Sensitivity of air quality to vehicle ammonia emissions in the United States.

The US Environmental Protection Agency (EPA) estimates on-road vehicles emissions using the Motor Vehicle Emission Simulator (MOVES). We developed updated ammonia emission rates for MOVES based on road-side exhaust emission measurements of light-duty gasoline and heavy-duty diesel vehicles. The resulting nationwide on-road vehicle ammonia emissions are 1.8, 2.1, 1.8, and 1.6 times higher than the MOVES3 estimates for calendar years 2010, 2017, 2024, and 2035, respectively, primarily due to an increase in light-duty gasoline vehicle NH3 emission rates. We conducted an air quality simulation using the Community Multi-Scale Air Quality (CMAQv5.3.2) model to evaluate the sensitivity of modeled ammonia and fine particulate matter (PM2.5) concentrations in calendar year 2017 using the updated on-road vehicle ammonia emissions. The average monthly urban ammonia ambient concentrations increased by up to 2.3 ppbv in January and 3.0 ppbv in July. The updated on-road NH3 emission rates resulted in better agreement of modeled ammonia concentrations with 2017 annual average ambient ammonia measurements, reducing model bias by 5.8 % in the Northeast region. Modeled average winter PM2.5 concentrations increased in urban areas, including enhancements of up to 0.5 µg/m3 in the northeast United States. The updated ammonia emission rates have been incorporated in MOVES4 and will be used in future versions of the NEI and EPA's modeling platforms.

Effect of Ambient Temperature on Total Organic Gas Speciation Profiles from Light-Duty Gasoline Vehicle Exhaust.

Total organic gases (TOG) emissions from motor vehicles include air toxic compounds and contribute to formation of ground-level ozone and secondary organic aerosol (SOA). These emissions are known to be affected by temperature; however previous studies have typically focused only on the temperature dependence of total emission factors and select toxic compounds. This study builds on the previous research by performing an evaluation of a comprehensive set of gas-phase organic compounds present in gasoline motor vehicle exhaust. A fleet of five vehicles using port fuel injection engine technology and running on E10 fuel was tested. Overall, three temperatures (0, 20, and 75 °F; or -18, -7, and 24 °C), two driving conditions (urban-FTP75 and aggressive driving-US06) and 161 compounds were evaluated; the emissions distributions were used to construct speciation profiles for each driving cycle and temperature. Overall, the speciation results indicated a significant increase in alkane and methane content, and decrease in alcohol, aldehyde and ketone content with decreasing temperature. These were verified using a statistical significance test. The fraction and composition of Mobile Source Air Toxics (MSATs) were significantly affected by temperature for both driving cycles. The ozone forming potentials of these profiles were evaluated using the maximum incremental reactivity (MIR) scale. Aromatic content was predicted to be a major driver behind the ozone forming potentials. Additionally, the decreasing ozone potential could be attributed to increased methane fractions with increasing temperature.

Development of organic gas exhaust speciation profiles for nonroad spark-ignition and compression-ignition engines and equipment.

UNLABELLED: The composition of exhaust emissions from nonroad engines and equipment varies based on a number of parameters, including engine type, emission control technology, fuel composition, and operating conditions. Speciated emissions data which characterize the chemical composition of these emissions are needed to develop chemical speciation profiles used for air quality modeling and to develop air toxics inventories. In this paper, we present results of an extensive review and analysis of available exhaust speciation data for total organic gases (TOG) for spark ignition (SI) engines running on gasoline/ethanol blends now in widespread use, and compression ignition (CI) engines running on diesel fuel. We identified two data sets best suited for development of exhaust speciation profiles. Neither of these data sets have previously been published. We analyzed the resulting speciation profiles for differences in SI engine exhaust composition between 2-stroke and 4-stroke engines using E0 (0% ethanol) and E10 (10% ethanol) blends, and differences in CI engine exhaust composition among engines meeting different emission standards. Exhaust speciation profiles were also analyzed to compare differences in maximum incremental reactivity (MIR) values; this is a useful indicator for evaluating how organic gases may affect ozone formation for air quality modeling. Our analyses found significant differences in speciated emissions from 2-stroke and 4-stroke SI engines, and between engines running on E0 and E10 fuels. We found significant differences in profiles from pre-Tier 1 CI engines, engines meeting Tier 1 standards, and engines meeting Tier 2 standards. Although data for nonroad CI engines meeting tier 4 standards with control devices such as particulate filters and selective catalyst reduction (SCR) devices were not available, data from highway CI engines suggest these technologies will substantially change profiles for nonroad CI engines as well (EPA, 2014c). IMPLICATIONS: The nonroad engine data sets analyzed in this study will substantially improve exhaust speciation profiles used to characterize organic gas emissions from nonroad engines. Since nonroad engines are major contributors to ambient air pollution, these profiles can considerably improve U.S. emission inventories for gaseous air toxics emitted from nonroad engines. The speciation profiles developed in this paper can be used to develop more accurate emission inputs to chemical transport models, leading to more accurate air quality modeling.

