Geostatistical Evaluation of Spatial Variation Related to Groundwater Quality Database: Case Study for Arak Plain Aquifer,Iran

Geostatistical methods are one of the advanced techniques to interpolate groundwater quality data. Geostatistical interpolation techniques employ both the mathematical and the statistical properties of the measured points. Compiling the data distribution on spatial and temporal domain is of crucial importance in order to evaluate its quality and safety. The main purpose of this paper is to assess groundwater quality of Arak plain, Iran, by an unbiased interpolated method so called Kriging. Therefore, seven quality variables of Arak plain aquifer including TDS, SAR, EC, Na⁺, TH, Cl^?, and SO₄ ^2? have been analyzed, studied, and interpreted statistically and geostatistically. Utilized data in this study were collected from 97 water well samples in Arak plain, in 2012. After normalizing data, variogram as a geostatistical tool for defining spatial regression was calculated and experimental variograms have been plotted by GS⁺ software, then the best theoretical model was fitted to each variogram based on minimum RSS error. Cross validation was used to determine the accuracy of the estimated data. The uncertainty of the method could be well assessed via this method since the method not only gave the average error (around 0 in this study) but also gave the standard deviation of the estimations. Therefore, more than 3800 points were estimated by ordinary Kriging algorithm in places which have not been sampled. Finally, estimation maps of groundwater quality were prepared and map of estimation variance, EV, has been presented to assess the quality of estimation in each estimated point. Results showed that the Kriging method is more accurate than the traditional interpolation algorithms not honoring the spatial properties of the database.     

Evaluation of R-EMAP Techniques for the Measurement of Ecological Integrity of Streams in Washington State's Coast Range Ecoregion

We used methods from EPA's Environmental Monitoring and Assessment Program (EMAP) to assess the regional status of streams within the Coast Range ecoregion of Washington State. Study objectives were: to determine the ecological condition of wadable, 1st-order through 3rd-order streams; to provide information for the development of water quality biological criteria; and to determine the applicability of EMAP-derived methods in Washington. Stream condition was assessed using EMAP indicators for habitat (chemical and physical) and biology (invertebrate and vertebrate assemblages). EMAP's probability survey was used to select 75 1st through 3rd-order stream sites from the USGS 1:100,000 series hydrographic layer. Of these, 45 sites were sampled. Multivariate techniques were used to identify community types and related physical and chemical habitat. Overall, about 25% of the sites were rated least-impacted. Most impacts were associated with non-point source pollution, mainly forestry practices. The R-EMAP method was a successful tool for assessment of regional status and ecological integrity; however, in order to use it for biological criteria development in Washington State, the method would require some modification to complement the current state protocols.     

清河流域水质目标管理技术应用示范

针对清河流域三级水生态功能分区功能定位和水质保护目标,按照"分类、分区、分级、分期"理念,应用流域水质目标管理技术方法,研究清河流域控制单元污染负荷核定、水环境容量计算与分配、污染负荷削减、污染物总量控制等关键技术应用示范,完善流域水质目标管理技术,为清河流域水环境管理提供科技支撑。     

A Generalized Water Quality Index for Taiwan

By exploring the behavior and limitations of conventional methods for quality evaluation, a better overall index for water quality in Taiwan and its application in Keya River are proposed. The thirteen variables are employed for the quality assessment. Numerical scales relating to the degree of quality are established for each variable to assess variations in quality and to convey findings in a comprehensive manner to others. The employed variables are categorized into three aspects referred to the principal components analysis, which are 'organics', 'particulates' and 'microorganisms'. The overall index consists of the geometric mean for the three defined aspects and multiplying by three coefficients, which reflect quality degrees of temperature, pH and toxicity, respectively. Besides, the arithmetic mean is addressed among the variables associated in the same aspect. Finally, a fresh, mixed aggregation function is developed for ensuring that the index score tells us something useful and does not hide important information. A case study of the Keya River is made to illustrate the application of this new index system. The new proposed index provides comprehensive information for water quality assessment, than the existing index used in Taiwan does.     

