Development of an empirical model to estimate real-world fine particulate matter emission factors: the traffic air quality model

The purpose of the study was to quantify the impact of traffic conditions, such as free flow and congestion, on local air quality. The Borman Expressway (I-80/94) in Northwest Indiana is considered a test bed for this research because of the high volume of class 9 truck traffic traveling on it, as well as the existing and continuing installation of the Intelligent Transportation System (ITS) to improve traffic management along the highway stretch. An empirical traffic air quality (TAQ) model was developed to estimate the fine particulate matter (PM2.5) emission factors (grams per kilometer) based solely on the measured traffic parameters, namely, average speed, average acceleration, and class 9 truck density. The TAQ model has shown better predictions that matched the measured emission factor values more than the U.S. Environmental Protection Agency (EPA)-PART5 model. During congestion (defined as flow-speeds < 50 km/hr [30 mi/hr]), the TAQ model, on average, overpredicted the measured values only by a factor of 1.2, in comparison to a fourfold underprediction using the EPA-PART5 model. On the other hand, during free flow (defined as flow-speeds > 80 km/hr [50 mi/hr]), the TAQ model was conservative in that it overpredicted the measured values by 1.5-fold.     

A Quantitative Approach to the Traffic Air Quality Problem: The Traffic Air Quality Index

Abstract

The Traffic Air Quality (TAQ) model is a simple tool to estimate traffic fine particulate emissions on roadways (g/km) and can be used for both real-time analysis and for localized conformity analysis (“hot-spot” analysis for nonattainment areas) as defined by 40 CFR 93.123. This paper is a follow-up to a study published earlier regarding the development of the TAQ model. This paper shows how local air quality levels can be a factor in traffic management in nonattainment areas. Similar to the industrial source quotas measured in tons per year, it is proposed that road segments are to be assigned emission quotas (or TAQ indices) measured in pollutant mass emitted per road length (g/km) above which traffic-measures have to be taken to reduce the fine-particulates emissions on such road links. The TAQ model as well as traffic-rerouting measures along with the Intelligent Transportation System (ITS) protocols can be used to have a real-time control of the traffic conditions along expressways to maintain the fine-particulates emissions below the quota assigned per road link and consequently improving the over all local air quality in nonattainment areas.     

A quantitative approach to the traffic air quality problem: the Traffic Air Quality index

The Traffic Air Quality (TAQ) model is a simple tool to estimate traffic fine particulate emissions on roadways (g/km) and can be used for both real-time analysis and for localized conformity analysis ("hot-spot" analysis for nonattainment areas) as defined by 40 CFR 93.123. This paper is a follow-up to a study published earlier regarding the development of the TAQ model. This paper shows how local air quality levels can be a factor in traffic management in nonattainment areas. Similar to the industrial source quotas measured in tons per year, it is proposed that road segments are to be assigned emission quotas (or TAQ indices) measured in pollutant mass emitted per road length (g/km) above which traffic-measures have to be taken to reduce the fine-particulates emissions on such road links. The TAQ model as well as traffic-rerouting measures along with the Intelligent Transportation System (ITS) protocols can be used to have a real-time control of the traffic conditions along expressways to maintain the fine-particulates emissions below the quota assigned per road link and consequently improving the over all local air quality in nonattainment areas.     

Scattering cross-section emission factors for visibility and radiative transfer applications: military vehicles traveling on unpaved roads

