Using condition factor and blood variable biomarkers in fish to assess water quality

The condition factor and blood variables, including erythrocyte lipid peroxidation (LPO) and the activity of antioxidant enzymes such as superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx), in two ecologically distinct fish species (Astyanax fasciatus and Pimelodus maculatus) were evaluated at five sites in the Furnas Hydroelectric Power Station reservoir (Brazil) to assess water quality. Aldrin/dieldrin, endosulfan, heptachlor epoxide, and metolachlor were detected at different concentrations in four of the sites. Condition factor was not directly affected by such contaminants. A negative correlation between hematocrit and heptachlor was detected in P. maculatus. Positive correlations between red blood cells and heptachlor as well as an interactive effect of metolachlor and aldrin/dieldrin were detected in A. fasciatus. The erythrocytes of both species collected from the contaminated sites showed high levels of LPO, an increase in SOD and GPx activities and a decrease in CAT activity. Although the leukocyte number and the differential percentage of leukocytes varied among the sites, the hematological variables, the LPO levels, and the antioxidant enzyme activities could be used to assess water quality, regardless of the differences in the responses of the fish species.     

Using discriminant analysis to assess polycyclic aromatic hydrocarbons contamination in Yongding New River

Yongding New River has been polluted by polycyclic aromatic hydrocarbons (PAHs) which are carcinogenic and mutagenic. In three periods (the abundant water period, mean water period, dry water period), ten sites (totally 30 samples) in Yongding New River were clustered into four categories by hierarchical cluster analysis (hierarchical CA). In the same cluster, the samples had the same approximate contamination situation. In order to eliminate the dimensional differences, the data in each sample, containing 16 kinds of PAHs, were standardized with normal standardization and maximum difference standardization. According to the results of the cubic clustering criterion, pseudo F, and pseudo t ² (PST2), the proper number of clustering for the 30 samples is 4. Before conducting hierarchical CA and K-means cluster analysis on the samples, we used principal component analysis to obtain another group data set. This data set was composed of the principal component scores which are uncorrelated variables. Hierarchical CA and K-means cluster analysis were used to classify the two data sets into four categories. With the classification results of hierarchical CA and K-means cluster analysis, discriminant analysis is applied to determine which method was better for normalization of the original data and which one was proper to cluster the samples and establish discriminant functions so that a new sample can be grouped into the right categories.     

Application of multimedia exposure assessment to drinking water

A potentially important exposure route for humans is the ingestion of chemicals via drinking water. If comprehensive exposure assessments are to be completed for either existing or proposed new chemicals and cost effective control strategies developed, then a quantitative understanding of multimedia transport and fate of specific chemical pollutants must be achieved. Mathematical models provide a powerful framework into which quantitative relationships may be placed to provide guidance in reaching water quality goals. Existing, state-of-the-art media-specific toxic organic transport and fate models for atmospheric (DiDOT), land surface (NPS) and surface water processes (EXAMS) and potable water treatment (WTP) have been linked to demonstrate the technical feasibility of such an approach. Limited application and sensitivity testing of this linked modeling system has shown that the impact of various source loadings and control strategies on drinking water quality can be estimated.     

Selection of suitable alternatives to reduce the environmental impact of road traffic noise using a fuzzy multi-criteria decision model

Road traffic noise is one of the most significant environmental impacts generated by transport systems. To this regard, the recent implementation of the European Environmental Noise Directive by Public Administrations of the European Union member countries has led to various noise action plans (NAPs) for reducing the noise exposure of EU inhabitants. Every country or administration is responsible for applying criteria based on their own experience or expert knowledge, but there is no regulated process for the prioritization of technical measures within these plans. This paper proposes a multi-criteria decision methodology for the selection of suitable alternatives against traffic noise in each of the road stretches included in the NAPs. The methodology first defines the main criteria and alternatives to be considered. Secondly, it determines the relative weights for the criteria and sub-criteria using the fuzzy extended analytical hierarchy process as applied to the results from an expert panel, thereby allowing expert knowledge to be captured in an automated way. A final step comprises the use of discrete multi-criteria analysis methods such as weighted sum, ELECTRE and TOPSIS, to rank the alternatives by suitability. To illustrate an application of the proposed methodology, this paper describes its implementation in a complex real case study: the selection of optimal technical solutions against traffic noise in the top priority road stretch included in the revision of the NAP of the regional road network in the province of Almeria (Spain).     

