Modeling the impacts of traffic emissions on air toxics concentrations near roadways

The dispersion formulation incorporated in the U.S. Environmental Protection Agency's AERMOD regulatory dispersion model is used to estimate the contribution of traffic-generated emissions of select VOCs – benzene, 1,3-butadiene, toluene – to ambient air concentrations at downwind receptors ranging from 10-m to 100-m from the edge of a major highway in Raleigh, North Carolina. The contributions are computed using the following steps: 1) Evaluate dispersion model estimates with 10-min averaged NO data measured at 7 m and 17 m from the edge of the road during a field study conducted in August, 2006; this step determines the uncertainty in model estimates. 2) Use dispersion model estimates and their uncertainties, determined in step 1, to construct pseudo-observations. 3) Fit pseudo-observations to actual observations of VOC concentrations measured during five periods of the field study. This provides estimates of the contributions of traffic emissions to the VOC concentrations at the receptors located from 10 m to 100 m from the road. In addition, it provides estimates of emission factors and background concentrations of the VOCs, which are supported by independent estimates from motor vehicle emissions models and regional air quality measurements. The results presented in the paper demonstrate the suitability of the formulation in AERMOD for estimating concentrations associated with mobile source emissions near roadways. This paper also presents an evaluation of the key emissions and dispersion modeling inputs necessary for conducting assessments of local-scale impacts from traffic emissions.     

Derivation of exemption formulas for air quality regulatory applications

The regulatory agencies and the industries have the responsibility for assessing the environmental impact from the release of air pollutants, and for protecting environment and public health. The simple exemption formula is often used as a criterion for the purpose of screening air pollutants. That is, the exemption formula is used for air quality review and to determine whether a facility applying for and described in a new, modified, or revised air quality plan is exempted from further air quality review. The Bureau of Ocean Energy Management’s (BOEM) air quality regulations are used to regulate air emissions and air pollutants released from the oil and gas facilities in the Gulf of Mexico. If a facility is not exempt after completing the air quality review, a refined air quality modeling will be required to regulate the air pollutants. However, at present, the scientific basis for BOEM’s exemption formula is not available to the author. Therefore, the purpose of this paper is to provide the theoretical framework and justification for the use of BOEM’s exemption formula. In this paper, several exemption formulas have been derived from the Gaussian and non-Gaussian dispersion models; the Gaussian dispersion model is a special case of non-Gaussian dispersion model. The dispersion parameters obtained from the tracer experiments in the Gulf of Mexico are used in the dispersion models. In this paper, the dispersion parameters used in the dispersion models are also derived from the Monin-Obukhov similarity theory. In particular, it has been shown that the total amount of emissions from the facility for each air pollutant calculated using BOEM’s exemption formula is conservative.

Implications:?The operation of offshore oil and gas facilities under BOEM’s jurisdiction is required to comply with the BOEM’s regulations. BOEM’s air quality regulations are used to regulate air emissions and air pollutants released from the oil and gas facilities in the Gulf of Mexico. The exemption formulas have been used by BOEM and other regulatory agencies as a screening tool to regulate air emissions emitted from the oil and gas and other industries. Because of the BOEM’s regulatory responsibility, it is important to establish the scientific basis and provide the justification for the exemption formulas. The methodology developed here could also be adopted and used by other regulatory agencies.     

Validation of Mobile Source Emission Estimates Using Mass Balance Techniques

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

Source apportionment of particulate matter (PM2.5) in an urban area using dispersion,receptor and inverse modelling

