Predicted Ground Level Concentrations Research Articles

Landfill air and odour emissions from an integrated waste management facility.

A mixture of gases and obnoxious odours are major components of landfill emission. A dispersion modelling on air pollutants and odour emissions anticipated from a proposed Integrated Waste Management Facility was conducted considering five operating scenarios. Impacts of the predicted ground level concentrations of air pollutants (including carbon monoxide, CO; oxides of nitrogen, NOX; sulphur dioxide, SO2; particulate matter, PM and hydrocarbons, HC) and odour on ambient air quality were investigated using the 10-min 1 OU/m3 odour limit, CH4 Lower Explosive Limit (LEL) and the daily limits of CO, NOx, SO2, PM and HC. The anticipated maximum ground level concentration of emitted odour and CH4 are 0.0040 OU/m3 and 0.0349ppm, respectively. Simultaneous operations of all the major components of the facility will generate the daily maximum concentrations of 7.34, 2.60, 7.31, 29.72 and 0.42μg/m3, for CO, NOX, SO2, PM and HC, respectively. Generally, the facility impacts on ambient air quality will be within the acceptable limit.

Dispersion of buoyant emissions from low level sources in urban areas: water channel modelling

A laboratory study was done to investigate dispersion of buoyant emissions from near surface sources in urban areas. Ground level concentrations under different surrounding building geometries were measured using a newly developed system based on laser induced fluorescence. In the presence of upstream buildings AERMOD (AMS/U.S. EPA regulatory dispersion model) is unable to explain concentrations close to the source. Plume visualisations and velocity measurements show that upstream buildings induce low velocity and a highly turbulent region near the stack, which increases the plume rise and induces rapid vertical mixing. Also, the urban canopy imposes a length scale on the horizontal turbulence, causing the plume to spread laterally with the square root of distance (~x1/2) rather than linearly as occurs in open terrain. A Gaussian-based dispersion model which accounts for these effects performs substantially better in predicting ground level concentrations associated with buoyant emissions from distributed power generators in urban areas.

An air dispersion model for the city of Toronto, Ontario, Canada

Air quality is a major concern for the public; therefore, the reliability of accurate models in predicting the air quality is of a major interest. In this study, a Gaussian air dispersion model, known as the Air dispersion model for Road Sources in Urban areaS (ARSUS), was developed to predict the ground level concentrations for a contaminant of interest. It was demonstrated that this model could be used successfully in place of or in conjunction with ambient air monitoring stations in determining the local Air Quality Index (AQI). The ARSUS model was validated against the US EPA ISC3 model before it was used to conduct two studies in this investigation. These two studies simulated weekday morning rush-hour tailpipe emissions of CO and predicted ground level concentrations. The first study used the ARSUS model to predict ground level concentrations of CO from the tailpipe emissions for roads and highways located in the vicinity of the Toronto West ambient air monitoring station. The second study involved an expansion of the domain to predict ground level concentrations of CO from tailpipe emissions from highways in the City of Toronto, Ontario, Canada. The predicted concentrations were then compared to the data collected from the Toronto West ambient air monitoring station. The results of the ARSUS model indicated that the air quality in the immediate vicinity of roads or highways is highly impacted by the tailpipe emissions. Higher concentrations were observed for the areas adjacent to the road and highway sources. The tailpipe emissions of CO from highways had a higher contribution to the local air quality. The predicted ground level concentrations from the ARSUS model under-predicted when compared to the observed data from the monitoring station; however, despite this, the predictive model is viable.

Inter-comparison of the AUSTAL2000 and CALPUFF dispersion models against the Kincaid data set

Regulatory air dispersion models AUSTAL2000 and CALPUFF are validated by comparing the predicted Ground Level Concentrations (GLC) with the measured dataset resulting from the dispersion of an elevated, buoyant plume release in the Kincaid power plant experiment. Standard statistical measures of the model results are inter-compared. Their performance is scrutinised using scattered, quartile-quartile, and residual plots. The predicted GLC of AUSTAL is twice of Kincaid under turbulent conditions, being less accurate than CALPUFF. The performance of AUSTAL is more consistent with respect to varying atmospheric parameters and distance from stack. The stochastic variability of AUSTAL is also examined.

