Impact evaluation of the run-of-river hydropower projects on the water quality dynamics of the Sindh River in the Northwestern Himalayas

As the run-of-river (RoR) hydropower projects remain understudied, we conducted this study to understand how these projects affect the hydro-chemical dynamics and water quality index (WQI) of the Sindh River in the Kashmir Himalayas. We used multivariate statistical techniques and WQI to identify the spatiotemporal dynamics of 18 physico-chemical parameters from 11 sampling stations distributed along the length of river Sindh from December 2017 to December 2019. The dataset was classified into three groups using hierarchical cluster analysis based on similarities between hydro-chemical characteristics, and the results were confirmed by discriminant analysis. Wilk's quotient distribution further showed that ions, nutrients, free carbon dioxide, water temperature, and pH contributed to the formation of clusters. Principle component analysis revealed that the chloride (Cl^-), total phosphorus (TP), ortho-phosphorus (PO₄-P), nitrate-nitrogen (NO₃-N), nitrite-nitrogen (NO₂-N), and sulfate ion (SO₄^2-) are significant factors that influence the water quality. Furthermore, our results suggest that diverting water for RoR operation did not significantly raise the WQI value to the point where water in the bypassed reaches could be declared unfit for drinking. Our analysis concluded that inclusive assessments are vital for framing policies on expanding RoR hydropower in the region.

Determination of trace levels of selenium in natural water, agriculture soil and food samples by vortex assisted liquid-liquid microextraction method: Multivariate techniques

A green vortex assisted based liquid-liquid microextraction (VA-LLME) method was developed for preconcentration of selenium. Ammonium pyrrolidine dithiocarbamate (APDC) was used to form a hydrophobic complex with selenium in natural water, agricultural soil and food samples by GFAAS. Whereas Triton X-114, a nonionic surfactant and 1-butyl-3-methylimidazolium hexafluorophosphate ionic liquid were used for Se extraction as a dispersing medium. The conical flasks contents were shack on a vortex mixer to increase the extraction efficiency. Multivariate techniques were used to evaluate extraction parameters; pH, vortex time, APDC amount, volume of ionic liquid and Triton X-114 and centrifugation rate on the recovery of Se. The central composite design (CCD) was used for further optimization of the essential extraction parameters. The enhancement factor and limit of detection were obtained as 98.7 and 0.07 µg L^-1. The certified reference materials was used for accuracy of method and the related standard deviation was found to be 3.51%. The resulted data indicated that concentrations of Se in all types of water samples were below the permissible limit recommended by WHO.

Seasonal changes in fish diversity, density, biomass, and assemblage alongside environmental variables in the Yangtze River Estuary

The present study used multivariate techniques, to analyze the fish species diversity and distribution patterns in order to determine the possible role of environmental parameters as drivers of fish community structure and composition in the Yangtze River Estuary (YRE). This analysis was conducted using data obtained in the YRE from February 2012 to December 2014. Analysis of the catch data showed that species composition, total density, and total biomass varied significantly between stations and seasons. Thirty-eight species belonging to 18 families were collected. Sciaenidae was the most dominant family accounting for 40.8% of total captured specimens. In descending order, Collichthys lucidus, Cynoglossus gracilis, Chaeturichthys stigmatias, and Lophiogobius ocellicauda dominated catches in the YRE. These four species constituted 64.2% of the total catches and showed average dissimilarities of 74.19% between stations and 81.3% between months. The highest number of fish specimens captured was recorded in August 2012 while the highest species richness was observed in December 2013. The mean fish density and biomass for the YRE was 0.35 individuals/m² and 2.5 g/m², respectively. The mean density and biomass for the most important and dominant species changed significantly between stations and seasons. Canonical correspondence analysis indicated that salinity and chlorophyll-a were the key variables that structured the fish assemblage in the YRE. High total species density and biomass were recorded in high saline stations (North Branch) of the YRE. This study confirms that most species captured in the YRE needs estuarine conditions to complete their growth and development. Hence, the findings in this study are important to understanding and developing suitable conservation plans for the management of fish resources in the YRE.

