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.     

A hybrid modeling approach to resolve pollutant concentrations in an urban area

A modeling tool that can resolve contributions from individual sources to the urban environment is critical for air-toxics exposure assessments. Air toxics are often chemically reactive and may have background concentrations originated from distant sources. Grid models are the best-suited tools to handle the regional features of these chemicals. However, these models are not designed to resolve pollutant concentrations on local scales. Moreover, for many species of interest, having reaction time scales that are longer than the travel time across an urban area, chemical reactions can be ignored in describing local dispersion from strong individual sources making Lagrangian and plume-dispersion models practical. In this study, we test the feasibility of developing an urban hybrid simulation system. In this combination, the Community Multi-scale Air Quality model (CMAQ) provides the regional background concentrations and urban-scale photochemistry, and local models such as Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT) and AMS/EPA Regulatory Model (AERMOD) provide the more spatially resolved concentrations due to local emission sources. In the initial application, the HYSPLIT, AERMOD, and CMAQ models are used in combination to calculate high-resolution benzene concentrations in the Houston area. The study period is from 18 August to 4 September of 2000. The Mesoscale Model 5 (MM5) is used to create meteorological fields with a horizontal resolution of 1×1 km². In another variation to this approach, multiple HYSPLIT simulations are used to create a concentration ensemble to estimate the contribution to the concentration variability from point sources. HYSPLIT simulations are used to model two sources of concentration variability; one due to variability created by different particle trajectory pathways in the turbulent atmosphere and the other due to different flow regimes that might be introduced when using gridded data to represent meteorological data fields. The ensemble mean concentrations determined by HYSPLIT plus the concentrations estimated by AERMOD are added to the CMAQ calculated background to estimate the total mean benzene concentration. These estimated hourly mean concentrations are also compared with available field measurements.     

National estimates of outdoor air toxics concentrations

The Clean Air Act identifies 189 hazardous air pollutants (HAPs), or "air toxics," associated with a wide range of adverse human health effects. The U.S. Environmental Protection Agency has conducted a modeling study with the Assessment System for Population Exposure Nationwide (ASPEN) to gain a greater understanding of the spatial distribution of concentrations of these HAPs resulting from contributions of multiple emission sources. The study estimates year 1990 long-term outdoor concentrations of 148 air toxics for each census tract in the continental United States, utilizing a Gaussian air dispersion modeling approach. Ratios of median national modeled concentrations to estimated emissions indicate that emission totals without consideration of emission source type can be a misleading indicator of air quality. The results also indicate priorities for improvements in modeling methodology and emissions identification. Model performance evaluation suggests a tendency for underprediction of observed concentrations, which is likely due, at least in part, to a number of limitations of the Gaussian modeling formulation. Emissions estimates for HAPs have a high degree of uncertainty and contribute to discrepancies between modeled and monitored concentration estimates. The model's ranking of concentrations among monitoring sites is reasonably good for most of the gaseous HAPs evaluated, with ranking accuracy ranging from 66 to 100%.     

Comparison of the performances of land use regression modelling and dispersion modelling in estimating small-scale variations in long-term air pollution concentrations in a Dutch urban area

