skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


This content will become publicly available on April 1, 2026

Title: Deployment of a Fixed-Wing Small Uncrewed Aircraft System for Urban Traffic–Related Air Pollution Assessment
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.  more » « less
Award ID(s):
2312996 2431471
PAR ID:
10595608
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Publisher / Repository:
American Meteorological Society
Date Published:
Journal Name:
Journal of Atmospheric and Oceanic Technology
Volume:
42
Issue:
4
ISSN:
0739-0572
Page Range / eLocation ID:
353 to 367
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; (, Frontiers in Built Environment)
    null (Ed.)
    Urban air pollution poses a major threat to human health. Understanding where and when urban air pollutant concentrations peak is essential for effective air quality management and sustainable urban development. To this end, we implement a mobile monitoring methodology to determine the spatiotemporal distribution of particulate matter (PM) and black carbon (BC) throughout Philadelphia, Pennsylvania and use hot spot analysis and heatmaps to determine times and locations where pollutant concentrations are highest. Over the course of 12 days between June 27 and July 29, 2019, we measured air pollution concentrations continuously across two 150 mile (241.4 km) long routes. Average daily mean concentrations were 11.55 ± 5.34 μg/m 3 (PM 1 ), 13.48 ± 5.59 μg/m 3 (PM 2.5 ), 16.13 ± 5.80 μg/m 3 (PM 10 ), and 1.56 ± 0.39 μg/m 3 (BC). We find that fine PM size fractions (PM 2.5 ) constitute approximately 84% of PM 10 and that BC comprises 11.6% of observed PM 2.5 . Air pollution hotspots across three size fractions of PM (PM 1 , PM 2.5 , and PM 10 ) and BC had similar distributions throughout Philadelphia, but were most prevalent in the North Delaware, River Wards, and North planning districts. A plurality of detected hotspots found throughout the data collection period (30.19%) occurred between the hours of 8:00 AM–9:00 AM. 
    more » « less
  2. ; (, Nature Communications)
    Abstract Energy transitions and decarbonization require rapid changes to a nation’s electricity generation mix. There are many feasible decarbonization pathways for the electricity sector, yet there is vast uncertainty about how these pathways will advance or derail the nation’s energy equality goals. We present a framework for investigating how decarbonization pathways, driven by a least-cost paradigm, will impact air pollution inequality across vulnerable groups (e.g., low-income, minorities) in the US. We find that if no decarbonization policies are implemented, Black and high-poverty communities may be burdened with 0.19–0.22 μg/m3higher PM2.5concentrations than the national average during the energy transition. National mandates requiring more than 80% deployment of renewable or low-carbon technologies achieve equality of air pollution concentrations across all demographic groups. Thus, if least-cost optimization capacity expansion models remain the dominant decision-making paradigm, strict low-carbon or renewable energy technology mandates will have the greatest likelihood of achieving national distributional energy equality. Decarbonization is essential to achieving climate goals, but myopic decarbonization policies that ignore co-pollutants may leave Black and high-poverty communities up to 26–34% higher PM2.5exposure than national averages over the energy transition. 
