Developing a high-resolution vehicular emission inventory by integrating an emission model and a traffic model: Part 1--Modeling fuel consumption and emissions based on speed and vehicle-specific power

To improve the accuracy and applicability of vehicular emission models, this study proposes a speed and vehicle-specific power (VSP) modeling method to estimate vehicular emissions and fuel consumption using data gathered by a portable emissions monitoring system (PEMS). The PEMS data were categorized into discrete speed-VSP bins on the basis of the characteristics of vehicle driving conditions and emissions in Chinese cities. Speed-VSP modal average rates of emissions (or fuel consumption) and the time spent in the corresponding speed-VSP bins were then used to calculate the total trip emissions (or fuel consumption) and emission factors (or fuel economy) under specific average link speeds. The model approach was validated by comparing it against measured data with prediction errors within 20% for trip emissions and link-speed-based emission factors. This analysis is based on the data of light-duty gasoline vehicles in China; however, this research approach could be generalized to other vehicle fleets in other countries. This modeling method could also be coupled with traffic demand models to establish high-resolution emissions inventories and evaluate the impacts of traffic-related emission control measures.     

Capability and limitations of first-order and diffusion approaches to describe long-term sorption of chlortoluron in soil

This paper compares the capability of a first-order and a spherical diffusion model to describe and predict long-term sorption and desorption processes of chlortoluron in two soils. Chlortoluron sorption was investigated at different time scales utilizing one rate experiment (120 days) and two sorption/desorption experiments. Experimental periods for sorption and desorption were set to 1 day (five desorption steps) and 30 days (three desorption steps), respectively. Upon fitting, the two models satisfactorily described the whole set of data. The spherical diffusion model performed better than the first-order model. We then tested the predictive capability of the models by predicting 30-day sorption/desorption data using kinetic parameters fitted on 1-day sorption/desorption data only. While the spherical diffusion model was able to predict the 30-day data set, the first-order model failed completely. Fitting both models to subsets of the data corresponding to different experimental time scales revealed that the rate parameter as well as the Freundlich coefficient of the first-order model are strongly time-dependent--a property that is not shared by parameters of the spherical diffusion model. The apparent stability of the spherical diffusion model with regard to time dependency of its parameters indicates that sorptive uptake may be diffusion-controlled. This also explains the models greater predictive power across different time scales compared to the first-order model. Finally, we investigate the suitability of solute class specific log-linear relationships between the first-order rate parameter and the Freundlich coefficient presented by earlier researchers in the light of the time dependency observed for the parameters of the first-order model.     

Development of the Relationship between Odor Intensity and Threshold Dilution Ratio for Swine Units

ABSTRACT

The following models of odor intensity for swine units were evaluated: the Weber-Fechner law model, the power law model, the Stevens model, and the Beidler model. Data were collected from four swine rooms (farrowing, finisher, gestation, and nursery) and odor threshold dilution ratios were measured by a panel using a dynamic forced-choice olfactometer. Odor intensity scales were determined by eight panelists using a six-point category scale method. A nonlinear parameter estimation method was used to estimate the parameters in each of the models. The widely used Weber-Fechner law did not adequately fit the data of odor intensity and threshold. Both the power law and the Beidler models described the data effectively, but the Beidler model showed the best fit of the data and was used as the model to represent the relationship between odor intensity and threshold dilution ratio for swine buildings.     

Model estimation and measurement of ammonia emission from naturally ventilated dairy cattle buildings with slatted floor designs

Laboratory experiments were carried out in a wind tunnel with a model of a slurry pit to investigate the characteristics of ammonia emission from dairy cattle buildings with slatted floor designs. Ammonia emission at different temperatures and air velocities over the floor surface above the slurry pit was measured with uniform feces spreading and urine sprinkling on the surface daily. The data were used to improve a model for estimation of ammonia emission from dairy cattle buildings. Estimates from the updated emission model were compared with measured data from five naturally ventilated dairy cattle buildings. The overall measured ammonia emission rates were in the range of 11-88 g per cow per day at air temperatures of 2.3-22.4 degrees C. Ammonia emission rates estimated by the model were in the range of 19-107 g per cow per day for the surveyed buildings. The average ammonia emission estimated by the model was 11% higher than the mean measured value. The results show that predicted emission patterns generally agree with the measured one, but the prediction has less variation. The model performance may be improved if the influence of animal activity and management strategy on ammonia emission could be estimated and more reliable data of air velocities of the buildings could be obtained.     

