Assessment of seasonal variations in surface water quality

A PCA–OLS Model for Assessing the Impact of Surface Biophysical Parameters on Land Surface Temperature Variations

Analysis of land surface temperature (LST) spatiotemporal variations and characterization of the factors affecting these variations are of great importance in various environmental studies and applications. The aim of this study is to propose an integrated model for characterizing LST spatiotemporal variations and for assessing the impact of surface biophysical parameters on the LST variations. For this purpose, a case study was conducted in Babol City, Iran, during the period of 1985 to 2018. We used 122 images of Landsat 5, 7, and 8, and products of water vapor (MOD07) and daily LST (MOD11A1) from the MODIS sensor of the Terra satellite, as well as soil and air temperature and relative humidity data measured at the local meteorological station over 112 dates for the study. First, a single-channel algorithm was applied to estimate LST, while various spectral indices were computed to represent surface biophysical parameters, which included the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), albedo, brightness, greenness, and wetness from tasseled cap transformation. Next, a principal component analysis (PCA) was conducted to determine the degree of LST variation and the surface biophysical parameters in the temporal dimension at the pixel scale based on Landsat imagery. Finally, the relationship between the first component of the PCA of LST and each surface biophysical parameter was investigated by using the ordinary least squares (OLS) regression with both regional and local optimizations. The results indicated that among the surface biophysical parameters, variations of NDBI, wetness, and greenness had the highest impact on the LST variations with a correlation coefficient of 0.75, −0.70, and −0.44, and RMSE of 0.71, 1.03, and 1.06, respectively. The impact of NDBI, wetness, and greenness varied geographically, but their variations accounted for 43%, 38%, and 19% of the LST variation, respectively. Furthermore, the correlation coefficient and RMSE between the observed LST variation and modeled LST variation, based on the most influential biophysical factors (NDBI, wetness, and greenness) yielded 0.85 and 1.06 for the regional approach and 0.93 and 0.26 for the local approach, respectively. The results of this study indicated the use of an integrated PCA–OLS model was effective for modeling of various environmental parameters and their relationship with LST. In addition, the PCA–OLS with the local optimization was found to be more efficient than the one with the regional optimization.

Assessment of pulp and paper mill effluent quality and its toxicity to fingerlings of Cyprinus carpio

Effluents from a pulp and paper mill in India were analyzed for various physicochemical parameters and heavy metals. Multivariate statistical analysis was used to interpret the data obtained and predict the toxicity of each component to Cyprinus carpio L. fingerlings. The results showed that most of these parameters exceeded recommended levels set by various agencies, and the effluent was not suitable for discharge into agricultural fields or water bodies without further treatment. The results of static renewal toxicity tests revealed median lethal concentrations at 24, 48, 72, and 96 h to be 42, 25, 18.6 and 14.5%, respectively, and the fingerlings exhibited severe behavioral anomalies. The sublethal dose of 2.9% (v/v) caused alterations in the gill and liver at durations of 7, 14, 21, and 28 d. Anomalies such as mucous secretion, leucocyte infiltration, curling of the secondary lamellae tip, clubbing, and the fusion of the secondary lamellae, aneurism, etc. were prominent. The liver showed inflammatory infiltration, cytoplasmic inclusion bodies, vacuolar degeneration, fatty degeneration, pyknosis, karyolysis, distorted pancreatic area, and piecemeal necrosis, among other pathological symptoms. The study concluded that the effluent, even in greatly diluted form, was highly toxic and the severity of responses depended on the length of exposure.

Statistical assessment of nonpoint source pollution in agricultural watersheds in the Lower Grand River watershed, MO, USA

The water quality in many Midwestern streams and lakes is negatively impacted by agricultural activities. Although the agricultural inputs that degrade water quality are well known, the impact of these inputs varies as a function of geologic and topographic parameters. To better understand how a range of land use, geologic, and topographic factors affect water quality in Midwestern watersheds, we sampled surface water quality parameters, including nitrate, phosphate, dissolved oxygen, turbidity, bacteria, pH, specific conductance, temperature, and biotic index (BI) in 35 independent sub-watersheds within the Lower Grand River Watershed in northern Missouri. For each sub-watershed, the land use/land cover, soil texture, depth to bedrock, depth to the water table, recent precipitation area, total stream length, watershed shape/relief ratio, topographic complexity, mean elevation, and slope were determined. Water quality sampling was conducted twice: in the spring and in the late summer/early fall. A pairwise comparison of water quality parameters acquired in the fall and spring showed that each of these factors varies considerably with season, suggesting that the timing is critical when comparing water quality indicators. Correlation analysis between water quality indicators and watershed characteristics revealed that both geologic and land use characteristics correlated significantly with water quality parameters. The water quality index had the highest correlation with the biotic index during the spring, implying that the lower water quality conditions observed in the spring might be more representative of the longer-term water quality conditions in these watersheds than the higher quality conditions observed in the fall. An assessment of macroinvertebrates indicated that the biotic index was primarily influenced by nutrient loading due to excessive amounts of phosphorus (P) and nitrogen (N) discharge from agricultural land uses. The PCA analysis found a correlation between turbidity, E. coli, and BI, suggesting that livestock grazing may adversely affect the water quality in this watershed. Moreover, this analysis found that N, P, and SC contribute greatly to the observed water quality variability. The results of this study can be used to improve decision-making strategies to improve water quality for the entire river basin.

New Patterns of Temporal and Spatial Variation in Water Quality of a Highly Artificialized Urban River-Course—a Case Study in the Tongzhou Section of the Beiyun River

This paper aims to gain a better understanding of urban river pollution through evaluation of water quality. Data for 10 parameters at eight sites of the Tongzhou Section of the Beiyun River (TSBR) are analyzed. Hierarchical cluster analysis, fuzzy comprehensive assessment, discriminant analysis and Spearman’s correlation analysis were used to estimate the water situation of each cluster and analyze its spatial-temporal variations. Principal component analysis/factor analysis were applied to extract and recognize the sources responsible for water-quality variations. The results showed that temporal variation is greater than spatial and sewage discharge is the dominant factor of the seasonal distribution. Moreover, during the rapid-flow period, water quality is polluted by a combination of organic matter, phosphorus, bio-chemical pollutants and nitrogen; during the gentle-flow period, water quality is influenced by domestic and industrial waste, the activities of algae, aquatic plants and phosphorus pollution. In regard to future improvement of water quality in TSBR, the control of reclaimed wastewater from adjacent factories should first be put in place, as well as other techniques, for example, an increase of the impervious area, low-impact development, and integrated management practices should also be proposed in managing storm water runoff.

Evaluation of surface water quality by using Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) method and discriminant analysis method: a case study Coruh River Basin

In this study, the water quality of the Coruh River Basin, which is located in the Eastern Black Sea Region of Turkey, was evaluated. The water quality data measurement results obtained by the State Hydraulic Works 26th Regional Directorate from four different sites over a course of 4 years between the years 2011 and 2014 in the Coruh River Basin were used as the data. In this study, the water quality was evaluated by using the Canadian Council of Ministers of the Environmental Water Quality Index (CCME WQI) method and discriminant analysis (DA). The water quality of the Coruh River Basin was calculated as 30.4 and 71.35 by using the CCME WQI and classified as "poor," "marginal," and "fair". These values show that the water of the Coruh River Basin is degraded and under threat and its overall quality is not close to natural or desired levels. The monitoring sites were divided into two groups by the cluster analysis (CA). DA is a multivariate analysis technique used to divide individuals or objects into different groups and assign them into predetermined groups. As a result of DA, calcium (Ca) and sulfate (SO₄) were determined to be significant parameters in the determination of the water quality of the Coruh River Basin. The success of DA depends on the percentage of correct classification. As a result of the analysis, 23% of the parameters in the first measurement point, 69.2% of the parameters in the second and third measurement points, and 76.9% of the parameters in the fourth measurement point were classified correctly. Since the second measurement point is the discharge point of a copper mine, it can be said that the water quality parameters measured may provide accurate results in detecting pollution at this point.