Using photographic image analysis to assess ground cover: a case study of forest road cutbanks

Road prisms, including cutbanks, road surfaces, and fillslopes, can be important contributors of sediment to streams in forested watersheds. Following road construction, cutbanks and fillslopes are often seeded, mulched, and sometimes fertilized to limit erosion and sedimentation. Assessing the success of vegetation establishment on cutbanks and fillslopes is a common task of forested land managers. This study developed and applied a photographic image analysis method to assess percent ground cover along an entire cutbank of a cut-and-fill haul road in the Monongahela National Forest in Tucker County, West Virginia. Variable-sized sections were employed to quantify the vegetative cover. Measurements obtained by this technique were similar to more commonly applied fixed-area plots, and it proved to be a useful tool for land managers who require a more repeatable quantification of ground cover than is possible through visual assessments. Cutbank slope and aspect also were analyzed to determine their potential impact on cutbank vegetation establishment. Slope was not a significant variable in explaining differences in vegetation cover; however, aspect did affect vegetation establishment. South-facing aspects had significantly lower percent vegetation cover than northeast, east, northwest, and north northwest aspects after the first year following seeding and throughout the entire study. Mean percent cover on the south-facing cutbanks was 32% over all time periods, compared to 60% to 73% for the other represented aspects. This result was expected since south-facing slopes generally are drier in the growing season and are subject to more freeze–thaw cycles in the winter. Timber felled onto the cutbank also decreased vegetative cover in the short term on north and north northwest aspects, but vegetation quickly became reestablished on these aspects with their favorable growing conditions.     

Multivariate analysis of drinking water quality parameters in Bhopal, India

Pollution of water bodies is one of the areas of major concern to environmentalists. Water quality is an index of health and well being of a society. Industrialization, urbanization and modern agriculture practices have direct impact on the water resources. These factors influence the water resources quantitatively and qualitatively. The study area selected were the Upper lake and Kolar reservoir of Bhopal, the state capital of Madhya Pradesh, India. The Upper lake and Kolar reservoir both are the important sources of potable water supply for the Bhopal city. The physico–chemical parameters like temperature, pH, turbidity, total hardness, alkalinity, BOD, COD, Chloride, nitrate and phosphate were studied to ascertain the drinking water quality.     

Development and sensitivity analysis of a global drinking water quality index

The UNEP GEMS/Water Programme is the leading international agency responsible for the development of water quality indicators and maintains the only global database of water quality for inland waters (GEMStat). The protection of source water quality for domestic use (drinking water, abstraction etc) was identified by GEMS/Water as a priority for assessment. A composite index was developed to assess source water quality across a range of inland water types, globally, and over time. The approach for development was three-fold: (1) Select guidelines from the World Health Organisation that are appropriate in assessing global water quality for human health, (2) Select variables from GEMStat that have an appropriate guideline and reasonable global coverage, and (3) determine, on an annual basis, an overall index rating for each station using the water quality index equation endorsed by the Canadian Council of Ministers of the Environment. The index allowed measurements of the frequency and extent to which variables exceeded their respective WHO guidelines, at each individual monitoring station included within GEMStat, allowing both spatial and temporal assessment of global water quality. Development of the index was followed by preliminary sensitivity analysis and verification of the index against real water quality data.     

Using a relative health indicator (RHI) metric to estimate health risk reductions in drinking water

When a new drinking water regulation is being developed, the USEPA conducts a health risk reduction and cost analysis to, in part, estimate quantifiable and non-quantifiable cost and benefits of the various regulatory alternatives. Numerous methodologies are available for cumulative risk assessment ranging from primarily qualitative to primarily quantitative. This research developed a summary metric of relative cumulative health impacts resulting from drinking water, the relative health indicator (RHI). An intermediate level of quantification and modeling was chosen, one which retains the concept of an aggregated metric of public health impact and hence allows for comparisons to be made across “cups of water,” but avoids the need for development and use of complex models that are beyond the existing state of the science. Using the USEPA Six-Year Review data and available national occurrence surveys of drinking water contaminants, the metric is used to test risk reduction as it pertains to the implementation of the arsenic and uranium maximum contaminant levels and quantify “meaningful” risk reduction. Uranium represented the threshold risk reduction against which national non-compliance risk reduction was compared for arsenic, nitrate, and radium. Arsenic non-compliance is most significant and efforts focused on bringing those non-compliant utilities into compliance with the 10 μg/L maximum contaminant level would meet the threshold for meaningful risk reduction.     