Emission factors for particulate matter (PM) are generally reported as mass emission factors (PM mass emitted per time or activity) as appropriate for air quality standards based on mass concentration. However, for visibility and radiative transfer applications, scattering, absorption, and extinction coefficients are the parameters of interest, with visibility standards based on extinction coefficients. These coefficients (dimension of inverse distance) equal cross-section concentrations, and, therefore, cross-section emission factors are appropriate. Scattering cross-section emission factors were determined for dust entrainment by nine vehicles, ranging from light passenger vehicles to heavy military vehicles, traveling on an unpaved road. Each vehicle made multiple passes at multiple speeds while scattering and absorption coefficients, wind velocity and dust plume profiles, and additional parameters were measured downwind of the road. Light absorption of the entrained PM was negligible, and the light extinction was primarily caused by scattering. The resulting scattering cross-section emission factors per vehicle kilometer traveled (vkt) range from 12.5 m2/vkt for a slow (16 km/ hr), light (1176 kg) vehicle to 3724 m2/vkt for a fast (64 km/hr), heavy (17,727 kg) vehicle and generally increase with vehicle speed and mass. The increase is approximately linear with speed, yielding emission factors per vkt and speed ranging from 4.2 m2/(vkt km/hr) to 53 m2/(vkt km/hr). These emission factors depend approximately linearly on vehicle mass within the groups of light (vehicle mass < or =3100 kg) and heavy (vehicle mass >8000 kg) vehicles yielding emission factors per vkt, speed, and mass of 0.0056 m2/(vkt km/hr kg) and 0.0024 m2/(vkt km/hr kg), respectively. Comparison of the scattering cross-section and PM mass emission factors yields average mass scattering efficiencies of 1.5 m2/g for the light vehicles and of 0.8 m2/g for the heavy vehicles indicating that the heavy vehicles entrain larger particles than the light vehicles.     

Estimations of Real-World N2O Emissions from Road Vehicles by Means of Measurements in a Traffic Tunnel

Abstract

Ambient air measurements of N₂O, NO_x, CO, and HC based on grab sampling were conducted in a major traffic tunnel in Sweden, that carries up to 4,000 vehicles per hour, in order to estimate real-world emissions of N₂O for road traffic. Two different methods—relative and mass balance—were used to calculate a N₂O emission factor for the mixed vehicle fleet, which gave an average emission factor, at average speeds of 30-70 km/h, of approximately 25 mg N₂O/ km, with a range of 7-56 mg/km.     

An incinerator air quality impact assessment for metals, PAH, dioxins and furans by using the MM5-CMAQ-EMIMO atmospheric modelling system: Spain case study

In this paper, we show the implementation of a modified version of MM5-CMAQ for carrying out an air quality impact analysis for installing an incinerator in the Basque Country model domain (Spain). The modified CMAQ model (EPA USA, 2004) includes Poly-Chlorinated Dibenzop-Dioxins and Dibenzo-Furans (dioxins and furans). This model represents their congeners as divided between gaseous and aerosol forms that exchange mass based on theoretical coefficients for gas to particle portioning. The emission model EMIMO – developed by UPM – has been adapted to incorporate the three metals and the benzo(a)pyrene according to the EMEP annual emission inventory. In addition, the PCDD/F EMEP emission inventory has been incorporated into the EMIMO model to produce proper 1 hr and 1 km × 1 km emission PCDD/F estimation. The emissions of the projected incinerator are incorporated by using the chimney technical parameters and the limit emission values (worst-case scenario) prescribed in the Directive/2000/76/CE.     

Emission factors of PCDD and PCDF for road vehicles obtained by tunnel experiment

Polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDF) were determined in air at the inlet and outlet of a longitudinally ventilated tunnel with separate tubes for each traffic direction. In addition, measurements of traffic density, traffic composition, ventilation rate and other air pollutants were carried out. The data allowed to estimate the PCDD/PCDF emissions based on a car fleet of about 20'–30'000 cars. Furthermore, the varying percentage of heavy duty vehicles made it possible to differentiate between emission factors for light (LDV) and heavy duty (diesel) vehicles (HDDV). Depending on driving conditions, the estimated emission factors were in the order of 0.04–0.5 ng/km 2,3,7,8-TEQ (Nordic model) for LDV and 0.8–9.5 ng/km for HDDV.     