Air pollution emission inventories are the basis for air quality assessment and management strategies. The quality of the inventories is of great importance since these data are essential for air pollution impact assessments using dispersion models. In this study, the quality of the emission inventory for fine particulates (PM_2.5) is assessed: first, using the calculated source contributions from a receptor model; second, using source apportionment from a dispersion model; and third, by applying a simple inverse modelling technique which utilises multiple linear regression of the dispersion model source contributions together with the observed PM_2.5 concentrations. For the receptor modelling the chemical composition of PM_2.5 filter samples from a measurement campaign performed between January 2004 and April 2005 are analysed. Positive matrix factorisation is applied as the receptor model to detect and quantify the various source contributions. For the same observational period and site, dispersion model calculations using the Air Quality Management system, AirQUIS, are performed. The results identify significant differences between the dispersion and receptor model source apportionment, particularly for wood burning and traffic induced suspension. For wood burning the receptor model calculations are lower, by a factor of 0.54, but for the traffic induced suspension they are higher, by a factor of 7.1. Inverse modelling, based on regression of the dispersion model source contributions and the PM_2.5 concentrations, indicates similar discrepancies in the emissions inventory. In order to assess if the differences found at the one site are generally applicable throughout Oslo, the individual source category emissions are rescaled according to the receptor modelling results. These adjusted PM_2.5 concentrations are compared with measurements at four independent stations to evaluate the updated inventory. Statistical analysis shows improvement in the estimated concentrations for PM_2.5 at all sites. Similarly, inverse modelling is applied at these independent sites and this confirms the validity of the receptor model results.     

An evaluation of the estimation of road traffic emission factors from tracer studies

Road traffic emission factors (EFs) are one of the main sources of uncertainties in emission inventories; it is necessary to develop methods to reduce these uncertainties to manage air quality more efficiently. Recently an alternative method has been proposed to estimate the EFs. In that work the emission factors were estimated from a long term tracer study developed in Ho Chi Minh City (HCMC) Vietnam. A passive tracer was continuously emitted from a finite line source placed in one side of an urban street canyon. Simultaneously, the resulting tracer concentrations were monitored at the other side of the street. The results of this experiment were used to calculate the dispersion factors and afterwards, these dispersion factors were used to estimate the EFs. In this paper we use the Computational Fluids Dynamics (CFD) model WinMISKAM to critically evaluate the proposed methodology.In a first step, we use the results of the tracer study to validate the CFD model. Results show that the model is able to simulate quite well the tracer dispersion in most of the cases. The model is then used to evaluate the effect of varying the source configuration and to correct the EFs. A comparison with available studies shows that the corrected EFs are within the range of the EFs reported in other studies. Finally, the CFD model is used to find a source configuration that better represents the vehicle emissions and that may be used in future studies to estimate the EFs more accurately. Results show that a 200 m line placed in the center of the street would represent very well the vehicle emissions. This work shows that it is possible to accurately estimate the EFs from tracer studies.     

Modeling traffic-related air pollution in street canyons of Beijing

It is important to develop a general model to accurately simulate the air pollution in urban street areas. In this paper, the Operational Street Pollution Model (OSPM) initially developed in Denmark is tested with measured data from a relatively wide and open street in Beijing. Major factors influencing the dispersion, such as emission factors, stationary source emissions, and solar radiation, are analyzed. Results show that the model can reflect the basic dispersion pattern in the street but gives systematically higher concentrations. After modifications to estimate street-level wind speed in the model, performance is obviously improved.     

Source apportionment of indoor air pollution

An understanding of the relative contributions from important pollutant sources to human exposures is necessary for the design and implementation of effective control strategies. In the past, societal efforts to control air pollution have focused almost exclusively on the outdoor (ambient) environment. As a result, substantial amounts of time and money have been spent to limit airborne discharges from mobile and stationary sources. Yet it is now recognized that exposures to elevated pollutant concentrations often occur as a result of indoor, rather than outdoor, emissions. While the major indoor sources have been identified, their relative impacts on indoor air quality have not been well defined. Application of existing source apportionment models to nonindustrial indoor environments is only just beginning. It is possible that these models might be used to distinguish between indoor and outdoor emissions, as well as to distinguish among indoor sources themselves. However, before the feasibility and suitability of source-apportionment methods for indoor applications can be assessed adequately, it is necessary to take account of model assumptions and associated data requirements. This paper examines the issue of indoor source apportionment and reviews the need for emission characterization studies to support such source-apportionment efforts.     

