Optimal monitoring network design and identification of unknown pollutant sources in polluted aquifers

Prakash, Om (2014) Optimal monitoring network design and identification of unknown pollutant sources in polluted aquifers. PhD thesis, James Cook University.

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Increasing stress from various anthropogenic activities has resulted in widespread pollution of groundwater resources. Often, when the pollutant is first detected in groundwater, little is known about the pollutant sources. Identification of source characteristics in terms of locations, activity initiation times, and source flux release histories and activity durations are vital in planning effective remediation measures and determining the liability of the polluter. Groundwater pollution source characterization is an inverse and ill-posed problem. Finding a solution to this inverse problem remains a challenging task due to uncertainties in accurately predicting the aquifer response to source flux injection, generally encountered sparsity of concentration measurements in the field, and the non-uniqueness in the aquifer response to the subjected hydraulic and chemical stresses. This study presents linked simulation-optimization, and sequential monitoring network design based methodologies for identification of unknown groundwater pollution source characteristics.

Pollution in groundwater aquifers is generally first detected in an arbitrarily located water supply well or a group of wells. Often pollutants are detected much after activity at the sources may have initiated, or even after it has ceased to exist. There may be a gap of years, or even decades, between the start of source activity and detection of pollutants in an aquifer. Other important issues in accurately identifying unknown groundwater pollution source characteristics are the quality, usability and extent of pollution measurement data from the study area. Existing methodologies for unknown groundwater pollution source characterization have several limitations. Methodologies developed in this study aim to address some of these limitations. The major limitations addressed in this study include:

i. sparsity of pollutant concentration measurement data,

ii. inefficient monitoring network for concentration measurements,

iii. difficulty in identifying the source locations,

iv. difficulty in establishing the pollutant source activity initiation time,

v. applicability of optimal source characterization with missing observation data.

In many cases of aquifer pollution, especially in clandestine underground disposal of toxic wastes, no information is available about the number and location of such sources. Moreover, monitoring wells where pollution is first detected may not be optimally located for accurately identifying the release history of unknown pollution sources. A large number of pollutant concentration measurements spread over time and space is necessary for accurate source identification. However, long term monitoring over a large number of monitoring locations has budgetary constraints. This study presents a sequential optimal monitoring network design methodology based on geostatistical kriging, a pollutant concentration gradient based search for identification of source locations, and a Genetic Programming (GP) based optimal monitoring network design model for collecting concentration measurements for efficient source characterization.

To address the issue of unknown starting times of activity of the sources, a new methodology is developed for simultaneously identifying the starting times of the activity of the sources and their flux release history. A new optimum decision model is formulated and solved such that the starting times of the activity of the sources are directly obtained as solution. Simulated Annealing (SA) is used for solving the optimization problem with the starting time of pollutant source activity incorporated as explicit decision variable.

Subsequent to the detection of pollution in an aquifer, a more formal methodology for source characterization is generally initiated only after large numbers of spatiotemporal concentration measurements, spaced over a sufficiently long period of time, are obtained. During this time, the spread of the pollutant continues while temporal measurements are being obtained at monitoring locations. A feedback-based sequential methodology for efficient identification of unknown pollutant source characteristics, integrating optimal monitoring network design and an optimization based source identification model, is developed. The main advantage of this methodology is that source characterization can start at the same time as when pollutant is first detected in the aquifer. In every sequence, feedback from the source identification model improves the optimal monitoring network design and vice-versa. This results in efficient and accurate source characterization, within a few sequences of source identification and monitoring network design.

The performances of the developed methodologies are evaluated for different scenarios of groundwater pollution incorporating transient flow and advective-dispersive transport in heterogeneous anisotropic conditions. The applicability of the developed methodologies is tested for a real aquifer site polluted with petrochemical waste (BTEX). These evaluation results demonstrate the potential applicability of the developed methodologies to correctly estimate the unknown source flux's magnitude, and location and source activity initiation times, while improving the accuracy of source flux identification. Results of performance evaluation of each of these methodologies indicate their potential for field application.

Item ID: 37017
Item Type: Thesis (PhD)
Keywords: aquifers; attributes; characteristics; ground water; groundwater; measurement; pollutants; pollution; simulation; source
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Publications arising from this thesis are available from the Related URLs field. The publications are:

Chapter 3: Prakash, Om, and Datta, Bithin (2014) Characterization of groundwater pollution sources with unknown release time history. Journal of Water Resource and Protection, 6 (4). pp. 337-350.

Chapter 3: Prakash, Om, and Datta, Bithin (2013) Sequential optimal monitoring network design and iterative spatial estimation of pollutant concentration for identification of unknown groundwater pollution source locations. Environmental Monitoring and Assessment, 185 (7). pp. 5611-5626.

Chapter 4: Datta, Bithin, Prakash, Om, Campbell, Sean, and Escalada, Gerry (2013) Efficient identification of unknown groundwater pollution sources using linked simulation-optimization incorporating monitoring location impact factor and frequency factor. Water Resources Management, 27 (14). pp. 4959-4976.

Chapter 4: Prakash, Om, and Datta, Bithin (2014) Multiobjective monitoring network design for efficient identification of unknown groundwater pollution sources incorporating genetic programming–based monitoring. Journal of Hydrologic Engineering, 19 (11). pp. 04014025-1-04014025-13.

Chapter 4: Prakash, Om, and Datta, Bithin (2014) Optimal monitoring network design for efficient identification of unknown groundwater pollution sources. International Journal of Geomate, 6 (1). pp. 785-790.

Chapter 4: Datta, Bithin, Prakash, Om, and Sreekanth, Janardhanan (2014) Application of genetic programming models incorporated in optimization models for contaminated groundwater systems management. In: Tantar, Alexandru-Adrian, Tantar, Emilia, Sun, Jian-Qiao, Zhang, Wei, Ding, Qian, Schütze, Oliver, Emmerich, Michael, Legrand, Pierrick, Del Moral, Pierre, and Coello Coello, Carlos A., (eds.) EVOLVE: a bridge between probability, set oriented numerics, and evolutionary computation V. Advances in Intelligent Systems and Computing, 288. Springer, New York, NY, USA, pp. 183-199.

Date Deposited: 06 Jan 2015 23:58
FoR Codes: 09 ENGINEERING > 0905 Civil Engineering > 090509 Water Resources Engineering @ 50%
09 ENGINEERING > 0907 Environmental Engineering > 090799 Environmental Engineering not elsewhere classified @ 50%
SEO Codes: 96 ENVIRONMENT > 9609 Land and Water Management > 960999 Land and Water Management of Environments not elsewhere classified @ 100%
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