Development of a microscale emission factor model for particulate matter for predicting real-time motor vehicle emissions

The U.S. Environmental Protection Agency's National Exposure Research Laboratory is pursuing a project to improve the methodology for modeling human exposure to motor vehicle emissions. The overall project goal is to develop improved methods for modeling the source through the air pathway to human exposure in significant exposure microenvironments. Current particulate matter (PM) emission models, particle emission factor model (used in the United States, except California) and motor vehicle emission factor model (used in California only), are suitable only for county-scale modeling and emission inventories. There is a need to develop a site-specific real-time emission factor model for PM emissions to support human exposure studies near roadways. A microscale emission factor model for predicting site-specific real-time motor vehicle PM (MicroFacPM) emissions for total suspended PM, PM less than 10 microm aerodynamic diameter, and PM less than 2.5 microm aerodynamic diameter has been developed. The algorithm used to calculate emission factors in MicroFacPM is disaggregated, and emission factors are calculated from a real-time fleet, rather than from a fleet-wide average estimated by a vehicle-miles-traveled weighting of the emission factors for different vehicle classes. MicroFacPM requires input information necessary to characterize the site-specific real-time fleet being modeled. Other variables required include average vehicle speed, time and day of the year, ambient temperature, and relative humidity.     

A Mathematical Model for Predicting Trends in Carbon Monoxide Emissions and Exposures on Urban Arterial Highways

Abstract

The roadway is one of the most important microenvironments for human exposure to carbon monoxide (CO). To evaluate long-term changes in pollutant exposure due to in-transit activities, a mathematical model has been developed to predict average daily vehicular emissions on highways. By utilizing measurements that are specific for a given location and year (e.g., traffic counts, fleet composition), this model can predict emissions for a specific roadway during various time periods of interest, allowing examination of long-term trends in human exposure to CO. For an arterial highway in northern California, this model predicts that CO emissions should have declined by 58% between 1980 and 1991, which agrees fairly well with field measurements of human exposure taken along that roadway during those two years. An additional reduction of up to 60% in CO emissions is predicted to occur between 1991 and 2002, due solely to the continued replacement of older cars with newer, cleaner vehicles.     

Observations and Model Simulations of Carbon Monoxide Emission Factors from a California Highway

ABSTRACT

A series of twelve intensively monitored 1-hr CO dispersion studies were conducted near Davis, CA, in winter 1996. The experimental equipment included twelve CO sampling ports at elevations up to 50 m, three sonic anemometers, a tethersonde station, aircraft measurements of wind and temperature profile aloft, and a variety of conventional meteorological equipment. The study was designed to explore the role of vehicular exhaust buoyancy during worst-case meteorological conditions, such as low winds oriented in near-parallel alignment with the road during a surface-based nocturnal inversion. From the study, field estimates of the CO emission factor (EF) from a California vehicle fleet were computed using two different methods. The analysis suggests that the CT-EMFAC/ EMFAC (EMission FACtor) models currently used to conduct federal conformity modeling significantly overpredict CO emissions for high-speed, free-flowing traffic on California highways.     

Changing Speed-VMT Distributions: The Effects on Emissions Inventories and Conformity

ABSTRACT

The emissions factor modeling component of the motor vehicle emissions inventory (MVEI) modeling suite is currently being revised by the California Air Resources Board (CARB). One of the proposed changes in modeling philosophy is a shift from using link-based travel activity data to trip-based travel data for preparing mobile emissions inventories. Also as part of the revisions, new speed correction factors (SCFs) will be developed by CARB for the revised model. The new SCFs will be derived from vehicle emissions on 15 new driving cycles, each constructed to represent a typical trip at a specific average speed. This paper discusses how the new SCFs will affect transportation conformity and emissions inventory development, and evaluates the differences in total emissions produced by trip-based and link-based distributions of speed and vehicle miles of travel (VMT).

We simulated both link-based and trip-based speed-VMT distributions using travel data from the Sacramento and San Diego travel demand models. On the basis of the simulation results, there is reason to expect that mobile emissions inventories constructed using the proposed trip-based philosophy will differ markedly from those constructed in the current manner. Noting that results may vary by region, increases are expected in the CO and HC inventory levels, with concomitant decreases in the NO_x mobile emissions inventories.     