A simple approach to estimate daily loads of total, refractory, and labile organic carbon from their seasonal loads in a watershed

Loads of naturally occurring total organic carbons (TOC), refractory organic carbon (ROC), and labile organic carbon (LOC) in streams control the availability of nutrients and the solubility and toxicity of contaminants and affect biological activities through absorption of light and complex metals with production of carcinogenic compounds. Although computer models have become increasingly popular in understanding and management of TOC, ROC, and LOC loads in streams, the usefulness of these models hinges on the availability of daily data for model calibration and validation. Unfortunately, these daily data are usually insufficient and/or unavailable for most watersheds due to a variety of reasons, such as budget and time constraints. A simple approach was developed here to calculate daily loads of TOC, ROC, and LOC in streams based on their seasonal loads. We concluded that the predictions from our approach adequately match field measurements based on statistical comparisons between model calculations and field measurements. Our approach demonstrates that an increase in stream discharge results in increased stream TOC, ROC, and LOC concentrations and loads, although high peak discharge did not necessarily result in high peaks of TOC, ROC, and LOC concentrations and loads. The approach developed herein is a useful tool to convert seasonal loads of TOC, ROC, and LOC into daily loads in the absence of measured daily load data.

Assessment of the Trophic Status of the South Lagoon of Tunis (Tunisia, Mediterranean Sea): Geochemical and Statistical Approaches

The trophic status assessment of the South Lagoon of Tunis, a shallow Mediterranean coastal area after its restoration, is addressed herein with respect to its various environmental settings which are taken as indicators of water quality. The lagoon had, in the past, witnessed severe environmental quality issues. To resolve these problems, a large restoration project of the lagoon was undertaken which consisted of dredging the bottom sediments removing areas of water stagnation and improving water circulation. After this restoration work, the lagoon morphology has radically changed. In this paper, we attempt to evaluate the lagoon water’s trophic state to analyze the eutrophication risk after almost 16 years. In order to achieve these purposes, two water quality monitoring campaigns were conducted (July 2013 and February 2014). Natural and anthropogenic factors controlling the nutrient content of the lagoon water have been assessed through both geochemical methods and multivariate statistical tools. The results show that the nutrients are from external sources due to the discharge of municipal and industrial wastewater from the surrounding city of the catchment in the lagoon’s south side. According to the TRIX index, the lagoon remains eutrophic presenting a “poor” water quality, notwithstanding the engineering project due to the high level of nutrients.

Seasonal assessment and apportionment of surface water pollution using multivariate statistical methods: Sinos River, southern Brazil

Assessment of surface water quality is an issue of currently high importance, especially in polluted rivers which provide water for treatment and distribution as drinking water, as is the case of the Sinos River, southern Brazil. Multivariate statistical techniques allow a better understanding of the seasonal variations in water quality, as well as the source identification and source apportionment of water pollution. In this study, the multivariate statistical techniques of cluster analysis (CA), principal component analysis (PCA), and positive matrix factorization (PMF) were used, along with the Kruskal-Wallis test and Spearman's correlation analysis in order to interpret a water quality data set resulting from a monitoring program conducted over a period of almost two years (May 2013 to April 2015). The water samples were collected from the raw water inlet of the municipal water treatment plant (WTP) operated by the Water and Sewage Services of Novo Hamburgo (COMUSA). CA allowed the data to be grouped into three periods (autumn and summer (AUT-SUM); winter (WIN); spring (SPR)). Through the PCA, it was possible to identify that the most important parameters in contribution to water quality variations are total coliforms (TCOLI) in SUM-AUT, water level (WL), water temperature (WT), and electrical conductivity (EC) in WIN and color (COLOR) and turbidity (TURB) in SPR. PMF was applied to the complete data set and enabled the source apportionment water pollution through three factors, which are related to anthropogenic sources, such as the discharge of domestic sewage (mostly represented by Escherichia coli (ECOLI)), industrial wastewaters, and agriculture runoff. The results provided by this study demonstrate the contribution provided by the use of integrated statistical techniques in the interpretation and understanding of large data sets of water quality, showing also that this approach can be used as an efficient methodology to optimize indicators for water quality assessment.

Influence of technological and municipal wastewaters on vulnerable karst riverine system, Krka River in Croatia

Exceptional natural value of karst rivers, as well as their potential as a source of drinking water supply, contributes to the importance of developing strict environmental protection. Although most of its watercourse is proclaimed national park, Krka River is impacted by technological and municipal wastewaters, which are released without proper purification only 2 km upstream of the park border. In order to assess water ecological status of the Krka River, vulnerability of karst ecosystem and potential threat to the Krka National Park, spatial and temporal variability of physico-chemical and microbiological water parameters, as well as concentrations of 25 total dissolved metals/metalloids were evaluated. All parameters indicated deteriorated water quality near the wastewater influences, which was for certain metals and bacterial counts even comparable to other world rivers of technological/rural catchment. Downstream location pointed to purification processes in karst river, although nutrients and conductivity were still in a range below good water quality and Al, Co, Fe, Li, Mn, Ni, Sr, Ti, and Zn levels remained comparable to their levels in the pollution impacted area, especially in autumn, indicating season-dependent processes. Presented data provided background status and identified pollution influences in the karst riverine system which indicated potential risk for protected area and a need for comprehensive water quality assessment.

Comparison of index systems for rating water quality in intermittent rivers

Water quality indexes (WQI) are a practical way to evaluate and compare the level of chemical contamination of different water bodies and to spatially and temporally compare levels of pollution. The purpose of this study was to check if these indexes are appropriate for intermittent rivers under arid and semi-arid climates. A literature review enabled the comparison of 25 water quality indexes to discern their capability to evaluate spatial (inter and intra catchment) and temporal (high and low water flow conditions) variations in water quality in three Mediterranean intermittent rivers: the River Vène (France) and the Oued Fez and the River Sebou (Morocco). Hierarchical cluster analysis identified groups of WQI with similar behavior and brought to light the 6 most distinguishing indexes. Whatever the hydrological conditions at the two sites, both the ME-MCATUHE and NCS indexes, which were developed for Morocco and Greece, and the CCMEWQI and BCWQI indexes, which were developed for non-arid or semi-arid zones, gave appropriate water quality evaluations.

Multi-Elements in Source Water (Drinking and Surface Water) within Five Cities from the Semi-Arid and Arid Region, NW China: Occurrence, Spatial Distribution and Risk Assessment

The purpose of this study was to identify the concentration of multi-elements (MEs) in source water (surface and drinking water) and assess their quality for sustainability. A total of 161 water samples including 88 tap drinking waters (DW) and 73 surface waters (SW) were collected from five cities in Xi’an, Yan’an, Xining, Lanzhou, and Urumqi in northwestern China. Eighteen parameters including pH, electrical conductivity (EC), total organic carbon (TOC) total nitrogen (TN), chemical compositions of anions (F⁻, Cl⁻, NO₃⁻, HCO₃⁻, SO₄²⁻), cations (NH₄⁺, K⁺, Na⁺, Ca²⁺, Mg²⁺), and metals (lead (Pb), chromium (Cr), cadmium (Cd), copper (Cu)) were analyzed in the first time at the five cities . The results showed that pH values and concentrations of Cl⁻, SO₄²⁻, Na⁺, K⁺, Ca²⁺, Mg²⁺ and Cd, Cr, Cu in DW were within the permissible limits of the Chinese Drinking Water Quality Criteria, whereas the concentrations of other ions (F⁻, NO₃⁻, NH₄⁺ and Pb) exceeded their permissible values. In terms of the SW, the concentrations of F⁻, Cl⁻, NO₃⁻, SO₄²⁻ were over the third range threshold i.e., water suitable for fishing and swimming of the Surface Water Quality Standards in China. The spatial distributions of most MEs in source water are similar, and there was no clear variation for all ions and metals. The metals in DW may be caused by water pipes, faucets and their fittings. The noncarcinogenic risk of metals in DW for local children are in decreasing order Cr > Cd > Pb > Cu. The carcinogenic risk from Cr exposure was at the acceptable level according to threshold of USEPA. Although the comprehensive index of potential ecological assessment of Cr, Cd, Pb and Cu in SW ranked at low risk level and was in the order of Huang River in Xining > Peaceful Canal in Urumqi > Yan River in Yan’an > Yellow River in Lanzhou, their adverse effects to ecology and human health at a low concentration in local semi-arid and arid areas should not be ignored in the long run.

