Modular neural network to predict the distribution of nitrate in ground water using on-ground nitrogen loading and recharge data

Artificial neural networks have proven to be an attractive mathematical tool to represent complex relationships in many branches of hydrology. Due to this attractive feature, neural networks are increasingly being applied in subsurface modeling where intricate physical processes and lack of detailed field data prevail. In this paper, a methodology using modular neural networks (MNN) is proposed to simulate the nitrate concentrations in an agriculture-dominated aquifer. The methodology relies on geographic information system (GIS) tools in the preparation and processing of the MNN input–output data. The basic premise followed in developing the MNN input–output response patterns is to designate the optimal radius of a specified circular-buffered zone centered by the nitrate receptor so that the input parameters at the upgradient areas correlate with nitrate concentrations in ground water. A three-step approach that integrates the on-ground nitrogen loadings, soil nitrogen dynamics, and fate and transport in ground water is described and the critical parameters to predict nitrate concentration using MNN are selected. The sensitivity of MNN performance to different MNN architecture is assessed. The applicability of MNN is considered for the Sumas-Blaine aquifer of Washington State using two scenarios corresponding to current land use practices and a proposed protection alternative. The results of MNN are further analyzed and compared to those obtained from a physically-based fate and transport model to evaluate the overall applicability of MNN

INTEGRATED AQUIFER VULNERABILITY ASSESSMENT OF NITRATE CONTAMINATION IN CENTRAL INDIANA

Groundwater is not easily contaminated, but it is difficult to restore once contaminated. Therefore, groundwater management is important to prevent pollutants from reaching groundwater. A common step in developing groundwater management plans is assessment of aquifer risk using computational models. Groundwater modeling with a geographic information system (GIS) for efficient groundwater management can provide maps of regions where groundwater is contaminated or may be vulnerable and also can help select the optimal number of groundwater monitoring locations

Value of Information in Design of Groundwater Quality Monitoring Network under Uncertainty

The increasing need for groundwater as a source for fresh water and the continuous deterioration in many places around the world of that precious source as a result of anthropogenic sources of pollution highlights the need for efficient groundwater resources management. To be efficient, groundwater resources management requires efficient access to reliable information that can be acquired through monitoring. Due to the limited resources to implement a monitoring program, a groundwater quality monitoring network design should identify what is an optimal network from the point of view of cost, the value of information collected, and the amount of uncertainty that will exist about the quality of groundwater. When considering the potential social impact of monitoring, the design of a network should involve all stakeholders including people who are consuming the groundwater. This research introduces a methodology for groundwater quality monitoring network design that utilizes state-of-the-art learning machines that have been developed from the general area of statistical learning theory. The methodology takes into account uncertainties in aquifer properties, pollution transport processes, and climate. To check the feasibility of the network design, the research introduces a methodology to estimate the value of information (VOI) provided by the network using a decision tree model. Finally, the research presents the results of a survey administered in the study area to determine whether the implementation of the monitoring network design could be supported. Applying these methodologies on the Eocene Aquifer, Palestine indicates that statistical learning machines can be most effectively used to design a groundwater quality monitoring network in real-life aquifers. On the other hand, VOI analysis indicates that for the value of monitoring to exceed the cost of monitoring, more work is needed to improve the accuracy of the network and to increase people’s awareness of the pollution problem and the available alternatives