    more » « less
  3. ; ; ; ; ; (, Environmental Science: Atmospheres)
    Fairbanks-North Star Borough (FNSB), Alaska perennially experiences some of the worst wintertime air quality in the United States. FNSB was designated as a “serious” nonattainment area by the U.S. Environmental Protection Agency in 2017 for excessive fine particulate matter (PM 2.5 ) concentrations. The ALPACA (Alaskan Layered Pollution And Chemical Analysis) field campaign was established to understand the sources of air pollution, pollutant transformations, and the meteorological conditions contributing to FNSB's air quality problem. We performed on-road mobile sampling during ALPACA to identify and understand the spatial patterns of PM across the study domain, which contained multiple stationary field sites and regulatory measurement sites. Our measurements demonstrate the following: (1) both the between-neighborhood and within-neighborhood variations in PM 2.5 concentrations and composition are large (>10 μg m −3 ). (2) Spatial variations of PM in Fairbanks are tightly connected to meteorological conditions; dramatic between-neighborhood differences exist during strong temperature inversion conditions, but are significantly reduced during weaker temperature inversions, where atmospheric conditions are more well mixed. (3) During strong inversion conditions, total PM 2.5 and black carbon (BC) are tightly spatially correlated and have high absorption Ångstrom exponent values (AAE > 1.4), but are relatively uncorrelated during weak inversion conditions and have lower AAE. (4) PM 2.5 , BC, and total particle number (PN) concentrations decreased with increasing elevation, with the fall-off being more dramatic during strong temperature inversion conditions. (5) Mobile sampling reveals important air pollutant concentration differences between the multiple fixed sites of the ALPACA study, and demonstrates the utility of adding mobile sampling for understanding the spatial context of large urban air quality field campaigns. These results are important for understanding both the PM exposure for residents of FNSB and the spatial context of the ALPACA study. 
    more » « less
  4. ; ; ; ; ; ; ; ; (, Nature Sustainability)
    Abstract Heavy-duty vehicles (HDVs) disproportionately contribute to the creation of air pollutants and emission of greenhouse gases—with marginalized populations unequally burdened by the impacts of each. Shifting to non-emitting technologies, such as electric HDVs (eHDVs), is underway; however, the associated air quality and health implications have not been resolved at equity-relevant scales. Here we use a neighbourhood-scale (~1 km) air quality model to evaluate air pollution, public health and equity implications of a 30% transition of predominantly diesel HDVs to eHDVs over the region surrounding North America’s largest freight hub, Chicago, IL. We find decreases in nitrogen dioxide (NO2) and fine particulate matter (PM2.5) concentrations but ozone (O3) increases, particularly in urban settings. Over our simulation domain NO2and PM2.5reductions translate to ~590 (95% confidence interval (CI) 150–900) and ~70 (95% CI 20–110) avoided premature deaths per year, respectively, while O3increases add ~50 (95% CI 30–110) deaths per year. The largest pollutant and health benefits simulated are within communities with higher proportions of Black and Hispanic/Latino residents, highlighting the potential for eHDVs to reduce disproportionate and unjust air pollution and associated air-pollution attributable health burdens within historically marginalized populations. 
    more » « less
  5. ; ; ; ; ; ; (, GeoHealth)
    Abstract The paucity of fine particulate matter (PM2.5) measurements limits estimates of air pollution mortality in Sub‐Saharan Africa. Well calibrated low‐cost sensors can provide reliable data especially where reference monitors are unavailable. We evaluate the performance of Clarity Node‐S PM monitors against a Tapered element oscillating microbalance (TEOM) 1400a and develop a calibration model in Mombasa, Kenya's second largest city. As‐reported Clarity Node‐S data from January 2023 through April 2023 was moderately correlated with the TEOM‐1400a measurements (R2 = 0.61) and exhibited a mean absolute error (MAE) of 7.03 μg m−3. Employing three calibration models, namely, multiple linear regression (MLR), Gaussian mixture regression and random forest (RF) decreased the MAE to 4.28, 3.93, and 4.40 μg m−3respectively. TheR2value improved to 0.63 for the MLR model but all other models registered a decrease (R2 = 0.44 and 0.60 respectively). Applying the correction factor to a five‐sensor network in Mombasa that was operated between July 2021 and July 2022 gave insights to the air quality in the city. The average daily concentrations of PM2.5within the city ranged from 12 to 18 μg m−3. The concentrations exceeded the WHO daily PM2.5limits more than 50% of the time, in particular at the sites nearby frequent industrial activity. Higher averages were observed during the dry and cold seasons and during early morning and evening periods of high activity. These results represent some of the first air quality monitoring measurements in Mombasa and highlight the need for more study. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email