Academic literature on the topic 'ISCLT3 Dispersion Model'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'ISCLT3 Dispersion Model.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "ISCLT3 Dispersion Model"
1
Safronov, A. N., N. F. Elansky, and A. I. Skorokhod. "DETECTION Of ATMOSPHERIC POLLUTION SOURCES BY USING CROSS-PLUME SCANNING METHOD AND MOBILE RAILwAY LABORATORY." GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY 11, no. 3 (September 29, 2018): 71–82. http://dx.doi.org/10.24057/2071-9388-2018-11-3-71-82.
Full textAbstract:
In this study the power of the sulfur dioxide emissions from the Mid-Urals copper-smelting enterprise (MUCE) was estimated by using plume cross-scanning. The combination of the observational data obtained by the TROICA experiments and information obtained by satellite photos of the Earth’s surface together with the ISCST3 dispersion model is promising for studies of the short-range atmospheric transport of chemically inactive pollutants. The results of ISCT3 model simulations indicate that the SO2 emissions in terms of sulfur make up about 3–4% of the plant sulfuric acid production. Also the cross validation between ISCST3 and NOAA HYSPLIT dispersion models was carried out. The emission rate obtained at the NOAA HYSPLIT model simulation is 1.5 times higher than the emission rate calculated at the ISCST3 simulation. It was emphasized, that the using of mobile platforms on electric traction has advantages in studying the environmental situation in comparison with the measurement system, constructed on the stationary Environmental Protection Stations. The cross-plume scanning method to a lesser degree depends on the wind rose, the features of the landscape and a relative location of emission sources and sensors.
2
Sharma, Sumit, and Avinash Chandra. "Simulation of Air Quality using an ISCST3 Dispersion Model." CLEAN – Soil, Air, Water 36, no. 1 (January 2008): 118–24. http://dx.doi.org/10.1002/clen.200700036.
Full text
3
VIDAL, César Marcelo Cajazeira, and Sérgio Machado CORRÊA. "PLUME DISPERSION STUDY IN A INDUSTRIAL COMPLEX." Periódico Tchê Química 08, no. 15 (January 20, 2011): 21–33. http://dx.doi.org/10.52571/ptq.v8.n15.2011.22_periodico15_pgs_21_33.pdf.
Full textAbstract:
The plume dispersion modeling is used to estimate the pollutants distribution in the vicinities of a chimney. It is based on a Gaussian model where input data are the emissions rate, physical data from the stack, meteorological data, and topographical characteristics. As this technique is new in Brazil, this work proposes to describe the methodology and its steps, indicating the most relevant parameters, the possible simplifications, and necessary details. The case study was done at the site of Brazilian Nuclear Industries and the results indicated that the edifications are the most relevant parameter, followed by the topographical characteristics. A comparison was also done between the two commercial softwares available, the ISCST3 and SCREEN. The results indicated that the SCREEN software can be used as an initial evaluation tool, whenever all input data necessary to process ISCST3 are not available.
4
Tseng, Wei Jen, Chao Heng Tseng, and Han Chi Liu. "The Study of AERMOD and ISCST3 for Area Source Simulation in Taiwan." Applied Mechanics and Materials 775 (July 2015): 491–95. http://dx.doi.org/10.4028/www.scientific.net/amm.775.491.
Full textAbstract:
This research compared the performance of ISCST3 model with the AERMOD simulated for six selected environmental impact assessment (EIA) cases which belongs four kinds of area in Taiwan (hotel, industrial area, road, reservoir). The influence of different terrain (complex or flat) and land use (countryside or urban) was then determined for the two air dispersion models.The results of the cross analysis indicated that there is no significant difference between of the complex terrain and flat terrain on the incremental concentration ratio. However, the biggest difference of incremental concentration of particulate matter (PM) is in the simulation for case in urban (ISCST3 is 1.96 times higher than AERMOD), and the gas pollutants in ISCST3 of incremental concentration simulation results ratio reach to 65.38% more than others. The highest incremental concentration of ISCST3 is 2.67 times to AERMOD. The concentration in AERMOD higher than that in ISTSC3 was 20% in the total 40 simulation values, due to the difference between their vertical diffusion simulations. The ratio of maximum incremental concentration in AERMOD was higher than ISCST3 by 42.5%. By the cross match of these incremental concentration, the ratio that maximum incremental concentration in AERMOD being less than ISCST3 was 22.5% after diffusion, which shows that the sinking rate in AERMOD is faster than ISCST3.The simulation of AERMOD considers more in complex terrain and surface characteristics. It uses the stratified flow over complex terrain and considers the effect in characteristics of the Earth’s surface. Thus, the theoretical basis of AERMOD is solider than ISCST3, and its simulation has more reliability.
