Emission Factor Model Research Articles

In-use measurement of the activity, fuel use, and emissions of front-loader refuse trucks

Field measurements were made for six front-loader refuse trucks for over 560 miles (901 km) and 47 h of operation using a portable emissions measurement system, electronic control unit data logger, and global positioning system receivers. Daily activity, fuel use rates, and emission rates are quantified in terms of operating mode bins defined by the U.S. Environmental Protection Agency for the MOVES emission factor model. On average, 44 (±4) percent of time was spent at idle, 5 (±1) percent braking or decelerating, 11 (±2) percent coasting, 23 (±3) percent cruising or accelerating at low speed (up to 25 mph, 40.2 kmph), 10 (±2) percent cruising or accelerating at moderate speed (25–50 mph, 40.2 to 80.4 kmph), and 7 (±3) percent cruising or accelerating at high speed (50 mph, 80.4 kmph or higher). Fuel use and emission rates varied among operating modes by factors of 6–24. The estimated daily activity cycle average fuel economy ranges from 2.3 to 3.2 mpg (0.98–1.4 kmpl). The PM emission rates for trucks with diesel particulate filters are 98 percent lower compared to those without. Variation in truck weight lead to differences in average fuel use and emission rates of 20 percent or less, except for hydrocarbons. The variation in the empirically-based daily activity cycle average rates were highly correlated with MOVES estimates, except for hydrocarbons. The data collected here are useful for quantifying daily activity specific to front-loaders, and for developing fuel use and emission estimates and models for this type of vehicle.

Estimation of PM10 in the traffic-related atmosphere for three road types in Beijing and Guangzhou, China

The levels of roadside PM10 in Beijing, China, were investigated in 2011 and 2012 on a seasonal basis to estimate the population exposure to particulates for three road types. The measurements of PM10 were also conducted in the southern Chinese megacity of Guangzhou for comparison purposes. The results showed that roadside PM10 in Beijing correlated strongly with the PM10 background in the urban atmosphere. The levels of PM10 in street canyons were markedly higher than those along the open roads and in crossroad areas because of limited ventilation. An elevation of PM10 was observed in April, which was possibly due to the sand storms that frequently occur in the spring. Based on these observations, roadside PM10 in Beijing could have multiple origins and was to some extent dispersion-governed. In Guangzhou, the roadside PM10 did not closely relate to the background values. The PM10 pollution was greatly affected by local traffic conditions. The simulation of PM10 for different road types was completed during the study period using the Motor Vehicle Emissions Factor Model (MOBILE6.2) as an emission model and the California Line Source Dispersion Model (CALINE4) and Operational Street Pollution Model (OSPM) as dispersion models. The MOBILE6.2/CALINE4 software package was demonstrated to be sufficient for the simulation of PM10 in the open roads and crossroad areas in both Beijing and Guangzhou, and the simulation results of roadside PM10 in the street canyons by the MOBILE6.2/OSPM package were in close agreement with those of the measurements.

Historic and future trends of vehicle emissions in Beijing, 1998–2020: A policy assessment for the most stringent vehicle emission control program in China

As a pioneer in controlling vehicle emissions within China, Beijing released the Clean Air Action Plan 2013–2017 document in August 2013 to improve its urban air quality. It has put forward this plan containing the most stringent emission control policies and strategies to be adopted for on-road vehicles of Beijing. This paper estimates the historic and future trends and uncertainties in vehicle emissions of Beijing from 1998 to 2020 by applying a new emission factor model for the Beijing vehicle fleet (EMBEV). Our updated results show that total emissions of CO, THC, NOx and PM2.5 from the Beijing vehicle fleet are 507 (395–819) kt, 59.1 (41.2–90.5) kt, 74.7 (54.9–103.9) kt and 2.69 (1.91–4.17) kt, respectively, at a 95% confidence level. This represents significant reductions of 58%, 59%, 31% and 62%, respectively, relative to the total vehicle emissions in 1998. The past trends clearly posed a challenge to NOx emission mitigation for the Beijing vehicle fleet, especially in light of those increasing NOx emissions from heavy-duty diesel vehicles (HDDVs) which have partly offset the reduction benefit from light-duty gasoline vehicles (LDGVs). Because of recently announced vehicle emission controls to be adopted in Beijing, including tighter emissions standards, limitations on vehicle growth by more stringent license control, promotion of alternative fuel technologies (e.g., natural gas) and the scrappage of older vehicles, estimated vehicle emissions in Beijing will continue to be mitigated by 74% of CO, 68% of THC, 56% of NOx and 72% of PM2.5 in 2020 compared to 2010 levels. Considering that many of the megacities in China are facing tremendous pressures to mitigate emissions from on-road vehicles, our assessment will provide a timely case study of significance for policy-makers in China.

