Characteristics of Iron Ore Tailing Slime in India and Its Test for Required Pond Size

The physical and chemical nature of the tailingslime depends on milling operations and water contentin the effluent. The characteristics of the tailingsdictate the type of disposal facility required.Characteristics of the tailings, transportation frombeneficiation plants and disposal system are described.Studies on tailing slime have been carried out at ironore mines in Orissa and the results are discussed. Thetailings contain toxic elements and find their wayinto the water environment. Sedimentation test was carriedout on tailings and the area required for tailing pondwas found to be 3155 m² in comparison to10 000 m² obtained from the use of an empiricalequation. Provision of tailing pond for the disposalof tailings is a conservation of resources in additionto pollution control, and sedimentation test is essentialfor required pond size calculation.     

Wilk’s Method Based Model for Regulations Related to Radiological Impact of Opencast Uranium Mining on Water Reservoirs

Opencast mining of uranium may lead to natural erosion of ore material due to overland flow of water accumulated from rainfall. The overland flow may ultimately reach the nearby surface water body. This process may lead to the release of ²³⁸U and its daughter products into the surface water body. A model is developed to assess the radiological impact of the erosion in terms of dose through drinking water pathway due to ²³⁸U and its progeny in the surface water body. The in-growth of progeny is taken into account using Bateman equations. The study brings out the importance of incorporating decay chain transport in the radiological impact assessment studies. It is also observed that ²¹⁰Po, ²¹⁰Pb, ²²⁶Ra, and ²³⁰Th together contribute to about 95.5 % of the total dose. The model is then extended to incorporate the uncertainty associated with the dose due to consumption of the reservoir water by employing Wilk’s Method. Such a model can be very useful in establishing regulations related to dose through drinking water pathway around an opencast mine. Wilk’s method is computationally less expensive as compared to the exact methods like Monte Carlo method. Wilk’s method is used to calculate a value greater than α percentile value for the dose to the public due to ²³⁸U and its progeny in the reservoir through drinking water pathway with confidence level β (α/β value). When applied to a hypothetical case using some literature data on surface water bodies, it is found that with increase in the value of α or β, the α/β value in general, shows an increasing trend as expected. Depending upon the nature of the problem under study, one can calculate an α/β value i.e. 95/95 value, 99/95 value etc., and that value can be helpful in establishing the regulatory limiting value. Also, the α/β value can be used to check whether dose due to a particular radioactive release is within the specified limits.     

Comprehensive monitoring of drinking well water quality in Seoul metropolitan city, Korea

In this research, the quality of drinking well waters from 14 districts around Seoul metropolitan city, Korea was assessed by measuring a number of parameters with established guideline (e.g., arsenic, fluoride, nitrate nitrogen, benzene, 1,2-dichloroethene, dichloromethane, copper, and lead) and without such criteria (e.g., hardness, chloride ion, sulfate ion, ammonia nitrogen, aluminum, iron, manganese, and zinc). Physical parameters such as evaporation residue (or total dissolved solids) and turbidity were also measured. The importance of each parameter in well waters was examined in terms of the magnitude and exceedance frequency of guideline values established by international (and national) health agencies. The results of this study indicate that among the eight parameters with well-established guidelines (e.g., WHO), arsenic and lead (guideline value of 0.01 mg?L^?1 for both) recorded the highest exceedance frequency of 18 and 16 well samples ranging in 0.06–136 and 2–9 mg?L^?1, respectively. As such, a number of water quality parameters measured from many well waters in this urban area were in critical levels which require immediate attention for treatment and continuous monitoring.     

Exploratory Analysis and a Stochastic Model for Humusdisintegration

Ulrich (1981) supposes in the hypothesis of humusdisintegrationthat the balance between polymerisation and breakdown of organicmaterial may be disturbed in chemically well buffered Europeanforest soils. This new aspect of aluminium toxicity may causenitrogen exceedance in forest ecosystems and subsequent seasonalnitrate output (Eichhorn and Hütterman, 1999).In a research program the substances in the seepage water aremonitored in a small woodland in central Germany. We explorethese multivariate data for examining possible influences on theprocess of humusdisintegration and its temporal evolution. As aresult, a regression model for carbon is developed, whichincludes covariables, i.e., other substances, as well as spatialand temporal terms describing systematic variability. Especiallyiron and aluminium turn out to be very influential in the model.So far our work is a basic step for monitoring the seepage waterdata by means of stochastic modelling.     

