Magnetic measurements and pollutants of sediments from Cauvery and Palaru River,India

Rock-magnetic techniques have become a useful tool in environmental issues; in particular, magnetic studies constitute an alternative way to study pollution in different media. The present contribution focuses on magnetic parameters as pollution indicators, especially from their relationship with contents of heavy metals. The work was carried out in two Indian rivers located in Tamil Nadu, southern India. Several sediment samples were collected and studied in the laboratory using magnetic techniques, magnetic susceptibility, anhysteric remanent magnetization, isothermal remanent magnetization, and chemical techniques to determine contents of heavy metals. Magnetic mineralogy indicates the predominance of ferrimagnetic minerals; although magnetite-like minerals are the main magnetic carriers, antiferromagnetic minerals can be present as subordinate carriers. Concentration-dependent magnetic parameters revealed noticeable differences between both rivers, e.g. magnetic susceptibility is four times higher in Cauvery than in Palaru River. Moreover, such increase can be interpreted as “magnetic enhancement” and therefore related to the pollution status. This magnetic enhancement indicated a different pollutant contribution in both rivers, and also, a different spatial distribution along these rivers, where critical (or more polluted) sites were identified. On the other hand, univariate and multivariate statistical analyses—e.g. PCoordA, Multifactorial Analysis of distance, PCA and RDA—were examined, revealing a link between magnetic and chemical variables. Among magnetic parameters, the concentration-dependent magnetic parameters (e.g. magnetic susceptibility) seem to be the most relevant for this study.     

A methodological approach to water quality assessment in an ungauged basin,Buenos Aires,Argentina

The Reconquista River is one of the most polluted rivers in Latin America. This paper aims at identifying the dynamics of water quality in an area with low or “background” concentrations of pollutants within the Reconquista River system in order to better define levels of pollution in the main system. In order to describe the dynamics of water quality in the background area, we propose a methodology based on flow estimation with the instantaneous unit hydrograph model and on measurements of physical and chemical water variables under different hydrological conditions. Because of high dissolved oxygen and low ammonium and o-phosphate concentrations, the Arroyo Durazno, a tributary stream of the Reconquista River, is defined as a background area. When a storm event begins, the concentration of nitrates and the electrical conductivity diminish. An increase in dissolved organic carbon suggests an important input of carbon from hillslope runoff. The proportion of fulvic and humic acids also increases. On the receding limb of the hydrograph, nitrate concentration was lower than during maximum flow and organic carbon concentration remained high. This behavior, known as the “flushing effect”, suggests that the soluble material accumulated in the drainage area during dry periods is transported to the stream by leaching or “lixiviation” and surface runoff, thus raising solute concentrations during the first few hours of the storm. Water quality changes rapidly, even in background areas, due to its dependence on the flow. The methodology followed in this paper can also be applied to other basins with similar characteristics. Due to the difficulty in defining baseline areas for surface waters, a knowledge of background water quality and its dynamics is essential for understanding pollution trends and anthropogenic impacts on rivers.     

Multivariate techniques as alternative statistical tools applied to magnetic proxies for pollution: a case study from Argentina and Antarctica

Several statistical analyses—as alternative tools—were applied to magnetic monitoring studies. Magnetic and chemical data from two environments have been gathered from previous papers and studied separately. Univariate and multivariate analyses were first examined, revealing a link between magnetic and chemical variables. The latter analyses, in particular, canonical correlation analysis, showed very good canonical correlations: R = 0.950 (Antarctica) and R = 0.891 (Argentina). On the other hand, in order to classify the data according to the degree of contamination, principal coordinates and discriminant analyses, as well as the comparison of several multivariate means were performed. Three groups were distinguished in both case studies, which were well classified at a low margin of error and quite different from each other at a significant level: 0.01 (Antarctica) and 0.05 (Argentina). The joint use of these statistical analyses also showed, in agreement with previous studies, that the relevant variables in order to identify atmospheric pollution are: magnetic susceptibility, saturation of isothermal remanent magnetisation, anhysteric susceptibility/magnetic susceptibility, remanent coercivity, and copper, lead, zinc and chromium contents.     

