Assessment of water quality and identification of pollution sources of plateau lakes in Yunnan (China)

The plateau lakes of Yunnan are important both ecologically and economically in China. Nevertheless, the human impact on water quality in these lakes has become increasingly highlighted. The water quality of 10 plateau lakes was monitored regularly over the period of 2000 through 2004 for 24 parameters. Multivariate statistical techniques, including cluster analysis (CA), factor analysis (FA), and principal component analysis (PCA), were employed to better interpret information about the water quality and its pollution sources. No obvious data reduction from CA/FA was found because three principal components (PCs) needed 14 variables to explain 85.01% of the total variance. However, three latent factors accounted for pollution mainly from the following sources: agricultural activities, residential activities and anthropogenic-toxic pollution from industrial effluents, or other special activities. Box-whiskers plots were employed to visually interpret the spatiotemporal variations of water quality variables, which were highly correlated with three PCs. Three types of water quality (i.e., low-, medium-, and high-polluted lakes) were determined through CA based on the similarity of water quality variables. Our results may provide helpful information for the authorities to effectively manage the water quality and make sound policies.     

A multivariate statistical analysis of surface water chemistry data--the Ankobra Basin, Ghana

R-mode hierarchical cluster and principal component analysis (PCA) were simultaneously applied to surface water hydrochemical data from three different locations, Ankwaso, Dominase and Prestea, along the Ankobra Basin, Ghana, to extract principal factors corresponding to the different sources of variation in the hydrochemistry, with the objective of defining the main controls on the hydrochemistry at the basin scale. Using the Kaiser criterion, principal components (PC) were extracted from the data and rotated using varimax normalization, for each location. The varimax rotation ensured that variation in the data was maximized for easy interpretation of the results. The analysis reduced 30, 33 and 33 data points, respectively, for Ankwaso, Dominase and Prestea to four, three and four PC representing the sources of variation in the hydrochemistry at the three different locations. Though the PC analysis proved to be more robust at unveiling the sources of variation in the hydrochemistry than the R-mode hierarchical cluster analysis (HCA), the combined use of both techniques resulted in more reliable interpretations of the hydrochemistry. On the basis of these analyses, the hydrochemistry of the basin is controlled largely by the weathering of minerals (silicates, carbonates, gypsum and apatite) from the underlying meta-sediments of the Birimian and Tarkwaian Systems, and the decay of organic matter from the heavily forested regions. Concentrations of the major chemical parameters are within naturally acceptable limits and do not pose threats to the local ecology and humans. There is no strong evidence of high anthropogenic impacts on the major anions and cations used for this research, though there are variations at the different locations studied. The hydrochemistry at Ankwaso is principally controlled by the weathering of silicate minerals, whereas those of Dominase and Prestea are, respectively, influenced by precipitation and domestic wastewaters, and the decay of organic matter.     

小珠山水库水质污染状况及趋势分析

小珠山水库作为青岛市黄岛区主要的供水水源地之一,本文根据近几年小珠山水库水质监测资料,采用污染指数法进行水库水质污染状况分析,探讨了小珠山水库水质的季节变化和年际变化规律及其成因,预测未来供水水质状况与水库水质发展趋势,并提出水资源保护对策.     

Application of best subset method for river water quality modeling: A study on Godavari River,India

The utilization of water quality analysis to inform optimal decision-making is imperative to achieve sustainable management of river water quality. A multitude of research works in the past has focused on river water quality modeling. Despite being a precise statistical regression technique that allows for fitting separate models for all potential combinations of predictors and selecting the optimal subset model, the application of best subset method in river water quality modeling is not widely adopted. The current research aims to validate the use of best subset method in evaluating the water quality parameters of the Godavari River, one of the largest rivers in India, by developing regression equations for different combinations of its physicochemical parameters. The study involves in formulating best subset regression equations to estimate the concentrations of river water quality parameters while also identifying and quantifying their variations. A total of 17 water quality parameters are analyzed at 13 monitoring sites using 13 years (1993–2005) of observed data for the monsoon (June–October) period and post-monsoon (November–February) period. The final subset model is selected among model combinations that are developed for each year's dataset through widely used statistical criteria such as R², F value, adjusted R²_a, AIC_c, and RSS. The final best subset model across all parameters exhibits R² values surpassing 0.8, indicating that the models possess the ability to account for over 80% of the variations in the concentrations of dependent parameters. Therefore, the findings demonstrated the appropriateness of this method in evaluating the water quality parameters in extensive rivers. This work is very useful for decision-making and in the management of river water quality for its sustainable use in the study area.     

