Chemometric characterization of river water quality

Evaluation of seasonal dynamics of the surface water hydrochemistry using multivariate statistical techniques and aquatic macrophyte productivity in a mountainous lake, Northeast India

The present work elucidates the effective application of multivariate statistics in understanding the probable relations between surface water hydrochemistry and aquatic macrophyte productivity and their underlying seasonal dynamics in a remote mountainous lake of northeast India. The result of hierarchical cluster analysis revealed three distinct clusters corresponding to the pre-monsoon (35.42%), post-monsoon (52.08%), and monsoon (12.50%) seasons. The factor analysis yielded three principal components suggesting the sediment flux, farming discharge, domestic waste, bacterial oxidation of sulfur compounds, and dissolution of plant matters associated with dissolved feldspar minerals as the influential factors. The lake hydrochemistry also varied significantly, both spatially and temporally implying geogenic weathering processes from rock-soil-water interactions. Overall, sixteen aquatic macrophytes were identified, and their monthly and daily net primary productivity varied considerably in different seasons. Regression analysis highlighted the effect of temperature, total dissolved solids, electrical conductivity, and turbidity on the seasonal fluctuations in macrophyte productivity. Overall, the study provides insights into seasonal variation in the lake water chemistry and highlights the role of statistical tools in understanding the fragile aquatic ecosystems over cost-, labor-, and time-intensive inventory studies.

Water quality assessment in the ecologically stressed lower and estuarine stretches of river Ganga using multivariate statistical tool

Water quality of the Ganga River system is changing day by day due to multifold increase in population, especially near the banks of river Ganga, and associated exponential amplification of anthropogenic activities also played a remarkable role in it. The ecologically important lower and estuarine stretch of river Ganga comprising 7 different sampling stations, i.e., Jangipur, Berhampore, Balagarh, Tribeni, Godakhali, Diamond Harbour and Fraserganj, were selected for the study as the stretch is enriched with the vast number of floral and faunal diversity. The study was conducted for a period of 5 years, i.e., from 2016 to 2020. In the study, various analytical tools and techniques were used for the assessment of riverine water quality, i.e., for calculation of water quality index (WQI); The National Sanitation Foundation Water Quality Index (NSF-WQI) and the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI) were used for the assessment. Along with WQI various statistical univariate as well as multivariate analytical tools like principal component analysis, correlation, ANOVA, and cluster analysis were also used to achieve the desired outputs. In the study, it has been observed that NSF-WQI varied from 61 to 2552, in which the higher value of NSF-WQI denoted the unsuitability of the water quality concerning the drinking water standards and vice versa. The CCME-WQI represented a similar trend as that of NSF-WQI, as it varied from 18 to 92 in which the lower value denoted degradation in the drinking water quality and vice versa. The study revealed that the Diamond Harbour-Fraserganj stretch is having an undesired level of water quality which were analyzed based on the drinking water guideline values of the Bureau of Indian Standards and that of NSF-WQI and CCME-WQI.

Application of geostatistical models to identify spatial distribution of groundwater quality parameters

Groundwater quality management is a priority in arid and semi-arid zones where water is scarce. Leachate from open dumping of municipal solid wastes may threaten groundwater quality. This research aimed at assessing groundwater quality of the aquifer of Shur river basin in Tehran province, Iran. The pollution potential of leachate from a landfill, located at the center of the basin, was estimated to assess its impact on the aquifer. Samples from 38 wells and 2 leachate ponds around the landfill were analyzed for their physico-chemical parameters and heavy metals. Leachate Pollution Index (LPI) and Water Quality Index (WQI) were calculated and multivariate statistical techniques were employed through geostatistical models to predict the spatial variability of groundwater quality and assess its contamination sources. The groundwater quality map was developed by GIS Interface. LPI indicated that leachate from the closed cell (LPI = 36) was more contaminating than that of the active site (LPI = 25). Kriging and cokriging geostatistical interpolation methods were applied to groundwater quality parameters. The best interpolation model was then identified through cross-validation with RMSE and GSD criteria. Cokriging yielded more accurate results than kriging. Spatial distribution maps showed high groundwater contamination and degraded water quality mainly in the central part of the basin, where the landfill was. Also, 293.7 ha of the study area possessed poor and very poor water quality, unsuitable for drinking. This study implicated multiple approaches for groundwater quality assessment and estimated its spatial structure as an effort toward effective groundwater quality management in Shur river basin.

