Variations In Surface Water Quality Research Articles

Implications of rainfall variability for seasonality and climate-induced risks concerning surface water quality in East Asia

Water resources in East Asia are considered particularly vulnerable to climate variability and extremes due to strong hydrologic variability inherent in the monsoon climate and rising water demand resulting from rapid economic growth. To obtain a better understanding of the current status and climate-induced risks concerning surface water quality in East Asia, seasonal and spatial variations in surface water quality were compared among 11 watersheds in eight countries during typical dry and wet periods from 2006 to 2008. While concentrations of dissolved ions tended to be higher during dry periods, concentrations of suspended sediments and dissolved organic matter were significantly higher during wet periods at most sampling locations. Metals with low solubility showed higher total concentrations during wet periods and had strong positive relationships with suspended sediment concentrations. Metals with high partitioning into the dissolved phase exhibited higher concentrations during dry periods at many sites. Seasonal and spatial patterns were distinct along the Lower Mekong River, including much higher monsoonal concentrations of sediment-associated metals and relatively high dry-season concentrations of dissolved As along upper reaches. The results suggest that rainfall variability is crucial in understanding seasonality and climate-induced risks concerning surface water quality in East Asia.

Spatial and temporal characterization of trace elements and nutrients in the Rawal Lake Reservoir, Pakistan using multivariate analysis techniques

Rawal Lake Reservoir is renowned for its ecological significance and is the sole source of drinking water of the third largest city of Pakistan. However, fish kill in recent years and anthropogenic impacts from human-related activities in its catchment area have resulted in deterioration of its surface water quality. This study aims to characterize spatial and temporal variations in surface water quality, identify contaminant sources, and compare their levels with quality guidelines. Surface water samples were collected from 10 sites and analyzed for 27 physicochemical parameters for a period of 2years on a seasonal basis. Concentration of metals in surface water in pre-monsoon were in the order: Fe>Mg>Ca>Mn>Zn>Ni>Cr>Cu>Co>Pb, whereas in post-monsoon, the order of elemental concentrations was: Ca>Mg>Na>Fe>K>Zn>Cr>Li>Pb>Co>Ni>Cu>Mn>Cd. Metals (Ni, Fe, Zn, and Ca), pH, electrical conductivity (EC), dissolved oxygen (DO), chemical oxygen demand (COD), and nutrients (PO (4) (3-) , NO(3)-N, and SO (4) (2-) ) were measured higher in pre-monsoon, whereas concentration of Cu, Mn, Cr, Co, Pb, Cd, K, Na, Mg, Li, Cl(-), and NH(4)-N were recorded higher in post-monsoon. Results highlighted serious metal pollution of surface water. Mean concentration of Zn, Cd, Ni, Cu, Fe, Cr, and Pb in both seasons and Mn in post-monsoon were well above the permissible level of surface water quality criteria. Results stress the dire need to reduce heavy-metal input into the lake basin and suggest that heavy-metal contamination should be considered as an integral part of future planning and management strategies for restoration of water quality of the lake reservoir.

Development of Water Quality Indexes to Identify Pollutants in Vietnam’s Surface Water

This study presents the first water quality indexes developed to evaluate surface water in Vietnam. The basic water quality index (WQIB) can be effectively used to evaluate the spatial and temporal variations of surface water quality as well as to identify water pollutants. The overall water quality index (WQIO) can provide additional information, particularly on toxic substances contributing to water pollution. The water quality indexes developed for this paper were applied to the national surface-water quality monitoring data taken from 1999 to 2007. Water pollutants were classified into three subcategories: organic and nutrients, particulates, and bacteria. Surface water in northern and central Vietnam was poor in quality and contained organic matter, nutrients, and bacteria. Water in the southern part was mainly polluted by bacteria. Trend analysis results reveal a deterioration in water quality in those provinces under pressure from rapid population growth, urbanization, and industrialization. Vietnam has established an official policy to ensure comprehensive nationwide water quality monitoring by 2020. The implementation of water quality indexes may provide the guiding data for sustainable water-resources management in Vietnam.