WATERBUS: A model to estimate boats’ emissions in “water cities”

An emissions factor model (WATERBUS) is developed to estimate boats emissions in Venice, Italy. Waterbuses and small private boats undertake most of the internal transport with the city and provide connections with other islands in the Venice lagoon and the mainland. Even though the engines of these boats are smaller, sailing conditions do not correspond to those of larger vessels. Consequently, emission factors typically associated with large boats are not readily transferrable. We thus estimate specific emissions for small boats and waterbus in the lagoon area. The WATERBUS model is used to assess emission changes that would occur if the fleet were fitted with electronic engines.

Spatial distribution of black carbon emissions in China

Based on the official statistics, locally measured emission factors, and the vehicular emission factor model most suitable for China, we developed a black carbon (BC) emission inventory for 2008 in China and at a spatial resolution of 0.5°×0.5°. In 2008, the total BC emissions in China were 1604.94 Gg. Industry and the residential sector were the dominant contributors, estimated at 695.03 Gg and 636.02 Gg of BC, respectively. Together, these two source types contributed 82.9% of the total emissions. Emissions from transportation were 194.63 Gg, accounting for 12.1% of the total. Since emission contributions from different sectors showed significant spatial diversity among the 31 administrative districts, we divided the districts into four categories: industry contribution district, residential contribution district, industry and residential contribution district, and transportation contribution district. As for energy consumption, coal and biofuel contributed 51.0% and 32.2%, respectively, of the total emissions. Spatially, BC emissions in China were unevenly distributed, higher in the east and lower in the west, corresponding to regional economic development and rural population density. High emission districts, covering 5.7% of the territory, contributed 41.2% of the total emissions. Shanxi, Hebei, Shandong, Henan, and Sichuan were the largest contributors to national BC emissions.

Air pollution and health risks due to vehicle traffic

Traffic congestion increases vehicle emissions and degrades ambient air quality, and recent studies have shown excess morbidity and mortality for drivers, commuters and individuals living near major roadways. Presently, our understanding of the air pollution impacts from congestion on roads is very limited. This study demonstrates an approach to characterize risks of traffic for on- and near-road populations. Simulation modeling was used to estimate on- and near-road NO2 concentrations and health risks for freeway and arterial scenarios attributable to traffic for different traffic volumes during rush hour periods. The modeling used emission factors from two different models (Comprehensive Modal Emissions Model and Motor Vehicle Emissions Factor Model version 6.2), an empirical traffic speed–volume relationship, the California Line Source Dispersion Model, an empirical NO2–NOx relationship, estimated travel time changes during congestion, and concentration–response relationships from the literature, which give emergency doctor visits, hospital admissions and mortality attributed to NO2 exposure. An incremental analysis, which expresses the change in health risks for small increases in traffic volume, showed non-linear effects. For a freeway, “U” shaped trends of incremental risks were predicted for on-road populations, and incremental risks are flat at low traffic volumes for near-road populations. For an arterial road, incremental risks increased sharply for both on- and near-road populations as traffic increased. These patterns result from changes in emission factors, the NO2–NOx relationship, the travel delay for the on-road population, and the extended duration of rush hour for the near-road population. This study suggests that health risks from congestion are potentially significant, and that additional traffic can significantly increase risks, depending on the type of road and other factors. Further, evaluations of risk associated with congestion must consider travel time, the duration of rush-hour, congestion-specific emission estimates, and uncertainties.

Estimation of Air Pollutant Emissions from Mobile Sources with Three Emission Factors Models

Vehicle emissions from mobile sources are major contributors to air pollution and varied with vehicle types, vehicle styles, traveled miles, temperature, oil types and the methods of operation and management. This study performs three emission factor models, Mobile-Taiwan 2, Mobile6.2 and EFDB to calculate emission factor of mobile sources from year 1986 to 2011. The emissions of primary air pollutants, MIRs and CO2emitted from mobile sources were calculated. The contribution ratios of varied vehicle types for different air pollutants would be compared and analyzed. Estimated emissions from mobile sources were 32.2, 177, 643, 197 and 401 kilotons/y for PM10, NOx, CO, THC and MIR for 2000; 31.3, 115, 305, 114 and 227 kilotons/y for 2011. Emissions of traditional air pollutants presented a decreasing trend because of fourth-stage emission standards for mobiles sources and CO2 revealed an increasing trend. According to presented control technology for greenhouse gases on mobile sources, ratio of emission for year 2011 to 2000 would be 1.38-1.49.