5
Hanna, Steven R., Robert Paine, David Heinold, Elizabeth Kintigh, and Dan Baker. "Uncertainties in Air Toxics Calculated by the Dispersion Models AERMOD and ISCST3 in the Houston Ship Channel Area." Journal of Applied Meteorology and Climatology 46, no. 9 (September 1, 2007): 1372–82. http://dx.doi.org/10.1175/jam2540.1.
Full textAbstract:
Abstract The uncertainties in simulations of annually averaged concentrations of two air toxics (benzene and 1,3-butadiene) are estimated for two widely used U.S. air quality models, the Industrial Source Complex Short-Term, version 3, (ISCST3) model and the American Meteorological Society–Environmental Protection Agency Model (AERMOD). The effects of uncertainties in emissions input, meteorological input, and dispersion model parameters are investigated using Monte Carlo probabilistic uncertainty methods, which involve simultaneous random and independent perturbations of all inputs. The focus is on a 15 km × 15 km domain in the Houston, Texas, ship channel area. Concentrations are calculated at hypothetical receptors located at the centroids of population census tracts. The model outputs that are analyzed are the maximum annually averaged maximum concentration at any single census tract or monitor as well as the annually averaged concentration averaged over the census tracts. The input emissions uncertainties are estimated to be about a factor of 3 (i.e., covering the 95% range) for each of several major categories. The uncertainties in meteorological inputs (such as wind speed) and dispersion model parameters (such as the vertical dispersion coefficient σz) also are estimated. The results show that the 95% range in predicted annually averaged concentrations is about a factor of 2–3 for the air toxics, with little variation by model. The input variables whose variations have the strongest effect on the predicted concentrations are on-road mobile sources and some industrial sources (dependent on chemical), as well as wind speed, surface roughness, and σz. In most scenarios, the uncertainties of the emissions input group contribute more to the total uncertainty than do the uncertainties of the meteorological/dispersion input group.
6
Lo, S. L., and H. A. Chu. "Evaluation of atmospheric deposition of nitrogen to the Feitsui Reservoir in Taipei." Water Science and Technology 53, no. 2 (January 1, 2006): 337–44. http://dx.doi.org/10.2166/wst.2006.068.
Full textAbstract:
This research studied how the air pollutants of urban areas affect a neighboring reservoir and its water quality. Through the atmospheric dispersion process, air pollutants move from the Taipei metropolitan to the Feitsui reservoir and enter the water body through dry and wet depositions. ISCST3 (Industrial Source Complex Short Term Model), an air quality model, was used to simulate dispersion, dry deposition and wet deposition of the air pollutants. Then the nitrogen loadings to the Feitsui Reservoir were evaluated. The results indicate that wet deposition places a greater burden than dry deposition does on the water body. Wet and dry deposition of NH4+ together make up a rather large proportion of the total pollution. The ratio ranged from 21.9 to 25.2%. Those of nitrate make up a smaller proportion, ranged from 2.0 to 2.3%. If we take indirect deposition into account and calculate the NO3− and NH4+ together, the proportion is 15.9–17.6%.
7
Ormerod, R. "Improving odour assessment by using better dispersion models: some examples." Water Science and Technology 44, no. 9 (November 1, 2001): 149–56. http://dx.doi.org/10.2166/wst.2001.0528.