Particulate matter speciation profiles for light-duty gasoline vehicles in the United States.

UNLABELLED: Representative profiles for particulate matter particles less than or equal to 2.5 microm (PM2.5) are developed from the Kansas City Light-Duty Vehicle Emissions Study for use in the US. Environmental Protection Agency (EPA) vehicle emission model, the Motor Vehicle Emission Simulator (MOVES), and for inclusion in the EPA SPECIATE database for speciation profiles. The profiles are compatible with the inputs of current photochemical air quality models, including the Community Multiscale Air Quality Aerosol Module Version 6 (AE6). The composition of light-duty gasoline PM2.5 emissions differs significantly between cold start and hot stabilized running emissions, and between older and newer vehicles, reflecting both impacts of aging/deterioration and changes in vehicle technology. Fleet-average PM2.5 profiles are estimated for cold start and hot stabilized running emission processes. Fleet-average profiles are calculated to include emissions from deteriorated high-emitting vehicles that are expected to continue to contribute disproportionately to the fleet-wide PM2.5 emissions into the future. The profiles are calculated using a weighted average of the PM2.5 composition according to the contribution of PM2.5 emissions from each class of vehicles in the on-road gasoline fleet in the Kansas City Metropolitan Statistical Area. The paper introduces methods to exclude insignificant measurements, correct for organic carbon positive artifact, and control for contamination from the testing infrastructure in developing speciation profiles. The uncertainty of the PM2.5 species fraction in each profile is quantified using sampling survey analysis methods. The primary use of the profiles is to develop PM2.5 emissions inventories for the United States, but the profiles may also be used in source apportionment, atmospheric modeling, and exposure assessment, and as a basis for light-duty gasoline emission profiles for countries with limited data. IMPLICATIONS: PM2.5 speciation profiles were developed from a large sample of light-duty gasoline vehicles tested in the Kansas City area. Separate PM2.5 profiles represent cold start and hot stabilized running emission processes to distinguish important differences in chemical composition. Statistical analysis was used to construct profiles that represent PM2.5 emissions from the U.S. vehicle fleet based on vehicles tested from the 2005 calendar year Kansas City metropolitan area. The profiles have been incorporated into the EPA MOVES emissions model, as well as the EPA SPECIATE database, to improve emission inventories and provide the PM2.5 chemical characterization needed by CMAQv5.0 for atmospheric chemistry modeling.

Reactive organic carbon air emissions from mobile sources in the United States.

Mobile sources are responsible for a substantial controllable portion of the reactive organic carbon (ROC) emitted to the atmosphere, especially in urban environments of the United States. We update existing methods for calculating mobile source organic particle and vapor emissions in the United States with over a decade of laboratory data that parameterize the volatility and organic aerosol (OA) potential of emissions from on-road vehicles, nonroad engines, aircraft, marine vessels, and locomotives. We find that existing emission factor information from Teflon filters combined with quartz filters collapses into simple relationships and can be used to reconstruct the complete volatility distribution of ROC emissions. This new approach consists of source-specific filter artifact corrections and state-of-the-science speciation including explicit intermediate-volatility organic compounds (IVOCs), yielding the first bottom-up volatility-resolved inventory of US mobile source emissions. Using the Community Multiscale Air Quality model, we estimate mobile sources account for 20 %-25 % of the IVOC concentrations and 4.4 %-21.4 % of ambient OA. The updated emissions and air quality model reduce biases in predicting fine-particle organic carbon in winter, spring, and autumn throughout the United States (4.3 %-11.3 % reduction in normalized bias). We identify key uncertain parameters that align with current state-of-the-art research measurement challenges.

2002-2017 anthropogenic emissions data for air quality modeling over the United States.

The United States Environmental Protection Agency (US EPA) has developed a set of annual North American emissions data for multiple air pollutants across 18 broad source categories for 2002 through 2017. The sixteen new annual emissions inventories were developed using consistent input data and methods across all years. When a consistent method or tool was not available for a source category, emissions were estimated by scaling data from the EPA's 2017 National Emissions Inventory with scaling factors based on activity data and/or emissions control information. The emissions datasets are designed to support regional air quality modeling for a wide variety of human health and ecological applications. The data were developed to support simulations of the EPA's Community Multiscale Air Quality model but can also be used by other regional scale air quality models. The emissions data are one component of EPA's Air Quality Time Series Project which also includes air quality modeling inputs (meteorology, initial conditions, boundary conditions) and outputs (e.g., ozone, PM2.5 and constituent species, wet and dry deposition) for the Conterminous US at a 12 km horizontal grid spacing.