Transboundary Pollution between Guangdong Province and Hong Kong: Threats to Water Quality in the Pearl River Estuary and Their Implications for Environmental Policy and Planning

The Pearl River (Zhujiang) is the largest river system in southern China. The river, which is approximately 2200 km long, discharges into the South China Sea through an extensive deltaic area to the west of Hong Kong. Water quality in the river is under threat from a variety of sources associated with industrializationand urbanization in the Pearl River Delta Region (PRDR). Hong Kong's location on the eastern bank of the Pearl River estuary means that the quality of its western marine waters is likely to be increasingly influenced by the Pearl's pollution burden. Little published material exists on pollution in the Pearl River, or the potential impacts of transboundary pollution on marine water quality in Hong Kong. This paper focuses on this issue of transboundary water pollution between the Delta Region and Hong Kong. Specifically, we present the results of a preliminary analysis of water quality data for the Pearl River. The paper demonstrates that the major potential problem affecting the Pearl River is organic pollution, and that the principal sources of pollution affecting the Pearl River estuary, and consequently Hong Kong's western waters, are the Shenzhen River, the upstream Guangzhou section of the Pearl River, and the Dongguan Canal. We estimate that less than 5% of untreated domestic sewage discharges affecting the estuary derive from Hong Kong itself. The paper also discusses the implications of transboundary pollution in the context of environmentalpolicy making in Hong Kong and argues that more extensive and effective co-operation and collaboration between Hong Kong and mainland agencies should be developed to address these concerns.     

Geographic information systems: a tool for strategic ground water quality management

Geographically‐related information is needed for several elements of an integrated ground water quality management programme, including ground water monitoring planning, prioritization of pollution sources, usage of permits and inspections for source control, and planning and completion of remedial actions. Geographic Information Systems (GISs) can be used to support these elements along with delineating wellhead protection areas (WHPAs), prioritizing existing contaminant sources and evaluating proposed changes in land usage in such areas. Eight case studies of the use of GISs in wellhead protection programmes are summarized, including examples from Rhode Island, Mississippi, New Jersey, New York, Pennsylvania, Kansas, Massachusetts and Texas. Six additional examples are mentioned relative to the use of GISs for evaluating ground water pollution potential, facilitating data analysis for environmental restoration of a large area with numerous waste sites, evaluating trends in ground water nitrate contamination, establishing a national database for ground water vulnerability to agricultural chemicals, simulating water table altitudes from stream and drainage basin locations, and selecting radioactive waste dump sites. The applicability of GISs and their associated advantages in wellhead protection and other ground water management studies are demonstrated via the case studies. The GIS technology provides a unique opportunity for analysing and visualizing spatial data. Contaminant and source prioritization within WHPAs is needed for both extant conditions and in the evaluation of proposed land use changes. The coupling of a GIS with contaminant/source prioritization would provide a strategic tool which could be used to plan targeted ground water monitoring programmes, to identify appropriate management or mitigation measures, minimize introduction of contaminants from existing sources into the subsurface environment, and to evaluate the potential of proposed land use activities for causing ground water contamination. GISs can be useful in providing current information for policy makers, planners and managers engaged in ground water quality decision making.     