Multivariate Statistical Techniques for the Assessment of Surface Water Quality at the Mid-Black Sea Coast of Turkey

The aim of this study was to investigate the seasonal and spatial variations in surface water quality at the mid-Black Sea coast of Turkey. The samples were collected from ten monitoring stations including rivers and sea water during the years from 2007 to 2008. The samples were analyzed for 25 parameters: total carbon, total inorganic carbon, total organic carbon, chromium, cadmium, copper, lead, iron, nickel, manganese, phenol, surfactants, ammonium, nitrite and nitrate-nitrogen, total phosphorus, adsorbable organic halogen, sulfate, hardness, dissolved oxygen, pH, temperature, total dissolved solids, electrical conductivity, and salinity. Multivariate statistical techniques, cluster analysis (CA) and factor analysis/principal component analysis (FA/PCA), were applied to analyze the similarities among the sampling sites to identify the source apportionment of pollution parameters in surface waters. The results indicate that seven factors for river water explained 82.24% of the variance. In seawater, seven factors account for 89.65% of the total variance. Varifactors obtained from factor analysis indicate that the parameters responsible for water quality variations are mainly related to organic pollution (municipal effluents), inorganic pollution (industrial effluents and waste disposal areas), nutrients (agricultural runoff), and dissolved salts (soil leaching and runoff process).

Study of seasonal and spatial variation in surface water quality of Cauvery river stretch in Karnataka

In this study, multivariate statistical techniques, such as discriminant, factor /principal component and cluster analyses were applied to water quality data set monitored in pre- and post- monsoon for twenty five locations during three years to investigate seasonal and spatial variations in river water quality. The variables were mainly divided into two categories viz., non-conservative – DO, BOD, COD, nitrates and phosphates and conservative parameters – TDS, conductivity, alkalinity, hardness, calcium, magnesium, sodium, potassium and sulfates. Trivial elevated values of all non conservative Characteristics in pre-monsoon and some conservative parameters (SO4, Cl) in post-monsoon period reflected contribution on temporal effect on surface water. Results of principal component analysis evinced that all the parameters equally and significantly contribute to water quality variations in the river basin for both the seasons. Factor 1 and factor 2 analysis revealed the inverse relation of DO, indicating the control of dissolved oxygen on organic load and nutrients in different seasons. Hierarchical cluster analysis grouped twenty five stations into three clusters in pre-monsoon and six clusters in post- monsoon with similar water quality features. Third clustered group of former and sixth of latter consisted one station (St.25), exhibiting significant spatial variation in physico-chemical composition. Key words: Multivariate analysis, cauvery, cluster, conservative parameters, temporal and spatial variations.

UTILIZATION OF TWO ARTIFICIAL NEURAL NETWORK METHODS IN SURFACE WATER QUALITY MODELING

Artificial neural networks (ANNs) are capable of learning the relationships between the dependent and predictor surface water quality variables and have a high ability to predict from the new data set. In this study, two different ANN methods, the feedforward network (FFN) and the cascadeforward network (CFN) were applied to predict two surface water quality variables such as chlorophyll-a (Chl-a) and fecal coliform (FC) using twelve other variables as an input to the models. Both methods, for developing and training the ANN, are multilayer, leading to the same architecture of the feedforward neural network trained by back-propagation learning algorithm. The networks were supplied with two subsets of water quality data, with two-thirds being used for training and one-third for testing the performance of the networks, after pre-processing of the data set (110 samples) by normalization and moving average techniques. The performances of both ANN methods were evaluated visually by plotting and quantitatively by using five statistical parameters. The training procedures of both ANN methods for Chl-a and FC were very successful and a perfect match was obtained between the measured and predicted values with the correlation coefficient (R) of 1.00. The testing or prediction results also produced good agreements with the measured data of Chl-a and FC in terms of R values of 0.83, 0.93 (for FFN), and 0.89, 0.91 (for CFN), respectively. Results of this study show that ANN methods are able to predict Chl-a and FC with reasonable accuracy, suggesting that ANN is a valuable and powerful tool for surface water quality modeling and management.