Reducing the Risk of Accidental Death Due to Vehicle-Related Carbon Monoxide Poisoning

ABSTRACT

Emissions of carbon monoxide (CO) from motor vehicles cause several hundred accidental fatal poisonings annually in the United States. The circumstances that could lead to fatal poisonings in residential settings with motor vehicles as the source of CO were explored. The risk of death in a garage (volume = 90 m³) and a single-family dwelling (400 m³) was evaluated using a Monte Carlo simulation with varying CO emission rates and ventilation rates. Information on emission rates was obtained from a survey of motor vehicle exhaust gas composition under warm idle conditions in California, and information on ventilation rates was obtained from a summary of published measurements in the U.S. housing stock. The risk of death ranged from 16 to 21% for a 3-hr exposure in a garage to 0% for a 1-hr exposure in a house. Older vehicles were associated with a disproportionately high risk of death. Removing all pre-1975 vehicles from the fleet would reduce the risk of death by one-fourth to two-thirds, depending on the exposure scenario. Significant efforts have been made to control CO emissions from motor vehicles with the goal of reducing CO concentrations in outdoor air. Substantial public health benefit could also be obtained if vehicle control measures were designed to take account of acute CO poisonings explicitly.     

Characterization of air manganese exposure estimates for residents in two Ohio towns

This study was conducted to derive receptor-specific outdoor exposure concentrations of total suspended particulate (TSP) and respirable (d_ae ≤ 10 µm) air manganese (air-Mn) for East Liverpool and Marietta (Ohio) in the absence of facility emissions data, but where long-term air measurements were available. Our “site-surface area emissions method” used U.S. Environmental Protection Agency’s (EPA) AERMOD (AMS/EPA Regulatory Model) dispersion model and air measurement data to estimate concentrations for residential receptor sites in the two communities. Modeled concentrations were used to create ratios between receptor points and calibrated using measured data from local air monitoring stations. Estimated outdoor air-Mn concentrations were derived for individual study subjects in both towns. The mean estimated long-term air-Mn exposure levels for total suspended particulate were 0.35 μg/m³ (geometric mean [GM]) and 0.88 μg/m³ (arithmetic mean [AM]) in East Liverpool (range: 0.014–6.32 μg/m³) and 0.17 μg/m³ (GM) and 0.21 μg/m³ (AM) in Marietta (range: 0.03–1.61 μg/m³). Modeled results compared well with averaged ambient air measurements from local air monitoring stations. Exposure to respirable Mn particulate matter (PM₁₀; PM <10 μm) was higher in Marietta residents.

Implications: Few available studies evaluate long-term health outcomes from inhalational manganese (Mn) exposure in residential populations, due in part to challenges in measuring individual exposures. Local long-term air measurements provide the means to calibrate models used in estimating long-term exposures. Furthermore, this combination of modeling and ambient air sampling can be used to derive receptor-specific exposure estimates even in the absence of source emissions data for use in human health outcome studies.     

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.     

Automated Chemical Mass Balance Receptor Modeling

Abstract

A nontrivial portion of heavy-duty vehicle emissions of NO_x and particulate matter (PM) occurs during idling. Regulators and the environmental community are interested in curtailing truck idling emissions, but current emissions models do not characterize them accurately, and little quantitative data exist to evaluate the relative effectiveness of various policies. The objectives of this study were to quantify the effect of accessory loading and engine speed on idling emissions from a properly functioning, modern, heavy-duty diesel truck and to compare these results with data from earlier model year vehicles. It was found that emissions during idling varied greatly as a function of engine model year, engine speed, and accessory load conditions. For the 1999 model year Class 8 truck tested, raising the engine speed from 600 to 1050 rpm and turning on the air conditioning resulted in a 2.5-fold increase in NO_x emissions in grams per hour, a 2-fold increase in CO₂ emissions, and a 5-fold increase in CO emissions while idling. On a grams per gallon fuel basis, NO_x emissions while idling were approximately twice as high as those at 55 mph. The CO₂ emissions at the two conditions were closer. The NO_x emissions from the 1999 truck while idling with air conditioning running were slightly more than those of two 1990 model year trucks under equivalent conditions, and the hydrocarbon (HC) and CO emissions were significantly lower. It was found that the NO_x emissions used in the California Air Resources Board’s (CARB) EMFAC2000 and the U.S. Environmental Protection Agency’s (EPA) MOBILE5b emissions inventory models were lower than those measured in all of the idling conditions tested on the 1999 truck.     