Full textAbstract:
A critical aspect of odour assessments is modelling to define exposure characteristics in affected communities, and to examine the effects of odour control options. In many cases, odour dispersion is influenced by complex or non steady-state meteorology that cannot be represented by the commonly used dispersion models, such as ISC3 and AUSPLUME. These models are based on a steady-state Gaussian plume assumption, which is often inaccurate. Recent developments in modelling of local meteorology and plume dispersion have enabled far more realistic predictions of odour dispersion. Three-dimensional models have been successfully applied to improve the predictions of odour impact and to better define the parameters for odour control options. These models more accurately represent features such as drainage flows along valley floors and around hills, and strong wind shear that can develop in stable conditions. Such conditions are often critical for a proper assessment of odour impact. Second-by-second fluctuations in odour concentrations can now be simulated using the KSP model developed by Yamartino et al. This model avoids the use of arbritrary methods of determining peak-to-mean ratios. New models can also provide detailed microscale wind fields, suitable for odour modelling in urban areas where odour dispersion is affected by very complex flows.
8
Porter, Raymond C., and Deborah Elenter. "Comparison of Odor Impacts from a Wastewater Treatment Plant Using the ISCST3 and AERMOD Dispersion Models." Proceedings of the Water Environment Federation 2007, no. 10 (October 1, 2007): 7637–54. http://dx.doi.org/10.2175/193864707787168594.
Full text
9
Faulkner, William B., Bryan W. Shaw, and Tom Grosch. "Sensitivity of Two Dispersion Models (AERMOD and ISCST3) to Input Parameters for a Rural Ground-Level Area Source." Journal of the Air & Waste Management Association 58, no. 10 (October 2008): 1288–96. http://dx.doi.org/10.3155/1047-3289.58.10.1288.
Full text
10
Gangcai, Chen, Yang Duoxing, and Wang Zhongqiong. "A comparison of the RCM and ISC3 dispersion models against the Alaska data set." Chinese Journal of Geochemistry 25, no. 3 (September 2006): 255–57. http://dx.doi.org/10.1007/bf02840420.
Full textDissertations / Theses on the topic "ISCLT3 Dispersion Model"
1
Can, Ali. "Investigation Of Turkey'." Phd thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/3/12607088/index.pdf.
Full textAbstract:
CO2 emission is very important, because it is responsible for about 60% of the "Greenhouse Effect"
. The major objectives of this study were to prepare a CO2 emission inventory of Turkey based on districts and provinces by using the fuel consumption data with respect to its sources, to find the CO2 uptake rate of forests in Turkey based on provinces and districts, and to estimate the ground level concentration of CO2 across Turkey using U.S. EPA'
s ISCLT3 model for the preparation of ground level concentration maps. The basic sources of the CO2 emission were taken as households, manufacturing industries, thermal power plants and road vehicles. The sinks of the CO2 were forests. The CO2 uptake by forests was calculated using the annual increment of forest biomass. The results of the CO2 emission inventory conducted in this study between the years 1990 and 2003 showed that the CO2 emission in 1990 was 142.45 million tones/year and the highest emission was calculated in 2000 with a value of 207.97 million tones/year. The regional distribution of CO2 emission showed that the Marmara Region emits the highest regional CO2 emission throughout the years with an average value of 54.76 million tones/year. It was also calculated that Marmara and Aegean Regions are responsible for half of the CO2 emission of Turkey. The results of the CO2 uptake calculations showed that the CO2 uptake of forests in the coastal zone was higher that that in the inland zone. The CO2 uptake in the Central Anatolia, Eastern Anatolia and South-Eastern Anatolia Regions were 2.6, 1.9 and 1.1 million tones/year, respectively. The maximum CO2 uptake is in the Black Sea Region with a value of 16.4 million tones/year. The highest ground level CO2 concentartions without any sink effect were always obtained in the Marmara Region. However, the forest areas in this region decrease the concentrations considerably. The dispersion model performance is determined highly without the results of the year 2002.
