Assessment of Poly (diallyl dimethyl ammonium chloride) and Lime for Surface Water Treatment (Pond, River, and Canal water): Seasonal Variations and Correlation Analyses

Assessment of Poly (diallyl dimethyl ammonium chloride) and Lime for Surface Water Treatment (Pond, River, and Canal water): Seasonal Variations and Correlation Analyses | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment of Poly (diallyl dimethyl ammonium chloride) and Lime for Surface Water Treatment (Pond, River, and Canal water): Seasonal Variations and Correlation Analyses Shagufta Jabin, J. K. Kapoor, Anupama Chadha, Anjali Gupta, Sapana Jadoun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4150081/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Sep, 2024 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 8 You are reading this latest preprint version Abstract This study investigates the basic characteristics of various surface water sources, including pond water, river water, and canal water, across four distinct seasons. The research endeavours to assess the impact of a cationic polyelectrolyte, specifically poly diallyl dimethyl ammonium chloride (PDADMAC), utilized as a coagulation aid in conjunction with lime for water treatment purposes. Employing a conventional jar test apparatus, turbidity removal from diverse water samples is examined. Furthermore, the samples undergo characterization utilizing X-ray diffraction (XRD) and Scanning Electron Microscopy (SEM) techniques. The study also conducts correlation analyses on various parameters such as electrical conductivity (EC), pH, total dissolved solids (TDS), turbidity of raw water, polyelectrolyte dosage, and percentage of turbidity removal across different water sources. Utilizing the Statistical Package for Social Science (SPSS) software, these analyses aim to establish robust relationships among initial turbidity, temperature, percentage of turbidity removal, dosage of coagulant aid, electrical conductivity, and total dissolved solids (TDS) in pond water, river water, and canal water. By elucidating these correlations, the study contributes to a deeper understanding of the effectiveness of PDADMAC and lime in water treatment processes across diverse environmental conditions. This research not only enhances our comprehension of surface water treatment methodologies but also provides valuable insights for optimizing water treatment strategies to address the challenges posed by varying water sources and seasonal fluctuations. Surface water SPSS software Polyelectrolyte Turbidity Different water Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Highlights Studied physicochemical properties of surface water across seasons. Employed PDADMAC polyelectrolyte as a coagulation aid. Analyzed using XRD and SEM peaks to explore sludge-lime-polyelectrolyte interaction. Utilized SPSS for parameter analysis. Identified a significant correlation between polyelectrolyte dosage, initial turbidity, and turbidity removal percentage. 1. Introduction Water quality is a critical concern influenced by a myriad of factors including geological and morphological attributes, vegetation, and human activities such as industrialization and urbanization (Sánchez-Martín et al. 2010 ; Soros et al. 2019 ). Turbidity, a measure of suspended and colloidal matter in water, is a key indicator of water quality, affecting both aesthetics and functionality (Asthana et al. 2017 ). High turbidity levels not only diminish water purity but also introduce unpleasant tastes and odors, impacting water treatment processes and increasing treatment costs (Muthuraman and Sasikala 2014 ). Moreover, turbid water inhibits respiratory processes and reduces visibility, underscoring the importance of minimizing turbidity levels in surface water (Frieder et al. 2012 ; Gautam 2011 ; Hargreaves and Tucker 2002 ; Li et al. 2013 ; Paul et al. 2019 ). In India, the pollution of rivers has reached alarming levels due to rapid urbanization and industrial growth, posing significant threats to aquatic ecosystems and human health (Roy and Shamim 2020 ). Pollution stems from various sources including industrial and sewage waste discharge, agricultural runoff, and solid waste deposition (Singh et al. 2005 ; Wang et al. 2019 ). The influx of untreated and treated suspensions further exacerbates water quality degradation, necessitating effective suspension removal strategies (Voulvoulis 2018 ). Polyelectrolytes have emerged as crucial agents for water treatment, offering enhanced flocculation capabilities and versatility in controlling properties such as charge units and molecular weight (Yadav and Goyal 2022 ). When used in conjunction with metal coagulants, polyelectrolytes aid in destabilizing suspended particles and enhancing flocculation processes (Jabin et al. 2021 , 2023 ). Poly diallyl dimethyl ammonium chloride (PDADMAC), a high-charge density