literature review on physicochemical analysis of water

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Examining the dynamics of the relationship between water pH and other water quality parameters in ground and surface water systems

Benjamin m. saalidong.

1 Department of Geosciences, Taiyuan University of Technology, Taiyuan, People’s Republic of China

Simon Appah Aram

2 College of Safety and Emergency Management Engineering, Taiyuan University of Technology, Taiyuan, People’s Republic of China

3 Department of Earth and Environmental Science, New Mexico Institute of Mining and Technology, Socorro, NM, United States of America

Patrick Osei Lartey

4 Ministry of Education Key Laboratory of Interface and Engineering in Advanced Materials, Research Center of Advanced Materials Science and Technology, Taiyuan University of Technology, Taiyuan, People’s Republic of China

Associated Data

The DOI for to data used for this Manuscript is provided below DOI: https://doi.org/10.6084/m9.figshare.14717355 .

This study evaluated the relationship between water pH and the physicochemical properties of water while controlling for the influence of heavy metals and bacteriological factors using a nested logistic regression model. The study further sought to assess how these relationships are compared across confined water systems (ground water) and open water systems (surface water). Samples were collected from 100 groundwater and 132 surface water locations in the Tarkwa mining area. For the zero-order relationship in groundwater, EC, TDS, TSS, Ca, SO 4 2- , total alkalinity, Zn, Mn, Cu, faecal and total coliform were more likely to predict optimal water pH. For surface water however, only TSS, turbidity, total alkalinity and Ca were significant predictors of optimal pH levels. At the multivariate level for groundwater, TDS, turbidity, total alkalinity and TSS were more likely to predict optimal water pH while EC, Mg, Mn and Zn were associated with non-optimal water pH. For the surface water system, turbidity, Ca, TSS, NO 3 , Mn and total coliform were associated with optimal water pH while SO 4 2- , EC, Zn, Cu, and faecal coliform were associated with non-optimal water pH. The non-robustness of predictors in the surface water models were conspicuous. The results indicate that the relationship between water pH and other water quality parameters are different in different water systems and can be influenced by the presence of other parameters. Associations between parameters are steadier in groundwater systems due to its confined nature. Extraneous inputs and physical variations subject surface water to constant variations which reflected in the non-robustness of the predictors. However, the carbonate system was influential in how water quality parameters associate with one another in both ground and surface water systems. This study affirms that chemical constituents in natural water bodies react in the environment in far more complicated ways than if they were isolated and that the interaction between various parameters could predict the quality of water in a particular system.

Introduction

Water is and will continue to be an important part of life. water bodies such as lakes, rivers and streams are the most essential reservoirs for freshwater [ 1 ]. Groundwater remains an essential source of potable water, serving as the primary water resource in arid regions. Compromising the quality of ground and surface water endangers the health and safety of residents within its catchment areas. Assessing the quality of water is mainly based on its physicochemical components, biological quality and heavy metals concentrations [ 2 ]. Water systems are considered contaminated when the presence of organic, inorganic, biological, thermal or radiological substances in them are at a level which tend to degrade or adversely affect the quality of water and consequently affecting it usefulness [ 3 ].

The quality of water in a reservoir is governed by anthropogenic processes such as industrial, agricultural, human exploitations and natural process including precipitation, weathering, erosions, mineral deposits and other geological phenomena [ 4 ]. Surface waters are the most susceptible and vulnerable water bodies to contamination as a result of being exposed to various types of waste and runoffs [ 5 ]. Ground water on the other hand is better protected against direct runoffs and waste disposals, however, once contaminated, it remains contaminated for longer periods [ 6 ], and as such there is the need to keep it safe for use.

pH is probably by far the most important physicochemical parameter controlling the behavior of other water quality parameters as well as metals concentration in the aquatic environments [ 7 ]. Chemical processes in aquatic systems such as acid-base reactions, solubility reactions, oxidation-reduction reactions and complexations are all influenced by hydrogen ions concentration (pH). Water bodies around the vicinity of mining activities are susceptible to receiving metals from dumpsite leachate and other waste discharge from the mining activities [ 8 ]. Metal pollution has become a major concern due to their ability to bioaccumulate along the food chain [ 9 ]. The availability of these metals can however be influenced by pH, making pH an important factor in determining the chemical and biological properties of water.

pH may also influence the lives of bacteria and the availability of other contaminants in water. In general, very high or very low pH can make water unpleasant for certain purposes. At very high pH, metals tend to precipitate while chemicals such as ammonia become toxic to aquatic life; water tend to have unpleasant smell and taste in alkaline conditions [ 10 ]. At low pH, solubility of metals tend to be high, chemicals like cyanide and sulphide become more toxic. Acidic waters also corrode metal pipes. Therefore, heavy metals in water with a low pH tend to be more toxic, as they become more soluble and bioavailable. Exposures to extreme water pH via drinking and skin contact are known to be associated with irritation to the eyes, skin, and mucous membranes [ 11 ]. Many municipal water suppliers voluntarily test the pH of their water to monitor for pollutants [ 12 ]. Thus, the determination of pH could serve as a sensitive indicator for contamination.

Water quality monitoring is given a high priority for the determination of current conditions and long-term trends for effective management. Given that water is one of the most important life requirements and taking into account the challenges of its quality management, there is the need to identify and assess the sources of contamination through monitoring and evaluation. However, high cost of data sampling and collection provides a challenge on the implementation of water quality monitoring programs. Field measurements may not always give a perfect view of the reality due to sensors having bad contact resulting from fouling, clogging or lack of maintenance. Measurement can also be influenced by external factors: humidity, temperature extremes or electromagnetic fields. The calibration of the measuring instrument may also give rise to problems. In an attempt to reduce the challenges in measurement and monitoring, water quality modelling provides an alternative for characterizing and predicting water quality parameters and to evaluate potential contamination using few measured parameters.

Several modelling techniques has been deployed in water quality monitoring and evaluation including cluster analysis (CA), principal component analysis (PCA), factor analysis (FA), Stepwise logistic regression and multiple linear regression (MLR). Given the large amount of data for assessing quality parameters, there is the need to develop indirect approaches (models) to predicts fluctuation in the factors affecting the quality of the environment. Multidisciplinary research techniques provide opportunities in addressing the challenges associated with understanding the links that exist between mining operations and how it affects the environment. These models offer an alternative approach to a better interpretation of data and to understand water quality [ 13 – 15 ], while making it possible to assess factors influencing the behavior of an environmental system and offers a valuable tool for managing resources as well as solution to pollution problems.

