Theoretical Analysis of Suspended Road Dust in Relation to Concrete Pavement Texture Characteristics

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Abstract
Particulate matter (PM) originating from road dust is an increasing concern in urban air quality, particularly as non-exhaust emissions from tire–pavement interactions gain prominence. Existing models often focus on meteorological and traffic-related variables while oversimplifying pavement surface characteristics, limiting their applicability across diverse spatial and traffic conditions. This study investigates the influence of concrete pavement macrotexture—specifically the Mean Texture Depth (MTD) and surface wavelength—on PM10 resuspension. Field data were collected using a vehicle-mounted DustTrak 8530 sensor following the TRAKER protocol, enabling real-time monitoring near the tire–pavement interface. A multivariable linear regression model was used to evaluate the effects of MTD, wavelength, and the interaction between silt loading (sL) and PM10 content, achieving a high adjusted R2 of 0.765. The surface wavelength and sL–PM10 interaction were statistically significant (p < 0.01). The PM10 concentrations increased with the MTD up to a threshold of approximately 1.4 mm, after which the trend plateaued. A short wavelength (30 mm), likely due to enhanced air-pumping effects caused by more frequent aggregate contact. Among pavement types, Transverse Tining (T.Tining) exhibited the highest emissions due to its high MTD and short wavelength, whereas Exposed Aggregate Concrete Pavement (EACP) and the Next-Generation Concrete Surface (NGCS) showed lower emissions with a moderate MTD (1.0–1.4 mm) and longer wavelength. Mechanistically, a low MTD means there is a lack of sufficient voids for dust retention but generates less turbulence, producing moderate emissions. In contrast, a high MTD combined with a very short wavelength intensifies tire contact and localized air pumping, increasing emissions. Therefore, an intermediate MTD and moderate wavelength configuration appears optimal, balancing dust retention with minimized turbulence. These findings offer a texture-informed framework for integrating pavement surface characteristics into PM emission models, supporting sustainable and emission-conscious pavement design.
Funding Information
  • Korea Agency for Infrastructure Technology Advancement (KAIA) Grant funded by the Ministry of Land, Infrastructure and Transport (21POQWB152342-03)

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