cationic polyelectrolyte, is particularly effective as a secondary coagulant for suspension removal (Jabin and Kapoor 2020 ). In this study, surface water samples were collected from various sources in India, including ponds, rivers, and canals, to assess turbidity levels and water quality parameters. Pond water, sourced from Thanthari, Palwal district, Haryana, river water from the Yamuna River, and canal water from the Kheri canal in Greater Faridabad, Haryana, were analyzed. The Yamuna River, a major tributary of the Ganga River, faces substantial pollution from treated and untreated waste, highlighting the urgency of effective water treatment measures. A view of the area and collection site of all three sources of surface water has been shown in Fig. 1 . The objective of this study was to evaluate the efficacy of lime in conjunction with PDADMAC for turbidity reduction in surface water. Additionally, correlation analyses were conducted to understand the relationships among various water quality parameters including initial turbidity, electrical conductivity, total dissolved solids, pH, polyelectrolyte dosage, and percentage removal of turbidity. By elucidating these correlations, the study aims to provide insights into effective water treatment strategies and facilitate informed decision-making for water quality management. 2. Materials and Methods Water samples were collected throughout the year of 2023 across four distinct seasons: March (spring), June (summer), September (autumn), and December (winter). Physicochemical parameters including pH, electrical conductivity (E.C.), turbidity, total dissolved solids (TDS), and dissolved oxygen were determined using standard methods outlined by the American Public Health Association (APHA, 1995) (Association 1926 ). The samples were collected in clean polyethylene bottles rinsed with de-ionized water to avoid contamination and stored in a refrigerator before analysis. This study provides insights into the quality of surface water and the impacts of anthropogenic activities on water resources. Inorganic coagulant lime was sourced from CDH (India) and prepared in distilled water to obtain a concentration of 10 mg/L. Polydiallyl dimethyl ammonium chloride (PDADMAC), obtained from Sigma Aldrich, was utilized as a coagulant aid. Water pH was measured using a pH meter (Anna HI 8314, USA), while electrical conductivity was assessed with a Philips conductivity bridge and dip-type cell at a temperature of 27°C ± 3°C. Turbidity measurements were conducted using a turbidity meter (Hanna HI93703, U.S.A.), and TDS values were calculated using a tubular TDS meter. The turbidity, temperature, E.C., TDS, and pH values of pond water, river water, and canal water across different seasons are presented in Tables 1 , 2 , and 3 , respectively, along with a summary of basic statistical analysis. These tables offer comprehensive insights into the variations of key water quality parameters across different seasons and water sources. 2.1 Raw pond water characteristics The E.C. of pond water exhibited notable variations throughout the investigation, reaching its peak in September and its lowest point in December. This fluctuation in E.C. mirrored the trends observed in TDS. Additionally, the pH levels of pond water displayed seasonal variations. In March, June, and December 2023, the mean pH was recorded at 8, whereas in September 2023, it decreased to 7.0. This pH variation is significant as it influences subsequent turbidity treatment processes. The turbidity levels of pond water also varied across different seasons. In March, June, September, and December 2023, the turbidity measurements were recorded at 175 NTU, 189 NTU, 96 NTU, and 145 NTU, respectively. These fluctuations in turbidity levels underscore the dynamic nature of water quality in ponds and emphasize the importance of continuous monitoring and treatment measures. Table 1 Characteristics of raw pond water with a summary of basic statistics Season Parameter Minimum Maximum Mean Median Standard Deviation March Turbidity(NTU) 166.0 183.0 175.0 175.5 8.29 Temperature( o C) 28 32 30 30 1.83 E.C(mho) 0.82 0.98 0.90 0.90 0.067 TDS(mg. L-1) 572 596 585 584 19.08 pH 7.8 8.2 8.0 8.0 0.18 June Turbidity(NTU) 179.0 198.0 189.0 189.5 9.9 Temperature( o C) 38.0 41.0 39.0 38.5 1.41 E.C(mho) 0.74 0.86 0.80 0.80 0.059 TDS(mg. L-1) 499.0 520.0 511.0 512.5 9.49 pH 7.90 8.50 8.00 8.24 0.31 September Turbidity(NTU) 85.0 104.0 96.0 96.5 10.55 Temperature( o C) 25.0 30.0 27.0 26.5 2.16 E.C(mho) 1.15 1.27 1.20 1.19 0.051 TDS(mg. L-1) 680 700 688 686 8.64 pH 6.8 7.4 7.0 7.1 0.29 December Turbidity(NTU) 138.0 156.0 145.0 143.0 8.08 Temperature( o C) 18.0 23.0 20.0 19.5 2.16 E.C(mho) 0.44 0.56 0.50 0.50 0.064 TDS(mg. L-1) 312 330 326 324 14.14 pH 7.4 7.8 8.0 7.6 0.16 2.2 Raw river water characteristics Upon evaluation of the raw water from the Yamuna, it was observed that