The performance of a Land Use Regression (LUR) model and a dispersion model (URBIS – URBis Information System) was compared in a Dutch urban area. For the Rijnmond area, i.e. Rotterdam and surroundings, nitrogen dioxide (NO₂) concentrations for 2001 were estimated for nearly 70 000 centroids of a regular grid of 100 × 100 m.A LUR model based upon measurements carried out on 44 sites from the Dutch national monitoring network and upon Geographic Information System (GIS) predictor variables including traffic intensity, industry, population and residential land use was developed. Interpolation of regional background concentration measurements was used to obtain the regional background. The URBIS system was used to estimate NO₂ concentrations using dispersion modelling. URBIS includes the CAR model (Calculation of Air pollution from Road traffic) to calculate concentrations of air pollutants near urban roads and Gaussian plume models to calculate air pollution levels near motorways and industrial sources. Background concentrations were accounted for using 1 × 1 km maps derived from monitoring and model calculations.Moderate agreement was found between the URBIS and LUR in calculating NO₂ concentrations (R = 0.55). The predictions agreed well for the central part of the concentration distribution but differed substantially for the highest and lowest concentrations. The URBIS dispersion model performed better than the LUR model (R = 0.77 versus R = 0.47 respectively) in the comparison between measured and calculated concentrations on 18 validation sites. Differences can be understood because of the use of different regional background concentrations, inclusion of rather coarse land use category industry as a predictor variable in the LUR model and different treatment of conversion of NO to NO₂.Moderate agreement was found between a dispersion model and a land use regression model in calculating annual average NO₂ concentrations in an area with multiple sources. The dispersion model explained concentrations at validation sites better.     

The Use of Artificial Activable Trace Elements to Monitor Pollutant Source Strengths and Dispersal Patterns

A tracer technique using certain of the rare earth elements which are easily activated by neutrons has been developed for the analysis of air pollution problems. Studies employing these tracers were made to determine whether the available meteorological dispersion models can be used effectively to describe pollution emissions from selected industries in the vicinity of Albany, Oregon. The Gaussian plume model was found to be satisfactory for the moderately intense turbulence fields which characterize Stability Types B, C, and D, including cases in which the pollution was trapped by an inversion layer aloft. For sources near ground level, however, it was necessary to make allowance for urban influences on plume dispersion. A box model best described the observed dispersal pattern when the upward penetration of the very intense turbulence of Stability Type A was limited by an inversion layer aloft. These meteorological models were applied using a “blind” experimental procedure to predict the emission rates of the effluent from multiple sources of air pollution in the Albany area. It was found that these techniques can be used to predict the rate of emission within a factor of two for multiple sources consisting of three stacks.     

Impact of emissions from natural gas production facilities on ambient air quality in the Barnett Shale area: A pilot study

Rapid and extensive development of shale gas resources in the Barnett Shale region of Texas in recent years has created concerns about potential environmental impacts on water and air quality. The purpose of this study was to provide a better understanding of the potential contributions of emissions from gas production operations to population exposure to air toxics in the Barnett Shale region. This goal was approached using a combination of chemical characterization of the volatile organic compound (VOC) emissions from active wells, saturation monitoring for gaseous and particulate pollutants in a residential community located near active gas/oil extraction and processing facilities, source apportionment of VOCs measured in the community using the Chemical Mass Balance (CMB) receptor model, and direct measurements of the pollutant gradient downwind of a gas well with high VOC emissions. Overall, the study results indicate that air quality impacts due to individual gas wells and compressor stations are not likely to be discernible beyond a distance of approximately 100 m in the downwind direction. However, source apportionment results indicate a significant contribution to regional VOCs from gas production sources, particularly for lower-molecular-weight alkanes (<C6). Although measured ambient VOC concentrations were well below health-based safe exposure levels, the existence of urban-level mean concentrations of benzene and other mobile source air toxics combined with soot to total carbon ratios that were high for an area with little residential or commercial development may be indicative of the impact of increased heavy-duty vehicle traffic related to gas production

ImplicationsRapid and extensive development of shale gas resources in recent years has created concerns about potential environmental impacts on water and air quality. This study focused on directly measuring the ambient air pollutant levels occurring at residential properties located near natural gas extraction and processing facilities, and estimating the relative contributions from gas production and motor vehicle emissions to ambient VOC concentrations. Although only a small-scale case study, the results may be useful for guidance in planning future ambient air quality studies and human exposure estimates in areas of intensive shale gas production.     