Inter-comparison of AERMOD and ISC3 modeling results to the Alaska tracer field experiment

AERMOD is an advanced plume model that incorporates updated treatments of the boundary layer theory, understanding of turbulence and dispersion, and includes handling of terrain interactions as well as the PRIME downwash algorithm. It was reported that the US EPA approved AERMOD for short-range dispersion modeling. It was the high time that AERMOD would replace ISC3. ISC3 is a traditional Gaussian plume model regarded as the regulatory model of US EPA with the capacity of building downwash similar to that of AERMOD. In this paper, the authors describe the advantages of AERMOD over the regulatory model of ISC3 by comparing their predicted ground level concentrations (GLC) along downwind distance to the Alaska tracer field data. The field experiment features buoyant release of effluent at elevated height over a flat terrain and local flows influenced by building downwash. Three measures to compare the observed and simulated concentration data, such as linear regression, quantile-quantile (QQ) and residual box are utilized. To sum up, AERMOD shows significantly better space-time correlation and probability distribution than the ISC3, which frequently overestimates the GLC for effluent released with significant plume rise under stable atmospheric conditions. The performance of AERMOD is greatly enhanced by introducing the state-of-the-art knowledge of boundary layer meteorology as well as the turbulence parameterization method. In particular, AERMOD takes into account the meander effect on coherent plume in stable condition with current state-of-the-art Planetary Boundary Layer (PBL) parameterizations, while ISC3 is not capable of producing such important effect. Generally speaking, 1.17 is the overall predicted-to-observed ratio for short-term averages using AERMOD. 1.94 is the overall predicted-to-observed ratio for short-term averages using ISC3.

A comparison of the RCM and ISC3 dispersion models against the Alaska data set

The USEPA (U. S. Environmental Protection Agency) approved ISC3 for short-range dispersion modeling. The regulatory model of China is a traditional Gaussian plume model similar to ISC. This paper demonstrates the advantages of ISC3 over the regulatory model of China by comparing their predicted ground level concentrations ( GLC ) along downwind distance to the Alaska tracer field data. The field experiment features buoyant release of effluent at elevated height over a flat terrain and local flows influenced by building downwash. ISC3 shows much better space-time correlation and probability distribution than the China regulatory model, which frequently underestimates the GLC for effluent released with significant plume rise under stable atmospheric conditions. The performance of ISC3 is greatly enhanced by incorporating the state-of-the-art knowledge of boundary layer meteorology. In particular, ISC3 employs the PRIME model for building downwash.

Real-time response system for the prediction of the atmospheric transport of hazardous materials

Human urge of exploiting earth resources has resulted into unprecedented industrial development in the last century resulting into production of large quantities of hazardous chemicals. Chemical, petrochemical, nuclear, biomedical and pharmaceutical industrial accidents release large quantities of hazardous chemicals into the atmosphere. The accidental discharge during production or storage or transportation have subjected the population to be exposed to exceptionally high concentration levels of hazardous chemicals, taking them by surprise, unprepared with fatal consequences. An emergency planning organization has to be trained to combat this situation in the shortest possible time to minimize the number of causalities. The present paper focuses on computation of dispersion model, using emission source, accident location and online metrological data near to the sources, to provide necessary and accurate results swiftly. The predicted ground level concentrations with the hazardous nature of the chemical, speed and direction of plume, the emergency team will be supplied with all the information in graphical easy to grasp form, superimposed over a GIS map or the latest satellite image of the area. The emergency team has to be trained for all past scenarios and their preparedness, response and actions must be practiced regularly to be able to abate chemical releases accidentally or intentionally. Accidental releases of chlorine and ammonia gases in residential and industrial areas are simulated. The predicted ground level concentrations in the effected areas are shown after different time intervals. For low vapor pressure chemical, the dispersion time is large and concentration levels are low but persist for prolonged time while for volatile chemical, the concentrations are high in short time and recovering to safe environment is quick.

SO2 dispersion and monthly evaluation of the industrial source complex short-term (ISCST32) model at Mina Al-Fahal refinery, Sultanate of Oman.

Modeling of air pollutant dispersion has been undertaken for emissions of sulfur dioxide (SO(2)) at the Mina Al-Fahal refinery in the Sultanate of Oman. The study was conducted during the period of November 1999 to October 2000. The Industrial Source Complex Short-Term (ISCST32) air pollution model was adopted to predict the ground level concentration of SO(2) in and around the refinery. The modeling results were validated against measured data during the study period. The comparison, based on the monthly average measurements, showed that the model underestimates the observed SO(2) concentrations. However, the predicted ground level concentrations of SO(2) during the months of September, October, November, and June were in better agreement with the observations. The predicted SO(2) values are presented in the form of concentration contours to determine the spatial distribution of SO(2) and to assess the impact on air quality over the survey area. Predicted SO(2) concentrations were found lower than the World Health Organisation (WHO) guideline value of 365 microg/m(3), with the maximum ground level concentrations being found to occur relatively close to the sources of emission. Moreover, concentration contour patterns for the modeled area vary with changes in meteorological conditions. On the basis of this study, the refinery is not likely to cause any significant deterioration in air quality, and predicted concentrations of SO(2) are well below those likely to influence health.