A Simplified Mathematical Formulation for Water Quality Index (WQI): A Case Study in the Kelani River Basin, Sri Lanka

Surface water quality is degraded due to industrialization; however, it is one of the widely used sources for water supply systems worldwide. Thus, the polluted water creates significant issues for the health of the end users. However, poor attention and concern can be identified on this important issue in most developing countries, including Sri Lanka. The Kelani River in Sri Lanka is the heart of the water supply of the whole Colombo area and has the water intake for drinking purposes near an industrialized zone (Biyagama). Therefore, this study intends to analyze the effect of industrialization on surface water quality variation of the Kelani River basin in Sri Lanka in terms of the water quality index (WQI). We proposed a regression model to predict the WQI using the water quality parameters. Nine water quality parameters, including pH, total phosphate, electric conductivity, biochemical oxygen demand, temperature, nitrates, dissolved oxygen, chemical oxygen demand, and chlorine evaluated the Kelani River water quality. The proposed regression model was used to examine the water quality of samples obtained at twelve locations from January 2005 to December 2012. The highest WQI values were found in Raggahawatte Ela throughout the 8 years, located near the Biyagama industrial zone. The relationship of industries to water quality in the Kelani River is stated. The surface water quality gradually decreased as a result of development and industrialized activities. Therefore, this work showcases and recommends the importance of introducing necessary actions and considerations for future water management systems.

A coherent approach of Water Quality Indices and Multivariate Statistical Models to estimate the water quality and pollution source apportionment of River Ganga System in Himalayan region, Uttarakhand, India

River Ganga covers around 26% of India's land area and sustains diverse ecosystems in this overly populated area. The globally accepted coherent approach of water quality indices (WQIs) and multivariate statistical models (principal component analysis (PCA) and cluster analysis (CA)) were applied on the dataset to evaluate the spatial-temporal variation and pollution source identification and apportionment. Twenty-two hydro-chemical parameters were analyzed by collecting the samples from 20 different vertically elevated monitoring locations for different seasons. The CA evaluation of data, grouped the monitoring locations into five clusters of varied water quality with human perturbations and geo-genic inputs. The PCA analysis of an extensive dataset indicated the seven significant principal components (PCs) explaining 93.0% of the total variance and finalized 8 water quality parameters out of preselected 22 to represent good aspects of the water quality. The seasonal variation in river water quality by the Canadian Council of Ministers for Environment Water Quality Index (CCMEWQI) showed the quality class at a marginal level in summer (62.16), monsoon (59.96), and post-monsoon (60.20) season, whereas in winters (71.18), water quality was in fair condition. The response of National Sanitation Foundation Water Quality Index (NSFWQI) classified the river water in medium quality class for summer, monsoon, post-monsoon, and winter season, respectively. The present observations contribute in the usefulness of these statistical methodologies to interpret and understand large dataset and also provide reliable information to reduce the tedious and cost of water quality monitoring and assessment programs.

Evaluation of Water Quality and Heavy Metals in Wetlands along the Yellow River in Henan Province

Assessing spatiotemporal variation in water quality and heavy metals concentrations in wetlands and identifying metal contamination source are crucial steps for the protection and sustainable utilization of water resources. Using the water quality identification index (Iwq), heavy metal pollution index (HPI), hierarchical cluster analysis (HCA) and redundancy analysis (RDA), we evaluated spatiotemporal variation in water quality and heavy metals concentrations, and their interrelation in wetlands along the middle and lower Yellow River. The average Iwq was highest during flood season but the average HPI was lowest in the same season. Meanwhile, the trend in mean HPI across three hydrological seasons was the opposite to that of mean Iwq. There was significant variation in wetlands water pollution status across seasons. During the flood season, the wetlands in the affected area with hanging river were seriously polluted. In other seasons, pollution in the artificial wetlands was even more severe. Moreover, serious pollution of wetlands in belt transect #03 (Yuanyang-Zhongmu) was more frequent. Dissolved oxygen and chemical oxygen demand strongly influenced heavy metal concentrations, while other water quality parameters had different influences on heavy metal concentrations in different hydrological seasons. The causes of water pollution were divided into natural factors and human disturbance (with potential relationships between them). The polluted wetlands were greatly affected by the Yellow River during the flood season while they were more impacted by agricultural and domestic sewage discharge in other seasons. However, heavy metal deposition and leaching into riparian wetlands were still affected by diverse channel conditions. If this trend is allowed to continue unabated, wetlands along the middle and lower Yellow River are likely to lose their vital ecological and social functions.

