Gaussian Dispersion Model Research Articles

Security-Constrained Unit Commitment Considering Differentiated Regional Air Pollutant Intensity

Conventional environmental-economic power dispatch methods constrain the total amount of emissions of power plants, and they succeed in reducing emissions from the power sector. However, they fail to address the mismatch between emission reductions and the resulting changes in regional air quality. This paper proposes an ecology- and security-constrained unit commitment (Eco-SCUC) model considering the differentiated impacts of generation-associated emissions on regional air quality. A Gaussian puff dispersion model is applied to capture the temporal-spatial transport of air pollutants. Additionally, an air pollutant intensity (API) index is defined for assessing the impacts of emissions on the air quality in regions with differentiated atmospheric environmental capacities. Then the API constraints are formulated based on air quality forecast and included in SCUC model. Moreover, the stochastic optimization is employed to accommodate wind power uncertainty, and the Benders decomposition technique is used to solve the formulated mixed-integer quadratic programming (MIQP) problem. Case studies demonstrate that the Eco-SCUC can cost-effectively improve air quality for densely-populated regions via shifting generation among units and can significantly reduce the person-hours exposed to severe air pollution. Furthermore, the benefits of wind power for air quality control are investigated.

Operational evaluation of the RLINE dispersion model for studies of traffic-related air pollutants

Exposure to traffic-related air pollutants (TRAP) remains a key public health issue, and improved exposure measures are needed to support health impact and epidemiologic studies and inform regulatory responses. The recently developed Research LINE source model (RLINE), a Gaussian line source dispersion model, has been used in several epidemiologic studies of TRAP exposure, but evaluations of RLINE's performance in such applications have been limited. This study provides an operational evaluation of RLINE in which predictions of NOx, CO and PM2.5 are compared to observations at air quality monitoring stations located near high traffic roads in Detroit, MI. For CO and NOx, model performance was best at sites close to major roads, during downwind conditions, during weekdays, and during certain seasons. For PM2.5, the ability to discern local and particularly the traffic-related portion was limited, a result of high background levels, the sparseness of the monitoring network, and large uncertainties for certain processes (e.g., formation of secondary aerosols) and non-mobile sources (e.g., area, fugitive). Overall, RLINE's performance in near-road environments suggests its usefulness for estimating spatially- and temporally-resolved exposures. The study highlights considerations relevant to health impact and epidemiologic applications, including the importance of selecting appropriate pollutants, using appropriate monitoring approaches, considering prevailing wind directions during study design, and accounting for uncertainty.

Radioactive Contamination Control by Atmospheric Dispersion Assessment of Airborne Indicator Contaminants: Numerical Model Validation

In this work, a numerical model is proposed to estimate air concentration of released airborne radioactive contaminants 131I and 137Cs. A Gaussian dispersion model is used to assess the atmospheric dispersion of radioactive contaminants released continuously from a nuclear power plant as a result of an accident. The model uses various input parameters such as source height, release rate, stability class, wind speed, and wind direction. The validation of the model was carried out by comparing its predicted values with published experimental data. The model was extensively tested by simulating several accidental situations. The main conclusion drawn from these tests is that for large downwind distances from the release point, the contaminant concentrations predicted by the model diverge drastically from measured data, while for short distances, the predicted values generally agree quite well with experimental data. The obtained activity concentrations range from 1.57 × 102 to 6.43 × 103 Bq/m3 for 131I and from 3.18 × 10−2 to 9.72 × 102 Bq/m3 for 137Cs. The estimated standard deviation coefficients values range of 7.2 to 6847.7 m, and the maximum absolute error predicted by the model for these parameters was less than 5%.