Near-roadway monitoring of vehicle emissions as a function of mode of operation for light-duty vehicles

Determination of the effect of vehicle emissions on air quality near roadways is important because vehicles are a major source of air pollution. A near-roadway monitoring program was undertaken in Chicago between August 4 and October 30, 2014, to measure ultrafine particles, carbon dioxide, carbon monoxide, traffic volume and speed, and wind direction and speed. The objective of this study was to develop a method to relate short-term changes in traffic mode of operation to air quality near roadways using data averaged over 5-min intervals to provide a better understanding of the processes controlling air pollution concentrations near roadways. Three different types of data analysis are provided to demonstrate the type of results that can be obtained from a near-roadway sampling program based on 5-min measurements: (1) development of vehicle emission factors (EFs) for ultrafine particles as a function of vehicle mode of operation, (2) comparison of measured and modeled CO₂ concentrations, and (3) application of dispersion models to determine concentrations near roadways. EFs for ultrafine particles are developed that are a function of traffic volume and mode of operation (free flow and congestion) for light-duty vehicles (LDVs) under real-world conditions. Two air quality models—CALINE4 (California Line Source Dispersion Model, version 4) and AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model)—are used to predict the ultrafine particulate concentrations near roadways for comparison with measured concentrations. When using CALINE4 to predict air quality levels in the mixing cell, changes in surface roughness and stability class have no effect on the predicted concentrations. However, when using AERMOD to predict air quality in the mixing cell, changes in surface roughness have a significant impact on the predicted concentrations.

Implications: The paper provides emission factors (EFs) that are a function of traffic volume and mode of operation (free flow and congestion) for LDVs under real-world conditions. The good agreement between monitoring and modeling results indicates that high-resolution, simultaneous measurements of air quality and meteorological and traffic conditions can be used to determine real-world, fleet-wide vehicle EFs as a function of vehicle mode of operation under actual driving conditions.     

Estimations of real-world N2O emissions from road vehicles by means of measurements in a traffic tunnel

Ambient air measurements of N2O, NOx, CO, and HC based on grab sampling were conducted in a major traffic tunnel in Sweden, that carries up to 4,000 vehicles per hour, in order to estimate real-world emissions of N2O for road traffic. Two different methods--relative and mass balance-were used to calculate a N2O emission factor for the mixed vehicle fleet, which gave an average emission factor, at average speeds of 30-70 km/h, of approximately 25 mg N2O/ km, with a range of 7-56 mg/km.     

Development of operating mode distributions for different types of roadways under different congestion levels for vehicle emission assessment using MOVES

The Motor Vehicle Emission Simulator (MOVES) quantifies emissions as a function of vehicle modal activities. Hence, the vehicle operating mode distribution is the most vital input for running MOVES at the project level. The preparation of operating mode distributions requires significant efforts with respect to data collection and processing. This study is to develop operating mode distributions for both freeway and arterial facilities under different traffic conditions. For this purpose, in this study, we (1) collected/processed geographic information system (GIS) data, (2) developed a model of CO₂ emissions and congestion from observations, (3) implemented the model to evaluate potential emission changes from a hypothetical roadway accident scenario. This study presents a framework by which practitioners can assess emission levels in the development of different strategies for traffic management and congestion mitigation.

Implications: This paper prepared the primary input, that is, the operating mode ID distribution, required for running MOVES and developed models for estimating emissions for different types of roadways under different congestion levels. The results of this study will provide transportation planners or environmental analysts with the methods for qualitatively assessing the air quality impacts of different transportation operation and demand management strategies.     

