Time Resolved Characterization of Diesel Particulate Emissions. 1. Instruments for Particle Mass Measurements

Moosmüller, Hans; Wp, Arnott; Cf, Rogers; Jl, Bowen; Ja, Gillies; Wr, Pierson; Jf, Collins; Td, Durbin; Jm, Norbeck

doi:10.1021/es0013935

Cited by 94 publications

(72 citation statements)

References 29 publications

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“…By volume, the standard consists of 1-3% particles with diameters less than 1 m, 36 -44% with diameters less than 4 m, 83-88% with diameters less than 7 m, and 97-100% with diameters less than 10 m. Niu et al 25 found that in comparing data from four DustTraks collocated in an indoor environment, the interinstrument variability was a reasonable 3%. Several authors have also reported that DustTrak measurements correlate well with filter-based measurements of diesel exhaust, 26 ambient urban particulate matter, 27 and indoor airborne particles, 25 though in all cases, investigators noted that the DustTrak deviated from filter-based measurements by a factor that depends on the nature of the aerosol measured. One shortcoming of using a nephelometer-style instrument is that light scattering response to changes in mass concentration can depend strongly on particle composition as well as particle size.…”

Section: Field Measurementsmentioning

confidence: 95%

Deposition and Removal of Fugitive Dust in the Arid Southwestern United States: Measurements and Model Results

Etyemezian

Ahonen

Nikolić

et al. 2004

Journal of the Air & Waste Management Association

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This work was motivated by the need to better reconcile emission factors for fugitive dust with the amount of geologic material found on ambient filter samples. The deposition of particulate matter with aerodynamic diameter less than or equal to 10 m (PM 10 ), generated by travel over an unpaved road, over the first 100 m of transport downwind of the road was examined at Ft. Bliss, near El Paso, TX. The field conditions, typical for warm days in the arid southwestern United States, represented sparsely vegetated terrain under neutral to unstable atmospheric conditions. Emission fluxes of PM 10 dust were obtained from towers downwind of the unpaved road at 7, 50, and 100 m. The horizontal flux measurements at the 7 m and 100 m towers indicated that PM 10 deposition to the vegetation and ground was too small to measure. The data indicated, with 95% confidence, that the loss of PM 10 between the source of emission at the unpaved road, represented by the 7 m tower, and a point 100 m downwind was less than 9.5%. A Gaussian model was used to simulate the plume. Values of the vertical standard deviation z and the deposition velocity V d were similar to the U.S. Environmental Protection Agency (EPA) ISC3 model. For the field conditions, the model predicted that removal of PM 10 unpaved road dust by deposition over the distance between the point of emission and 100 m downwind would be less than 5%. However, the model results also indicated that particles larger than 10 m (aerodynamic diameter) would deposit more appreciably. The model was consistent with changes observed in size distributions between 7 m and 100 m downwind, which were measured with optical particle counters. The Gaussian model predictions were also compared with another study conducted over rough terrain and stable atmospheric conditions. Under such conditions, measured PM 10 removal rates over 95 m of downwind transport were reported to be between 86% and 89%, whereas the Gaussian model predicted only a 30% removal. One explanation for the large discrepancy between measurements and model results was the possibility that under the conditions of the study, the dust plume was comparable in vertical extent to the roughness elements, thereby violating one of the model assumptions. Results of the field study reported here and the previous work over rough terrain bound the extent of particle deposition expected to occur under most unpaved road emission scenarios. IMPLICATIONSThis work was motivated by the well-documented disagreement between estimates of road and other geological dust obtained by emissions inventory methods and the actual amount of inorganic minerals observed on filter samples at ambient monitoring sites. This study provides a basis for modeling the magnitude of PM 10 dust removal by deposition close to the emission source. The analysis focuses on emissions from unpaved roads, though the results may be pertinent to other sources of fugitive dust. TECHNICAL PAPER INTRODUCTIONFugitive dust is emitted from an unpaved road when a vehicle ...

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Section: Field Measurementsmentioning

confidence: 95%

Deposition and Removal of Fugitive Dust in the Arid Southwestern United States: Measurements and Model Results

Etyemezian

Ahonen

Nikolić

et al. 2004

Journal of the Air & Waste Management Association

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“…It was equipped with a comprehensive suite of state-of-the-art fast response instruments, including an aethalometer (AE-16, Magee Scientific) for BC; a photoionization aerosol sensor (EcoChem PAS 2000CE) for particle-bound PAHs; a non-dispersive infrared (NDIR) unit (Li-Cor LI 6262) for carbon dioxide (CO 2 ); an Aerodyne tunable infrared laser differential absorption spectrometer (TILDAS) for nitrogen dioxide (NO 2 ) and formaldehyde (HCHO); an NDIR analyzer for carbon monoxide (CO); a chemiluminescent analyzer (Thermo 42C) for nitrogen oxides (NO x ); a proton transfer reaction mass spectrometer (PTR-MS) for speciated volatile organic compounds (VOCs); and an aerosol photometer for PM 2.5 (TSI DustTrak 8520). Fast results for the aethalometer, which recorded measurements at one-minute intervals, were obtained by applying the time signature of the aerosol photometer data, as previous results have shown a strong correlation between BC and DustTrak measurements (Moosmüller et al, 2001). In strict terms, the chemiluminescent analyzer was configured to detect total reactive nitrogen species (NO y ); however NO x accounts for nearly all of NO y in fresh vehicle exhaust.…”