Models such as PCA provides understanding to the underlying relationships between the variables. Verma and Singh [ 16 ] successfully used an artificial neural network (ANN) model to predict water quality parameters of coalmine discharge, Individual techniques such as multiple linear regression (MLR) might not be very useful in addressing problems involving complex and non-linear data and thus might not provide the best and accurate prediction [ 17 ], it is also difficult to describe the quality of water in a quantitative manner by relying solely on models. However, methods that combines these models will allow a more accurate prediction. The task of monitoring water quality can be facilitated if the relationship between various water quality parameters can be established, the inter-parameter relationship offers remarkable information on the source and pathway of parameters. The existence of such associations can help predict the existence of other parameters. Knowledge of these relationships can also help assess conditions of unmonitored water bodies by inferring from already measured parameters and also identify human activities that significantly contribute to pollution as well as areas that are at risk and promote management practices to reduce non-point source pollution [ 18 ].

In this study, a nested logistic regression model was used to examine the relationship between water pH levels and physicochemical factors while controlling for heavy metals and biological factors in water systems (surface and ground water) in the Tarkwa mining area. Modelled and predicted pH could serve as means of detecting abnormal values, discontinuities and recording drifts from routine measurements. In as much as pH affects the biological, physical and chemical properties of water, it is also affected by the water’s geochemistry. Ewusi et al. [ 13 ] affirmed in their study that regression models were appropriate for water quality modelling.

Materials and methods

Samples for this study were taken in Tarkwa, a mining town in the Western Region of Ghana. Tarkwa lies within the south-western equatorial climate zone. The country falls between latitudes 4° 0ʹ 0″ N and 5° 40ʹ 0″ N and longitudes 1° 45ʹ 0″ W and 2° 1ʹ 0″ W. A total of 232 locations (ground water = 100 and surface water = 132) were sampled for this study. Sampling was done on quarterly basis between January 2019 and December 2019. Tarkwa is one of the areas in the country that experience high rainfall. This causes heavy runoffs and leaching of surface soil chemicals. The area was selected for this study due to the high-level of anthropogenic activities including mining activities, welding and other mechanical servicing activities that serve as the main sources of pollution to water supply systems [ 19 , 20 ].

Boreholes, hand-dug wells, streams and rivers are the major source of water supply in Tarkwa for both domestic and commercial purpose. The majority of these water supply systems serve as a source of drinking water for nearby communities. The average well depth in the area is about 35.4m [ 21 ]. The quality of the water supply systems in the area is highly affected by mine contaminants and mining-related activities, leakage from underground storage tanks, improper waste disposal and agrochemicals from agricultural fields. The study area is located within a drainage basin of the Ankobra River Basin. The Bonsa, Huni and Ankobra Rivers and their tributaries are the main sources of drainage system in the area [ 21 , 22 ].

Data description

A total of 17 parameters, which include 10 physicochemical parameters (electrical conductivity, total dissolved solids, total suspended solids, turbidity, total alkalinity, magnesium, calcium, sulphate, nitrate and phosphate), 5 heavy metals (arsenic, zinc, iron, manganese and copper) and 2 biological parameters (faecal coliform and total coliform) were obtained from ground and surface water systems in the study area. These parameters were carefully chosen based on their data availability, significance and concentrations with respect to the WHO guideline values.

Water pH was the focus variable of this research. pH was selected as the response variable because pH is notably one of the most important physicochemical parameters that controls the behavior of other water quality parameters as well as metals concentration in aquatic environments [ 7 ]. Chemical processes in aquatic systems such as acid-base reactions, solubility reactions, oxidation-reduction reactions and complexation are all influenced by pH. The WHO has a standard drinking water guideline for pH. In this instance, all drinking waters should be within a pH range of 6.5–8.5. In this study, pH values that were within this range were classified as optimal and coded with “1” while pH values that were outside of this standard were classified as “non-optimal” and coded as “0” to get a binary outcome (non-optimal/ optimal).

Logistic regression analysis

In this study, a logistic regression statistical model was deployed, This model relates to the response variable through a link function by allowing the magnitude of the variance of each measurement to be a function of its predicted value under the assumption of binary response (non-optimal/ optimal) [ 23 ], Via the link function, there are several potential techniques that could be deployed for a logistic regression analysis: the logit model, probit model, negative log-log and complementary log-log model. Both logit and probit link functions have the same property, that is the probability that an observation in a specified category of a binary outcome variable has the same probability of approaching 0 as well as approaching 1 (50% non-optimal, 50% optimal). Given that, the observations of a binary outcome have an asymmetrical success of probability, that is, fewer 0s than 1s or more 0s than 1s, then the link function complementary log-log or negative log-log is chosen respectively. In this study 64% and 79.5% of the locations had optimal pH for drinking water for ground water and surface water respectively. For this reason, the complementary log-log link function was appropriate for modelling water pH levels.

The odds ratios (OR) were built in a nested model starting from the physicochemical model, heavy metals model and bacteriological model. An OR of 1 meant that higher values of the predictor did not affect the odds of optimum or non-optimum water pH; OR > 1 meant that the predictor was associated with odds of optimum water pH; and OR < 1 meant the predictor variable was associated with odds of non-optimum water pH.

All statistical analyses were performed using Stata 15 (StataCorp, College Station, Texas) SE software at a statistical significance of 0.05 and at a confidence interval of 95%.

Water sampling

The sampling was carried out in accordance with the protocols developed by the America Public Health Association (APHA) [ 24 ]. Sampling bottles were washed with detergent and rinsed with 10% hydrochloric acid and double-distilled water prior to sampling. At each of the sampling locations, bottles used to collect samples were thoroughly rinsed with the water to be sampled three times to reduce possible contamination of the sampling bottles. Surface water samples were taken midstream with conscious effort not to disturb water sediments by gently submerging the sample bottle horizontally into the water to fill the bottles while facing upstream, taking reasonable measures to avoid suspended/floating debris. Thus, surface water samples were collected at the subsurface in order to avoid the colloidal layer as this can influence the concentration of certain parameters. Personnel entry into the water body was minimized as much as possible. 1000 mL of water was collected from each sample location using two 500 mL transparent plastic bottles, which were placed in an opaque material (black polyethylene bag), tied and finally kept in a cooler box. Bottles containing samples were labelled using first letters of sampling site and numbers. This procedure minimized the possible growth of micro bacteria, flocculation and reduce any adsorption on container surfaces, processes which could affect the results.

Water from the community boreholes was collected at the faucet after it had been pumped for a while to obtain a steady flow before sampling. This was to be sure that the water being collected is freshly extracted from the borehole.

Field analysis

pH, conductivity and turbidity were measured in situ during the sampling. Calibrations were conducted in the field at the sample site. The pH probe was calibrated with pH 7 and 10 buffer solutions on the day of sampling.