Indicator and probability kriging methods for delineating Cu, Fe, and Mn contamination in groundwater of Najafgarh Block, Delhi, India

Two non-parametric kriging methods such as indicator kriging and probability kriging were compared and used to estimate the probability of concentrations of Cu, Fe, and Mn higher than a threshold value in groundwater. In indicator kriging, experimental semivariogram values were fitted well in spherical model for Fe and Mn. Exponential model was found to be best for all the metals in probability kriging and for Cu in indicator kriging. The probability maps of all the metals exhibited an increasing risk of pollution over the entire study area. Probability kriging estimator incorporates the information about order relations which the indicator kriging does not, has improved the accuracy of estimating the probability of metal concentrations in groundwater being higher than a threshold value. Evaluation of these two spatial interpolation methods through mean error (ME), mean square error (MSE), kriged reduced mean error (KRME), and kriged reduced mean square error (KRMSE) showed 3.52% better performance of probability kriging over indicator kriging. The combined result of these two kriging method indicated that on an average 26.34%, 65.36%, and 99.55% area for Cu, Fe, and Mn, respectively, are coming under the risk zone with probability of exceedance from a cutoff value is 0.6 or more. The groundwater quality map pictorially represents groundwater zones as ??desirable?? or ??undesirable?? for drinking. Thus the geostatistical approach is very much helpful for the planners and decision makers to devise policy guidelines for efficient management of the groundwater resources so as to enhance groundwater recharge and minimize the pollution level.     

Multi-level modelling of chlorination by-product presence in drinking water distribution systems for human exposure assessment purposes

During drinking water treatment and distribution, chlorine reacts with organic matter occurring in water to form various chlorination by-products (CBPs) such as trihalomethanes (THMs) and haloacetic acids (HAAs). This paper presents the occurrence of THMs and HAAs in different water distribution systems (DS) of the same region and their modelling for exposure assessment purposes. This study was conducted in eight DS supplying chlorinated water to the population of Québec City, Canada. These systems differ in type of water source (i.e. surface, ground or mixed water), in treatment applied at the plant, and in size and structure of the DS. Two spatio-temporal databases for THMs and HAAs were implemented, one for model development and the other for model validation. The analysis of the data demonstrates significant seasonal and spatial variations of these compounds. A multi-level statistical modelling approach was applied to estimate the ranges for occurrence of THMs and HAAs in the eight DS (i.e. a single model for the study region for each CBP species). The modelling approach integrates available or easily measurable parameters. For both THMs and HAAs, a two-level model considering a sampling-site random effect was selected among various models initially developed. The model capacity for estimating the presence of THMs and HAAs in drinking water and its usefulness for exposure assessment purposes in the studied region was demonstrated.     

Polynomial Surrogates for Open-Channel Flows in Random Steady State

Assessing epistemic uncertainties is considered as a milestone for improving numerical predictions of a dynamical system. In hydrodynamics, uncertainties in input parameters translate into uncertainties in simulated water levels through the shallow water equations. We investigate the ability of generalized polynomial chaos (gPC) surrogate to evaluate the probabilistic features of water level simulated by a 1-D hydraulic model (MASCARET) with the same accuracy as a classical Monte Carlo method but at a reduced computational cost. This study highlights that the water level probability density function and covariance matrix are better estimated with the polynomial surrogate model than with a Monte Carlo approach on the forward model given a limited budget of MASCARET evaluations. The gPC-surrogate performance is first assessed on an idealized channel with uniform geometry and then applied on the more realistic case of the Garonne River (France) for which a global sensitivity analysis using sparse least-angle regression was performed to reduce the size of the stochastic problem. For both cases, Galerkin projection approximation coupled to Gaussian quadrature that involves a limited number of forward model evaluations is compared with least-square regression for computing the coefficients when the surrogate is parameterized with respect to the local friction coefficient and the upstream discharge. The results showed that a gPC-surrogate with total polynomial degree equal to 6 requiring 49 forward model evaluations is sufficient to represent the water level distribution (in the sense of the \(\ell _2\) norm), the probability density function and the water level covariance matrix for further use in the framework of data assimilation. In locations where the flow dynamics is more complex due to bathymetry, a higher polynomial degree is needed to retrieve the water level distribution. The use of a surrogate is thus a promising strategy for uncertainty quantification studies in open-channel flows and should be extended to unsteady flows. It also paves the way toward cost-effective ensemble-based data assimilation for flood forecasting and water resource management.     

