Prediction of PM10 concentrations at urban traffic intersections using semi-empirical box modelling with instantaneous velocity and acceleration

At urban traffic intersections, vehicles frequently stop with idling engines during the red-light period and speed up rapidly during the green-light period. The changes of driving patterns (i.e., idle, acceleration, deceleration and cruising patterns) generally produce uncertain emission. Additionally, the movement of pedestrians and the influence of wind further result in the random dispersion of pollutants. It is, therefore, too complex to simulate the effects of such dynamics on the resulting emission using conventional deterministic causal models.For this reason, a modified semi-empirical box model for predicting the PM₁₀ concentrations on roadsides is proposed in this paper. The model constitutes three parts, i.e., traffic, emission and dispersion components. The traffic component is developed using a generalized force traffic model to obtain the instantaneous velocity and acceleration when vehicles move through intersections. Hence the distribution of vehicle emission in street canyon during the green-light period is calculated. Then the dispersion component is investigated using a semi-empirical box model combining average wind speed, box height and background concentrations. With these considerations, the proposed model is applied and evaluated using measured data at a busy traffic intersection in Mong Kok, Hong Kong. In order to test the performance of the model, two situations, i.e., the data sets within a sunny day and between two sunny days, were selected to examine the model performance. The predicted values are generally well coincident with the observed data during different time slots except several values are overestimated or underestimated. Moreover, two types of vehicles, i.e., buses and petrol cars, are separately taken into account in the study. Buses are verified to contribute most to the emission in street canyons, which may be useful in evaluating the impact of vehicle emissions on the ambient air quality when there is a significant change in a specific vehicular population.     

Effects of improved spatial and temporal modeling of on-road vehicle emissions

Numerous emission and air quality modeling studies have suggested the need to accurately characterize the spatial and temporal variations in on-road vehicle emissions. The purpose of this study was to quantify the impact that using detailed traffic activity data has on emission estimates used to model air quality impacts. The on-road vehicle emissions are estimated by multiplying the vehicle miles traveled (VMT) by the fleet-average emission factors determined by road link and hour of day. Changes in the fraction of VMT from heavy-duty diesel vehicles (HDDVs) can have a significant impact on estimated fleet-average emissions because the emission factors for HDDV nitrogen oxides (NOx) and particulate matter (PM) are much higher than those for light-duty gas vehicles (LDGVs). Through detailed road link-level on-road vehicle emission modeling, this work investigated two scenarios for better characterizing mobile source emissions: (1) improved spatial and temporal variation of vehicle type fractions, and (2) use of Motor Vehicle Emission Simulator (MOVES2010) instead of MOBILE6 exhaust emission factors. Emissions were estimated for the Detroit and Atlanta metropolitan areas for summer and winter episodes. The VMT mix scenario demonstrated the importance of better characterizing HDDV activity by time of day, day of week, and road type. More HDDV activity occurs on restricted access road types on weekdays and at nonpeak times, compared to light-duty vehicles, resulting in 5-15% higher NOx and PM emission rates during the weekdays and 15-40% lower rates on weekend days. Use of MOVES2010 exhaust emission factors resulted in increases of more than 50% in NOx and PM for both HDDVs and LDGVs, relative to MOBILE6. Because LDGV PM emissions have been shown to increase with lower temperatures, the most dramatic increase from MOBILE6 to MOVES2010 emission rates occurred for PM2.5 from LDGVs that increased 500% during colder wintertime conditions found in Detroit, the northernmost city modeled.     