Tracer studies to characterize the effects of roadside noise barriers on near-road pollutant dispersion under varying atmospheric stability conditions

A roadway toxics dispersion study was conducted at the Idaho National Laboratory (INL) to document the effects on concentrations of roadway emissions behind a roadside sound barrier in various conditions of atmospheric stability. The homogeneous fetch of the INL, controlled emission source, lack of other manmade or natural flow obstructions, and absence of vehicle-generated turbulence reduced the ambiguities in interpretation of the data. Roadway emissions were simulated by the release of an atmospheric tracer (SF₆) from two 54 m long line sources, one for an experiment with a 90 m long noise barrier and one for a control experiment without a barrier. Simultaneous near-surface tracer concentration measurements were made with bag samplers on identical sampling grids downwind from the line sources. An array of six 3-d sonic anemometers was employed to measure the barrier-induced turbulence. Key findings of the study are: (1) the areal extent of higher concentrations and the absolute magnitudes of the concentrations both increased as atmospheric stability increased; (2) a concentration deficit developed in the wake zone of the barrier with respect to concentrations at the same relative locations on the control experiment at all atmospheric stabilities; (3) lateral dispersion was significantly greater on the barrier grid than the non-barrier grid; and (4) the barrier tended to trap high concentrations near the “roadway” (i.e. upwind of the barrier) in low wind speed conditions, especially in stable conditions.     

A Multivariable Model for Atmospheric Dispersion Predictions

In conjunction with a 15-month air quality survey of Jacksonville, Fia., a mathematical model has been developed to describe the dispersion of atmospheric pollutants. The source inventory used with the model was compiled, in part, from the data obtained from the sampling of all major sources within the area. The major sources were considered separately from the one-mile square area sources which accounted for the remainder of the emissions. Meteorological data was recorded continuously in the city including vertical temperature observations to 750 ft. The model was compiled in FORTRAN and can be used for both gaseous and particulate pollutants, by utilizing proper decay rates. The variant nature of meteorological parameters and emission rates are considered. The ground level concentrations of several pollutants which were determined for 24 hr periods at 11 sites and continuously at two other sites were used to check the model. A limited tracer study was carried out in conjunction with the project.     

A method of assessing air toxics concentrations in urban areas using mobile platform measurements

The objective of this paper is to demonstrate an approach to characterize the spatial variability in ambient air concentrations using mobile platform measurements. This approach may be useful for air toxics assessments in Environmental Justice applications, epidemiological studies, and environmental health risk assessments. In this study, we developed and applied a method to characterize air toxics concentrations in urban areas using results of the recently conducted field study in Wilmington, DE. Mobile measurements were collected over a 4- x 4-km area of downtown Wilmington for three components: formaldehyde (representative of volatile organic compounds and also photochemically reactive pollutants), aerosol size distribution (representing fine particulate matter), and water-soluble hexavalent chromium (representative of toxic metals). These measurements were,used to construct spatial and temporal distributions of air toxics in the area that show a very strong temporal variability, both diurnally and seasonally. An analysis of spatial variability indicates that all pollutants varied significantly by location, which suggests potential impact of local sources. From the comparison with measurements at the central monitoring site, we conclude that formaldehyde and fine particulates show a positive correlation with temperature, which could also be the reason that photochemically generated formaldehyde and fine particulates over the study area correlate well with the fine particulate matter measured at the central site.     

High time-resolved measurements of organic air toxics in different source regimes