Application of water quality index for assessment of surface water quality surrounding integrated industrial estate-Pantnagar

Water pollution as a consequence of accelerated industrial growth has drawn concerns over public health and environment. In order to assess the extent of environmental impact due to integrated industrial estate-Pantnagar (IIE-Pantnagar), surface water was monitored for duration of one year. Grab surface water samples from 12 locations were collected, processed and analyzed for 11 pre-identified variables. Besides providing the raw baseline data, the information was normalized and integrated by applying Water Quality Index (WQI). The average surface water quality surrounding IIE-Pantnagar was found to be satisfactory in terms of its potability after conventional treatment and disinfection. During summer season, the WQI of Baigul River at Haldi Road illustrated good water quality (83.3), which however, deteriorates in its downstream at Rudrapur (55.5), signifying moderate quality. The WQI inside IIE-Pantnagar varied from 47.4 to 66.6, revealing moderate to good surface water quality. However, in monsoon and post-monsoon seasons, WQI demonstrated a modest increase in quality for all sampling points, with a few exceptions due to dilution caused by monsoonal rainfall. In this period, average WQI varied from 49.6 to 81.7. During winter season, WQI further declined due to cumulative effects of industrial discharge from IIE-Pantnagar and other adjacent industrial set-ups coupled with municipal waste water from Rudrapur city. The lowest WQI for entire sampling network was found within IIE-Pantnagar as 37.1, revealing poor water quality. The application of WQI to assess temporal variations in surface water quality was therefore found satisfactory.

Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques

In this study, spatial and seasonal variations of water quality in Haraz River Basin were evaluated using multivariate statistical techniques, such as cluster analysis, principal component analysis and factor analysis. Water quality data collected from 8 sampling stations in river during 4 seasons (Summer and Autumn of 2007, Winter and Spring of 2008) were analyzed for 10 parameters (dissolved oxygen, Fecal Coliform, pH, water temperature, biochemical oxygen demand, nitrate, total phosphate, turbidity, total solid and discharge). Cluster analysis grouped eight sampling stations into three clusters of similar water quality features and thereupon the whole river basin may be categorized into three zones, i.e. low, moderate and high pollution. The principle component analysis/factor analysis assisted to extract and recognize the factors or origins responsible for water quality variations in four seasons of the year. The natural parameters (temperature and discharge), the inorganic parameter (total solid) and the organic nutrients (nitrate) were the most significant parameters contributing to water quality variations for all seasons. Result of principal component analysis and factor analysis evinced that, a parameter that can be significant in contribution to water quality variations in river for one season, may less or not be significant for another one.

Surface water quality and land use in Wisconsin, USA – a GIS approach

To explore effective water pollution mitigation, it is essential to study the complex relationship between surface water quality and land-use patterns in different regions under various spatial scales. A geographic information system (GIS) based approach together with statistical analysis was used to examine this complex relationship in the State of Wisconsin, USA. The data on watershed, land use/cover and water quality of 1988–1996 were collected and integrated from the Wisconsin Department of Natural Resources, the Wisconsin Initiative for State-wide Cooperation on Landscape Analysis and Data, and the US EPA, respectively. Nonparametric statistical analysis techniques were employed to analyse the relationship between land-use categories and surface water quality variables such as calcium, total phosphorus, lead and fecal coliform at the watershed level. Then a GIS-based spatial analysis was further conducted for variables that were significantly related to land use. The results showed that 14 out of 21 water quality variables were significantly and positively related to the urban and agricultural lands, and were significantly and negatively related to the forest and wetland land-use types. In addition, a strong positive rank correlation between electric conductivity related water quality variables and urban/agricultural land use was also observed. The results and the information extracted from this research would be useful to land-use planners and environmental management agencies for monitoring and reducing the water pollution.