An Analysis of Michigan and California CO Remote Sensing Measurements

Abstract

Remote sensing measurements of CO emissions from on-road vehicles were made in California in 1991 and in Michigan in 1992. It was determined that both fleets had a small linear increase in the high emitter frequency (vehicles emitting more than 4% CO) as a function of vehicle age for 1986 and newer model vehicles. Although high emitting vehicles were only a small minority of the fleet, they had a dominant impact on the mean CO and total CO emitted by the fleet. In Michigan, the highest emitting 5% of passenger cars generated 45% of the CO from cars. In California, the highest emitting 5% of passenger cars generated 38% of the CO from cars. There was a high correlation between the mean CO emitted by each model year of vehicle and the frequency of high emitting vehicles within the model year for both the Michigan and California fleets. The frequency of high emitters within any model year had no obvious relation to that model year’s certification standards. The high emitter frequencies for vehicles less than nine years old were very similar for the California and Michigan fleets. An increase in the high emitter frequency in the ten-year-old and older Michigan passenger car fleet (relative to the California passenger car fleet), suggests, but does not conclusively demonstrate, that the rate of high emitters in Michigan and California is reduced by the inspection and maintenance (I/M) programs.     

The Impact of California Phase 2 Reformulated Gasoline on Real-World Vehicle Emissions

ABSTRACT

In mid-1996, California implemented Phase 2 Reformulated Gasoline (RFG). The new fuel was designed to further decrease emissions of hydrocarbons (HC_s), oxides of nitrogen (NO_x), carbon monoxide (CO), sulfur dioxide (SO₂), and other toxic species. In addition, it was formulated to reduce the ozone-forming potential of the HCs emitted by vehicles. Previous studies have observed that emissions from on-road vehicles can differ significantly from those predicted by mobile source emissions models, and so it is important to quantify the change in emissions in a real-world setting. In October 1995, prior to the introduction of California Phase 2 RFG, the Desert Research Institute (DRI) performed a study of vehicle emissions in Los Angeles' Sepulveda Tunnel. This study provided a baseline against which the results of a second experiment, conducted in July 1996, could be compared to evaluate the impact of California Phase 2 RFG on emissions from real-world vehicles. Compared with the 1995 experiment, CO and NO_x emissions exhibited statistically significant decreases, while the decrease in non-methane hydrocarbon emissions was not statistically significant.

Changes in the speciated HC emissions were evaluated. The benzene emission rate decreased by 27% and the overall emission rate of aromatic compounds decreased by 22% comparing the runs with similar speeds. Emissions of alkenes were virtually unchanged; however, emissions of combustion related unsaturates (e.g., acetylene, ethene) increased, while heavier alkenes decreased. The emission rate of methyl tertiary butyl ether (MTBE) exhibited a larger increase. Overall changes in the ozone-forming potential of the emissions were not significantly different, with the increased contributions to reactivity from paraffins, ole-fins, and MTBE being offset by a large decrease in reactivity due to aromatics.     

Radical precursors and related species from traffic as observed and modeled at an urban highway junction

