Less Polluted Research Articles

Health risk posed by direct ingestion of yeasts from polluted river water

River water is an essential human resource that may be contaminated with hazardous microorganisms. However, the risk of yeast infection through river water exposure is unclear because it is highly dependant on individual susceptibility and has therefore not been well-studied, to date. To evaluate this undefined risk, we analysed the fungal communities in less polluted (LP) and highly polluted (HP) river water, as determined using principal coordinate analysis of pollution indicators. We enumerated culturable yeasts using a thermally selective isolation procedure (37 °C) and thus promoted the growth of potentially opportunistic species. Yeast species identified as clinically relevant were then tested for antifungal resistance. In addition, we propose a quantitative microbial risk assessment (QMRA) framework to quantitatively assess the potential risk of yeast infection. Our results indicated that pollution levels significantly altered fungal communities (p = 0.007) and that genera representing opportunistic and pathogenic members were significantly more abundant in HP waters (p = 0.038). Additionally, the yeast species Candida glabrata and Clavispora lusitaniae positively correlated with other pollution indicators, demonstrating the species’ indicator potential. Our QMRA results further indicate that higher risk of infection is associated with increased water pollution levels (considering both physicochemical and bacterial indicators). Furthermore, yeast species with higher pathogenic potential present an increased risk of infection despite lower observed concentrations in the river water. Interestingly, the bloom of Meyerozyma guilliermondii during the wet season suggests that other environmental factors, such as dissolved oxygen levels and water turbulence, might affect growth characteristics of yeasts in river water, which consequently affects the distribution of annual infection risks. The presence of antifungal resistant yeasts, observed in this study, could further contribute to variation in risk distribution. Research on the ecophysiology of yeasts in these environments is therefore necessary to ameliorate the uncertainty and sensitivity of the proposed QMRA model. In addition to the vital knowledge on opportunistic and pathogenic yeast occurrence in river water and their observed association with pollution, this study provides valuable methods and insights to initiate future QMRAs of yeast infections.

Impact of Water Pollution on Trophic Transfer of Fatty Acids in Fish, Microalgae, and Zoobenthos in the Food Web of a Freshwater Ecosystem.

This research work was carried out to determine the effects of water contamination on the fatty acid (FA) profile of periphyton, zoobenthos, two Chinese carps and a common carp (Hypophthalmichthys molitrix, Ctenopharygodon idella and Cyprinus carpio), captured from highly polluted (HP), less polluted (LP), and non-polluted (NP) sites of the Indus river. We found that the concentration of heavy metals in the river water from the polluted locations exceeded the permissible limits suggested by the World Health Organization (WHO) and the US Environmental Protection Agency (EPA). Fatty acid profiles in periphyton, zoobenthos, H. molitrix, C. idella, and C. carpio in the food web of river ecosystems with different pollution levels were assessed. Lauric acid and arachidic acids were not detected in the biomass of periphyton and zoobenthos from HP and LP sites compared to NP sites. Alpha-linolenic acid (ALA), eicosadienoic acid and docosapentaenoic acid were not recorded in the biomass samples of periphyton and zoobenthos in both HP and LP sites. Caprylic acid, lauric acid, and arachidic acid were not found in H. molitrix, C. idella, and C. carpio captured from HP. In this study, 6 and 9 omega series FAs were identified in the muscle samples of H. molitrix, C. idella and C. carpio captured from HP and LP sites compared to NP sites, respectively. Less polyunsaturated fatty acids were observed in the muscle samples of H. molitrix, C. idella, and C. carpio collected from HP than from LP. The heavy metals showed significant negative correlations with the total FAs in periphyton, zoobenthos, and fish samples.

Risk characterization and surface water quality assessment of Manas River, Assam (India) with an emphasis on the TOPSIS method of multi-objective decision making

The present study centers on the investigation of surface water quality with the aid of quality indices and explores the application of a multi-objective decision-making method (TOPSIS) in arranging decisions for policy makers on the basis of overall ranking of the sampling locations. A case study has been performed on the Manas River, Assam (India). Water Quality Index (WQI) involving physico-chemical parameters, and heavy metal pollution index (HPI) and contamination index (CI) involving heavy metal influences were employed for water quality assessment. WQI graded two sampling locations “very poor” and all other locations “poor”. HPIs of all the locations were below the critical value of 100, but the CI depicted that two locations were “moderately contaminated”. Risk assessment to human health was done using hazard quotient and hazard index. Cluster analysis (CA) demonstrated site similarity by grouping the relatively more polluted and less polluted (LP) sites into two major clusters. However, there surfaced difficulty in discerning the overall water quality, as all the three quality indices included different parameters and contradicted each other. A multi-objective decision-making tool, TOPSIS was therefore employed for ranking the locations on the basis of their relative pollution levels. The novelty of the study reflects in the identification of the relatively more or relatively less polluted sites within the same cluster in CA by the application of TOPSIS. The study justifies the effectiveness of TOPSIS method in prioritizing decisions in complex scenarios for policy makers.