AERMOD as a Gaussian dispersion model for planning tracer gas dispersion tests for landfill methane emission quantification

The measurement of methane emissions from landfills is important to the understanding of landfills’ contribution to greenhouse gas emissions. The Tracer Dispersion Method (TDM) is becoming widely accepted as a technique, which allows landfill emissions to be quantified accurately provided that measurements are taken where the plumes of a released tracer-gas and landfill-gas are well-mixed. However, the distance at which full mixing of the gases occurs is generally unknown prior to any experimental campaign.To overcome this problem the present paper demonstrates that, for any specific TDM application, a simple Gaussian dispersion model (AERMOD) can be run beforehand to help determine the distance from the source at which full mixing conditions occur, and the likely associated measurement errors. An AERMOD model was created to simulate a series of TDM trials carried out at a UK landfill, and was benchmarked against the experimental data obtained. The model was used to investigate the impact of different factors (e.g. tracer cylinder placements, wind directions, atmospheric stability parameters) on TDM results to identify appropriate experimental set ups for different conditions.The contribution of incomplete vertical mixing of tracer and landfill gas on TDM measurement error was explored using the model. It was observed that full mixing conditions at ground level do not imply full mixing over the entire plume height. However, when full mixing conditions were satisfied at ground level, then the error introduced by variations in mixing higher up were always less than 10%.

Experimental determination of the dispersion of ions from a point source in the environment

ABSTRACTThe dispersion of ions from a point source has been extensively modelled but there have been very few attempts to experimentally verify the theoretical findings. The main reason for this has been the difficulty of discriminating between cluster ion and charged particle concentrations in the air. In this paper, we describe a novel technique for the experimental determination of the dispersion of ions from a point source in air. Laboratory experiments showed that the lifetime of cluster ions in an aerosol cloud was of the order of minutes. However, once they attached to aerosols, the particles retained the charge for at least 30 min, suggesting that they may be carried long distances in natural winds. A negative air ionizer was used to produce ions and charged particles in an open field in the presence of a steady horizontal wind. A neutral cluster and air ion spectrometer was used to measure cluster ion and charged particle concentrations as a function of downwind distance from the source. The results are broadly consistent with the Gaussian dispersion model for a continuous point source. We estimate that cluster ions can be carried up to a distance of several hundred metres before they fully attach to particles which can then be carried as far as 3–4 km. Therefore, these observations have important bearing on exposure to cluster ions and charged particles downwind of ion sources such as high voltage power lines and busy roads.

Validation of dispersion models using Cabauw field experiments and numerical weather re-analysis

The Gaussian atmospheric dispersion model AEROPOL is validated against the classical Cabauw dispersion experiment, driven by (1) on-site meteorological data and (2) meteorological data from the numerical weather model HARMONIE. The meteorological forecast time used at the time of the release is 10 to 14 hours ahead of the analysis time. The results from the combination of AEROPOL with on-site data are slightly better than with re-analysed meteorology. The near-surface concentrations based on the combination of AEROPOL with the model forecasts are in general lower than the concentrations from the combination of AEROPOL with on-site data and are also lower than the observed concentrations. The combination of AEROPOL with meteorological model results in a mismatch between the modelled position of the plume axis with the observed plume axis. The results show a significant improvement in the HARMONIE version 38 with respect to the HARMONIE version 37.

New plume rise modeling in a turbulent atmosphere via hybrid RANS-LES numerical simulation

New plume rise formulas are developed to overcome the drawbacks of the conventional models in predicting the rise of buoyant jets in different atmospheric stability conditions. A hybrid Reynolds averaged Navier Stokes (RANS) and large eddy simulation (LES) approach with a mixed scale sub-grid parameterization technique is applied. By using the aforementioned simulation results, new plume rise formulas are derived. The direct effects of atmospheric turbulence intensity at stack height (IAir) and the vertical derivative of wind velocity are introduced in plume rise formulas. The quantile-quantile plots show that new formulas can predict the simulated plume rise in the turbulent crossflow with a deviation factor of 1.18, 1.0025 and 1.17 for stable, neutral and unstable conditions, respectively whereas the conventional models overestimate the final plume rise at least by a factor of 6.1, 3.4 and 2.7. Moreover, by applying the new plume rise formulas in Gaussian dispersion model, the accuracy of new formulas is evaluated. The results show that by applying the new plume rise formulas instead of the conventional Briggs formulas in the Gaussian model, the normalized mean square error reduces by 20% and the fraction of predictions within a factor of two increases by 50%.