Modeling Traffic-Related Air Pollution in Street Canyons of Beijing

ABSTRACT

It is important to develop a general model to accurately simulate the air pollution in urban street areas. In this paper, the Operational Street Pollution Model (OSPM) initially developed in Denmark is tested with measured data from a relatively wide and open street in Beijing. Major factors influencing the dispersion, such as emission factors, stationary source emissions, and solar radiation, are analyzed. Results show that the model can reflect the basic dispersion pattern in the street but gives systematically higher concentrations. After modifications to estimate street-level wind speed in the model, performance is obviously improved.     

Review of Singapore's air quality and greenhouse gas emissions: current situation and opportunities

Singapore has many environmental accomplishments to its credit. Accessible data on air quality indicates that all criteria pollutants satisfy both U.S. Environmental Protection Agency (EPA) and World Health Organization (WHO) air quality standards and guidelines, respectively. The exception is PM2.5 (particles with an aerodynamic diameter < or = 2.5 microm), which is not currently considered a criteria pollutant in Singapore but may potentially be the major local air pollution problem and cause for health concern. Levels of other airborne pollutants as well as their physical and chemical processes associated with local formation, transformation, dispersion, and deposition are not known. According to available emission inventories, Singapore contribution to the total atmospheric pollution and carbon budget at the regional and global scales is small. Emissions per unit gross domestic product (GDP) are low compared with other countries, although Singapore's per-capita GDP and per-capita emissions are among the highest in the world. Some information is available on health effects, but the impacts on the ecosystem and the complex interactions of air pollution and climate change at a regional level are also unknown. This article reviews existing available information on atmospheric pollution and greenhouse gas emissions and proposes a multipollutant approach to greenhouse gas mitigation and local air quality. Singapore, by reducing its per-capita emissions, increasing the availability of information (e.g., through regularly publishing hourly and/or daily PM2.5 concentrations) and developing a research agenda in this area, would likely be seen to be a model of a high-density, livable, and sustainable city in Southeast Asia and other tropical regions worldwide.     

Managing emissions from highly industrialized areas: Regulatory compliance under uncertainty

The use of regulatory and compliance-based modeling for air quality impact assessment is invariably relied upon to predict future air quality under various management scenarios particularly where air quality monitoring data are limited. This paper examines the dispersion from a multi-stack cement manufacturing complex with associated quarries and transport activities for regulatory compliance under uncertain emission and meteorological conditions. The concentrations of CO, NO_x, SO₂ and PM at sensitive receptor locations were used as indicators in comparison to World Health Organization (WHO) interim guidelines. Exceedance exposure areas were delineated under bounded uncertainties in input emission factors and meteorological parameters. Planning and management initiatives were tested to control/minimize potential exposure. Compared to the case of low emissions and actual meteorological conditions, the consideration of worst emissions coupled to worst meteorological conditions enlarged the boundaries of the exceedance exposure areas considerably. The implementation of best available technologies and enforcement of emission standards improved air quality in the region significantly and lowered the exposure at many population centers to below health standards. Uncertainty in the output of atmospheric dispersion models continues to play a significant role to be considered at the point where science is translated into political decision making.     

Relating meteorological variables and trends in motor vehicle emissions to monthly urban carbon monoxide concentrations

A model which quantifies the relationship between the monthly time series for CO emissions, the monthly time series in ambient CO concentration, and meteorologically driven dispersion was developed. Fifteen cities representing a wide range of geographical and climatic conditions were selected. An eight-year time series (1984–1991 inclusive) of monthly averaged data were examined in each city. A new method of handling missing ambient concentration values which is designed to calculate city-wide average concentrations that follow the trend seen at individual monitor sites is presented. This method is general and can be used in other applications involving missing data. The model uses emissions estimates along with two meteorological variables (wind speed and mixing height) to estimate monthly averages of ambient air pollution concentrations. The model is shown to have a wide range of applicability; it works equally well for a wide range of cities that have very different temporal CO distributions. The model is suited for assessing long-term trends in ambient air pollutants and can also be used for estimating seasonal variations in concentration, estimation of trends in emissions, and for filling in gaps in the ambient concentration record.     