FRFI model application in groundwater non-point source pollution evaluation: a case study in the Luoyang Basin of North Henan province, China

The traditional non-point source (NPS) pollution models mainly focus on the flow path of NPS pollutants and attenuation during the flow. Extensive data set preparation and complex results analysis for these models are the most common problems encountered by the model user. In this study a new model, fuzzy-rough sets and fuzzy inference (FRFI), was introduced to evaluate groundwater NPS pollution. The proposed model involves two steps: the algorithm of fuzzy-rough sets attribute reduction (FRSAR) was applied to yield minimal decision rules from the fuzzy information system (FIS); the fuzzy inference technique was then used to forecast a groundwater synthesis pollution index based on the minimal decision rules. This model was applied in the Luoyang Basin, examining NPS pollution factors and hydrochemical variables data to validate the effectiveness of this model. The results indicate that it is only required to collect five NPS pollution factors or three hydrochemical variables; the groundwater synthesis pollution index can be predicted using the FRFI model. The prediction error is restricted to 2.9–6.1 % and 0.8–1.6 %, respectively. Therefore, the costs of computation and monitoring can be decreased, and the user is not required to prepare massive model parameters for the FRFI model. According to analyze the correlation between NPS pollution factors and hydrochemical variables, prevention measures are provided for treatment of the endemic disease and eutrophication. The FRFI model can be suitable for groundwater NPS pollution evaluation systems.     

Survival of the thinnest: rediscovery of Bauer’s (1898) ichthyosaur tooth sections from Upper Jurassic lithographic limestone quarries,south Germany

During the late Early Cretaceous, the shallow-water domains of the western Tethys are characterized by the widespread deposition of Urgonian-type carbonates rich in rudists, corals and other oligotrophic, shallow-marine organisms. In the Helvetic Alps, the Urgonian occurrences have been dated by ammonite biostratigraphy as Late Barremian and Early Aptian. For the more proximal occurrences in the western Swiss Jura, a recent age model based on bio-, chemo- and sequence stratigraphy has been proposed, which allows for an improved correlation with the Helvetic counterparts. In order to corroborate the recently proposed age model for the Jura, a set of well-preserved rhynchonellids collected from five different lithostratigraphical formations and members (“Marnes bleues d’Hauterive”, “Marnes d’Uttins”, basal marly layers within the “Urgonien Jaune”, “Marnes de la Russille”, “Urgonien Blanc”) has been analysed for its strontium–isotope ratios (⁸⁷Sr/⁸⁶Sr). In addition, K–Ar dating was performed on well-preserved glauconite grains from two different levels (“Marnes d’Uttins” and a basal layer within the “Urgonien Jaune”). The correlation of the Sr–isotope data set with a belemnite-based, ammonite-calibrated reference curve provides an age model which is coherent with recently published ages based on calcareous nannofossil biostratigraphy and the correlation of trends in chemo- and sequence stratigraphy. K–Ar dating on well-preserved glauconite grains from the “Marnes d’Uttins” and lowermost part of the “Urgonien Jaune” delivered ages of 127.5 ± 2.3 and 130.7 ± 2.6 Ma, respectively. Whereas the age of the glauconitic level near the base of the “Urgonien Jaune” is chronostratigraphically meaningful, the K–Ar age of the “Marnes d’Uttins” appears too young relative to the presently used time scale. This may be related to rejuvenation of the K–Ar chronometer due to post-depositional Ar loss, most likely during hardground formation. The ages obtained here confirm the Late Barremian age for the onset of the Urgonian platform, an age which is conform with ages obtained in the Helvetic Alps and elsewhere along the northern Tethyan margin.     

Groundwater pollution risk mapping for the Eocene aquifer of the Oum Er-Rabia basin, Morocco

Sustainable development requires the management and preservation of water resources indispensable for all human activities. When groundwater constitutes the main water resource, vulnerability maps therefore are an important tool for identifying zones of high pollution risk and taking preventive measures in potential pollution sites. The vulnerability assessment for the Eocene aquifer in the Moroccan basin of Oum Er-Rabia is based on the DRASTIC method that uses seven parameters summarizing climatic, geological, and hydrogeological conditions controlling the seepage of pollutant substances to groundwater. Vulnerability maps were produced by using GIS techniques and applying the “generic” and “agricultural” models according to the DRASTIC charter. Resulting maps revealed that the aquifer is highly vulnerable in the western part of the basin and areas being under high contamination risk are more extensive when the “agricultural” model was applied.     