WATER QUALITY ANALYSIS OF THE HALIFAX RIVER,FLORIDA1

ABSTRACT: An application of the receiving water block of the EPA Storm Water management Model (SWMM) is presented to quantify water quality impacts and evaluated control alternatives for a 208 areawide waste water management plan in Volusia Country, Florida. The water quality impact analyses were conducted for dry-and wet-weather conditions to simulate dissolved oxygen (DO), chlorides, total nitrogen (TN), and total phosphorus (TP) in the Halifax Rivers, Florida, a 40-kilometer-long tidal estuary located on the Atlantic coast of Florida near Daytona Beach. Dry-weather analysis was performed using conventional 7-day, 10-year low flow conditions to determine a set of unit transfer coefficients which estimate the pollutant concentration transferred to any point in the estuary from a constant unit discharge of pollutants at the existing waste water treatment plant outfall locations. Wet-weather analysis was performed by continuous simulation of a typical three-month summer wet season in Florida. Three-month cumulative duration curves of DO, TN and TP concentrations were constructed to estimate the relative value of controlling urban runoff of waste water treatment plant effluent on the Halifax River. The three-month continuous simulation indicated that the greatest change in DO, TN, and TP duration curves is possible by abatement of waste water treatment plant pollution.     

2006-2010年浑江通化江段地表水系状况分析与对策

通过分析2006-2010年浑江通化江段水质监测数据,评价水质状况,针对主要污染因子超标现象,分析成因,从工矿企业污染源、生活污染源、农业污染源等方面阐述污染因素。在此基础上,从加强上游管理、污染源头控制、加强监管、发展循环经济、推广农业技术等方面提出改善水质的对策。     

MULTIVARIATE ANALYSIS OF WATER QUALITY AND PHYSICAL CHARACTERISTICS OF SELECTED WATERSHEDS IN PUERTO RICO1

ABSTRACT: Multivariate analyses were used to develop equations that could predict certain water quality (WQ) conditions for unmonitored watersheds in Puerto Rico based on their physical characteristics. Long term WQ data were used to represent the WQ of 15 watersheds in Puerto Rico. A factor analysis (FA) was performed to reduce the number of chemical constituents. Cluster analysis (CA) was used to group watersheds with similar WQ characteristics. Finally, a discriminant analysis (DA) was performed to relate the WQ clusters to different physical parameters and generate predicting equations. The FA identified six factors (77 percent of variation explained): nutrients, dissolved ions, sodium and chloride, silicacious geology, red ox conditions, and discharge. From the FA, specific conductance, sodium, phosphorous, silica, and dissolved oxygen were selected to represent the WQ characteristics in the CA. The CA determined five groups of watersheds (forested, urban polluted, mixed urban/rural, forested plutonic, and limestone) with similar WQ properties. From the five WQ clusters, two categories can be observed: forested and urban watersheds. The DA found that changes in forest cover, percent of limestone, mean annual rainfall, and watershed shape factor were the most important physical features affecting the WQ of watersheds in Puerto Rico.     

地震对城乡地表水水质长期影响分析——以四川省宝兴县为例

为研究地震前后地表水水质变化规律,以2013年4月20日雅安地震重灾区—宝兴县地表水水质变化为例,对2012 ～2019年宝兴县赵家坝断面地表水水质监测数据收集整理,单因子评价法、污染指数法(单项污染指数、综合污染指数和有机污染综合指数)和水质指数模式优化法对地震前后地表水质进行分析.结果 表明,此次地震引起水体中污染...     

DETERMINING THE SOURCE OF STREAM CONTAMINATION IN A KARST‐WATER SYSTEM,SOUTHWEST VIRGINIA,USA1

ABSTRACT: Karst terrane provides a linkage between surface water and ground water regimes by means of caves, sinkholes and swallets, and sinking streams, and facilitates the inter‐watershed transfer of water and contaminants through these subsurface systems. The goal of this study was to develop procedures to identify the sources of degradation of a creek situated in a complex karst‐water system. The study approach consisted of using dye tracing technique to determine subsurface flow paths through the karst system, a water‐sampling network to identify and characterize pollution sources within the surface watershed and subsurface flow regime, and evaluation of analytical data for several water quality parameters. The results of this study provide an interesting perspective of water and contaminant movement in karst‐water systems and pinpoint the sources of stream contamination for a case study site in southwest Virginia where two springs supply water to a contaminated freshwater stream.     

