Hydrogeochemistry and Water Quality Assessment in the Thamirabarani River Stretch by Applying GIS and PCA Techniques

Tharmar, Esakkimuthu; Abraham, Marykutty; Prakash, Ramaiah; Sundaram, Akila; Flores, Erick Saavedra; Canales, Cristian; Alam, Mohammad Ayaz

doi:10.3390/su142416368

Cited by 10 publications

(4 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this context, the assessment of water quality, whether through the Water Quality Index (WQI) in groundwater [6][7][8] or surface water [9,10], using neural networks and machine learning [11,12], or through mathematic models employing computer software are widely used to simulate hydrodynamics, dispersion, and kinetics of pollutants in the natural environment [13]. They allow for the simulation of water usage situations, defining the ability of assimilation through the limits of disposal of finding sources or for the extractions of certain outflows, in a manner that implies calculated risk about the deterioration of water quality according to legal standards [14].…”

Section: Introductionmentioning

confidence: 99%

Water Quality Simulation in the Bois River, Goiás, Central Brazil

2023

View full text Add to dashboard Cite

The Cerrado is a hotspot for biodiversity conservation and holds the headwater springs that are major Brazilian river basins. The development of industry, agriculture, and mining causes water quality deterioration. Mathematical models appear as a management tool to simulate water quality parameters and the dispersion of pollutants in water bodies. Thus, this study aimed to evaluate the behavior of dissolved oxygen (DO) and other parameters through the QUAL2Kw (Stream Water Quality Model) model in a river in the Brazilian Cerrado. Complementary data were obtained in four experimental measurement campaigns. The calibration results showed a good fit, especially for the DO. The most critical situation occurred in October, where DO remained below 5 mg/L for a long stretch, and the ammoniacal nitrogen (NH4) and biochemical oxygen demand (BOD) presented non-compliance concerning the legal Brazilian requirements. In all campaigns, BOD remained above 5 mg/L for at least 5 km in length, disagreeing with the legislation for exceeding the distance from the mixing zone. The uncertainty analysis for the DO confirmed the critical scenario of October, and the sensitivity analysis by the Monte Carlo Simulation showed the significance of the reaeration coefficient for DO. Thus, it is concluded that the QUAL2Kw model proved dependable for the simulation of point launches in the Bois River, supplying a good fit in the calibration act. Because BOD does not meet the legal requirements in all samplings, the water use of the downstream population may be impaired by the activities found in the basin. Activities such as sand extraction, tanneries, and other food industries increase the organic burden of waterbodies and, therefore, require greater environmental inspections.

show abstract

Section: Introductionmentioning

confidence: 99%

Water Quality Simulation in the Bois River, Goiás, Central Brazil

2023

View full text Add to dashboard Cite

show abstract

“…Urban zones produce both nonpoint and point sources of contaminants, and alongside these industrial effluents, poisonous metals are discharged, as a result the health of rivers worldwide has been impacted by the nation's economy's and population's rapid industrialization. Most rivers that flow through cities are polluted by municipal waste and industrial effluents [5,6]. The contamination of water due to heavy metals (HMs) is a big concern for humankind; however, global studies related to this topic are rare [7].…”

Section: Introductionmentioning

confidence: 99%

Hydrogeochemical Study and Geospatial Analysis of Water Quality Using GIS based Water Index and Multivariate Statistics in Kombolcha City, Ethiopia