An evaluation of four box models for instantaneous dense-gas releases

Four box models for instantaneous hazardous gas releases have been evaluated with data from the Burro field scale tests and also laboratory scale experiments performed at the University of Arkansas. The models are the OME model, the DENS1 model, the SLAB model and the BOX model. The BOX model, developed during this study, incorporates the proposed equation for translational speed by Wheatley and Prince (ref. 1), of a dense gas cloud. The models are evaluated using statistical measures similar to the interim guidelines on air quality model evaluation given by the United States Environmental Protection Agency. The non parametric bootstrap resampling procedure, which is relatively new method, was used to obtain confidence limits on the various statistical measures. In predicting ground level concentrations, the SLAB model performed well in all atmospheric conditions and calm conditions; and the BOX model showed a similar performance in unstable cases, neutral cases and calm conditions. The DENS1 model predictions of the cloud speed under all the conditions are in good agreement with observations.

Comparison between the results of a Monte Carlo atmospheric diffusion model and tracer experiments

A Lagrangian statistical (Monte Carlo) model for airborne pollutant dispersion is presented. Its ability to simulate the atmospheric dispersion both in homogeneous and inhomogeneous turbulence by comparison with an analytical solution and with the Willis and Deardorff water tank experiments, respectively, has been stated in previous papers. In the present paper the model is used to simulate dispersion in the real atmospheric PBL. The numerical results obtained are verified against experimental data from the Karlsruhe Nuclear Research Center tracer experiments. The model is applied to the problem of predicting the ground level concentration of two different tracers simultaneously released from two heights (160 and 195 m) at the Karlsruhe meteorological tower. Convectively unstable and neutral conditions were prevailing during the two tracer experiments which have been simulated. Model performance was evaluated through two statistical indexes: relative mean bias and normalized mean square error. The cumulative frequency distribution of the point-by-point ratio between observed and predicted ground level concentrations (glcs) was also computed. The simulated concentrations agree very well with observations. The tracer data were also compared to the simulations of 10 Gaussian models. They differed one another for the choice of dispersion sigma curves and for the way to insert the wind speed and direction. None of them proved to perform better than our particle model in all the exercises.

A comparison of ambient and workplace dioxin levels from testing in and around modern resource recovery facilities with predicted ground level concentrations of dioxins from stack emission testing with corresponding workplace health risks

Pre-operational background ambient dioxin testing was performed around two modern resource recovery facilities in Bristol, CT and Hillsborough County, FL. Recent stack testing at the Bristol, CT facility and at a facility in Tulsa, OK (similar to the Hillsborough County facility) showed that ambient levels of dioxins determined by computer modeling from stack emissions at the point of maximum impact were insignificant compared to the actual background levels. With current ambient dioxin sampling equipment, the stack contribution to the ambient level of dioxins would be basically immeasurable whether or not the facility was operating. In addition, a sampling program was conducted to evaluate the potential for employee exposure to dioxins and heavy metals in the Hillsborough County facility. Results indicate that levels of dioxins and heavy metals were similar to ambient levels outside the facility before the facility began operation. The potential for employee exposure was determined to be orders of magnitude below U.S. governmental safety levels for workplace exposures to heavy metals. The estimated health risk from dioxin exposure was approximately one in ten million using conservative assumptions regarding the workplace and the employee.

Dispersion from safety valves and other momentum emission sources: Continuous

Safety valves are atypical pollutant emission sources in petroleum refineries and chemical plants. Their releases are characterized by very high velocities (from 20ms −1 to sonic) and near-ambient temperatures, so they are referred to as momentum sources, as opposed to bouyant sources. Since releases from these sources to the atmosphere may contain relatively high (often 100%) pollutant concentrations we have developed a method of accurately predicting the critical ground level concentrations of pollutants resulting from such sources. (The critical ground level concentration is the highest ambient pollutant concentration at any downwind location or meterological condition.) Since no data were available on which to base an air dispersion model for predicting ground level concentrations for momentum sources, an experimental program was undertaken, consisting of a full scale simulation of a momentum source emission using an inert tracer gas and downwind sampling to determine resulting critical ground level concentrations. Based on the data collected, a dispersion calculation method for estimating ground level concentrations from momentum sources was developed. The general form of this correlation is similar to the previously suggested, but not validated, American Society of Mechanical Engineers (ASME) momentum plume equation. The modified equation contains an additional parameter to account for the large gas density differences often encountered with momentum source releases. The equation was designed to be somewhat conservative to compensate for the randomness of atmospheric phenomena, the limited amount of experimental data, and the fact that safety valve releases can include dangerous substances. The calculation method described in this report is recommended to predict peak ambient concentrations for any source dominated by momentum plume conditions.

The effect of the turning of the wind vector with height on the ground level trajectory of a diffusing cloud

Most simple models for the meso-scale transport of gaseous pollutants are unable to take into account the change of direction of the wind vector with height above the surface, and this can lead to errors in predicting ground level concentrations. Expressions are derived for the ground level trajectory of a diffusing cloud taking the height of emission and the depth of the mixing layer into account. These are used to define an effective wind direction dependent upon travel time which can be used to improve dispersion estimates.