Evaluation of total volatile organic compound emissions from adhesives based on chamber tests

In 1997, Homeswest in western Australia and Murdoch University developed a project to construct low-allergen houses (LAHs) in a newly developed suburb. Before the construction of LAHs, all potential volatile organic compound (VOC) emission materials used in LAHs are required to be measured to ensure that they are low total VOC (TVOC) emission materials. This program was developed based on this purpose. In recent times, the number of complaints about indoor air pollution caused by VOCs has increased. A number of surveys of indoor VOCs have indicated that many indoor materials contribute to indoor air pollution. Although some studies have been conducted on the characteristics of VOC emissions from adhesives, most of them were focused on VOC emissions from floor adhesives. Few measurements of VOC emissions from adhesives used for wood, fabrics, and leather are available. Furthermore, most research on VOC emissions from adhesives has been done in countries with cool climates, where ventilation rates in the indoor environment are lower than those in Mediterranean climates, due to energy conservation. VOCs emitted from adhesives have not been sufficiently researched to prepare an emission inventory to predict indoor air quality and to determine both exposure levels for the Australian population and the most appropriate strategies to reduce exposure. An environmental test chamber with controlled temperature, relative humidity, and airflow rate was used to evaluate emissions of TVOCs from three adhesives used frequently in Australia. The quantity of TVOC emissions was measured by a gas chromatography/flame ionization detector. The primary VOCs emitted from each adhesive were detected by gas chromatography/mass spectrometry. The temporal change of TVOC concentrations emitted from each adhesive was tested. A double-exponential equation was then developed to evaluate the characteristics of TVOC emissions from these three adhesives. With this double-exponential model, the physical processes of TVOC emissions can be explained, and a variety of emission parameters can be calculated. These emission parameters could be used to estimate real indoor TVOC concentrations in Mediterranean climates.     

Biosorption of Pb(II) by biomass of KC-2: kinetic, equilibrium and characteristic studies

Performance and characteristics of biosorption of Pb(II) had been studied in a batch system using the fungal strain biomass, KC-2. The biosorption performance was investigated by analysing the effects of such factors as the initial pH, initial Pb(II) concentration, and contact time at 303 K. The maximum Pb(II) adsorption was obtained at pH 5.0. The experimental data were described by the pseudo first-order, pseudo second-order and intraparticle diffusion kinetic models, and were closely followed the pseudo second-order kinetic model. The equilibrium experimental data were well fitted to Langmuir model and the maximum biosorption capacity was 84.03 mg g(-1). The adsorption mechanism was examined by FTIR, SEM and EDAX analysis. Results indicated that carboxylic, hydroxyl and amine groups were involved in the biosorption and ion exchange mechanism existed.     

Validation of Mobile Source Emission Estimates Using Mass Balance Techniques

Air quality models must consider error in the emissions estimates as well as in the model itself. Currently, the accepted method for determining emission factors for mobile sources is through the use of the EPA model MOBILE₃. In this paper, emission factor calculations from a technique based on the conservation of mass are presented from experimental data. These emission factors are then compared to MOBILE₃ simulations using the experimental conditions and two different methods of obtaining the vehicle scenario. In both cases, the mass balance gave emission factors that were from 1.5 to 2.2 times those predicted by MOBILE₃. Next, mass balance and MOBILE₃ emission factors were used in the dispersion models CALINE3 and TXLINE in an effort to predict the observed CO concentrations. In both models, enhanced performance was observed when mass balance data were implemented.     

Approaches for quantifying reactive and low-volatility biogenic organic compound emissions by vegetation enclosure techniques - part B: applications