On the use of multivariate statistical methods for combining in-stream monitoring data and spatial analysis to characterize water quality conditions in the White River Basin,Indiana, USA

Mechanistic hydrologic and water quality models provide useful alternatives for estimating water quality in unmonitored streams. However, developing these elaborate models for large watersheds can be time-consuming and expensive, in addition to challenges that arise during calibration when there is limited spatial and/or temporal monitored in-stream water quality data. The main objective of this research was to investigate different approaches for developing multivariate analysis models as alternative methods for rapidly assessing relationships between spatio-temporal physical attributes of the watershed and water quality conditions in monitored streams, and then using the developed relationships for estimating water quality conditions in unmonitored streams. The study compares the use of various statistical estimates (mean, geometric mean, trimmed mean, and median) of monitored water quality variables to represent annual and seasonal water quality conditions. The relationship between these estimates and the spatial data is then modeled via linear and non-linear multivariate methods. Overall, the non-linear techniques for classification outperformed the linear techniques with an average cross-validation accuracy of 79.7%. Additionally, the geometric mean based models outperformed models based on other statistical indicators with an average cross-validation accuracy of 80.2%. Dividing the data into annual and quarterly datasets also offered important insights into the behavior of certain water quality variables impacted by seasonal variations. The research provides useful guidance on the use and interpretation of the various statistical estimates and statistical models for multivariate water quality analyses.     

Qualidex – A New Software for Generating Water Quality Indice

Water quality indice are necessary for resolving lengthy, multi-parameter, water analysis reports into single digit scores. This, in turn, is essential for comparing the water quality of different sources and in monitoring the changes in the water quality of a given source as a function of time and other influencing factors.In this paper we present the computer-automated tool QUALIDEX (water QUALIty inDEX), which has been developed by us to generate and operate water quality indice. Several popular indice – such as the Oregon Water Quality Index developed in the 1970s by Oregon Department of Environmental Quality and later updated in 1995, the Aquatic Toxicity Index developed by Wepener and coworkers for protection of aquatic life at the Olifants river, Kruger National Park, South Africa, the water quality index developed by Dinius in 1987, the Overall Index of Pollution (of surface waters) developed at the National Environmental Engineering Research Institute (NEERI), and the water quality index of the Central Pollution Control Board (CPCB) – have also been added to QUALIDEX in modular form. There is also a New water quality index sub-module which enables the user to generate his or her own index and compare its performance with these well-known indice. The package is also capable of analyzing the variations in the water quality of different sites at different times. The software has been coded in Visual C++ and has been integrated with MS Access database.     

Maryland Biological Stream Survey: A State Agency Program to Assess the Impact of Anthropogenic Stresses on Stream Habitat Quality and Biota

The Maryland Department of Natural Resources is conducting the Maryland Biological Stream Survey, a probability-based sampling program, stratified by river basin and stream order, to assess water quality, physical habitat, and biological conditions in first through third order, non-tidal streams. These streams comprise about 90% of all lotic water miles in the state. About 300 sites (75 m segments) are being sampled during spring and summer each year. All basins in the state will be sampled over a three-year period, 1995-97. MBSS developments in 1995-96 included (1) an electrofishing capture efficiency correction method to improve the accuracy of fish population estimates, (2) two indices of biotic integrity (IBI) for fish assemblages to identify degraded streams, and (3) land use information for catchments upstream of sampled sites to investigate associations between stream condition and anthropogenic stresses. Based on fish IBI scores at 270 stream sites in six basins sampled in 1995, 11% of non-tidal stream miles in Maryland were classified as very poor, 15% as poor, 24% as fair, and 27% as good. IBIs have not yet been developed for stream sites with catchment areas less than 120 hectares (23% of non-tidal stream miles). IBI scores declined with stream acid neutralizing capacity (ANC) and pH, an association that was also evident for fish species richness, biomass, and density. Low IBI scores were associated with several measures of degraded stream habitat, but not with local riparian buffer width. There was a significant negative association between IBI scores and urban land use upstream of sampled sites in the only extensively urbanized basin assessed in 1995. Future plans for the MBSS include (1) identifying all benthic macroinvertebrate samples to genus, (2) developing benthic macroinvertebrate, herpetofaunal, and physical habitat indicators, and (3) enhancing the analysis of stream condition-stressor associations by refining landscape metrics and using multi-variate techniques.     

