Gas- and particle-phase primary emissions from in-use, on-road gasoline and diesel vehicles

May, Andrew A.; Nguyen, Ngoc; Presto, Albert A.; Gordon, T. D.; Lipsky, Eric M.; Karve, Mrunmayi; Gutierrez, A.; Robertson, William H.; Zhang, Mang; Brandow, Christopher; Chang, Oliver; Chen, Shiyan; Cicero-Fernández, Pablo; Dinkins, Lyman; Fuentes, Mario Luis; Huang, Sampson C.; Ling, Richard; Long, Jeff; Maddox, Christine; Massetti, John; McCauley, E.; Miguel, Antonio H.; Na, Kwangsam; Ong, Richard; Pang, Yanbo; Rieger, Paul; Sax, Todd; Truong, Tin; Vo, Thu; Chattopadhyay, Sulekha; Maldonado, Hector; Maricq, M. Matti; Robinson, Allen L.

doi:10.1016/j.atmosenv.2014.01.046

Cited by 205 publications

(332 citation statements)

References 58 publications

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“…1 indicates that gasoline emissions have much higher SOAto-POA ratios than diesel emissions, yet the total OA from those sources was similar; therefore gasoline vehicles effectively "catch up" to diesel vehicles when one accounts for SOA production. For on-road gasoline vehicles, there was an order of magnitude reduction in the NMOG emissions from newer, lower-mileage vehicles that met stricter emissions standards (pre-LEV to LEV-I to LEV-II) (25). However, those reductions did not translate into a similar reduction in SOA (10).…”

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confidence: 99%

“…To do this, we quantified the emissions of speciated SOA precursors (e.g., aromatics and larger alkanes) using standard onedimensional GC-based techniques. The speciation data for onroad vehicles are in May et al (25); the biomass burning data are in SI Appendix, Table S2. The ratio of speciated SOA precursors to the sum of the speciated NMOG emissions compared well with literature data (SI Appendix, Fig.…”

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confidence: 99%

“…The total NMOG mass was measured using a flame ionization detector, calibrated with propane, at low dilution (high OA concentrations), following standard protocols (25). This measurement did not include any compounds evaporated from the POA between measurements made at low dilution (defined by standard sampling protocols) and the more atmospheric-like conditions inside the smog chamber.…”

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confidence: 99%

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Unspeciated organic emissions from combustion sources and their influence on the secondary organic aerosol budget in the United States

Jathar

Gordon

Hennigan

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Secondary organic aerosol (SOA) formed from the atmospheric oxidation of nonmethane organic gases (NMOG) is a major contributor to atmospheric aerosol mass. Emissions and smog chamber experiments were performed to investigate SOA formation from gasoline vehicles, diesel vehicles, and biomass burning. About 10-20% of NMOG emissions from these major combustion sources are not routinely speciated and therefore are currently misclassified in emission inventories and chemical transport models. The smog chamber data demonstrate that this misclassification biases model predictions of SOA production low because the unspeciated NMOG produce more SOA per unit mass than the speciated NMOG. We present new source-specific SOA yield parameterizations for these unspeciated emissions. These parameterizations and associated source profiles are designed for implementation in chemical transport models. Box model calculations using these new parameterizations predict that NMOG emissions from the top six combustion sources form 0.7 Tg y −1 of first-generation SOA in the United States, almost 90% of which is from biomass burning and gasoline vehicles. About 85% of this SOA comes from unspeciated NMOG, demonstrating that chemical transport models need improved treatment of combustion emissions to accurately predict ambient SOA concentrations.particulate matter | air quality | photochemical oxidation | volatile organic compounds | emissions inventory

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confidence: 99%

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Unspeciated organic emissions from combustion sources and their influence on the secondary organic aerosol budget in the United States

Jathar

Gordon

Hennigan

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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294

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“…Figure 4 shows that the gasoline vehicle factor lies along a mixing line defined by source profiles of a series of vehicles tested by May et al (2014). Figure 3 shows that the contribution of the gasoline vehicle factor to measured m+p-xylene concentrations is low at night and higher in the day, as would be expected based on traffic patterns.…”

