Research suggests that re-entrained road dust may be a major source of atmospheric particulate matter (PM), particularly in the western United States where arid conditions are more prevalent. As engines become cleaner and electric vehicles more common, road dust emissions are likely to become a dominant factor in near-road air quality. Being able to quantify these road dust emissions accurately will become more important in the foreseeable future. However, current methodologies to estimate road dust emissions are outdated, based on numerous assumptions, and lack the ability to fully quantify temporal and spatial variability.

This research report summarizes the state-of-the-science for calculating road dust emission factors and provides recommendations for ways to update and improve those methods. Future research is needed to account for seasonal effects, changes to average driving speeds, geographic differences, and to update PM2.5-to-PM10 ratios (and investigate if those ratios are temporally and/or spatially dependent). Future research could also leverage more novel measurement techniques, such as mobile monitoring, utilize more advanced and accurate measurement equipment, and measure PM2.5 from road dust emissions directly.

• 693JJ319N300053

• Planning, Environment, and Realty

• FY 2002-2022 / Planning, Environment, and Realty / Modeling and Analysis Tools

• Analysis, Modeling, and Simulation
• Data and Analysis

• Methodology report.
• Final synthesis and gap analysis report.
• Slide deck summarizing report findings and seasonal road dust measurement studies.

Findings from this research report suggest a number of avenues for improving the accuracy of road dust emission calculations and increasing confidence and geographic coverage of models. FHWA may use findings to tailor future research studies on road dust emissions, including:

• Investigating how EPA’s AP-42 methodology might be updated using the latest version of the MOVES model.
• Conducting field measurement campaigns, particularly with use of new monitoring methods, such as mobile monitoring technology.
• Pairing measurement data with the latest MOVES model to produce more accurate emissions estimates for today’s roadway conditions.
• Developing road dust emissions datasets which are representative of a variety of meteorological and geographical conditions. This might include adding variables to the AP-42 equations to account for seasonality or incorporating land-use data.
• Developing quantification methods for roadways with low silt loading and high vehicle speed (e.g., interstates), and low fleet-average vehicle weight and low vehicle speed.
• Investigating whether dispersion modeling can be leveraged to estimate constants in road dust equations.