Laboratory analysis

Laboratory tests were conducted in compliance with “Standard Methods for the Examination of Water and Wastewater” of the American Public Health Association, 1998 Edition. Analysis of metals As, Fe, Mn, Cu and Zn were carried out by homogenizing samples, filtered and acid-digested in accordance with USEPA protocol 2002 and analyzed using flame atomic absorption spectroscopy (AA240FS) following USEPA protocol 2002 [ 25 ]. Unprocessed water samples were also analyzed for electrical conductivity, and for chloride, sulphate, nitrate, phosphate, and alkalinity concentrations. Faecal coliform and total coliform were also determined by the membrane filtration technique.

Descriptive statistics

Table 1 shows a statistical summary (mean, standard deviation, minimum and maximum) of pH and the predicting variables selected for the study. A total of 100 ground water locations were sampled. Maximum and minimum pH values recorded were 7.850 and 5.240 respectively with a mean pH of 6.737, indicating an acidic to slightly alkaline groundwater samples. Out of the 100 locations, 36 recorded pH values outside the range of the WHO standard for drinking water [ 26 ]. The remaining 64 locations however recorded pH values within the WHO guidelines for drinking water quality.

For surface water sources, there were132 sample locations. The mean pH value recorded was 7.005 With maximum and minimum values of 9.950 and 4.160 respectively. Out of the 132 locations, 27 recorded pH values outside the range of the WHO standard for drinking water quality.

Most of the other physicochemical parameters were within the guideline limits with few sample locations recording values above the guideline limit. On average, surface water recorded high values for heavy metals as compared to groundwater. Coliform bacteria were relatively high in ground water than surface water sources.

Correlation analysis for water quality parameters in groundwater and surface water sources

Pearson’s correlation analysis (r) for the selected parameters were carried out. From the correlation matrix for ground water ( Table 2 ), conductivity was highly correlated with total dissolved solids (r = 0.963). The strong correlation between conductivity and total dissolved solids gives an indication of the extent to which salts dissociate into ions and influence conductivity. Conductivity is the ability of water to conduct electrical current and it is related to the concentration of ionized substances in the water. Total suspended solids was moderately correlated with magnesium (r = 0.601), calcium (r = 0.556) and sulphate (r = 0.682). Magnesium showed a moderate correlation with total alkalinity (r = 0.633), while phosphate was also moderately correlated with total coliform (r = -0.525). pH was moderately correlated with nitrate (r = -0.525). For surface water sources in Table 3 , conductivity again showed a strong correlation with total dissolved solids (r = 0.963) and moderately correlated with nitrate (r = 0.581). pH was weakly correlated with all parameters selected for the study.

Note: * In bold are significant correlations.

Zero-order relationship between pH and selected water quality parameters

Table 4 shows the results of the association between individual parameters and their odds of predicting pH levels in ground and surface water sources. For ground water physicochemical factors, conductivity (OR = 1.004, p < 0.001), total dissolved solids (OR = 1.005, p < 0.001), total suspended solids (OR = 1.003, p < 0.05), calcium (OR = 1.023, p < 0.05) and sulphate (OR = 1.009, p < 0.05) were significantly associated with higher odds of optimum water pH. Similarly, higher values of total alkalinity (OR = 1.720, p < 0.05) was significantly associated with higher odds of optimum water pH. Turbidity, magnesium, nitrate and phosphate showed no association with pH in groundwater. Among the heavy metals, zinc (OR = 0.091, p < 0.05), manganese (OR = 1.37E-13, p < 0.05) and copper (OR = 0.578, p < 0.05) were statistically significant in predicting water pH levels. Here, higher values of zinc, manganese and copper were associated with non-optimal water pH. Of the bacteriological factors, faecal coliform (OR = 1.000, p < 0.05) showed significant association with pH levels, however, odds of faecal coliform did not affect the odds of predicting pH levels in ground water.

Note: * In bold are significant predictors.

For surface water sources, only total suspended solids (OR = 1.008, p < 0.05), turbidity (OR = 1.014, p < 0.05), calcium (OR = 1.023, p < 0.05) and total alkalinity (OR = 2.014, p < 0.001) were statistically associated with predicting pH levels. Here, higher values of total suspended solids, turbidity, calcium and total alkalinity were associated with higher odds of optimum water pH. None of the heavy metals and bacteriological factors was significant in predicting pH levels in surface water at the bivariate level.

Multivariate complementary log–log regression model showing the relationship between pH and selected water quality parameters for groundwater

Table 5 shows the results of the multivariate regression analysis of three different models for ground water. Model 1 presents the results for the physicochemical parameters. Model 2 accounted for physicochemical and heavy metals and in the third model, physicochemical factors together with heavy metals and biological parameters were accounted for. Model 1 showed that, conductivity (OR = 0.990, p < 0.05) and magnesium (OR = 0.735, p < 0.05) were less likely to be associated with optimal water pH. Total dissolved solids (OR = 1.020, p < 0.05), turbidity (OR = 1.002, p < 0.05) and total alkalinity (OR = 2. 780, p < 0.05) were statistically associated with higher odds of pH, thus higher values of total dissolved solids, turbidity and total alkalinity were associated with higher odds of predicting optimal water pH for drinking groundwater sources.

In the second model, where heavy metals were accounted for, the odds of prediction for conductivity and magnesium remained less likely in predicting optimal water pH. Total dissolved solids, turbidity and total alkalinity were still significantly associated with predicting optimal pH in ground water systems. There was however partial mediation by the heavy metals (changes in significant values). It was observed that, total suspended solids was not statistically significant in model 1, but became statistically significant in model 2, indicating mediation by the heavy metals. In this instance, total suspended solids (OR = 0.009, p < 0.05) was associated with predicting optimal pH levels. Among the heavy metals, zinc (OR = 0.108, p < 0.05) and manganese (OR = 5.71*10–22, p < 0.05) were less likely to predict optimal pH for ground water systems.

In the third model in which biological parameters were accounted for, the model showed similar characteristics of model 2. Of the physicochemical parameters, the relationship between conductivity and magnesium and the likelihood of predicting non-optimal pH levels persisted in the third model. Total dissolved solids, total suspended solids, turbidity and total alkalinity remained significantly associated with higher odds of predicting optimal pH and their odds of prediction persisted as observed in model 2. Among the heavy metals, the relationship between zinc, manganese and the odds of predicting pH levels also remained robust and persisted. In this case zinc and manganese were associated with predicting odds of non-optimal water pH. None of the bacteriological factors were statistically significant in predicting pH levels in ground water systems in this study.

Multivariate regression model showing the relationship between pH and selected water quality parameters for surface water

Multivariate regression model for surface water is shown in Table 6 . Physicochemical parameters were accounted for in model 1. Model 2 accounted for heavy metals and the third model controlled for biological parameters. In the first model, turbidity (OR = 1.034, p < 0.05), calcium (OR = 1.055, p < 0.001) and sulphate (OR = 0.994, p < 0.05) showed statistically significant association with water pH levels. In this case, turbidity and calcium were more likely to predict optimal water pH levels. Contrariwise, sulphate was associated with non-optimal values of pH in surface water.

