scholarly journals Source Characterization of Multiple Reactive Species at an Abandoned Mine Site Using a Groundwater Numerical Simulation Model and Optimization Models

Author(s):  
Michael Saah Hayford ◽  
Bithin Datta

The most important first step in the management and remediation of contaminated groundwater aquifers is unknown contaminant source characterization. Often, the hydrogeological field data available for accurate source characterization are very sparse. In addition, hydrogeological and geochemical parameter estimates and field measurements are uncertain. Particularly in complex contaminated sites such as abandoned mine sites, the geochemical processes are very complex and identifying the sources of contamination in terms of location, magnitude, and duration, and determination of the pathways of pollution become very difficult. The reactive nature of the contaminant species makes the geochemical transport process very difficult to model and predict. Additionally, the source identification inverse problem is often non-unique and ill posed. This study is about developing and demonstrating a source characterization methodology for a complex contaminated aquifer with multiple reactive species. This study presents linked simulation optimization-based methodologies for characterization of unknown groundwater pollution source characteristics, i.e., location, magnitude and duration or timing. Optimization models are solved using an adaptive simulated annealing (ASA) optimization algorithm. The performance of the developed methodology is evaluated for different complex scenarios of groundwater pollution such as distributed mine waste dumps with reactive chemical species. The method is also applied to a real-life contaminated aquifer to demonstrate the potential applicability and optimal characterization results. The illustrative example site is a mine site in Northern Australia that is no longer active.

2012 ◽  
Vol 61 (3) ◽  
pp. 611-623 ◽  
Author(s):  
Diego Arosio ◽  
Laura Longoni ◽  
Monica Papini ◽  
Luigi Zanzi

Fact Sheet ◽  
1997 ◽  
Author(s):  
Michael L. Pomes ◽  
W.R. Green ◽  
E.M. Thurman ◽  
W.H. Orem ◽  
H.T. Lerch

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 384
Author(s):  
Rocío Hernández-Sanjaime ◽  
Martín González ◽  
Antonio Peñalver ◽  
Jose J. López-Espín

The presence of unaccounted heterogeneity in simultaneous equation models (SEMs) is frequently problematic in many real-life applications. Under the usual assumption of homogeneity, the model can be seriously misspecified, and it can potentially induce an important bias in the parameter estimates. This paper focuses on SEMs in which data are heterogeneous and tend to form clustering structures in the endogenous-variable dataset. Because the identification of different clusters is not straightforward, a two-step strategy that first forms groups among the endogenous observations and then uses the standard simultaneous equation scheme is provided. Methodologically, the proposed approach is based on a variational Bayes learning algorithm and does not need to be executed for varying numbers of groups in order to identify the one that adequately fits the data. We describe the statistical theory, evaluate the performance of the suggested algorithm by using simulated data, and apply the two-step method to a macroeconomic problem.


2014 ◽  
Vol 41 ◽  
pp. 176-188 ◽  
Author(s):  
Michael C. Moncur ◽  
Carol J. Ptacek ◽  
Masaki Hayashi ◽  
David W. Blowes ◽  
S. Jean Birks

Environments ◽  
2016 ◽  
Vol 3 (4) ◽  
pp. 15 ◽  
Author(s):  
Hendra Prasetia ◽  
Masayuki Sakakibara ◽  
Yuri Sueoka ◽  
Koichiro Sera

ACS Catalysis ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 11663-11670 ◽  
Author(s):  
Kohei Ueda ◽  
Kazuhisa Isegawa ◽  
Kenta Amemiya ◽  
Kazuhiko Mase ◽  
Hiroshi Kondoh

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