PM Emissions Emanating from Limited-Access Highways

ABSTRACT

Motor vehicle contributions to primary particulate matter (PM) emissions include exhaust, tire wear, brake and clutch wear, and resuspended road dust. Relatively few field studies have been conducted to quantify fleetaverage exhaust emissions for actual on-road conditions. Therefore, direct measurements of motor vehicle-related PM emissions are warranted. In this study, PM₁₀ and PM_2.5 mass concentrations were measured near two major highways in the St. Louis area over the period from February–April 1997. Samplers were deployed both upwind and downwind of the roadways to capture the transport and dispersion of PM with distance from the roadway. The observed microscale concentration fields were compared to estimates using the PART5 emission factor model together with the CALINE4 highway dispersion model. Traffic- induced PM mass concentrations observed downwind of the roadway were always less than PART5/CALINE4 predictions; average percent differences for observed traffic-induced mass concentrations compared to predicted values were ?34% for PM_2.5 and -70% for PM₁₀. In most cases, the observed PM concentration decay with increasing distance from the roadway was steeper than predicted by dispersion modeling. Motor vehicle-induced emission factors were reconstructed by fitting CALINE4 to the observed concentration data with the emission factor as the sole adjustable parameter. Reconstructed fleet-average motor vehicle emission factors for the urban interstate highway were 0.03–0.04 g/VMT for both PM_2.5 and PM₁₀, while the fleet-average emission factors for the rural interstate highway were 0.2 and 0.3 g/VMT for PM_2.5 and PM₁₀, respectively.     

Mesoscale modeling of wintertime particulate matter episodes in eastern Washington, USA

The Spokane, Washington area is classified as a non-attainment area for the 24-h PM₁₀ standard due to a history of high particulate matter concentrations. A Eulerian regional air quality model (CALMET/CALGRID) has been used to characterize the emission, transport and dispersion of PM₁₀ and PM_2.5 in Spokane. Observations from a residential site (Rockwood, RW) and an industrial site (Crown Zellerbach, CZ), spanning July 1994–August 1996 were used to evaluate the current emission inventory. Two major tasks were devised to conduct the objectives of this investigation. First, a simple and efficient urban dispersion model (WYNDValley) was used to simulate important episodes characterized by the highest PM₁₀ and PM_2.5 concentrations. The selected episodes included four days with wet conditions for which no roads would have been emitting and seven days with dry conditions for which roads would emit. In the second step, a single road-emitting event was selected from the previous predicted results for further analysis using the Eulerian regional air quality model to examine the emission inventory. The urban and regional models predicted the observed concentration distributions reasonably well for the source emissions inventoried in Spokane. The mass concentrations of PM₁₀ were well predicted for the roads emitting case examined by both models indicating that the emission inventory based primarily upon area sources including roads is reasonably well characterized, at least at the RW site. The area sources around CZ are less well characterized, so that the PM₁₀ concentrations are underpredicted at CZ. The models appear unable to reach an equilibrium mass balance status at the beginning of the simulation, and the urban model seems unable to properly resolve the nocturnal boundary layer.     

Computational fluid dynamics modeling of laboratory flames and an industrial flare

A computational fluid dynamics (CFD) methodology for simulating the combustion process has been validated with experimental results. Three different types of experimental setups were used to validate the CFD model. These setups include an industrial-scale flare setups and two lab-scale flames. The CFD study also involved three different fuels: C₃H₆/CH₄/Air/N₂, C₂H₄/O₂/Ar, and CH₄/Air. In the first setup, flare efficiency data from the Texas Commission on Environmental Quality (TCEQ) 2010 field tests were used to validate the CFD model. In the second setup, a McKenna burner with flat flames was simulated. Temperature and mass fractions of important species were compared with the experimental data. Finally, results of an experimental study done at Sandia National Laboratories to generate a lifted jet flame were used for the purpose of validation. The reduced 50 species mechanism, LU 1.1, the realizable k-? turbulence model, and the EDC turbulence–chemistry interaction model were used for this work. Flare efficiency, axial profiles of temperature, and mass fractions of various intermediate species obtained in the simulation were compared with experimental data and a good agreement between the profiles was clearly observed. In particular, the simulation match with the TCEQ 2010 flare tests has been significantly improved (within 5% of the data) compared to the results reported by Singh et al. in 2012. Validation of the speciated flat flame data supports the view that flares can be a primary source of formaldehyde emission.