High time-resolved (HTR) measurements can provide significant insight into sources and exposures of air pollution. In this study, an automated instrument was developed and deployed to measure hourly concentrations of 18 gas-phase organic air toxics and 6 volatile organic compounds (VOCs) at three sites in and around Pittsburgh, Pennsylvania. The sites represent different source regimes: a site with substantial mobile-source emissions; a residential site adjacent to a heavily industrialized zone; and an urban background site. Despite the close proximity of the sites (less than 13 km apart), the temporal characteristic of outdoor concentrations varied widely. Most of the compounds measured were characterized by short periods of elevated concentrations or plume events, but the duration, magnitude and composition of these events varied from site to site. The HTR data underscored the strong role of emissions from local sources on exposure to most air toxics. Plume events contributed more than 50% of the study average concentrations for all pollutants except chloroform, 1,2-dichloroethane, and carbon tetrachloride. Wind directional dependence of air toxic concentrations revealed that emissions from large industrial facilities affected concentrations at all of the sites. Diurnal patterns and weekend/weekday variations indicated the effects of the mixing layer, point source emissions patterns, and mobile source air toxics (MSATs) on concentrations. Concentrations of many air toxics were temporally correlated, especially MSATs, indicating that they are likely co-emitted. It was also shown that correlations of the HTR data were greater than lower time resolution data (24-h measurements). This difference was most pronounced for the chlorinated pollutants. The stronger correlations in HTR measurements underscore their value for source apportionment studies.     

Sampling frequency guidance for ambient air toxics monitoring

The U.S. Environmental Protection Agency (EPA) is in the process of designing a national network to monitor hazardous air pollutants (HAPs), also known as air toxics. The purposes of the expanded monitoring are to (1) characterize ambient concentrations in representative areas; (2) provide data to support and evaluate dispersion and receptor models; and (3) establish trends and evaluate the effectiveness of HAP emission reduction strategies. Existing air toxics data, in the form of an archive compiled by EPA's Office of Air Quality Planning and Standards (OAQPS), are used in this paper to examine the relationship between estimated annual average (AA) HAP concentrations and their associated variability. The goal is to assess the accuracy, or bias and precision, with which the AA can be estimated as a function of ambient concentration levels and sampling frequency. The results suggest that, for several air toxics, a sampling schedule of 1 in 3 days (1:3) or 1:6 days maybe appropriate for meeting some of the general objectives of the national network, with the more intense sampling rate being recommended for areas expected to exhibit relatively high ambient levels.     

developing a practical dispersion model for an air quality region

A series of computer models have been developed to predict air quality in the New York/New Jersey/Connecticut Air Quality Region. Efforts have been directed at models which have a shorter time scale than climatological models, and which are capable of providing better recommendations for effective abatement and planning, but use input data presently available.

The basic dispersion model for these investigations is a steady-state,nondivergent Gaussian-type model. A modified inventory of SO₂ sources,based on published data for the New York/New Jersey/Connecticut Air Quality Region, was prepared for use with the model. The basic model has been subjected to various internal sensitivity analyses, in which was isolated the variation produced in the pollutant concentration by a given change in each of the factors that contribute, e.g., wind speed, wind direction,mixing depth, stability conditions, source strengths, and grid size for the area sources.

To date, validation tests of the model have been made against the July and August 1969 data for the ten telemetering stations of the New York City Aerometric Network. Hourly as well as averaged concentrations were considered. Various sets of meteorological data from the network stations and the three area airports, were compared and tested. Additional tests, particularly for the winter season, are needed to substantiate the preliminary conclusions suggested by the results to date.

Considerable insight into the relative importance of model components has been acquired from the sensitivity studies. Furthermore the validation results lend support to the belief that a reasonably simple, practical dispersion model can be developed for the region.     

Modeling of Personal Exposures to Ambient Air Toxics in Camden,New Jersey: An Evaluation Study