Simulation of arsenic partitioning in tributaries to drinking water resevoirs

Arsenic released by bottom sediments was determined by experiments in which the sediments were artificially re-suspended using a particle entrainment simulator (PES) to simulate river conditions. Sediment cores were collected from various tributaries to drinking water reservoirs in Connecticut spiked with arsenic, and run in the PES at simulated bed-flow shear stresses from 0.0 to 0.6 N/m². Under equilibrium conditions, the dissolved fraction of arsenic was found to range from 8.3 to 22.1 μg/l, which in most cases exceeded EPA Maximum Contaminant Level (MCL) of 10 μg/l. Experimental results from these simulations have shown that bed-flow shear stress causes an increased concentration of dissolved arsenic, most notably at shear stresses of 0.4, 0.5, and 0.6 N/m². For the solid phase under equilibrium, the concentrations of arsenic ranged between 71 and 275 mg/kg. The average concentration of arsenic on the solid phase as well as partitioning coefficient values (K _p) were highest at initial shear stress. This was attributed to the higher fraction of colloidal material and finer organic particles in the suspended solid mixture. Particles of such nature proved to have higher affinity to arsenic. K _p values were determined from PES data and were found to range from 4,687 to 24,090 l/kg. However, on a mass load basis, the amount of arsenic found in suspended sediment increased with the increase of shear stress. Similarly, the amount of arsenic in the solid phase increased significantly for sites with high Volatile Organic Carbon (VOC) content. Because of the influence of Total Suspended Solids (TSS) and VOC concentrations on K _p, the use of the PES is more appropriate in obtaining K _p values that would be found under real stream conditions when compared to the traditional way of measuring K _p using a jar study technique.     

Using the analytical hierarchy process to assess the environmental vulnerabilities of basins in Taiwan

Every year, Taiwan endures typhoons and earthquakes; these natural hazards often induce landslides and debris flows. Therefore, watershed management strategies must consider the environmental vulnerabilities of local basins. Because many factors affect basin ecosystems, this study applied multiple criteria analysis and the analytical hierarchy process (AHP) to evaluate seven criteria in three phases (geographic phase, hydrologic phase, and societal phase). This study focused on five major basins in Taiwan: the Tan-Shui River Basin, the Ta-Chia River Basin, the Cho-Shui River Basin, the Tseng-Wen River Basin, and the Kao-Ping River Basin. The objectives were a comprehensive examination of the environmental characteristics of these basins and a comprehensive assessment of their environmental vulnerabilities. The results of a survey and AHP analysis showed that landslide area is the most important factor for basin environmental vulnerability. Of all these basins, the Cho-Shui River Basin in central Taiwan has the greatest environmental vulnerability.     

Using NOAA AVHRR data to assess flood damage in China

The article used two NOAA-14 Advanced Very High ResolutionRadiometer (AVHRR) datasets to assess flood damage in the middleand lower reaches of China's Changjiang River (Yangtze River) in 1998. As the AVHRR is an optical sensor, it cannot penetratethe clouds that frequently cover the land during the flood season, and this technology is greatly limited in flood monitoring. However the widely used normalized difference vegetation index (NDVI) can be used to monitor flooding, sincewater has a much lower NDVI value than other surface features.Though many factors other than flooding (e.g. atmospheric conditions, different sun-target-satellite angles, and cloud) can change NDVI values, inundated areas can be distinguished fromother types of ground cover by changes in the NDVI value beforeand after the flood after eliminating the effects of other factors on NDVI. AVHRR data from 26 May and 22 August, 1998 wereselected to represent the ground conditions before and after flooding. After accurate geometric correction by collecting GCPs,and atmospheric and angular corrections by using the 6S code, NDVI values for both days and their differences were calculatedfor cloud-free pixels. The difference in the NDVI values betweenthese two times, together with the NDVI values and a land-use map, were used to identify inundated areas and to assess the arealost to the flood. The results show a total of 358 867 ha, with 207 556 ha of cultivated fields (paddy and non-irrigated field) inundated during the flood of 1998 in the middle and lower reaches of the Changjiang River Catchment; comparing with the reported total of 321 000 and 197 000 ha, respectively. The discrimination accuracy of this method was tested by comparing the results from two nearly simultaneous sets of remote-sensingdata (NOAA's AVHRR data from 10 September, 1998, and JERS-1 synthetic aperture radar (SAR) data from 11 September, 1998, with a lag of about 18.5 hr) over a representative flooded regionin the study area. The results showed that 67.26% of the total area identified as inundated using the NOAA data was also identified as inundated using the SAR data.     