Assessment of the water quality monitoring network of the Piabanha River experimental watersheds in Rio de Janeiro, Brazil, using autoassociative neural networks

Water quality monitoring is a complex issue that requires support tools in order to provide information for water resource management. Budget constraints as well as an inadequate water quality network design call for the development of evaluation tools to provide efficient water quality monitoring. For this purpose, a nonlinear principal component analysis (NLPCA) based on an autoassociative neural network was performed to assess the redundancy of the parameters and monitoring locations of the water quality network in the Piabanha River watershed. Oftentimes, a small number of variables contain the most relevant information, while the others add little or no interpretation to the variability of water quality. Principal component analysis (PCA) is widely used for this purpose. However, conventional PCA is not able to capture the nonlinearities of water quality data, while neural networks can represent those nonlinear relationships. The results presented in this work demonstrate that NLPCA performs better than PCA in the reconstruction of the water quality data of Piabanha watershed, explaining most of data variance. From the results of NLPCA, the most relevant water quality parameter is fecal coliforms (FCs) and the least relevant is chemical oxygen demand (COD). Regarding the monitoring locations, the most relevant is Poço Tarzan (PT) and the least is Parque Petrópolis (PP).

The Impact of Cropland Balance Policy on Ecosystem Service of Water Purification—A Case Study of Wuhan, China

Urbanization has been responsible for the loss of cropland worldwide, especially in China. Since this trend is expected to continue in the near future, China has implemented the strictest cropland protection policies in the world, to guarantee its national food security. However, the negative impact of cropland protection policies on ecosystem services has always been ignored. In this paper, we used LANDSCAPE (Land System Cellular Automata model for Potential Effects) model to assess the ecological lands loss under different scenarios in Wuhan, China during S2010–2020. Our scenarios differ in whether or not the cropland protection policy is imposed. Then, the InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model was used to calculate the amount of nutrient export under two different scenarios and to analyze the mechanism of impact of Cropland Balance Policy on water purification. Results show that the scenarios with strict cropland protection (CP) will lead to more losses of ecological lands compared with scenarios without cropland protection (NCP). Besides, the nitrogen export in the CP scenario is average 8.6% higher than the NCP scenario, which indicates that the Cropland Balance Policy has a negative impact on water purification. The nitrogen export is transported mainly by subsurface, which is 1.73 times higher than the surface averaged over the two scenarios. Accordingly, this study proposed that reasonable land use planning, and lowering the nutrient delivery ratio would be more beneficial to the ecosystem service of water purification.

Seasonally related effects on natural organic matter characteristics from source to tap in Korea

In this study, natural organic matter (NOM) characteristics were investigated over three years of monthly monitoring to determine the effect of seasonal variations on NOM levels from source to tap. Liquid chromatography with organic carbon detection (LC-OCD) was used to determine NOM characteristics and the level of reduction of biodegradable dissolved organic carbon (BDOC). The average dissolved organic matter concentration in the source water (Lake Paldang, Korea) was not significantly different between summer and winter. However, the distribution of NOM components, such as biopolymers, building blocks, low molecular weight (MW) neutrals and acids, identified by LC-OCD, varied seasonally. While high MW NOM was preferentially removed by coagulation/sedimentation/rapid sand filtration (CSR), no seasonal effects were observed on the removal of high MW NOM. CSR and biological activated carbon (BAC) filtration showed a better efficiency of BDOC removal in winter and summer, respectively. High concentrations of chlorine used in the treatment plants in summer resulted in 10% higher DOC concentrations during disinfection. Overall NOM removal efficiencies from source to tap were 45% and 35% for summer and winter, respectively. Principal component analysis also indicated that seasonal variations (principal component 1) showed the strongest positive correlation with the overall performance of water treatment. The long-term monitoring of drinking water treatment processes showed that seasonal variations were important factors affecting NOM characteristics during water treatment.

Spatial and Seasonal Surface Water Quality Assessment in a Tropical Urban Catchment: Burío River, Costa Rica

Water quality assessments are essential for providing information regarding integrated water resource management processes. This study presents the results of a spatial and seasonal surface water quality assessment of the Burío river sub-catchment in Costa Rica. Fourteen sample campaigns were conducted at eight sample sites between 2005 and 2010. Seasonal variations were evaluated using linear mixed-effects models where dissolved oxygen, total solids, and nitrate showed significant differences between dry and wet seasons (p < 0.05). Cluster analysis identified three clusters at the top, middle, and bottom of the catchment that were consistent with land use patterns, and principal component analysis identified the main parameters that were affecting 84% of the total variance in water quality (biochemical oxygen demand, dissolved oxygen, total phosphate, and nitrate). The National Sanitation Foundation Water Quality Index (NSF-WQI) results indicated the majority of the river consisted of mainly “medium” water quality, although “bad” and “good” water quality results were identified depending on sample site and season. This methodological approach provides a useful monitoring technique for local governments that can be used for further remediation strategies.

Dynamic water quality evaluation based on fuzzy matter-element model and functional data analysis, a case study in Poyang Lake

Comprehensively evaluating water quality with a single method alone is challenging because water quality evaluation involves complex, uncertain, and fuzzy processes. Moreover, water quality evaluation is limited by finite water quality monitoring that can only represent water quality conditions at certain time points. Thus, the present study proposed a dynamic fuzzy matter-element model (D-FME) to comprehensively and continuously evaluate water quality status. D-FME was first constructed by introducing functional data analysis (FDA) theory into a fuzzy matter-element model and then validated using monthly water quality data for the Poyang Lake outlet (Hukou) from 2011 to 2012. Results showed that the finite water quality indicators were represented as dynamic functional curves despite missing values and irregular sampling time. The water quality rank feature curve was integrated by the D-FME model and revealed comprehensive and continuous variations in water quality. The water quality in Hukou showed remarkable seasonal variations, with the best water quality in summer and worst water quality in winter. These trends were significantly correlated with water level fluctuations (R = -0.71, p < 0.01). Moreover, the extension weight curves of key indicators indicated that total nitrogen and total phosphorus were the most important pollutants that influence the water quality of the Poyang Lake outlet. The proposed D-FME model can obtain scientific and intuitive results. Moreover, the D-FME model is not restricted to water quality evaluation and can be readily applied to other areas with similar problems.

Natural and anthropic effects on hydrochemistry and major and trace elements in the water mass of a Spanish Pyrenean glacial lake set

This study presents the key hydrochemical characteristics and concentration levels of major (Ca, Mg, Na, Si, K, Sr, Fe) and trace (Ba, Sc, Cr, Mn, Al, As, Li, Co, Cu, U, Pb, Hg, Au, Sn, Zn, Cd, Ag, Ni) elements in the water mass of four selected Pyrenean cirque glacial lakes (Sabocos, Baños, Truchas and Escalar tarns) with different catchment features, between 2010 and 2013. Resulting data set is statistically analyzed to discriminate between the natural or anthropic origin of the elements. Analyses indicate that in all cases, the main source of most major and trace elements is geological weathering, being thus individual bedrock composition the main driver of differences between lakes. Several anthropogenic sources of airborne Cu, Sc, Co, and Cr must be also considered. The shallowness of the lake is also a factor that may influence element cycling and concentration levels in its water mass. Concentrations of anthropogenic elements were low, comparable to those reported in other glacial lakes, way below the WHO, US EPA, EC, and Spanish legal limits for drinking water quality, indicating the absence of serious pollution. Toxic heavy metals Cd, Pb, Hg, and Zn were not detected in any of the tarns.