the turbidity levels peaked at 249 NTU in June, while reaching their lowest point at 94 NTU in March. Concurrently, the temperature of the water samples exhibited fluctuations ranging from 19°C to 38°C, with the highest temperatures recorded in June and the lowest in March. Similarly, the E.C. demonstrated its highest values in June and its lowest in March. The elevated E.C. values in June indicate the presence of a significant amount of dissolved inorganic substances in the ionized water. TDS serves as an indicator of the overall salinity of water, with the highest TDS recorded in June at 849 mg/L and the lowest in September at 595 mg/L. The pH levels of the water samples ranged from 7.4 to 8.6, showcasing variations in acidity and alkalinity. These findings highlight the dynamic nature of water quality parameters in the Yamuna, emphasizing the importance of continued monitoring and management strategies to ensure water safety and purity. Table 2 Characteristics of raw river water with a summary of basic statistics Season Parameter Minimum Maximum Mean Median Standard Deviation March Turbidity(NTU) 86.0 102.0 94.0 94.0 7.70 Temperature( o C) 17.0 23.0 19.5 19.0 2.65 E.C(mho) 0.94 1.23 1.10 1.115 0.147 TDS(mg. L-1) 838 866 849 846 11.944 pH 8.3 8.8 8.6 8.65 0.244 June Turbidity(NTU) 236.0 268.0 249.0 246.0 15.01 Temperature( o C) 35.9 39.2 37.4 37.25 1.49 E.C(mho) 1.48 1.69 1.60 1.615 0.092 TDS(mg. L-1) 834 861 849 850.5 12.027 pH 8.1 8.5 8.3 8.30 0.182 September Turbidity(NTU) 99 121.0 110.0 110.0 9.13 Temperature( o C) 28.0 30.0 29.0 29.0 1.15 E.C(mho) 1.13 1.30 1.20 1.185 0.084 TDS(mg. L-1) 398 684 595 647 136.142 pH 8.00 8.50 8.30 8.35 0.244 December Turbidity(NTU) 146.0 172.0 158.0 157.0 10.71 Temperature( o C) 16.0 26.0 21.0 21.0 4.16 E.C(mho) 1.20 1.42 1.30 1.29 0.099 TDS(mg. L-1) 674 706 689 688 14.00 pH 7.2 7.6 7.4 7.4 0.163 2.3 Raw canal water characteristics The turbidity levels in canal water were notably higher compared to other surface water sources. In June 2023, the average turbidity reached 333 NTU, whereas it decreased to a minimum of 198 NTU in December 2023. Concurrently, the temperature of the water samples exhibited fluctuations ranging from 22°C to 39°C, with June 2023 recording the highest temperatures and December 2023 the lowest. Similarly, the E.C. values fluctuated between 0.5 to 1.4 mho, mirroring the trend observed in TDS. Notably, the E.C. peaked in December 2023 and was at its lowest in June 2023, demonstrating seasonal variations in water quality parameters. Table 3 Characteristics of raw canal water with a summary of basic statistics Seasons Parameters Minimum Maximum Mean Median Standard Deviation March Turbidity(NTU) 240.0 262.0 248.0 247.0 12.110 Temperature( o C) 22.0 25.0 23.0 22.5 1.414 E.C(mho) 0.70 0.92 0.8 0.79 0.107 TDS(mg. L-1) 270 300 285 285 16.206 pH 7.1 7.5 7.3 7.3 0.182 June Turbidity(NTU) 312.0 350.0 333.0 335.0 15.705 Temperature( o C) 38.0 40.0 39.0 39.0 1.154 E.C(mho) 0.43 0.57 0.50 0.50 0.070 TDS(mg. L-1) 195 226 210 209.5 13.440 pH 7.20 8.10 7.60 7.55 0.424 September Turbidity(NTU) 192.0 220.0 205.0 204.0 12.49 Temperature( o C) 30 33 31 30.5 1.414 E.C(mho) 1.11 1.33 1.20 1.18 0.983 TDS(mg. L-1) 302 333 315 312.5 13.038 pH 7.0 7.2 7.1 7.1 0.115 December Turbidity(NTU) 180.0 215.0 198.0 198.5 18.055 Temperature( o C) 20.0 24.0 22.0 22.0 1.993 E.C(mho) 1.35 1.46 1.40 1.39 0.058 TDS(mg. L-1) 336 360 348 348 12.754 pH 8.0 8.2 8.1 8.1 0.115 2.4 Removal of turbidity using polyelectrolyte To eliminate turbidity, a combination of PDADMAC and lime was employed across all types of surface water. Initially, the raw water underwent filtration using a stainless sieve followed by filter paper with a pore size of 7–8µm. Subsequently, a jar test procedure was conducted, a widely adopted laboratory method for assessing coagulation and flocculation processes at a bench scale. The objective was to ascertain the optimal operating conditions, including pH, temperature, and chemical dosage, for effective water treatment. Previous studies have demonstrated the efficacy of this method in determining the optimal doses of polyelectrolyte for turbidity removal. For this experiment, a conventional jar test apparatus, featuring the Phipps and Bird six-paddle stirrer with an illuminated base, was utilized within 2 L square Plexiglas containers. After a 30-minute sedimentation period, a 10 ml aliquot was extracted from the mid-depth of the beaker, and the residual turbidity was measured (Chiavola et al. 2023 ; Haghiri et al. 2018 ). This process was repeated for all types of water samples. Variations in results were observed due to disparities in the physicochemical properties of water quality. 