Differences in airborne particle and gaseous concentrations in urban air between weekdays and weekends

Airborne particle number concentrations and size distributions as well as CO and NO_x concentrations monitored at a site within the central business district of Brisbane, Australia were correlated with the traffic flow rate on a nearby freeway with the aim of investigating differences between weekday and weekend pollutant characteristics. Observations over a 5-year monitoring period showed that the mean number particle concentration on weekdays was (8.8±0.1)×10³ cm⁻³ and on weekends (5.9±0.2)×10³ cm⁻³—a difference of 47%. The corresponding mean particle number median diameters during weekdays and weekends were 44.2±0.3 and 50.2±0.2 nm, respectively. The differences in mean particle number concentration and size between weekdays and weekends were found to be statistically significant at confidence levels of over 99%. During a 1-year period of observation, the mean traffic flow rate on the freeway was 14.2×10⁴ and 9.6×10⁴ vehicles per weekday and weekend day, respectively—a difference of 48%. The mean diurnal variations of the particle number and the gaseous concentrations closely followed the traffic flow rate on both weekdays and weekends (correlation coefficient of 0.86 for particles). The overall conclusion, as to the effect of traffic on concentration levels of pollutant concentration in the vicinity of a major road (about 100 m) carrying traffic of the order of 10⁵ vehicles per day, is that about a 50% increase in traffic flow rate results in similar increases of CO and NO_x concentrations and a higher increase of about 70% in particle number concentration.     

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.     

A composite receptor and dispersion model approach for estimation of effective emission factors for vehicles

It is difficult to estimate vehicular emission factors at traffic junctions for use in dispersion modelling studies. Firstly, because the vehicles are in various modes of operation and secondly, it is difficult to delineate the effects of other contributing sources, mainly the effects of road dust and deposited constituents, which are very prominent at traffic junctions in India. Factor analysis-multiple regression (FA-MR), a receptor modelling technique has been used in this study for apportioning the contributing sources. The measurement data consist of one year's temporal variation of suspended particulate matter (SPM), analysed for its trace metal constituents, and two gaseous components NO₂ and SO₂ at two traffic junctions in Mumbai (India). FA-MR apportioned 40% of the observed SPM to road dust and 15% to vehicular sources. Of the total Pb observed in the SPM, FA-MR apportioned 60% to vehicular sources and 20% to road dust. The field-observed vehicular counts, meteorological parameters and road geometry were used in California line source dispersion model to estimate the effective vehicular emission factor for Pb at one traffic junction. This derived emission factor was used to predict the Pb concentration at second (independent observation) traffic junction. The result was found to be more satisfactory than using default emission factors obtained from literature. Similarly, effective vehicular emission factor for NO₂ was also evaluated for one site and tested for predicting concentrations at the other site.     

Modeling weekday/weekend ozone differences in the Los Angeles region for 1997

Numerous studies of ambient ozone (O3) in the Los Angeles (LA) area have found both increases and decreases in elevated O3 levels on weekends, depending on location and year. Since the mid-1990s, average daily maximum O3 levels have been higher on weekends than on weekdays throughout most of the area. We used the Comprehensive Air-Quality Model with extensions to investigate causes of weekday/weekend O3 differences in the LA area for August 3-7, 1997, from the Southern California Ozone Study. Weekday/weekend emission changes were estimated, because explicit weekend inventories are not yet available from regulatory agencies. Changes to on-road motor vehicle (MV) emissions were derived from observed weekday/weekend traffic differences. The estimated changes in MV emissions of nitrogen oxides (NOx) were a 5% increase on Friday, a 27% decrease on Saturday, and a 37% decrease on Sunday, relative to Monday-Thursday levels. The corresponding changes in MV volatile organic carbon (VOC) emissions were an 8% increase on Friday, an 8% decrease on Saturday, and a 15% decrease on Sunday. Modeling these MV emissions changes explained the observed weekend O3 effect very well. Furthermore, changes to the mass of MV NOx emissions were the main contributor to O3 differences rather than changes to the timing of MV emissions. Ozone increases on weekends were caused by NOx emission decreases, because O3 formation is strongly VOC-limited throughout most of the LA area.     