Characterization of organic aerosol at a rural site influenced by olive waste biomass burning

Biomass burning is a major air pollution problem all around the world. However, the identification and quantification of its contribution to ambient aerosol levels is a difficult task due to the generalized lack of observations of molecular markers. This paper presents the results of a yearlong study of organic constituents of the atmospheric aerosol at a rural site in southern Spain (Villanueva del Arzobispo, Jaén). Sampling was performed for PM₁₀ and PM_2.5, and a total of 116 and 115 samples, respectively, were collected and analyzed by GC/MS, quantifying 77 organic compounds. Higher levels of organic pollutants were recorded from November to March, coinciding with the cold season when domestic combustion is a common practice in rural areas. This jointly with adverse meteorological conditions, e.g. strong atmospheric stability, produced severe pollution episodes with high PM_x ambient levels. High daily concentrations of tracers were reached, up to 26 ng m^-3 for B(a)P and 6065 ng m^-3 for levoglucosan in PM_2.5, supporting that biomass burning is a major source of pollution at rural areas. A multivariate statistical study based on factor and cluster analysis, was applied to the data set with the aim to distinguish sources of organic compounds. The main resulting sources were related with biomass combustion, secondary organic aerosol (SOA), biogenic emissions, lubricating oil and soil organic components. A preliminary organic source profile for olive wastes burning was evaluated, based on cluster results, showing anhydrosacharides and xylitol are the main emitted compounds, accounting for more than 85% of the quantified compounds. Other source compounds were fatty acids, diacids, aliphatics, sugars, sugar alcohols, PAHs and quinones.

T-patterns in the study of movement and behavioral disorders

Aim of the present review is to offer an outline of the application of T-pattern analysis (TPA) in the study of neurological disorders characterized by anomalies of movement and, more in general, of behavior. TPA is a multivariate technique to detect real time patterns of behavior on the basis of statistically significant constraints among the events in sequence. TPA is particularly suitable to analyse the structure of behavior. The application of TPA to study movement and behavioral disorders is able to offer, with a high level of detail, hidden characteristics of behavior otherwise impossible to detect. For its intrinsic features, TPA is completely different not only from quantitative evaluations of behavior such as assessments of frequencies, durations, percent distributions etc. of individual behavioral components, but also from the largest extent of multivariate approaches based, for instance, on the analysis of transition matrices. Various applications of TPA in the study of behavior in human patients and in animal models of neurological disorders are discussed. TPA is a suitable tool to study the movement and behavioral disorders. This review represents a useful background for researchers, therapists, physicians etc. who intend to use this technique.

Assessment of spatial variations in the surface water quality of the Velhas River Basin, Brazil, using multivariate statistical analysis and nonparametric statistics

The Velhas River sub-basin, which is located in the third-largest river basin in Brazil (São Francisco), is in an advanced state of degradation. In this work, the surface water quality of the Velhas River Basin was studied at 65 monitoring sites; 16 water quality parameters were sampled quarterly for 11 years (2008 to 2013). Cluster analysis (CA) and a nonparametric Kruskal-Wallis test were associated with the analysis of violations to water quality standards to interpret the water quality data set from the Velhas River Basin and assess its spatial variations. The CA grouped the 65 monitoring sites into four groups. The Kruskal-Wallis test identified significant differences (p < 0.05) between the groups formed by CA. The results show that watercourses located in the upper region of the Velhas River Basin are more affected by the release of industrial effluent and domestic sewage, and the lower region is more affected by diffuse pollution and erosion. This association between multivariate statistical techniques and nonparametric tests was effective for the classification and processing of large water quality datasets and the identification of major differences between water pollution sources in the basin. Therefore, these results provide an understanding of the factors affecting water quality in the Velhas River Basin. The results can aid in decision-making by water managers and these methods can be applied to other river basins.

Pollution assessment of heavy metals in soils of India and ecological risk assessment: A state-of-the-art

Soil is substantive component of biosphere, which is exposed to plethora of pollutants including heavy metals. These are added by natural as well as anthropogenic activities. Upsurge in heavy metal content affects all organisms by biomagnification. So, it becomes vital to create a database of heavy metals concentration in soil. This is relevant in countries where unsustainable intensive agriculture, industrial and urban development is in progress. The present review of the scientific literature from 1991 to 2018 on heavy metals in soils in India shows that Zn and Pb exceeded their limits for Indian natural soil guidelines (Zn 22.1 and Pb 13.1 μg/g), Canada (Zn 200 μg/g), Swedish (80 μg/g) and Poland (Zn 300 μg/g) soil guidelines. The mean values of As and Cu for all soil types except for roadside soils, exceeded the limits. The average value of Cd for all soil types exceeded their limit. The mean values obtained for soils of India are Fe (23774.84 μg/g), Mn (872.54 μg/g), Zn (359.94 μg/g), Cu (183.67 μg/g), Cr (161.42 μg/g), As (148.70 μg/g), Ni (112.41 μg/g), Pb (61.87 μg/g), Co (37.63 μg/g) and Cd (14.16 μg/g). Cluster analysis and factor analysis were employed to different soil types and showed multiple sources of these metals. The contamination factor (CF), enrichment factor (EF) and potential contamination index (Cp) showed that Cd and As are the main contaminants. The results of ecological risk index indicated that Cd is the main pollutant in the different soils of India.