The pristine nature of river Ganges: its qualitative deterioration and suggestive restoration strategies

The river Ganges, the National Heritage, and the lifeline of millions of Indians, unfortunately, ranked the second most polluted rivers of the world in 2017. This review reveals the current trends of the water quality of the Ganges assessed around 36 stretches during 2012-2016, to indicate an improvement around 6 (16.7%), deterioration around 14 (38.9%), and non-significant changes around 16 (44.4%) stretches. An increase in dissolved oxygen and a decrease in biochemical oxygen demand were observed at six stretches (Devprayag [S₅], Rishikesh upstream [S₇], Varanasi upstream [S₁₉], Mokama upstream [S₂₅], Mokama downstream [S₂₆], and Munger [S₂₇]). The total and fecal coliform contamination decreased at seven stretches (Rudraprayag [S₂ and S₃], Devprayag [S₅ and S₆], Rishikesh [S₇], Varanasi upstream [S₁₉], and Munger [S₂₇]) due to improved hygienic conditions, but it increased subsequently at eight stretches (Haridwar [S₈], Kanpur [S₁₅], Raibareili [S₁₆], Prayagraj [S₁₇ and S₁₈], Patna [S₂₄], Berhampore [S₃₀], and Serampore [S₃₁]) due to improper defecation and mass bathing during 2007-2016. Dissolved oxygen level declined significantly, and biochemical oxygen demand increased (> 3 ppm), alarmingly at places receiving heavy untreated sewage water. The water quality of the Ganges was good up to Rishikesh, because of an undisrupted flow of the uncontaminated water from the higher altitudes (≥ 372 m) with higher forest cover, lower temperatures (< 21 °C), and higher dissolved oxygen (≥ 8.5 ppm) and due to the dissolution of antipathogenic chemical constituents of the medicinal herbs, pollutant degrading alkaline phosphatase, and bacteriophages. The present review is a systematic collection of data on river pollution, its scientific analyses, and its relationship with 6Ps (namely population, poverty, pollution, precipitation, plantation, and periodicity). Not only that, but the river water restoration measures have also suggested through the novel interlinked water working groups for implementing integrated water management strategies.

Assessment of landfill leachate in semi-arid climate and its impact on the groundwater quality case study: Hamedan, Iran

To evaluate environmental impacts of solid waste landfilling, groundwater quality near the MSW landfill in a semi-arid climate of Iran (Hamedan) and its leachates were analyzed. To this aim, heavy metal concentrations, COD, BOD₅, TOC, EC, NO₃^-, Cl^-, TDS, and pH of two leachate ponds (active and closed sites) as the sources of contamination as well as the shallow groundwater of the area were measured. Monthly and seasonal monitoring program of 13 sampling points in the area were designed during the period of 2014-2016. Principal components analysis has been carried out using chemical data to deduce relationship between the samples. A special statistical approach including a main factor (age of leachate) and a subfactor (distance from the source of pollutant) was designed in order to identify the landfill role on the groundwater contamination. The physicochemical analysis of the leachate characteristics confirmed a high variation in the contaminants (i.e., organic compounds, salts, and heavy metals) related to leachate age. The BOD₅/COD ratio of the active (0.73) and closed (0.77) sites ponds indicated that the leachates were in a biodegradable and unstabilized condition. The seasonal physicochemical analysis of the leachates showed that rainfall events increase the decomposition rate of the waste and affect pollutant concentration of the leachate. The proposed statistical analysis illustrated a direct relationship between the groundwater quality parameters and the leachates physicochemical characteristics.

Environmental monitoring of water resources around a municipal landfill of the Rio Grande do Sul state, Brazil

In Brazil, landfills are commonly used as a method for the final disposal of waste that is compliant with the legislation. This technique, however, presents a risk to surface water and groundwater resources, owing to the leakage of metals, anions, and organic compounds. The geochemical monitoring of water resources is therefore extremely important, since the leachate can compromise the quality and use of surface water and groundwater close to landfills. In this paper, the results of analyses of metals, anions, ammonia, and physicochemical parameters were used to identify possible contamination of surface water and groundwater in a landfill area. A statistical multivariate approach was used. The values found for alkali metals, nitrate, and chloride indicate contamination in the regional groundwater and, moreover, surface waters also show variation when compared to the other background points, mainly for ammonia. Thus, the results of this study evidence the landfill leachate influence on the quality of groundwater and surface water in the study area.