Levels of polycyclic aromatic hydrocarbons and dibenzothiophenes in wetland sediments and aquatic insects in the oil sands area of Northeastern Alberta, Canada

An immense volume of tailings and tailings water is accumulating in tailings ponds located on mine leases in the oil sands area of Alberta, Canada. Oil sands mining companies have proposed to use tailings- and tailings water-amended lakes and wetlands as part of their mine remediation plans. Polycyclic aromatic hydrocarbons (PAHs) are substances of concern in oil sands tailings and tailings water. In this study, we determined concentrations of PAHs in sediments, insect larvae and adult insects collected in or adjacent to three groups of wetlands: experimental wetlands to which tailings or tailings water had been purposely added, oil sands wetlands that were located on the mine leases but which had not been experimentally manipulated and reference wetlands located near the mine leases. Alkylated PAHs dominated the PAH profile in all types of samples in the three categories of wetlands. Median and maximum PAH concentrations, especially alkylated PAH concentrations, tended to be higher in sediments and insect larvae in experimental wetlands than in the other types of wetlands. Such was not the case for adult insects, which contained higher than expected levels of PAHs in the three types of ponds. Overlap in PAH concentrations in larvae among pond types suggests that any increase in PAH levels resulting from the addition of tailings and tailings water to wetlands would be modest. Biota-sediment accumulation factors were higher for alkylated PAHs than for their parent counterparts and were lower in experimental wetlands than in oil sands and reference wetlands. Research is needed to examine factors that affect the bioavailability of PAHs in oil sands tailings- or tailings water-amended wetlands.     

ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study

Coagulation is the most important stage in drinking water treatment processes for the maintenance of acceptable treated water quality and economic plant operation, which involves many complex physical and chemical phenomena. Moreover, coagulant dosing rate is non-linearly correlated to raw water characteristics such as turbidity, conductivity, pH, temperature, etc. As such, coagulation reaction is hard or even impossible to control satisfactorily by conventional methods. Traditionally, jar tests are used to determine the optimum coagulant dosage. However, this is expensive and time-consuming and does not enable responses to changes in raw water quality in real time. Modelling can be used to overcome these limitations. In this study, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was used for modelling of coagulant dosage in drinking water treatment plant of Boudouaou, Algeria. Six on-line variables of raw water quality including turbidity, conductivity, temperature, dissolved oxygen, ultraviolet absorbance, and the pH of water, and alum dosage were used to build the coagulant dosage model. Two ANFIS-based Neuro-fuzzy systems are presented. The two Neuro-fuzzy systems are: (1) grid partition-based fuzzy inference system (FIS), named ANFIS-GRID, and (2) subtractive clustering based (FIS), named ANFIS-SUB. The low root mean square error and high correlation coefficient values were obtained with ANFIS-SUB method of a first-order Sugeno type inference. This study demonstrates that ANFIS-SUB outperforms ANFIS-GRID due to its simplicity in parameter selection and its fitness in the target problem.     

等离子发射光谱-质谱(ICP-MS)直接用于饮用水中的砷、锑、硒测定研究

用等离子发射光谱和等离子发射光谱-质谱作比较,等离子发射光谱-质谱用于饮用水As、Sb、Se的测量时需考虑校正系数,通过不确定度分析,确定了ICP-MS测定方法不确定度大小,得出结论:不要氢化物发生器,不加EDTA作为掩蔽剂,也可以直接用于实际.     