Section: Mobile Laboratorymentioning

confidence: 99%

“…The method depends on scattering efficiencies, which are a function of particle size distributions and optical properties. Although scattering efficiencies of ambient and diesel particles are similar (Waggoner et al, 1981), the efficiencies may differ for gasoline particles; and the calibration for individual vehicles may vary by a factor of two or more (Moosmüller et al, 2001). …”

Section: Mobile Laboratorymentioning

confidence: 99%

Vehicle fleet emissions of black carbon, polycyclic aromatic hydrocarbons, and other pollutants measured by a mobile laboratory in Mexico City

et al. 2005

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Abstract. Black carbon (BC) and polycyclic aromatic hydrocarbons (PAHs) are of concern due to their effects on climate and health. The main goal of this research is to provide the first estimate of emissions of BC and particle-phase PAHs (PPAHs) from motor vehicles in Mexico City. The emissions of other pollutants including carbon monoxide (CO), oxides of nitrogen (NO x ), volatile organic compounds (VOCs), and particulate matter of diameter 2.5 µm and less (PM 2.5 ) are also estimated. As a part of the Mexico City Metropolitan Area field campaign in April 2003 (MCMA-2003, a mobile laboratory was driven throughout the city. The laboratory was equipped with a comprehensive suite of gas and particle analyzers, including an aethalometer that measured BC and a photoionization aerosol sensor that measured PPAHs. While driving through traffic, the mobile lab continuously sampled exhaust plumes from the vehicles around it. We have developed a method of automatically identifying exhaust plumes, which are then used as the basis for calculation of fleet-average emissions. In the approximately 75 h of on-road sampling during the field campaign, we have identified ∼30 000 exhaust measurement points that represent a variety of vehicle types and driving conditions. The large sample provides a basis for estimating fleet-average emission factors and thus the emission inventory. Motor vehicles in the Mexico City area are estimated to emit 1700±200 metric tons BC, 57±6 tons PPAHs, 1 190 000±40 000 tons CO, 120 000±3000 tons NO x , 240 000±50 000 tons VOCs, andCorrespondence to: L. C. Marr (lmarr@vt.edu) 4400±400 tons PM 2.5 per year, not including cold start emissions. The estimates for CO, NO x , and PPAHs may be low by up to 10% due to the slower response time of analyzers used to measure these species. Compared to the government's official motor vehicle emission inventory for the year 2002, the estimates for CO, NO x , VOCs, and PM 2.5 are 38% lower, 23% lower, 27% higher, and 25% higher, respectively. The distributions of emission factors of BC, PPAHs, and PM 2.5 are highly skewed, i.e. asymmetric, while those for benzene, measured as a surrogate for total VOCs, and NO x are less skewed. As a result, the total emissions of BC, PPAHs, and PM 2.5 could be reduced by approximately 50% if the highest 20% of data points were removed, but "super polluters" are less influential on overall NO x and VOC emissions.

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“…A number of articles and reports have been published regarding the contribution of traffic to OC and EC in Europe and the USA (Kerminen et al, 1997;Miguel et al, 1998;Fraser et al, 1999;Moosmüller et al, 2000;Shi et al, 2000;Kohler et al, 2001;Ruellan et al, 2001). …”

Section: Introductionmentioning

confidence: 99%

Characterization of Roadside Fine Particulate Carbon and its Eight Fractions in Hong Kong

Cao

Lee

et al. 2006

Aerosol Air Qual. Res.

104

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Simultaneous measurements of PM 2.5 mass, OC and EC and eight carbon fractions were conducted in a roadside microenvironment around Hong Kong for a week in May-June 2002 to obtain the characterization of freshly emitted traffic aerosols. Traffic volume (diesel-powered, liquefied-petroleum gas and gasoline-powered vehicles), meteorological data, and sourcedominated samples were also measured. PM 2.5 samples were collected on pre-fired quartz filters with a mini-volume sampler and a portable fine-particle sampler, then analyzed for OC and EC using thermal optical reflectance (TOR) method, following the IMPROVE protocol. High levels of PM 2.5 mass (64.4 μg m -3 ), OC (16.7 μg m -3 ) and EC (17.1 μg m -3 ) observed in the roadside microenvironment were found to be well-correlated with each other. The average OC/EC ratio was 1.0, indicating that OC and EC were both primary pollutants. Marked diurnal PM 2.5 mass OC and EC concentration profiles were observed in accordance with the traffic pattern (especially for diesel vehicles). Average daytime concentrations were 1.3-1.5 times greater than nighttime values.Carbon profiles from source-dominated samples (diesel, LPG and gasoline vehicles) and diurnal variations of eight carbon fractions (OC1, OC2, OC3, OC4, EC1, EC2, EC3 and OP) demonstrated EC2 and OC2 were the major contributors to the diesel exhaust, and OC3 and OC2were the larger contributors to the LPG and gasoline exhaust. Thus, carbon fractions derived from the IMPROVE protocol could be used to identify different carbon sources.

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Time Resolved Characterization of Diesel Particulate Emissions. 1. Instruments for Particle Mass Measurements

Cited by 94 publications

References 29 publications

Deposition and Removal of Fugitive Dust in the Arid Southwestern United States: Measurements and Model Results

Deposition and Removal of Fugitive Dust in the Arid Southwestern United States: Measurements and Model Results

Vehicle fleet emissions of black carbon, polycyclic aromatic hydrocarbons, and other pollutants measured by a mobile laboratory in Mexico City

Characterization of Roadside Fine Particulate Carbon and its Eight Fractions in Hong Kong

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