Sensitivity Analysis and Evaluation of MicroFacCO: A Microscale Motor Vehicle Emission Factor Model for CO Emissions

ABSTRACT

This paper presents a sensitivity analysis of a microscale emission factor model (MicroFacCO) for predicting realtime site-specific motor vehicle CO emissions to input variables, as well as a limited field study evaluation of the model. The sensitivity analysis has shown that MicroFacCO emission estimates are very sensitive to vehicle fleet composition, speed, and ambient temperature. For the present U.S. traffic fleet, the CO emission rate (g/mi) is increased by more than 500% at 5 mph in comparison with a speed greater than 40 mph and by ~67% at ambient temperatures of 45 °F and ≥95 °F in comparison with an ambient temperature of 75 °F.     

A Constant-Volume Rapid Exhaust Dilution System for Motor Vehicle Particulate Matter Number and Mass Measurements

Abstract

An improved version of the constant volume sampling (CVS) methodology that overcomes a number of obstacles that exist with the current CVS dilution tunnel system used in most diesel and gasoline vehicle emissions test facilities is presented. The key feature of the new sampling system is the introduction of dilution air immediately at the vehicle tailpipe. In the present implementation, this is done concentrically through a cylindrical air filter. Elimination of the transfer hose conventionally used to connect the tailpipe to the dilution tunnel significantly reduces the hydrocarbon and particulate matter (PM) storage release artifacts that can lead to wildly incorrect particle number counts and to erroneous filter-collected PM mass. It provides accurate representations of particle size distributions for diesel vehicles by avoiding the particle coagulation that occurs in the transfer hose. Furthermore, it removes the variable delay time that otherwise exists between the time that emissions exit the tailpipe and when they are detected in the dilution tunnel. The performance of the improved CVS system is examined with respect to diesel, gasoline, and compressed natural gas vehicles.     

Evaluation of the Effects of Emission Reductions on Secondary Particulate Matter in the South Coast Air Basin of California

An Aerosol Trajectory Model (ATM) is applied to the South Coast Air Basin of California for a two-day episode in August 1982 to evaluate proposed control strategies that aim to reduce atmospheric aerosols. Model predictions Indicate that secondary organic aerosols decrease linearly with reactive hydrocarbon emissions. In addition, the model shows that If sulf ate is produced only in the gas phase by oxidation of SO₂, then reduction In SO₂ emissions yields a nearly proportional decrease In sulfate levels. Reduction in ammonia emissions, combined with reduction of NO_x emissions, gives the best results In terms of nitrate control. The order in which the emission controls are implemented Is predicted to have a major impact on the reduction of secondary atmospheric aerosols.     

Application and Evaluation of Two Air Quality Models for Particulate Matter for a Southeastern U.S. Episode

Abstract

The Models-3 Community Multiscale Air Quality (CMAQ) Modeling System and the Particulate Matter Comprehensive Air Quality Model with extensions (PMCAMx) were applied to simulate the period June 29–July 10, 1999, of the Southern Oxidants Study episode with two nested horizontal grid sizes: a coarse resolution of 32 km and a fine resolution of 8 km. The predicted spatial variations of ozone (O₃), particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM_2.5), and particulate matter with an aerodynamic diameter less than or equal to 10 μm (PM₁₀) by both models are similar in rural areas but differ from one another significantly over some urban/suburban areas in the eastern and southern United States, where PMCAMx tends to predict higher values of O₃ and PM than CMAQ. Both models tend to predict O₃ values that are higher than those observed. For observed O₃ values above 60 ppb, O₃ performance meets the U.S. Environmental Protection Agency's criteria for CMAQ with both grids and for PMCAMx with the fine grid only. It becomes unsatisfactory for PMCAMx and marginally satisfactory for CMAQ for observed O₃ values above 40 ppb.