Dust-particle migration around flotation tailings ponds: pine needles as passive samplers

The potential use of dust particles trapped on Scots pine needles for tracking dust migration around flotation tailings ponds in the Silesian-Krakowian ore district, Southern Poland was tested. 1-, 2- and 3-years old needles were studied using a scanning electron microscope and energy dispersive X-ray microanalysis. Particle sizes and morphologies, and the nature of the compounds hosting metals ions, were examined. A large majority of the particles examined are <10 microm in size. Almost 80% of particles represent respirable dust. All the components in the flotation wastes appear among the chemical compounds identified in particles on the pine needles. The quantity of the waste particles decreases with distance from the flotation tailings ponds. The analysis of dust particles on pine needles could be a useful method for estimating vectors and travel distances of suspended dust migrating around open flotation tailings ponds and around other point sources emitting dust particles with chemical compositions differing from the environmental background.     

Sensitivity analysis of water quality for Delhi stretch of the River Yamuna,India

Simulation models are used to aid the decision makers about water pollution control and management in river systems. However, uncertainty of model parameters affects the model predictions and hence the pollution control decision. Therefore, it often is necessary to identify the model parameters that significantly affect the model output uncertainty prior to or as a supplement to model application to water pollution control and planning problems. In this study, sensitivity analysis, as a tool for uncertainty analysis was carried out to assess the sensitivity of water quality to (a) model parameters (b) pollution abatement measures such as wastewater treatment, waste discharge and flow augmentation from upstream reservoir. In addition, sensitivity analysis for the “best practical solution” was carried out to help the decision makers in choosing an appropriate option. The Delhi stretch of the river Yamuna was considered as a case study. The QUAL2E model is used for water quality simulation. The results obtained indicate that parameters K ₁ (deoxygenation constant) and K ₃ (settling oxygen demand), which is the rate of biochemical decomposition of organic matter and rate of BOD removal by settling, respectively, are the most sensitive parameters for the considered river stretch. Different combinations of variations in K ₁ and K ₂ also revealed similar results for better understanding of inter-dependability of K ₁ and K ₂. Also, among the pollution abatement methods, the change (perturbation) in wastewater treatment level at primary, secondary, tertiary, and advanced has the greatest effect on the uncertainty of the simulated dissolved oxygen and biochemical oxygen demand concentrations.     

Impact of model parameterization on predictive uncertainty of regional groundwater models in the context of environmental impact assessment

Parameterization strategy impacts models' outputs and the associated uncertainty. This is particularly true for transient regional groundwater models where parameters can only be weakly constrained by insufficient observations. However, this is rarely investigated under any particular model structure. This study bridges this gap using a regional groundwater model developed to understand the impact of coal seam gas extraction on groundwater systems in a probabilistic framework. Two different parameterization schemes were implemented for hydraulic conductivity and specific storage. The first method solely relies on the relationship between hydraulic properties and burial depths. The second more complex strategy allows more spatial variations of hydraulic parameters using pilot points. The study provides new insights and practical guidance on the application of groundwater modelling for environmental impact assessment. The results suggest that the choice of model parameterization has a significant influence on predictive uncertainty. The model using the simple parameterization provides predictions with a much wider range than the model with a more sophisticated parameterization. This is because that the lowly parameterized model tends to generate more extreme effective hydraulic parameter fields unless the parameterization simplification converts the inverse problem to a (close to) well-posed problem that rarely exists for applied regional groundwater modelling. The potential impact of model parameterization should be discussed explicitly in groundwater modelling applications to support decision making to avoid misinterpretation of the modelling results.     

