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Updated: Apr 22, 2026
Measuring Sub-23 Nanometer Real Driving Particle Number Emissions Using the Portable DownToTen Sampling System
Published on: May 22, 2020
Modeling particle number concentrations along Interstate 10 in El Paso, Texas
Hector A Olvera1, Omar Jimenez2, Elias Provencio-Vasquez3
1Center for Environmental Resource Management, University of Texas at El Paso, 500 W. University Ave., El Paso TX 79968, USA ; School of Nursing, University of Texas at El Paso, 500 W. University Ave., EL Paso TX 79968, USA ; Hispanic Health Disparities Research Center, University of Texas at El Paso, 500 W. University Ave., EL Paso TX 79968, USA.
Researchers estimated particle number concentrations near a highway using atmospheric dispersion and land use regression models. The models accurately predicted concentrations, showing the feasibility of mapping air pollution along major roadways.
Area of Science:
- Environmental Science
- Atmospheric Chemistry
- Air Quality Modeling
Background:
- Particle number concentrations near highways impact urban air quality.
- Accurate estimation of these concentrations is crucial for public health assessments.
- Existing models may not fully capture localized traffic impacts.
Purpose of the Study:
- To estimate annual average daily particle number concentrations along Interstate 10 in El Paso, Texas.
- To integrate atmospheric dispersion and land use regression models for improved accuracy.
- To assess the feasibility of generating particle concentration surfaces along major roadways.
Main Methods:
- Utilized an atmospheric dispersion model with wind speed and traffic data.
- Employed a land use regression model incorporating vehicle kilometers traveled for background adjustments.
- Validated model estimates against measured concentrations at seven sites.
Main Results:
- Estimated particle number concentrations ranged from 9.8 × 10³ to 1.3 × 10⁵ particles/cc, averaging 2.5 × 10⁴ particles/cc.
- Model estimates showed a low average fractional error of 6% compared to measurements.
- Accuracy was influenced by emission factors and background condition adjustments.
Conclusions:
- The integrated modeling approach effectively captured traffic impacts from both highways and arterial roads.
- The methodology is performant, economical, and feasible for urban air quality mapping.
- Readily available traffic and meteorological data support widespread application of this technique.
