Impact of city effluents on water quality of Indus River: assessment of temporal and spatial variations in the southern region of Khyber Pakhtunkhwa, Pakistan

Khan, Irfanullah; Khan, Azim; Khan, Muhammad Sohail; Zafar, Shabnam; Hameed, A. Shahul; Badshah, Shakeel; Réhman, Shafiq Ur; Yasmeen, Ghazala

doi:10.1007/s10661-018-6621-7

Cited by 12 publications

(3 citation statements)

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“…FA analysis indicated that the major responsible parameters for degrading the quality of the water system were discharge and organic pollutions. More recently, Khan et al and Zafar et al [30,31] have used the multivariate statistics for assessing the effluent discharge on the irrigation water and Indus River water quality in KPK, Pakistan, and found that effluent discharge results in the sever degradation of water quality for irrigation purpose. Moreover, using PCA and CA, they conclude that the Indus River is found strongly affecting city effluent in KPK Province of Pakistan.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Impact of Anthropogenic and Natural Sources of Pollution on Quality of Water in Upper Indus Basin (UIB) by Using Multivariate Statistical Analysis

Baluch

Hashmi

2019

Journal of Chemistry

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Water quality of the Indus River around the upper basin and the main river was evaluated with the help of statistical analysis. In order to analyze the similarities and dissimilarities for identifying the spatial variations in water quality of the Indus River and sources of contamination, multivariate statistical analysis, i.e., principle component analysis (PCA), cluster analysis, and descriptive analysis, was done. Data of 8 physicochemical quality parameters from 64 sampling stations belonging to 6 regions (labeled as M1, M2, M3, M4, M5, and M6) were used for analysis. The parameters used for assessing the water quality were pH, dissolved oxygen (DO), oxygen reducing potential (ORP), electrical conductivity (EC), total dissolved solids (TDS), salinity (%), and concentration of arsenic (As) and lead (Pb), respectively. PCA assisted in extracting and recognizing the responsible variation factors of water quality over the region, and the results showed three underlying factors including anthropogenic source pollution along with runoff due to rain and soil erosion were responsible for explaining the 93.87% of total variance. The parameters which were significantly influenced by anthropogenic impact are DO, EC, TDS (negative), and concentration of Pb (positive), while the concentration of As, % salinity, and ORP are affected by erosion and runoff due to rain. The worst pollution situation for regions M1 and M6 was due to the concentration of As which was approximately 400 μg/l (i.e., 40 times higher than minimum WHO recommendation). Furthermore, the results also indicated that, in the Indus River, three monitoring stations and five quality parameters are sufficient to have a reasonable confidence about the quality of water in this most important reserve of Pakistan.

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Section: Introductionmentioning

confidence: 99%

Investigating the Impact of Anthropogenic and Natural Sources of Pollution on Quality of Water in Upper Indus Basin (UIB) by Using Multivariate Statistical Analysis

Baluch

Hashmi

2019

Journal of Chemistry

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“…To achieve the desired results, it is important to understand the water quality parameters (WQPs) causing impairment and their characterization. In general, these WQPs are categorized as chemical (including cations and anions such as potassium (K + ), sodium(Na + ), Calcium (Ca 2+ ), magnesium (Mg 2+ ), chloride (Cl − ), fluoride (F − ) nitrate (NO 3 − ), and sulfate (SO 4 2− )), physical (including TSSs, electrical conductivity (EC), dissolved oxygen (DO), and salinity or TDSs), and biological (including total coliforms and Escherichia coli (E. coli)) parameters [1,2,[20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variability in Total Dissolved Solids and Total Suspended Solids along the Colorado River