2
Dolek, Emre. "Comparison Of Iscst3 And Aermod Air Dispersion Models: Case Study Of Cayirhan Thermal Power Plant." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12609207/index.pdf.
Full textAbstract:
In this study, emission inventory was prepared and pollutant dispersion studies were carried out for the area around Çayirhan Thermal Power Plant to determine the effects of the plant on the environment. Stack gas measurement results were used for the emissions from the power plant and emission factors were used for calculating the emissions from residential sources and coal stockpiles in the study region. Ground level concentrations of SO2, NOx and PM10 were estimated by using EPA approved dispersion models
namely ISCST3 and AERMOD. The ground level concentrations predicted by two models were compared with the results of ambient air pollution measurements for November 2004. Predictions of both ISCST3 and AERMOD were underestimating the ground level SO2 concentrations. However, AERMOD predictions are better than ISCST3 predictions. The results of both models had good correlation with the results of NOx measurements. It has been shown that the contribution of the power plant to SO2, NOx and PM10 pollution in the area studied is minimal.
3
Tan, Stella. "Assessing near-field black carbon variability due to wood burning and evaluating regression models and ISC dispersion modeling." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/626.
Full textAbstract:
PM2.5 variability within the neighborhood scale has not been thoroughly studied for wood burning communities. High variability in near-field PM2.5 concentration may lead to harmful public exposure since monitoring does not occur on that scale. This study measures near-field PM2.5 variability by measuring black carbon (BC), a component of PM2.5, in a 1 km2 area located in Cambria, California. BC and meteorological data (when meteorological instruments were available) were measured over thirteen 12-hour intensive operation periods (IOPs) occurring over the winters of 2009 and 2010. Near-field BC variability was measured to understand the type of exposures found in communities where many homes are burning wood simultaneously within a small area. In addition, relationships between meteorological, geographical, and burning source characteristics and BC were observed as tools for understanding BC concentration. The computer air dispersion modeling programs, ISC-PRIME and ISCST3, were also evaluated for applicability to the near field.
BC concentrations were measured using 1- to 2-minute resolution aethalometers and 12 hour resolution Personal Environmental Monitors (PEMs). On average, over all IOPs and sites, aethalometer and PEM BC averages were very similar, ranging between 200 and 250 ng/m3, or 4 and 5 µg/m3 for PM2.5, and standard deviations were often high. Averaging all BC measurements, aethalometer BC standard deviation values were 360 percent of the average BC concentration and PEM BC standard deviations were 120 percent the average BC concentration. The average standard deviation detected during each IOP was 190 percent of the average BC concentration for aethalometers and 79 percent of the average BC concentration for PEMs. The average standard deviation detected at each site was 220 percent of the average BC concentration for aethalometers and 76 percent of the average BC concentration for PEMs. The larger standard deviations measured by higher resolution aethalometers demonstrated that low resolution instruments, such as PEMs, are unable to detect high concentrations that may occur.
In addition to examining BC variability, multiple linear regression analyses were conducted to determine the impact of meteorological variables and geographic and burning source characteristics on BC concentration and a weighted BC deviation function (BC standard deviation divided by average BC concentration). Time impacts, humidity, and wind speed, accounted for about 50 percent of variability in aethalometer average BC and BC deviation. However, because all model assumptions were not satisfied, improvements are needed. Regression models based on PEM BC found wind speed and direction to account for about 80 percent of average PEM BC variability and number of burning sources to account for about 30 percent of PEM BC deviation. Although PEM BC models accounted for a high percentage of BC variability, few data points were available for the PEM analyses and more IOPs are needed to determine their accuracy.
When evaluating correlations between geographic and burning source characteristics and PEM BC concentrations, specific IOP and PEM sampling location explained almost 70 percent of variability in BC concentration, though model residuals suggested model bias. IOP likely explained variation in burning patterns and meteorology over each night while sampling location was likely a proxy for housing density, tree coverage, and/or elevation. Because all regression model assumptions could not be satisfied, the predictors were also observed graphically. Plotting BC concentration versus the number of burning sources suggested that number of burning sources may affect BC concentration in areas of low tree coverage and high housing density and in the case that the level of surrounding vegetation and structures are minimal. More data points will be needed to determine whether or not these relationships are significant.