A STATISTICAL APPROACH TO ASSESS FACTORS AFFECTING WATER CHEMISTRY USING MONITORING DATA1

ABSTRACT: A method to partition the variation in concentrations of water chemistry parameters in a river is described. The approach consists of fitting a family of curves for each chemical parameter. Each curve indicates the response of the parameter to river flow for a particular time period or location. An analysis of covariance is then used to identify statistically significant differences between curves. Such differences result largely from two factors: (1) the discharge of effluents and (2) river flow-concentration relationships. The deviations from the fitted curves indicate month-to-month variations unrelated to river flow that are controlled by factors such as temperature-related seasonal patterns. Underlying statistical assumptions are discussed with respect to water chemistry data. The technique is applied to a data set consisting of monthly samples of 22 water chemistry parameters from the Sulphur River of Texas and Arkansas. Several patterns of response to river flow and to two effluent discharges were revealed.     

A statistical study of the quality of surface water intended for human consumption near Valencia (Spain)

Water quality in the European Union is subject to legislation through directives that are applicable in all Member States. The directives specify a set of physical and chemical parameters that should be regularly controlled using a network of sampling points, with sampling based on the intended use of the water. This paper presents the results of a statistical comparison of the quality of water intended for human consumption at two different locations (the Canal de Benagéber and the Canal Júcar-Turia) near the town of Valencia (Spain). These are currently the only canals that could supply Valencia and other nearby towns with drinking water. The parameters considered in this paper are the ones specified in the European directives for determining the quality of water intended for human consumption. These directives specify that the 95th percentile of the distribution of each parameter should be used to assess water quality, so accurate estimation of these percentiles was essential in this paper. We found statistically significant differences among locations in chlorides, sulfates, temperature, coloration, and manganese, but the results varied depending on the statistical tests that we used; parametric methods gave better results than nonparametric methods.     

Determination of arsenic levels in the water resources of Aksaray Province, Turkey

Arsenic levels were determined in 62 stations utilized as drinking and potable water resources by local community for Turkey's Aksaray Province (4589 km(2); 980 m above sea level). The samplings were implemented every two months for 1 year. The arsenic values were found to be ranging between 10 and 50 μg/L in 22 points and were found to be >50 μg/L in 5 stations, according to the mean value of the 6 samples. WHO and the Turkish Standards have permitted an arsenic concentration of 10 μg/L in drinking waters. The multivariate statistical technique, cluster analysis (CA), followed by principal component analysis (PCA) were applied to the data on 17 water quality parameters in 47 stations that are used for drinking and other domestic resources. Two significant sampling locations were detected based on the similarity of their water quality. The chemical correlations were observed in the two sub-sampling locations by Principal Component Analysis.     

GROUPWISE MODELING STUDY OF BACTERIALLY IMPAIRED WATERSHEDS IN TEXAS: CLUSTERING ANALYSIS1

ABSTRACT: Under the Clean Water Act (CWA) program, the Texas Commission on Environmental Quality (TCEQ) listed 110 stream segments in the year 2000 with pathogenic bacteria impairment. A study was conducted to evaluate the probable sources of pollution and characterize the watersheds associated with these impaired water bodies. The primary aim of the study was to group the water bodies into clusters having similar watershed characteristics and to examine the possibility of studying them as a group by choosing models for total maximum daily load (TMDL) development based on their characteristics. This approach will help to identify possible sources and determine appropriate models and hence reduce the number of required TMDL studies. This in turn will help in reducing the effort required to restore the health of the impaired water bodies in Texas. The main characteristics considered for the classification of water bodies were land use distribution within the watershed, density of stream network, average distance of land of a particular use to the closest stream, household population, density of on‐site sewage facilities (OSSFs), bacterial loading from different types of farm animals and wildlife, and average climatic conditions. The climatic data and observed instream fecal coliform bacteria concentrations were analyzed to evaluate seasonal variability of instream water quality. The grouping of water bodies was carried out using the multivariate statistical techniques of factor analysis/principal component analysis, cluster analysis, and discriminant analysis. The multivariate statistical analysis resulted in six clusters of water bodies. The main factors that differentiated the clusters were found to be bacterial contribution from farm animals and wildlife, density of OSSFs, density of households connected to public sewers, and land use distribution.     