Multi-phase distribution and comprehensive ecological risk assessment of heavy metal pollutants in a river affected by acid mine drainage

To date, there is a lack of a comprehensive research on heavy metals detection and ecological risk assessment in river water, sediments, pore water (PW) and suspended solids (SS). Here, the concentrations of heavy metals, including Cu, Zn, Mn, Cd, Pb and As, and their distribution between the four phases was studied. Samples for analysis were taken from twelve sites of the Hengshi River, Guangdong Province, China, during the rainy and dry seasons. A new comprehensive ecological risk index (CERI) based on considering metal contents, pollution indices, toxicity coefficients and water categories is offered for prediction of potential risk on aquatic organisms. The results of comprehensive analysis showed that the highest concentrations of Cu, Zn and Mn of 6.42, 87.17 and 98.74mg/L, respectively, in PW were comparable with those in water, while concentrations of Cd, Pb and As of 609.5, 2757 and 96.38μg/L, respectively, were 2-5 times higher. The sum of the exchangeable and carbonate fractions of target metals in sediments followed the order of Cd > Mn > Zn > Pb > Cu > As. The distribution of heavy metals in phases followed the order of sediment > SS > water > PW, having the sum content in water and PW lower than 2% of total. The elevated ecological risk for a single metal and the phase were 34,585 for Cd and 1160 for water, respectively, implied Cd as a priority pollutant in the considered area. According to the CERI, the maximum risk value of 769.3 was smaller than 1160 in water, but higher than those in other phases. Out of considering the water categories and contribution coefficients, the CERI was proved to be more reliable for assessing the pollution of rivers with heavy metals. These results imply that the CERI has a potential of adequate assessment of multi-phase composite metals pollution.

Studying Water Quality Using Socio-Environmental Synthesis Approach: A Case Study in Baltimore’s Watershed

While almost 87% of the world’s population now has access to an improved drinking water source, the risk of water pollution remains, often due to environmental factors such as increasing urbanization and industrialization. Last year, as the country watched the tragic Flint, Michigan tap water quality deterioration unfold, the issue was brought closer to home: How good is Baltimore’s water system? Baltimore’s water source is primarily surface water, which feeds into the Liberty, Loch Raven and Prettyboy reservoirs. The Socio-Environmental Synthesis (SES) approach was used to investigate the factors that contribute to water quality impairment. SES is a research method that integrates existing knowledge and data from natural and social sciences to advance understanding of socio-environmental systems. The study found out that while the quality of the drinking water is generally good, there is a growing concern with the quality of water in the watersheds. The high levels of nitrate-nitrogen and increased concentrations of carbon dioxide are especially alarming. The high levels of Biological Oxygen Demand are also good indicators of the intensity of agriculture and urbanization in the watersheds. This study believes that maximizing the current watershed conservation and restoration efforts would reduce the treatment costs and safeguard the urban water supplies.

Assessment of water quality using multivariate techniques in River Sosiani, Kenya

Multivariate techniques can infer intrinsic characteristics of complex data by generating correlation, similarity, dissimilarity, and covariance vector matrix to ascertain their relationships. The study evaluated the effect of anthropogenic activities by analyzing selected physicochemical water quality parameters (WQP) as indicators of pollution in River Sosiani, located in western Kenya, at six stations from August 2012 to February 2013 (Aug-Oct ≡ wet season, Nov-Feb ≡ Dry season). Temperature, pH, Total Dissolved Solids (TDS), conductivity, and Dissolved Oxygen (DO) were measured in situ while Total Phosphorus (TP), Total Organic Nitrogen (TON), and Biologial Oxygen Demand (BOD) were measured in vitro using standard methods. Except for DO and pH, the other variables were increasing in concentration downstream. Cluster analysis grouped stations with municipal discharge, to be the most distant linked to other stations in both seasons. Multidimensional scaling had four categories of stations with similar WQP: before, after, and wet and dry seasons. Principal component analysis with (60.5 and 26.1% for components 1 and 2) evaluated TON and TP as key pollutants in both seasons. Factor analysis with varifactor two at 35.3 and 27.1% variance in wet and dry seasons, respectively, had strong absolute factor loading of BOD (wet 0.878, dry 0.915) and TP (wet 0.839, dry 0.709) inferring sites with organic pollution also had nutrient pollution. Assessment of pollution with the selected WQP identified two major effects: nutrient and organic. Additional variables may identify other pollutants along the river. Multiple pollution effects, changing environment, and intrinsic characteristics of aquatic ecosystems generate complex data which are better assessed with multivariate techniques.

Using multivariate techniques to assess the effects of urbanization on surface water quality: a case study in the Liangjiang New Area, China

Rapid urbanization in China has been causing dramatic deterioration in the water quality of rivers and threatening aquatic ecosystem health. In this paper, multivariate techniques, such as factor analysis (FA) and cluster analysis (CA), were applied to analyze the water quality datasets for 19 rivers in Liangjiang New Area (LJNA), China, collected in April (dry season) and September (wet season) of 2014 and 2015. In most sampling rivers, total phosphorus, total nitrogen, and fecal coliform exceeded the Class V guideline (GB3838-2002), which could thereby threaten the water quality in Yangtze and Jialing Rivers. FA clearly identified the five groups of water quality variables, which explain majority of the experimental data. Nutritious pollution, seasonal changes, and construction activities were three key factors influencing rivers' water quality in LJNA. CA grouped 19 sampling sites into two clusters, which located at sub-catchments with high- and low-level urbanization, respectively. One-way ANOVA showed the nutrients (total phosphorus, soluble reactive phosphorus, total nitrogen, ammonium nitrogen, and nitrite), fecal coliform, and conductivity in cluster 1 were significantly greater than in cluster 2. Thus, catchment urbanization degraded rivers' water quality in Liangjiang New Area. Identifying effective buffer zones at riparian scale to weaken the negative impacts of catchment urbanization was recommended.

Spatial assessment of water quality parameters in Jhelum city (Pakistan)

In this study, we assess the drinking water quality of Jhelum city. Two hundred and ninety-two drinking water samples were randomly collected in the study area. These samples were chemically analyzed for three key toxic (in excess) elements such as pH, total dissolved solids (TDS), and calcium. Geostatistical techniques such as variogram and kriging were used to investigate the spatial variations of these minerals across the city. The spatial structure for each element was found to be anisotropic, and thus, anisotropic variograms were used. The kriging predictions revealed significant concentrations of the above-stated elements at some locations in the study area. While comparing with the World Health Organization, United States Environmental Protection Agency, and Pakistan Environmental Protection Agency standards, the water samples were found to be unsatisfactory for drinking. We conclude that the drinking water in this region is of poor quality and needs proper treatment to make it palatable.

Characterizing and explaining spatio-temporal variation of water quality in a highly disturbed river by multi-statistical techniques

Assessing the spatio-temporal variations of surface water quality is important for water environment management. In this study, surface water samples are collected from 2008 to 2015 at 17 stations in the Ying River basin in China. The two pollutants i.e. chemical oxygen demand (COD) and ammonia nitrogen (NH3-N) are analyzed to characterize the river water quality. Cluster analysis and the seasonal Kendall test are used to detect the seasonal and inter-annual variations in the dataset, while the Moran's index is utilized to understand the spatial autocorrelation of the variables. The influence of natural factors such as hydrological regime, water temperature and etc., and anthropogenic activities with respect to land use and pollutant load are considered as driving factors to understand the water quality evolution. The results of cluster analysis present three groups according to the similarity in seasonal pattern of water quality. The trend analysis indicates an improvement in water quality during the dry seasons at most of the stations. Further, the spatial autocorrelation of water quality shows great difference between the dry and wet seasons due to sluices and dams regulation and local nonpoint source pollution. The seasonal variation in water quality is found associated with the climatic factors (hydrological and biochemical processes) and flow regulation. The analysis of land use indicates a good explanation for spatial distribution and seasonality of COD at the sub-catchment scale. Our results suggest that an integrated water quality measures including city sewage treatment, agricultural diffuse pollution control as well as joint scientific operations of river projects is needed for an effective water quality management in the Ying River basin.