The effects of congestions tax on air quality and health

The “Stockholm Trial” involved a road pricing system to improve the air quality and reduce traffic congestion. The test period of the trial was January 3–July 31, 2006. Vehicles travelling into and out of the charge cordon were charged for every passage during weekdays. The amount due varied during the day and was highest during rush hours (20 SEK = 2.2 EUR, maximum 60 SEK per day). Based on measured and modelled changes in road traffic it was estimated that this system resulted in a 15% reduction in total road use within the charged cordon. Total traffic emissions in this area of NO_x and PM10 fell by 8.5% and 13%, respectively. Air quality dispersion modelling was applied to assess the effect of the emission reductions on ambient concentrations and population exposure. For the situations with and without the trial, meteorological conditions and other emissions than from road traffic were kept the same. The calculations show that, with a permanent congestion tax system like the Stockholm Trial, the annual average NO_x concentrations would be lower by up to 12% along the most densely trafficked streets. PM10 concentrations would be up to 7% lower. The limit values for both PM10 and NO₂ would still be exceeded along the most densely trafficked streets. The total population exposure of NO_x in Greater Stockholm (35 × 35 km with 1.44 million people) is estimated to decrease with a rather modest 0.23 μg m^?3. However, based on a long-term epidemiological study, that found an increased mortality risk of 8% per 10 μg m^?3 NO_x, it is estimated that 27 premature deaths would be avoided every year. According to life-table analysis this would correspond to 206 years of life gained over 10 years per 100 000 people following the trial if the effects on exposures would persist. The effect on mortality is attributed to road traffic emissions (likely vehicle exhaust particles); NO_x is merely regarded as an indicator of traffic exposure. This is only the tip of the ice-berg since reductions are expected in both respiratory and cardiovascular morbidity. This study demonstrates the importance of not only assessing the effects on air quality limit values, but also to make quantitative estimates of health impacts, in order to justify actions to reduce air pollution.     

Real-World Energy Use and Emission Rates for Idling Long-Haul Trucks and Selected Idle Reduction Technologies

Abstract

Long-haul freight trucks typically idle for 2000 or more hours per year, motivating interest in reducing idle fuel use and emissions using auxiliary power units (APUs) and shore-power (SP). Fuel-use rates are estimated based on electronic control unit (ECU) data for truck engines and measurements for APU engines. Engine emission factors were measured using a portable emission measurement system. Indirect emissions from SP were based on average utility grid emission factors. Base engine fuel use and APU and SP electrical load were analyzed for 20 trucks monitored for more than 1 yr during 2.76 million mi of activity within 42 U.S. states. The average base engine fuel use varied from 0.46 to 0.65 gal/hr. The average APU fuel use varied from 0.24 to 0.41 gal/hr. Fuel-use rates are typically lowest in mild weather, highest in hot or cold weather, and depend on engine speed (revolutions per minute [RPM]). Compared with the base engine, APU fuel use and emissions of carbon dioxide (CO₂) and sulfur dioxide (SO₂) are lower by 36–47%. Oxides of nitrogen (NO_x) emissions are lower by 80–90%. Reductions in particulate matter (PM), carbon monoxide (CO), and hydrocarbon emissions vary from approximately 10 to over 50%. SP leads to more substantial reductions, except for SO₂. The actual achievable reductions will be lower because only a fraction of base engine usage will be replaced by APUs, SP, or both. Recommendations are made for reducing base engine fuel use and emissions, accounting for variability in fuel use and emissions reductions, and further work to quantify real-world avoided fuel use and emissions.     

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.     

Near-source air quality impacts of large olefin flares

Large petrochemical flares, common in the Houston Ship Channel (the Ship Channel) and other industrialized areas in the Gulf of Mexico region, emit hundreds to thousands of pounds per hour of highly reactive volatile organic compounds (HRVOCs). We employed fine horizontal resolution (200 m?×?200 m) in a three-dimensional (3D) Eulerian chemical transport model to simulate two historical Ship Channel flares. The model reasonably reproduced the observed ozone rise at the nearest monitoring stations downwind of the flares. The larger of the two flares had an olefin emission rate exceeding 1400 lb/hr. In this case, the model simulated a rate of increase in peak ozone greater than 40 ppb/hr over a 12 km?×?12 km horizontal domain without any unusual meteorological conditions. In this larger flare, formaldehyde emissions typically neglected in official inventories enhanced peak ozone by as much as 16 ppb and contributed over 10 ppb to ambient formaldehyde up to ～8 km downwind of the flare. The intense horizontal gradients in large flare plumes cannot be simulated by coarse models typically used to demonstrate ozone attainment. Moreover, even the relatively dense monitoring network in the Ship Channel may not be able to detect many transient high ozone events (THOEs) caused by industrial flare emissions in the absence of stagnant air recirculation or stalled sea breeze fronts, even though such conditions are unnecessary for the occurrence of THOEs.