The focus of the studies presented in the preceding companion paper (Part A: Review) and here (Part B: Applications) is on defining representative emission rates from vegetation for determining the roles of biogenic volatile organic compound (BVOC) emissions in atmospheric chemistry and aerosol processes. The review of previously published procedures for identifying and quantifying BVOC emissions has revealed a wide variety of experimental methods used by various researchers. Experimental details become increasingly critical for quantitative emission measurements of low volatility monoterpenes (MT) and sesquiterpenes (SQT). These compounds are prone to be lost inadvertently by uptake to materials in contact with the sample air or by reactions with atmospheric oxidants. These losses become more prominent with higher molecular weight compounds, potentially leading to an underestimation of their emission rates. We present MT and SQT emission rate data from numerous experiments that include 23 deciduous tree species, 14 coniferous tree species, 8 crops, and 2 shrubs. These data indicate total, normalized (30 degrees C) basal emission rates from <10 to 5600ngCg(-1)h(-1) for MT, and from <10 to 1150ngCg(-1)h(-1) for SQT compounds. Both MT and SQT emissions have exponential dependencies on temperature (i.e. rates are proportional to e(betaT)). The inter-quartile range of beta-values for MT was between 0.12 and 0.17K(-1), which is higher than the value commonly used in models (0.09K(-1)). However many of the MT emissions also exhibited light dependencies, making it difficult to separate light and temperature influences. The primary light-dependent MT was ocimene, whose emissions were up to a factor of 10 higher than light-independent MT emissions. The inner-quartile range of beta-values for SQT was between 0.15 and 0.21K(-1).     

Model for estimation of traffic pollutant levels in northern communities

Using models to estimate the contribution of traffic to air pollution levels from known traffic data typically requires the knowledge of model parameters such as emission factors and meteorological conditions. This paper presents a state-space model analysis method that does not require the knowledge of model parameters; these parameters are identified from measured traffic and ambient air quality data. This method was used to analyze carbon monoxide (CO) in downtown Fairbanks, AK, which is the community of focus for this paper. It was found that traffic contributed, on average, 53% to the total CO levels over the last six winters. The correlation coefficient between the measured and model-predicted daily profiles of the CO concentration was 0.98, and the results were in good agreement with earlier findings obtained via a thorough CO emission inventory. This justified the usability of the method and it was further used to analyze fine particulate matter (PM2.5) in downtown Fairbanks. It was found that traffic contributed, on average, approximately 30% to the total PM2.5 levels over the last six winters. The correlation coefficient between the measured and model-predicted daily profiles of the PM2.5 concentration was 0.98.     

A study on dynamic volatile organic compound emission characterization of water-based paints

Volatile organic compounds (VOCs) emitted from surface coatings have caused growing public concern for air quality. Even the low-emitted VOC impact from water-based paints on indoor air quality in urban areas has caused concern. This paper presents experimental data using a mathematical model to simulate dynamic VOC emissions from water-based paints that is based on mass transfer and molecular diffusion theories. A series of field-analogous experiments were carried out to continuously measure the VOCs emitted from two typical water-based paints using a gas chromatography-flame-ionization detector monitor in an artificial wind tunnel system. In the study cases, the mass flux of VOCs emitted from the water-based paints was up to 50 microg/m2sec. It was found that the time needed to completely emit VOCs from water-based paints is just hundreds of seconds. However, the order of magnitude of the VOC emission rate from water-based paints is not lower than that from some dry building materials and solvent-based paints. The experimental data were used to produce a useful semiempirical correlation to estimate the VOC emission rates for water-based paints. This correlation is valid under appropriate conditions as suggested by this work with a statistical deviation of +/- 7.6%. With this correlation, it seems feasible to predict the dynamic emission rates for VOCs during a painting process. This correlation is applicable for assessing the hazardous air pollutant impact on indoor air quality or for environmental risk assessment. Associated with the dynamic VOC emission characterization, the air-exchange rate effect on the VOC emission rates is also discussed.     

Estimating Emissions from Air Concentration Measurements

A one-year-long experiment in which two different tracers were simultaneously released from two different locations was used to test various hybrid receptor modeling techniques to estimate the tracer emissions using the measured air concentrations and a meteorological model. Air concentrations were measured over an 8-hour averaging time at three sites 14 to 40 km downwind. When the model was used to estimate emissions at only one tracer source, 6 percent of the short-term (8-h) emission estimates were within a factor of 2 of the actual emissions. Temporal averaging of the 8-h data enhanced the precision of the estimate such that after 10 days 42 percent of the estimates were within a factor of 2 and after six months all of them (each source-receptor pair) were within a factor of 2. To test the ability of the model to separate two sources, both tracer sources were combined, and a multiple linear regression technique was used to determine the emissions from each source from a time series of air concentration measurements representing the sum of both tracers. In general, 50 percent of the short-term estimates were within a factor of 10, 25 percent were biased low, and in another 25 percent the regression technique failed. The bias and failures are attributed to low or no correlation between measured air concentrations and model calculated dispersion factors. In the regression method increased temporal averaging did not consistently improve the emission estimate since the ability of the model to distinguish emissions between sources was diminished with increased averaging time. However, including progressively longer time periods (more data) into the regression or spatially averaging the data over all the receptors was found to be the most effective method to improve the estimated emissions. At best about 75 percent of the estimated monthly emission data were within a factor of 10 of the measured values. This suggests that the usefulness of meteorological models and statistical methods to address questions of source attribution requires many data points to reduce the uncertainty in the emission estimates.     