ImplicationsValidated computational fluid dynamics (CFD) models can be a useful tool to predict destruction and removal efficiency (DRE) and combustion efficiency (CE) under steam/air assist conditions in the face of many other flare operating variables such as fuel composition, exit jet velocity, and crosswind. Augmented with rigorous combustion chemistry, CFD is also a powerful tool to predict flare emissions such as formaldehyde. In fact, this study implicates flares emissions as a primary source of formaldehyde emissions. The rigorous CFD simulations, together with available controlled flare test data, can be fitted into simple response surface models for quick engineering use.     

A gas/aerosol air pollutants study over the urban area of Rome using a comprehensive chemical transport model

The aim of this study was to identify areas of potential relevant exposure to pollutants within Rome's urban core. To meet this goal, intensive field campaigns were conducted and simulations were performed, using the flexible air quality regional model (FARM), to study winter and summer pollution episodes. The simulations were performed using a complete emission inventory that included traffic flow model results of the Roman street network to better describe, with respect to the available diffuse national emission inventory, the hourly variation of traffic emissions in the city. The meteorological reconstruction was performed by means of both prognostic and diagnostic models by using experimental data collected during the field campaigns. To evaluate the capability of the FARM model to capture the main features of the selected episodes, a comparison of modelled results against observed air quality data for different pollutants was performed at urban and rural sites. FARM performed well in predicting ozone (O₃) and nitrogen dioxide (NO₂) concentrations, showing a good reproduction of both daily peaks and their diurnal variations. The model also showed a good capability to reproduce the magnitude of volatile alkane, aromatic and carbonyl compound concentrations. PM₁₀ model results revealed the tendency to under-predict the observed values. PM composition model results were compared with observed data, evidencing good results for elemental carbon (EC), nitrate (NO₃⁻) and ammonium (NH₄⁺), underestimation for sulphate (SO₄²⁻) and poor performance for organic matter (OM). The soil components of PM were found to be significantly under-predicted by the model, especially during Saharan dust episodes. Overall, the study results show large areas of high O₃ and PM₁₀ concentrations where levels of pollutants should be carefully monitored and population exposure evaluated.     

Sensitivity analyses of factors influencing CMAQ performance for fine particulate nitrate

Improvement of air quality models is required so that they can be utilized to design effective control strategies for fine particulate matter (PM_2.5). The Community Multiscale Air Quality modeling system was applied to the Greater Tokyo Area of Japan in winter 2010 and summer 2011. The model results were compared with observed concentrations of PM_2.5 sulfate (SO₄^2-), nitrate (NO₃^?) and ammonium, and gaseous nitric acid (HNO₃) and ammonia (NH₃). The model approximately reproduced PM_2.5 SO₄^2? concentration, but clearly overestimated PM_2.5 NO₃^? concentration, which was attributed to overestimation of production of ammonium nitrate (NH₄NO₃). This study conducted sensitivity analyses of factors associated with the model performance for PM_2.5 NO₃^? concentration, including temperature and relative humidity, emission of nitrogen oxides, seasonal variation of NH₃ emission, HNO₃ and NH₃ dry deposition velocities, and heterogeneous reaction probability of dinitrogen pentoxide. Change in NH₃ emission directly affected NH₃ concentration, and substantially affected NH₄NO₃ concentration. Higher dry deposition velocities of HNO₃ and NH₃ led to substantial reductions of concentrations of the gaseous species and NH₄NO₃. Because uncertainties in NH₃ emission and dry deposition processes are probably large, these processes may be key factors for improvement of the model performance for PM_2.5 NO₃^?.

Implications: The Community Multiscale Air Quality modeling system clearly overestimated the concentration of fine particulate nitrate in the Greater Tokyo Area of Japan, which was attributed to overestimation of production of ammonium nitrate. Sensitivity analyses were conducted for factors associated with the model performance for nitrate. Ammonia emission and dry deposition of nitric acid and ammonia may be key factors for improvement of the model performance.     