Statistical Characterization of a Random Velocity Field Using Stacking Velocity Profiles

In petroleum exploration and production, it is important to have good estimations of the uncertainties on the reserves. Uncertainties in the velocity model used during the data processing are of major importance in this estimation. The use of geo-statistical tools can help in dealing with these uncertainties. Up to now, a strong limitation has been the inability to properly merge velocity functions measured in the wells with seismic velocity data. This was due to the different “supports” among the two, i.e. the well velocity may be regarded as a direct measurement of the instantaneous velocity field, while the seismic velocities correspond to an “average along the travelled paths” of this field. The problem is that, apart from the well positions, the instantaneous velocity field is out of reach. Luckily, for many practical applications, it is enough to know just its covariance model. However, no algorithmic method is available in the literature to yield the covariance model, and geologists are forced to use arbitrary distributions. The present paper proposes an original method to obtain a good estimate of this covariance model, using widely available information, mainly seismic stacking velocities. This method was first developed in a simple one-layer case with constant velocity, and then extended to more realistic situations. Finally, a real data application is performed, which highlights the robustness of the resulting estimation.     

A pre-edge analysis of Mn K-edge XANES spectra to help determine the speciation of manganese in minerals and glasses

High-resolution manganese K-edge X-ray absorption near edge structure spectra were collected on a set of 40 Mn-bearing minerals. The pre-edge feature information (position, area) was investigated to extract as much as possible quantitative valence and symmetry information for manganese in various “test” and “unknown” minerals and glasses. The samples present a range of manganese symmetry environments (tetrahedral, square planar, octahedral, and cubic) and valences (II to VII). The extraction of the pre-edge information is based on a previous multiple scattering and multiplet calculations for model compounds. Using the method described in this study, a robust estimation of the manganese valence could be obtained from the pre-edge region at 5% accuracy level. This method applied to 20 “test” compounds (such as hausmannite and rancieite) and to 15 “unknown” compounds (such as axinite and birnessite) provides a quantitative estimate of the average valence of manganese in complex minerals and silicate glasses. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

可变模糊理论与模糊粗糙集在地下水污染评估中的应用

传统的地下水污染评估方法需检测多项地下水化学变量,成本较高且无法考虑水质指标区间量值的影响。为此,笔者利用可变模糊理论对地下水污染进行综合评估,在此基础上通过模糊粗糙集理论挖掘关键变量,并利用生成的最小决策规则库对样品点的污染综合指数进行预测。将该方法应用于洛阳盆地,对其有效性进行验证,计算结果表明:As、Hg、Se、I、NH3-N对研究区地下水污染分类起控制作用。通过与实测的13种化学变量所计算的地下水污染综合指数相比,测试数据中7组样品的相对误差为0.104 0～0.172 5;剩余15组样品的相对误差为0.001 5～0.093 7,基本满足地下水污染评估的要求。可变模糊集与模糊粗糙集理论能够通过较少的化学变量对污染综合指数进行预测,从而降低地下水污染评估成本,为后续的地下水污染控制与修复提供可靠的数据来源。     

Magnetic studies and elemental analysis of river sediments: a case study from the Ponnaiyar River (Southeastern India)

The Ponnaiyar River is one of the largest rivers of the Tamil Nadu state (India), flowing a distance of 430 km from its point of origin to the sea. This work contributes with new data of magnetic and elemental composition of river sediments, and improves the knowledge obtained by preliminary and previous studies of rivers from Southeastern India. Magnetic susceptibility, anhysteretic and isothermal remanent magnetization and chemical determinations (major and trace metals) were measured. Magnetic results reveal the predominance of magnetite-like mineral with magnetic grain size variations along the river and in depth. Most of the uppermost samples have the major presence of trace metals and higher values of magnetic concentration. Magnetic and chemical variables were also analysed as potential pollution indicators using multivariate statistical techniques: canonical correlation and fuzzy c-means clustering analyses, which confirmed the existence of relationships, but not in a simple way, between magnetic and chemical variables. Furthermore, fuzzy analysis allows classifying the data in different well-differentiated groups regarding the trace metal load, concentration and feature-dependent parameters. The most polluted samples show high concentration of trace elements and magnetic carriers, softer and coarser magnetic minerals; on the contrary, the unpolluted samples (from the deepest sediments) have the opposite characteristics.     

The effect of grain size on the Cu, Pb, Ni, Cd speciation and distribution in sediments: a case study of Dongping Lake, China

This paper examines the surface sediments collected from Dongping Lake in China for speciation and distribution of toxic heavy metals (Cu, Pb, Ni, Cd) in different grain size fractions, and for the factors that need to be considered in potential hazard of metals to the environment. Four grain size fractions (<63, 63–78, 78–163 and 163–280 μm), divided in wet condition, and bulk samples less than 280 μm in diameter were analyzed for their distribution, density and appearance. A three-stage extraction procedure following the BCR protocol was used to chemically fractionate metals into “acid soluble”, “reducible”, “oxidizable” and “residual” fractions. Correlation analysis was used to analyze the datasets. The results showed that <63 μm grain size part constitutes the major proportion of the sediments, but its density is the smallest among the four grain size fractions. In general, the metal content curve against grain size presents “S” distribution, and the highest concentrations do not exist in <63 μm grain size. Appearance observation indicates that the adsorbed substance increases gradually along with the decreasing grain size. The dominant speciation of elements and the extent of pollution are responsible for the metal distribution in different grain size sediments. While studying bioavailability and mobilization of metals, it is advisable to take metal speciation, grain size distribution and density into consideration.     