Abstract

This study presents the Individual Based Exposure Modeling (IBEM) application of MENTOR (Modeling ENvironment for TOtal Risk studies) in a hot spot area, where there are concentrated local sources on the scale of tens to hundreds of meters, and an urban reference area in Camden, NJ, to characterize the ambient concentrations and personal exposures to benzene and toluene from local ambient sources. The emission-based ambient concentrations in the two neighborhoods were first estimated through atmospheric dispersion modeling. Subsequently, the calculated and measured ambient concentrations of benzene and toluene were separately combined with the time-activity diaries completed by the subjects as inputs to MENTOR/IBEM for estimating personal exposures resulting from ambient sources. The modeling results were then compared with the actual personal measurements collected from over 100 individuals in the field study to identify the gaps in modeling personal exposures in a hot spot. The modeled ambient concentrations of benzene and toluene were generally in agreement with the neighborhood measurements within a factor of 2, but were underestimated at the high-end percentiles. The major local contributors to the benzene ambient levels are from mobile sources, whereas mobile and stationary (point and area) sources contribute to the toluene ambient levels in the study area. This finding can be used as guidance for developing better air toxic emission inventories for characterizing, through modeling, the ambient concentrations of air toxics in the study area. The estimated percentage contributions of personal exposures from ambient sources were generally higher in the hot spot area than the urban reference area in Camden, NJ, for benzene and toluene. This finding demonstrates the hot spot characteristics of stronger local ambient source impacts on personal exposures. Non-ambient sources were also found as significant contributors to personal exposures to benzene and toluene for the population studied.     

Correlating emissions with time and temperature to predict worst-case emissions from open liquid area sources

The two primary factors influencing ambient air pollutant concentrations are emission rate and dispersion rate. Gaussian dispersion modeling studies for odors, and often other air pollutants, vary dispersion rates using hourly meteorological data. However, emission rates are typically held constant, based on one measured value. Using constant emission rates can be especially inaccurate for open liquid area sources, like wastewater treatment plant units, which have greater emissions during warmer weather, when volatilization and biological activity increase. If emission rates for a wastewater odor study are measured on a cooler day and input directly into a dispersion model as constant values, odor impact will likely be underestimated. Unfortunately, because of project schedules, not all emissions sampling from open liquid area sources can be conducted under worst-case summertime conditions. To address this problem, this paper presents a method of varying emission rates based on temperature and time of the day to predict worst-case emissions. Emissions are varied as a linear function of temperature, according to Henry's law, and a tenth order polynomial function of time. Equation coefficients are developed for a specific area source using concentration and temperature measurements, captured over a multiday period using a data-logging monitor. As a test case, time/temperature concentration correlation coefficients were estimated from field measurements of hydrogen sulfide (H2S) at the Rowlett Creek Wastewater Treatment Plant in Garland, TX. The correlations were then used to scale a flux chamber emission rate measurement according to hourly readings of time and temperature, to create an hourly emission rate file for input to the dispersion model ISCST3. ISCST3 was then used to predict hourly atmospheric concentrations of H2S. With emission rates varying hourly, ISCST3 predicted 384 acres of odor impact, compared with 103 acres for constant emissions. Because field sampling had been conducted on relatively cool days (85-90 degrees F), the constant emission rate underestimated odor impact significantly (by 73%).     

Modeling of dispersion near roadways based on the vehicle-induced turbulence concept

A mathematical model is developed for dispersion near roadways by incorporating vehicle-induced turbulence (VIT) into Gaussian dispersion modeling using computational fluid dynamics (CFD). The model is based on the Gaussian plume equation in which roadway is regarded as a series of point sources. The Gaussian dispersion parameters are modified by simulation of the roadway using CFD in order to evaluate turbulent kinetic energy (TKE) as a measure of VIT. The model was evaluated against experimental carbon monoxide concentrations downwind of two major freeways reported in the literature. Good agreements were achieved between model results and the literature data. A significant difference was observed between the model results with and without considering VIT. The difference is rather high for data very close to the freeways. This model, after evaluation with additional data, may be used as a framework for predicting dispersion and deposition from any roadway for different traffic (vehicle type and speed) conditions.     