Implications: Flare minimization may be an important strategy to attain the U.S. federal ozone standard in industrialized areas, and to avoid inordinate exposure to formaldehyde in neighborhoods surrounding petrochemical facilities. Moreover, air quality monitoring networks, emission inventories, and chemical transport models with higher spatial and temporal resolution and more refined speciation of HRVOCs are needed to better account for the near-source air quality impacts of large olefin flares.     

The impact of transportation control measures on emission reductions during the 2008 Olympic Games in Beijing,China

Traffic congestion and air pollution were two major challenges for the planners of the 2008 Olympic Games in Beijing. The Beijing municipal government implemented a package of temporary transportation control measures during the event. In this paper, we report the results of a recent research project that investigated the effects of these measures on urban motor vehicle emissions in Beijing. Bottom–up methodology has been used to develop grid-based emission inventories with micro-scale vehicle activities and speed-dependent emission factors. The urban traffic emissions of volatile organic compounds (VOC), carbon monoxide (CO), nitrogen oxides (NO_x) and particulate matter with an aerodynamic diameter of 10 μm or less (PM₁₀) during the 2008 Olympics were reduced by 55.5%, 56.8%, 45.7% and 51.6%, respectively, as compared to the grid-based emission inventory before the Olympics. Emission intensity was derived from curbside air quality monitoring at the North 4th Ring Road site, located about 7 km from the National Stadium. Comparison between the emission intensity before and during the 2008 Olympics shows a reduction of 44.5% and 49.0% in daily CO and NO_x emission from motor vehicles. The results suggest that reasonable traffic system improvement strategies along with vehicle technology improvements can contribute to controlling total motor vehicle emissions in Beijing after the Olympic Games.     

On-road emission characteristics of heavy-duty diesel vehicles in Shanghai

On-road vehicle tests of nine heavy-duty diesel trucks were conducted using SEMTECH-D, an emissions measuring instrument provided by Sensors, Inc. The total length of roads for the tests was 186 km. Data were obtained for 37,255 effective driving cycles, including 17,216 on arterial roads, 15,444 on residential roads, and 4595 on highways. The impacts of speed and acceleration on fuel consumption and emissions were analyzed. Results show that trucks spend an average of 16.5% of the time in idling mode, 25.5% in acceleration mode, 27.9% in deceleration mode, and only 30.0% at cruise speed. The average emission factors of CO, total hydrocarbons (THC), and NO_x for the selected vehicles are (4.96±2.90), (1.88±1.03) and (6.54±1.90) g km⁻¹, respectively. The vehicle emission rates vary significantly with factors like speed and acceleration. The test results reflect the actual traffic situation and the current emission status of diesel trucks in Shanghai. The measurements show that low-speed conditions with frequent acceleration and deceleration, particularly in congestion conditions, are the main factors that aggravate vehicle emissions and cause high emissions of CO and THC. Alleviating congestion would significantly improve vehicle fuel economy and reduce CO and THC emissions.     

Preliminary Measurements of Biogenic Sulfur-Containing Gas Emissions From Soils

Emission factors for particulate matter and carbon monoxide have been measured for wood, anthracite coal, and bituminous coal burned in residential heaters operated at less than 15 kW (50,000 Btu/hr). In these studies the stove effluent is mixed with about four volumes of outside air and samples are collected about two minutes after mixing. The main purpose of using this sampling method is to simulate atmospheric conditions more closely. Particulate samples are collected on 20 × 25 cm filters after cyclone preseparation of particles larger than 4 μm. Carbon monoxide concentrations are measured with Draeger tubes.

Particulate emission factors for wood ranged from 1.6 to 6.4 g/kg (fuel) and were found to depend on the fuel load and the firing rate, as indicated by earlier studies. These values are substantially less than the values obtained previously for stoves operated under similar conditions. The average particulate emission factors for bituminous and anthracite coal are 10.4 and 0.50 g/kg, respectively. Carbon monoxide emission factors for wood, bituminous, and anthracite coal are 100,116, and 21 g/kg, respectively.     