Simulating industrial emissions using Atmospheric Dispersion Modeling System: Model performance and source emission factors

In this paper, the Gaussian Atmospheric Dispersion Modeling System (ADMS4) was coupled with field observations of surface meteorology and concentrations of several air quality indicators (nitrogen oxides (NO_X), carbon monoxide (CO), fine particulate matter (PM₁₀) and sulfur dioxide (SO₂)) to test the applicability of source emission factors set by the European Environment Agency (EEA) and the United States Environmental Protection Agency (USEPA) at an industrial complex. Best emission factors and data groupings based on receptor location, type of terrain and wind speed, were relied upon to examine model performance using statistical analyses of simulated and observed data. The model performance was deemed satisfactory for several scenarios when receptors were located at downwind sites with index of agreement d values reaching 0.58, fractional bias “FB” and geometric mean bias “MG” values approaching 0 and 1, respectively, and normalized mean square error “NMSE” values as low as 2.17. However, median ratios of predicted to observed concentrations “Cp/Co” at variable downstream distances were 0.01, 0.36, 0.76 and 0.19 for NO_X, CO, PM₁₀ and SO₂, respectively, and the fraction of predictions within a factor of two of observations “FAC2” values were lower than 0.5, indicating that the model could not adequately replicate all observed variations in emittant concentrations. Also, the model was found to be significantly sensitive to the input emission factor bringing into light the deficiency in regulatory compliance modeling which often uses internationally reported emission factors without testing their applicability.

Implications In the absence of site-specific source emission factors, the use of internationally reported emission factors without testing their validity may generate significant errors. Instead, recorded field measurements and meteorological data may be combined with atmospheric transport and dispersion models to better estimate source emissions, particularly in regulatory compliance studies. In this context, lower model performance is expected at higher wind speeds for most indicators such as CO, PM₁₀, and SO₂.     

Estimating Taiwan biogenic VOC emission: Leaf energy balance consideration

The goal of the present study is to provide a comprehensive model to estimate biogenic volatile organic compounds (BVOCs) in Taiwan. In addition to metrological data, the model consists of (1) 83 land-use patterns, (2) emission factors for various vegetations, (3) energy balance equation to account for leaf temperature, and (4) correction terms for photosynthetically active radiation. The model output includes 4 categories of 33 BVOCs [isoprene, methylbutenol (MBO), 14 species of monoterpenes and 17 other BVOCs]. The results of model verification based on several approaches include: (1) predicted isoprene emission flux correlates relatively good with the observed isoprene concentration (R² = 0.66); (2) correlation between leaf temperature and observed isoprene levels is better than that between ambient temperature and isoprene concentrations (R² = 0.63 vs. 0.58); (3) model-predicted isoprene fluxes match well with observed 3-day diurnal isoprene concentration variations; and (4) subsequent model-predicted O₃ concentrations with the BVOC input obtained in the present study match well than that with previous estimated BVOC data with the observed 6-day diurnal O₃ levels in 8 air quality monitoring stations.Based on the meteorological data in 2000, the total emission of BVOCs in Taiwan was simulated to be about 433,000 ton (33% of total VOCs) of which both isoprene and 14 species of monoterpenes account for about 34%, with 17 species of other BVOCs being 31% and <2% contribution from MBO. Total emissions of BVOCs are higher in lower and medium altitude (300–1000 m) mountain areas with an average of around 15–30 ton km⁻² y⁻¹. The implication of the other results is also discussed.     

Estimation and validation of PM2.5/PM10 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 PM₁₀ 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 PM₁₀ and PM_2.5 emission factors are lower in the summer month than the rest of the year. For example, PM₁₀ 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 PM₁₀, 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 PM₁₀ 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 PM₁₀ levels.     

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

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

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

Fine particulate speciation profile and emission factor of municipal solid waste incinerator established by dilution sampling method

In this study, fine particulate matter (PM_2.5) emitted from a municipal solid waste incinerator (MSWI) was collected using dilution sampling method. Chemical compositions of the collected PM_2.5 samples, including carbon content, metal elements, and water-soluble ions, were analyzed. Traditional in-stack hot sampling was simultaneously conducted to compare the influences of dilution on PM_2.5 emissions and the characteristics of the bonded chemical species. The results, established by a dilution sampling method, show that PM_2.5 and total particulate matter (TPM) emission factors were 61.6 ± 4.52 and 66.1 ± 5.27 g ton-waste^?1, respectively. The average ratio of PM_2.5/TPM is 0.93, indicating that more than 90% of PM emission from the MSWI was fine particulate. The major chemical species in PM_2.5 included organic carbon (OC), Cl^?, NH₄⁺, elemental carbon (EC) and Si, which account for 69.7% of PM_2.5 mass. OC was from the unburned carbon in the exhaust, which adsorbed onto the particulate during the cooling process. High Cl^? emission is primarily attributable to wastes containing plastic bags made of polyvinyl chloride, salt in kitchen refuse and waste biomass, and so on. Minor species that account for 0.01–1% of PM_2.5 mass included SO₄^2-, K⁺, Na, K, NO₃^?, Al, Ca²⁺, Zn, Ca, Cu, Fe, Pb, and Mg. The mean ratio of dilution method/in-stack hot method was 0.454. The contents of water-soluble ions (Cl^?, SO₄^2-, NO₃^?) were significantly enriched in PM_2.5 via gas-to-particle conversion in the dilution process. Results indicate that in-stack hot sampling would underestimate levels of these species in PM_2.5.