Nonlinear Growth Models for Modelling Time Series of Groundwater Nitrate Concentrations

In this paper, a novel approach of modelling time series of nitrate concentrations in drinking water by nonlinear growth functions is proposed. An almost constant increase in the average values of nitrate concentrations occurred at the Vinokov??ak wellfield in the county of Vara?din, Croatia, in the period between 1998 and 2013. Our hypothesis was that the time series would be well adjusted to the nonlinear growth function with asymptotic properties after smoothing performed by the moving average method. We used a general growth model which does not have asymptotic properties and the logistic, Gompertz and Richards functions which do have asymptotic properties. Last three growth functions were modified based on the knowledge of a constant presence of basic nitrate concentrations in the drinking water before growth. The fit of growth curves to experimental data was assessed using R ² and Aikake’s information criterion. The modified growth functions gave the best fit to the smoothed time series of nitrate concentrations with R ² ranging from 0.9960 to 0.9986.     

Probability of misclassifying biological elements in surface waters

Measurement uncertainties are inherent to assessment of biological indices of water bodies. The effect of these uncertainties on the probability of misclassification of ecological status is the subject of this paper. Four Monte-Carlo (M-C) models were applied to simulate the occurrence of random errors in the measurements of metrics corresponding to four biological elements of surface waters: macrophytes, phytoplankton, phytobenthos, and benthic macroinvertebrates. Long series of error-prone measurement values of these metrics, generated by M-C models, were used to identify cases in which values of any of the four biological indices lay outside of the “true” water body class, i.e., outside the class assigned from the actual physical measurements. Fraction of such cases in the M-C generated series was used to estimate the probability of misclassification. The method is particularly useful for estimating the probability of misclassification of the ecological status of surface water bodies in the case of short sequences of measurements of biological indices. The results of the Monte-Carlo simulations show a relatively high sensitivity of this probability to measurement errors of the river macrophyte index (MIR) and high robustness to measurement errors of the benthic macroinvertebrate index (MMI). The proposed method of using Monte-Carlo models to estimate the probability of misclassification has significant potential for assessing the uncertainty of water body status reported to the EC by the EU member countries according to WFD. The method can be readily applied also in risk assessment of water management decisions before adopting the status dependent corrective actions.     

Selection of appropriate sampling stations in a lake through mapping

Much valuable information is obtained from water quality measurements and monitoring of lakes around the world. A powerful tool is the use of mapping techniques, as it offers potential use in water quality research. Both remote sensing techniques and traditional water quality monitoring are required to collect data at sampling stations. This study suggests another approach to determine the most appropriate distribution of sampling stations in water reservoirs that will be mapped for water quality parameters. Tests were conducted for the proposed approach for Secchi disc depth (SDD), chlorophyll-a, turbidity and suspended solids parameters in Lake Beysehir, Turkey. Results of analysis are available for a total of 30 sampling stations in August 2006. Ten sampling stations were used to model Lake Beysehir while the others were used for validation of the model. Sampling stations that offered the best representation of the lake for each parameter were determined. Then, the best representative sampling stations for all parameters in the study were determined. Moreover, in order to confirm the accuracy of these re-determined sampling stations, modelling was performed on the results of the analysis of June 2006, and it was found that the values obtained from the re-determined sampling stations were acceptable.     

Statistical Simulation to Estimate Uncertain Behavioral Parameters of Hybrid Energy-Economy Models

In energy-economy modeling, new hybrid models attempt to combine the technological explicitness of bottom-up models with the macroeconomic feedbacks and statistically estimated behavioral parameters of top-down models. However, statistical estimation of behavioral parameters (portraying firm and household technology choices) with such models is challenged by the number of uncertain variables and the lack of historical data on technologies in terms of capital costs, operating costs, and market shares. Multiple combinations of parameter values might equally explain past technology choices. This paper reports on the application of a Bayesian statistical simulation approach for estimating the most likely values for these key behavioral parameters in order to best explain past technology choices and then simulate policies to influence future technology choices. The method included (1) data collection of key technology market shares, capital costs, and operating costs over the past; (2) backcasting a hybrid energy-economy model over a historical time period; and (3) the application of Markov chain Monte Carlo statistical simulation using the Metropolis–Hastings algorithm as a tool for estimating distributions for key parameters in the model. The results provide a means of indicating the uncertainty bounds around key behavioral parameters when generating forecasts of the effect of certain policies. However, the results also indicate that this approach may have limited applicability, given that future available technologies may differ substantially from past technologies and that it is difficult to separate the effects of parameter uncertainty from model structure uncertainty.     