Review of Singapore's air quality and greenhouse gas emissions: Current situation and opportunities

Singapore has many environmental accomplishments to its credit. Accessible data on air quality indicates that all criteria pollutants satisfy both U.S. Environmental Protection Agency (EPA) and World Health Organization (WHO) air quality standards and guidelines, respectively. The exception is PM_2.5 (particles with an aerodynamic diameter ≤2.5 μm), which is not currently considered a criteria pollutant in Singapore but may potentially be the major local air pollution problem and cause for health concern. Levels of other airborne pollutants as well as their physical and chemical processes associated with local formation, transformation, dispersion, and deposition are not known. According to available emission inventories, Singapore's contribution to the total atmospheric pollution and carbon budget at the regional and global scales is small. Emissions per unit gross domestic product (GDP) are low compared with other countries, although Singapore's per-capita GDP and per-capita emissions are among the highest in the world. Some information is available on health effects, but the impacts on the ecosystem and the complex interactions of air pollution and climate change at a regional level are also unknown. This article reviews existing available information on atmospheric pollution and greenhouse gas emissions and proposes a multipollutant approach to greenhouse gas mitigation and local air quality. Singapore, by reducing its per-capita emissions, increasing the availability of information (e.g., through regularly publishing hourly and/or daily PM_2.5 concentrations) and developing a research agenda in this area, would likely be seen to be a model of a high-density, livable, and sustainable city in Southeast Asia and other tropical regions worldwide.

Implications Singapore is widely recognized for its environmental achievements and often cited as a model of a high-density, livable, and sustainable city. This article reviews available information with the aim to provide a reference for future scientific research of strategic relevance for Singapore's air quality and greenhouse gas mitigation management under a multipollutant framework. However, the limited publicly accessible data and little scientific information prevent a comprehensive assessment of the local air quality and greenhouse gas emissions. Singapore's dynamic economy and strong profile in advanced science and technological innovation have the potential to enhance the research agenda in this area, which is not yet well developed in tropical cities.     

Air Pollution Problems of the Steel Industry

Urban air pollution has traditionally been modeled using annual, or at best, seasonal emissions inventories and climatology. These averaging techniques may introduce uncertainty into the analysis, if specific emissions (e.g. SO₂) are correlated with dispersion factors on a short-term basis. This may well be the case for space heating emissions. An analysis of this problem, using hourly climatological and residential emission estimates for six U.S. cities, indicates that the errors introduced using such averages are modest (~ ± 12%) for annual average concentrations. Maximum hourly concentrations vary considerably more, since maximum heat demand and worst case dispersion are in general not coincident. The paper thus provides a basis for estimating more realistic air pollution Impacts due to residential space heating.     

Comparison of the industrial source complex and AERMOD dispersion models: case study for human health risk assessment

Air quality models are typically used to predict the fate and transport of air emissions from industrial sources to comply with federal and state regulatory requirements and environmental standards, as well as to determine pollution control requirements. For many years, the U.S. Environmental Protection Agency (EPA) widely used the Industrial Source Complex (ISC) model because of its broad applicability to multiple source types. Recently, EPA adopted a new rule that replaces ISC with AERMOD, a state-of-the-practice air dispersion model, in many air quality impact assessments. This study compared the two models as well as their enhanced versions that incorporate the Plume Rise Model Enhancements (PRIME) algorithm. PRIME takes into account the effects of building downwash on plume dispersion. The comparison used actual point, area, and volume sources located on two separate facilities in conjunction with site-specific terrain and meteorological data. The modeled maximum total period average ground-level air concentrations were used to calculate potential health effects for human receptors. The results show that the switch from ISC to AERMOD and the incorporation of the PRIME algorithm tend to generate lower concentration estimates at the point of maximum ground-level concentration. However, the magnitude of difference varies from insignificant to significant depending on the types of the sources and the site-specific conditions. The differences in human health effects, predicted using results from the two models, mirror the concentrations predicted by the models.     