Correlating emissions with time and temperature to predict worst-case emissions from open liquid area sources

The two primary factors influencing ambient air pollutant concentrations are emission rate and dispersion rate. Gaussian dispersion modeling studies for odors, and often other air pollutants, vary dispersion rates using hourly meteorological data. However, emission rates are typically held constant, based on one measured value. Using constant emission rates can be especially inaccurate for open liquid area sources, like wastewater treatment plant units, which have greater emissions during warmer weather, when volatilization and biological activity increase. If emission rates for a wastewater odor study are measured on a cooler day and input directly into a dispersion model as constant values, odor impact will likely be underestimated. Unfortunately, because of project schedules, not all emissions sampling from open liquid area sources can be conducted under worst-case summertime conditions. To address this problem, this paper presents a method of varying emission rates based on temperature and time of the day to predict worst-case emissions. Emissions are varied as a linear function of temperature, according to Henry's law, and a tenth order polynomial function of time. Equation coefficients are developed for a specific area source using concentration and temperature measurements, captured over a multiday period using a data-logging monitor. As a test case, time/temperature concentration correlation coefficients were estimated from field measurements of hydrogen sulfide (H2S) at the Rowlett Creek Wastewater Treatment Plant in Garland, TX. The correlations were then used to scale a flux chamber emission rate measurement according to hourly readings of time and temperature, to create an hourly emission rate file for input to the dispersion model ISCST3. ISCST3 was then used to predict hourly atmospheric concentrations of H2S. With emission rates varying hourly, ISCST3 predicted 384 acres of odor impact, compared with 103 acres for constant emissions. Because field sampling had been conducted on relatively cool days (85-90 degrees F), the constant emission rate underestimated odor impact significantly (by 73%).     

Environmental tobacco smoke particles in multizone indoor environments

Environmental tobacco smoke (ETS) is a major source of human exposure to airborne particles. To better understand the factors that affect exposure, and to investigate the potential effectiveness of technical control measures, a series of experiments was conducted in a two-room test facility. Particle concentrations, size distributions, and airflow rates were measured during and after combustion of a cigarette. Experiments were varied to obtain information about the effects on exposure of smoker segregation, ventilation modification, and air filtration. The experimental data were used to test the performance of an analytical model of the two-zone environment and a numerical multizone aerosol dynamics model. A respiratory tract particle deposition model was also applied to the results to estimate the mass of ETS particles that would be deposited in the lungs of a nonsmoker exposed in either the smoking or nonsmoking room. Comparisons between the experimental data and model predictions showed good agreement. For time-averaged particle mass concentration, the average bias between model and experiments was less than 10%. The average absolute error was typically 35%, probably because of variability in particle emission rates from cigarettes. For the conditions tested, the use of a portable air filtration unit yielded 65–90% reductions in predicted lung deposition relative to the baseline scenario. The use of exhaust ventilation in the smoking room reduced predicted lung deposition in the nonsmoking room by more than 80%, as did segregating the smoker from nonsmokers with a closed door.     

Quantification of emission factor uncertainty

Emissions factors are important for estimating and characterizing emissions from sources of air pollution. There is no quantitative indication of uncertainty for these emission factors, most factors do not have an adequate data set to compute uncertainty, and it is very difficult to locate the data for those that do. The objectives are to compare the current emission factors of Electric Generating Unit NOx sources with currently available continuous emission monitoring data, develop quantitative uncertainty indicators for the Environmental Protection Agency (EPA) data quality rated emission factors, and determine the possible ranges of uncertainty associated with EPA's data quality rating of emission factors. EPA's data letter rating represents a general indication of the robustness of the emission factor and is assigned based on the estimated reliability of the tests used to develop the factor and on the quantity and representativeness of the data. Different sources and pollutants that have the same robustness in the measured emission factor and in the representativeness of the measured values are assumed to have a similar quantifiable uncertainty. For the purposes of comparison, we assume that the emission factor estimates from source categories with the same letter rating have enough robustness and consistency that we can quantify the uncertainty of these common emission factors based on the qualitative indication of data quality which is known for almost all factors. The results showed that EPA's current emission factor values for NOx emissions from combustion sources were found to be reasonably representative for some sources; however AP-42 values should be updated for over half of the sources to reflect current data. The quantified uncertainty ranges were found to be 25-62% for A rated emission factors, 45-75% for B rated emission factors, 60-82% for C rated emission factors, and 69-86% for D rated emission factors, and 82-92% for E rated emission factors.     