Detection of seasonal variations in surface water quality using discriminant analysis

In the study, discriminant analysis (DA) was applied to water quality data set monitored in winter and summer months along 3 years to investigate seasonal variations of Tahtali Reservoir water quality. The variables were divided into two categories as "parameters naturally found in surface waters--Al, Fe, Mn, F, Ba, Cl, and SO(4)" and "pollutants-biochemical oxygen demand, chemical oxygen demand, and NO(3)". Linear discriminant functions were calculated to distinguish between winter and summer samples. Results revealed that water quality variables (naturally found in surface water and also pollutants) were distinctly different between two seasons. Seasonal changes of water quality caused by anthropogenic activities should be taken into consideration by the water managers especially in summer seasons in which higher concentrations were observed. The study showed that DA can be applied successfully to establish the seasonal variation in surface water quality and make it possible to design a future temporal sampling strategy in an optimal manner.

Simulating Ensembles of Source Water Quality Using a K-Nearest Neighbor Resampling Approach

Climatological, geological, and water management factors can cause significant variability in surface water quality. As drinking water quality standards become more stringent, the ability to quantify the variability of source water quality becomes more important for decision-making and planning in water treatment for regulatory compliance. However, paucity of long-term water quality data makes it challenging to apply traditional simulation techniques. To overcome this limitation, we have developed and applied a robust nonparametric K-nearest neighbor (K-nn) bootstrap approach utilizing the United States Environmental Protection Agency's Information Collection Rule (ICR) data. In this technique, first an appropriate "feature vector" is formed from the best available explanatory variables. The nearest neighbors to the feature vector are identified from the ICR data and are resampled using a weight function. Repetition of this results in water quality ensembles, and consequently the distribution and the quantification of the variability. The main strengths of the approach are its flexibility, simplicity, and the ability to use a large amount of spatial data with limited temporal extent to provide water quality ensembles for any given location. We demonstrate this approach by applying it to simulate monthly ensembles of total organic carbon for two utilities in the U.S. with very different watersheds and to alkalinity and bromide at two other U.S. utilities.

Environmental Impact of Mining Activities in the Southern Sector of the Guadiana Basin (SW of the Iberian Peninsula)

The southern sector of the Guadiana River basin (GRB) drains the central-western part of the Iberian Pyrite Belt, an area with many polymetallic sulfide deposits and residues of mining activities that under oxidizing conditions generate an acidic leachate with large quantities of sulfates, metals, and metalloids in solution. These acidic leachates seep into the fluvial system contaminating the surface water bodies and increasing the contamination risk for local populations and riparian habitats. The present study was carried out both in Portugal and Spain with the main aim of identifying the principal contamination sources that produce acid mine drainage (AMD) in the southern part of the GRB and to evaluate the seasonal variations of water quality affected by AMD. The physicochemical parameters determined in the field (temperature, electrical conductivity, pH, redox potential, and dissolved oxygen) are discussed and interpreted together with the hydrochemical analysis of surface water samples collected at 79 points of observation. The data show a strong seasonal variation of surface water quality with poorer water quality standards during the dry season. It is also possible to observe that there is a natural decrease in pollution levels with increasing distance from the pollution source (mining areas). Acidic leachates are gradually neutralized as they drain away from the mining areas depositing Fe-(Cu-Al) bearing secondary minerals. There is also an important contaminant load reduction in the estuary area as a result of the mixing process with seawater. This contributes to a loss of the metals in solution due to both dilution and precipitation, as a result of pH increase.