ISC-PRIME and ISCST3 modeling overall tended to under predict BC concentrations with average modeled-to-measured ratios averaging 0.25 and 0.15, for ISC-PRIME and ISCST3, respectively. Correction factors of 9.75 and 18.2 for ISC-PRIME and ISCST3, respectively, were determined to bring modeled BC concentrations closer to unity, but the range of ratios was still high. Both programs were unable to consistently capture BC variability in the area and more investigation will be needed to improve models.
The results of the study indicate high BC variability exists on the near-field scale, but that the variability is not clearly explained by existing regression and air dispersion models. To prevent public exposure to harmful concentrations, more investigation will be needed to determine factors that largely influence pollutant variability on the neighborhood scale.
4
Clemente, Daniela de Amorim. "Estudo do impacto ambiental das fontes industriais de poluição do ar no municipio de Paulinia - SP : empregando o modelo ISCST3." [s.n.], 2000. http://repositorio.unicamp.br/jspui/handle/REPOSIP/267562.
Full textAbstract:
Orientador: Edson TomazDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Quimica
Made available in DSpace on 2018-07-27T04:00:38Z (GMT). No. of bitstreams: 1 Clemente_DanieladeAmorim_M.pdf: 5859986 bytes, checksum: bfce7cb30592ab8fb4a24cc8aa1eb816 (MD5) Previous issue date: 2000
Resumo: O município de Paulínia - SP possui um parque industrial expressivo e constitui um pólo atrativo para expansão industrial, apresentando sinais importantes de degradação ambiental em vários aspectos. No entanto, poucos estudos têm sido realizados no sentido de avaliar a situação atual de poluição do ar na região, visando desenvolver uma base de conhecimento para subsidiar o Estado na definição de políticas públicas e mesmo os empreendedores na decisão de novos investimentos na região. O presente trabalho tem como objetivo fazer um estudo sobre poluição do ar na região, estimando-se a qualidade do ar em todo domínio considerado, para identificar as regiões mais atingidas pelos efeitos da dispersão dos poluentes, bem como, para auxiliar no planejamento da etapa subseqüente do trabalho desenvolvido na Unicamp que é a monitorização da qualidade do ar empregando um laboratório móvel. A estimativa da qualidade do ar na região está baseada num minucioso inventário de emissões de poluentes do ar, em dados meteorológicos de três anos e no uso de um modelo matemático aceito por diversos órgãos ambientais nacionais e internacionais. São realizadas comparações entre os dados medidos por uma estação de monitorização da Cetesb e os dados no estudo, obtendo uma razoável coerência entre os resultados. Da análise das curvas de isoconcentração para os diversos poluentes estudados é possível identificar as regiões críticas quanto à alteração da qualidade do ar, servindo como base para o planejamento dos estudos de campo com a estação móvel de monitorização de qualidade do ar
Abstract: The municipal district of Paulínia-SP possesses an expressive industrial park and it constitutes an attractive pole for industrial expansion, presents important signs of environmental degradation in several aspects. However, few studies have been accomplished in the sense of evaluating the current situation of air pollution in the area, seeking to develop a knowledge base to subsidize the state in the definition of public politics and even the entrepreneurs in the decision of new investments in the area. The present work has as objective to do a study about air pollution in this area, being made estimates about air quality in whole considered domain, to identify the areas more reached by the effects of pollutants dispersion, as well as, to aid in the planning of the subsequent stage of the work developed in Unicamp that is the air quality monitoring, by means of a mobile laboratory. Estimate of air quality in the area is based on a meticulous air pollutants emission inventory, in three-year meteorological data and in the use of a mathematical model accepted by national and international environmental agencies. Comparisons are accomplished among data measured by an monitoring station of Cetesb (State Environmental Agency - São Paulo) and the data estimated in the study, obtaining a reasonable coherence among the results. From analysis of isoconcentration curves for the several pollutants studied it is possible to identify the critical areas with relationship to air quality alteration, being good as base for planning field measures with the air quality monitoring mobile station
Mestrado
Desenvolvimento de Processos Químicos
Mestre em Engenharia Química
5
HASAN, KHALID. "STUDY OF SPATIAL/TEMPORAL PATTERNS OF RADON RELEASES FROM THE K-65 SILOS, USING DISPERSION MODELING AND GIS: A CASE STUDY AT THE DEPARTMENT OF ENERGY'S FERNALD ENVIRONMENTAL MANAGEMENT PROJECT, CINCINNATI, OHIO." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1008268951.