Zebrafish as a possible bioindicator of organic pollutants in drinking waters with effects on reproduction: are effects cumulative or reversible?

Organic pollutants are present in drinking waters due to inefficient detection and removal treatments. For this reason, zebrafish is proposed as a complementary indicator in conventional potabilization treatments. Based on the most sensitive parameters detected in our previous work, in this study we attempted to examine the possible cumulative effect between generations of environmental pollutants likely present in drinking waters, when specimens were cultured in the same water and/or the possible reversibility of these effects when cultured in control water. To this end, embryos with the chorion intact were cultured in three drinking waters from different sources and in one control water for up to 5 months in 20 l glass tanks. Four replicates were performed in all water groups. Results in water group C (tap water from a city also located in a region with intensive agricultural activity, but from the hydrological basin of the river Xúquer) revealed a non-reversible effect on fertility rate. Also in water C there was an alteration of sex ratio towards females, although in this case the alteration was reversible. A transgenerational alteration in the germ-line via an epigenetic mechanism from the previous generation is proposed as the most plausible explanation of this effect.

Seasonal rationalization of river water quality sampling locations: a comparative study of the modified Sanders and multivariate statistical approaches

The design of surface water quality sampling location is a crucial decision-making process for rationalization of monitoring network. The quantity, quality, and types of available dataset (watershed characteristics and water quality data) may affect the selection of appropriate design methodology. The modified Sanders approach and multivariate statistical techniques [particularly factor analysis (FA)/principal component analysis (PCA)] are well-accepted and widely used techniques for design of sampling locations. However, their performance may vary significantly with quantity, quality, and types of available dataset. In this paper, an attempt has been made to evaluate performance of these techniques by accounting the effect of seasonal variation, under a situation of limited water quality data but extensive watershed characteristics information, as continuous and consistent river water quality data is usually difficult to obtain, whereas watershed information may be made available through application of geospatial techniques. A case study of Kali River, Western Uttar Pradesh, India, is selected for the analysis. The monitoring was carried out at 16 sampling locations. The discrete and diffuse pollution loads at different sampling sites were estimated and accounted using modified Sanders approach, whereas the monitored physical and chemical water quality parameters were utilized as inputs for FA/PCA. The designed optimum number of sampling locations for monsoon and non-monsoon seasons by modified Sanders approach are eight and seven while that for FA/PCA are eleven and nine, respectively. Less variation in the number and locations of designed sampling sites were obtained by both techniques, which shows stability of results. A geospatial analysis has also been carried out to check the significance of designed sampling location with respect to river basin characteristics and land use of the study area. Both methods are equally efficient; however, modified Sanders approach outperforms FA/PCA when limited water quality and extensive watershed information is available. The available water quality dataset is limited and FA/PCA-based approach fails to identify monitoring locations with higher variation, as these multivariate statistical approaches are data-driven. The priority/hierarchy and number of sampling sites designed by modified Sanders approach are well justified by the land use practices and observed river basin characteristics of the study area.

Spatial patterns of water quality in Xingu River Basin (Amazonia) prior to the Belo Monte dam impoundment

The Xingu River, one of the most important of the Amazon Basin, is characterized by clear and transparent waters that drain a 509.685 km2 watershed with distinct hydrological and ecological conditions and anthropogenic pressures along its course. As in other basins of the Amazon system, studies in the Xingu are scarce. Furthermore, the eminent construction of the Belo Monte for hydropower production, which will alter the environmental conditions in the basin in its lower middle portion, denotes high importance of studies that generate relevant information that may subsidize a more balanced and equitable development in the Amazon region. Thus, the aim of this study was to analyze the water quality in the Xingu River and its tributaries focusing on spatial patterns by the use of multivariate statistical techniques, identifying which water quality parameters were more important for the environmental changes in the watershed. Data sampling were carried out during two complete hydrological cycles in twenty-five sampling stations. The data of twenty seven variables were analyzed by Spearman's correlation coefficients, cluster analysis (CA), and principal component analysis (PCA). The results showed a high auto-correlation between variables (> 0.7). These variables were removed from multivariate analyzes because they provided redundant information about the environment. The CA resulted in the formation of six clusters, which were clearly observed in the PCA and were characterized by different water quality. The statistical results allowed to identify a high spatial variation in the water quality, which were related to specific features of the environment, different uses, influences of anthropogenic activities and geochemical characteristics of the drained basins. It was also demonstrated that most of the sampling stations in the Xingu River basin showed good water quality, due to the absence of local impacts and high power of depuration of the river itself.

Characterization of spatial and temporal variability in hydrochemistry of Johor Straits, Malaysia

Characterization of hydrochemistry changes in Johor Straits within 5 years of monitoring works was successfully carried out. Water quality data sets (27 stations and 19 parameters) collected in this area were interpreted subject to multivariate statistical analysis. Cluster analysis grouped all the stations into four clusters ((Dlink/Dmax) × 100<90) and two clusters ((Dlink/Dmax) × 100<80) for site and period similarities. Principal component analysis rendered six significant components (eigenvalue>1) that explained 82.6% of the total variance of the data set. Classification matrix of discriminant analysis assigned 88.9-92.6% and 83.3-100% correctness in spatial and temporal variability, respectively. Times series analysis then confirmed that only four parameters were not significant over time change. Therefore, it is imperative that the environmental impact of reclamation and dredging works, municipal or industrial discharge, marine aquaculture and shipping activities in this area be effectively controlled and managed.

Development, application, and sensitivity analysis of a water quality index for drinking water management in small systems

The aim of this study was to produce a drinking water assessment tool for operators of small distribution systems. A drinking water quality index (DWQI) was developed and applied to small systems based on the water quality index of the Canadian Council of Ministers of Environment. The drinking water quality index was adapted to specific needs by creating four drinking water quality scenarios. First, the temporal and spatial dimensions of drinking water quality variability were taken into account. The DWQI was designed to express global drinking water quality according to different monitoring frequencies. Daily, monthly, and seasonal assessment was also considered. With the data made available, it was possible to use the index as a spatial monitoring tool and express water quality in different points in the distribution system. Moreover, adjustments were made to prioritize the type of contaminant to monitor. For instance, monitoring contaminants with acute health effects led to a scenario based on daily measures, including easily accessible and affordable water quality parameters. On the other hand, contaminants with chronic effects, especially disinfection by-products, were considered in a seasonal monitoring scenario where disinfection by-product reference values were redefined according to their seasonal variability. A sensitivity analysis was also carried out to validate the index. Globally, the DWQI developed is adapted to the needs of small systems. In fact, expressing drinking water quality using the DWQI contributes to the identification of problematic periods and segments in the distribution system. Further work may include this index in the development of a customized decision-making tool for small-system operators and managers.

Vulnerability assessment and application of bacterial technology on urban rivers for pollution eradication

To protect against the environmental pollution, the present research was undertaken to enumerate the Bacterial Technologies (BTs) on the restoration of polluted urban rivers, that is, Fenghu-Song Yang River (FSR) and Xuxi River (XXR). Experimental research accounted for the physiochemical parameters (pH; temperature; dissolved oxygen (DO); chemical oxygen demand (COD); total phosphorus (TP); total nitrogen (TN); and ammonia nitrogen (NH₃N)) before and after the BT operation. The results declared that the BT is efficient to restore the polluted rivers up to reliable condition. These results were analyzed by using multivariate statistical techniques (principal component analysis (PCA) and cluster analysis (CA)). These techniques interpreted the complex data sets and expressed the point source information about the water quality of these rivers at SA5, SA6, and SB3 under highly polluted regions. For better understanding, water quality index (WQI) was applied to compute the single numeric value. WQI results are evidence of the above results which prove the water quality of both rivers faced under outrageous condition (below 50 WQI scores) before the BT treatment, but, after the treatment, the rivers were restored from fair to good level (above 50 WQI scores) and overall output of these scores was quite similar to detect the point source of pollution. These results described an abrupt recovery of the urban rivers up to reliable condition for aquatic organism and clear effluents from the rivers.