2.5 X-Ray Diffraction (XRD) analysis The studies of X-ray diffraction were carried out using a Rigaku D/Max-2500 X-ray diffractometer. This instrument utilized a Cu-Kα X-ray tube with a wavelength of 1.540538 angstroms, operating at a current of 20 mA and an input voltage of 40 kV. Throughout the study, two diffractograms were generated to investigate the interactions within the sludge-lime-polymer system-one for sludge with lime, and another for sludge with lime and polyelectrolyte. These analyses aimed to provide insights into the complex interactions occurring within the composite material. 2.6 Scanning Electron Microscopy (SEM) analysis Materials were imaged using an SEM apparatus manufactured by Jeol (Japan), specifically the JSM 6510Lv model. Operating at a 15 kV accelerating voltage, this SEM enabled detailed visualization of the specimens. A typical SEM allows for scanning areas ranging from 1 cm to 5 µm, offering magnifications between 20x and 30,000x with a spatial resolution of 50–100 nm. In SEM analysis, a focused beam of electrons interacts with the specimen, producing images that unveil the surface topography of the sample. 3. Result and Discussion 3.1 Result of different surface water with basic statistics Sixteen water samples were collected from various surface water sources over the course of one year. The optimization of lime dosage for four different samples is summarized in Table 4 . Table 4 Optimum lime dosages in different surface water samples in different seasons Water Sample Season Dosage of Lime (mg/L) Turbidity of Raw water (NTU) Pond Water March 2023 7.5 175 River Water 10 94 Canal Water 5 248 Pond Water June 2023 7.5 189 River Water 5 249 Canal Water 5 333 Pond Water September 2023 10 96 River Water 10 110 Canal Water 5 205 Pond Water December 2023 7.5 145 River Water 7.5 158 Canal Water 5 198 The optimal lime dosage varied across different water sources and seasons, with specific dosages identified for each scenario. In March, the optimal lime dosage was determined to be 7.5 mg/L for pond water and 10 mg/L for river water. Conversely, canal water consistently required 5 mg/L of lime dosage throughout all seasons due to its consistently high turbidity compared to other surface water sources. Notably, an inverse relationship was observed between turbidity levels and optimal lime dosage, with higher turbidity necessitating lower lime dosages. This trend was consistent across all four seasons. To address residual lime concentration in treated water and further reduce turbidity, cationic polyelectrolyte PDADMAC was employed in conjunction with lime for turbidity removal from various water samples. Enhanced performance was achieved when the coagulant aid was added to the water sample after proper lime mixing, in contrast to the simultaneous addition of polyelectrolyte and lime. PDADMAC, characterized by its quaternary ammonium salt nature and synthetic polyelectrolyte properties, demonstrated efficacy in turbidity removal. Its high charge density and molecular weight facilitated the flocculation of negatively charged suspended particles through adsorption and charge neutralization mechanisms. This effectiveness was particularly notable in high turbidity water compared to low turbidity water (Kapoor et al. 2015 ; Piaskowski et al. 2023 ). The treatment results for pond water across four different seasons are depicted in Fig. 2 (A-B). Following treatment, the average residual turbidity of pond water ranged from 4.76 NTU to 9.0 NTU for March, June, September, and December 2023, respectively. Interestingly, the lowest residual turbidity was observed when the raw water exhibited the highest turbidity levels. This underscores PDADMAC's superior performance in highly turbid water conditions compared to situations with lower turbidity levels. The efficacy of PDADMAC in turbidity removal from river water is illustrated in Fig. 3 (A-D) across the months of March, June, September, and December 2023. Notably, the combined action of lime and PDADMAC proved notably more effective at higher initial turbidity levels compared to lower turbidity conditions. This heightened effectiveness can be attributed to the presence of ample colloidal suspensions during periods of maximum turbidity, facilitating adsorption and charge neutralization processes. June 2023 exhibited the highest turbidity levels in river water, as depicted in Fig. 3 (B). In canal water, the use of polyelectrolyte as a coagulant aid in the flocculation process resulted in reduced lime dosage and residual turbidity, as shown in Fig. 4 (A-B). Given that canal water exhibited the highest raw turbidity in June compared to other surface water sources, the removal of turbidity peaked during this period (97.99%), as illustrated in Fig. 4 . Following jar testing, minimal changes in pH (within ± 0.1) were observed across all treated water samples. Table 5 provides a summary of the optimal polyelectrolyte dosage alongside the percentage of turbidity removal across various surface water sources. Polyelectrolyte dosage emerged as a critical factor in turbidity removal, with the highest