Contribution of lubricating oil to particulate matter emissions from light-duty gasoline vehicles in Kansas City.

The contribution of lubricating oil to particulate matter (PM) emissions representative of the in-use 2004 light-duty gasoline vehicles fleet is estimated from the Kansas City Light-Duty Vehicle Emissions Study (KCVES). PM emissions are apportioned to lubricating oil and gasoline using aerosol-phase chemical markers measured in PM samples obtained from 99 vehicles tested on the California Unified Driving Cycle. The oil contribution to fleet-weighted PM emission rates is estimated to be 25% of PM emission rates. Oil contributes primarily to the organic fraction of PM, with no detectable contribution to elemental carbon emissions. Vehicles are analyzed according to pre-1991 and 1991-2004 groups due to differences in properties of the fitting species between newer and older vehicles, and to account for the sampling design of the study. Pre-1991 vehicles contribute 13.5% of the KC vehicle population, 70% of oil-derived PM for the entire fleet, and 33% of the fuel-derived PM. The uncertainty of the contributions is calculated from a survey analysis resampling method, with 95% confidence intervals for the oil-derived PM fraction ranging from 13% to 37%. The PM is not completely apportioned to the gasoline and oil due to several contributing factors, including varied chemical composition of PM among vehicles, metal emissions, and PM measurement artifacts. Additional uncertainties include potential sorption of polycyclic aromatic hydrocarbons into the oil, contributions of semivolatile organic compounds from the oil to the PM measurements, and representing the in-use fleet with a limited number of vehicles.

Evaluation of 15 years of modeled atmospheric oxidized nitrogen compounds across the contiguous United States.

Atmospheric nitrogen oxide and nitrogen dioxide (NO + NO2, together termed as NO X ) estimates from annual photochemical simulations for years 2002-2016 are compared to surface network measurements of NO X and total gas-phase-oxidized reactive nitrogen (NO Y ) to evaluate the Community Multiscale Air Quality (CMAQ) modeling system performance by U.S. region, season, and time of day. In addition, aircraft measurements from 2011 Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality are used to evaluate how emissions, chemical mechanism, and measurement uncertainty each contribute to the overall model performance. We show distinct seasonal and time-of-day patterns in NO X performance. Summertime NO X is overpredicted with bimodal peaks in bias during early morning and evening hours and persisting overnight. The summertime morning NO X bias dropped from between 28% and 57% for earlier years (2002-2012) to between -2% and 7% for later years (2013-2016). Summer daytime NO X tends to be unbiased or underpredicted. In winter, the evening NO X overpredictions remain, but NO X is unbiased or underpredicted overnight, in the morning, and during the day. NO X overpredictions are most pronounced in the Midwestern and Southern United States with Western regions having more of a tendency toward model underpredictions of NO X . Modeled NO X performance has improved substantially over time, reflecting updates to the emission inputs and the CMAQ air quality model. Model performance improvements are largest for years simulated with CMAQv5.1 or later and for emission inventory years 2014 and later, coinciding with reduced onroad NO X emissions from vehicles with newer emission control technologies and improved treatment of chemistry, deposition, and vertical mixing in CMAQ. Our findings suggest that emissions temporalization of specific mobile source sectors have a small impact on model performance, while chemistry updates improve predictions of NO Y but do not improve summertime NO X bias in the Baltimore/DC area. Sensitivity runs performed for different locations across the country suggest that the improvement in summer NO X performance can be attributed to updates in vertical mixing incorporated in CMAQv5.1.

Variability of particle number emissions from diesel and hybrid diesel-electric buses in real driving conditions.

A linear mixed model was developed to quantify the variability of particle number emissions from transit buses tested in real-world driving conditions. Two conventional diesel buses and two hybrid diesel-electric buses were tested throughout 2004 under different aftertreatments, fuels, drivers, and bus routes. The mixed model controlled the confounding influence of factors inherent to on-board testing. Statistical tests showed that particle number emissions varied significantly according to the after treatment, bus route, driver, bus type, and daily temperature, with only minor variability attributable to differences between fuel types. The daily setup and operation of the sampling equipment (electrical low pressure impactor) and mini-dilution system contributed to 30-84% of the total random variability of particle measurements among tests with diesel oxidation catalysts. By controlling for the sampling day variability, the model better defined the differences in particle emissions among bus routes. In contrast, the low particle number emissions measured with diesel particle filters (decreased by over 99%) did not vary according to operating conditions or bus type but did vary substantially with ambient temperature.