Identification of key factors affecting the water pollutant concentration in the sluice-controlled river reaches of the Shaying River in China via statistical analysis methods

The construction of sluices creates a strong disturbance in water environmental factors within a river. The change in water pollutant concentrations of sluice-controlled river reaches (SCRRs) is more complex than that of natural river segments. To determine the key factors affecting water pollutant concentration changes in SCRRs, river reaches near the Huaidian Sluice in the Shaying River of China were selected as a case study, and water quality monitoring experiments based on different regulating modes were implemented in 2009 and 2010. To identify the key factors affecting the change rates for the chemical oxygen demand of permanganate (CODMn) and ammonia nitrogen (NH3-N) concentrations in the SCRRs of the Huaidian Sluice, partial correlation analysis, principal component analysis and principal factor analysis were used. The results indicate four factors, i.e., the inflow quantity from upper reaches, opening size of sluice gates, water pollutant concentration from upper reaches, and turbidity before the sluice, which are the common key factors for the CODMn and NH3-N concentration change rates. Moreover, the dissolved oxygen before a sluice is a key factor for the permanganate concentration from CODMn change rate, and the water depth before a sluice is a key factor for the NH3-N concentration change rate. Multiple linear regressions between the water pollutant concentration change rate and key factors were established via multiple linear regression analyses, and the quantitative relationship between the CODMn and NH3-N concentration change rates and key affecting factors was analyzed. Finally, the mechanism of action for the key factors affecting the water pollutant concentration changes was analyzed. The results reveal that the inflow quantity from upper reaches, opening size of sluice gates, permanganate concentration from CODMn from upper reaches and dissolved oxygen before the sluice have a negative influence and the turbidity before the sluice has a positive influence on the permanganate concentration from CODMn change rates and that the opening size of sluice gates, NH3-N concentration from upper reaches, and water depth before the sluice have a negative influence and the inflow quantity from upper reaches and turbidity before the sluice have a positive influence on the NH3-N concentration change rates, which provides a scientific grounding for pollution control and sluice operations in SCRRs.

Design of sampling locations for river water quality monitoring considering seasonal variation of point and diffuse pollution loads

The design of a water quality monitoring network (WQMN) is a complicated decision-making process because each sampling involves high installation, operational, and maintenance costs. Therefore, data with the highest information content should be collected. The effect of seasonal variation in point and diffuse pollution loadings on river water quality may have a significant impact on the optimal selection of sampling locations, but this possible effect has never been addressed in the evaluation and design of monitoring networks. The present study proposes a systematic approach for siting an optimal number and location of river water quality sampling stations based on seasonal or monsoonal variations in both point and diffuse pollution loadings. The proposed approach conceptualizes water quality monitoring as a two-stage process; the first stage of which is to consider all potential water quality sampling sites, selected based on the existing guidelines or frameworks, and the locations of both point and diffuse pollution sources. The monitoring at all sampling sites thus identified should be continued for an adequate period of time to account for the effect of the monsoon season. In the second stage, the monitoring network is then designed separately for monsoon and non-monsoon periods by optimizing the number and locations of sampling sites, using a modified Sanders approach. The impacts of human interventions on the design of the sampling net are quantified geospatially by estimating diffuse pollution loads and verified with land use map. To demonstrate the proposed methodology, the Kali River basin in the western Uttar Pradesh state of India was selected as a study area. The final design suggests consequential pre- and post-monsoonal changes in the location and priority of water quality monitoring stations based on the seasonal variation of point and diffuse pollution loadings.

Seasonal variation of fecal contamination in drinking water sources in developing countries: a systematic review

Accounting for fecal contamination of drinking water sources is an important step in improving monitoring of global access to safe drinking water. Fecal contamination varies with time while its monitoring is often infrequent. We sought to understand seasonal trends in fecal contamination to guide best practices to capture seasonal variation and ascertain the extent to which the results of a single sample may overestimate compliance with health guidelines. The findings from 22 studies from developing countries written in English and identified through a systematic review were analyzed. Fecal contamination in improved drinking water sources was shown to follow a statistically significant seasonal trend of greater contamination during the wet season (p<0.001). This trend was consistent across fecal indicator bacteria, five source types, twelve Köppen-Geiger climate zones, and across both rural and urban areas. Guidance on seasonally representative water quality monitoring by the World Health Organization and national water quality agencies could lead to improved assessments of access to safe drinking water.

Evaluation of river water quality variations using multivariate statistical techniques: Sava River (Croatia): a case study

For the evaluation of seasonal and spatial variations and the interpretation of a large and complex water quality dataset obtained during a 7-year monitoring program of the Sava River in Croatia, different multivariate statistical techniques were applied in this study. Basic statistical properties and correlations of 18 water quality parameters (variables) measured at 18 sampling sites (a total of 56,952 values) were examined. Correlations between air temperature and some water quality parameters were found in agreement with the previous studies of relationship between climatic and hydrological parameters. Principal component analysis (PCA) was used to explore the most important factors determining the spatiotemporal dynamics of the Sava River. PCA has determined a reduced number of seven principal components that explain over 75 % of the data set variance. The results revealed that parameters related to temperature and organic pollutants (CODMn and TSS) were the most important parameters contributing to water quality variation. PCA analysis of seasonal subsets confirmed this result and showed that the importance of parameters is changing from season to season. PCA of the four seasonal data subsets yielded six PCs with eigenvalues greater than one explaining 73.6 % (spring), 71.4 % (summer), 70.3 % (autumn), and 71.3 % (winter) of the total variance. To check the influence of the outliers in the data set whose distribution strongly deviates from the normal one, in addition to standard principal component analysis algorithm, two robust estimates of covariance matrix were calculated and subjected to PCA. PCA in both cases yielded seven principal components explaining 75 % of the total variance, and the results do not differ significantly from the results obtained by the standard PCA algorithm. With the implementation of robust PCA algorithm, it is demonstrated that the usage of standard algorithm is justified for data sets with small numbers of missing data, nondetects, and outliers (less than 4 %). The clustering procedure highlighted four different groups in which the sampling sites have similar characteristics and pollution levels. The first and the second group correspond to relatively low and moderately polluted sites while stations which are located in the middle of the river belong to the third and fourth group and correspond to highly and moderately polluted sites.

Lethal and sublethal effects of selected PPCPs on the freshwater rotifer, Plationus patulus

Pharmaceuticals and personal care products (PPCPs) have been reported in surface waters around the world. The continuous input of these pollutants into freshwaters and their potential effects on aquatic life are of increasing concern. The rotifer Plationus patulus, a basal member of riverine food webs, was used to test acute and chronic toxicity of 4 PPCPs (acetamidophenol, caffeine, fluoxetine, triclosan). A population from a remote site in Mexico (reference population) and one from an urbanized stretch of the Rio Grande were exposed. Acute toxicity tests show that both populations were more sensitive to fluoxetine. Chronic exposure to acetamidophenol (10 mg/L, 15 mg/L, and 20 mg/L) inhibited reference population growth, whereas Rio Grande population growth was inhibited only at 15 mg/L and 20 mg/L. Population growth was inhibited at 200 mg/L and 300 mg/L of caffeine for both populations. Chronic exposure to fluoxetine (0.020 mg/L) significantly inhibited population growth for the Rio Grande population only. Triclosan (0.05 mg/L, 0.075 mg/L, 0.10 mg/L) had the most deleterious effects, significantly reducing both populations' growth rates. Sublethal effects of chronic exposure to PPCPs included decreased egg production and increased egg detachment. A mixed exposure (6 PPCPs, environmentally relevant concentrations) did not affect population growth in either population. However, the continuous introduction of a broad suite of PPCPs to aquatic ecosystems still may present a risk to aquatic communities. Environ Toxicol Chem 2015;34:913-922. © 2014 SETAC.