removal rates achieved at minimal polyelectrolyte dosages across all water samples. In the presence of suspended solids, low molecular mass polymers primarily react with soluble organics, while high molecular mass polymers, at low dosages, preferentially interact with suspended solids (Kapoor et al. 2015 ; Piaskowski et al. 2023 ). Consequently, PDADMAC, being a high molecular weight polyelectrolyte, operates optimally at a dosage of 2 mg/L for highly turbid canal and river water in June. It is crucial to maintain an optimal dosage of coagulant aid to prevent excessive adsorption, which could lead to poor adsorption site accessibility. Moreover, the optimal dosage of polyelectrolyte was identified as 4 mg/L in river water for the March, June, and December seasons. This optimized dosage strategy aims to balance effective turbidity removal while minimizing excess polyelectrolyte adsorption, ensuring optimal treatment outcomes. Table 5 The optimum dosage of polyelectrolyte in the removal of turbidity in different surface Types of surface water Seasons Optimum dosage of polyelectrolyte (mg L − 1 ) Turbidity of raw water (ntu) Percentage of removal of turbidity (%) pond Water march 4 175 97.28 june 4 189 97.04 september 6 96 90.62 december 4 145 97.09 river Water mach 6 94 92.09 june 2 249 97.14 september 6 110 96.36 december 4 158 94.87 canal Water march 4 248 97.81 june 2 333 97.91 september 4 205 95.70 december 4 198 96.78 3.2 X-ray powder diffraction (XRD) analysis The XRD spectra obtained for the samples containing sludge with lime and sludge with lime with polyelectrolyte provide valuable insights into the structural composition and interaction of these components. The XRD spectra obtained for the sludge with lime combination reveal distinctive diffraction peaks corresponding to the crystalline phases present in the sample. Typically, the XRD pattern for sludge with lime exhibits peaks corresponding to the crystalline phases of lime, including calcium hydroxide (Ca(OH) 2 ), calcium carbonate (CaCO 3 ), and calcium oxide (CaO). These peaks align well with the characteristic peaks of calcite at 2θ ~ 29.20°, indicative of the (111) plane, as observed in Card No. 5-586 from the International Center for Diffraction Data (ICDD) (Galván-Ruiz et al. 2009 ; Pires 2015 ). However, the intensity of the peak at 39.02° corresponding to the (200) plane is comparatively low due to the overlay of sludge samples onto the lime (Nasrazadani and Eureste 2008 ), Fi 5 (A). These peaks facilitate the identification of crystalline phases formed upon the addition of lime to the sludge, providing insights into the degree of crystallinity and phase composition of the sample. Moreover, shifts or changes in peak intensity suggest potential interactions or reactions between the sludge and lime components. In contrast, the XRD spectra obtained for the sludge with lime plus polyelectrolyte combination exhibit alterations in peak intensity, position, or appearance compared to the sludge with lime alone. These changes indicate potential modifications in the crystalline structure or phase composition induced by the addition of polyelectrolyte. Specifically, a sharp peak near 2θ ~ 30° becomes more intense and undergoes a slight shift in angle after interaction with polyelectrolyte, while a peak observed at 2θ ~ 17 disappears upon the addition of polyelectrolyte. Furthermore, a new peak emerges at 2θ ~ 31.68°, characteristic of PDADMAC, suggesting an increase in the semicrystalline nature of the sample following the addition of polyelectrolyte (Tyagi and Sharma 2016 ). The disappearance of the peak at 2θ ~ 34° from the spectra indicates the dominance of polyelectrolyte in the material over lime, Fig. 5 (B). These alterations in peak characteristics signify interactions between the polyelectrolyte and the sludge-lime matrix, potentially leading to the formation of new crystalline phases or changes in crystallographic parameters. Overall, XRD analysis provides valuable insights into the structural modifications induced by the incorporation of polyelectrolyte into the sludge-lime composite, shedding light on its influence on the overall properties of the material. 