Comparing estimates from the R-LINE near road dispersion model using model-derived and observation-derived meteorology

Observed meteorological conditions, usually measured at airports or weather monitoring stations, have long provided the only source of meteorology for many Gaussian air pollution dispersion models. This introduces uncertainty and limitations in numerical model estimates, especially for locations of interest far removed from these monitoring stations. Hence, it is advantageous to employ predicted meteorology from a prognostic meteorological model as a substitute. The objective of this study was to compare estimates from the R-LINE near road dispersion model at three inland sites and one coastal site in Connecticut using observation-derived (weather station) and model-derived (Weather Research and Forecasting Model) meteorology. Both the graphical and statistical comparisons indicated less pronounced discrepancies in model estimations in the time periods generally characterized by unstable atmospheric conditions than those characterized by stable atmospheric conditions. There were also more pronounced differences at larger distances from roadways. Comparison of the estimated surface characteristic variables using both the observation-derived and model-derived meteorology displayed similar diurnal trends.

Odour Dispersion Modelling for Raw Rubber Processing Factories

An odour dispersion modelling approach was adopted to assess odour dispersion, particularly from the drying stage of raw rubber processing factories, identified as a major source of malodour pollutant in the rubber industry. The Ausplume modelling software which uses a Gaussian plume dispersion model was used to predict odour dispersion from the water scrubber, a malodour control system used by rubber processing factories. The modelling involved a selected range of parameters in correlation with characteristics of raw rubber malodour pollutants and interpretations of the predicted data were discussed in terms of effects of stack height of the water scrubber, odour concentration and local meteorological conditions at several locations in peninsular Malaysia. In general, it was found that a higher stack of water scrubber minimises effects of different emission concentration characteristics and local meteorological conditions. This was demonstrated by profiles of predicted odour concentration dispersion and minimal dispersion variation within variables as attributed by standard deviation curves. Furthermore, higher stacks predicted lower odour concentration carried over from the point of emission to ground level receptors by taking into consideration all of the modelling assumptions. In summary, the initialfindings from this work are beneficial to designing a better odour control system which subsequently minimises odour impact to the surrounding environment.

The adverse effects of gas-burning CHP systems on the local air quality

One of the benefits of the combined heat and power (CHP) systems is reducing greenhouse gases (GHG) in the national level. These systems are installed in the close vicinity of local communities and their pollution can directly affect the local environment. This paper studies the adverse environmental effects of a 2 MW gas-burning CHP (GB-CHP) system in a residential community in Mashhad, Iran. We compare the amount of CO2 and NOx emissions from the GB-CHP with the conventional choice of supplying electricity from the central power plants and heat from the locally installed boilers. The results show that the GB-CHP system increases local CO2 and NOx emissions by approximately two and six times, respectively. The NOx emission is increased from 3.1 tonnes to 18.7 tonnes. The CO2 emission increases from 3623 tonnes to 7238 tonnes. Furthermore, we use Gaussian Dispersion model to estimate the concentration of these emissions in the local area using the weather conditions of the most polluted times of the previous year. The results show that for a 3-meter stack, the maximum concentration of CO2 and NOx are 14.8 mg/m3 and 38.1 μg/m3, respectively. Our analysis shows that increasing the height of the GB-CHP stack from 3 meters to 10 meters reduces the concentration by 40%.

Dust emission and dispersion from mineral storage piles.

Dust pollution is a complex problem of growing interest because of its environmental, health, economic and political impact. Environmental impact assessment methods for dust pollution management are often based on the simulation of dust dispersion, which requires a precise characterization of the source term and of the source parameters. The source term model should be as simple and as accurate as possible and requires low time consumption in order to be easily connected to a more complex algorithm for the dispersion calculations. This work focuses on dust emissions from mineral storage piles, which are usually modelled as source terms by means of the algorithm proposed in the AP-42 US EPA standard. Unfortunately, this algorithm tends to overestimate emissions, and when coupled with a Gaussian dispersion model, it leads to inaccurate results in terms of estimation of both concentration and spatial distribution. This paper proposes a new methodology drawn from the original standard US EPA AP-42 https://www3.epa.gov/ttnchie1/ap42/ch13/ scheme with the purpose to account for the actual dynamics of erosion and to enhance the accuracy of the concentration and the pollutant spatial distribution assessment, thereby considering the effects of the wind interactions. The standard EPA methodology and the new one were compared by means of the AERMOD and CALPUFF dispersion models. Results are superimposable in terms of concentration values, leading to a quantification of the same order of magnitude, although with a different and more variable spatial distribution.