The contribution of drinking water to exposure to toxic substances in Canada

Factors influencing the contribution made by drinking water to an individual's exposure to chemical contaminants are reviewed. Deficiencies inherent in simple computational procedures (such as those which rely on the impact of average values to estimate the relative importance of air, food and water and other vehicles by which man is exposed to contaminants) are examined. An alternative approach is described in which a mathematical model incorporating a probabilistic approach is used to predict the distribution of the total exposure of a population. Values of the means and standard deviations for various quantities including contaminant concentrations and food and drinking water intake are used as inputs to the model. The model enables estimations to be made of the proportion of individuals whose exposure exceeds a certain critical value and the relative contribution each exposure pathway makes to the overall distribution of exposure; its versatility is illustrated utilizing data available for lead. The paper also includes a critical assessment of the value of various techniques employed for monitoring drinking water supplies in providing data suitable for exposure assessment and summarizes conclusions from a recent survey of Canadian drinking water consumption habits.     

Multi-criteria decision analysis with probabilistic risk assessment for the management of contaminated ground water

Traditionally, environmental decision analysis in subsurface contamination scenarios is performed using cost–benefit analysis. In this paper, we discuss some of the limitations associated with cost–benefit analysis, especially its definition of risk, its definition of cost of risk, and its poor ability to communicate risk-related information. This paper presents an integrated approach for management of contaminated ground water resources using health risk assessment and economic analysis through a multi-criteria decision analysis framework. The methodology introduces several important concepts and definitions in decision analysis related to subsurface contamination. These are the trade-off between population risk and individual risk, the trade-off between the residual risk and the cost of risk reduction, and cost-effectiveness as a justification for remediation. The proposed decision analysis framework integrates probabilistic health risk assessment into a comprehensive, yet simple, cost-based multi-criteria decision analysis framework. The methodology focuses on developing decision criteria that provide insight into the common questions of the decision-maker that involve a number of remedial alternatives. The paper then explores three potential approaches for alternative ranking, a structured explicit decision analysis, a heuristic approach of importance of the order of criteria, and a fuzzy logic approach based on fuzzy dominance and similarity analysis. Using formal alternative ranking procedures, the methodology seeks to present a structured decision analysis framework that can be applied consistently across many different and complex remediation settings. A simple numerical example is presented to demonstrate the proposed methodology. The results showed the importance of using an integrated approach for decision-making considering both costs and risks. Future work should focus on the application of the methodology to a variety of complex field conditions to better evaluate the proposed methodology.     

Using GA-Ridge regression to select hydro-geological parameters influencing groundwater pollution vulnerability

For groundwater conservation and management, it is important to accurately assess groundwater pollution vulnerability. This study proposed an integrated model using ridge regression and a genetic algorithm (GA) to effectively select the major hydro-geological parameters influencing groundwater pollution vulnerability in an aquifer. The GA-Ridge regression method determined that depth to water, net recharge, topography, and the impact of vadose zone media were the hydro-geological parameters that influenced trichloroethene pollution vulnerability in a Korean aquifer. When using these selected hydro-geological parameters, the accuracy was improved for various statistical nonlinear and artificial intelligence (AI) techniques, such as multinomial logistic regression, decision trees, artificial neural networks, and case-based reasoning. These results provide a proof of concept that the GA-Ridge regression is effective at determining influential hydro-geological parameters for the pollution vulnerability of an aquifer, and in turn, improves the AI performance in assessing groundwater pollution vulnerability.     

Interlaboratory trial to determine the analytical state-of-the-art of bromate determination in drinking water

The new European Directive for water intended for human consumption has established a regulatory level for bromate at 10 microg L(-1). This Maximum Admissible Concentration requires analytical methods with detection limits of a least 2.5 microg L(-1). A project funded by the Standards, Measurements and Testing Programme of the European Commission has enabled the improvement and/or development of methods for the determination of bromate at such concentration levels. This collaborative work was concluded by the organisation of an interlaboratory trial involving 26 European laboratories, which enabled the testing of both a draft ISO Standard method and alternative methods. This paper presents the results of this interlaboratory trial, along with results of a bromate stability study. The progress made with respect to the analytical state-of-the-art for bromate will greatly benefit the quality of measurements carried out in water quality monitoring.     