Application of multivariate statistical techniques in assessment of surface water quality in Second Songhua River basin, China

Multivariate statistical techniques, such as cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were applied to evaluate and interpret the surface water quality data sets of the Second Songhua River (SSHR) basin in China, obtained during two years (2012-2013) of monitoring of 10 physicochemical parameters at 15 different sites. The results showed that most of physicochemical parameters varied significantly among the sampling sites. Three significant groups, highly polluted (HP), moderately polluted (MP) and less polluted (LP), of sampling sites were obtained through Hierarchical agglomerative CA on the basis of similarity of water quality characteristics. DA identified pH, F, DO, NH3-N, COD and VPhs were the most important parameters contributing to spatial variations of surface water quality. However, DA did not give a considerable data reduction (40% reduction). PCA/FA resulted in three, three and four latent factors explaining 70%, 62% and 71% of the total variance in water quality data sets of HP, MP and LP regions, respectively. FA revealed that the SSHR water chemistry was strongly affected by anthropogenic activities (point sources: industrial effluents and wastewater treatment plants; non-point sources: domestic sewage, livestock operations and agricultural activities) and natural processes (seasonal effect, and natural inputs). PCA/FA in the whole basin showed the best results for data reduction because it used only two parameters (about 80% reduction) as the most important parameters to explain 72% of the data variation. Thus, this work illustrated the utility of multivariate statistical techniques for analysis and interpretation of datasets and, in water quality assessment, identification of pollution sources/factors and understanding spatial variations in water quality for effective stream water quality management.

Assessment of Water Quality using Multivariate Statistical Techniques: A Case Study of the Nakdong River Basin, Korea

This study estimated spatial and seasonal variation of water quality to understand characteristics of Nakdong river basin, Korea. All together 11 parameters (discharge, water temperature, dissolved oxygen, 5-day biochemical oxygen demand, chemical oxygen demand, pH, suspended solids, electrical conductivity, total nitrogen, total phosphorus, and total organic carbon) at 22 different sites for the period of 2003-2011 were analyzed using multivariate statistical techniques (cluster analysis, principal component analysis and factor analysis). Hierarchical cluster analysis grouped whole river basin into three zones, i.e., relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) based on similarity of water quality characteristics. The results of factor analysis/principal component analysis explained up to 83.0%, 81.7% and 82.7% of total variance in water quality data of LP, MP, and HP zones, respectively. The rotated components of PCA obtained from factor analysis indicate that the parameters responsible for water quality variations were mainly related to discharge and total pollution loads (non-point pollution source) in LP, MP and HP areas; organic and nutrient pollution in LP and HP zones; and temperature, DO and TN in LP zone. This study demonstrates the usefulness of multivariate statistical techniques for analysis and interpretation of multi-parameter, multi-location and multi-year data sets.

Water Quality Assessment of Mahmoudia Canal in Northern West of Egypt

The main water source for Alexandria and Behiera governorate which locate at northern west of Egypt are Mahmoudia canal (receive about 15 Mm3/day), which receive water from Rosetta branch at Mahmoudia city. The canal receives domestic and agriculture wastes from Zarcon Drain and other non-point sources. Drinking water in Alexandria is supplied from surface water sources of Mahmoudia canal. The water is transferred to the eight water treatment plants for treatment operations. The primary objective of conventional drinking water treatment and distribution is to deliver to the consumer water that satisfy the requirements and does not constitute human health risk. To achieve this, water utilities employ a range of physical and chemical barriers in order to reduce risks. The present study offered comprehensive water quality information of Mahmoudia canal. Cluster analysis grouped 10 sampling sites into three groups, less polluted (LP), medium polluted (MP) and highly polluted (HP). The dissolved oxygen concentrations, as indicated by the results of Mahmoudia canal during the period of study, ranged from 4.9 mg O2/l at the beginning of canal after mixing with discharge of Zarcon drain to 5.9 mg O2/l at the Manshia water treatment plant intake while DO values in Khandak canal, (at 15.2 km from starting of the canal) varied from 6 to 6.2 mg O2/l. Nutrient concentrations did not comply with the permissible limits of ammonia, as well as the values of COD and BOD were exceeded the Egypt standards. Ammonia, 20 samples out of 36 samples were not complying with Egypt standard. All COD and BOD data obtained were exceeding of Egypt standard.