Implications: PM_2.5 samples from a municipal solid waste incinerator (MSWI) were collected simultaneously by a dilution sampling technique and a traditional in-stack method. PM_2.5 emission factors and chemical speciation profiles were established. Dilution sampling provides more reliable data than in-stack hot sampling. The results can be applied to estimate the PM_2.5 emission inventories of MSWI, and the source profile can be used for contribution estimate of chemical mass balance modeling.     

Quantifying trace gas emissions from composite landscapes: A mass-budget approach with aircraft measurements

Quantifying trace gas emissions and the influence of surface exchange processes on the atmosphere is a necessary step towards the control of global greenhouse gas emissions and reliability of air quality models. This paper proposes a procedure based on the mass balance method and implemented on highly resolved aircraft data. It allows one to estimate surface exchanges on areas of several km² and heterogeneous features exploiting the characteristics of convective boundary layer during steady state conditions that permit the estimation of emission/absorption terms as functions of advective fluxes only. A nonparametric approach is adopted and the fluxes on the surface of a virtual box surrounding the area of interest are reconstructed on the basis of scalar densities and wind vectors using Shepard functions. Two different techniques are also proposed to face lack of data on the top surface of the box. The method has been applied to experimental data coming from measurement campaigns on two different sites. It provides realistic estimates of the CO₂ emission/absorption in the considered areas that are in good agreement with CO₂ fluxes evaluated by Airborne Eddy Covariance and confirm the suitability of the proposed approach for the assessment of turbulent exchange of trace gases by composite landscapes. Uncertainties on the estimated emissions due to both propagation of the experimental error and interpolation have been quantified by bootstrap analysis as 6%.     

Characterization of emissions from portable household combustion devices: particle size distributions,emission rates and factors,and potential exposures

A series of source tests were conducted to characterize emissions of particulate matter (PM), carbon monoxide (CO), carbon dioxide (CO₂), methane (CH₄), and total hydrocarbon (THC ) from five types of portable combustion devices. Tested combustion devices included a kerosene lamp, an oil lamp, a kerosene space heater, a portable gas range, and four unscented candles. All tests were conducted either in a well-mixed chamber or a well-mixed room, which enables us to determine emission rates and emission factors using a single-compartment mass balance model. Particle mass concentrations and number concentrations were measured using a nephelometric particle monitor and an eight-channel optical particle counter, respectively. Real-time CO concentrations were measured with an electrochemical sensor CO monitor. CO₂, CH₄, and THC were measured using a GC-FID technique. The results indicate that all particles emitted during steady burning in each of the tested devices were smaller than 1.0 μm in diameter with the vast majority in the range between 0.1 and 0.3 μm. The PM mass emission rates and emission factors for the tested devices ranged from 5.6±0.1 to 142.3±40.8 mg h⁻¹ and from 0.35±0.06 to 9.04±4.0 mg g⁻¹, respectively. The CO emission rates and emission factors ranged from 4.7±3.0 to 226.7±100 mg h⁻¹ and from 0.25±0.12 to 1.56±0.7 mg g⁻¹, respectively. The CO₂ emission rates and emission factors ranged from 5500±700 to 210,000±90,000 mg h⁻¹ and from 387±45 to 1689±640 mg g⁻¹, respectively. The contributions of CH₄ and THC to emission inventories are expected to be insignificant due both to the small emission factors and to the relatively small quantity of fuel consumed by these portable devices. An exposure scenario analysis indicates that every-day use of the kerosene lamp in a village house can generate fine PM exposures easily exceeding the US promulgated NAAQS for PM_2.5.     