Nitrous acid (HONO) and formaldehyde (HCHO) are important precursors for radicals and are believed to favor ozone formation significantly. Traffic emission data for both compounds are scarce and mostly outdated. A better knowledge of today's HCHO and HONO emissions related to traffic is needed to refine air quality models. Here the authors report results from continuous ambient air measurements taken at a highway junction in Houston, Texas, from July 15 to October 15, 2009. The observational data were compared with emission estimates from currently available mobile emission models (MOBILE6; MOVES [MOtor Vehicle Emission Simulator]). Observations indicated a molar carbon monoxide (CO) versus nitrogen oxides (NO_x) ratio of 6.01 ± 0.15 (r ² = 0.91), which is in agreement with other field studies. Both MOBILE6 and MOVES overestimate this emission ratio by 92% and 24%, respectively. For HCHO/CO, an overall slope of 3.14 ± 0.14 g HCHO/kg CO was observed. Whereas MOBILE6 largely underestimates this ratio by 77%, MOVES calculates somewhat higher HCHO/CO ratios (1.87) than MOBILE6, but is still significantly lower than the observed ratio. MOVES shows high HCHO/CO ratios during the early morning hours due to heavy-duty diesel off-network emissions. The differences of the modeled CO/NO_x and HCHO/CO ratios are largely due to higher NO_x and HCHO emissions in MOVES (30% and 57%, respectively, increased from MOBILE6 for 2009), as CO emissions were about the same in both models. The observed HONO/NO_x emission ratio is around 0.017 ± 0.0009 kg HONO/kg NO_x which is twice as high as in MOVES. The observed NO₂/NO_x emission ratio is around 0.16 ± 0.01 kg NO₂/kg NO_x, which is a bit more than 50% higher than in MOVES. MOVES overestimates the CO/CO₂ emission ratio by a factor of 3 compared with the observations, which is 0.0033 ± 0.0002 kg CO/kg CO₂. This as well as CO/NO_x overestimation is coming from light-duty gasoline vehicles.

Implications: Nitrous acid (HONO) and formaldehyde (HCHO) are important precursors for radicals that ultimately contribute to ozone formation. There still exist uncertainties in emission sources of HONO and HCHO and thus regional air quality modeling still tend to underestimate concentrations of free radicals in the atmosphere. This paper demonstrates that the latest U.S. Environmental Protection Agency (EPA) traffic emission model MOVES still shows significant deviations from observed emission ratios, in particular underestimation of HCHO/CO and HONO/NO_x ratios. Improving the performance of MOVES may improve regional air quality modeling.     

Near-road air pollutant concentrations of CO and PM2.5: A comparison of MOBILE6.2/CALINE4 and generalized additive models

The contribution of vehicular traffic to air pollutant concentrations is often difficult to establish. This paper utilizes both time-series and simulation models to estimate vehicle contributions to pollutant levels near roadways. The time-series model used generalized additive models (GAMs) and fitted pollutant observations to traffic counts and meteorological variables. A one year period (2004) was analyzed on a seasonal basis using hourly measurements of carbon monoxide (CO) and particulate matter less than 2.5 μm in diameter (PM_2.5) monitored near a major highway in Detroit, Michigan, along with hourly traffic counts and local meteorological data. Traffic counts showed statistically significant and approximately linear relationships with CO concentrations in fall, and piecewise linear relationships in spring, summer and winter. The same period was simulated using emission and dispersion models (Motor Vehicle Emissions Factor Model/MOBILE6.2; California Line Source Dispersion Model/CALINE4). CO emissions derived from the GAM were similar, on average, to those estimated by MOBILE6.2. The same analyses for PM_2.5 showed that GAM emission estimates were much higher (by 4–5 times) than the dispersion model results, and that the traffic-PM_2.5 relationship varied seasonally. This analysis suggests that the simulation model performed reasonably well for CO, but it significantly underestimated PM_2.5 concentrations, a likely result of underestimating PM_2.5 emission factors. Comparisons between statistical and simulation models can help identify model deficiencies and improve estimates of vehicle emissions and near-road air quality.     

A Multipurpose Flare Stack for Control of Chemical Process Wastes

Abstract

To test the effectiveness of California’s vehicle inspection/ maintenance (I/M) program, exclusive of vehicle-owner intervention, a fleet of more than 1,100 vehicles that previously had failed California’s Smog Check test were sent to randomly selected Smog Check stations in the Los Angeles area for covert inspections and repairs. The two-speed idle test was used for repairs. For those vehicles that were repaired at the first inspection, their FTP emission reductions were 25%, 14%, and 11% for hydrocarbons (HC), carbon monoxide (CO), and nitrogen oxides (NO_x), respectively, although emissions testing for NO_x was not performed at the Smog Check stations. Idle HC and CO emissions increased for 35% and 43% of the vehicles, respectively, after repairs. This data set shows that most vehicles that fail the Smog Check inspection are only marginal emitters, with 61% and 44% of the total potential for HC and CO emission reductions, respectively, coming from only 10% of the vehicles that currently fail the inspection. When the vehicles were rank-ordered by idle emissions from dirtiest to cleanest, emission reduction costs for the highest-emitting 10% of the fleet averaged $l,100/ton and $250/ton for HC and CO, respectively, attributing all the costs to each pollutant exclusively. For the remaining vehicles, costs increased dramatically.     