Comparative sensitivity to the fungicide tebuconazole of biofilm and plankton microbial communities in freshwater ecosystems

Stream and lake ecosystems in agricultural watersheds are exposed to fungicide inputs that can threaten the structure and functioning of aquatic microbial communities. This research analyzes the impact of the triazole fungicide tebuconazole (TBZ) on natural biofilm and plankton microbial communities from sites presenting different degrees of agricultural contamination. Biofilm and plankton communities from less-polluted (LP) and polluted (P) sites were exposed to nominal concentrations of 0 (control), 2 and 20μgTBZL−1 in 3-week microcosm experiments. Descriptors of microbial community structure (bacterial density and chlorophyll-a concentration) and function (bacterial respiration and production and photosynthesis) were analyzed to chart the effects of TBZ and the kinetics of TBZ attenuation in water during the experiments. The results showed TBZ-induced effects on biofilm function (inhibition of substrate-induced respiration and photosynthetic activity), especially in LP-site communities, whereas plankton communities experienced a transitory stimulation of bacterial densities in communities from both LP and P sites. TBZ attenuation was stronger in biofilm (60–75%) than plankton (15–18%) experiments, probably due to greater adsorption on biofilms. The differences between biofilm and plankton responses to TBZ were likely explained by differences in community structure (presence of extracellular polymeric substances (EPS) matrix) and microbial composition. Biofilm communities also exhibited different sensitivity levels according to their in-field pre-exposure to fungicide, with P-site communities demonstrating adaptation capacities to TBZ. This study indicates that TBZ toxicity to non-targeted aquatic microbial communities essentially composed by microalgae and bacteria was moderate, and that its effects varied between stream and lake microbial communities.

Multivariate statistical techniques for the assessment of seasonal variations in surface water quality of pasture ecosystems

This study investigates the applicability of multivariate statistical techniques including cluster analysis (CA), discriminant analysis (DA), and factor analysis (FA) for the assessment of seasonal variations in the surface water quality of tropical pastures. The study was carried out in the TPU catchment, Kuala Lumpur, Malaysia. The dataset consisted of 1-year monitoring of 14 parameters at six sampling sites. The CA yielded two groups of similarity between the sampling sites, i.e., less polluted (LP) and moderately polluted (MP) at temporal scale. Fecal coliform (FC), NO3, DO, and pH were significantly related to the stream grouping in the dry season, whereas NH3, BOD, Escherichia coli, and FC were significantly related to the stream grouping in the rainy season. The best predictors for distinguishing clusters in temporal scale were FC, NH3, and E. coli, respectively. FC, E. coli, and BOD with strong positive loadings were introduced as the first varifactors in the dry season which indicates the biological source of variability. EC with a strong positive loading and DO with a strong negative loading were introduced as the first varifactors in the rainy season, which represents the physiochemical source of variability. Multivariate statistical techniques were effective analytical techniques for classification and processing of large datasets of water quality and the identification of major sources of water pollution in tropical pastures.

Assessment of groundwater quality in Karur block of Tamil Nadu using multivariate techniques: A case study

In this case study, multivariate statistical techniques, such as principal component analysis, factor analysis and cluster analyses were applied for evaluation of temporal/spatial variations in the groundwater quality. These techniques were employed for the better interpretation of large complex water quality data set monitored in the four seasons from twenty five groundwater locations of Karur block, Tamil Nadu during the year 2012. The water samples were characterized for the physico-chemical parameters such as temperature, pH, total alkalinity, electrical conductivity, total hardness, calcium ions, magnesium ions, total dissolved solids, fluorides, chlorides and sulphates. The data obtained were subjected to principal component analysis (PCA) for simplifying its interpretation and to define the parameters responsible for the main variability in water quality variance. The results of principal component analysis evinced that all the parameters equally and significantly contributed to water quality variations in the study area in all the seasons. Hierarchical cluster analysis grouped twenty five sampling stations into three clusters (i.e.) relatively less polluted (LP), moderately polluted (MP) and highly polluted (HP) sites based on the similarity of water quality characteristics. The water quality index (WQI) of these groundwater samples ranged from 47 to 107, 53 to 96 and 45 to 94 in post-monsoon, summer and pre-monsoon seasons, respectively. This investigation revealed that the groundwater of the study area needs some degree of water treatment before consumption. Thus, this study demonstrates the usefulness of

Assessment of surface water quality using multivariate statistical techniques: a case study of Behrimaz Stream, Turkey

Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis, and factor analysis, were applied for the evaluation of temporal/spatial variations and for the interpretation of a water quality data set of the Behrimaz Stream, obtained during 1 year of monitoring of 20 parameters at four different sites. Hierarchical CA grouped 12 months into two periods (the first and second periods) and classified four monitoring sites into two groups (group A and group B), i.e., relatively less polluted (LP) and medium polluted (MP) sites, based on similarities of water quality characteristics. Factor analysis/principal component analysis, applied to the data sets of the two different groups obtained from cluster analysis, resulted in five latent factors amounting to 88.32% and 88.93% of the total variance in water quality data sets of LP and MP areas, respectively. Varifactors obtained from factor analysis indicate that the parameters responsible for water quality variations are mainly related to discharge, temperature, and soluble minerals (natural) and nutrients (nonpoint sources: agricultural activities) in relatively less polluted areas; and organic pollution (point source: domestic wastewater) and nutrients (nonpoint sources: agricultural activities and surface runoff from villages) in medium polluted areas in the basin. Thus, this study illustrates the utility of multivariate statistical techniques for analysis and interpretation of data sets and, in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective stream water quality management.