Application of air pollution dispersion modeling for source-contribution assessment and model performance evaluation at integrated industrial estate-Pantnagar

Source-contribution assessment of ambient NO₂ concentration was performed at Pantnagar, India through simulation of two urban mathematical dispersive models namely Gaussian Finite Line Source Model (GFLSM) and Industrial Source Complex Model (ISCST-3) and model performances were evaluated. Principal approaches were development of comprehensive emission inventory, monitoring of traffic density and regional air quality and conclusively simulation of urban dispersive models. Initially, 18 industries were found responsible for emission of 39.11 kg/h of NO₂ through 43 elevated stacks. Further, vehicular emission potential in terms of NO₂ was computed as 7.1 kg/h. Air quality monitoring delineates an annual average NO₂ concentration of 32.6 μg/m³. Finally, GFLSM and ISCST-3 were simulated in conjunction with developed emission inventories and existing meteorological conditions. Models simulation indicated that contribution of NO₂ from industrial and vehicular source was in a range of 45-70% and 9-39%, respectively. Further, statistical analysis revealed satisfactory model performance with an aggregate accuracy of 61.9%.     

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

ABSTRACT

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

Comparison of the MOVES2010a,MOBILE6.2, and EMFAC2007 mobile source emission models with on-road traffic tunnel and remote sensing measurements

The Desert Research Institute conducted an on-road mobile source emission study at a traffic tunnel in Van Nuys, California, in August 2010 to measure fleet-averaged, fuel-based emission factors. The study also included remote sensing device (RSD) measurements by the University of Denver of 13,000 vehicles near the tunnel. The tunnel and RSD fleet-averaged emission factors were compared in blind fashion with the corresponding modeled factors calculated by ENVIRON International Corporation using U.S. Environmental Protection Agency's (EPA's) MOVES2010a (Motor Vehicle Emissions Simulator) and MOBILE6.2 mobile source emission models, and California Air Resources Board's (CARB's) EMFAC2007 (EMission FACtors) emission model. With some exceptions, the fleet-averaged tunnel, RSD, and modeled carbon monoxide (CO) and oxide of nitrogen (NO_x) emission factors were in reasonable agreement (±25%). The nonmethane hydrocarbon (NMHC) emission factors (specifically the running evaporative emissions) predicted by MOVES were insensitive to ambient temperature as compared with the tunnel measurements and the MOBILE- and EMFAC-predicted emission factors, resulting in underestimation of the measured NMHC/NO_x ratios at higher ambient temperatures. Although predicted NMHC/NO_x ratios are in good agreement with the measured ratios during cooler sampling periods, the measured NMHC/NO_x ratios are 3.1, 1.7, and 1.4 times higher than those predicted by the MOVES, MOBILE, and EMFAC models, respectively, during high-temperature periods. Although the MOVES NO_x emission factors were generally higher than the measured factors, most differences were not significant considering the variations in the modeled factors using alternative vehicle operating cycles to represent the driving conditions in the tunnel. The three models predicted large differences in NO_x and particle emissions and in the relative contributions of diesel and gasoline vehicles to total NO_x and particulate carbon (TC) emissions in the tunnel.

Implications: Although advances have been made to mobile source emission models over the past two decades, the evidence that mobile source emissions of carbon monoxide and hydrocarbons in urban areas were underestimated by as much as a factor of 2–3 in past inventories underscores the need for on-going verification of emission inventories. Results suggest that there is an overall increase in motor vehicle NMHC emissions on hot days that is not fully accounted for by the emission models. Hot temperatures and concomitant higher ratios of NMHC emissions relative to NO_x both contribute to more rapid and efficient formation of ozone. Also, the ability of EPA's MOVES model to simulate varying vehicle operating modes places increased importance on the choice of operating modes to evaluate project-level emissions.     

Emission factors of fine particulate matter,organic and elemental carbon,carbon monoxide,and carbon dioxide for four solid fuels commonly used in residential heating by the U.S. Navajo Nation

Most homes in the Navajo Nation use wood as their primary heating fuel, often in combination with locally mined coal. Previous studies observed health effects linked to this solid-fuel use in several Navajo communities. Emission factors (EFs) for common fuels used by the Navajo have not been reported using a relevant stove type. In this study, two softwoods (ponderosa pine and Utah juniper) and two high-volatile bituminous coals (Black Mesa and Fruitland) were tested with an in-use residential conventional wood stove (homestove) using a modified American Society for Testing and Materials/U.S. Environmental Protection Agency (ASTM/EPA) protocol. Filter sampling quantified PM_2.5 (particulate matter with an aerodynamic diameter ≤2.5 μm) and organic (OC) and elemental (EC) carbon in the emissions. Real-time monitoring quantified carbon monoxide (CO), carbon dioxide (CO₂), and total suspended particles (TSP). EFs for these air pollutants were developed and normalized to both fuel mass and energy consumed. In general, coal had significantly higher mass EFs than wood for all pollutants studied. In particular, coal emitted, on average, 10 times more PM_2.5 than wood on a mass basis, and 2.4 times more on an energy basis. The EFs developed here were based on fuel types, stove design, and operating protocols relevant to the Navajo Nation, but they could be useful to other Native Nations with similar practices, such as the nearby Hopi Nation.

