RECENT DEVELOPMENTS IN IDENTIFICATION OF UNKNOWN CONTAMINATION SOURCES AND MONITORING NETWORK DESIGN FOR CONTAMINATED GROUNDWATER SYSTEMS Bithin Datta1, 2, Om Prakash1, Mahsa Amirabdollahian1,2, Manish K. Jha1, Hamed K. Esfahani1, Michael S. Hayford1, Chadalavada Sreenivasulu2, Ravi Naidu2 1
College of Science Technology and Engineering, James Cook University, Townsville QLD 4811, AUSTRALIA 2 CRC for Contamination Assessment and Remediation of the Environment, Mawson Lakes, SA 5095, AUSTRALIA
[email protected] INTRODUCTION In order to design an effective aquifer contamination remediation strategy two important steps are: (i) identification of unknown groundwater pollution sources once contamination is detected in an aquifer, and (ii) efficient and effective monitoring of contaminant plume movement. James Cook University, Australia and CRC-CARE are collaborating in developing comprehensive and easy to use computer software and state of art methodologies that at can be utilized for (i) identification of unknown pollution source magnitudes, location, and time of activity; (2) optimal design of a contamination monitoring network that can be implemented in any contaminated groundwater site incorporating site specific information. One part of the collaborative research between James Cook University team and CRC-CARE has resulted in the development of software that enables water resources managers and engineers to solve the difficult problems of identifying sources of pollution in a contaminated groundwater systems, and to design optimal monitoring networks that can detect the extent and movement of contaminants in contaminated groundwater systems. The developed computer software make it possible for practitioners with limited knowledge of hydrogeology and pollutant transport processes to address the source identification issue. These software are expected to be immensely useful for proper management of contaminated sites with unknown sources of contamination. The capabilities of the two developed software, the contamination source identification and the monitoring network design, are briefly introduced. Figure 1 shows the architecture of the two developed software packages. There are various challenging issues that make the source characterization problem very difficult to solve. Some of the complexities arise due to uncertainties in modelling the aquifer system and the associated physical processes, due to sparsity of measurement data in typical contaminated aquifers, due to errors in field measurements, and due to difficulty in predicting the complex geochemical processes involving reactive chemical species such as those occurring in mine sites, and also due to the non-uniqueness of the aquifer response to various stresses i.e., contaminant injection. Few recent advances towards improving the available methodologies are focussed towards making the source identification methodology more versatile and valid under different real-life scenarios e.g., urban spills and abandoned mine sites where very complex reactive geochemical processes are occurring. The extension of source identification methodology to incorporate complex geochemical reactive environment, incorporation uncertainties through fuzzy quantification, incorporation of uncertainties through Fuzzy quantification, use of Fractal Singularity Index to delineate potential pollution plumes, development of new surrogate or meta models for ensuring the computational feasibility of the developed methodologies are some of the recent advances and possible future trends. The current state of development of these methodologies for more accurate and reliable contaminant source identification and the improvements achieved by implementing a designed monitoring network will be presented.
SOURCE IDENTIFICATION AND MONITORING NETWORK DESIGN SOFTWARE Some of the source identification methodologies for source identification are reported in: Datta et al.(1989); Mahar and Datta (1997 and 2001); Datta et. al. (2009, and 2010); Sreenivasulu et al. (2012); and Jha and Datta (2012). (Amirabdollahian and Datta, 2013) In the source identification software (CARE-GWSID) the linked simulation-optimization methodology is utilized. THE MONITORING NETWORK DESIGN SOFTWARE (CARE-GWMND) A comprehensive UI based software (CARE-GWMND, 2013) for Optimal Monitoring Network Design to address various aspects of groundwater management is developed.. Some of the objectives and methodologies adopted are outlined in Mahar and Datta, (1997), Singh (2008), and Sreenivasulu et.al (2010). (CARE-GWSID manual, 2013). Fig. 1. Software Architectures RECENT DEVELOPMENTS Some of the recent developments and solution results obtained for complex contaminated sites, i.e., urban aquifers contaminated by leakage of tanks, abandoned mine sites with unknown sources and pathways of contamination exhibiting clear evidence of critical pollution, use of fuzzy quantification of modelling uncertainties to address errors and uncertainties in describing the aquifer system and processes, and some results obtained from validation study of some of the developed methodologies will be presented and discussed. Figure 2a shows a contaminate aquifer and the pollution plume. The candidate contamination source locations are marked in Figure 2a. Figure 2b shows the estimated source fluxes for each of the candidate source locations using the developed software. Solution results show that the contamination sources are identified satisfactorily.
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Fig.2. Contaminant aquifer; characteristics.
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Source identification and pathway determination for a contaminated mine site in Queensland and in Northern territories, was also carried out, with surrogate models based on Genetic programming and also Self Organizing Maps were developed. These sites also represent complex geochemistry. The source identification exercise will be described for these sites with reactive or radioactive chemicals.
Fig. 3. Selected Monitoring Wells Delineating the Plume Boundary
Fig. 4. a) Model definition, b) observation wells location for the mine site aquifer
CONCLUSIONS Recent developments, future trends and potential use of developed dedicated software related to unknown pollution source identification in contaminated aquifers are discussed. The utility and application of the user-friendly software for source identification and design of effective and economically efficient monitoring network are presented. The extension of source identification methodology to incorporate complex geochemical reactive environment, incorporation uncertainties through fuzzy quantification, incorporation of uncertainties through Fuzzy quantification, use of Fractal Singularity Index to delineate potential pollution plumes, development of new surrogate or meta models for ensuring the computational feasibility of the developed methodologies, are some of the recent advances and possible future trends. The current state of development of these methodologies and some the application results are discussed. REFERENCES CARE-GWMND User Manual (2013). Groundwater Monitoring Network Design Software, (Datta et. al.) School of Engineering & Physical Sciences, James Cook University, Townsville, QLD 4814, Australia. CARE-GWSID User Manual (2013). Groundwater Contamination Source Identification (Datta et. al.), School of Engineering & Physical Sciences, James Cook University, Townsville, QLD 4814, Australia. Datta, B. et al. (1989). Development of an expert system embedding pattern recognition techniques for pollution source identification. Technical Report. Department of Civil Engineering, University of California Davis, USA. Datta, B. Chakrabarty, D. and Dhar, A. (2008). Optimal dynamic monitoring network design and identification of unknown groundwater pollution sources. Water Res. Man. 23(10):2031-2049. Jha, M. and Datta, B. (2012). Three dimensional groundwater contamination source identification using adaptive simulated annealing. J. Hydro. Eng. 18(3):307-313. Mahar, P.S. and Datta, B. (1997). Optimal monitoring network and ground water pollution source identification. J Water Res. Plan. Man. 123(4):199-207. Prakash, O., and Datta, B. (2012). Sequential optimal monitoring network design and iterative spatial estimation of pollutant concentration for identification of unknown groundwater pollution source locations. Environ. Monit. Assess. 185(7). Sreenivasulu, C., Datta, B., and Naidu, R. (2012) Optimal identification of groundwater pollution sources using feedback monitoring information: a case study. Environ. Forensics. 13(2):140-153.