Section: Pmf and Factor Identificationmentioning

confidence: 67%

“…HAPs include a wide variety of particulate and gaseous pollutants emitted by both mobile and stationary sources. Mobile source air toxics (MSATs) include volatile organic compounds (VOCs) such as benzene, toluene, ethyl benzene, and the xylene isomers, collectively known as BTEX, and carbonyl compounds (May et al, 2014;Schauer et al, 1999Schauer et al, , 2002. Diesel particulate matter is also classified as an MSAT, and is often inferred from concentrations of particulate black carbon (BC).…”

Section: Introductionmentioning

confidence: 99%

BTEX exposures in an area impacted by industrial and mobile sources: Source attribution and impact of averaging time

Presto

Dallmann

et al. 2016

Journal of the Air & Waste Management Association

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The impacts of emissions plumes from major industrial sources on black carbon (BC) and BTEX (benzene, toluene, ethyl benzene, xylene isomers) exposures in communities located >10 km from the industrial source areas were identified with a combination of stationary measurements, source identification using positive matrix factorization (PMF), and dispersion modeling. The industrial emissions create multihour plume events of BC and BTEX at the measurement sites. PMF source apportionment, along with wind patterns, indicates that the observed pollutant plumes are the result of transport of industrial emissions under conditions of low boundary layer height. PMF indicates that industrial emissions contribute >50% of outdoor exposures of BC and BTEX species at the receptor sites. Dispersion modeling of BTEX emissions from known industrial sources predicts numerous overnight plumes and overall qualitative agreement with PMF analysis, but predicts industrial impacts at the measurement sites a factor of 10 lower than PMF. Nonetheless, exposures associated with pollutant plumes occur mostly at night, when residents are expected to be home but are perhaps unaware of the elevated exposure. Averaging data samples over long times typical of public health interventions (e.g., weekly or biweekly passive sampling) misapportions the exposure, reducing the impact of industrial plumes at the expense of traffic emissions, because the longer samples cannot resolve subdaily plumes. Suggestions are made for ways for future distributed pollutant mapping or intervention studies to incorporate high time resolution tools to better understand the potential impacts of industrial plumes.Implications: Emissions from industrial or other stationary sources can dominate air toxics exposures in communities both near the source and in downwind areas in the form of multihour plume events. Common measurement strategies that use highly aggregated samples, such as weekly or biweekly averages, are insensitive to such plume events and can lead to significant under apportionment of exposures from these sources.PAPER HISTORY

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A dual‐chamber method for quantifying the effects of atmospheric perturbations on secondary organic aerosol formation from biomass burning emissions

et al. 2017

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Biomass burning (BB) is a major source of atmospheric pollutants. Field and laboratory studies indicate that secondary organic aerosol (SOA) formation from BB emissions is highly variable. We investigated sources of this variability using a novel dual‐smog‐chamber method that directly compares the SOA formation from the same BB emissions under two different atmospheric conditions. During each experiment, we filled two identical Teflon smog chambers simultaneously with BB emissions from the same fire. We then perturbed the smoke with UV lights, UV lights plus nitrous acid (HONO), or dark ozone in one or both chambers. These perturbations caused SOA formation in nearly every experiment with an average organic aerosol (OA) mass enhancement ratio of 1.78 ± 0.91 (mean ± 1σ). However, the effects of the perturbations were highly variable ranging with OA mass enhancement ratios ranging from 0.7 (30% loss of OA mass) to 4.4 across the set of perturbation experiments. There was no apparent relationship between OA enhancement and perturbation type, fuel type, and modified combustion efficiency. To better isolate the effects of different perturbations, we report dual‐chamber enhancement (DUCE), which is the quantity of the effects of a perturbation relative to a reference condition. DUCE values were also highly variable, even for the same perturbation and fuel type. Gas measurements indicate substantial burn‐to‐burn variability in the magnitude and composition of SOA precursor emissions, even in repeated burns of the same fuel under nominally identical conditions. Therefore, the effects of different atmospheric perturbations on SOA formation from BB emissions appear to be less important than burn‐to‐burn variability.

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Gas- and particle-phase primary emissions from in-use, on-road gasoline and diesel vehicles

Cited by 205 publications

References 58 publications

Unspeciated organic emissions from combustion sources and their influence on the secondary organic aerosol budget in the United States

Unspeciated organic emissions from combustion sources and their influence on the secondary organic aerosol budget in the United States

BTEX exposures in an area impacted by industrial and mobile sources: Source attribution and impact of averaging time

A dual‐chamber method for quantifying the effects of atmospheric perturbations on secondary organic aerosol formation from biomass burning emissions

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