Modeling water quality in an urban river using hydrological factors--data driven approaches

Contrasting seasonal variations occur in river flow and water quality as a result of short duration, severe intensity storms and typhoons in Taiwan. Sudden changes in river flow caused by impending extreme events may impose serious degradation on river water quality and fateful impacts on ecosystems. Water quality is measured in a monthly/quarterly scale, and therefore an estimation of water quality in a daily scale would be of good help for timely river pollution management. This study proposes a systematic analysis scheme (SAS) to assess the spatio-temporal interrelation of water quality in an urban river and construct water quality estimation models using two static and one dynamic artificial neural networks (ANNs) coupled with the Gamma test (GT) based on water quality, hydrological and economic data. The Dahan River basin in Taiwan is the study area. Ammonia nitrogen (NH3-N) is considered as the representative parameter, a correlative indicator in judging the contamination level over the study. Key factors the most closely related to the representative parameter (NH3-N) are extracted by the Gamma test for modeling NH3-N concentration, and as a result, four hydrological factors (discharge, days w/o discharge, water temperature and rainfall) are identified as model inputs. The modeling results demonstrate that the nonlinear autoregressive with exogenous input (NARX) network furnished with recurrent connections can accurately estimate NH3-N concentration with a very high coefficient of efficiency value (0.926) and a low RMSE value (0.386 mg/l). Besides, the NARX network can suitably catch peak values that mainly occur in dry periods (September-April in the study area), which is particularly important to water pollution treatment. The proposed SAS suggests a promising approach to reliably modeling the spatio-temporal NH3-N concentration based solely on hydrological data, without using water quality sampling data. It is worth noticing that such estimation can be made in a much shorter time interval of interest (span from a monthly scale to a daily scale) because hydrological data are long-term collected in a daily scale. The proposed SAS favorably makes NH3-N concentration estimation much easier (with only hydrological field sampling) and more efficient (in shorter time intervals), which can substantially help river managers interpret and estimate water quality responses to natural and/or manmade pollution in a more effective and timely way for river pollution management.

Heavy metal pollution and health risk assessment in the Wei River in China

From data collected monthly at 26 monitoring cross sections in the Wei River in the Shaanxi Region of China during the period 2008-2012, the temporal pollution characteristics of heavy metals (Hg, Cd, Cr(VI), Pb, and As) were analyzed based on a heavy metal pollution index (HPI). The monthly HPI values of the five heavy metals in the river fluctuated greatly in 2008 and then declined gradually with time. This general trend of reduction in HPI appears not to have a seasonal variation and most likely resulted from the continued improvement in heavy metal pollution control strategies implemented by local environmental agencies combined with a significant improvement in wastewater treatment capacities. Among the five heavy metals, Cd and Pb were below 0.1 and 3 μg L(-1), respectively, at all the sampling points in the studied areas in the year 2012. The detection rates of As, Hg, and Cr(VI) were in the order of Hg > Cr(VI) > As. Hg, Cr(VI), and As exceeded, in a month of the dry season in 2012, the standard limits for category III surface waters according to the China Environment Quality Standards for Surface Water (CEQSSW). Based on the assessment using the HPI method, the pollution status of these heavy metals in water of the Wei River in the Shaanxi Region was generally at an acceptable level, but exhibited distinctive characteristics between the main stream river and tributaries. Most of the tributaries were more seriously polluted than the main river. A health risk assessment was conducted based on the Human Health Risk Assessment (HHRA) method recommended by the United States Environmental Protection Agency (USEPA). Apart from As, the health risk for the five heavy metals in the region were at acceptable levels for drinking water sources (hazard quotient (HQ) < 1, carcinogenic risk (CR) ranged from 10(-4)-10(-6)) according to the Risk Assessment Guidance for Superfund (RAGS), USEPA. Arsenic was identified as the most important pollutant of concern among the five heavy metals; both its values of the HQ and CR indicated potentially adverse health risks for the local population.

Seasonal and spatial variations of source and drinking water quality in small municipal systems of two Canadian regions

A one-year sampling program covering twenty-five small municipal systems was carried out in two Canadian regions to improve our understanding of the variability of water quality in small systems from water source to the end of the distribution system (DS). The database obtained was used to develop a global portrait of physical, chemical and microbiological water quality parameters. More precisely, the temporal and the spatial variability of these parameters were investigated. We observed that the levels of natural organic matter (NOM) were variable during different seasons, with maxima in the fall for both provinces. In the regions under study, the highest trihalomethane (THM) and haloacetic acid (HAA) levels were achieved in warmer seasons (summer, fall), as observed in previous studies involving large systems. Observed THM and HAA levels were three times higher in systems in the province of Newfoundland & Labrador than in the province of Quebec. Taste and odor indicators were detected during the summer and fall, and higher heterotrophic plate count (HPC) levels were associated with lower free chlorine levels. To determine spatial variations, stepwise statistical analysis was used to identify parameters and locations in the DS that act as indicators of drinking water quality. As observed for medium and large systems, free chlorine consumption, THM and HAA levels were dependent on their location in the DS. We also observed that the degradation of HAAs is more important in small systems than in medium or large DS reported in the literature, and this degradation can occur from the beginning of the DS. The results of this research may contribute to providing precious information on drinking water quality to small system operators and pave the way for several opportunities to improve water quality management.

Use of multivariate statistical techniques for the evaluation of temporal and spatial variations in water quality of the Kaduna River, Nigeria

Multivariate statistical techniques, such as cluster analysis (CA) and principal component analysis/factor analysis (PCA/FA), were used to investigate the temporal and spatial variations and to interpret large and complex water quality data sets collected from the Kaduna River. Kaduna River is the main tributary of Niger River in Nigeria and represents the common situation of most natural rivers including spatial patterns of pollutants. The water samples were collected monthly for 5 years (2008-2012) from eight sampling stations located along the river. In all samples, 17 parameters of water quality were determined: total dissolved solids (TDS), pH, Thard, dissolved oxygen (DO), 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), NH4-N, Cl, SO4, Ca, Mg, total coliform (TColi), turbidity, electrical conductivity (EC), HCO3 (-), NO3 (-), and temperature (T). Hierarchical CA grouped 12 months into two seasons (dry and wet seasons) and classified eight sampling stations into two groups (low- and high-pollution regions) based on seasonal differences and different levels of pollution, respectively. PCA/FA for each group formed by CA helped to identify spatiotemporal dynamics of water quality in Kaduna River. CA illustrated that water quality progressively deteriorated from headwater to downstream areas. The results of PCA/FA determined that 78.7 % of the total variance in low pollution region was explained by five factor, that is, natural and organic, mineral, microbial, organic, and nutrient, and 87.6 % of total variance in high pollution region was explained by six factors, that is, microbial, organic, mineral, natural, nutrient, and organic. Varifactors obtained from FA indicated that the parameters responsible for water quality variations are resulted from agricultural runoff, natural pollution, domestic, municipal, and industrial wastewater. Mann-Whitney U test results revealed that TDS, pH, DO, T, EC, TColi, turbidity, total hardness (THard), Mg, Ca, NO3 (-), COD, and BOD were identified as significant variables affecting temporal variation in river water, and TDS, EC, and TColi were identified as significant variables affecting spatial variation. In addition, box-whisker plots facilitated and supported multivariate analysis results. This study illustrates the usefulness of multivariate statistical techniques for classification and processing of large and complex data sets of water quality parameters, identification of latent pollution factors/sources and their spatial-temporal variations, and determination of the corresponding significant parameters in river water quality.

