Final Report: Ambient Particulate Concentration Model for Traffic Intersections

EPA Grant Number: R825427C004
Subproject: this is subproject number 004 , established and managed by the Center Director under grant R825427
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).

Center: Urban Waste Management and Research Center (University New Orleans)
Center Director: McManis, Kenneth
Title: Ambient Particulate Concentration Model for Traffic Intersections
Investigators: Kura, Bhaskar
Institution: University of New Orleans
EPA Project Officer: Lasat, Mitch
Project Period: January 1, 1998 through December 31, 1999
RFA: Urban Waste Management & Research Center (1998) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Targeted Research

Objective:

The objective of this research was to develop a model to predict ambient particulate concentration at traffic intersections based on the traffic volume and meteorological conditions. Field monitoring was performed to collect data on (1) ambient PM10 and PM25 concentrations, (2) traffic volume, (3) traffic composition (types of vehicles), (4) vehicle speed, (5) time of the day. All meteorological observations — wind speed and direction, cloud cover, temperature, relative humidity and rainfall — were obtained from the local weather station. Sun altitude data was obtained from the United States Naval Observatory (USNO). The Rupprecht & Patashnick TEOM Series 1400a Ambient Particulate Monitor, which is approved by the USE PA for continuous PM10 monitoring, was used to monitor PM10 concentrations. Nu-Metrics Hi-Star® Model NC-97 counters were used to obtain traffic data. These counters utilize Vehicle Magnetic Imaging (VMI) to measure vehicle parameters. One counter was installed at or near the center of each lane of traffic exiting the intersection using a rubber mat for a protective covering.

Due to a number of constraints including time, resources and logistics, limited data was available to develop a reliable predictive model for coarse ambient particulate matter, PM10. For, fine particulate matter, PM2.5, the following preliminary models were developed:

For wind speed < 6 m/s:
y = 182.220+4.07E-3X1-1.605X2-0.411X3 (95% confidence limits: y'+0.993)

For wind speeds > 6 m/s:
y = -24.777+3.41E-3X1-0.155X2-0.475X3 (95% confidence limits: y'+2.198)

where:
y - hourly average PM2.5 concentration ( µg/m3)
X1 - average hourly traffic volume (vehicles/hour)
X2 - average hourly temperature (°F)
X3 - average hourly relative humidity (%)

While the above models can serve in screening air pollution episodes of traffic intersections, caution should be exercised in using the above models as additional observations are necessary for validating and refining the models.

The objective of this study was to develop a model that can be used to predict particulate concentrations at traffic intersections based upon traffic and meteorological parameters. The research was undertaken with several expectations. The first was that the particle concentrations would increase as traffic volume increased. Secondly, PM2.5 data were projected to yield much stronger relationships to traffic volumes than PM10. Another belief was that as atmospheric stability increased, overall particle concentrations would also increase.

The results of this study provide a preliminary framework for model development. Data collection was limited due to a number of constraints including time, resources and logistics. Because the results are based on limited data from a single site, it should not be assumed that the results are applicable on a broad scale.

The specific objectives of this project were:

  • To measure ambient particulate concentrations at traffic intersections.
  • To obtain traffic and meteorological data.
  • To develop a model that predicts ambient particulate concentrations based on traffic and meteorological parameters.

Summary/Accomplishments (Outputs/Outcomes):

The PM10 results obtained for stability categories A through C using simple linear regression showed an increase in concentration with increased traffic volume, however did not yield a strong correlation. When data for relative humidity values less than 80% were analyzed separately, the relationship improved, but not significantly enough to develop a predictive model based on traffic volumes alone. Category D data yielded a relationship opposite of that which was expected; PM10 concentrations decreased with increased traffic volumes. This generality held true for both humidity conditions tested. The correlation for both cases, however, was significantly greater than that for categories A through C. The low humidity conditions again yielded the stronger relationship.

The negative slopes obtained for the category D PM10, versus traffic trend lines were of major concern. PM concentrations attributable to vehicular activity are mostly due to road dust and tire and brake wear. Neither of these items is dependent directly upon traffic volume or vehicle exhaust, but rather on roadway and vehicle conditions. Both streets at the intersection are paved and neither has any unpaved shoulder area. Further investigation is needed to determine the exact cause of the negative trend.

Multivariate regression analysis for PM10 also yielded weak results. For wind speeds over 6 m/s, the errors did not meet the assumptions for regression modeling, hence no equations were developed. For wind speeds 6 m/s and below, the assumptions were met; however, the model yielded a very weak correlation when compared to the results obtained for PM2.5. Because of the weakness of this correlation, no further calculations were performed.

