Determination of average emission factors for vehicles on a busy road

Gramotnev, Galina; Brown, Richard J.; Ristovski, Zoran; Hitchins, J.; Morawska, Lidia

doi:10.1016/s1352-2310(02)00923-8

Cited by 84 publications

(55 citation statements)

References 11 publications

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“…In general, the shift in the maximum towards larger diameters is typically accompanied with a significant decrease in the total particle number concentration, as the air mass is transported from the roadside to the background. Pirjola et al (2006), Gramotnev et al (2003), and Zhu et al (2002) showed that the total particle number concentration decreased by 35-45% while transported from the roadside to a distance of about 60-80 m from the road. Both the number concentration decrement and the increment of the location of the maximum of the particle number size distribution have been attributed to dilution process (e.g.…”

Section: Downwind Versus Upwind Nearby a Roadsidementioning

confidence: 98%

Evaluation and modeling of the size fractionated aerosol particle number concentration measurements nearby a major road in Helsinki – Part II: Aerosol measurements within the SAPPHIRE project

et al. 2007

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Abstract. This study presents an evaluation and modeling exercise of the size fractionated aerosol particle number concentrations measured nearby a major road in Helsinki during 23 August-19 September 2003 and 14 January-11 February 2004. The available information also included electronic traffic counts, on-site meteorological measurements, and urban background particle number size distribution measurement. The ultrafine particle (UFP, diameter<100 nm) number concentrations at the roadside site were approximately an order of magnitude higher than those at the urban background site during daytime and downwind conditions. Both the modal structure analysis of the particle number size distributions and the statistical correlation between the traffic density and the UFP number concentrations indicate that the UFP were evidently from traffic related emissions. The modeling exercise included the evolution of the particle number size distribution nearby the road during downwind conditions. The model simulation results revealed that the evaluation of the emission factors of aerosol particles might not be valid for the same site during different time.

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Section: Downwind Versus Upwind Nearby a Roadsidementioning

confidence: 98%

Evaluation and modeling of the size fractionated aerosol particle number concentration measurements nearby a major road in Helsinki – Part II: Aerosol measurements within the SAPPHIRE project

et al. 2007

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“…(1) tunnel measurements, 4 (2) inverse-dispersion modeling of streets 5 and street canyons, 6 and (3) mass conservation box model of open road measurements. 7 Other means of estimating the real-world emission factors include direct measurements of the tail pipe emissions either by using remote sensing techniques, such as open path Fourier transform infrared spectroscopy, 8,9 or using on-board instruments to measure emissions.…”

Section: Introductionmentioning

confidence: 99%

Development of an Empirical Model to Estimate Real-World Fine Particulate Matter Emission Factors: The Traffic Air Quality Model

Soliman

Jacko

Palmer

2006

Journal of the Air & Waste Management Association

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The purpose of the study was to quantify the impact of traffic conditions, such as free flow and congestion, on local air quality. The Borman Expressway (I-80/94) in Northwest Indiana is considered a test bed for this research because of the high volume of class 9 truck traffic traveling on it, as well as the existing and continuing installation of the Intelligent Transportation System (ITS) to improve traffic management along the highway stretch. An empirical traffic air quality (TAQ) model was developed to estimate the fine particulate matter (PM 2.5 ) emission factors (grams per kilometer) based solely on the measured traffic parameters, namely, average speed, average acceleration, and class 9 truck density. The TAQ model has shown better predictions that matched the measured emission factor values more than the U.S. Environmental Protection Agency (EPA)-PART5 model. During congestion (defined as flow-speeds Ͻ50 km/hr [30 mi/hr]), the TAQ model, on average, overpredicted the measured values only by a factor of 1.2, in comparison to a fourfold underprediction using the EPA-PART5 model. On the other hand, during free flow (defined as flowspeeds Ͼ80 km/hr [50 mi/hr]), the TAQ model was conservative in that it overpredicted the measured values by 1.5-fold.

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“…The CL4 program is a dispersion modeling software to estimate exposures to CO from mobile sources (Benson, 1992) and has been adapted to estimate urban and near roadway concentrations of PM (Gramotnev et al, 2003;Batterman et al, 2010). The CL4 model has not been validated for dispersion of UFP and EMFAC is only equipped to generate emission factors for PM 2.5 .…”

Section: Model Estimation Of Pm 25mentioning

confidence: 99%

Particle Concentration on Freeways: Affecting Factors and a Simple Model Development

Fung

Zhou

Hinds

et al. 2013

Aerosol Air Qual. Res.

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A few field studies have reported high levels of in-cabin ultrafine particles and discussed certain contributing factors such as vehicle operations and atmospheric meteorological conditions. However, to generalize limited field study results to a wide range of conditions, a deeper understanding of affecting factors and a simple model that can predict on-road particle concentrations are essential. This paper has two objectives, first, to analyze the effects of surrounding vehicle density, relative freeway lane position, and vehicle speed on measured particle concentrations on two California freeways, I-405 (5% diesel) and I-710 (25% diesel). The second objective is to test the use of Emission Factor (EMFAC) 2007 and Caline4 (CL4) models in predicting on-road particle concentrations. Particle number and mass concentrations were collected using a mobile laboratory driving on the I-710 and I-405 freeways. Particle number concentration was found to be affected by the density of surrounding heavy duty diesel trucks which is consistent with previous studies. The highest particle number concentrations were measured in the outer lanes on the freeway and the lowest was in the inner lanes. The highest levels of particles were measured at vehicle speeds of 40-50 mph. Analysis of CL4 model was conducted using factor-of-two test. For the I-710, 85% of values were within the factor-of-two envelope, and for the I-405, 77% were within the envelope. Regression analysis showed that the model is able to account for 43% of the variability on the I-710 and 26% of the variability on the I-405. The use of CL4 in conjunction with EMFAC shows promise as a simple tool to estimate on-road PM 2.5 concentration.

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Determination of average emission factors for vehicles on a busy road

Cited by 84 publications

References 11 publications

Evaluation and modeling of the size fractionated aerosol particle number concentration measurements nearby a major road in Helsinki – Part II: Aerosol measurements within the SAPPHIRE project

Evaluation and modeling of the size fractionated aerosol particle number concentration measurements nearby a major road in Helsinki – Part II: Aerosol measurements within the SAPPHIRE project

Development of an Empirical Model to Estimate Real-World Fine Particulate Matter Emission Factors: The Traffic Air Quality Model

Particle Concentration on Freeways: Affecting Factors and a Simple Model Development

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