Total organic carbon analyzers as tools for measuring carbonaceous matter in natural waters

For some utilities, new US drinking water regulations may require the removal of disinfection byproduct (DBP) precursor material as a means of minimizing DBP formation. The Environmental Protection Agency's Stage 1 DBP Rule relies on total organic carbon (TOC) concentrations as a measure of the effectiveness of treatment techniques for removing organic material that could act as DBP precursors. Accordingly, precise and accurate methods are needed for the determination of TOC and dissolved organic carbon (DOC) concentrations in raw and finished potable water supplies. This review describes the current analytical technologies and summarizes the key factors affecting measurement quality. It provides a look into the fundamental principles and workings of TOC analyzers. Current peroxydisulfuric acid wet ashing methods and combustion methods are discussed. Issues affecting quality control, such as non-zero blanks and preservation, are covered. Some of the difficulties in analyzing water for TOC and DOC that were identified up to 20 years ago still remain problematic today. Limitations in technology, reagent purity, operator skill and knowledge of natural organic matter (NOM) can preclude the level of precision and accuracy desirable for compliance monitoring.     

Evaluation of groundwater quality in and around Peenya industrial area of Bangalore, South India using GIS techniques

Groundwater resource forms a significant component of the urban water supply. Declining groundwater levels in Bangalore Urban District is generally due to continuous overexploitation during the last two decades or more. There is a tremendous increase in demand in the city for good quality groundwater resource. The present study monitors the groundwater quality using geographic information system (GIS) techniques for a part of Bangalore metropolis. Thematic maps for the study area are prepared by visual interpretation of SOI toposheets on 1:50,000 scale using MapInfo software. Physicochemical analysis data of the groundwater samples collected at predetermined locations form the attribute database for the study, based on which spatial distribution maps of major water quality parameters are prepared using MapInfo GIS software. Water quality index was then calculated by considering the following water quality parameters--pH, total dissolved solids, total hardness, calcium hardness, magnesium hardness, alkalinity, chloride, nitrate and sulphate to find the suitability of water for drinking purpose. The water quality index for these samples ranged from 49 to 502. The high value of water quality index reveals that most of the study area is highly contaminated due to excessive concentration of one or more water quality parameters and that the groundwater needs pretreatment before consumption.     

Visual techniques for the detection of water quality trends: Double-mass curves and cusum functions

There is a great need for quantitative techniques to assess changes in water quality related to progressive watershed land-use developments, water-related impoundments or to evaluate the impact of recent sanitation programs. In choosing a physically representative variate for the water quality of the run-off, both concentrations and fluxes of pollutants must be taken into account. The importance of the climatic seasonal and hydrological factors associated with unstable event-related contributions of point and non-point pollution sources of the pollutants has lead us to simultaneously study water-discharge and pollutant flux time-series. The mass-discharge time-series are, in practice, far from being ideal for the application of classical trend analysis: they are relatively short and inaccurate: their distribution, orginating from mixed parent populations is very often highly skewed; they show a high level of serial dependence and the seasonal effects represent a large percentage of the variance, concealing possible long-term trends. Faced with the poor structure of these series which prohibits the use of statistical tests, experiments have been carried out with progressive-regressive inertial techniques, which imply the stationarity of water discharges. The double-mass technique was developed originally as a visual technique, to assess the homogeneity of precipitation records and was extended to study variations in sediment transport in modified watersheds. More recently confidence ‘rails’ and slope change detection have rendered its use more quantitative. Based on the same inertial principles, the Cumulative Sum (CUSUM) functions allow simultaneous evaluation of the covariability of the two series. An example involving weekly sampled nitrate concentrations and continuously monitored water discharges is developed.     