Strategies to optimize monitoring schemes of recreational waters from Salta, Argentina: a multivariate approach

Several recreational surface waters in Salta, Argentina, were selected to assess their quality. Seventy percent of the measurements exceeded at least one of the limits established by international legislation becoming unsuitable for their use. To interpret results of complex data, multivariate techniques were applied. Arenales River, due to the variability observed in the data, was divided in two: upstream and downstream representing low and high pollution sites, respectively, and cluster analysis supported that differentiation. Arenales River downstream and Campo Alegre Reservoir were the most different environments, and Vaqueros and La Caldera rivers were the most similar. Canonical correlation analysis allowed exploration of correlations between physicochemical and microbiological variables except in both parts of Arenales River, and principal component analysis allowed finding relationships among the nine measured variables in all aquatic environments. Variable's loadings showed that Arenales River downstream was impacted by industrial and domestic activities, Arenales River upstream was affected by agricultural activities, Campo Alegre Reservoir was disturbed by anthropogenic and ecological effects, and La Caldera and Vaqueros rivers were influenced by recreational activities. Discriminant analysis allowed identification of subgroup of variables responsible for seasonal and spatial variations. Enterococcus, dissolved oxygen, conductivity, E. coli, pH, and fecal coliforms are sufficient to spatially describe the quality of the aquatic environments. Regarding seasonal variations, dissolved oxygen, conductivity, fecal coliforms, and pH can be used to describe water quality during dry season, while dissolved oxygen, conductivity, total coliforms, E. coli, and Enterococcus during wet season. Thus, the use of multivariate techniques allowed optimizing monitoring tasks and minimizing costs involved.

Estimating impacts of land use on groundwater quality using trilinear analysis

Groundwater is connected to the landscape above and is thus affected by the overlaying land uses. This study evaluated the impacts of land uses upon groundwater quality using trilinear analysis. Trilinear analysis is a display of experimental data in a triangular graph. Groundwater quality data collected from agricultural, septic tank, forest, and wastewater land uses for a 6-year period were used for the analysis. Results showed that among the three nitrogen species (i.e., nitrate and nitrite (NO(x)), dissolved organic nitrogen (DON), and total organic nitrogen (TON)), NO(x) had a high percentage and was a dominant species in the groundwater beneath the septic tank lands, whereas TON was a major species in groundwater beneath the forest lands. Among the three phosphorus species, namely the particulate phosphorus (PP), dissolved ortho phosphorus (PO4(3-)) and dissolved organic phosphorus (DOP), there was a high percentage of PP in the groundwater beneath the septic tank, forest, and agricultural lands. In general, Ca was a dominant cation in the groundwater beneath the septic tank lands, whereas Na was a dominant cation in the groundwater beneath the forest lands. For the three major anions (i.e., F(-), Cl(-), and SO4(2-)), F(-) accounted for <1% of the total anions in the groundwater beneath the forest, wastewater, and agricultural lands. Impacts of land uses on groundwater Cd and Cr distributions were not profound. This study suggests that trilinear analysis is a useful technique to characterize the relationship between land use and groundwater quality.

Spatial and temporal variability of surface water pollution in the Mekong Delta, Vietnam

Surface water pollution in the Vietnamese Mekong Delta (MD) could threaten human, animal and ecosystem health given the fact that this water source is intensively used for drinking, irrigation and domestic services. We therefore determined the levels of pollution by organic pollutants, salts, metals and microbial indicators by (bi)monthly monitoring of canals between November 2011 and July 2012 at 32 sampling locations, representing fresh and saline/brackish environments. The results were compared with national water quality guidelines, between the studied regions and with water quality data from main waterways. Key factors explaining the observed levels of pollution in surface water were identified through principal component analysis (PCA). Temporal variations due to tidal regime and seasonality were also assessed. Based on regression models, the spatial variability of five water quality parameters was visualized using GIS based maps. Results indicate that pH (max. 8.6), turbidity (max. 461 FTU), maximum concentrations of ammonium (14.7 mg L(-1)), arsenic (44.1 μg L(-1)), barium (157.5 μg L(-1)), chromium (84.7 μg L(-1)), mercury (45.5 μg L(-1)), manganese (1659.7 μg L(-1)), aluminum (14.5 mg L(-1)), iron (17.0 mg L(-1)) and the number of Escherichia coli (87,000 CFU 100 mL(-1)) and total coliforms (2,500,000 CFU 100 mL(-1)) in canals exceed the thresholds set by Vietnamese quality guidelines for drinking and domestic purposes. The PCA showed that i) urbanization; ii) metal leaching from soils; iii) aquaculture; and iv) tidal regime explain 85% of the variance of surface water quality attributes. Significant differences in water quality were found due to daily tidal regime and as a result of seasonality. Surface water quality maps for dissolved oxygen, ammonium, ortho-phosphate, manganese and total coliforms were developed to highlight hot-spot areas of pollution. The results of this study can assist policy makers in developing water management strategies and drinking water companies in selecting optimum water extraction locations.

Uncertainty of SWAT model at different DEM resolutions in a large mountainous watershed

The objective of this study was to enhance understanding of the sensitivity of the SWAT model to the resolutions of Digital Elevation Models (DEMs) based on the analysis of multiple evaluation indicators. The Xiangxi River, a large tributary of Three Gorges Reservoir in China, was selected as the study area. A range of 17 DEM spatial resolutions, from 30 to 1000 m, was examined, and the annual and monthly model outputs based on each resolution were compared. The following results were obtained: (i) sediment yield was greatly affected by DEM resolution; (ii) the prediction of dissolved oxygen load was significantly affected by DEM resolutions coarser than 500 m; (iii) Total Nitrogen (TN) load was not greatly affected by the DEM resolution; (iv) Nitrate Nitrogen (NO₃-N) and Total Phosphorus (TP) loads were slightly affected by the DEM resolution; and (v) flow and Ammonia Nitrogen (NH₄-N) load were essentially unaffected by the DEM resolution. The flow and dissolved oxygen load decreased more significantly in the dry season than in the wet and normal seasons. Excluding flow and dissolved oxygen, the uncertainties of the other Hydrology/Non-point Source (H/NPS) pollution indicators were greater in the wet season than in the dry and normal seasons. Considering the temporal distribution uncertainties, the optimal DEM resolutions for flow was 30-200 m, for sediment and TP was 30-100 m, for dissolved oxygen and NO₃-N was 30-300 m, for NH₄-N was 30 to 70 m and for TN was 30-150 m.

Assessment of physico-chemical qualities and heavy metal concentrations of Umgeni and Umdloti Rivers in Durban, South Africa

We assessed the effects of seasonal dynamics on the physico-chemical qualities and heavy metals concentrations of the Umgeni and Umdloti Rivers in Durban, South Africa. Water samples were taken from nine different sampling points and analysed for the following parameters; temperature, pH, turbidity, electrical conductivity (EC), biological oxygen demand (BOD5), chemical oxygen demand (COD), phosphate (PO4(2-)), nitrate (NO3(2-)), ammonium (NH4(+)), sulphate (SO4(2-)), lead (Pb(2+)), mercury (Hg(2+)), cadmium (Cd(2+)), aluminium (Al(3+)), and copper (Cu(2+)) using standard methods. The data showed variations it terms of the seasonal fluctuations and sampling regime as follows: temperature 12-26.5 °C; pH 5.96-8.45; turbidity 0.53-18.8 NTU; EC 15.8-5180 mS m(-1); BOD5 0.60-7.32 mg L(-1); COD 10.5-72.9 mg L(-1); PO4 (2-) < 500-2,460 μg L(-1); NO3 (2-) <0.05-4.21 mg L(-1); NH4 (+) < 0.5-1.22 mg L(-1); SO4 (2-) 3.90-2,762 mg L(-1); Pb(2+) 0.023-0.135 mg L(-1); Hg(2+) 0.0122-0.1231 mg L(-1) Cd(2+) 0.068-0.416 mg L(-1); Al(3+) 0.037-1.875 mg L(-1), and Cu(2+)0.006-0.144 mg L(-1). The concentrations of most of the investigated parameters exceeded the recommended limit of the South African Guidelines and World Health Organization tolerance limits for freshwater quality. We conclude that these water bodies are potentially hazardous to public health and this highlights the need for implementation of improved management strategies of these river catchments for continued sustainability.