A GIS-based atmospheric dispersion model for pollutants emitted by complex source areas

Gaussian dispersion models are widely used to simulate the concentrations and deposition fluxes of pollutants emitted by source areas. Very often, the calculation time limits the number of sources and receptors and the geometry of the sources must be simple and without holes. This paper presents CAREA, a new GIS-based Gaussian model for complex source areas. CAREA was coded in the Python language, and is largely based on a simplified formulation of the very popular and recognized AERMOD model. The model allows users to define in a GIS environment thousands of gridded or scattered receptors and thousands of complex sources with hundreds of vertices and holes. CAREA computes ground level, or near ground level, concentrations and dry deposition fluxes of pollutants. The input/output and the runs of the model can be completely managed in GIS environment (e.g. inside a GIS project). The paper presents the CAREA formulation and its applications to very complex test cases. The tests shows that the processing time are satisfactory and that the definition of sources and receptors and the output retrieval are quite easy in a GIS environment. CAREA and AERMOD are compared using simple and reproducible test cases. The comparison shows that CAREA satisfactorily reproduces AERMOD simulations and is considerably faster than AERMOD.

Evaluation of regional and local atmospheric dispersion models for the analysis of traffic-related air pollution in urban areas

Dispersion of road transport emissions in urban metropolitan areas is typically simulated using Gaussian models that ignore the turbulence and drag induced by buildings, which are especially relevant for areas with dense downtown cores. To consider the effect of buildings, street canyon models are used but often at the level of single urban corridors and small road networks. In this paper, we compare and validate two dispersion models with widely varying algorithms, across a modelling domain consisting of the City of Montreal, Canada accounting for emissions of more 40,000 roads. The first dispersion model is based on flow decomposition into the urban canopy sub-flow as well as overlying airflow. It takes into account the specific height and geometry of buildings along each road. The second model is a Gaussian puff dispersion model, which handles complex terrain and incorporates three-dimensional meteorology, but accounts for buildings only through variations in the initial vertical mixing coefficient. Validation against surface observations indicated that both models under-predicted measured concentrations. Average weekly exposure surfaces derived from both models were found to be reasonably correlated (r = 0.8) although the Gaussian dispersion model tended to underestimate concentrations around the roadways compared to the street canyon model. In addition, both models were used to estimate exposures of a representative sample of the Montreal population composed of 1319 individuals. Large differences were noted whereby exposures derived from the Gaussian puff model were significantly lower than exposures derived from the street canyon model, an expected result considering the concentration of population around roadways. These differences have large implications for the analyses of health effects associated with NO2 exposure.

Comparison of AERMOD, ADMS and ISC3 for incomplete upper air meteorological data (case study: Steel plant)

In this paper three well known Gaussian dispersion models have been evaluated for a case study of a steel plant using complete and incomplete upper air meteorological data.In developing countries, the availability of surface and upper air meteorological data is limited. AMS/EPA Regulatory Model (AERMOD), Advanced Dispersion Modeling System (ADMS) and Industrial Source Complex Model (ISC3) have been evaluated for both real and estimated upper meteorological data and the results have been compared with field measurements both in the horizontal and vertical directions.The results show significant differences in predicted concentrations when modeling with real (actual) and estimated upper meteorological data. The differences ranged from 100% to 450%. Comparison of model performance suggests that AERMOD and ADMS with real meteorological data produce consistent results in the horizontal direction while ISC3 output over-predicts in general. In AERMOD and ISC3 the predicted concentrations have a similar trend of variation in the vertical direction but in ADMS the concentration variation in the vertical direction exhibited different trends. In general, the ADMS predicted concentrations under-estimated field observations.The paper suggests that upper data must be used for modeling and the default values must be used with care. In absence of upper meteorological data, users could estimate upper meteorological data by different available algorithm rather than only default option of models.