Use of Tracer Gas for Direct Calibration of Emission-Factor Measurements in a Traffic Tunnel

ABSTRACT

Pollutant measurements in traffic tunnels have been used to estimate motor-vehicle emissions for several decades. The objective in this type of study is to use the traffic tunnel as a tool for characterizing motor vehicles rather than seeking a tunnel design with acceptably low pollutant concentrations. In the past, very simple aerodynamic models have been used to relate measured concentrations to vehicle emissions. Typically, it is assumed that velocities and concentrations are uniform across the tunnel cross section. In the present work, a vehicle emitting a known amount of sulfur hexafluoride (SF₆) was driven repeatedly through a 730-m-long traffic tunnel in Vancouver, Canada. Comparing the measured SF₆ concentrations to the known emission rates, it is possible to directly assess the accuracy of the simple tunnel aerodynamic models typically used to interpret tunnel data. Correction factors derived from this procedure were then applied to measurements of carbon monoxide and other pollutants to obtain gram-per-kilometer emission factors for vehicles. Although the specific correction factors measured here are valid only for the tunnel tested, the magnitude of the factors (up to two or more) suggests that the phenomena observed here should be considered when interpreting data from other tunnels.     

Evaluation of pollution prevention options to reduce styrene emissions from fiber-reinforced plastic open molding processes

Pollution prevention (P2) options to reduce styrene emissions, such as new materials and application equipment, are commercially available to the operators of open molding processes. However, information is lacking on the emissions reduction that these options can achieve. To meet this need, the U.S. Environmental Protection Agency's (EPA) Air Pollution Prevention and Control Division, working in collaboration with Research Triangle Institute, measured styrene emissions for several of these P2 options. In addition, the emission factors calculated from these test results were compared with the existing EPA emission factors for gel coat sprayup and resin applications. Results show that styrene emissions can be reduced by up to 52% by using controlled spraying (i.e., reducing overspray), low-styrene and styrene-suppressed materials, and nonatomizing application equipment. Also, calculated emission factors were 1.6-2.5 times greater than the mid-range EPA emission factors for the corresponding gel coat and resin application. These results indicate that facilities using existing EPA emission factors to estimate emissions in open molding processes are likely to underestimate actual emissions. Facilities should investigate the applicability and feasibility of these P2 options to reduce their styrene emissions.     

Power models and average ship parameter effects on marine emissions inventories

Maritime greenhouse gas emissions are projected to increase significantly by 2050, highlighting the need for reliable inventories as a first step in analyzing ship emission control policies. The impact of ship power models on marine emissions inventories has garnered little attention, with most inventories employing simple, load-factor-based models to estimate ship power consumption. The availability of more expansive ship activity data provides the opportunity to investigate the inventory impacts of adopting complex power models. Furthermore, ship parameter fields can be sparsely populated in ship registries, making gap-filling techniques and averaging processes necessary. Therefore, it is important to understand of the impact of averaged ship parameters on ship power and emission estimations. This paper examines power estimation differences between results from two complex, resistance-based and two simple, load-factor-based power models on a baseline inventory with unique ship parameters. These models are additionally analyzed according to their sensitivities toward average ship parameters. Automated Identification System (AIS) data from a fleet of commercial marine vessels operating over a 6-month period off the coast of the southwestern United States form the basis of the analysis. To assess the inventory impacts of using averaged ship parameters, fleet-level carbon dioxide (CO₂) emissions are calculated using ship parameter data averaged across ship types and their subtype size classes. Each of the four ship power models are used to generate four CO₂ emissions inventories, and results are compared with baseline estimates for the same sample fleet where no averaged values were used. The results suggest that a change in power model has a relatively high impact on emission estimates. They also indicate relatively little sensitivity, by all power models, to the use of ship characteristics averaged by ship and subtype.