Modelling spatio-temporal heterogeneities in groundwater quality in Ghana: a multivariate chemometric approach

Chemometric techniques were applied to evaluate the spatial and temporal heterogeneities in groundwater quality data for approximately 740 goldmining and agriculture-intensive locations in Ghana. The strongest linear and monotonic relationships occurred between Mn and Fe. Sixty-nine per cent of total variance in the dataset was explained by four variance factors: physicochemical properties, bacteriological quality, natural geologic attributes and anthropogenic factors (artisanal goldmining). There was evidence of significant differences in means of all trace metals and physicochemical parameters (p < 0.001) between goldmining and non-goldmining locations. Arsenic and turbidity produced very high value F's demonstrating that 'physical properties and chalcophilic elements' was the function that most discriminated between non-goldmining and goldmining locations. Variations in Escherichia coli and total coliforms were observed between the dry and wet seasons. The overall predictive accuracy of the discriminant function showed that non-goldmining locations were classified with slightly better accuracy (89%) than goldmining areas (69.6%). There were significant differences between the underlying distributions of Cd, Mn and Pb in the wet and dry seasons. This study emphasizes the practicality of chemometrics in the assessment and elucidation of complex water quality datasets to promote effective management of groundwater resources for sustaining human health.

Assessing the impacts of climate change and socio-economic changes on flow and phosphorus flux in the Ganga river system

Anthropogenic climate change has impacted and will continue to impact the natural environment and people around the world. Increasing temperatures and altered rainfall patterns combined with socio-economic factors such as population changes, land use changes and water transfers will affect flows and nutrient fluxes in river systems. The Ganga river, one of the largest river systems in the world, supports approximately 10% global population and more than 700 cities. Changes in the Ganga river system are likely to have a significant impact on water availability, water quality, aquatic habitats and people. In order to investigate these potential changes on the flow and water quality of the Ganga river, a multi-branch version of INCA Phosphorus (INCA-P) model has been applied to the entire river system. The model is used to quantify the impacts from a changing climate, population growth, additional agricultural land, pollution control and water transfers for 2041-2060 and 2080-2099. The results provide valuable information about potential effects of different management strategies on catchment water quality.

An assessment of selected hydrochemical parameter trend of the Nakdong River water in South Korea, using time series analyses and PCA

Time series analyses (autocorrelation, spectral density, and cross-correlation) and principal component analysis (PCA) were used to understand the characteristics of the selected hydrochemical parameters pH, turbidity, alkalinity, Cl, hardness, total dissolved solids (TDS), and metals Fe and Mn in the Nakdong River, South Korea. Autocorrelation and spectral density for pH, alkalinity, hardness, and Cl were very similar to TDS, whereas Fe, Mn, and turbidity showed different trends from TDS. Cross-correlograms of pH, alkalinity, hardness, and Cl versus TDS were very similar to each other. Those of Fe and turbidity represented the opposite relations with other components. Cross-correlation coefficients had the highest values at zero lag, indicating that pH, alkalinity, hardness, and Cl are controlling factors for TDS. On the other hand, Fe and turbidity showed the highest values at 6-month lag and Mn at a month lag. PCA indicated that TDS had very close relation with hardness, pH, and Cl and very small relation with Mn. Turbidity and Fe had relatively opposite relations with TDS. It was concluded that the geostatistical methods were very useful for evaluating the hydrochemical characteristics of the Nakdong River water in South Korea.

Source apportionment of wastewater pollutants using multivariate analyses

A faster and cost-effective methodology has been developed to estimate the spatial and seasonal variations in wastewater quality and apportion the influencing sources through multivariate statistical techniques, cluster analysis and principal component analysis (PCA). Partially treated or untreated wastewater is released into the river from various industrial and domestic sources, which poses a serious threat to human health. Wastewater samples were collected from five stations along the river bank. PCA performed on overall wastewater samples revealed that in present study all the five sampling stations were influenced by sewage and industrial effluents mixed together. However, the pollutant levels were significantly different in the three groups of wastewater samples, which were confirmed by univariate analysis of principal component (PC) scores. Based on wastewater similarities, cluster analysis identified three groups (central, upstream and downstream) of sampling stations, which further confirmed univariate analysis of PCs scores. Spatial variations in wastewater quality reveled that the highest pollutant concentration was noted for group 1 and lowest for group 2. Seasonal variations in the wastewater quality revealed that highest values of pollutants were observed in low flow and lowest in high flow. Results of the present study obtained through multivariate analyses may be used to classify wastewater and identify the influencing sources of pollutants. The present study may be useful in reducing 11 % of the cost in future investigations. Thus, in future quality estimation of the representative wastewater samples would be faster as well as cost-effective approach.