Emissions of VOC from a Landfill and its Plume Disperison Modelling

The main purpose of this work is to evaluate the VOC emissions by a landfill, located at Niteroi city, Rio de Janeiro State, Brazil. Twenty six samples were collected at 500 mL min-1 using a battery-operated air pump during 10 minutes, at four days in May and December, 2009. It was used a cylindrical 30 liters PVC flux chamber, which open bottom side was inserted 5 cm inside the landfill soil, and the samples were collected using a valve at the upper closed side. It were used double bed activated charcoal cartridges and the chemical analyses were done by gas chromatography with a mass spectrometry detector. The results indicated that the landfill contain several chemical compounds, mainly VOC, causing injures to vegetation and human health at the vicinities. The results indicated an elevated value of 1,980 kg km-2 h-1, when compared with metropolitan areas of São Paulo (38 kg km-2 h-1) and Rio de Janeiro (26 kg km-2 h-1). It was used a Gaussian dispersion model implemented at ISCSC3 mathematical model to calculate the pollutants diffusion and transport, in order to estimate the concentrations at the neighborhood, using the emissions, meteorological, and topographical data. Maximum values of 525 μg m-3 for VOC were found near 500 m from the landfill. As the landfill will be used for a long time it is necessary to evaluate the possibility to change the location of several schools, churches, residences, and hospitals to avoid the impacts.

Modeling emission rates and exposures from outdoor cooking

Approximately 3 billion individuals rely on solid fuels for cooking globally. For a large portion of these – an estimated 533 million – cooking is outdoors, where emissions from cookstoves pose a health risk to both cooks and other household and village members. Models that estimate emissions rates from stoves in indoor environments that would meet WHO air quality guidelines (AQG), explicitly don't account for outdoor cooking. The objectives of this paper are to link health based exposure guidelines with emissions from outdoor cookstoves, using a Monte Carlo simulation of cooking times from Haryana India coupled with inverse Gaussian dispersion models. Mean emission rates for outdoor cooking that would result in incremental increases in personal exposure equivalent to the WHO AQG during a 24-h period were 126 ± 13 mg/min for cooking while squatting and 99 ± 10 mg/min while standing. Emission rates modeled for outdoor cooking are substantially higher than emission rates for indoor cooking to meet AQG, because the models estimate impact of emissions on personal exposure concentrations rather than microenvironment concentrations, and because the smoke disperses more readily outdoors compared to indoor environments. As a result, many more stoves including the best performing solid-fuel biomass stoves would meet AQG when cooking outdoors, but may also result in substantial localized neighborhood pollution depending on housing density. Inclusion of the neighborhood impact of pollution should be addressed more formally both in guidelines on emissions rates from stoves that would be protective of health, and also in wider health impact evaluation efforts and burden of disease estimates. Emissions guidelines should better represent the different contexts in which stoves are being used, especially because in these contexts the best performing solid fuel stoves have the potential to provide significant benefits.

External cost of photovoltaic oriented silicon production: A case in China

The rapidly increasing demand for silicon panels has resulted in severe pollution from its manufacture in the underdeveloped regions of China, where environmental control usually concedes to economic development. The study aimed to estimate the external costs of silicon production to inform policy on the sustainable exploitation of solar energy. Specifically, the study estimated the cost of health damage and agricultural loss resulting from the pollution of silicon production. The data was collected from a silicon industrial zone in Southwest China. The cost of agricultural loss was estimated using a production function method, while the cost of health damage was estimated using dose-response functions, in which the air pollutant concentration was estimated based on a Gaussian dispersion model. The results show that the annual external cost of agricultural loss is CNY 0.76–4.13 million and that of health damage is CNY 26.53–44.63 million, or a total external cost is CNY 27.29–48.76 million. The average external cost is CNY 447.35 (CNY 321.1–473.82) per ton of metallurgical silicon. This information has implication for making policies on the control of pollution and the sustainable development of solar energy industry.

Simulation of gaseous pollutant dispersion around an isolated building using the k–ω SST (shear stress transport) turbulence model