Cost and Benefits of Road Dust Control in Seattle’s Industrial Valley

The paving of gravel roads with an average daily traffic (ADT) over 15 is a least cost method for reducing suspended particulate In the air in Seattle. It is also a good business investment when the ADT exceeds 100. Clean roads, gutters, and parking lots may reduce or eliminate Seattle’s most serious environmental constraint on economic development. In a study done in Seattle’s Duwamish Valley the impact of road dust on air quality was measured by obtaining dust emission factors for vehicles traveling at 10, 20, and 30 mph on gravel as well as dusty paved roads. A University of Washington Mark II Cascade Impactor was mounted on a trailer and towed behind a car to determine the concentration and size distribution of this dust. It was found that each vehicle mile at 20 mph on unpaved roads contributed 7.0 Ib of dust to the air, 1.9 Ib consisting of particles smaller than 10 microns in diameter and 0.24 Ib below 2 microns. Three to eight percent by weight of this respirable dust was free silica, which is potentially toxic. The quantity of dust generated varies as an exponent of the speed. The concentrations of dust found in the air near a dry gravel road with an ADT of 250 reached 584 μg/m3 for an 8 hr work day. A 24 hr suspended particulate reading of 463 μg/m3 total and 3.83 μg/m3 free silica was found beside a dusty paved road with an ADT of 18,000. Nineteen miles of gravel roads and 110 miles of dusty paved roads contributed 2700 tons/year of particulate, of which 700 tons were below 10 microns. Paving or oiling such roads will produce benefits of $3,881,000 yearly in household cleaning, health care, sewer, vehicle operation, and road maintenance costs as well as an increase in property values. Clean roads can lower the cost of clean air in Seattle.     

Measurement of Hydrocarbon Emissions Fluxes from Refinery Wastewater Impoundments Using Atmospheric Tracer Techniques

Sulfur hexafluoride (SF6) tracer was used in a series of the experiments to simulate emissions of benzene, toluene, ethyl-benzene, and xylenes (BTEX) from a refinery wastewater basin. The ratio of the measured tracer release to the ambient tracer concentration established a dilution factor which was then used to calculate the mass flux of BTEX from the wastewater basin using the ambient BTEX concentration data. Measured fluxes of BTEX varied from 7 g/min to 70 g/min.

The CHEMDAT7 air emissions model was then used to predict emissions for comparison with the emissions measured using the tracer flux simulation. CHEMDAT7 typically overpredicted total measured BTEX emissions by factors of twelve to seventeen. The degree of overprediction varied both by the individual compound and the module of CHEMDAT7 used to predict emission fluxes.     

A measurement campaign in a street canyon in Helsinki and comparison of results with predictions of the OSPM model

In 1997, a measuring campaign was conducted in a street canyon (Runeberg St.) in Helsinki. Hourly mean concentrations of CO, NO_x, NO₂ and O₃ were measured at street and roof levels, the latter in order to determine the urban background concentrations. The relevant hourly meteorological parameters were measured at roof level; these included wind speed and direction, temperature and solar radiation. Hourly street level measurements and on-site electronic traffic counts were conducted throughout the whole of 1997; roof level measurements were conducted for approximately two months, from 3 March to 30 April in 1997. CO and NO_x emissions from traffic were computed using measured hourly traffic volumes and evaluated emission factors. The Operational Street Pollution Model (OSPM) was used to calculate the street concentrations and the results were compared with the measurements. The overall agreement between measured and predicted concentrations was good for CO and NO_x (fractional bias were −4.2 and +4.5%, respectively), but the model overpredicted the measured NO₂ concentrations (fractional bias was +22%). The agreement between the measured and predicted values was also analysed in terms of its dependence on wind speed and direction; the latter analysis was performed separately for two categories of wind velocity. The model qualitatively reproduces the observed behaviour very well. The database, which contains all measured and predicted data, is available for further testing of other street canyon dispersion models. The dataset contains a larger proportion of low wind speed cases, compared with other available street canyon measurement datasets.