Weekday versus weekend activity patterns for ozone precursor emissions in California's South Coast Air Basin

Ambient O3 concentrations in California's South Coast Air Basin (SoCAB) can be as much as 55% higher on weekends than on weekdays under comparable meteorological conditions. This is paradoxical because emissions of O3 precursors (hydrocarbons, CO, and nitrogen oxides [NOx]) are lower on weekends. Day-of-week emissions activity data were collected and analyzed to investigate the hypothesized causes of the "weekend O3 effect." Emission activity data were collected for various mobile, area, and point sources throughout the SoCAB, including on-road vehicles, lawn and garden equipment, barbecues, fireplaces, solvent use, and point sources with continuous emission monitoring data. The results of this study indicate significant differences between weekday and weekend emission activity patterns and emissions. Their combined effect results in a 12-18% decrease in reactive organic gases (ROGs) and a 35-41% decrease in NOx emissions on Saturdays and Sundays, respectively, relative to weekdays in summer 2000. These changes in emissions result in an increase of more than 30% in the ROG/NOx ratio on weekends compared with weekdays, which, along with lower NOx emissions, leads to increased O3 production on weekends.     

Developing a high-resolution vehicular emission inventory by integrating an emission model and a traffic model: Part 2--A case study in Beijing

A grid-based, bottom-up method has been proposed by combining a vehicle emission model and a travel demand model to develop a high-resolution vehicular emission inventory for Chinese cities. Beijing is used as a case study in which the focus is on fuel consumption and emissions from hot-stabilized activities of light-duty gasoline vehicles (LGVs) in 2005. The total quantity of emissions, emission intensity, and spatial distribution of emissions at 1- by 1-km resolution are presented and compared with results from other inventory methods commonly used in China. The results show that the total daily fuel consumption and vehicular emissions of carbon dioxide, carbon monoxide, hydrocarbons, and oxides of nitrogen from LGVs in the Beijing urban area in 2005 were 1.95 x 10(7) L, 4.28 x 10(4) t, 1.97 x 10(3) t, 0.28 x 10(3) t, and 0.14 x 10(3) t, respectively. Vehicular fuel consumption and emissions show spatial variations that are consistent with the traffic characteristics. The grid-based inventory developed in this study reflects the influence of traffic conditions on vehicle emissions at the microscale and may be applied to evaluate the effectiveness of traffic-related measures on emission control in China.     

Weekday vs. Weekend Ambient Ozone Concentrations: Discussion and Hypotheses with Focus on Northern California

Abstract

Since the early 1970s, researchers and data analysts have reported differences between weekday and weekend ozone concentrations, with higher ozone concentrations occurring on Sundays in some locations. At that time, the phenomenon was referred to as the “Sunday effect.” In the late 1980s, additional papers focused on weekday/weekend differences in air quality in the South Coast (Los Angeles) Air Basin.

Analyses of ozone concentrations measured at a number of locations in northern California reveal that average ozone concentrations are frequently higher on weekends than on weekdays. Violations of the California 0.09 ppm 1-hour air quality standard for ozone also occur in disproportionately greater frequency on weekends. We hypothesize that this phenomenon is based largely on the differences between weekday and weekend emission patterns. We believe that the observed differences may provide information regarding which pollutant reduction strategy, NO_x or ROG control, may be more effective in reducing ambient ozone concentrations. For the northern California region, the presence of higher weekend ozone concentrations suggests the need for ROG control is greater than for NO_x control. If both NO_x and ROG are to be controlled, it is important to understand the interdependence of the two pollutants in forming ozone. With the current uncertainty and debate regarding official vehicular emission inventories, this phenomenon emphasizes the importance of using observation-based data to examine ambient pollution and emission relationships. This natural experiment of varying emissions provides an interesting test case for sophisticated air pollution model performance and evaluation.

Using a Bay Area emission inventory and an estimate of its change from weekday to weekend, combined with a generic Empirical Kinetic Modeling Approach (EKMA) diagram, we demonstrate the weekend effect. In addition, changes in the Bay Area emission inventory from 1980 to 1990, when combined with the EKMA diagram, also show why the weekend effect is more evident in the 1990s.