Note: *In bold are significant predictors.

In the second model, where heavy metals were accounted for, only turbidity (OR = 1.023, p < 0.05) and calcium (OR = 1.083, p < 0.05) persisted in predicting water pH levels. However, new relationships appeared, indicating mediation by the heavy metals. Here, conductivity (OR = 0.987, p < 0.05) and total suspended solids (OR = 1.0180, p < 0.05) were statistically significant in predicting water pH levels. Conductivity in this scenario was less likely to predict optimum water pH levels while total suspended solids was more likely to predict optimal water pH. Of the heavy metals, manganese (OR = 168614.3, p < 0.05), zinc (OR = 0.003, p < 0.001) and copper (OR = 0.593, p < 0.05) were statistically associated with the odds of predicting water pH. In this instance, manganese was more likely to predict optimal water pH levels. Contrariwise, higher values of zinc and copper were associated with non-optimal levels of pH in surface water systems.

Total suspended solids and conductivity lost their significance in the third model when biological factors were accounted for. Indicating mediation by the biological factors. Turbidity (OR = 1.116, p < 0.05) and calcium (OR = 1.089, p < 0.05) remained robust and persisted in predicting pH levels. In this case, they were more likely to predict optimal water pH levels. A new relationship also appeared. Here, nitrate (OR = 1.028, p < 0.05) became statistically significant in predicting pH levels. Thus, higher values of nitrate was more likely to predict optimal pH levels. Of the heavy metals, manganese (OR = 1.5*107, p < 0.05) remained significant with high odds of predicting optimal pH in surface water locations. Among the bacteriological factors, total coliform (OR = 1.005, p < 0.05) and faecal coliform (OR = 0.985, p < 0.001) were statistically associated with predicting water pH levels. Total coliform in this scenario was associated with the odds of predicting optimal pH values while faecal coliform was associated with non-optimal pH values.

This study analyzed the association between pH and physicochemical parameters while controlling for heavy metals and bacteriological factors for ground water and surface water systems in the Tarkwa mining area. Nested logistic regression model was used to evaluate the dynamics of these relationships in groundwater and surface water systems. The chemistry of water systems, especially heavy metals, are much affected by pH and vice versa [ 27 ]. Knowledge of the association between water quality parameters is important for the sustainability and quality management of water. This study used heavy metals, bacteriological and physicochemical factors as predicting variables to assess the association between water quality parameters and pH and how their associations vary in the different water systems. The models in this study indicated that higher values of pH can be associated with some water quality parameters and can give an idea of the quality of water system.

In a zero-order relationship for the ground water system, six physicochemical parameters (conductivity, total dissolves solids, total suspended solids, total alkalinity, calcium and sulphate) were associated with predicting optimum water pH levels. High values of these parameters implied high probability of having water optimal for drinking.

At the multivariate level for ground water, total alkalinity was consistent with the odds of prediction. The addition of heavy metals and bacteriological factors further strengthened the relationship between alkalinity and odds of predicting optimal pH. The carbonate system is a function of alkalinity while the various forms of carbonates (carbon dioxide, bicarbonate and carbonates) govern pH conditions of water [ 28 ]. High alkaline water often has high pH and so the strong association of total alkalinity with optimal pH indicates that the alkalinity values reported in this study are not high and thus corresponds to non-optimal pH.

Total dissolved solids were robust in its association with pH. The major contributors of total dissolved solids are carbonates, bicarbonates and salts of sulphates, phosphates, chlorides and nitrates. The dissolution of these salts (sulphate and phosphate) influences the availability of dissolved solids, the presence of dissolved solids indicates the dissolution of salts. Tlili-Zrelli [ 29 ] observed a linear relationship between total dissolved solids and major ions in groundwater. Solids that dissolve in water break into positively and negatively charged ions thereby increasing the conducting ability of water [ 30 ]. Conductivity, a property that depends mainly on dissolved salts can be taken as indirect measure for total dissolve solids [ 31 ]. These dissolved ions consequently become the conductors for electric current. This linear relationship between conductivity and total dissolved solids was further manifested in the correlation analysis as a strong positive correlation was observed. The relationship between conductivity and dissolved solids indicates the degree to which salts dissociate into ions. Armah [ 32 ] reported a significant association between conductivity and total dissolved solids with the distribution of pH in a ground water system. Interestingly, in this study, increased total dissolved solids indicates optimal pH while increased conductivity indicates non-optimal water pH.

Magnesium was insignificant while sulphate and calcium were significantly associated with pH levels in the zero-order relationship, however, the opposite occurred in the multivariate model where other physicochemical parameters, heavy metals and bacteriological factors were accounted for. In all three models for groundwater, magnesium was significantly associated with lower odds of predicting optimum water pH with slight decrease in odds as heavy metals and biological factors were accounted for, indicating that higher values of magnesium is associated with non-optimal groundwater pH. The association of magnesium with pH at the multivariate level could be mediated by sulphate ions. Kura [ 33 ] in his study reported that sulphate and magnesium ions were significantly associated and could have influence each other in a complex system. Magnesium bearing minerals such as dolomite is a very common mineral in groundwater resulting from rock-water interactions. The dissolution of dolomite is a function of pH, moreover, the fractionation of carbonate rocks is also influenced by water pH [ 34 ]. Greiserman [ 35 ] also reported similar observation where the dissolution of dolomite into calcium and magnesium ions were effective in acidic medium. These studies are in line with this current study that, high concentration of magnesium ions in water suggest non-optimal pH and that the availability of magnesium in water especially in groundwater system is influenced by pH.

Turbidity was not significant in predicting pH levels in the zero-order relationship however, it became significantly associated with pH at the multivariate level for groundwater, with higher values of turbidity in this case indicating an optimal water pH. Suspended solids contribute to the turbidity of water and could be the main parameter that mediated the association between turbidity and pH. Many studies including Acheampong [ 36 ] and Mustapha [ 30 ] also observed a significant relationship between total suspended solids and turbidity. Interestingly, total suspended solids lost its significance with pH in the physicochemical model. However, when heavy metals and bacteriological factors were controlled for, the relationship between pH and total suspended solids reappeared, with higher values of total suspended solids indicating optimal water pH.

Among the heavy metals, only zinc and manganese were significantly associated with pH; predicting non-optimal pH levels. The environment of every chemical specie has influence on its behavior and thus affect its reactions with other species. Although solubility of metals depends on pH, the chemical composition of water systems can influence metal dissolution. The availability of metals in a groundwater system is a complex function of many factors including chemical, biological, and environmental processes [ 37 ]. For the bacteriological factors, both faecal and total coliform were significant predictors of pH in the zero-order analysis but was however insignificant in the multivariate analysis. Thus, the relationship between coliform bacteria and pH in a water system can be mediated by the physicochemical factors and heavy metals.