Application of an unsupervised artificial neural network technique to multivariant surface water quality data

AbstractSurface water contamination from agricultural and urban runoff and wastewater discharges from industrial and municipal activities is of major concern to people worldwide. Classical models can be insufficient to visualise the results because the water quality variables used to describe dynamic pollution sources are complex, multivariable, and nonlinearly related. Artificial intelligence techniques with the ability to analyse multivariant water quality data by means of a sophisticated visualisation capacity can offer an alternative to current models. In this study, the Kohonen self‐organising feature maps (SOM) neural network was initially applied to analyse the complex nonlinear relationships among multivariable surface water quality variables using the component planes of the variables to determine the complex behaviour of water quality parameters. The dependencies between water quality variables were extracted and interpreted using the pattern analysis visualised in component planes. For further investigation, the k‐means clustering algorithm was used to determine the optimal number of clusters by partitioning the maps and utilising the Davies–Bouldin clustering index, leading to seven groups or clusters corresponding to water quality variables. The results reveal that the concentrations of Na, K, Cl, NH4‐N, NO2‐N, o‐PO4, component planes of organic matter (pV), and dissolved oxygen (DO) were significantly affected by seasonal changes, and that the SOM technique is an efficient tool with which to analyse and determine the complex behaviour of multidimensional surface water quality data. These results suggest that this technique could also be applied to other environmentally sensitive areas such as air and groundwater pollution.

Temporal variations of surface water quality in urban, suburban and rural areas during rapid urbanization in Shanghai, China

As the economic and financial center of China, Shanghai has experienced an extensive urban expansion since the early 1980s, with an attendant cost in environmental degradation. We use an integrated pollution index to study the temporal variations of surface water quality in urban, suburban and rural areas between 1982 and 2005. Data on monitored cross-sections were collected from the Shanghai Environmental Monitoring Center. The results indicated that the spatial pattern of surface water quality was determined by the level of urbanization. Surface water qualities in urban and suburban areas were improved by strengthening the environmental policies and management, but were worsening in rural areas. The relationship between economic growth and surface water quality in Shanghai showed an inversed- U-shaped curve, which reflected a similar pattern in most developed countries. This research suggests that decision makers and city officials should be more aware of the recent pollution increases in Shanghai.

Assessment of seasonal variations in surface water quality

Assessment of seasonal changes in surface water quality is an important aspect for evaluating temporal variations of river pollution due to natural or anthropogenic inputs of point and non-point sources. In this study, surface water quality data for 16 physical and chemical parameters collected from 22 monitoring stations in a river during the years from 1998 to 2001 were analyzed. The principal component analysis technique was employed to evaluate the seasonal correlations of water quality parameters, while the principal factor analysis technique was used to extract the parameters that are most important in assessing seasonal variations of river water quality. Analysis shows that a parameter that is most important in contributing to water quality variation for one season may not be important for another season except for DOC and electrical conductance, which were always the most important parameters in contributing to water quality variations for all four seasons.

Multivariate statistical techniques for the assessment of surface water quality of Fuji River Basin, Japan

Different multivariate statistical techniques were used to evaluate temporal and spatial variations of surface water-quality of Fuji river basin using data sets of 8 years monitoring at 13 different sites. The hierarchical cluster analysis grouped thirteen sampling sites into three clusters i.e. relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) sites based on the similarity of water quality characteristics. The principal component analysis/factor analysis indicated that the parameters responsible for water quality variations are mainly related to discharge and temperature (natural), organic pollution (point sources) in LP areas; organic pollution (point sources) and nutrients (non point sources) in MP areas; and organic pollution and nutrients (point sources) in HP areas. The discriminant analysis showed that six water quality parameters (discharge, temperature, dissolved oxygen, biochemical oxygen demand, electrical conductivity and nitrate nitrogen) account for most of the expected temporal variations whereas seven water quality parameters (discharge, temperature, biochemical oxygen demand, pH, electrical conductivity, nitrate nitrogen and ammonical nitrogen) account for most of the expected spatial variations in surface water quality of Fuji river basin.