Both models predict similar amounts of sulfate (SO₄ ^2?) and organic matter, and both predict SO₄ ^2? to be the largest contributor to PM_2.5. PMCAMx generally predicts higher amounts of ammonium (NH₄ ⁺), nitrate (NO₃ ^?), and black carbon (BC) than does CMAQ. PM performance for CMAQ is generally consistent with that of other PM models, whereas PMCAMx predicts higher concentrations of NO₃ ^?,NH₄ ⁺, and BC than observed, which degrades its performance. For PM₁₀ and PM_2.5 predictions over the southeastern U.S. domain, the ranges of mean normalized gross errors (MNGEs) and mean normalized bias are 37–43% and –33–4% for CMAQ and 50–59% and 7–30% for PMCAMx. Both models predict the largest MNGEs for NO₃ ^? (98–104% for CMAQ, 138–338% for PMCAMx). The inaccurate NO₃ ^? predictions by both models may be caused by the inaccuracies in the ammonia emission inventory and the uncertainties in the gas/particle partitioning under some conditions. In addition to these uncertainties, the significant PM overpredictions by PMCAMx may be attributed to the lack of wet removal for PM and a likely underprediction in the vertical mixing during the daytime.     

Guidance for the Performance Evaluation of Three-Dimensional Air Quality Modeling Systems for Particulate Matter and Visibility

ABSTRACT

Guidance for the performance evaluation of three-dimensional air quality modeling systems for particulate matter and visibility is presented. Four levels are considered: operational, diagnostic, mechanistic, and probabilistic evaluations. First, a comprehensive model evaluation should be conducted in at least two distinct geographical locations and for several meteorological episodes. Next, streamlined evaluations can be conducted for other similar applications if the comprehensive evaluation is deemed satisfactory. In all cases, the operational evaluation alone is insufficient, and some diagnostic evaluation must always be carried out. Recommendations are provided for designing field measurement programs that can provide the data needed for such model performance evaluations.     

Airshed Model Evaluation of Reactivity Adjustment Factors Calculated with the Maximum Incremental Reactivity Scale for Transitional-Low Emission Vehicles

Abstract

The California Air Resources Board recently adopted regulations for light- and medium-duty vehicles that require reductions in the ozone-forming potential or “reactivity,” rather than the mass, of nonmethane organic gas (NMOG) emissions. The regulations allow sale of all alternatively fueled vehicles (AFVs) that meet NMOG exhaust emission standards equivalent in reactivity to those set for vehicles fueled with conventional gasoline. Reactivity adjustment factors (RAFs), the ratio of the reactivity (per gram) of the AFV exhaust to that of the conventionally fueled vehicle (CFV), are used to correct the stringent exhaust emission standards. Complete chemical speciation of the exhaust and conversion of each NMOG species to an appropriate mass of ozone using the maximum incremental reactivity (MIR) scale of Carter determines the RAF. The MIR approach defines reactivity where NMOG control is the most effective strategy in reducing ozone concentrations, and assumes it is not important to define reactivity at other conditions, i.e., where NO_x is the limiting precursor.

This study used the Carnegie/California Institute of Technology airshed model to evaluate whether the RAF-adjusted AFV emissions result in ozone impacts equivalent to those of CFV emissions. A matrix of two ozone episodes in the South Coast Air Basin (SoCAB) of California, two base emission inventories, and exhaust emissions from three alternative fuels that meet the first level of the low emission vehicle standards bounds the expected range of conditions. Although very good agreement was found previously for individual NMOG species,² this study noted deviations of up to ±15 percent from the equal ozone impacts for any vehicle/fuel combination required by the California regulations. These deviations appear to be attributable to differences in spatial and temporal patterns of emissions between vehicle fleets, rather than a problem with the MIR approach. The first formally adopted RAF, a value of 0.41 for 85 percent methanol/15 percent gasoline-fueled vehicles, includes a 10 percent increase based on the airshed modeling. The correction to the RAF is different for other fuels and may be different for air basins other than the SoCAB.     