For the zero-order analysis in the surface water system, total suspended solids, turbidity, total alkalinity and calcium were all associated with optimal water pH. However, only turbidity and calcium were significantly associated with pH in the multivariate regression model. Sulphate also became significantly associated with pH.

Calcium showed a strong association with high odds of predicting pH, and was persistent in all three models, this result indicated that higher values of turbidity and calcium are associated with optimum water pH. The interaction between carbon dioxide and solid carbonates in the form of calcium carbonate (calcite) from bedrocks liberates calcium ions and bicarbonate species in water. Calcium is an important component of the carbonate system and its liberation from carbonate system is affected by pH. Holland [ 38 ] observed a linear relationship between calcium ions and bicarbonate ions in river waters. The association of calcium with pH in this study further suggest that pH affect the carbonate system. The dissolution of metals contribute to dissolved ions and thus the introduction of heavy metals influenced the availability of dissolved ions and consequently mediating the association of conductivity with pH.

Total alkalinity and total suspended solids were significantly associated with high odds of prediction in the zero-order analysis but lost their significance in the physicochemical model of the multivariate analysis for surface water. However, the addition of heavy metals mediated the association of total suspended solids with surface water pH. Insoluble metal ions in the form of solid elemental metal precipitates (metal colloids) and solid metal compounds might have contributed suspended solids. The nature of surface water makes it susceptible to receiving solids in all forms through runoffs, agricultural inputs, waste water discharge, etc. and could also contribute to solid particles.

All metals in the zero-order analysis for surface water had no significant association with pH levels. However, zinc, manganese and copper were significantly associated with pH at the multivariate level with manganese values associated with high odds of predicting optimum water pH. Zinc and copper were however not robust in the multivariate model accounting for biological and physicochemical parameters. The nature of surface water allows it to be much affected by climatic conditions; the physical conditions of surface water such as temperature, turbulence and transparency influence its chemical and biological process. Temperature is known to affect mobility and solubility of chemical species while turbulence affect turbidity via water overturn (mixing) and consequently affecting water temperature. The variations in physical conditions of surface water subject water parameters to constant changes and could account for the non-robustness of some surface water variables.

When biological factors were accounted for in the surface water model, both faecal and total coliforms showed significant association with pH. Interestingly, total coliforms were associated with optimal pH values while faecal coliform were associated with non-optimal water pH, a similar observation was reported by Aram et al. [ 39 ]. This further implied that total coliform bacteria survival is much favored in optimal pH conditions. The distribution of feacal bacteria in water are much affected by physical and climatic factors such as runoffs, temperature and solar radiations than pH. Both total and faecal coliform bacteria are considered pollution indication bacteria and are used as a measure for sanitary parameters for water in a particular environment. Several studies have also reported a significant relationship between various bacteria in water environment [ 40 – 43 ]. The introduction of biological factors also saw nitrate gaining significant association with pH. The association of nitrate with pH suggests that an increase in the nitrogenous species occur in optimal pH levels. Armah et al. [ 44 ] reported a significant association between nitrate and coliform bacteria and thus nitrate’s significance in the biological model could be influenced by the introduction of coliform bacteria. The variations in chemical composition of surface water are highly influenced by topography, climate, and mineralogical composition of the bed rock [ 45 ]. Empirical research has shown that the quality of surface water in a particular region is controlled by these natural factors [ 46 ]. Surface water is much affected by temperature and could be an influential factor in the availability of both faecal and total coliform [ 47 ] and consequently their association with water pH.

This study sough to evaluate the relationship between water pH and physicochemical properties of water while controlling for the effect of heavy metals and bacteriological factors using a nested logistic regression model. The study also compared the relationship between water quality parameters in confined water systems (ground water) and open water systems (surface water). The findings of this study give the joint effect of water quality parameters on pH and how they affect each other in a confined and open water system. For the zero order relationship in groundwater, EC, TDS, TSS, Ca, SO 4 2- , total alkalinity, Zn, Mn, Cu, faecal and total coliform were more likely to predict optimal water pH. For surface water however, only TSS, turbidity, total alkalinity and Ca were significant predictors of optimal pH levels. At the multivariate level for groundwater, TDS, turbidity, total alkalinity and TSS were associated with optimal water pH while EC, Mg, Mn and Zn were associated with non-optimal water pH. For surface water multivariate regression model, turbidity, Ca, TSS, NO 3 , Mn and total coliform were associated with optimal pH while SO 4 2- , EC, Zn, Cu, and faecal coliform were associated with non-optimal water pH. The non-robustness of predictors in the surface water models were conspicuous. The results indicate that the relationship between water pH and other water quality parameters are different in different water systems and can be influenced by the presence of other parameters. Associations between the parameters are steadier in groundwater systems due to its confined nature. Extraneous inputs and physical variations subject surface water to constant variations which reflected in the non-robustness of predictors. The carbonate system was influential in how water quality parameters associate with one another in both ground and surface water systems. This study affirms that chemical constituents in natural water bodies react in a more complicated ways than if they were isolated and that the interaction between various parameters could predict the quality of water in a particular system.

Funding Statement

The author received no specific funding for this work.

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Physico-Chemical Parameters for Water Quality Check: A Comprehensive Review

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Home > Books > River Basin Management - Under a Changing Climate

Evaluation of Water Quality Using Physicochemical Parameters and Aquatic Insects Diversity

Submitted: 17 June 2022 Reviewed: 03 October 2022 Published: 14 November 2022

DOI: 10.5772/intechopen.108423

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Biomonitoring studies focus on the component of biodiversity, its natural habitats, and species populations which display the ongoing variations in ecosystem and landscape. Physicochemical parameters are important water quality parameters of river water i.e., pH, temperature, turbidity, conductivity, total dissolved solids, total suspended solids, total alkalinity, sulfate, nitrate, heavy metals, and phosphate. This chapter focuses on assessing water quality through Physicochemical Parameters and Aquatic Insects Diversity. The case study investigated the effect of pollutants produced by the human dwelling, agricultural and industrial activities on aquatic invertebrate communities of water of part of Soan River, Pakistan. Four sites were selected based on variation in microhabitat accessibility to examine the pollution in water. Samples were collected from these sites during spring, 2015. Water samples for physio-chemical analysis and macroinvertebrates were collected from all sites. Results showed that conductivity, dissolved oxygen, sodium, and cadmium at all sites were higher than the drinking water quality of WHO standards while potassium, chromium, and manganese were higher in concentration at most downstream sites. However, all other studied parameters were within recommended range of WHO standards. A total of 412 individuals of aquatic insects were collected from the studied sites, belonged to 6 orders and they were the most abundant in April. Total abundance was used to estimate the quality of water at the sites. Most biotic indexes showed that water was of good quality at upstream stations rather than downstream stations, while water quality index (WQI) showed fair water quality at downstream sites. This study showed that aquatic insects could be useful as bioindicators for biomonitoring of water quality along with physiochemical parameters.