Magnetic properties of urban topsoils and correlation with heavy metals: a case study from the city of Xuzhou, China

Heavy metals in urban topsoils have been shown to be very useful tracers of environmental pollution. Thus, their detailed studies are of great importance. Apart from expensive and time-consuming chemical methods, several simple, rapid and cheap proxy methods have been developed recently, one of them being based on rock-magnetic parameters. This examines the use of rock-magnetic methods designed to assess the degree of heavy-metal pollution of urban topsoils from the city of Xuzhou (China). The aim was to identify the magnetic properties and to link the “magnetic pollution” to the concentrations of the heavy metals. Since a strong correlation has been found between saturation isothermal remanent magnetization (SIRM) and the heavy metals, namely, Fe, Se, Ti, Sc, Ba, Bi, Pb, Cu, Zn, Cr and Mo, an anthropogenic contamination origin is thought to be the cause. The present study shows that SIRM is a fast, inexpensive and non-destructive method for the detection and mapping of heavy-metal-contaminated urban topsoils.     

Topic 3: State of the art of mathematical modelling of agricultural impact on groundwater pollution

The state of the art of mathematical modelling of groundwater pollution by agricultural activities is well advanced at the present time and a large number of models are available for a wide variety of water pollution problems. Also a variety of computational methods are available from very simple ones to the most advanced. In general, there are two ways of obtaining a water quality model: “from below,” when water quality data based on actual observation or measurement are explicitly analyzed by the calculus; and “from above,” when the calculus is available in advance as a theory and the user tries to find its interpretation for a given case and from rather limited data. Classifications may be based on the type of modelled processes, the mathematical approach, or on the purpose of the models. Eleven papers were presented in the Symposium on this topic, with most of the models being of the dynamic type.     

Comparison of Mathematical Methods of Potential Modeling

Various attempts are known to turn the “catalogue” of mineral deposit models compiled by Cox and Singer (1986) operational, and have initiated activities called “potential mapping”, “potential modeling”, or “targeting”. The common ultimate objective is to estimate the probability for a given location that a mineralization of a given type occurred. The mathematics range from “weights of evidence” and others featuring a Bayesian approach to logistic regression by maximum likelihood, and include other realizations by means of fuzzy methods, genetic programming, and artificial neural nets. Once developed and coded, applications are not restricted to mineral prospection and exploration but include any kind of occurrences and their estimated probabilities, e.g., risk assessment of land slides and many others.     

Landslide Hazard Zonation using Remote Sensing and GIS: a case study of Dikrong river basin, Arunachal Pradesh, India

Landslides are among the most costly and damaging natural hazards in mountainous regions, triggered mainly under the influence of earthquakes and/or rainfall. In the present study, Landslide Hazard Zonation (LHZ) of Dikrong river basin of Arunachal Pradesh was carried out using Remote Sensing and Geographic Information System (GIS). Various thematic layers namely slope, photo-lineament buffer, thrust buffer, relative relief map, geology and land use / land cover map were generated using remote sensing data and GIS. The weighting-rating system based on the relative importance of various causative factors as derived from remotely sensed data and other thematic maps were used for the LHZ. The different classes of thematic layers were assigned the corresponding rating value as attribute information in the GIS and an “attribute map” was generated for each data layer. Each class within a thematic layer was assigned an ordinal rating from 0 to 9. Summation of these attribute maps were then multiplied by the corresponding weights to yield the Landslide Hazard Index (LHI) for each cell. Using trial and error method the weight-rating values have been re-adjusted. The LHI threshold values used were: 142, 165, 189 and 216. A LHZ map was prepared showing the five zones, namely “very low hazard”, “low hazard”, “moderate hazard”, “high hazard” and “very high hazard” by using the “slicing” operation.     