Sampling Frequency Guidance for Ambient Air Toxics Monitoring

Abstract

The U.S. Environmental Protection Agency (EPA) is in the process of designing a national network to monitor hazardous air pollutants (HAPs), also known as air toxics. The purposes of the expanded monitoring are to (1) characterize ambient concentrations in representative areas; (2) provide data to support and evaluate dispersion and receptor models; and (3) establish trends and evaluate the effectiveness of HAP emission reduction strategies. Existing air toxics data, in the form of an archive compiled by EPA’s Office of Air Quality Planning and Standards (OAQPS), are used in this paper to examine the relationship between estimated annual average (AA) HAP concentrations and their associated variability. The goal is to assess the accuracy, or bias and precision, with which the AA can be estimated as a function of ambient concentration levels and sampling frequency. The results suggest that, for several air toxics, a sampling schedule of 1 in 3 days (1:3) or 1:6 days may be appropriate for meeting some of the general objectives of the national network, with the more intense sampling rate being recommended for areas expected to exhibit relatively high ambient levels.     

An urban trajectory model for sulfur in Asian megacities: model concepts and preliminary application

This paper explores the adaptation of a regional Lagrangian approach for making long-term simulations of SO₂ and sulfate ambient concentrations at the resolution needed for health effects risk assessment in Asian megacities and their surroundings. A Lagrangian trajectory model (UR-BAT) is described which simulates transport and diffusion of sulfur within and near urban areas, originating from area and major point sources. The long-range contribution is accounted for by the ATMOS model, simulating all Asian sources. The model has been applied to Beijing and Bombay, by using preliminary emission figures, and the results have been compared with available monitoring data. The computed concentrations in different cities are in the correct range, indicating the potential use of the model in an integrated assessment framework such as RAINS-Asia.     

A complex terrain dispersion model for regulatory applications

This paper demonstrates the development of a model designed to estimate concentrations associated with a source situated in complex terrain. The model is designed to provide estimates of concentration distributions and is thus primarily suitable for regulatory applications. The model assumes that the concentration at a receptor is a combination of concentrations caused by two asymptotic states: the plume remains horizontal, and the plume climbs over the hill. The factor that weights the two states is a function of the fractional mass of the plume above the dividing streamline height. The model has been evaluated against data from four complex terrain sites. The evaluation shows that the model performs at least as well as CTDMPLUS (Perry, S.G., 1992. CTDMPLUS, a dispersion model for sources near complex topography. Part I: technical formations. Journal of Applied Meteorology 31, 633–645), a more comprehensive model designed for complex terrain applications.     

Correlating Emissions with Time and Temperature to Predict Worst-Case Emissions from Open Liquid Area Sources

Abstract

The two primary factors influencing ambient air pollutant concentrations are emission rate and dispersion rate. Gaussian dispersion modeling studies for odors, and often other air pollutants, vary dispersion rates using hourly meteorological data. However, emission rates are typically held constant, based on one measured value. Using constant emission rates can be especially inaccurate for open liquid area sources, like wastewater treatment plant units, which have greater emissions during warmer weather, when volatilization and biological activity increase. If emission rates for a wastewater odor study are measured on a cooler day and input directly into a dispersion model as constant values, odor impact will likely be underestimated. Unfortunately, because of project schedules, not all emissions sampling from open liquid area sources can be conducted under worst-case summertime conditions. To address this problem, this paper presents a method of varying emission rates based on temperature and time of the day to predict worst-case emissions. Emissions are varied as a linear function of temperature, according to Henry’s law, and a tenth order polynomial function of time. Equation coefficients are developed for a specific area source using concentration and temperature measurements, captured over a multiday period using a data-logging monitor. As a test case, time/temperature concentration correlation coefficients were estimated from field measurements of hydrogen sulfide (H₂S) at the Rowlett Creek Wastewater Treatment Plant in Garland, TX. The correlations were then used to scale a flux chamber emission rate measurement according to hourly readings of time and temperature, to create an hourly emission rate file for input to the dispersion model ISCST3. ISCST3 was then used to predict hourly atmospheric concentrations of H₂S. With emission rates varying hourly, ISCST3 predicted 384 acres of odor impact, compared with 103 acres for constant emissions. Because field sampling had been conducted on relatively cool days (85–90 °F), the constant emission rate underestimated odor impact significantly (by 73%).