Sensory characterisation of black ripe table olives from Spanish Manzanilla and Hojiblanca cultivars

A lexicon from the literature has been used for the characterisation of black ripe table olives from Spanish Manzanilla and Hojiblanca cultivars by Quantitative Descriptive Analysis (QDA). After confirming the acceptable reproducibility and repeatability of the panel, the descriptors that received the widest range of scores and significantly contributed to sample discrimination were: skin green, flesh green, skin sheen, flesh red, fibrousness, firmness, skin red, moisture release, fishy smell/ocean and flesh yellow. The effects of cultivar, growing area and storage period on the sensory profiles were relevant, as showed by spider graphs and multivariate methods. The map of variables, using bootstrapping techniques, associated descriptors like fibrousness, firmness, chewiness, skin red, flesh red, and skin sheen to PC1, which can then be related to texture, while PC2 was linked to skin green and astringency (related to phenols) or vinegar and fishy smell/ocean (possibly connected to cultivars). Centring data by panelist had a strong influence on the segregation of samples but increasing the number of panelists had a reduced additional effect. The diverse sensory profiles of samples were also summarised by biclustering.

The assessment and prediction of temporal variations in surface water quality-a case study

In order to optimize the processes of sampling, monitoring, and management, the initial aim of this paper was to develop a model for the definition and prediction of temporal changes of water quality. In the case of the Morava River Basin (Serbia), the patterns of temporal changes have been recognized by applying different multivariate statistical techniques. The results of the conducted cluster analysis are the indicators of the existence of the three monitoring periods: the low-water, transitional, and high-water periods, which is in accordance with changes in the water flow in the analyzed river basin. A possibility of reducing the initial data set and recognizing the main pollution sources was examined by carrying out the principal component/factor analysis. The results indicate that the natural factor has a dominant influence in temporal groups. In order to recognize the discriminatory water quality parameters, a discriminant analysis (DA) was carried out. Conducting the DA enabled a significant reduction in the data set by the extraction of two parameters (the water temperature and electrical conductivity). Furthermore, the artificial neural network technique was used for testing the possibility of predicting changes in the values of the discriminant factors in the monitoring periods. The reliability of this method for the prediction of temporal variations of both extracted parameters within all temporal clusters has been proven.

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.

Water quality assessment of river Beas, India, using multivariate and remote sensing techniques

River Beas originates in the Himalayas and merges into river Sutlej at Harike, a Ramsar wetland. This river is a habitat of the endangered freshwater dolphin, Platanista gangetica minor R. Twenty-five water quality parameters, including eight heavy metals, were studied at four sampling sites over a stretch of 63 km between Beas and Harike towns for pre-monsoon, post-monsoon and winter seasons. Principal component analysis of the data proved to be an effective tool for data reduction as the first three principal components of all the water quality parameters explained 100% variance. Factor analysis delineated three factors underlying the water quality. Factor 1 comprised pollution-related parameters like BOD, COD, DO, PO4(-3) and hardness. Factor 2 was a natural water quality determinant and explained maximum variance in turbidity, alkalinity and TDS. Factor 3 comprised NO3(-1), a fertilizer-related parameter. Reflectance values from bands 2 (green), 3 (red) and 4 (near infra-red) of Landsat (TM) digital data were regressed on PO4(-3), turbidity and TDS using multiple linear regression analysis. PO4(-3) contributed positively to the spectral radiance, whereas TDS contributed negatively. Beta regression analysis revealed that PO4(-3) had a positive relation with BOD, whereas turbidity and TDS were negatively regressed with BOD. Artificial neural network models were fitted to the data. Correlations between the target values from ANN for turbidity, BOD and bands 2 (green), 3 (red) and 4 (near infra-red) were highly significant.