Full text
6
Liu, Tsing-Wei, and 劉沁瑋. "Estimation of Total Emissions in Hsinchu Science-Based Industrial Park and Application of ISCST3 Dispersion Model." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/01941530958705232088.
Full text
7
Botlaguduru, Venkata Sai V. "Comparison of Aermod and ISCST3 Models for Particulate Emissions from Ground Level Sources." 2009. http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7600.
Full textAbstract:
Emission factors (EFs) and results from dispersion models are key components in the air pollution regulatory process. The EPA preferred regulatory model changed from ISCST3 to AERMOD in November, 2007. Emission factors are used in conjunction with dispersion models to predict 24-hour concentrations that are compared to National Ambient Air Quality Standards (NAAQS) for determining the required control systems in permitting sources. This change in regulatory models has had an impact on the regulatory process and the industries regulated.
In this study, EFs were developed for regulated particulate matter PM10 and PM2.5 from cotton harvesting. Measured concentrations of TSP and PM10 along with meteorological data were used in conjunction with the dispersion models ISCST3 and AERMOD, to determine the emission fluxes from cotton harvesting. The goal of this research was to document differences in emission factors as a consequence of the models used. The PM10 EFs developed for two-row and six-row pickers were 154 + 43 kg/km2 and 425 + 178 kg/km2, respectively. From the comparison between AERMOD and ISCST3, it was observed that AERMOD EFs were 1.8 times higher than ISCST3 EFs for Emission factors (EFs) and results from dispersion models are key components in the air pollution regulatory process. The EPA preferred regulatory model changed from ISCST3 to AERMOD in November, 2007. Emission factors are used in conjunction with dispersion models to predict 24-hour concentrations that are compared to National Ambient Air Quality Standards (NAAQS) for determining the required control systems in permitting sources. This change in regulatory models has had an impact on the regulatory process and the industries regulated.
In this study, EFs were developed for regulated particulate matter PM10 and PM2.5 from cotton harvesting. Measured concentrations of TSP and PM10 along with meteorological data were used in conjunction with the dispersion models ISCST3 and AERMOD, to determine the emission fluxes from cotton harvesting. The goal of this research was to document differences in emission factors as a consequence of the models used. The PM10 EFs developed for two-row and six-row pickers were 154 + 43 kg/km2 and 425 + 178 kg/km2, respectively. From the comparison between AERMOD and ISCST3, it was observed that AERMOD EFs were 1.8 times higher than ISCST3 EFs for absence of solar radiation. Using AERMOD predictions of pollutant concentrations off property for regulatory purposes will likely affect a source?s ability to comply with limits set forth by State Air Pollution Regulatory Agencies (SAPRAs) and could lead to inappropriate regulation of the source.
8
Lin, Chun-You, and 林春佑. "A Study of the Atmospheric Environmental Impact Assessment of the Significant Stationary Air Pollution Sources in Hualien County─Using the ISCST3 Gaussian Dispersion Model." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/49017567941332119659.