Ammonia emissions from a representative in-use fleet of light and medium-duty vehicles in the California South Coast Air Basin

We used Fourier Transform Infrared Spectroscopy (FTIR) to measure tailpipe ammonia emissions from a representative fleet of 41 light and medium-duty vehicles recruited in the California South Coast Air Basin. A total of 121 chassis dynamometer emissions tests were conducted on these vehicles and the test results were examined to determine the effects of several key variables on ammonia emissions. Variables included vehicle type, driving cycle, emissions technology, ammonia precursor emissions (i.e. CO and NOx) and odometer readings/model year as a proxy for catalyst age. The mean ammonia emissions factor was 46 mg km^?1 (σ = 48 mg km^?1) for the vehicle fleet. Average emission factors for specific vehicle groups are also reported in this study. Results of this study suggest vehicles with the highest ammonia emission rates possess the following characteristics: medium-duty vehicles, older emissions technologies, mid-range odometer readings, and higher CO emissions. In addition, vehicles subjected to aggressive driving conditions are likely to be higher ammonia emitters. Since the vehicles we studied were representative of recent model year vehicles and technologies in urban airsheds, the results of our study will be useful for developing ammonia emissions inventories in Los Angeles and other urban areas where California-certified vehicles are driven. However, efforts should also be made to continue emissions testing on in-use vehicles to ensure greater confidence in the ammonia emission factors reported here.     

An Integrated Modeling System for the Estimation of Motor Vehicle Emissions

ABSTRACT

The paper provides a summary of accomplished and ongoing activities in the field of motor vehicle emission modeling in Europe. These activities have led to the development of a system of methods and conesponding computer models that address all the issues related to motor vehicle emissions that are of interest to policy-makers, institutions, and the automotive and oil industries. The Coordination of Information on Air Emissions/Computer Program to Calculate Emissions from Road Traffic (CORLNAIR/COPERT) methodology for the estimation of emissions from road vehicles is presented and compared with other models. A COPERT-based approach for microscale traffic emission estimation, with direct application in regional and urban emission inventories, is outlined, and relevant case studies are briefly discussed. The FOREMOVE model, developed for forecasts of motor vehicle emissions, is presented, together with some results from its application in the European Auto/Oil program. Particular attention is given to modeling the deterioration of in-use vehicles. Finally, the major areas of further research in the field of vehicle emissions in Europe are indicated.     

Idle Emissions from Heavy-Duty Diesel and Natural Gas Vehicles at High Altitude

ABSTRACT

Idle emissions of total hydrocarbon (THC), CO, NO_x, and particulate matter (PM) were measured from 24 heavy-duty diesel-fueled (12 trucks and 12 buses) and 4 heavy-duty compressed natural gas (CNG)-fueled vehicles. The volatile organic fraction (VOF) of PM and aldehyde emissions were also measured for many of the diesel vehicles. Experiments were conducted at 1609 m above sea level using a full exhaust flow dilution tunnel method identical to that used for heavy-duty engine Federal Test Procedure (FTP) testing. Diesel trucks averaged 0.170 g/min THC, 1.183 g/min CO, 1.416 g/min NO_x, and 0.030 g/min PM. Diesel buses averaged 0.137 g/min THC, 1.326 g/min CO, 2.015 g/min NO_x, and 0.048 g/min PM.

Results are compared to idle emission factors from the MOBILE5 and PART5 inventory models. The models significantly (45-75%) overestimate emissions of THC and CO in comparison with results measured from the fleet of vehicles examined in this study. Measured NO_x emissions were significantly higher (30-100%) than model predictions. For the pre-1999 (pre-consent decree) truck engines examined in this study, idle NO_x emissions increased with Health and Environment; June 30, 1999 (available from the authors).     