Evaluation of Water Quality in the Chillán River (Central Chile) Using Physicochemical Parameters and a Modified Water Quality Index

The Chillán River in Central Chile plays a fundamental role in local society, as a source of irrigation and drinking water, and as a sink for urban wastewater. In order to characterize the spatial and temporal variability of surface water quality in the watershed, a Water Quality Index (WQI) was calculated from nine physicochemical parameters, periodically measured at 18 sampling sites (January-November 2000). The results indicated a good water quality in the upper and middle parts of the watershed. Downstream of the City of Chillán, water quality conditions were critical during the dry season, mainly due to the effects of the urban wastewater discharge. On the basis of the results from a Principal Component Analysis (PCA), modifications were introduced into the original WQI to reduce the costs associated with its implementation. WQIDIR2 and WQIDIR, which are both based on a laboratory analysis (Chemical Oxygen Demand) and three (pH, temperature and conductivity), respectively, four field measurements (pH, temperature, conductivity and Dissolved Oxygen), adequately reproduce the most important spatial and temporal variations observed with the original index. They are proposed as useful tools for monitoring global water quality trends in this and other, similar agricultural watersheds in the Chilean Central Valley. Possibilities and limitations for the application of the used methodology to watersheds in other parts of the world are discussed.

Aspects of the ecology of the Asian tapeworm, <i>Bothriocephalus acheilognathi</i> Yamaguti, 1934 in yellowfish in the Vaal Dam, South Africa

Seasonal surveys were conducted at the Vaal Dam between April 2000 and January 2001. Twenty smallmouth yellowfish (Labeobarbus aeneus) and 20 largemouth yellowfish (Labeobarbus kimberleyensis) were collected with the aid of gill nets. Surface water quality variables were included. The cestodes were identified as either Bothriocephalus acheilognathi Yamaguti, 1934 or "other cestode spp.". The majority (99.8%) of the cestodes found in both yellowfish species were identified as B. acheilognathi (Asian tapeworm). The prevalence, mean intensity and abundance of B. acheilognathi in both yellowfish species were calculated. Ecological parameters including species specificity, seasonality, gender specificity and relationships between fish size and the Asian tapeworm prevalence were also included. In this study, B. acheilognathi preferred L. kimberleyensis over L. aeneus although a low intensity was observed in smallmouth yellowfish. Furthermore, the infection (in terms of prevalence, abundance and mean intensity) in largemouth yellowfish was markedly higher. Seasonal patterns observed in the Asian tapeworm's infection of smallmouth yellowfish are attributed to breeding and subsequent feeding patterns of this fish species with relatively high infections recorded in winter and spring. For L. kimberleyensis no explanation can be given regarding the seasonal patterns observed for the mean intensity and abundance of B. acheilognathi. The maximum and minimum mean intensity and abundance values in largemouth yellowfish were recorded in autumn and spring, respectively. In addition, the prevalence of B. acheilognathi was consistently high in all four seasons.

Association of Landscape Metrics to Surface Water Biology in the Savannah River Basin

Surface water quality for the Savannah River basin was assessed using water biology and landscape metrics. Two multivariate analyses, partial least square and canonical correlation, were used to describe how the structural variation in landscape metrics may affect surface water biology and to define the key landscape variable(s) that contribute the most to variation in surface water quality. The results showed that the key landscape metrics in this study area were: percent forest, percent of total area in agriculture (row crops + pasture) on moderately erodible soils, percent of total area with slopes greater than 3% and stream density. The first two canonical variates describe the linear combinations of the two data sets (r = 0.74 and 0.63), weighted mainly by percent of total area with slopes greater than 3%, taxa richness of sensitive insects to pollution (EPT; Ephemeroptera-Plecoptera-Trichoptera Index), algal growth potential and percent of total area in agriculture on moderately erodible soils.