Texas Water Resources: Vulnerability from Contaminants

Numerical models of flow and transport are commonly applied for the sustainable management of water resources and for the selection of appropriate remediation techniques. However, these numerical models are not always accurate due to uncertain parameters and the disparity of scales across which observations are made, hydrological processes occur, and modeling is conducted. The modeling framework becomes further complex because hydrologic processes are coupled with chemical and biological processes. This dissertation focuses on the most widespread contaminants of surface and ground water, which are E. coli and nitrate, respectively. Therefore, this research investigates the linkages between bio-chemical and hydrologic processes for E. coli transport, explores the spatio-temporal variability of nitrate, quantifies uncertainty, and develops models for both E. coli and nitrate transport that better characterize these biogeochemical linkages. A probabilistic framework in the form of Bayesian Neural Networks (BNN) was used to estimate E. coli loads in surface streams and was compared with a conventional model LOADEST. This probabilistic framework is crucial when water quality data are scarce, and most models require a large number of mechanistic parameters to estimate E. coli concentrations. Results indicate that BNN provides better characterization of E. coli at higher loadings. Results also provide the physical, chemical, and biological factors that are critical in the estimation of E. coli concentrations in Plum Creek, Texas. To explore model parameters that control the transport of E. coli in the groundwater (GW) and surface water systems, research was conducted in Lake Granbury, Texas. Results highlight the importance of flow regimes and seasonal variability on E. coli transport. To explore the spatio-temporal variability of nitrate across the Trinity and Ogallala aquifers in Texas, an entropy-based method and a numerical study were employed. Results indicate that the overall mean nitrate-N has declined from 1940 to 2008 in the Trinity Aquifer as opposed to an increase in the Ogallala Aquifer. The numerical study results demonstrate the effect of different factors like GW pumping, flow parameters, hydrogeology of the site at multiple spatial scales. To quantify the uncertainty of nitrate transport in GW, an ensemble Kalman filter was used in combination with the MODFLOW-MT3DMS models. Results indicate that the EnKF notably improves the estimation of nitrate-N concentrations in GW. A conceptual modeling framework with deterministic physical processes and stochastic bio-chemical processes was devised to independently model E. coli and nitrate transport in the subsurface. Results indicate that model structural uncertainty provides useful insights to modeling E. coli and nitrate transport

Integrated Environmental Modelling Framework for Cumulative Effects Assessment

Global warming and population growth have resulted in an increase in the intensity of natural and anthropogenic stressors. Investigating the complex nature of environmental problems requires the integration of different environmental processes across major components of the environment, including water, climate, ecology, air, and land. Cumulative effects assessment (CEA) not only includes analyzing and modeling environmental changes, but also supports planning alternatives that promote environmental monitoring and management. Disjointed and narrowly focused environmental management approaches have proved dissatisfactory. The adoption of integrated modelling approaches has sparked interests in the development of frameworks which may be used to investigate the processes of individual environmental component and the ways they interact with each other. Integrated modelling systems and frameworks are often the only way to take into account the important environmental processes and interactions, relevant spatial and temporal scales, and feedback mechanisms of complex systems for CEA. This book examines the ways in which interactions and relationships between environmental components are understood, paying special attention to climate, land, water quantity and quality, and both anthropogenic and natural stressors. It reviews modelling approaches for each component and reviews existing integrated modelling systems for CEA. Finally, it proposes an integrated modelling framework and provides perspectives on future research avenues for cumulative effects assessment

Nitrates and phosphates in cave waters of Kraków-Częstochowa Upland, southern Poland

The paper presents the varied presence of nitrates and phosphates in water from caves located in Częstochowa and Kraków, in urban, strongly anthropogenic conditions, representing the vadose zone of the fissure-karstic-porous massif of Upper Jurassic limestones. Hydrochemical research was carried out by the authors in the Cave on the Stone in Częstochowa in 2012–2015, in caves of the Zakrzówek horst from 1996 to 2002, and in the Dragon’s Cave by the research team of J. Motyka in 1995–1998. A number of NO3 and PO4 measurements were performed in waters sampled at these research sites: 20 measurements each ofNO3 and PO4 at the Cave on the Stone, 228 of NO3 and 422 of PO4 at Zakrzówek, and 19 each of NO3 and PO4 at the Dragon’s Cave. To assess the quality aspect of N and P compounds in waters from the Cave on the Stone, the results of geochemical modelling were processed using PHREEQC software. In cave waters, the oxidised form of nitrogen NO3 − predominates; in surface waters in the vicinity, unoxidised forms prevail: NH4+, NH3, and NH4SO4 −. Among phosphorus speciations, dissolved forms are dominant: HPO4 2−, H2PO4 −, and the insoluble form CaHPO4; in surface waters, these forms are practically absent. Transformations of water chemistry in ‘urban’ caves, often centuries old, manifest themselves in, inter alia, the occurrence of multi-ionic waters, including seasonal variations and extremely diversified concentrations, with very high concentrations in subpopulations of NO3 (0.2–485 mg dm−3) and P (0.02–6.87 mg dm−3). The common presence of NO3 in waters of the phreatic zone of the Częstochowa Upland, an area developed in an agricultural direction, is documented by, inter alia, the exploitation of intakes supplying the city of Częstochowa (10–57 mg dm−3, 2011) and crenological studies from 2008 to 2015 (NO3, 2–58 mg dm−3), at simultaneously low phosphate concentrations (PO4, 0.02–0.24 mg dm−3)