A probabilistic water quality index for river water quality assessment: a case study

Available water quality indices have some limitations such as incorporating a limited number of water quality variables and providing deterministic outputs. This paper presents a hybrid probabilistic water quality index by utilizing fuzzy inference systems (FIS), Bayesian networks (BNs), and probabilistic neural networks (PNNs). The outputs of two traditional water quality indices, namely the indices proposed by the National Sanitation Foundation and the Canadian Council of Ministers of the Environment, are selected as inputs of the FIS. The FIS is trained based on the opinions of several water quality experts. Then the trained FIS is used in a Monte Carlo analysis to provide the required input-output data for training both the BN and PNN. The trained BN and PNN can be used for probabilistic water quality assessment using water quality monitoring data. The efficiency and applicability of the proposed methodology is evaluated using water quality data obtained from water quality monitoring system of the Jajrood River in Iran.     

Monitoring, assessment and modelling using water quality data in the Saale River Basin, Germany

The European Water Framework Directive (WFD) is the overall driver for this environmental study and currently requires the identification of patterns and sources of pollution (monitoring) to support objective ecological sound decision making and specific measures to enhance river water quality (modelling). The purpose of this paper is to demonstrate in a case study the interrelationship between (1) hydrologic and water quality monitoring data for river basin characterization and (2) modelling applications to assess resources management alternatives. The study deals with monitoring assessment and modelling of river water quality data of the main stem Saale River and its principal tributaries. For a period of 6 years the data, which was sampled by Environmental Agencies of the German states of Thuringia, Saxony and Saxony-Anhalt, was investigated to assess sources and indicators of pollution. In addition to the assessment a modelling exercise of the routing of different pollutants was carried out in the lower part of the test basin. The modelling is a tool to facilitate the evaluation of alternative measures to reduce contaminant loadings and improve ecological status of a water body as required by WFD. The transport of suspended solids, salts and heavy metals was modelled along a selected Saale reach under strong anthropogenic influence in terms of contaminants and river morphology between the city of Halle and the confluence with the Elbe River. The simulations were carried out with the model WASP5 which is a dynamic flood-routing and water quality model package developed by the US Environmental Protection Agency.     

A multi criteria analog model for assessing the vulnerability of rural catchments to road spills of hazardous substances

Road spills of hazardous substances are common in developing countries due to increasing industrialization and traffic accidents, and represent a serious threat to soils and water in catchments. There is abundant literature on equations describing the wash-off of pollutants from roads during a storm event and there are a number of watershed models incorporating those equations in storm water quality algorithms that route runoff and pollution yields through a drainage system towards the catchment outlet. However, methods describing catchment vulnerability to contamination by road spills based solely on biophysical parameters are scarce. These methods could be particularly attractive to managers because they can operate with a limited amount of easily collectable data, while still being able to provide important insights on the areas more prone to contamination within the studied watershed. The purpose of this paper was then to contribute with a new vulnerability model. To accomplish the goal, a selection of medium properties appearing in wash-off equations and routing algorithms were assembled and processed in a parametric framework based on multi criteria analysis to define the watershed vulnerability. However, parameters had to be adapted because wash-off equations and water quality models have been developed to operate primarily in the urban environment while the vulnerability model is meant to run in rural watersheds. The selected parameters were hillside slope, ground roughness (depending on land use), soil permeability (depending on soil type), distance to water courses and stream density. The vulnerability model is a spatially distributed algorithm that was prepared to run under the IDRISI Selva software, a GIS platform capable of handling spatial and alphanumeric data and execute the necessary terrain model, hydrographic and thematic analyses. For illustrative purposes, the vulnerability model was applied to the legally protected Environmental Protection Area (APA), located in the Uberaba region, state of Minas Gerais, Brazil. In this region, the risk of accidents causing chemical spills is preoccupying because large quantities of dangerous materials are transported in two important distribution highways while the APA is fundamental for the protection of water resources, the riverine ecosystems and remnants of native vegetation. In some tested scenarios, model results show 60% of vulnerable areas within the studied area. The most sensitive parameter to vulnerability is soil type. To prevent soils from contamination, specific measures were proposed involving minimization of land use conflicts that would presumably raise the soil's organic matter and in the sequel restore the soil's structural functions. Additionally, the present study proposed the preservation and reinforcement of riparian forests as one measure to protect the quality of surface water.     