Some basic notions of theoretical hydrochemistry

This report gives the conceptual approaches to the development of the theory of the formation of natural water composition in view of the primary properties of aquatic ecosystems: the “principle of uncertainty” in the estimation of the conditions, the “continuity” of the changes proceeding, and the “plurality” of water’s natural structure and components (biotic and abiotic), as well as their interaction processes. The importance and features of each of these basical criteria for characterizing the conditions of an aquatic ecosystems are substantiated in theory.     

Kriging with imprecise (fuzzy) variograms. I: Theory

Imprecise variogram parameters are modeled with fuzzy set theory. The fit of a variogram model to experimental variograms is often subjective. The accuracy of the fit is modeled with imprecise variogram parameters. Measurement data often are insufficient to create good experimental variograms. In this case, prior knowledge and experience can contribute to determination of the variogram model parameters. A methodology for kriging with imprecise variogram parameters is developed. Both kriged values and estimation variances are calculated as fuzzy numbers and characterized by their membership functions. Besides estimation variance, the membership functions are used to create another uncertainty measure. This measure depends on both homogeneity and configuration of the data.     

An approach for assessing soil health: a practical guide for optimal ecological management

How to integrate environmental geographic information and biodiversity data combined with management measures to effectively assess soil health is still an unresolved problem. This paper suggests an approach for systematically estimating soil quality and guiding ecological management. First, canonical correspondence analysis is used to predict the distributions of plant species or microorganism communities, principle pollutants and environmental variables from which spatial and environmental data are extracted by the geographic information system (GIS). Secondly, geostatistical methodologies are then used to estimate and quantify the spatial distribution characteristic of the species and pollutants and to create maps of spatial uncertainty and hazard assessment through ArcGis technology. Finally, redundancy analysis provides a suggestion about better management strategy and environmental factor for improving soil health and biodiversity. The combination of these methods with “3S” techniques as an assessment approach effectively meets the challenges for estimation and management in different soil environments.     

Conditioning DRASTIC model to simulate nitrate pollution case study: Hamadan–Bahar plain

One of the major causes of groundwater pollution in Hamadan–Bahar aquifer in western Iran is a non-point source pollution resulting from agricultural activities. Withdrawal of over 88% of drinking water from groundwater resources, adds urgency to the studies leading to a better management of water supplies in this region. In this study, the DRASTIC model was used to construct groundwater vulnerability maps based on the “intrinsic” (natural conditions) and “specific” (including management) concepts. As DRASTIC has drawbacks to simulate specific contaminants, we conditioned the rates on measured nitrate data and optimized the weights of the specific model to obtain a nitrate vulnerability map for the region. The performance of the conditioned DRASTIC model improved significantly (R ² = 0.52) over the intrinsic (R ² = 0.12) and specific (R ² = 0.19) models in predicting the groundwater nitrate concentration. Our study suggests that a locally conditioned DRASTIC model is an effective tool for predicting the region’s vulnerability to nitrate pollution. In addition, comparison of groundwater tables between two periods 30 years apart indicated a drawdown of around 50 m in the central plain of the Hamadan–Bahar region. Our interpretation of the vulnerability maps for the two periods showed a polluted zone developing in the central valley requiring careful evaluation and monitoring.     

Chemodynamics of trace metal fractions in surface sediments of the Pandoh Lake,Lesser Himalaya,India

The seasonal variation in the trace metals’ concentrations (Cd, Co, Cu, Fe, Mn, Ni, Pb, and Zn) were investigated in surface sediments of the Pandoh Lake. The horizontal distribution of TC, TN, and TP reflects spatial and temporal differences in sedimentary organic production. The chemical sequential extraction of heavy metals was carried out by seven-step fractionation scheme (Leleyter and Probst in Int J Environ Chem 73:109–128, 1999). The significant concentrations of Ni and Cd were associated with “water soluble (Eua)” fraction in the monsoon and winter, respectively, while “exchangeable (Exch)” and “carbonate-bound (Carb)” fractions for Ni and Cd were abundant in winter and summer. The Cd, Cu, and Pb associated with “Exch” fraction in the summer season support their availability on exchange sites due to oxidized nature of surface sediments. Enrichment of Co, Fe, Mn, and Zn in “AFeO” fraction showed poor bioavailability, while Cd, Cu, and Mn in the monsoon, Co in the winter and summer, and Zn in the winter season showed significant “organically bound (Org)” fraction. The ANOVA was significant for chemical fractions of trace elements except “Carb” fraction of Pb and Zn and “CFeO” fraction of Pb. Factor analysis revealed that the “Eua”, “Exch”, and “Carb” fractions together control the metal enrichment of “MnO”, “AFeO”, and “CFeO” fractions in the summer season.