• Biomonitoring
• Physicochemical

Author Information

Muhammad xaaceph khan *.

• Institute of Zoology, University of the Punjab, Lahore, Pakistan

*Address all correspondence to: [email protected]

1. Introduction

1.1 physicochemical problems and emergences of biomonitoring.

Common methods that rely on chemicals to monitor river pollution are increasingly suitable for monitoring systems as they can detect physical and environmental pressures occurring over time and on multiple scales [ 1 , 2 , 3 , 4 , 5 ]. However, concepts, and principles of biomonitoring, which are more efficient and effective than traditional methods, have been developed and widely used worldwide to monitor river pollution. Compared to common and uncommon species, bioindicators are more tolerant to environmental change. They are sensitive enough to detect environmental change thanks to their tolerance, but they are also resilient enough to deal with some variability and represent the overall biotic response [ 6 ]. However, this new initiative has liberated tropical areas by allowing the model and adaptation of current geologically developed non-tropical species using natural freshwater organisms. These bio-monitoring indicators are often developed for specific regions to respond to regional variables using local biotic collections that reflect regional variability based on sensitivity or biological tolerance. Such variations may affect the strength, performance, and reliability of biomonitoring indicators developed in non-tropical areas (mean temperature between 21 and 30°C and rainfall 100 inches a year) when used in tropical rivers (mean temperatures above 18°C and monsoonal patterns rainfall 79 to 394 inches) [ 7 ]. Similarly, modification of non-thermal bio-monitoring indicators used in tropical areas is often captured by incomplete taxonomical resolution and unknown levels of tropical taxa [ 4 ]. Abiotic variables or physiochemical samples are problematic in identifying a change or impact in some environmental conditions. For example, contamination can be present in toxic quantities or bio-accumulated, which causes adverse biological deterioration. However, contaminated concentrations may be too small to be detected using this procedure [ 8 ]. Consequently, changes in behavioral or pathological responses, population dynamics, environmental pollution, and impacts have been measured using biological rather than physic-chemical indicators by many scientists because of the direct interaction of an organism with the ecosystem [ 9 , 10 , 11 ].

1.2 Importance of biologic index

The use of biological indicators to assess the health of the river ecosystem has become increasingly important because the function of life, biodiversity, population density, human settlement, and the activity of aquatic organisms are affected by all changes in the water ecosystem. River life decisions can be made based on biodiversity and quantity. Many aquatic species such as fish [ 12 , 13 ], algae [ 14 ], plankton [ 15 ], and benthic macroinvertebrates [ 16 , 17 ] are common biologic indicators of water pollution and are used in biotic reliability for the aquatic ecosystem [ 18 , 19 , 20 , 21 ]. The types of indicators are those taxes that are known to be more sensitive to certain environmental factors so that changes that occur or in large quantities can directly reflect local change [ 22 ]. The usage of biomonitoring techniques in river ecosystems have many advantages compared to physiochemical techniques [ 23 ]. Freshwater organisms play an important role in the continuously monitoring water quality and pollutants that enter at different time intervals. In most cases, the disorder occurs during at least one stage (egg, larva, caterpillar, adult) of the invertebrate animal life cycle. If this category is affected by the disruption, changes will be seen in the community structure if sampled over time [ 24 ]. Macroinvertebrates are also sensitive to stress; it can be natural or human-based. This change will lead to an impaired community. The aquatic insects show the effect of point and non-point contaminants, physical habitat alteration, and pollutant accumulation over the life cycle [ 25 ].

2. Estimation of physiochemical and biological index, a case study of Soan River

2.1 study site.

Sampling sites have been selected that vary in their physiochemical characteristics of the Soan River, Pakistan, at the time of spring 2015. These sites were named A (N 33°43.120, E 073°20.44), B (N 33°37.133, E 073°17.88), C (N 33°33.174, E 073°08.547), and D (N 33°32.906, E073°05.844) which was starts from site A and end at D site as mentioned in [ 26 ]. Their positions are shown on the map ( Figure 1 ). The starting point was Simply dam, and the sampling sites were selected thereafter. Photos for Site A ( Figure 2 ), Site B ( Figure 3 ), Site C ( Figure 4 ), and Site D ( Figure 5 ) are presented. In addition, the site descriptions are listed in Table 1 . Site A was considered upstream, site B and C considered mid-stream, while all the sites were categorized with respect to major water pollution entry in the river. i.e., site A has no major pollution activities; site B receives poultry farms wastage; site C receives industry pollution wastage without treatment; site D is a dumping point of sewage from Rawalpindi and Islamabad. After that, no major pollution point was spotted.

Map of study area and location of sampling sites at Soan River, Pakistan.

The geographical position of sampling sites at Soan River.

2.2 Sampling and analysis procedure

From each site, ten water and macroinvertebrates samples were collected. Physical parameters of the area was also recorded. pH, conductivity, total suspended solids (T.D.S), and the water temperature were measured in the field using a portable instrument (HANNA HI 9811-5), and dissolved oxygen (DO) was calculated by using DO-510. The other parameters recorded in Table 2 were determined in the laboratory using a flame (air-acetylene) atomic absorption spectrophotometer (Hitachi, Model Z-5000) and tritremetric methods following standard procedure [ 27 , 28 , 29 , 30 , 31 ].

Physio-chemical analysis of water samples collected from Soan River.

A: Simly Dam; B: Aari Syedan; C: Hummak; D: Soan Adae: WHO: World Health Organization; S.E in brackets. Bold indicates that the values didn’t follow the WHO Standards.

2.3 Sampling of aquatic insects

Macroinvertebrates were collected by D-net of 30 cm × 30 cm × 60 cm with 0.5 mm mesh. The net was held vertically right angle to the current. The collection was done by shaking the net at a depth of the river for 1 minute. The collection was done in the morning for 1 week. The collection was done from upstream (A) to downstream (D). For maximizing the complete assemblage of samples, ten replicates from each site were selected every day according to the geographic conditions of the surrounding, i.e., anthropogenic interference and natural causes. After the collecting of water and macroinvertebrates, samples were carried out into 500 ml plastic bottles with enough water, so that samples were not damaged during transportation.

In the laboratory, macroinvertebrates were preserved within a few hours of collection in 90% alcohol. The collection was sorted within a month by RIVPACS (River Invertebrate Prediction and Classification System) standard procedure (Environment Agency, 1997) using taxonomic keys [ 32 ]. Small aquatic insects were sorted under a dissecting microscope, whereas large insects were sorted with naked eyes. All samples were then kept in properly labeled vials containing 90% alcohol.