On applications of remote sensing for environmental monitoring

Modern airborne and satellite remote sensing techniques offer attractive opportunities to coastal monitoring systems. Improvements of the evaluation of larger scales phenomena and processes due to the synopticity of the remote sensing data are of particular interest. However, some uncertainties and limitations about remote sensing must be considered. Microwave, infrared and visible radiation methods and their applications are briefly discussed and some applications are demonstrated. Special attention is paid to the remote sensing of various pollutants in the sea, in particular with respect to oil pollution.Promising developments of the remote sensing methods for coastal monitoring are to be expected from the European remote sensing satellite missions ERS 1 and ERS 2.Combination of these observations with simultaneous in situ measurements from ships (sea truth) appears to be most advantageous for the interpretation of the collected data.     

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.     

A decision support system for air quality network design

In the last few years, due to the implementation of European and Italian laws, public agencies are in urgent need of objective criteria and methodologies for the correct location of air quality monitoring networks. This paper describes MoNet, a decision support system oriented to network designers, to help them in the choice of the number and the location of instruments in an assigned area. The package includes a model base consisting of a set of pollution diffusion simulation models and two optimization algorithms for network location and allows the user to integrate his or her own subjective knowledge of the problem with the results of the simulations performed by means of objective techniques. The package has been applied to revise the air quality network located in an industrial and urban area of Northern Italy. The results of the simulations performed by using different diffusion models, location algorithms and problem constraints are shown and discussed in the concluding section.     

Software for Sustainability Assessment: a Case Study in Quang Tri Province,Vietnam

Sustainable development is a widespread concept. Indicator selection and sustainability assessment are basic methods to analyse the sustainable development process. This study aims to develop a software application to select indicators and to allocate weightings to the indicators and the sub-indexes, so that they can be listed in a sustainability assessment. The Visual Basic software is used to develop a software package based on Delphi, analytic hierarchy, normalization and combination processes. Software for sustainability assessment was developed. Thirty-five indicators are selected as a first step of which eight health indicators and eight environmental ones and their sub-indexes are given weightings. The indicators are normalized. Sub-indexes are combined into a composite sustainable development index. The model is validated showing the ‘main features’ of the sustainability situation on health and environmental aspects in the Quang Tri province during the period 2000–2005. The results support the decision-making process of the competent authorities.     

Principal response curves technique for the analysis of multivariate biomonitoring time series

Although chemical and biological monitoring is often used to evaluate the quality of surface waters for regulatory purposes and/or to evaluate environmental status and trends, the resulting biological and chemical data sets are large and difficult to evaluate. Multivariate techniques have long been used to analyse complex data sets. This paper discusses the methods currently in use and introduces the principal response curves method, which overcomes the problem of cluttered graphical results representation that is a great drawback of most conventional methods. To illustrate this, two example data sets are analysed using two ordination techniques, principal component analysis and principal response curves. Whereas PCA results in a difficult-to-interpret diagram, principal response curves related methods are able to show changes in community composition in a diagram that is easy to read. The principal response curves method is used to show trends over time with an internal reference (overall mean or reference year) or external reference (e.g. preferred water quality or reference site). Advantages and disadvantages of both methods are discussed and illustrated.     

钱塘江南源流域异常水质预测及影响因素分析

基于数据驱动的水质预测模型存在局限性,对突发水质异常事件的预测效果不佳。该研究选取钱塘江南源流域马戍口监测断面为研究对象,综合采用相关性分析方法对水质异常事件的主要影响因素进行分析,明确流域内、外因及土壤条件对水质异常指标的影响程度,探究造成模型局限性的深层原因。结果表明:异常水质的影响因素及其耦合机制复杂多变,异常浊度受降雨量、径流量的直接影响更大,异常高锰酸盐指数与温度、降雨量相关关系更明显,而总磷、总氮的异常变化与相对湿度、降雨量、径流量具有相关关系。研究结果对于提高气候变化背景下的水质预报能力具有重要的参考价值,可为自然流域水污染防治提供科学参考。