3.3 Scanning electron microscopy (SEM) analysis: SEM analysis was conducted on sludge samples (after filtration) treated with lime alone and in combination with polyelectrolyte. The impact of lime as a standalone metal coagulant on sludge was examined at magnification scales of 500X (Figure A), 25KX (Figure B), and 50KX (Figure C). Similarly, sludge treated with lime in conjunction with polyelectrolyte was studied at the same magnification scales of 500X (Figure D), 25KX (Figure E), and 50KX (Figure F). Figure 6 (A) presents the original sludge particles treated with lime alone. These particles exhibit smaller size with an unevenly dispersed arrangement on the surface and strong adhesion to water. Figures 6 (B) and 6 (C) depict the unadjusted spaces observable on the surface without uniform consistency. These observations provide insight into the structural changes induced by the application of lime as a solo metal coagulant. Following the addition of polyelectrolyte, molecules permeate and occupy spaces within the structure, indicating the formation of a sludge-lime-polyelectrolyte composite (Fig. 6 E and F). Analysis of the sludge treated with lime and polyelectrolyte confirms the development of dense and smooth floc structures during the treatment process (Fig. 6 D). Under SEM examination, strong bonding within the sludge-lime-polyelectrolyte composite is evident, showcasing unique and well-defined features (Fig. 6 D-F). Polyelectrolyte plays a crucial role in enlarging sludge particles and facilitating strong aggregation through charge neutralization mechanisms (Figure E and F). This phenomenon enhances the separation of water from sludge, leading to improved water purification. Furthermore, the processes of adsorption and charge neutralization transform destabilized particles into larger aggregates and flocs, aiding in effective water cleaning. 3.4 SPSS Pearson correlations Monitoring water quality is facilitated through correlation studies among various parameters (Chekkala et al. 2023 ; Shroff et al. 2015 ). A correlation matrix for different variables is constructed using SPSS to establish correlations between different aspects of surface water. The degree of correlation between variables is assessed through the Pearson correlation coefficient. This coefficient, denoted as r, indicates the strength and direction of linear relationships between pairs of continuous variables. The analysis determines whether variables are strongly correlated with each other. Essentially, the Pearson Correlation assesses whether there is statistical evidence supporting a linear relationship among the parameters. 3.4.1 Analysis of pond water To analyze the influence of certain factors on pond water characteristics, the Statistical Package for Social Science (SPSS) software was employed. Table 6 presents the Pearson correlation coefficient, illustrating the relationship between Turbidity, Temperature, E.C., TDS, pH, percentage removal of Turbidity, and Initial Turbidity. The correlation analysis of pond water revealed several significant relationships. There was a strong positive linear correlation between the percentage removal of turbidity and initial turbidity (r = 0.887), which was statistically significant (p = 0.000). Additionally, the dosage of polyelectrolyte exhibited a strong negative correlation with the percentage of removal (r = -0.881), also statistically significant. Furthermore, the turbidity of raw water displayed a strong positive correlation with the percentage of removal. Higher initial turbidity corresponded to a greater percentage of turbidity removal. Moreover, the E.C. showed a significant positive correlation with TDS (r = + 0.977), indicating a direct relationship between the two parameters. Lastly, there was a direct correlation observed between temperature and the initial turbidity of raw water (r = + 0.570). Table 6 Pearson Correlation Coefficient for Pond Water Parameter Temp E.C. TDS pH Dosage % removal of turbidity Initial Turbidity Temp Pearson correlation 1 0.293 0.372 0.612 * -0.164 0.334 0.570 * Sig(2-tailed) 0.270 0.156 0.012 0.543 0.206 0.021 N 16 16 16 16 16 16 16 E.C Pearson correlation 0.293 1 0.977 ** -0.431 0.784 ** -0.596 * -0.453 Sig(2-tailed) 0.270 0.000 0.095 0.000 0.015 0.078 N 16 16 16 16 16 16 16 TDS Pearson correlation 0.372 0.977 ** 1 -0.293 0.699 ** -0.501 * -0.350 Sig(2-tailed) 0.156 0.000 0.270 0.003 0.048 0.183 N 16 16 16 16 16 16 16 pH Pearson correlation 0.612 * -0.431 − .293 1 -0.796 ** 0.821 ** 0.905 ** Sig(2-tailed) 0.012 0.095 .270 0.000 0.000 0.000 N 16 16 16 16 16 16 16 Dosage Pearson correlation -0.164 0.784 ** 0.699 ** -0.796 ** 1 -0.881 ** -0.879 ** Sig(2-tailed) 0.543 0.000 0.003 0.000 0.000 0.000 N 16 16 16 16 16 16 16 % removal of turbidity Pearson correlation .334 − .596 * − .501 * .821 ** − .881 ** 1 0.887 ** Sig(2-tailed) 0.206 0.015 0.048 0.000 0.000 0.000 N 16 16 16 16 16 16 16 Initial turbidity Pearson correlation 0.570 * -0.453 -0.350 0.905 ** -0.879 ** 0.887 ** 1 Sig(2-tailed) 0.021 0.078 0.183 0.000 0.000 0.000 N 16 16 16 16 16 16 16 *Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). 