The world’s big rivers and their floodplains were central to development of civilization and are now home to c. 2.7 billion people. They are economically vital whilst also constituting some of the most diverse habitats on Earth. However, a number of anthropogenic stressors, including large-scale damming, hydrological change, pollution, introduction of non-native species and sediment mining, challenge their integrity and future, as never before. The rapidity and extent of such change is so great that large-scale, and potentially irreparable, transformations may ensue in periods of years to decades, with ecosystem collapse being possible in some big rivers. Prioritizing the fate of the world’s great river corridors on an international political stage is imperative. Future sustainable management, and establishment of environmental flow requirements for the world’s big rivers, must be supported through co-ordinated international funding, and trans-continental political agreement to monitor these rivers, finance their continual upkeep and help ameliorate increasing anthropogenic pressures. To have any effect, all of these must be set within an inclusive governance framework across scales, organizations and local populace. Stressors such as large-scale damming, hydrological change, pollution, the introduction of non-native species and sediment mining are challenging the integrity and future of large rivers, according to a synthesis of the literature on the 32 biggest rivers.

Anthropogenic stresses on the world’s big rivers

Large world rivers are significant sources of dissolved organic matter (DOM) to the oceans. Watershed geomorphology and land use can drive the quality and reactivity of DOM. Determining the molecular composition of riverine DOM is essential for understanding its source, mobility and fate across landscapes. In this study, DOM from the main stem of 10 global rivers covering a wide climatic range and land use features was molecularly characterized via ultrahigh-resolution Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR-MS). FT-ICR mass spectral data revealed an overall similarity in molecular components among the rivers. However, when focusing specifically on the contribution of nonoxygen heteroatomic molecular formulas (CHON, CHOS, CHOP, etc.) to the bulk molecular signature, patterns relating DOM composition and watershed land use became apparent. Greater abundances of N- and S-containing molecular formulas were identified as unique to rivers influenced by anthropogenic inputs, whereas ...

Linking the Molecular Signature of Heteroatomic Dissolved Organic Matter to Watershed Characteristics in World Rivers

The objective of this work is to evaluate the physico-chemical quality of the groundwater of the Merdja plain and to determine the sources of mineralization. This quality is influenced by several environmental and anthropogenic factors such as geological context, climate, precipitation and interaction between groundwater and aquifers and human activities.  A Principal Component Analysis (PCA) on samples taken from several wells spread over the entire Tebessa plain (Merdja) allowed us to detect two axes that explain 73.4% of the information. The first axis describes the variables related to mineralisation and the second one describes those related to agricultural activity. Multidimensional Positioning (MDS) confirmed the interaction of physico-chemical parameters between them and their influence on groundwater quality by highlighting three groups of wells according to their physico-chemical characteristics, particularly those containing high concentrations of nitrates. This contamination is mainly the result of spreading the fertilisers and wastes that are dumped into the plain without treatment. Salinization is the result of long-term interactions between groundwater and geological formations.

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Hydrochemical Characterisation of Groundwater Quality: Merdja Plain (Tebessa Town, Algeria)

Rapid urban development has led to a critical negative impact on water bodies flowing in and around urban areas. In the present study, 25 physiochemical and biological parameters have been studied on water samples collected from the entire section of a small river originating and ending within an urban area. This study envisaged to assess the water quality status of river body and explore probable sources of pollution in the river. Weighted arithmetic water quality index (WQI) was employed to evaluate the water quality status of the river. Multivariate statistical techniques namely cluster analysis (CA) and principal component analysis (PCA) were applied to differentiate the sources of variation in water quality and to determine the cause of pollution in the river. WQI values indicated high pollution levels in the studied water body, rendering it unsuitable for any practical purpose. Cluster analysis results showed that the river samples can be divided into four groups. Use of PCA identified four important factors describing the types of pollution in the river, namely (1) mineral and nutrient pollution, (2) heavy metal pollution, (3) organic pollution, and (4) fecal contamination. The deteriorating water quality of the river was demonstrated to originate from wide sources of anthropogenic activities, especially municipal sewage discharge from unplanned housing areas, wastewater discharge from small industrial units, livestock activities, and indiscriminate dumping of solid wastes in the river. Thus, the present study effectively demonstrates the use of WQI and multivariate statistical techniques for gaining simpler and meaningful information about the water quality of a lotic water body as well as to identify of the pollution sources.

Use of water quality index and multivariate statistical techniques for the assessment of spatial variations in water quality of a small river

The Ganges River is one of the biggest transboundary streams in the Indian sub-continent. The significant part of this waterway channel drains one of the most densely populated areas on the planet so it is unequivocally influenced by human activities. Unprecedented high-temporal-resolution samples were collected for investigating the seasonal variability of water quality. Water quality index (WQI) reveals large seasonal variation among three major seasons and also indicates that the river water is not suitable for drinking and other household uses. The dominant water facies is bicarbonate (HCO3-). The water quality indices, %Na, Kelley's ratio (KR), sodium adsorption ratio (SAR), and magnesium adsorption ratio (MAR), reveal that the water is appropriate for irrigation. The permeability index (PI) indicated that the water is moderate to poorly useable for agricultural purposes. Heavy metals concentrations demonstrated significant seasonal variations with high concentrations during the monsoon due to flushing of pollutants from catchment areas by intense monsoonal precipitations. In addition, local activities such as oil spills from the boat, vehicle washing water, and agricultural runoff may also added pollutants. The single-factor pollution index (Ii) and Nemerow pollution index (Ni) exhibits minor pollution. The values of heavy metal pollution index (HPI) are far below than the critical limit (100) for the studied month, although relatively higher HPI values found for April, August, and November than other months might come from domestic wastes and agricultural activities. The heavy metal evaluation index (HEI) values of all the months indicated a low degree of pollution. Even though the river water pollution level is low, the authority should take proper management and monitoring strategy for sustainable use.

Variability of water quality and metal pollution index in the Ganges River, Bangladesh

In present study multivariate statistical approaches are used; interpretation of large and complex data matrix obtained during a monitoring of the river Ganges in Varanasi. 16 physicochemical and bacteriological variables have been analyzed in water samples collected every three months for two years from six sampling sites where river affected by man made and seasonal influences. The dataset was treated using Principal Component Analysis (PCA) to extract the parameters that are most important in assessing variation in water quality. Four Principal Factor were identified as responsible for the data structure explaining 90% of the total variance of the dataset, in which nutrient factor (39.2%), sewage and feacal contamination (29.3%), physicochemical sources of variability (6.2%) and waste water pollution from industrial and organic load (5.8%) that represents total variance of water quality in the Ganges River. The present study suggests that PCA techniques are useful tools for identification of important surface water quality parameters.

A. Mishra

Papers

Assessment of water quality using principal component analysis: A case study of the river Ganges