Assessing water quality of drinking water distribution system in the South Taiwan

This investigation conducted a full-scale survey the drinking water distribution system in Kaohsiung city, Taiwan. The aim was to investigate whether the distribution system was capable of maintaining high water quality from the water treatment facilities through to the end user. The results showed that the distribution system can maintain high water quality, except for suitable chlorine residuals. The authors plotted chlorine residual contour maps to identify areas with low chlorine residuals, helping them prioritize sections that must be flushed or renewal. The contour maps also provide sufficient and clear information for locating booster chlorination stations. Contour maps enable water facilities to identify how water quality decays in the distribution systems and the locations of such decay. Water quality decay can be caused by properties of pipeline materials, hydraulic conditions, and/or biofilm thickness. However, understanding the exact reasons is unnecessary because the contour maps provide sufficient information for trouble-shooting the distribution systems.     

Use of biological indicators to assess water quality of the Ul River (Portugal)

Diatoms and macroinvertebrates have been extensively used as water quality indicators in Europe for the last two decades. In Portugal, the use of biological indicators to assess water quality in rivers has increased greatly. The aim of this work was to assess the water quality and ecological status of the Ul River in order to evaluate its ability for the establishment of a fluvial beach, using periphytic diatoms and macroinvertebrates as indicators. Four sites were selected along the Ul River. At each site, biological, physical, and chemical parameters were investigated. Epilithic diatoms and macroinvertebrates were sampled. The Specific Polluosensitivity Index and the Biological Diatom Index were applied to diatom data, while for macroinvertebrates, the Iberian Biological Monitoring Working Party (IBMWP) was used. According to the results obtained, it was possible to conclude that up to now, this river does not possess the ideal conditions for the establishment of a fluvial beach. We concluded that epilithic diatoms and macroinvertebrates provided consistent information on water quality assessment and can be used as biological indicators of the water quality in Ul River.     

Determination of volatile organic compounds pollution sources in malaysian drinking water using multivariate analysis

A field investigation was conducted at all water treatment plants throughout 11 states and Federal Territory in Peninsular Malaysia. The sampling points in this study include treatment plant operation, service reservoir outlet and auxiliary outlet point at the water pipelines. Analysis was performed by solid phase micro-extraction technique with a 100 μm polydimethylsiloxane fibre using gas chromatography with mass spectrometry detection to analyse 54 volatile organic compounds (VOCs) of different chemical families in drinking water. The concentration of VOCs ranged from undetectable to 230.2 μg/l. Among all of the VOCs species, chloroform has the highest concentration and was detected in all drinking water samples. Average concentrations of total trihalomethanes (THMs) were almost similar among all states which were in the range of 28.4--33.0 μg/l. Apart from THMs, other abundant compounds detected were cis and trans-1,2-dichloroethylene, trichloroethylene, 1,2-dibromoethane, benzene, toluene, ethylbenzene, chlorobenzene, 1,4-dichlorobenzene and 1,2-dichloro - benzene. Principal component analysis (PCA) with the aid of varimax rotation, and parallel factor analysis (PARAFAC) method were used to statistically verify the correlation between VOCs and the source of pollution. The multivariate analysis pointed out that the maintenance of auxiliary pipelines in the distribution systems is vital as it can become significant point source pollution to Malaysian drinking water.     

Accuracy of bottled drinking water label content

The accurate predictions of ground ozone concentrations are required for proper management, control, and making public warning strategies. Due to the difficulties in handling phenomenological models that are based on complex chemical reactions of ozone production, neural network models gained popularity in the last decade. These models also have some limitations due to problems of overfitting, local minima, and tuning of network parameters. In this study, the predictions of daily maximum ozone concentrations are attempted using support vector machines (SVMs). The comparison between the accuracy of SVM and neural network predictions is performed to evaluate their performance. For this, the daily maximum ozone concentration data observed during 2002–2004 at a site in Delhi is utilized. The models are developed using the available meteorological parameters. The results indicated the promising performance of SVM over neural networks in predicting daily maximum ozone concentrations.