Influence of Mercury and Chlorine Content of Coal on Mercury Emissions from Coal-Fired Power Plants in China

China is the largest mercury emitter in the world and coal combustion is the most important mercury source in China. This paper updates the coal quality database of China and evaluates the mercury removal efficiency of air pollution control devices (APCDs) based on 112 on-site measurements. A submodel was developed to address the relationship of mercury emission factor to the chlorine content of coal. The mercury emissions from coal-fired power plants (CFPPs) in China were estimated using deterministic mercury emission factor model, nonchlorine-based and chlorine-based probabilistic emission factor models, respectively. The national mercury emission from CFPPs in 2008 was calculated to be 113.3 t using the deterministic model. The nonchlorine-based probabilistic emission factor model, which addresses the log-normal distribution of the mercury content of coal, estimates that the mercury emission from CFPPs is 96.5 t (P50), with a confidence interval of 57.3 t (P10) to 183.0 t (P90). The best estimate by the chlorine-based probabilistic emission factor model is 102.5 t, with a confidence interval of 71.7 to 162.1 t. The chlorine-based model addresses the influence of chlorine and reduces the uncertainties of mercury emission estimates.

Mitigation Potential of Mercury Emissions from Coal-Fired Power Plants in China

With the rapid increase of coal consumption in China, the mercury emissions from coal-fired power plants have drawn global attention. In this study, a literature review on the mercury content of coal in China and the mercury removal efficiencies of particulate matter, SO2, and NOx control devices was conducted thoroughly. A probabilistic emission factor model was established to develop the mercury emission inventory for coal-fired power plants in China. The best estimate for total mercury emissions from coal-fired power plants in China was 96.5 tons (P50) in 2008, with the confidence interval from 57.3 tons (P10) to 183.0 tons (P90). The synergetic mercury removal benefit from the SO2 control measures during 2005–2008 was 33.9 tons. Two energy scenarios and three pollution control scenarios were developed to forecast the future trend of mercury emissions in China. The change of the energy structure and energy saving will play an important role in the mercury emission reduction in the next 2 decades. Under the current energy consumption pattern and air pollution control policies, the mercury emissions would increase to 196 tons in 2020. The installation of selective catalytic reduction (SCR) will result in 75 tons of mercury emission reduction during 2008–2020. Under the current energy consumption pattern and extended emission controls, the mercury emission in 2030 is 47% lower than that in 2020, because of the widespread application of SCR and the application of fabric filter (FF) and mercury-specific control technologies. Further reduction can be contributed by the enhancement of mercury-specific control technologies. Through the implementation of energy policies with accelerated control technologies, the mercury emission in 2030 can be decreased by 71% from the level of 2008, which shows the significant mitigation potential of mercury emissions from coal-fired power plants in China in the future.

Fertilizer Management Effects on Nitrate Leaching and Indirect Nitrous Oxide Emissions in Irrigated Potato Production

Potato ( L.) is a N-intensive crop, with high potential for nitrate (NO) leaching, which can contribute to both water contamination and indirect nitrous oxide (NO) emissions. Two approaches that have been considered for reducing N losses include conventional split application (CSA) of soluble fertilizers and single application of polymer-coated urea (PCU). The objectives of this study were to: (i) compare NO leaching using CSA and two PCUs (PCU-1 and PCU-2), which differed in their polymer formulations, and (ii) use measured NO leaching rates and published emissions factors to estimate indirect NO emissions. Averaged over three growing seasons (2007-2009), NO leaching rates were not significantly different among the three fertilizer treatments. Using previously reported direct NO emissions data from the same experiment, total direct plus indirect growing season NO emissions with PCU-1 were estimated to be 30 to 40% less than with CSA. However, PCU-1 also resulted in greater residual soil N after harvest in 2007 and greater soil-water NO in the spring following the 2008 growing season. These results provide evidence that single PCU applications for irrigated potato production do not increase growing season NO leaching compared with multiple split applications of soluble fertilizers, but have the potential to increase N losses after the growing season and into the following year. Estimates of indirect NO emissions ranged from 0.8 to 64% of direct emissions, depending on what value was assumed for the emission factor describing off-site conversion of NO to NO. Thus, our results also demonstrate how more robust models are needed to account for off-site conversion of NO to NO, since current emission factor models have an enormous degree of uncertainty.