Implications: Indoor wood and coal combustion is an important contributor to public health burdens in the Navajo Nation. Currently, there exist no emission factors representative of Navajo homestoves, fuels, and practices. This study developed emission factors for PM_2.5, OC, EC, CO, and CO₂ using a representative Navajo homestove. These emission factors may be utilized in regional-, national-, and global-scale health and environmental models. Additionally, the protocols developed and results presented here may inform on-going stove design of the first EPA-certified wood and coal combination stove.     

The change in O3, SO2 and NO2 concentrations in Lithuania

Due to the dynamic nature of the atmosphere, substantial amounts of gaseous and particulate pollutants are transported to the areas distant from their sources. In order to determine the regional concentration levels of atmospheric pollutants in Lithuania, concentrations of gaseous O₃, SO₂, NO₂ and other pollutants have been measured at the Preila background station (55°20′ N and 21°00′ E, 5 m a.s.l.) since 1981. The long-term concentration data set enabled us to get temporal trends, both on a seasonal and longer time scale, to identify source areas of pollutants and to relate them to the emission data. Based on the data obtained, the different tendencies in the pollutant concentration changes were revealed. Positive trends for ozone (of 2.9% per year during 1983–2000) and a distinct negative trend for both sulphur dioxide (of 3.8% per year during 1981–2000) and nitrogen dioxide (of 3.8% per year during 1983–2000) were found. The air mass back-trajectory analysis was used to assess the source region of air pollutants transported to Lithuania. The pollutant concentration levels were compared with their emission changes in Europe and Lithuania. The general trends in SO₂ as well as in NO₂ concentrations observed are consistent with changes in SO₂ and NO₂ emissions in Europe and Lithuania.     

Measurements and modelling of the ozone deposition velocity to concrete tiles,including the effect of diffusion

A range of models were fitted to the experimental time-dependent curves for the deposition velocity of O₃ to concrete floor tile samples. The models included modified Langmuir isotherms assuming adsorption of O₃ on the material surfaces and models assuming direct reaction on and diffusion of O₃ into the material from the air. The best fit was obtained with a simple two-parameter model assuming direct reaction of O₃ with adsorbed surface water and direct diffusion of O₃ into the material from the air. However, models assuming an additional second-order reaction of O₃ with an adsorbed surface species with a given start mass gave improved curve fit in the first 500 min. Applying the best model to experimental data obtained over the whole air humidity range resulted in markedly lower equilibrium deposition velocities than those measured after 48 h. The modelling gave a deposition velocity minimum in the 50–70% relative air humidity range in agreement with observations. The deposition velocity minimum could be explained with a reduced constant for the reaction of O₃ with water or OH⁻ ions on the surface.     

Source apportionment of fine particles at urban background and rural sites in the UK atmosphere

Airborne fine particulate matter (PM_2.5) has been collected at two sites in the West Midlands conurbation, UK, representing urban background and rural locations. Chemical analyses have been carried out for major anions, trace metals, total OC and EC, and for individual organic marker species including n-alkanes, hopanes, PAHs, organic acids and sterols. Source apportionment has been conducted using both a pragmatic mass closure model and the US EPA chemical mass balance (CMB) model. The pragmatic mass closure model is well able to account for the measured PM_2.5 mass in terms of chemical/source components, and the chemical mass balance model has been used to apportion the carbonaceous component of the aerosol. The dominant components of PM_2.5 at both sites are secondary inorganic (sulphate and nitrate) and carbonaceous particles. The CMB model shows the latter to arise mainly from road traffic sources, with smaller contributions from vegetative detritus, wood smoke, natural gas, coal, and dust/soil. The CMB model also identifies an important component of the organic aerosol not associated with these primary sources, which correlates very strongly with secondary organic aerosol estimated from the OC/EC ratio. The split between different automotive source types does not relate well to UK emission inventories, and may indicate that CMB source profiles from North American studies and different carbon analysis protocols may lead to erroneous conclusions.     

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.