Evaluation of risks from urban air pollutants in the southeast Chicago area

Region V of the U.S. Environmental Protection Agency has conducted a comprehensive study of cancer risks from urban exposure to air pollutants in the Southeast Chicago area. This study estimated emissions of a list of 30 air carcinogens from a broad range of nontraditional, as well as traditional, source types. Using dispersion modeling and applying the appropriate unit risk factors and population data, this study estimated the risks at each receptor location and the total number of cancer cases attributable to air pollution in the area. This analysis estimated that current concentrations would cause 77 cases of cancer over the next 70 years, an average risk of 2.0 X 10(-4). Contributions from different source types and different pollutants were estimated. The total contribution from nontraditional source types was less than 0.3 percent. Although these estimates are highly uncertain, the study does suggest the nature and general magnitude of cancer risks from air pollution in the urban area studied.     

Road traffic impact on urban water quality: a step towards integrated traffic,air and stormwater modelling

Methods for simulating air pollution due to road traffic and the associated effects on stormwater runoff quality in an urban environment are examined with particular emphasis on the integration of the various simulation models into a consistent modelling chain. To that end, the models for traffic, pollutant emissions, atmospheric dispersion and deposition, and stormwater contamination are reviewed. The present study focuses on the implementation of a modelling chain for an actual urban case study, which is the contamination of water runoff by cadmium (Cd), lead (Pb), and zinc (Zn) in the Grigny urban catchment near Paris, France. First, traffic emissions are calculated with traffic inputs using the COPERT4 methodology. Next, the atmospheric dispersion of pollutants is simulated with the Polyphemus line source model and pollutant deposition fluxes in different subcatchment areas are calculated. Finally, the SWMM water quantity and quality model is used to estimate the concentrations of pollutants in stormwater runoff. The simulation results are compared to mass flow rates and concentrations of Cd, Pb and Zn measured at the catchment outlet. The contribution of local traffic to stormwater contamination is estimated to be significant for Pb and, to a lesser extent, for Zn and Cd; however, Pb is most likely overestimated due to outdated emissions factors. The results demonstrate the importance of treating distributed traffic emissions from major roadways explicitly since the impact of these sources on concentrations in the catchment outlet is underestimated when those traffic emissions are spatially averaged over the catchment area.     

Algorithms and analytical solutions for rapidly approximating long-term dispersion from line and area sources

Predicting long-term mean pollutant concentrations in the vicinity of airports, roads and other industrial sources are frequently of concern in regulatory and public health contexts. Many emissions are represented geometrically as ground-level line or area sources. Well developed modelling tools such as AERMOD and ADMS are able to model dispersion from finite (i.e. non-point) sources with considerable accuracy, drawing upon an up-to-date understanding of boundary layer behaviour. Due to mathematical difficulties associated with line and area sources, computationally expensive numerical integration schemes have been developed. For example, some models decompose area sources into a large number of line sources orthogonal to the mean wind direction, for which an analytical (Gaussian) solution exists. Models also employ a time-series approach, which involves computing mean pollutant concentrations for every hour over one or more years of meteorological data. This can give rise to computer runtimes of several days for assessment of a site. While this may be acceptable for assessment of a single industrial complex, airport, etc., this level of computational cost precludes national or international policy assessments at the level of detail available with dispersion modelling. In this paper, we extend previous work [S.R.H. Barrett, R.E. Britter, 2008. Development of algorithms and approximations for rapid operational air quality modelling. Atmospheric Environment 42 (2008) 8105–8111] to line and area sources. We introduce approximations which allow for the development of new analytical solutions for long-term mean dispersion from line and area sources, based on hypergeometric functions. We describe how these solutions can be parameterized from a single point source run from an existing advanced dispersion model, thereby accounting for all processes modelled in the more costly algorithms. The parameterization method combined with the analytical solutions for long-term mean dispersion are shown to produce results several orders of magnitude more efficiently with a loss of accuracy small compared to the absolute accuracy of advanced dispersion models near sources. The method can be readily incorporated into existing dispersion models, and may allow for additional computation time to be expended on modelling dispersion processes more accurately in future, rather than on accounting for source geometry.     