ABSTRACTThe dispersion of gaseous pollutant around buildings is complex due to complex turbulence features such as flow detachment and zones of high shear. Computational fluid dynamics (CFD) models are one of the most promising tools to describe the pollutant distribution in the near field of buildings. Reynolds-averaged Navier-Stokes (RANS) models are the most commonly used CFD techniques to address turbulence transport of the pollutant. This research work studies the use of closure model for the gas dispersion around a building by fully resolving the viscous sublayer for the first time. The performance of standard model is also included for comparison, along with results of an extensively validated Gaussian dispersion model, the U.S. Environmental Protection Agency (EPA) AERMOD (American Meteorological Society/U.S. Environmental Protection Agency Regulatory Model). This study’s CFD models apply the standard and the turbulence models to obtain wind flow field. A passive concentration transport equation is then calculated based on the resolved flow field to simulate the distribution of pollutant concentrations. The resultant simulation of both wind flow and concentration fields are validated rigorously by extensive data using multiple validation metrics. The wind flow field can be acceptably modeled by the model. However, the model fails to simulate the gas dispersion. The model outperforms in both flow and dispersion simulations, with higher hit rates for dimensionless velocity components and higher “factor of 2” of observations (FAC2) for normalized concentration. All these validation metrics of model pass the quality assurance criteria recommended by The Association of German Engineers (Verein Deutscher Ingenieure, VDI) guideline. Furthermore, these metrics are better than or the same as those in the literature. Comparison between the performances of and AERMOD shows that the CFD simulation is superior to Gaussian-type model for pollutant dispersion in the near wake of obstacles. AERMOD can perform as a screening tool for near-field gas dispersion due to its expeditious calculation and the ability to handle complicated cases. The utilization of to simulate gaseous pollutant dispersion around an isolated building is appropriate and is expected to be suitable for complex urban environment.Implications: Multiple validation metrics of turbulence model in CFD quantitatively indicated that this turbulence model was appropriate for the simulation of gas dispersion around buildings. CFD is, therefore, an attractive alternative to wind tunnel for modeling gas dispersion in urban environment due to its excellent performance, and lower cost.

Modelled and observed surface soil pollen deposition distance curves for isolated trees of Carpinus betulus, Cedrus atlantica, Juglans nigra and Platanus acerifolia

Source–distance relationships for pollen deposited directly into surface soil have been rarely undertaken, particularly for a single or isolated source, rather than a forest, grove or plantation. This study aimed to determine surface soil pollen deposition patterns from single, isolated source trees and to compare the results to Gaussian model curves for the same trees. Four isolated tree pollen sources were chosen in Worcester, UK: Carpinus betulus, Cedrus atlantica, Juglans nigra and Platanus acerifolia. Surface soil samples were collected at 1, 5 and then every 10 m, up to 100 m distance from the main trunk of each source along the prevailing wind direction during flowering. A Gaussian dispersion model was used to estimate source strength using tree height and width and wind speeds on days when flowering was occurring and when the wind direction flowed along the sampling transect. This model simulated the expected concentration and deposition along the sampling transect. Modelled and observed results showed that most pollen was deposited beneath the canopy (range 63–94%) in an exponentially decreasing curve and the tailing off started from around the outer edge of the canopy in most cases. The amount of pollen deposited at 50 m was no more than 2.6% of total deposition in the samples for any tree and at 100 m no more than 0.2%. Tree height, width and wind speed during the pollination period were found to be the main parameters affecting deposition away from the source.

Dispersion of volatile organic compounds (VOCs) emissions from a biofilter at an electronic manufacturing facility

Printed circuit board (PCB) manufacturing industries, known as PCB facilities, emit odorous and toxic volatile organic compounds (VOCs). Such compounds not only harm the health but also create a nuisance environment for people who live in the neighborhood of PCB facilities. Biofilter technology which is based on the bio‐oxidation of pollutants has been used by many industries to remove odorous VOCs. When the removal efficiency drops due to a design or operational deficiency, odorous VOCs are released from the biofilter and this becomes evident by the increased odor complaints. Poor biofilter performance, geographical location of the facility, and meteorological conditions contribute to increased dispersion of VOCs. This research investigates dispersion of a VOC pollutant known as propylene glycol monomethyl ether acetate (PGMEA) which is released from a commercial biofilter unit installed at a PCB facility in Ontario, Canada. A Gaussian dispersion model and California puff model (CALPUFF) were used to examine the dispersion effects due to changes in wind speed, wind direction, temperature, mixing height, atmospheric stability classes as well as biofilter performance. Simulations were done for the three modeling periods (January, May, September) to account for seasonal effects on PGEMEA dispersion and predictions from both models are compared. The contour hourly wind and concentration plots confirm that the seasonal changes have a direct impact on the PGMEA concentration and plume path. The results presented under variable meteorological conditions show that the average daily concentration of PGMEA is the highest in the month of September. © 2017 American Institute of Chemical Engineers Environ Prog, 36: 1100–1107, 2017