Biotic Index, Biological Monitoring Working Party-Average Score Per Taxon (BMWP-ASPT), The HBI (Hilsenhoff Biotic Index) or Family Biotic Index (FBI), Ephemeroptera, Plecoptera, and Trichoptera (EPT) Index and Water Quality Index were assessed using the data gathered. Small description for each of the indexes are also given as follows.

3. Biotic index

Aquatic insects of the freshwater river and stream ecosystems have frequently been examined to assess the species-habitat relationship concerning the water quality of the habitat [ 33 ]. Aquatic insects can indicate the water quality of streams, rivers, and lakes. Once aquatic insects are collected, and after analysis, the data can be compared between different sites by using four standard indices, i.e., Hilsenhoff’s Biotic Index [ 34 ], EPT (Ephemeroptera, Plecopterans, and Trichopterans) Index [ 35 ], the Benthic Index of Biotic Integrity [ 25 ] and Beck’s Biotic Index [ 36 ]. The EPT Index stands for Ephemeroptera, Plecopterans, and Trichopterans, three orders of Class Insecta which can easily be sorted, identified, and commonly used as water quality indicators. The EPT index is based upon a high-quality stream ecosystem with a great species richness. Biotic Index shows the quality of the environment by the presence of different organisms present in it, this index is also known as the “Family Biotic Index”. This index is commonly used for river water quality. Biotic Index shows four basic water quality, i.e., (Excellent, Good, Fair, or Poor) measured as 1 to 10. 1 is good, and 10 is poor.

3.1 Biological monitoring working party-average score per taxon (BMWP-ASPT)

Biological Monitoring Working Party-Average Score Per Taxon (BMWP-ASPT) is a biotic index method. That index also estimates the diversity of organisms concerning pollution level. According to different indexes, specimens are placed at various levels from 1-to 10. Plecoptera (rock fly larvae), Ephemeroptera (mayfly larvae), and Crustacea (pole shrimp) are on level 10, Gastropoda (freshwater limpet), Odonata (kini – kini) are at level 8, Trichoptera (caddisfly larvae) at level 7, Odonata (dragonfly larvae), Crustacea (freshwater shrimp) and Bivalvia (shell) at 6 levels, Hemiptera (backswimmer), Diptera (fly larvae), Trichoptera and Coleoptera (water scorpion, diving beetle) at 5, Arachnida (water mite) and Platyhelminthes (flatworm) at four levels, Syrphidae (rattail maggot), Hirudinea (leech), Gamaridae (water pig bug), Gastropoda (snail) and Bivalvia (shell) at 3, Chironomidae (mosquito larvae) at level 2 and Oligochaeta (worm) at level 1. The sequence starts from 10 as excellent and 1 as poorer. Many aquatic species are pollutants intolerant, i.e., levels 10–7 and absent in polluted water bodies. The greater the pollution, the lower the number of insects because few species are tolerant to pollutants, i.e., level 1. The BMWP score equals the sum of the tolerance score of all families in the sample. ASPT was calculated by taking the average number of the tolerance scores of all families of macroinvertebrates which varies from 0 to 10.

3.2 The HBI (Hilsenhoff biotic index) or family biotic index (FBI)

The HBI (Hilsenhoff Biotic Index), also known as the family biotic index (FBI) calculates the level of tolerance in the community of a specific area and the categorization of each taxonomic group by relative abundance. Organisms are grouped with a tolerance number 0–10, and 10 is the most tolerant, while 0 is the most sensitive to organic pollutants [ 1 , 37 , 38 , 39 ]. The tolerance values were modified for macroinvertebrates for application in the Modified Family Biotic Index.

The family biotic index (FBI) is calculated as [ 37 ].

Where a xi is the number of individuals within a taxon, ti is the tolerance value of a taxon, and n is the total number of organisms in the sample. If the value is between 0.00–3.75, then excellent, 3.76–4.25 very good, 4.26–5.00 good, 5.01–5.75 fair, 5.76–6.50 fairly poor, 6.51–7.25 poor, and 7.26–10.00 very poor.

3.3 Ephemeroptera, Plecoptera, and Trichoptera (EPT) index

EPT index was estimated by summing a total number of individuals group Ephemeroptera (Mayflies), Plecoptera (Stoneflies), and Trichoptera (Caddisflies). If the EPT number is greater than 7 per sample, then it is considered excellent, between 2 and 7 is good, and below 2 is considered poor [ 40 ].

4. Water quality index

Water quality was identified based on the water quality index (WQI) [ 41 ].

F 1 = [Water variables that do not meet objectives/Total number of water variables]*100

F 2 = [Number of tests that do not meet objectives/Total number of tests]*100

F 3  = nse/0.01 nse + 0.01

nse (normalized sum of excursions) = Σ n departure/number of tests.

The variables that do not meet objectives are those parameters that exceed the WHO permissible limits. At the same time, the number of tests that do not meet objectives is the number of replicates that do not fall between permissible limits. WQI, the score was scaled between 0 to 100; higher values represent better quality of water, e.g., excellent >80, good 60–80, fair 60–40, and poor <40.

The water quality parameters of the upstream section of Soan river tested in this study are summarized in Table 2 . The pH was normal in all sites except in site D. Conductivity, D.O, sodium, and cadmium had high values concerning the standards. While, T.D.S, chloride, sulfate, nitrate, calcium, magnesium, total hardness, potassium, aluminum, and copper did not exceed the limit, and fell within the normal range. Potassium, chromium, and manganese show the normal range at sites A & B while surpassing ranges at sites C & D. The water temperature was 25–27°C from upstream to downstream.

A total of 412 individuals of macroinvertebrates were captured, which represents 7 orders. Site A has shown more variety of insects than all the other sites. Total abundance was higher at site C. The variety of insects drops from upstream to downstream stations and only limits to order Diptera ( Table 3 ). In the biotic index, the FBI shows good water quality in upstream stations (sites A & B) while quality decreases fairly in site C and becomes very poor at the last site. The BMWP shows the same trend just like FBI but starts from fair biological quality to very poor biological quality from upstream to downstream. The ASPT represents good water quality at site A and decreases the status to very poor at site D. The EPT only shows good water quality at site A and poor in all remaining sites. In the Physicochemical index, the WQI shows good water quality at sites A & B while fair in sites C & D ( Table 4 ).

Percent composition (%) of aquatic insects in four different stations of Soan River, Pakistan.

The water quality of the Soan River is based on biological indices and water quality index.

Notes: FBI = Family Biotic Index; BMWP = Biological Monitoring Work Party; ASPT = Average Score Per Taxon; EPT = Ephemeroptera, Plecoptera and Trichoptera; Water Quality Index = WQI.