2023

JWR

View full text Add to dashboard Cite

The study was carried out to evaluate hydro geochemistry and the risk of groundwater and surface water pollution in the Kombolcha area. To achieve this, hydrogeochemical analysis, water heavy metal, geospatial data analysis, ion ratio, correlation matrix, principal component analysis, Heavy metal Pollution Index (HPI), and Water Quality Index (WQI) methodologies were employed. A total of 36 samples (both water and effluent samples) had been collected and assessed for major physicochemical variables and heavy metals and samples were taken from hand dug wells, Borehole, springs, and rivers in the area. Hydrogeochemical methods showed groundwater mineralization due to (1) silicate weathering, (2) cation exchange processes, and (3) anthropogenic sources (i.e., contaminated discharge of sulphate, carbonate, and trace metal effluents). The study result revealed that major ions dominating the area are Ca2+ >Na+ > Mg2+ >K+, HCO3- >SO4 2- > Cl- > NO3-, and Fe> Mn >Pb> Cr> Cd for cations, anions and trace metals respectively with all heavy netals had mean concentrations above the WHO recommended limits. Calculated Pollution indices revealed 50.7% of the sample belongs to a low level of pollution, while 35% and 14.3% belong to a medium and high level of pollution respectively which consequently translating the area into high groundwater pollution zones. The correlation matrix revealed that no significant correlation exists between the water quality variables (Cl and NO3- with Fe, Pb, Cr, Mn, and Cd). PCA was applied on the data set to identify the spatial sources of pollution in groundwater and in the first principal component analysis, Mn, Fe, Cr, Pb, and Cd (in descending order) were found in amounts greater than 0.5, confirming that these metals were from anthropogenic sources. The combined assessments based on WQI and HPI, the study showed that water samples in the proximity of industrial sites are polluted by factories effluent and uncontrolled waste disposal due to urbanization.

show abstract

“…However, these methods are subjective and cannot effectively solve the one-sidedness of the evaluation. Esakkimuthu Tharmar used principal component analysis (PCA) to find out the dominant factors of the overall water quality and its variance coverage [11]. Neural networks excel in solving nonlinear problems and can approximate nonlinear functions with sufficient training data.…”

Section: Introductionmentioning

confidence: 99%

Application of Improved Particle Swarm Optimization SVM in Water Quality Evaluation of Ming Cui Lake

et al. 2023

View full text Add to dashboard Cite

Water quality directly determines our living environment. In order to establish a more scientific and reasonable water quality evaluation model, it needs a lot of data support, but it will lead to a large increase in the calculation time of the evaluation model. This paper proposes an improved particle swarm optimization SVM model (CPOS-SVM) to solve this problem. In this paper, the Pareto optimal solution concept is used to sparsely process the training set, which can ensure that the number of training sets is reduced without loss of data characteristics, thus reducing the training time. In order to solve the problem of the kernel parameter g and penalty factor c on the SVM algorithm, which affects the accuracy of the SVM model but it is difficult to select why, a particle swarm optimization algorithm is used in this paper to optimize the kernel parameter and penalty factor and improve the accuracy of the model. In this paper, 480 sets of data from Ming Cui Lake from 2014 to 2022 are taken as the research object, and examples are analyzed in MATLAB 2020a. The results show that the training time of the CPOS-SVM model can be completed within 2 s and does not increase with the increase of data volume. Meanwhile, by comparing the SVM model, POS-SVM model, and POS-BP model, training time increases dramatically with the amount of data. The accuracy of the POS-SVM model is the highest, and the accuracy of the CPOS-SVM model is basically consistent with that of the POS-SVM, reaching 94%, while the accuracy of the SVM model and the POS-BP model are slightly worse. This indicates that the CPOS-SVM model has good application value in water quality evaluation.

show abstract

Hydrogeochemistry and Water Quality Assessment in the Thamirabarani River Stretch by Applying GIS and PCA Techniques

Cited by 10 publications

References 35 publications

Water Quality Simulation in the Bois River, Goiás, Central Brazil

Water Quality Simulation in the Bois River, Goiás, Central Brazil

Hydrogeochemical Study and Geospatial Analysis of Water Quality Using GIS based Water Index and Multivariate Statistics in Kombolcha City, Ethiopia

Application of Improved Particle Swarm Optimization SVM in Water Quality Evaluation of Ming Cui Lake

Contact Info

Product

Resources

About