Trace Level Determination of U, Zn, Cd, Pb and Cu in Drinking Water Samples

The concentration of uranium has been assessed in drinking water samples collected from different locations in Bathinda district, Punjab, India. The water samples are taken from hand pumps and tube wells. Uranium is determined using fission track technique. Uranium concentration in the water samples varies from 2.23± 0.05 to 87.05± 0.29 μg/L. These values are compared with safe limit values recommended for drinking water. The uranium concentration in almost all drinking water samples is found to be more than the safe limit. Analysis of some heavy metals viz. Zn, Cd, Pb and Cu in water is made. The concentration of sodium, potassium, calcium, magnesium, chlorine and total hardness along with the pH value and conductivity of the water samples are measured. Some of the samples show stunningly high values of these parameters.     

State of the marine environment at Little Bay Arm,Newfoundland and Labrador,Canada, 10 years after a "do nothing" response to a mine tailings spill

In 1989, the tailings pond dam at the site of a former copper mine near Little Bay, Newfoundland and Labrador, Canada, ruptured and tailings spilled into Little Bay Arm. At the time, no action was taken to arrest the flow of tailings or to mitigate the effects of the spill. To date, no action has been taken to repair the dam and tailings continue to flow into Little Bay Arm. As a result, the marine environment around Little Bay Arm has become contaminated with heavy metals from the tailings. However, the tailings are not the only source of heavy metals to the ecosystem. An old slag heap and what is presumably concentrated copper ore spilled during the loading of ore freighters, are also contributing to the ecosystem's metal load. Marine sediment throughout the Arm contained elevated concentrations of Cu, Ni, Zn, As, V, Co, and Mn. Beach material also contained elevated concentrations of metals with material near the slag heap being the most contaminated. At this site, Cu concentrations were in excess of 5000 mg kg(-1) dry weight, Zn greater than 3000 mg kg(-1) and Co concentrations exceeded 700 mg kg(-1). The highest concentrations of metals in biota were found near the slag heap, near the tailings dam breach, and at the site of the former concentrate loading dock. Despite elevated metal concentrations, the tailings and nearby sediment were not devoid of life. Bivalves and seaweed were abundant in the area and there were no obvious signs of tissue damage or disease in soft shell clams (Mya arenaria) living in the tailings. These clams may be suffering from chronic exposure to the tailings, however, evidence of lipid peroxidation in the clams was inconclusive.     

Accounting for Uncertainty in Value Judgements when Applying Multi-Attribute Value Theory

In environmental decisions, analysts commonly face substantial uncertainties around stakeholders’ values judgments. Multi-Attribute Value Theory (MAVT), a family of multi-criteria decision analysis techniques, is applied in participative settings to articulate stakeholders’ values in decision-making. In MAVT, value judgments represent the intensity of individuals’ preferences in a set of objectives, which are operationalized as scaling factors or weights. Different sets of weights may express variation in people’s preferences or value judgments. Unfortunately, there are still important methodological gaps regarding how to incorporate uncertainty and the substantial variation commonly encountered in stakeholders’ preferences. This article presents a model of uncertainty that encompasses the dispersion of value judgments in MAVT. To achieve this goal, we draw on info-gap theory, which provides a mathematically grounded method for exploring sensitivity to preference weights when there are relatively high levels of uncertainties. We experimentally tested the uncertainty model in an environmental decision problem. We found that MAVT can use info-gap analysis to deal with multiple value judgments, avoiding exclusive reliance on nominal expected values to inform decisions. We explored a mechanism to explicitly consider the trade-offs between the performance of alternatives and the level of uncertainty that in any specified context a decision maker is willing to accept. Findings emphasize the potential of MAVT to support environmental management decisions, particularly in situations where multiple stakeholders and their contested value judgments have to be considered simultaneously to explore uncertainties around value trade-offs.