6. Discussion

In this study, water quality was examined using chemical, physical, and biomonitoring methods. In chemical analysis, different parameters were tested, such as magnesium, chromium, aluminum, copper, cadmium, dissolved oxygen (DO), nitrogen (N), and phosphorous (P) [ 42 ]. Physical parameters include pH, total suspended solids (T.D.S), conductivity, chloride, sulfate, nitrate, calcium, total hardness, sodium, and potassium. In bio-monitoring FBI, EPT, ASPT, and BMWP were calculated to determine water quality.

The concentration of metals was almost normal but some of them were high such as cadmium, sodium, potassium, and chromium. The pH values were low at the start and exceeded in site D. This shows that Organic influx wastage in the monsoon season may lower the water pH at that site D. The pH affects the biochemical process as well. It also indicates water quality and the extent of pollution in the watershed [ 42 ]. Among the different heavy metals, Cd shows a higher level of concentration which did not support any life or for drinking purposes (3 μg L −1 ) [ 43 ]. Cd is the typical anthropogenic metal affected by human activities [ 44 ] and showed enrichment. It is shown that Cd is associated to a greater extent with colloidal materials in surface runoff which can easily be transported into river flow [ 45 ]. Metals, such as Cu, Zn, and Pb, have a high affinity to human substances present in organic matter. The presence and quality of organic matter differentially influence the binding of metals within the sediments, reducing the adsorption of Cd and Co and increasing the adsorption of Zn [ 46 ]. Discharge of industrial, sewage, and poultry waste largely untreated forms may cause the elevation of metals in the water [ 47 , 48 , 49 ]. The water quality shows poor quality at those sites which drain sewage of the twin cities, while at downstream sites, the natural process shows some recovery from stress conditions due to the huge amount of sewage waste from the urban [ 50 ]. Activities of humans can change the smallest change in the ecosystem, especially downstream of the Soan River. The poor quality of water at downstream rather than upstream stations can result from several human activities, sewage, nutrient, sedimentation, and agriculture pesticides residue run-off. Wahizatul et al. [ 51 ] also studied in the Sekayu stream and found that agricultural and recreational activities were directly related to the destruction of aquatic species diversity in the Sekayu recreational forest. The higher organism abundance at site A is related to greater availability of coverage of riparian vegetation, which offers them a great supply of hiding places, allochthonous material, and food availability. Roque et al. [ 52 ] pointed out that the area with greater vegetation coverage has greater taxonomic richness. Although, at sites C and D, low diversity is found which could be related to the loss of riverbank vegetation and replaced by waste material, shrubby, exotic vegetation, and a lower quantity of heterogeneous substrate. This phenomenon was noted by [ 53 ]. Adamu Mustapha and Geidam [ 54 ] reported that high nutrients loading at urban sites are due to discharge of sewage wastewater of Rawalpindi and Islamabad.

Chironomidae (Diptera) were most abundant at downstream of the Soan River. They show no variation and are found in all stations. Yule and Sen [ 32 ] reported that in Malysia, Chironomidae is probably the most abundant and diverse group of all macroinvertebrate’s streams. The sandy or muddy areas and slow-flowing or standing streams with a high number of sediment particles are the best areas where Chironomidae can excel [ 32 , 55 ]. Due to heavy rainfall, the flood affected the macroinvertebrates from all the sites. Thus, effect seasonal taxa richness. The member of Chironomidae was most affected by the flood. The mayflies (Ephemeroptera) and true bug (Hemiptera) did not show any response to heavy rainfall because they are morphologically better adapted, attachment abilities to stones, mobility in water and behavioral pattern during mating. Holomuzki and Biggs [ 56 ] studied the behavioral pattern in response to the flood. The fluctuation of water level in the winter season remained very low. This is the major stressing factor for littoral organisms. Nairn et al. and Gopal [ 57 , 58 ] also recorded a similar finding on littoral destruction. The macroinvertebrate density in the Soan River was found to be the lowest when the monsoon season starts and increases when the monsoon stops. Wallace et al. and Jakob et al. [ 59 , 60 ] pointed out that monsoon floods decrease macroinvertebrates’ density, especially Chironomid species known as two-winged flies. EPT was not found abundantly in any collection points, especially downstream. EPT members are known to be the most sensitive insects to environmental stress. Therefore, the presence of EPT upstream indicates a relatively clean environment [ 61 , 62 ]. Therefore, the EPT can be used for potential bioindicator purposes. The BMWP index shows poor results for all the sampling sites, but some sites show fair biological quality at site A as well as ASPT [ 62 ]. Therefore, the presence and absence of macroinvertebrates along with water physicochemical analysis at upstream and downstream shows the influence of anthropogenic and natural influences. This suggests that aquatic insects can be used to access the water management in Pakistan as the role of potential bio-indicators.

7. Conclusion

The biological and WQI index shows water quality was good at upstream rather than at downstream stations. The biotic index shows a clear variation throughout the sites. While the physicochemical index shows the same trend at site D and it shows fair water quality despite the biotic index is Poor. The biotic index is detailed and efficient while the individual can gather information on the spot, but physicochemical parameters are costly, laboratory intensive, and time-consuming. These two indexes can be used alternatively concerning the situation.

Acknowledgments

This study was sponsored by the World Wildlife Fund (WWF) Pakistan through the young Ecologist Scholarship. Special funds for a multipurpose water analysis kit were provided by the University of the Punjab, Lahore. The authors are extremely thankful to both organizations.

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A critical and intensive review on assessment of water quality parameters through geospatial techniques

• Review Article
• Published: 08 June 2021
• Volume 28 , pages 41612–41626, ( 2021 )

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• Jaydip Dey 1 , 2 &
• Ritesh Vijay   ORCID: orcid.org/0000-0002-3198-6007 1  

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Evaluation of water quality is a priority work nowadays. In order to monitor and map, the water quality for a wide range on different scales (spatial, temporal), the geospatial technique has the potential to minimize the field and laboratory work. The review has emphasized the advance of remote sensing for the effectiveness of spectral analysis, bio-optical estimation, empirical method, and application of machine learning for water quality assessment. The water quality parameters (turbidity, suspended particles, chlorophyll, etc.) and their retrieval techniques are described in a scientific manner. Available satellite, bands, resolution, and spectrum ranges for specific parameters are critically described in this review with challenges in remote sensing for water quality analysis, considering non-optical active parameters. The application of statistical programmes like linear (multiple regression analysis) and non-linear approaches is discussed for better assessment of water quality. Emphasis is given on comparison between different models to increase the accuracy level of remote sensing of water quality assessment. A direction is suggested for future development in the field of estimation of water pollution assessment through geospatial techniques.

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Authors are thankful to the Director of CSIR-National Environmental Engineering Research Institute (NEERI), Nagpur, for providing the necessary infrastructure and support to carry out this study.

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Dey, J., Vijay, R. A critical and intensive review on assessment of water quality parameters through geospatial techniques. Environ Sci Pollut Res 28 , 41612–41626 (2021). https://doi.org/10.1007/s11356-021-14726-4

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