Field Measurements of Particulate Matter Emissions,Carbon Monoxide,and Exhaust Opacity from Heavy-Duty Diesel Vehicles

ABSTRACT

Diesel particulate matter (PM) is a significant contributor to ambient air PM₁₀ and PM_2.5 particulate levels. In addition, recent literature argues that submicron diesel PM is a pulmonary health hazard. There is difficulty in attributing PM emissions to specific operating modes of a diesel engine, although it is acknowledged that PM production rises dramatically with load and that high PM emissions occur during rapid load increases on turbocharged engines. Snap-acceleration tests generally identify PM associated with rapid transient operating conditions, but not with high load. To quantify the origin of PM during transient engine operation, continuous opacity measurements have been made using a Wager 650CP full flow exhaust opacity meter. Opacity measurements were taken while the vehicles were operated over transient driving cycles on a chassis dynamometer using the West Virginia University (WVU) Transportable Heavy Duty Vehicle Emissions Testing Laboratories. Data were gathered from Detroit Diesel, Cummins, Caterpillar, and Navistar heavy-duty (HD) diesel engines. Driving cycles used were the Central Business District (CBD) cycle, the WVU 5-Peak Truck cycle, the WVU 5-Mile route, and the New York City Bus (NYCB) cycle. Continuous opacity measurements, integrated over the entire driving cycle, were compared to total integrated PM mass. In addition, the truck was subjected to repeat snap-acceleration tests, and PM was collected for a composite of these snap-acceleration tests. Additional data were obtained from a fleet of 1996 New Flyer buses in Flint, MI, equipped with electronically controlled Detroit Diesel Series 50 engines. Again, continuous opacity, regulated gaseous emissions, and PM were measured. The relationship between continuous carbon monoxide (CO) emissions and continuous opacity was noted. In identifying the level of PM emissions in transient diesel engine operation, it is suggested that CO emissions may prove to be a useful indicator and may be used to apportion total PM on a continuous basis over a transient cycle. The projected continuous PM data will prove valuable in future mobile source inventory prediction.     

Comparison and Evaluation of Chemically Speciated Mobile Source PM2.5 Particulate Matter Profiles

ABSTRACT

Mobile sources are significant contributors to ambient PM_{2 5}, accounting for 50% or more of the total observed levels in some locations. One of the important methods for resolving the mobile source contribution is through chemical mass balance (CMB) receptor modeling. CMB requires chemically speciated source profiles with known uncertainty to ensure accurate source contribution estimates. Mobile source PM profiles are available from various sources and are generally in the form of weight fraction by chemical species. The weight fraction format is commonly used, since it is required for input into the CMB receptor model. This paper examines the similarities and differences in mobile source PM_2.5 profiles that contain data for elements, ions, elemental carbon (EC) and organic carbon (OC), and in some cases speciated organics (e.g., polycyclic aromatic hydrocarbons [PAHs]), drawn from four different sources.

Notable characteristics of the mass fraction data include variability (relative contributions of elements and ions) among supposedly similar sources and a wide range of average EC:OC ratios (0.60 ± 0.53 to 1.42 ± 2.99) for light-duty gasoline vehicles (LDGVs), indicating significant EC emissions from LDGVs in some cases. For diesel vehicles, average EC:OC ratios range from 1.09 ± 2.66 to 3.54 ± 3.07. That different populations of the same class of emitters can show considerable variability suggests caution should be exercised when selecting and using profiles in source apportionment studies.     

Application of a Chemical Mass Balance Receptor Model to Respirable Particulate Matter in Mexico City

Mexico City frequently experiences high levels of air pollution. This is due mainly to its topography and meteorology that suppress pollutant diffusion and dispersion. The atmospheric mixing is extremely poor, especially during the dry winter months. The levels of certain pollutants, such as particulate matter, are of concern since they have severe effects on public health. Visibility deterioration is one of the most noticeable effects in large cities. Biological effects of particulate matter on man and animals, ranging from mild eye irritation to death, have been reported. The effects depend on the size of the particles, their solubility, and toxicity. The main objective of this paper is to present the results of a chemical mass balance receptor model applied to a well-characterized data set of particulate matter collected in the Mexico City Metropolitan Area (MCMA). Samples of particulate matter were collected using a denuder and a Hi-Vol system for the respi-rable fraction and total suspended particles, respectively. In this paper the analysis of a database consisting of the chemical composition of 33 samples of respirable particulate matter (aerosols with diameter less than 2.5 µm) is presented. The 12-hour samples were acquired during day and night periods in a typical medium-income neighborhood from December 19, 1989 through February 5, 1990.     

