. Monitoring ambient particulate matter concentrations can provide information on potential exposures and inform mitigation strategies, but ground-based measurements are sparse. Data fusion approaches that integrate data from multiple sources can complement existing observation networks and reveal insights that single-sensor data might miss to better manage pollutant exposure risks. However, data fusion approaches face multiple challenges, including incompatible measurement units, varying data resolutions, and differing levels of uncertainty. As a result, the optimal method for data fusion remains an open question. Here, we propose a probabilistic spatiotemporal model, based on the stochastic advection–diffusion (SAD) equation, as a data fusion method to process multimodal air quality data to predict hourly concentrations of fine particulate matter (PM2.5). We employ a variational inference method to calibrate the probabilistic model using ground-level observations and the numerical output of two simulation models. We then evaluate the prediction performance of our model for two scenarios: (1) incorporating simulation outputs and ground-level observations from sparse regulatory-grade stations and (2) using ground-level observations from both low-cost and regulatory-grade stations. For the first scenario, the data fusion method reduces prediction error by 14% compared to the nearest regulatory-grade air monitor located 20 km away. For the second scenario, error is reduced by 40% compared to the nearest regulatory-grade monitor and 11% compared to the nearest low-cost sensor located approximately 1 km away. The model captures 78% of observed data within a 75% confidence interval across both scenarios, demonstrating its ability to accurately represent uncertainty. Our findings demonstrate that the proposed SAD model can effectively integrate multimodal data to provide improved prediction of particulate matter concentrations at high spatial resolution. Model outputs can inform individual and community-level decision-making to mitigate air pollutant exposures.
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- Award ID(s):
- 2231557
- PAR ID:
- 10565360
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Environmental Research Letters
- Volume:
- 20
- Issue:
- 1
- ISSN:
- 1748-9326
- Format(s):
- Medium: X Size: Article No. 014065
- Size(s):
- Article No. 014065
- Sponsoring Org:
- National Science Foundation
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