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切換: 標籤 | MARC模式 | ISBD

Numerical modeling of arsenic transp...

da Silva, Eduardo Figueira.

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Numerical modeling of arsenic transport in groundwater.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Numerical modeling of arsenic transport in groundwater./
作者:	da Silva, Eduardo Figueira.
面頁冊數:	211 p.
附註:	Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 1101.
Contained By:	Dissertation Abstracts International66-02B.
標題:	Engineering, Environmental. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3163933
ISBN:	0496985981

Numerical modeling of arsenic transport in groundwater.
da Silva, Eduardo Figueira.

Numerical modeling of arsenic transport in groundwater. - 211 p.

Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 1101.

Thesis (Ph.D.)--University of Michigan, 2005.

Design and implementation of remediation strategies for arsenic contaminated groundwater requires the development of numerical models capable of accurately predicting arsenic fate and transport. Adsorption of arsenic onto mineral oxides present in groundwater is an important mechanism controlling transport. Two alternative modeling approaches for sorption were investigated to determine their ability to provide accurate numerical predictions for arsenic transport: the isotherm approach and the surface complexation model (SCM) approach.

ISBN: 0496985981Subjects--Topical Terms:

783782
Engineering, Environmental.

Numerical modeling of arsenic transport in groundwater.
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245 1 0 $a Numerical modeling of arsenic transport in groundwater.
300 $a 211 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 1101.
500 $a Chair: Linda M. Abriola.
502 $a Thesis (Ph.D.)--University of Michigan, 2005.
520 $a Design and implementation of remediation strategies for arsenic contaminated groundwater requires the development of numerical models capable of accurately predicting arsenic fate and transport. Adsorption of arsenic onto mineral oxides present in groundwater is an important mechanism controlling transport. Two alternative modeling approaches for sorption were investigated to determine their ability to provide accurate numerical predictions for arsenic transport: the isotherm approach and the surface complexation model (SCM) approach.
520 $a Results from the isotherm modeling approach showed that traditional finite element expansions of the mass-term of the contaminant transport equation produce poor transport results, especially for the Freundlich isotherm, due to numerical difficulties related to the non-linearity of the isotherm slope. A non-traditional finite element approach was developed that resulted in accurate and mass-conservative numerical solutions for the transport of a contaminant subject to non-linear isotherm sorption, including Freundlich.
520 $a The self-consistent SCM developed in this dissertation for adsorption of arsenate onto goethite using sorption data from batch reactor studies and spectroscopic information on surface speciation led to the selection of four surface species of the monodentate complex for modeling adsorption data between pH 2.5 and 11.0 over the added arsenate concentration range of 133 to 266 micromolars.
520 $a Results from a sensitivity analysis using an arsenate transport model to simulate columns containing iron oxides indicated that the SCM approach was able to account for the influence of the variability in pH, pH buffering, surface area of the oxide, concentration of surface sites, and concentration of injected arsenate on arsenate transport. The effects of variable pH and changing arsenate concentrations on transport simulations could not be predicted using the isotherm transport model when the pH was not buffered. Changing pH effects upon arsenate adsorption were also not expected based on the isotherm model. These results suggest that the surface complexation modeling approach can produce a more versatile and realistic transport model, which uses independent batch sorption measurements to account for the effect of variations in geochemical conditions on arsenic transport predictions.
590 $a School code: 0127.
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791 $a Ph.D.
792 $a 2005
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