A Pilot Indoor-Outdoor Study of Organic Particulate Matter and Particulate Mutagenicity

Abstract

Many large metropolitan areas experience elevated concentrations of ground-level ozone pollution during the summertime “smog season”. Local environmental or health agencies often need to make daily air pollution forecasts for public advisories and for input into decisions regarding abatement measures and air quality management. Such forecasts are usually based on statistical relationships between weather conditions and ambient air pollution concentrations. Multivariate linear regression models have been widely used for this purpose, and well-specified regressions can provide reasonable results. However, pollution-weather relationships are typically complex and nonlinear—especially for ozone—properties that might be better captured by neural networks. This study investigates the potential for using neural networks to forecast ozone pollution, as compared to traditional regression models. Multiple regression models and neural networks are examined for a range of cities under different climate and ozone regimes, enabling a comparative study of the two approaches. Model comparison statistics indicate that neural network techniques are somewhat (but not dramatically) better than regression models for daily ozone prediction, and that all types of models are sensitive to different weather-ozone regimes and the role of persistence in aiding predictions.     

Sensitivity of Source Apportionment of Urban Particulate Matter to Uncertainty in Motor Vehicle Emissions Profiles

Abstract

A sensitivity analysis was conducted to characterize sources of uncertainty in results of a molecular marker source apportionment model of ambient particulate matter using mobile source emissions profiles obtained as part of the Gasoline/Diesel PM Split Study. A chemical mass balance (CMB) model was used to determine source contributions to samples of fine particulate matter (PM_2.5) collected over 3 weeks at two sites in the Los Angeles area in July 2001. The ambient samples were composited for organic compound analysis by the day of the week to investigate weekly trends in source contributions. The sensitivity analysis specifically examined the impact of the uncertainty in mobile source emissions profiles on the CMB model results. The key parameter impacting model sensitivity was the source profile for gasoline smoker vehicles. High-emitting gasoline smoker vehicles with visible plumes were seen to be a significant source of PM in the area, but use of different measured profiles for smoker vehicles in the model gave very different results for apportionment of gasoline, diesel, and smoker vehicle tailpipe emissions. In addition, the contributions of gasoline and diesel emissions to total ambient PM varied as a function of the site and the day of the week.     

Particulate Emission Rates for Unpaved Shoulders along a Paved Road

ABSTRACT

This paper reports the first empirical estimate of particle emissions from unpaved shoulders along paved roads.¹ Its objectives are to develop and demonstrate an emission rate measurement methodology that can be applied in different areas; identify the mechanisms that suspend dust from unpaved shoulders and the observables related to this suspension process; and quantify PM₁₀ mass emissions in the form of an emission rate. To achieve these objectives, fast-response observations from nephelometers and a sonic anemometer were used to characterize shortlived dust plumes generated by passing vehicles. In addition, detailed soil surface measurements determined the mechanical properties of the shoulder surfaces.

Large traffic-induced turbulence events that led to significant dust entrainment were almost exclusively caused by “large” vehicles such as trucks, semis, and vehicles pulling trailers, all traveling 50-65 mph. PM₁₀ emission rates for these large, fast-traveling vehicles were determined to be 8 ± 4 grams per vehicle kilometer traveled under dry conditions. Emissions due to smaller vehicles such as cars, vans, and sport utility vehicles were negligible for normal on-road driving. These results indicate that the majority of PM₁₀ emissions from unpaved shoulders is caused by relatively few vehicles.     