Full textAbstract:
碩士國立東華大學
化學研究所
88
Most of western counties of Taiwan have been developed rapidly in the last 20 years. A lot of factories have been set up in these counties, and the populations of these counties have grown rapidly. Because of numerous economics activities, many western areas of Taiwan have been suffered from the air pollution for a long time. As a result, the government of Taiwan was pushing the “Industrial Eastern-Movement Strategy” policy in 1990. Hualien County is located on the eastern corner of Taiwan. Its economic development is much slower than the western counties of Taiwan. This county has few factories and inhabitants, therefore its environmental quality is very good. Unfortunately, in recent years, the government of Taiwan has approved many commercial and industrial projects in Hualien County. Such projects will perhaps discharge a large number of air pollutants , and will affect the air quality of Hualien County. Consequently, we have to study the air pollution impact of the operated industrial sources in Hualien County. A Gaussian Dispersion Model, Industrial Sources Complex Short Term Version 3 (ISCST3), can be used to predict the primary air pollutants concentration in urban and rural areas based on the emission inventories, the meteorological data and the terrain data. In most of the developed countries, ISCST3 model is the most widely used as a Air Quality Model to assess the atmospheric environmental impact of commercial and industrial projects. In this study, we use this model to assess the air pollution impact of the significant industrial stationary sources in Hualien County. By way of this study, we gained the spatial and temporal concentration distribution of exhaust air pollutants from the significant industrial stationary sources in Hualien County. In accordance with the study results, we proved the primary air pollutants of these significant industrial stationary air pollution sources in Hualien County did not significantly impact the air quality of Hualien County.
Book chapters on the topic "ISCLT3 Dispersion Model"
1
Ciucci, Iliano, Marino Mazzini, and Stefano Strinati. "A Study on the Aerosol Dispersion Model Implemented in ISC3 Code of US-EPA." In Air Pollution Modelling and Simulation, 565–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04956-3_57.
Full textConference papers on the topic "ISCLT3 Dispersion Model"
1
Thomas P Curran, Vincent A Dodd, and William L Magette. "Evaluation of ISC3 and CALPUFF Atmospheric Dispersion Models for Odor Nuisance Prediction." In 2007 Minneapolis, Minnesota, June 17-20, 2007. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2007. http://dx.doi.org/10.13031/2013.23276.
Full text
2
Alfaro Degan, G., G. Di Bona, D. Lippiello, and M. Pinzari. "PM10 dispersion model in quarrying activities: a comparison of an ISC3 approach to a mono/multivariate geostatistical estimation." In AIR POLLUTION 2006. Southampton, UK: WIT Press, 2006. http://dx.doi.org/10.2495/air06012.
Full text
3
Ghenai, C., A. Nagaboina, and L. Lagos. "Dispersion Modeling of Beryllium Airborne Particulate Released During the Demolition of Contaminated Building." In 14th International Conference on Nuclear Engineering. ASMEDC, 2006. http://dx.doi.org/10.1115/icone14-89830.
Full textAbstract:
During the demolition of contaminated building, the Beryllium on the surface of the building can be released to the atmosphere. Workers on the site and also off site public can be exposed to the Beryllium airborne particulate that can cause health problems. The objective of this study is to assess the impacts of beryllium airborne particulate released during the demolition of building on workers and off site public. For the source inventory, data from building 333 at DOE Hanford site are used as an example to estimate the total amount of Beryllium on the surface of this building. Samples from the interior surfaces were collected in previous. The surface contaminations were measured and the contamination levels ranging from 1 to 11 μg/100 cm2 were found in this building. The fraction of Beryllium released to the atmosphere during the demolition process was estimated. The amount of Beryllium released from the demolition process are transported and dispersed in the air. The short term Industrial Source Complex (ISC3) model was used to predict the ambient Beryllium concentrations. The receptors are located at downwind distances between 52 m and 2000 m from the center of emission source for every 10-degree flow vector around the emission source (thirty six receptors for each downwind distance). The results presented in this paper show the total 24-hours averaged total airborne air concentration and the 8-hours averages Beryllium air concentration at each of the receptor location. A comparison between the maximum predicted concentration of Beryllium and the compliance benchmark for the site workers (0.2 μg/m3 over 8-hours time averaged) and off site public (0.01 μg/m3) was performed. The risk assessment analysis will help the decision makers to assess the risks from exposure to Beryllium during the demolition of buildings.