The PM2.5 results obtained for stability categories A through C using simple linear regression showed an increase in concentration with increased traffic volume, and again did not yield a strong correlation. Data for relative humidity values less than 80% were analyzed separately and the relationship improved, but not significantly enough to develop a predictive model based on traffic volumes alone. Category D data also yielded a positive slope for the linear trendline, indicating an increase in concentration with increased traffic volume as expected. The low humidity conditions again yielded the stronger relationship, however the relationship obtained for category D were not as strong as those obtained for categories A through C.

Multivariate regression analysis for PM2.5 yielded much more acceptable results. For each wind speed category, all of the parameters (traffic, relative humidity and temperature) proved to have an effect on ambient concentrations. The relationship between each parameter and PM2.5 concentrations for wind speeds of 6 m/s or less were very similar, therefore the data for all of these wind speeds were treated together as low wind conditions. Data for wind speeds greater than 6 m/s behaved quite differently and were treated separately as high wind conditions.

Equations 1 and 2 are the preliminary models obtained for predicting ambient PM2.5 concentrations under the wind speed conditions specified.

For wind speed < 6 m/s:
y = 182.220+4.07E-3X1-1.605X2-0.411X3 (1)
Where the 95% confidence limits are y'+0.993

For wind speeds > 6 m/s
y = -24.777+3.41E-3X1-0.155X2-0.475X3 (2)

where 95% confidence limits were y'+2.198. In both the equations y represents the hourly average PM2.5 concentration ( µg/m3), X1 represents the average hourly traffic volume (vehicles per hour), X2 represents average hourly temperature (°F) and X3 represents average hourly relative humidity (%). Both equations show a minimal contribution of traffic, which is supported by the findings of the Swiss study cited in the detailed technical report. The prevailing west and northwest winds have affected this observation in the current study. Another factor to consider is that most of the traffic was found to be passenger vehicles, which primarily have gasoline engines. Vehicles having diesel engines are most likely to produce more particulate pollution.

The large value of the intercept (a0 parameter) in the equation for wind speeds less than 6 m/s is obviously too high to represent a background concentration. This indicates that other variability is present that is not accounted for by the traffic volume, relative humidity and temperature parameters. Additionally it was noted that for wind speeds in excess 6 m/s, the estimated a0 parameter has a negative value. Because ambient particle concentrations are influenced by a large number of particles all of which could not be accounted for in this study, it is plausible that the factors not included in the model could produce a negative effect when compared to the parameters that are included.

As expected, PM2.5 data were over all more closely related to traffic volumes. This is primarily because PM2.5 can be attributed more directly to vehicle exhaust as the source other than roadway and other external conditions as with PM2.5

It was observed that atmospheric stability increased, so did the concentration of PM2.5. This was attributed to the fact that because PM2.5 are small, they can remain air bound for extended periods of time. When the atmosphere is stable, less dispersion occurs, which allows fine particles to remain entrained in the air at the monitoring site. In case of PM10, more stable conditions yielded lower particle concentrations. With less turbulence, larger particles are able to settle; therefore they are not as likely to be measured as PM2.5.

This research obtained data on traffic density, ambient particulate concentration, and meteorological conditions through field monitoring to develop models to predict ambient particulate concentration at traffic intersections. No acceptable model could be developed for PM10 due to interference. Preliminary models were developed for ambient concentration of PM2.5 for wind speed above 6 m/s and below 6 m/s. Caution should be used in using these models as additional observations are necessary to refine and validate these models.

Supplemental Keywords:

Ambient particulate concentration, PM10 concentration, PM2.5 concentration, coarse particulate matter, fine particulate matter, traffic pollution, urban particulate pollution, TEOM Series 1400a Ambient Particulate Monitor, RFA, Scientific Discipline, Air, Geographic Area, Waste, particulate matter, Ecology, Municipal, Environmental Chemistry, State, mobile sources, Ecological Risk Assessment, Ecology and Ecosystems, atmospheric particulate matter, waste minimization, motor vehicle emissions, air quality models, automotive emissions, particulate emissions, airborne particulate matter, municipal waste, automotive exhaust, groundwater quality, ambient particle pollution, New Orleans (NO), waste management, outreach, technology transfer, aersol particles

Progress and Final Reports:

Original Abstract
  • 1998

  • Main Center Abstract and Reports:

    R825427    Urban Waste Management and Research Center (University New Orleans)

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