3.4.2 Analysis of river water The correlation analysis of river water variables was conducted using IBM SPSS software, and the Pearson correlation coefficients are presented in Table 7 . The Pearson correlation coefficient indicates the strength and direction of the relationship between different parameters. The correlation matrix revealed several significant relationships. Initial turbidity exhibited a strong positive correlation (r = 0.740) with the temperature of river water, which was statistically significant. Furthermore, the dosage of polyelectrolyte displayed a strong negative linear relationship with initial turbidity (r = -0.879), which was also statistically significant (p = 0.000). Additionally, initial turbidity showed a strong negative correlation (r = -0.914) with E.C., also statistically significant (p = 0.000). Table 7 Correlation coefficient values for river water Parameter Temp E.C TDS pH Dosage % removal of turbidity Initial Turbidity Temp Pearson correlation 1 0.783 ** 0.121 -0.190 -0.628 ** 0.687 ** .740 ** Sig(2-tailed) 0.000 .655 .482 .009 .003 .001 N 16 16 16 16 16 16 16 E.C Pearson correlation 0.783 ** 1 0.323 -0.091 -0.879 ** 0.825 ** 0.914 ** Sig(2-tailed) 0.000 0.222 0.737 0.000 0.000 0.000 N 16 16 16 16 16 16 16 TDS Pearson correlation 0.121 0.323 1 0.293 -0.415 -0.138 0.381 Sig(2-tailed) 0.655 0.222 0.270 0.110 0.611 0.146 N 16 16 16 16 16 16 16 pH Pearson correlation -0.190 -0.091 0.293 1 0.240 -0.352 -0.218 Sig(2-tailed) 0.482 0.737 0.270 0.371 0.181 0.418 N 16 16 16 16 16 16 16 Dosage Pearson correlation -0.628 ** -0.879 ** -0.415 0.240 1 -0.825 ** -0.977 ** Sig(2-tailed) 0.009 0.000 0.110 0.371 0.000 0.000 N 16 16 16 16 16 16 16 % removal of turbidity Pearson correlation 0.687 ** 0.825 ** -0.138 -0.352 -0.825 ** 1 0.851 ** Sig(2-tailed) 0.003 0.000 0.611 0.181 0.000 0.000 N 16 16 16 16 16 16 16 Initial turbidity Pearson correlation 0.740 ** 0.914 ** 0.381 -0.218 -0.977 ** 0.851 ** 1 Sig(2-tailed) 0.001 0.000 0.146 0.418 0.000 0.000 N 16 16 16 16 16 16 16 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). Moreover, the correlation matrix indicated a strong negative linear relationship between the dosage of polyelectrolyte and initial turbidity (r = -0.977), with statistical significance (p = 0.000). Furthermore, the percentage removal of turbidity showed a strong positive linear relation with initial turbidity (r = 0.851), which was statistically significant (p = 0.000). Additionally, there was a statistically significant relationship between the percentage of removal and the dosage of polyelectrolyte (p = 0.000), with both parameters negatively correlated (r = -0.825) with each other. 3.4.3 Analysis of canal water The correlation analysis for various variables was conducted using SPSS software, revealing the relationships between different parameters. Table 8 presents the Pearson correlation coefficients for different parameters of canal water. The correlation matrix indicated several significant findings. Initial turbidity demonstrated a statistically significant (p = 0.002) strong positive correlation (r = 0.712) with the temperature of canal water. Furthermore, initial turbidity exhibited a strong negative correlation with E.C. (r = -0.917) and TDS (r = -0.945), both of which were statistically significant (p = 0.000). However, there was no significant correlation observed between initial turbidity and pH. Moreover, the dosage of polyelectrolyte displayed a strong negative linear relationship with initial turbidity (r = -0.910), which was statistically significant (p = 0.000). Additionally, the correlation matrix showed a strong positive linear relationship between the percentage removal of turbidity and initial turbidity (r = 0.690), which was statistically significant (p = 0.003). Furthermore, there was a negative correlation (r = -0.584) observed between the dosages of polyelectrolyte and the percentage of removal of turbidity. The correlation analysis across different surface water types revealed consistent trends. Initial turbidity showed a strong correlation with temperature across all surface water types, and the dosage of polyelectrolyte exhibited a strong correlation with initial turbidity across all surface water types. Table 8 Correlation coefficient values for canal water Parameter Temp E.C TDS pH Dosage % removal of turbidity Initial Turbidity Temp Pearson correlation 1 -0.621 * -0.765 ** 0.152 -0.692 ** 0.013 0.712 ** Sig(2-tailed) .010 .001 .574 .003 .963 .002 N 16 16 16 16 16 16 16 E.C Pearson correlation -0.621 * 1 0.970 ** 0.266 0.973 ** -0.680 ** -0.917 ** Sig(2-tailed) .010 0.000 0.319 0.000 0.004 0.000 N 16 16 16 16 16 16 16 TDS Pearson correlation -0.765 ** 0.970 ** 1 0.222 0.948 ** -0.574 * -0.945 ** Sig(2-tailed) 0.001 0.000 0.409 0.000 0.020 0.000 N 16 16 16 16 16 16 16 pH Pearson correlation 0.152 0.266 0.222 1 0.125 -0.478 -0.241 Sig(2-tailed) 0.574 0.319 0.409 0.644 0.061 .369 N 16 16 16 16 16 16 16 Dosage Pearson correlation -0.692 ** .973 ** 0.948 ** 0.125 1 -0.584 * -0.910 ** Sig(2-tailed) 0.003 0.000 0.000 0.644 0.017 0.000 N 16 16 16 16 16 16 16 % removal of turbidity Pearson correlation 0.013 -0.680 ** -0.574 * -0.478 -0.584 * 1 0.690 ** Sig(2-tailed) .963 .004 .020 .061 .017 .003 N 16 16 16 16 16 16 16 Initial turbidity Pearson correlation .712 ** − .917 ** − .945 ** − .241 − .910 ** .690 ** 1 Sig(2-tailed) .002 .000 .000 .369 .000 .003 N 16 16 16 16 16 16 16 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). 