Effect of Soil Type and Momentum on Unpaved Road Particulate Matter Emissions from Wheeled and Tracked Vehicles

Excluding windblown dust, unpaved road dust PM 10 emissions in the US EPA's 2002 National Emission Inventory account for more than half of all PM 10 emissions in the arid states of the western U.S. (i.e., CA, AZ, NV, NM, and TX). Despite the large size of the source, substantial uncertainty is associated with both the vehicle activity (i.e., number of kilometers traveled at a particular speed) and the emission factors (i.e., grams of PM 10 per kilometer traveled). In this study, emission factors were measured using the flux tower method for both tracked and wheeled military vehicles at three military bases in the Western U.S. Test vehicle weights ranged from 2400 kg to 60,000 kg. Results from both previously published and unpublished field studies are combined to link emission factors to three related variables: soil type, vehicle momentum, and tred type (i.e., tire or track). Current emission factor models in US EPA's AP-42 Emission Factor Compendium do not factor both speed and weight into unpaved road ...

Estimating Diesel Vehicle Emission Factors at Constant and High Speeds for Short Road Segments

Vehicle emission estimates with high spatial and temporal resolution are needed to estimate near-roadway air quality and human exposure to emissions. The U.S. Environmental Protection Agency's (EPA) MOBILE6 emission factor model is based on test cycles with transient speeds and durations on the order of 10 min. MOBILE6 does not report emission factors for average speeds higher than 65 mph. However, for near-roadway studies, emission factors are needed for short highway segments that represent only a few seconds of vehicle travel time at approximately constant speed and speeds greater than 65 mph. Constant-speed and high-speed correction factors for nitrogen oxides, hydrocarbons, carbon dioxide (CO2), and carbon monoxide were developed on the basis of 59,286 s of EPA dynamometer emissions data for diesel vehicles from which speed–acceleration modal models were derived. The constant speed correction factor is the ratio of the emission factor at constant speed to the emissions factor during transient cycles with an equivalent average speed. For example, the constant speed correction factor for CO2 increases from 0.71 to 1 as speed increases from 31 to 78 mph. High-speed correction factors are based on the ratio of emission factors at a constant speed greater than 65 mph to emission factors at a constant speed of 65 mph. For example, at 80 mph, the high-speed correction factor is approximately 1.5 for CO2. Sensitivity analysis of emission estimates to these correction factors was conducted for speeds of 40 to 80 mph.

Light duty gasoline vehicle emission factors at high transient and constant speeds for short road segments

Vehicle emissions estimates are needed at high spatial and temporal resolution to estimate near-roadway air quality and human exposures. The MOBILE6 emission factor model is based on transient test cycles of less than 65 mph. Correction factors for high speed and constant speed are developed based on vehicle-specific power-based modal models for light duty gasoline vehicles, using data from portable emission measurement systems. At 80 mph versus 65 mph, the estimated average emission rates are greater by 30%, 20%, 80%, and 10% for NO x , HC, CO, and CO 2. The ratio of constant to average of transient speed emission rates range from 0.49 to 0.94 for NO x at speeds of 20 mph and 80 mph. The high speed and constant speed correction factors are applied to estimate vehicle emissions for a freeway segment that includes vehicle cruising speeds between 65 and 80 mph. The potential error for not accounting for constant speed operation on a short segment of highway could be 49% at moderate speed and 24% at high speed.

Diurnal and Seasonal Variability of Gasoline-Related Volatile Organic Compound Emissions in Riverside, California

On- and off-road mobile sources are the dominant contributors to urban anthropogenic volatile organic compound (AVOC) emissions. Analyses of gasoline samples from California for both summer and winter indicate significant differences in liquid fuel and vapor chemical composition due to intentional seasonal adjustments. Ambient concentrations of 55 VOCs were measured via in situ gas chromatography in the 2005 Study of Organic Aerosols at Riverside (SOAR) during both summer and fall. A chemical mass balance analysis was used to differentiate vapor pressure-driven VOC emissions from other motor vehicle-related emissions such as tailpipe exhaust. Overall, fuel vapor emissions accounted for 31 +/- 2% of gasoline-related VOC in Riverside; California's emission factor model similarly estimates 31% of gasoline-related VOC emissions are fuel vapor. The diurnal pattern of vapor pressure-driven VOC source contributions is relatively stable around 10 microg/m3, while whole gasoline (i.e., tailpipe) contributions peak at approximately 60 microg/m3 during the morning commute. There is no peak in whole gasoline source contributions during the afternoon, due to rapid dilution associated with high mixing heights and wind speeds in the Riverside area. The relationship between estimated gasoline-related VOC and observed carbon monoxide concentrations in this study is similar to California's 2005 emission inventory; we calculated a VOC to CO mass ratio of 0.086 +/- 0.006 (95% CI) compared to 0.097 in the emission inventory for all gasoline-related sources.

