- author = "Om Prakash",
- title = "Optimal monitoring network design and identification of unknown pollutant sources in polluted aquifers",
- school = "School of Engineering and Physical Sciences, James Cook University",
- year = "2014",
- address = "Australia",
- month = apr,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://researchonline.jcu.edu.au/37017/",
-
URL = "
http://researchonline.jcu.edu.au/37017/1/37017-prakash-2014-thesis.pdf",
- size = "229 pages",
- abstract = "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.",
-
absttract = "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.",
-
absttract = "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.",
- absttract = "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.",
-
notes = "Genetic Programming Models for Impact Factor
Assessment and Frequency Factor Assessment
Item ID: 37017 Supervisor Bithin Datta",