A Portable Fiber-Optic Chemical Device for the Quantitative Determination of Carbon Monoxide from Automobile Exhaust Emissions

ABSTRACT

A colorimetxic method for the quantitative determination of CO by diffuse reflectance is described. This method is based on the reduction by CO of Mo (VI) from the indicator reagent molybdosilicic acid (H₈Si[Mo₂O₇]₆). The reduction yielded a change of color from clear yellow to dark green on white disk filter chart paper wetted with reagent indicator solution. The gaseous mixture containing CO was forced to pass through this chart paper, initiating the reaction. The intensity of the color produced, measured by diffuse reflectance, was proportional to the CO concentration present in exhaust gases in the range from 0.02 to 12% volume/volume (v/v). A 650-nm light-emitting diode was used as a light source. A two-fiber-optic system carried the light from the source to the detection system, which was composed of a photodiode, an amplification circuit, and a digital display. The method was applied with success in field measurements for automobiles in the Otto cycle. In a previous paper, this method was used for the quantitative determination of exhaust emissions from diesel-fueled vehicles.¹     

Multiple-year black carbon measurements and source apportionment using Delta-C in Rochester,New York

Black carbon (BC), an important component of the atmospheric aerosol, has climatic, environmental, and human health significance. In this study, BC was continuously measured using a two-wavelength aethalometer (370 nm and 880 nm) in Rochester, New York, from January 2007 to December 2010. The monitoring site is adjacent to two major urban highways (I-490 and I-590), where 14% to 21% of the total traffic was heavy-duty diesel vehicles. The annual average BC concentrations were 0.76 μg/m³, 0.67 μg/m³, 0.60 μg/m³, and 0.52 μg/m³ in 2007, 2008, 2009, and 2010, respectively. Positive matrix factorization (PMF) modeling was performed using PM_2.5 elements, sulfate, nitrate, ammonia, elemental carbon (EC), and organic carbon (OC) data from the U.S. Environmental Protection Agency (EPA) speciation network and Delta-C (UVBC_370nm – BC_880nm) data. Delta-C has been previously shown to be a tracer of wood combustion factor. It was used as an input variable in source apportionment models for the first time in this study and was found to play an important role in separating traffic (especially diesel) emissions from wood combustion emissions. The result showed the annual average PM_2.5 concentrations apportioned to diesel emissions in 2007, 2008, 2009, and 2010 were 1.34 μg/m³, 1.25 μg/m³, 1.13 μg/m³, and 0.97 μg/m³, respectively. The BC conditional probability function (CPF) plots show a large contribution from the highway diesel traffic to elevated BC concentrations. The measurements and modeling results suggest an impact of the U.S Environmental Protection Agency (EPA) 2007 Heavy-Duty Highway Rule on the decrease of BC and PM_2.5 concentrations during the study period.

Implications: This study suggests that there was an observable impact of the U.S EPA 2007 Heavy-Duty Highway Rule on the ambient black carbon concentrations in Rochester, New York. Aethalometer Delta-C was used as an input variable in source apportionment models and it allowed the separation of traffic (especially diesel) emissions from wood combustion emissions.     

Development of an individual exposure model for application to the Southern California children's health study

The Southern California Children's Health Study (CHS) investigated the relationship between air pollution and children's chronic respiratory health outcomes. Ambient air pollutant measurements from a single CHS monitoring station in each community were used as surrogates for personal exposures of all children in that community. To improve exposure estimates for the CHS children, we developed an Individual Exposure Model (IEM) to retrospectively estimate the long-term average exposure of the individual CHS children to CO, NO₂, PM₁₀, PM_2.5, and elemental carbon (EC) of ambient origin. In the IEM, pollutant concentrations due to both local mobile source emissions (LMSE) and meteorologically transported pollutants were taken into account by combining a line source model (CALINE4) with a regional air quality model (SMOG). To avoid double counting, local mobile sources were removed from SMOG and added back by CALINE4. Limited information from the CHS survey was used to group each child into a specific time-activity category, for which corresponding Consolidated Human Activity Database (CHAD) time-activity profiles were sampled. We found local traffic significantly increased within-community variability of exposure to vehicle-related pollutants. PM-associated exposures were influenced more by meteorologically transported pollutants and local non-mobile source emissions than by LMSE. The overall within-community variability of personal exposures was highest for NO₂ (±20–40%), followed by EC (±17–27%), PM₁₀ (±15–25%), PM_2.5 (±15–20%), and CO (±9–14%). Between-community exposure differences were affected by community location, traffic density, and locations of residences and schools in each community. Proper siting of air monitoring stations relative to emission sources is important to capture community mean exposures.