Implications: Commercial marine vessel emissions inventories were calculated using four different models for ship engine power. The calculations used 6 months of Automated Identification System (AIS) data from a sample of 248 vessels as input data. The results show that more detailed, resistance-based models tend to estimate a lower propulsive power, and thus lower emissions, for ships than traditional load-factor-based models. Additionally, it was observed that emission calculations using averaged values for physical ship parameters had a minimal impact on the resulting emissions inventories.     

Dependence of the Wind Profile Power Law on Stability for Various Locations

Recent environmental regulations have increased the need for construction of meteorological towers at power generation facilities. Due to practical and economic considerations, tower heights are usually lower than effluent release heights. At heights where wind speed data are not available, the wind speed is usually estimated from the measured wind speed using the %th wind profile power law and assuming neutral stability conditions. This study examines published data for many locations and shows that the %th wind profile power law is often unrepresentative of actual conditions because the degree of variation of wind speed with height depends greatly on atmospheric stability. The frequency of neutral stability conditions also varies appreciably by site. These two considerations are especially important in dispersion models which extrapolate wind speed at stack height from low level wind speed data.     

Modelling the dissipation kinetics of six commonly used pesticides in two contrasting soils of New Zealand

We used three non-linear bi-phasic models, bi-exponential (BEXP), first-order double exponential decay (FODED), and first-order two-compartment (FOTC), to fit the measured degradation data for six commonly used pesticides (atrazine, terbuthylazine, bromacil, diazinon, hexazinone and procymidone) in two New Zealand soils. Corresponding DT₅₀ and DT₉₀ values for each compound were numerically obtained and compared against those estimated by simple first-order kinetic (SFOK) model. All 3 non-linear models gave good fit of the measured data under both soil depths and were well supported by the values obtained for the respective statistical indices (RMSE, CRM and r ²). The FOTC model gave by far the best fit for most compounds, followed by the FODED and BEXP models. Overall, DT₅₀ values derived by non-linear models for the six compounds in soils from both sites were lower than the values obtained by the SFOK model. Differences in the SFOK and the three non-linear models derived DT₉₀were, however, an order of magnitude higher for some compounds, while for others differences were very small. Although all three non-linear models described most data by giving excellent fits, in a few instances > 5–10% asymptotes hindered the estimation of DT₉₀ values. This work shows that when degradation deviates from first-order kinetic, application of non-linear decay models to describe the kinetics of degradation becomes important in order to derive the true end-points for pesticides in soil.     

Development and preliminary evaluation of a particulate matter emission factor model for European motor vehicles

Although modeling of gaseous emissions from motor vehicles is now quite advanced, prediction of particulate emissions is still at an unsophisticated stage. Emission factors for gasoline vehicles are not reliably available, since gasoline vehicles are not included in the European Union (EU) emission test procedure. Regarding diesel vehicles, emission factors are available for different driving cycles but give little information about change of emissions with speed or engine load. We have developed size-specific speed-dependent emission factors for gasoline and diesel vehicles. Other vehicle-generated emission factors are also considered and the empirical equation for re-entrained road dust is modified to include humidity effects. A methodology is proposed to calculate modal (accelerating, cruising, or idling) emission factors. The emission factors cover particle size ranges up to 10 microns, either from published data or from user-defined size distributions. A particulate matter emission factor model (PMFAC), which incorporates virtually all the available information on particulate emissions for European motor vehicles, has been developed. PMFAC calculates the emission factors for five particle size ranges [i.e., total suspended particulates (TSP), PM10, PM5, PM2.5, and PM1] from both vehicle exhaust and nonexhaust emissions, such as tire wear, brake wear, and re-entrained road dust. The model can be used for an unlimited number of roads and lanes, and to calculate emission factors near an intersection in user-defined elements of the lane. PMFAC can be used for a variety of fleet structures. Hot emission factors at the user-defined speed can be calculated for individual vehicles, along with relative cold-to-hot emission factors. The model accounts for the proportions of distance driven with cold engines as a function of ambient temperature and road type (i.e., urban, rural, or motorway). A preliminary evaluation of PMFAC with an available dispersion model to predict the airborne concentration in the urban environment is presented. The trial was on the A6 trunk road where it passes through Loughborough, a medium-size town in the English East Midlands. This evaluation for TSP and PM10 was carried out for a range of traffic fleet compositions, speeds, and meteorological conditions. Given the limited basis of the evaluation, encouraging agreement was shown between predicted and measured concentrations.