Multi-Output Adaptive Neuro-Fuzzy Inference System for Prediction of Dissolved Metal Levels in Acid Rock Drainage: a Case Study

Pyrite oxidation, Acid Rock Drainage (ARD) generation, and associated release and transport of toxic metals are a major environmental concern for the mining industry. Estimation of the metal loading in ARD is a major task in developing an appropriate remediation strategy. In this study, an expert system, the Multi-Output Adaptive Neuro-Fuzzy Inference System (MANFIS), was used for estimation of metal concentrations in the Shur River, resulting from ARD at the Sarcheshmeh porphyry copper deposit, southeast Iran. Concentrations of Cu, Fe, Mn and Zn are predicted using pH, sulphate (SO4) and magnesium (Mg) concentrations in the Shur River as input to the MANFIS. Three MANFIS models were implemented, Grid Partitioning (GP), the Subtractive Clustering Method (SCM) and the Fuzzy C-Means Clustering Method (FCM).A comparison was made between these three models and the results show the superiority of the MANFIS-SCM model. The results obtained indicate that the MANFIS-SCM model has potentialfor estimation of the metals with high a degree of accuracy and robustness

The occurrence and origin of salinity in non-coastal groundwater in the Waikato region

Aims The aims of this project are to describe the occurrence, and determine the origin of non-coastal saline groundwater in the Waikato region. High salinity limits the use of the water for supply and agricultural use. Understanding the origin and distribution of non-coastal salinity will assist with development and management of groundwater resources in the Waikato. Method The occurrence of non-coastal groundwater salinity was investigated by examining driller’s records and regional council groundwater quality information. Selected wells were sampled for water quality analyses and temperatures were profiled where possible. Water quality analyses include halogens such as chloride, fluoride, iodide and bromide. Ratios of these ions are useful to differentiate between geothermal and seawater origins of salinity (Hem, 1992). Other ionic ratio approaches for differentiating sources and influences on salinity such as those developed by Alcala and Emilio (2008) and Sanchez-Martos et al., (2002), may also be applied. Potential sources of salinity include seawater, connate water, geothermal and anthropogenic influences. The hydrogeologic settings of saline occurrence were also investigated, to explore the potential to predict further occurrence. Results Numerous occurrences of non-coastal saline groundwater have been observed in the Waikato region. Where possible, wells with relatively high total dissolved solids (TDS) were selected for further investigation. Several groundwater samples are moderately saline and exceed the TDS drinking water aesthetic guideline of 1,000 g m-3 (Ministry of Health, 2008). Selected ion ratios (predominantly halogens) were used to assist in differentiating between influences on salinity such as seawater and geothermal. Bromide to iodide ratios, in particular, infer a greater geothermal influence on salinity, although other ratios are not definitive. The anomalously elevated salinity observed appears natural but nevertheless has constrained localised groundwater resource development for dairy factory, industrial and prison water supply use. Further work may show some relationship with geology or tectonics, which could assist prediction of inland saline groundwater occurrence