Application of a Chemical Mass Balance Receptor Model to Respirable Particulate Matter in Mexico City

Mexico City frequently experiences high levels of air pollution. This is due mainly to its topography and meteorology that suppress pollutant diffusion and dispersion. The atmospheric mixing is extremely poor, especially during the dry winter months. The levels of certain pollutants, such as particulate matter, are of concern since they have severe effects on public health. Visibility deterioration

is one of the most noticeable effects in large cities. Biological effects of particulate matter on man and animals, ranging from mild eye irritation to death, have been reported. The effects depend on the size of the particles, their solubility, and toxicity. The main objective of this paper is to present the results of a chemical mass balance receptor model applied to a well-characterized data set of particulate matter collected in the Mexico City Metropolitan Area (MCMA). Samples of particulate matter were collected using a denuder and a Hi-Vol system for the respirable fraction and total suspended particles, respectively.

In this paper the analysis of a database consisting of the chemical composition of 33 samples of respirable particulate matter (aerosols with diameter less than 2.5 μm) is presented. The 12-hour samples were acquired during day and night periods in a typical medium-income neighborhood from December 19, 1989 through February 5, 1990.

The results show that the main contributors to suspended particles are vehicles without catalytic converters and heavy-duty diesel vehicles. The contribution of refineries, smelters, cement plants, resuspended dust, natural sources, and secondary aerosols were taken into account. In particular, the vehicles without catalytic converters represent the major contribution to PM2.5. They contribute with 50% during the day and 38% at night. Most of the source profiles were taken from the model library SPECIATE EPA. However, native profiles for soil, vehicles, and refinery were designed.     

The Relationship Between Total Suspended Particulate Matter (TSP) and British Smoke Measurements in London: Development of a Simple Model

Abstract

This article develops an empirical relationship between the British Smoke (BS) measurement, coefficient of haze (CoH), and Total Suspended Particulate Matter (TSP) for London winter periods of the early 1950s and 1960s. A bounded nonlinear model of form BS = TSP³ / (TSP² + [200 μg/m³]² ) fits the available BS/TSP data and meets the urban boundary conditions that BS→0 as TSP→ 0, and BS→ TSP as TSP→∞. A derivation is presented for the form of the equation from basic principles. Equations of a similar form may be useful on a site- and season-specific basis for developing relations between other fractions of PM.     

Fugitive Dust Emission Source Profiles and Assessment of Selected Control Strategies for Particulate Matter at Gravel Processing Sites in Taiwan

Abstract

Particles emitted from gravel processing sites are one contributor to worsening air quality in Taiwan. Major pollution sources at gravel processing sites include gravel and sand piles, unpaved roads, material crushers, and bare ground. This study analyzed fugitive dust emission characteristics at each pollution source using several types of particle samplers, including total suspended particulates (TSP), suspended particulate (PM₁₀), fine suspended particulate (PM_2.5), particulate sizer, and dust-fall collectors. Furthermore, silt content and moisture in the gravel were measured to develop particulate emission factors. The results showed that TSP (<100 µm) concentrations at the boundary of gravel sites ranged from 280 to 1290 µg/m³, which clearly exceeds the Taiwan hourly air quality standard of 500 µg/m³. Moreover, PM₁₀ concentrations, ranging from 135 to 550 µg/m³, were also above the daily air quality standard of 125 µg/m³ and approximately 1.2 and 1.5 times the PM_2.5 concentrations, ranging from 105 to 470 µg/m³. The size distribution analysis reveals that mass mean diameter and geometric standard deviation ranged from 3.2 to 5.7 µm and from 2.82 to 5.51, respectively. In this study, spraying surfactant was the most effective control strategy to abate windblown dust from unpaved roads, having a control efficiency of approximately 93%, which is significantly higher than using paved road strategies with a control efficiency of approximately 45%. For paved roads, wet suppression provided the best dust control efficiencies ranging from 50 to 83%. Re-vegetation of disturbed ground had dust control efficiencies ranging from 48 to 64%.     