Sulfur dioxide dispersion and source contribution to receptors of downtown Patras, Greece

Background The development of the city of Patras, including harbour relocation, in conjunction with the protection of the regional ecosystems, requires air quality assessment and management. For this reason, a model applicable in the Patras area is necessary and valuable. The goal of this study was to validate a model suitable for predicting the dispersion of sulfur dioxide (SO₂), based on particular activity, topography and weather conditions. Methods We used the US-EPA ISCLT3 integral dispersion model to predict SO₂ concentrations for Patras, Greece. We assumed that the major contribution to Patras air pollution came from central heating, harbour and traffic. We calculated traffic emissions using COPERTIII. Results and Discussion Assigning suitable values of the mixing height, the model predicted the local and spatial distribution of the mean monthly SO₂ concentrations in downtown Patras, as well computed the contribution of the SO₂ emissions originating from each particular source at each receptor location on a seasonal and annual basis. The comparison between predictions and measurements shows that the model performance for estimating the SO₂ concentrations and period pattern is satisfactory. Conclusion The mixing height was the critical parameter for calibrating the model. Model validation promises satisfactory predictions for SO₂ pollution levels on monthly basis. Recommendations and Outlook The model could be used in predicting SO₂ concentrations and source contribution for several downtown Patras receptors using pertinent meteorological and emission information. It could be also extended to predict the dispersion of other primary air pollutants. The calibrated model predictions could be used to fill gaps in monitoring data, saving money and time, and help in assess and manage air quality as Patras develops.     

Evaluation of Risks from Urban Air Pollutants in the Southeast Chicago Area

Region V of the U.S. Environmental Protection Agency has conducted a comprehensive study of cancer risks from urban exposure to air pollutants in the Southeast Chicago area. This study estimated emissions of a list of 30 air carcinogens from a broad range of nontraditional, as well as traditional, source types. Using dispersion modeling and applying the appropriate unit risk factors and population data, this study estimated the risks at each receptor location and the total number of cancer cases attributable to air pollution in the area.

This analysis estimated that current concentrations would cause 77 cases of cancer over the next 70 years, an average risk of 2.0 × 10^?4. Contributions from different source types and different pollutants were estimated. The total contribution from nontraditional source types was less than 0.3 percent. Although these estimates are highly uncertain, the study does suggest the nature and general magnitude of cancer risks from air pollution in the urban area studied.     

Wind Tunnel Modelling of Roadways: Comparison with Mathematical Models

The assessment of air quality impacts from roadways is a major concern to urban planners. In order to assess future road and building configurations, a number of techniques have been developed, including mathematical models, which simulate traffic emissions and atmospheric dispersion through a series of mathematical relationships and physical models. The latter models simulate emissions and dispersion through scaling of these processes in a wind tunnel. Two roadway mathematical models, HIWAY-2 and CALINE-4, were applied to a proposed development in a large urban area. Physical modelling procedures developed by Rowan Williams Davies & Irwin Inc. (RWDI) in the form of line source simulators were also applied, and the resulting carbon monoxide concentrations were compared. The results indicated a factor of two agreement between the mathematical and physical models. The physical model, however, reacted to changes in building massing and configuration. The mathematical models did not, since no provision for such changes was included in the mathematical models. In general, the RWDI model resulted in higher concentrations than either HIWAY-2 or CALINE-4. Where there was underprediction, it was often due to shielding of the receptor by surrounding buildings. Comparison of these three models with the CALTRANS Tracer Dispersion Experiment showed good results although concentrations were consistently underpredicted.