2023

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The Colorado River is a principal source of water for 40 million people and farmlands in seven states in the western US and the Republic of Mexico. The river has been under intense pressure from the effects of climate change and anthropogenic activities associated with population growth leading to elevated total dissolved solid (TDS) and total suspended solid (TSS) concentrations. Elevated TDS- and TSS-related issues in the basin have a direct negative impact on the water usage and the ecological health of aquatic organisms. This study, therefore, analyzed the spatiotemporal variability in the TDS and TSS concentrations along the river. Results from our analysis show that TDS concentration was significantly higher in the Upper Colorado River Basin while the Lower Colorado River Basin shows a generally high level of TSSs. We found that the activities in these two basins are distinctive and may be a factor in these variations. Results from the Kruskal–Wallis significance test show there are statistically significant differences in TDSs and TSSs from month to month, season to season, and year to year. These significant variations are largely due to seasonal rises in consumptive use, agriculture practices, snowmelts runoffs, and evaporate rates exacerbated by increased temperature in the summer months. The findings from this study will aid in understanding the river’s water quality, detecting the sources and hotspots of pollutions to the river, and guiding legislative actions. The knowledge obtained forms a strong basis for management and conservation efforts and consequently helps to reduce the economic damage caused by these water quality parameters including the over USD 300 million associated with TDS damages.

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“…TDS is a known physical WQP [3,13,14], categorized by the United States Environmental Protection Agency (US EPA) in its secondary drinking water regulations (SDWRs) with an allowable limit in drinking water set to 500 mg/L [3,12,15]. These SDWRs are described as nonenforceable federal guidelines that cause cosmetic and aesthetic effects.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature

Adjovu

Stephen

Ahmad

2023

Water

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Total dissolved solids (TDS) concentration determination in water bodies is sophisticated, time-consuming, and involves expensive field sampling and laboratory processes. TDS concentration has, however, been linked to electrical conductivity (EC) and temperature. Compared to monitoring TDS concentrations, monitoring EC and temperature is simpler, inexpensive, and takes less time. This study, therefore, applied several machine learning (ML) approaches to estimate TDS concentration in Lake Mead using EC and temperature data. Standalone models including the support vector machine (SVM), linear regressors (LR), K-nearest neighbor model (KNN), the artificial neural network (ANN), and ensemble models such as bagging, gradient boosting machine (GBM), extreme gradient boosting (XGBoost), random forest (RF), and extra trees (ET) models were used in this study. The models’ performance were evaluated using several performance metrics aimed at providing a holistic assessment of each model. Metrics used include the coefficient of determination (R2), mean absolute error (MAE), percent mean absolute relative error (PMARE), root mean square error (RMSE), the scatter index (SI), Nash–Sutcliffe model efficiency (NSE) coefficient, and percent bias (PBIAS). Results obtained showed varying model performance at the training, testing, and external validation stage of the models, with obtained R2 of 0.77–1.00, RMSE of 2.28–37.68 mg/L, an MAE of 0.14–22.67 mg/L, a PMARE of 0.02–3.42%, SI of 0.00–0.06, NSE of 0.77–1.00, and a PBIAS of 0.30–0.97 across all models for the three datasets. We utilized performance rankings to assess the model performance and found the LR to be the best-performing model on the external validation datasets among all the models (R2 of 0.82 and RMSE of 33.09 mg/L), possibly due to the established existence of a relationship between TDS and EC, although this may not always be linear. Similarly, we found the XGBoost to be the best-performing ensemble model based on the external validation with R2 of 0.81 and RMSE of 34.19 mg/L. Assessing the overall performance of the models across all the datasets, however, revealed GBM to produce a superior performance based on the ranks, possibly due to its ability to reduce overfitting and improve generalizations. The findings from this study could be employed in assisting water resources managers and stakeholders in effective monitoring and management of water resources to ensure their sustainability.

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Impact of city effluents on water quality of Indus River: assessment of temporal and spatial variations in the southern region of Khyber Pakhtunkhwa, Pakistan

Cited by 12 publications

References 11 publications

Investigating the Impact of Anthropogenic and Natural Sources of Pollution on Quality of Water in Upper Indus Basin (UIB) by Using Multivariate Statistical Analysis

Investigating the Impact of Anthropogenic and Natural Sources of Pollution on Quality of Water in Upper Indus Basin (UIB) by Using Multivariate Statistical Analysis

Spatiotemporal Variability in Total Dissolved Solids and Total Suspended Solids along the Colorado River

A Machine Learning Approach for the Estimation of Total Dissolved Solids Concentration in Lake Mead Using Electrical Conductivity and Temperature

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