It is our hypothesis that the presence of the weekend effect, positive or negative, combined with changes in emission changes, provides a simple clue to whether an area is NO_xor ROG limited with respect to ozone formation.     

Nonparametric factorial analysis of daily weigh-in-motion traffic: implications for the ozone “weekend effect” in Southern California

The Ozone Weekend Effect (OWE) has become increasingly more frequent and widespread in southern California since the mid-1970s. Although a number of hypotheses have been suggested to explain the effect, there remains uncertainty associated with the root factors contributing to elevated weekend ozone concentrations. Targeting the time window of the 1997 Southern California Ozone Study (SCOS97), this paper examines traffic activity data for 14 vehicle classes at 27 weigh-in-motion (WIM) stations in southern California. Nonparametric factorial analyses of light-duty vehicle (LDV) and heavy-duty truck (HDT) traffic volumes indicate significant differences in daily volumes by day of week and between the weekly patterns of daily LDV and HDT volumes. Across WIM stations, the daily LDV volume was highest on Friday and decreased by 10% on weekends compared to that on midweek days. In contrast, daily HDT volumes showed dramatic weekend drops of 53% on Saturday and 64% on Sunday. As a result, LDV to HDT ratios increased by 145% on weekends. Nonparametric tests also suggest that weekly traffic patterns varied significantly between WIM stations located close to (central) and far from (peripheral) the Los Angeles Metro area. Weekend increases in LDV/HDT ratios were more pronounced at central WIM sites due to greater weekend declines of HDT relative to LDV traffic. The implications of these weekly traffic patterns for the OWE in southern California were investigated by estimating daily WIM traffic on-road running exhaust emissions of total organic gas (TOG) and oxides of nitrogen (NO_x) using EMFAC2002 emission factors. The results support the California Air Resource Board's (CARB's) NO_x reduction hypothesis that greater weekend NO_x reductions relative to volatile organic compound (VOC) emissions, in combinations with the VOC-limited ozone system, contribute to the OWE observed in the region. The results from this study can be used to develop weekend on-road mobile emission inventories for the purpose of air quality modeling.     

Multiobjective optimization model of intersection signal timing considering emissions based on field data: A case study of Beijing

Most existing signal timing models are aimed to minimize the total delay and stops at intersections, without considering environmental factors. This paper analyzes the trade-off between vehicle emissions and traffic efficiencies on the basis of field data. First, considering the different operating modes of cruising, acceleration, deceleration, and idling, field data of emissions and Global Positioning System (GPS) are collected to estimate emission rates for heavy-duty and light-duty vehicles. Second, multiobjective signal timing optimization model is established based on a genetic algorithm to minimize delay, stops, and emissions. Finally, a case study is conducted in Beijing. Nine scenarios are designed considering different weights of emission and traffic efficiency. The results compared with those using Highway Capacity Manual (HCM) 2010 show that signal timing optimized by the model proposed in this paper can decrease vehicles delay and emissions more significantly. The optimization model can be applied in different cities, which provides supports for eco-signal design and development.

Implications: Vehicle emissions are heavily at signal intersections in urban area. The multiobjective signal timing optimization model is proposed considering the trade-off between vehicle emissions and traffic efficiencies on the basis of field data. The results indicate that signal timing optimized by the model proposed in this paper can decrease vehicle emissions and delays more significantly. The optimization model can be applied in different cities, which provides supports for eco-signal design and development.     

The influence of tropospheric ozone on the air temperature of the city of Toronto,Ontario, Canada

Weekday/weekend variations in tropospheric ozone concentrations were examined to determine whether ground-level greenhouse gases have a significant impact on local climate. The city of Toronto, Canada, was chosen due to a high volume of commuter traffic and frequent exposure to high ozone episodes. Due to day-of-the-week variations in commuter traffic, ozone concentrations were shown to vary significantly between weekdays and weekends. During high ozone episodes weekend air temperatures were significantly higher than those observed on weekdays. As no meteorological phenomenon is known to occur over a 7 day cycle the observed temperature variations were attributed to anthropogenic activity.     