Current Status of Air Quality Models for Particulate Matter

ABSTRACT

The current status of the mathematical modeling of atmospheric particulate matter (PM) is reviewed in this paper. Simulating PM requires treating various processes, including the formation of condensable species, the gas/ particle partitioning of condensable compounds, and in some cases, the evolution of the particle size distribution. The algorithms available to simulate these processes are reviewed and discussed. Eleven 3-dimensional (3-D) Eulerian air quality models for PM are reviewed in terms of their formulation and past applications. Results of past performance evaluations of 3-D Eulerian PM models are presented. Currently, 24-hr average PM_2.5 concentrations appear to be predicted within 50% for urban-scale domains. However, there are compensating errors among individual particulate species. The lowest errors tend to be associated with SO₄ ^2-, while NO₃ ^-, black carbon (BC), and organic carbon (OC) typically show larger errors due to uncertainties in emissions inventories and the prediction of the secondary OC fraction. Further improvements and performance evaluations are recommended.     

Characterization of Particulate Matter for Three Sites in Kuwait

Abstract

Many studies have shown strong associations between particulate matter (PM) levels and a variety of health outcomes, leading to changes in air quality standards in many regions, especially the United States and Europe. Kuwait, a desert country located on the Persian Gulf, has a large petroleum industry with associated industrial and urban land uses. It was marked by environmental destruction from the 1990 Iraqi invasion and subsequent oil fires. A detailed particle characterization study was conducted over 12 months in 2004–2005 at three sites simultaneously with an additional 6 months at one of the sites. Two sites were in urban areas (central and southern) and one in a remote desert location (northern). This paper reports the concentrations of particles less than 10 µm in diameter (PM₁₀) and fine PM (PM_2.5), as well as fine particle nitrate, sulfate, elemental carbon (EC), organic carbon (OC), and elements measured at the three sites. Mean annual concentrations for PM₁₀ ranged from 66 to 93 µg/m³ across the three sites, exceeding the World Health Organization (WHO) air quality guidelines for PM₁₀ of 20 µg/m³. The arithmetic mean PM_2.5 concentrations varied from 38 and 37 µg/m³ at the central and southern sites, respectively, to 31 µg/m³ at the northern site. All sites had mean PM_2.5 concentrations more than double the U.S. National Ambient Air Quality Standard (NAAQS) for PM_2.5. Coarse particles comprised 50–60% of PM₁₀. The high levels of PM₁₀ and large fraction of coarse particles comprising PM₁₀ are partially explained by the resuspension of dust and soil from the desert crust. However, EC, OC, and most of the elements were significantly higher at the urbanized sites, compared with the more remote northern site, indicating significant pollutant contributions from local mobile and stationary sources. The particulate levels in this study are high enough to generate substantial health impacts and present opportunities for improving public health by reducing airborne PM.     

On-Road Vehicle Particulate Matter and Gaseous Emission Distributions in Las Vegas,Nevada, Compared with Other Areas

Abstract

During the spring and summer of 2000, 2001, and 2002, gaseous and particulate matter (PM) fuel-based emission factors for ～150,000 low-tailpipe, individual vehicles in the Las Vegas, NV, area were measured via on-road remote sensing. For the gaseous pollutants (carbon monoxide, hydrocarbons, and nitrogen oxide), a commercial vehicle emissions remote sensing system (VERSS) was used. The PM emissions were determined using a Lidar-based VERSS. Emission distributions and their shapes were analyzed and compared with previous studies. The large skewness of the distributions is evident for both gaseous pollutants and PM and has important implications for emission reduction policies, because the majority of emissions are attributed to a small fraction of vehicles. Results of this Las Vegas study and studies at other geographical locations were compared. The gaseous pollutants were found to be close to those measured by VERSS in other U.S. cities. The PM emission factors for spark ignition and diesel vehicles are in the range of previous tunnel and dynamometer studies.