Evaluation of total particulate matter emission factors for copper slag in dry abrasive blasting

Abrasive blasting was performed in enclosed conditions to evaluate the effect of blast pressure, feed rate and surface contamination on Total Particulate Matter (TPM) emission factors for copper slag. Stack sampling methods were used to collect uncontrolled TPM. Emission factors were calculated as grams of TPM emitted per pound of copper slag used (g/lb) and grams of TPM emitted per square foot of area cleaned (g/sq.ft). Emission factor models were developed to study variation of TPM emission factors with pressure, feed rate and surface contamination. These models can be used to reduce emissions by selecting optimum operating condition as well as to determine emission factors at any operating conditions, within the tested range, for copper slag.

Impact of Underestimating the Effects of Cold Temperature on Motor Vehicle Start Emissions of Air Toxics in the United States

Analyses of U.S. Environmental Protection Agency (EPA) certification data, California Air Resources Board surveil-lance testing data, and EPA research testing data indicated that EPA’s MOBILE6.2 emission factor model substantially underestimates emissions of gaseous air toxics occurring during vehicle starts at cold temperatures for light-duty vehicles and trucks meeting EPA Tier 1 and later standards. An unofficial version of the MOBILE6.2 model was created to account for these underestimates. When this unofficial version of the model was used to project emissions into the future, emissions increased by almost 100% by calendar year 2030, and estimated modeled ambient air toxics concentrations increased by 6–84%, depending on the pollutant. To address these elevated emissions, EPA recently finalized standards requiring reductions of emissions when engines start at cold temperatures.

Climate change impact on California on-road mobile source emissions

Continued climate change could have widespread impacts on California’s economy and ecosystems, and on the health of its citizens. Climate change can impact meteorology, emissions, and chemical processing and thereby influence air quality and its associated effects on public health and welfare. Some mobile source emission control technologies in the distant future are likely to be very different than current ones; it is not possible to make quantitative predictions of California’s mobile source emissions inventory for 2050 and 2100. However, investigating the response of the current on-road mobile source inventory to possible future temperatures can provide insights for the near future and suggest actions which are needed to protect California’s air quality. The results of this study indicate that for California surface temperatures predicted by the B1 and A2 IPCC-carbon dioxide (CO2) emissions scenarios, summer-time on-road total (exhaust plus evaporative) reactive organic gases (ROG) emissions from the 2005 motor vehicle fleet will increase by 4 to 5% using temperature projections for mid-century and by 13 to 16% for end-of-century temperature projections. Increases of as much as 30% are seen in evaporative emissions in 2100. California Air Resources Board’s (CARB) mobile source emission factor model (EMFAC) predicts decreases of similar magnitudes for oxides of nitrogen (NOX) emissions. The medium-high emissions scenario results in a positive feedback loop for greenhouse gas (GHG) emissions from on-road motor vehicles, with 4 to 5% increase in methane and 8 to 9% increases in CO2 by 2100. These emissions estimates are strictly a test of the sensitivity of the current California mobile sources emission inventory model to temperature increases, as they do not take into account future growth in mobile source activity, new control regulations, or possible vehicle or fuel technological advances.

Estimation and validation of PM 2.5/PM 10 exhaust and non-exhaust emission factors for practical street pollution modelling