Day-of-week patterns of particulate matter and its chemical components at selected sites in California

This paper analyzes day-of-week variations in concentrations of particulate matter (PM) in California. Because volatile organic compounds (VOCs) and oxides of nitrogen (NOx) are not only precursors of ozone (O3) but also of secondary PM, it is useful to know whether the variations by day of week in these precursors are also evident in PM data. Concentrations of PM < or = 10 microm (PM10) and < or = 2.5 microm in aerodynamic diameter (PM2.5) were analyzed. PM concentrations exhibit a general weekly pattern, with the maximum occurring late in the workweek and the minimum occurring on weekends (especially Sunday); however, this pattern does not prevail at all sites and areas. PM nitrate (NO3-) data from Size Selective Inlet (SSI) samplers in the South Coast Air Basin (SoCAB) tend to be somewhat lower on weekends compared with weekdays. During 1988-1991, the weekend average was lower than the weekday average at 8 of 13 locations, with an average decrease of 1%. During 1997-2000, the weekend average was lower than the weekday average at 10 of 13 locations, with an average decrease of 6%. The weekend averages are generally lower than weekday averages for sulfates, organic carbon, and elemental carbon. Because heavy-duty trucks typically represent a major source of elemental carbon, the weekend decrease in heavy-duty truck traffic may also result in a decrease in ambient elemental carbon concentrations.     

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 modal approach to vehicular emissions and fuel consumption model development

This study reports on the analysis of emissions and fuel consumption from motor vehicles using a modal approach. The four standard driving modes are idling, accelerating, cruising, and decelerating. On-road data were collected using instrumented test vehicles traveling many times through the urban areas of Hong Kong. A model was developed for estimating vehicular fuel consumption and emissions as a function of instantaneous speed and driving mode. Piecewise interpolation functions were proposed for each nonidling driving mode. Idling emission and fuel consumption rates were estimated as negative exponential functions of idling time. Preliminary modeling results showed good agreements for the test vehicles and indicated that the on-road measurements are feasible for the development of modal emission and fuel consumption models.     

机动车尾气VOCs排放特征及减排措施研究

分析了机动车尾气挥发性有机物(VOCs)的排放特征,发现尾气VOCs排放具有明显的日变化和季节变化特征。不同区域不同车型机动车尾气VOCs成分谱略有差异,轻型汽油车尾气VOCs中芳香烃和烷烃含量较高,柴油车烷烃含量较高。尾气排放受机动车保有量、行驶里程、维护保养水平、行驶速度和燃油标准、排放标准等因素影响。从优先控制汽油车、加快机动车更新、采取本地化减排措施、加强多元管理措施、提高科研水平等方面提出了针对性的减排措施。     

Vehicle wake factor for heterogeneous traffic in urban environments

Vehicles' 'wakes' are generated as a result of vehicular movements. They are one of the dominant factors in dispersing the pollutants in 'calm' meteorological conditions when wind velocity is <1 m/sec (Chock, 1978). They are used as a wind-speed-correction factor in several air quality models considering the effects of traffic movements on the pollutant dispersion. In this study, the vehicle wake factor (VWF) has been estimated using the inverse general finite line source model, GFLSM, (Luhar and Patil, 1989) for heterogeneous traffic conditions at one of the busiest traffic intersections of the Delhi city, near the Income Tax Office (ITO). The results show that in 'unstable' conditions, the VWF varies between 1.63 and 0.3 (for wind direction, θ = 90°) and 2.5 and 0.8 (for wind direction, θ = 180°). During 'neutral' and 'stable' conditions, it is in the range of 0.84–0.4, 1.91–0.85, (for wind direction θ = 270°) and 1.7–0.7, 3.1–0.3 for wind direction θ = 360°, respectively.     