Assessment of surface water quality using multivariate statistical techniques in red soil hilly region: a case study of Xiangjiang watershed, China

In the study, multivariate statistical methods including factor, principal component and cluster analysis were applied to analyze surface water quality data sets obtained from Xiangjiang watershed, and generated during 7 years (1994-2000) monitoring of 12 parameters at 34 different profiles. Hierarchical cluster analysis grouped 34 sampling sites into three clusters, including relatively less polluted (LP), medium polluted (MP) and highly polluted (HP) sites, and based on the similarity of water quality characteristics, the watershed was divided into three zones. Factor analysis/principal component analysis, applied to analyze the data sets of the three different groups obtained from cluster analysis, resulted in four latent factors accounting for 71.62%, 71.77% and 72.01% of the total variance in water quality data sets of LP, MP and HP areas, respectively. The PCs obtained from factor analysis indicate that the parameters for water quality variations are mainly related to dissolve heavy metals. Thus, these methods are believed to be valuable to help water resources managers understand complex nature of water quality issues and determine the priorities to improve water quality.

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.

Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan

Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA), were applied for the evaluation of temporal/spatial variations and the interpretation of a large complex water quality data set of the Fuji river basin, generated during 8 years (1995–2002) monitoring of 12 parameters at 13 different sites (14 976 observations). Hierarchical cluster analysis grouped 13 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. Factor analysis/principal component analysis, applied to the data sets of the three different groups obtained from cluster analysis, resulted in five, five and three latent factors explaining 73.18, 77.61 and 65.39% of the total variance in water quality data sets of LP, MP and HP areas, respectively. The varifactors obtained from factor analysis indicate that the parameters responsible for water quality variations are mainly related to discharge and temperature (natural), organic pollution (point source: domestic wastewater) in relatively less polluted areas; organic pollution (point source: domestic wastewater) and nutrients (non-point sources: agriculture and orchard plantations) in medium polluted areas; and organic pollution and nutrients (point sources: domestic wastewater, wastewater treatment plants and industries) in highly polluted areas in the basin. Discriminant analysis gave the best results for both spatial and temporal analysis. It provided an important data reduction as it uses only six parameters (discharge, temperature, dissolved oxygen, biochemical oxygen demand, electrical conductivity and nitrate nitrogen), affording more than 85% correct assignations in temporal analysis, and seven parameters (discharge, temperature, biochemical oxygen demand, pH, electrical conductivity, nitrate nitrogen and ammonical nitrogen), affording more than 81% correct assignations in spatial analysis, of three different sampling sites of the basin. Therefore, DA allowed a reduction in the dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective river water quality management.

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 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 five water quality parameters account for most of the expected temporal variations whereas six water quality parameters account for most of the expected spatial varitations in surface water quality of Fuji river basin.

Radioguided excision of nonpalpable breast cancer with simultaneous sentinel lymph node biopsy

Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA), were applied for the evaluation of temporal/spatial variations and the interpretation of a large complex water quality data set of the Fuji river basin, generated during 8 years (1995–2002) monitoring of 12 parameters at 13 different sites (14 976 observations). Hierarchical cluster analysis grouped 13 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. Factor analysis/principal component analysis, applied to the data sets of the three different groups obtained from cluster analysis, resulted in five, five and three latent factors explaining 73.18, 77.61 and 65.39% of the total variance in water quality data sets of LP, MP and HP areas, respectively. The varifactors obtained from factor analysis indicate that the parameters responsible for water quality variations are mainly related to discharge and temperature (natural), organic pollution (point source: domestic wastewater) in relatively less polluted areas; organic pollution (point source: domestic wastewater) and nutrients (non-point sources: agriculture and orchard plantations) in medium polluted areas; and organic pollution and nutrients (point sources: domestic wastewater, wastewater treatment plants and industries) in highly polluted areas in the basin. Discriminant analysis gave the best results for both spatial and temporal analysis. It provided an important data reduction as it uses only six parameters (discharge, temperature, dissolved oxygen, biochemical oxygen demand, electrical conductivity and nitrate nitrogen), affording more than 85% correct assignations in temporal analysis, and seven parameters (discharge, temperature, biochemical oxygen demand, pH, electrical conductivity, nitrate nitrogen and ammonical nitrogen), affording more than 81% correct assignations in spatial analysis, of three different sampling sites of the basin. Therefore, DA allowed a reduction in the dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective river water quality management.