Multivariate statistical characterization of water quality in Lake Lanier, Georgia, USA

Watershed monitoring programs depend on water quality characterization data collected for many parameters, at many times and places, and with limited resources. Our objective is to present a strategy that reduces the measured parameters, locations, and frequency without compromising the quality of the monitoring program. One year of twice-monthly (growing season) and monthly (dormant season) water quality data collected from 17 lake and 10 tributary sites are used in conjunction with multivariate statistical techniques to improve the utility of collected data by identifying key parameters and monitoring locations. Factor analysis shows that tributary water quality data consists of three components-stormwater runoff, municipal and industrial discharges, and ground water-which can be distinguished using total suspended solids, total dissolved solids, and alkalinity plus soluble reactive P, respectively. Lake water quality characterization is more ambiguous than tributary water quality characterization, but factor analysis indicates that anoxia associated with lake stratification is the largest source of lake water quality variation, followed by nutrient abundance, and finally by biomass abundance. Cluster analysis suggests that tributary and lake monitoring stations can be consolidated. Reducing the number of parameters and stations frees up resources for increased monitoring elsewhere.     

锦州地区地下水饮用水源污染因素及防治对策研究

通过对锦州地区地下水饮用水源水质监测数据分析,评价地下水饮用水源水质现状;针对部分水源水质超标现象,分析其成因,并在实地考察的基础上从工矿企业污染源、生活污染源、养殖业污染源、农业污染源和不合理开采等几方面进一步阐述水源地潜在污染因素;在此基础上从预防、治理、生态修复及加强监管等方面提出地下水饮用水源污染防治对策。     

A METHOD FOR OBTAINING SLOPE INFORMATION FOR HYDROLOGIC MODELS1

: A method is described for obtaining surface slope information for analysis with other land resource and water quality data in hydrologic models of nonpoint sources of water pollution. The method described requires a point sampling scheme, topographic maps, and a coordinate digitizer. Sample point elevation, slope direction, and slope magnitude are calculated from locations of the sample point and the nearest upper and lower contour lines. Details of the data collection methodology and associated problems are discussed.     

Mapping Three‐Dimensional Water‐Quality Data in the Chesapeake Bay Using Geostatistics1

Abstract:  Data interpretation and visualization software tools with geostatistical capabilities were adapted, customized, and tested to assist the Chesapeake Bay Program in improving its water‐quality modeling protocols. Tools were required to interpolate, map, and visualize three‐dimensional (3D) water‐quality data, with the capability to determine estimation errors. Components of the software, originally developed for ground‐water modeling, were customized for application in estuaries. Additional software components were developed for retrieval, and for pre‐ and post‐ processing of data. The Chesapeake Bay Program uses the 3D mapped data for input to the Bay water‐quality model that projects the future health of the Bay and its tidal tributary system. In determining water‐quality attainment criteria, 3D kriging estimation errors are needed as a statistical measure of uncertainty. Furthermore, given the high cost of installing and operating new monitoring stations, geostatistical techniques can assist the Chesapeake Bay Program in the identification of suitable data collection locations. Following the evaluation, selection, and development of the software components phase, 3D ordinary kriging techniques with directional semi‐variograms to account for anisotropy were successfully demonstrated for mapping 3D fixed station water‐quality data, such as dissolved oxygen and salinity. Additionally, an improved delineation tool was implemented to simulate the upper and lower pycnocline boundary surfaces allowing the segregation of the interpolated 3D data into three separate zones for a better characterization of the pycnocline layer.