Effects of advanced traffic signal status warning systems on vehicle emission reductions at signalized intersections

Signalized intersections have been identified as vehicle emission hotspots, where drivers decelerate, idle, and accelerate their vehicles in response to signal changes. Advanced traffic signal status warning systems (ATSSWSs) can be applied to reduce traffic emissions at intersections by mitigating unnecessary braking and acceleration. In this study, two types of ATSSWSs, variable message sign (VMS) based and vehicle-to-infrastructure (V2I) based, were designed, and their environmental effectiveness was evaluated through driving simulator-based experiments. Three scenarios were designed and tested: (1) baseline without an ATSSWS, (2) with the VMS-based ATSSWS, and (3) with the V2I-based ATSSWS. The Motor Vehicle Emission Simulator model was used to evaluate and compare the environmental effectiveness of these two types of ATSSWSs. The results indicate that the proposed ATSSWSs can reduce traffic emissions at signalized intersections. In particular, the V2I-based ATSSWS can substantially reduce CO₂, NOx, CO, and HC emissions. The results will help transportation practitioners with implementing advanced driver information systems and decision making on emission reduction policies.

Implications: Signalized intersection has been identified as one of hottest spots for vehicle emissions where signal control causes vehicles to frequently decelerate, idle, and accelerate. Advanced Traffic Signal Status Warning Systems (ATSSWS) can be applied to reduce traffic emission at intersections by decreasing vehicles’ unnecessary brakes and accelerations. The results of this study will assist transportation practitioners in implementing advanced driver information systems and making decisions on emission reduction policies.     

Day-of-Week Patterns of Particulate Matter and Its Chemical Components at Selected Sites in California

Abstract

This paper analyzes day-of-week variations in concentrations of particulate matter (PM) in California. Because volatile organic compounds (VOCs) and oxides of nitrogen (NO_x) are not only precursors of ozone (O₃) but also of secondary PM, it is useful to know whether the variations by day of week in these precursors are also evident in PM data. Concentrations of PM ≤10 μm (PM₁₀) and ≤2.5[H9262]m in aerodynamic diameter (PM_2.5) were analyzed. PM concentrations exhibit a general weekly pattern, with the maximum occurring late in the workweek and the minimum occurring on weekends (especially Sunday); however, this pattern does not prevail at all sites and areas. PM nitrate (NO₃ ^-) data from Size Selective Inlet (SSI) samplers in the South Coast Air Basin (SoCAB) tend to be somewhat lower on weekends compared with weekdays. During 1988–1991, the weekend average was lower than the weekday average at 8 of 13 locations, with an average decrease of 1%. During 1997–2000, the weekend average was lower than the weekday average at 10 of 13 locations, with an average decrease of 6%. The weekend averages are generally lower than weekday averages for sulfates, organic carbon, and elemental carbon. Because heavy-duty trucks typically represent a major source of elemental carbon, the weekend decrease in heavy-duty truck traffic may also result in a decrease in ambient elemental carbon concentrations.     

A vehicle emissions system using a car simulator and a geographical information system: Part 1--System description and testing

A methodology for estimating vehicular emissions comprising a car simulator, a basic traffic model, and a geographical information system is capable of estimating vehicle emissions with high time and space resolution. Because of the extent of the work conducted, this article comprises two sections: In Part 1 of this work, we describe the system and its components and use examples for testing it. In Part 2 we will study in more detail the emissions of the sample fleet using the system and will make comparisons with another emission model. The experimental data for the car simulator is obtained using on-board measuring equipment and laboratory Fourier transform IR (FTIR) measurements with a dynamometer following typical driving cycles. The car simulator uses this data to generate emission factors every second. These emission factors, together with information on car activity and velocity profiles of highways and residential and arterial roads in Mexico City in conjunction with a basic traffic model, provide emissions per second of a sample fleet. A geographical information system is used to localize these road emissions.