In order to carry out efficient traffic and air quality management, validated models and PM emission estimates are needed. This paper compares current available emission factor estimates for PM 10 and PM 2.5 from emission databases and different emission models, and validates these against eight high quality street pollution measurements in Denmark, Sweden, Germany, Finland and Austria. The data sets show large variation of the PM concentration and emission factors with season and with location. Consistently at all roads the PM 10 and PM 2.5 emission factors are lower in the summer month than the rest of the year. For example, PM 10 emission factors are in average 5–45% lower during the month 6–10 compared to the annual average. The range of observed total emission factors (including non-exhaust emissions) for the different sites during summer conditions are 80–130 mg km −1 for PM 10, 30–60 mg km −1 for PM 2.5 and 20–50 mg km −1 for the exhaust emissions. We present two different strategies regarding modelling of PM emissions: (1) For Nordic conditions with strong seasonal variations due to studded tyres and the use of sand/salt as anti-skid treatment a time varying emission model is needed. An empirical model accounting for these Nordic conditions was previously developed in Sweden. (2) For other roads with a less pronounced seasonal variation (e.g. in Denmark, Germany, Austria) methods using a constant emission factor maybe appropriate. Two models are presented here. Further, we apply the different emission models to data sets outside the original countries. For example, we apply the “Swedish” model for two streets without studded tyre usage and the “German” model for Nordic data sets. The “Swedish” empirical model performs best for streets with studded tyre use, but was not able to improve the correlation versus measurements in comparison to using constant emission factors for the Danish side. The “German” method performed well for the streets without clear seasonal variation and reproduces the summer conditions for streets with pronounced seasonal variation. However, the seasonal variation of PM emission factors can be important even for countries not using studded tyres, e.g. in areas with cold weather and snow events using sand and de-icing materials. Here a constant emission factor probably will under-estimate the 90-percentiles and therefore a time varying emission model need to be used or developed for such areas. All emission factor models consistently indicate that a large part (about 50–85% depending on the location) of the total PM 10 emissions originates from non-exhaust emissions. This implies that reduction measures for the exhaust part of the vehicle emissions will only have a limited effect on ambient PM 10 levels.

Sensitivity Analysis and Evaluation of MicroFacPM: A Microscale Motor Vehicle Emission Factor Model for Particulate Matter Emissions

A microscale emission factor model (MicroFacPM) for predicting real-time site-specific motor vehicle particulate matter emissions was presented in the companion paper titled “Development of a Microscale Emission Factor Model for Particulate Matter (MicroFacPM) for Predicting Real-Time Motor Vehicle Emissions.” The emission rates discussed are in mass per unit distance with the model providing estimates of fine particulate matter (PM2.5) and coarse particulate matter. This paper complements the companion paper by presenting a sensitivity analysis of the model to input variables and evaluation model outputs using data from limited field studies. The sensitivity analysis has shown that MicroFacPM emission estimates are very sensitive to vehicle fleet composition, speed, and the percentage of high-emitting vehicles. The vehicle fleet composition can affect fleet emission rates from 8 mg/mi to 1215 mg/mi; an increase of 5% in the smoking (high-emitting) current average U.S. light-duty vehicle fleet (compared with 0%) increased PM2.5 emission rates by ∼272% for 2000; and for the current U.S. fleet, PM2.5 emission rates are reduced by a factor of ∼0.64 for speeds >50 miles per hour (mph) relative to a speed of 10 mph. MicroFacPM can also be applied to examine the contribution of emission rates per vehicle class, model year, and sources of PM. The model evaluation is presented for the Tuscarora Mountain Tunnel, Pennsylvania Turnpike, PA, and some limited evaluations at two locations: Sepulveda Tunnel, Los Angeles, CA, and Van Nuys Tunnel, Van Nuys, CA. In general, the performance of MicroFacPM has shown very encouraging results.

A high-resolution NO x emission factor model for North American motor vehicles

Criteria air contaminant inventories in Canada show that approximately 60% of NO x is produced from the transportation sector alone. NO 2, which is present at higher levels in the winter than in the summer, has been identified as the main pollutant responsible for non-traumatic deaths and hospitalization in Canadian cities. Therefore, accurate emission estimates of vehicle-generated NO x are needed, especially at the micro-scale level. The MOBILE emission model, which is widely used in Canada, provides only aggregated emission factors at very low resolution. Many studies have shown that MOBILE emission data are unreliable for many applications—particularly for air-quality modelling. In contrast to the aggregated nature of the MOBILE data, the new disaggregated model, MicroFacNO x , uses time-dependent site-specific vehicle fleet information to calculate emission factors at high spatial and temporal resolution. MicroFacNO x evaluation in a range of traffic fleet and meteorological conditions shows very encouraging results. Sensitivity analyses of the model reveal that its results depend on traffic fleet composition, speed and ambient temperature. MicroFacNO x results are suitable for use in human exposure assessment and by city planners in decision making for growth management. The effect of vehicle-related NO x on ozone in the vicinity of a large urban area is a good example of an unsolved problem that would benefit from the MicroFacNO x modelling approach.