4. Conclusion The results from the SPSS correlation analysis revealed a robust correlation between initial turbidity and various variables. Specifically, there was a notable correlation observed between initial turbidity and temperature, dosage of polyelectrolyte, and percentage of turbidity removal. Moreover, in pond and canal water, E.C. and TDS were found to be strongly correlated with each other, whereas they exhibited a moderate correlation in river water. Further insights into the interaction of impurities with lime and polyelectrolyte were gained through XRD studies of soil samples. Additionally, SEM analysis confirmed that treating sludge with lime and polyelectrolyte resulted in the formation of dense flocs, aiding in water purification via charge neutralization. The pH of water emerged as a crucial factor, determining the electrical charges of organic and inorganic colloids. Notably, the performance of lime in conjunction with polyelectrolyte was significantly more effective at high initial turbidity levels compared to low turbidity levels. The fluctuating turbidity levels of water at different stages further complicated the treatment process. Overall, the study underscored the efficacy of PDADMAC as a coagulant aid, even at very low dosages, for turbidity removal across diverse water sources. The addition of PDADMAC alongside lime led to an enhancement in flocculation size, indicating improved treatment efficiency. Thus, the findings support the successful application of polyelectrolyte as a coagulant aid for turbidity removal in various surface water contexts. Declarations Ethical and Disclosures: The submitted work is original and is not published or submitted elsewhere in any form or language. Acknowledgment The authors are thankful to the School of Engineering and Technology, Manav Rachna International Institute of Research & Studies for providing lab facilities for practical work. The corresponding author Sapana Jadoun is grateful for the support National Research and Development Agency of Chile (ANID) for the project FONDECYT project 3200850. Funding Declarations This work is not supported by any funding. Competing Interests: The authors have no relevant financial or non-financial interests to disclose. Author Contributions: All authors contributed to the study's conception and design. Lab work, data collection, and analysis were performed by Dr. Shagufta jabin, Jitander Kumar Kapoor guided her during the work. Dr. Anupama Chadha and Dr. Anjali Gupta have analyzed SPSS. The first draft of the manuscript was written by Dr. Shagufta Jabin. Dr. Sapana Jadoun has done the review, editing, and finalizing of the manuscript for publication. All authors read and approved the final manuscript . Consent to Participate: Consent was obtained from all individual participants included in the study. Consent to Publish: The Author confirms that the work described has not been published before and is not under consideration for publication elsewhere. The work has been approved by all co-authors. Data availibility statement Data will ve made available on request. References Association, A. P. H. (1926). Standard methods for the examination of water and wastewater (Vol. 6). American Public Health Association. Asthana, M., Kumar, A., & Sharma, B. S. (2017). Wastewater treatment. Principles and applications of environmental biotechnology for a sustainable future , 173–232. Chekkala, A., Atasoy, M., Williams, C., & Cetecioglu, Z. (2023). Statistical Analysis of SARS-CoV-2 Using Wastewater-Based Data of Stockholm, Sweden. International Journal of Environmental Research and Public Health, 20 (5), 4181. Chiavola, A., Di Marcantonio, C., D’Agostini, M., Leoni, S., & Lazzazzara, M. (2023). A combined experimental-modeling approach for turbidity removal optimization in a coagulation–flocculation unit of a drinking water treatment plant. Journal of Process Control, 130 , 103068. Frieder, C. A., Nam, S. H., Martz, T. R., & Levin, L. A. (2012). High temporal and spatial variability of dissolved oxygen and pH in a nearshore California kelp forest. Biogeosciences, 9 (10), 3917–3930. Galván-Ruiz, M., Hernández, J., Baños, L., Noriega-Montes, J., & Rodríguez-García, M. E. (2009). Characterization of calcium carbonate, calcium oxide, and calcium hydroxide as starting point to the improvement of lime for their use in construction. Journal of Materials in Civil Engineering, 21 (11), 694–698. Gautam, D. K. (2011). Effect of pollution on dissolved oxygen concentration in stream of Shivalik Himalayas: a case study. International Journal of Life science & Pharma Research, 1 (1). Haghiri, S., Daghighi, A., & Moharramzadeh, S. (2018). Optimum coagulant forecasting by modeling jar test experiments using ANNs. Drinking Water Engineering and Science, 11 (1), 1–8. Hargreaves, J. A., & Tucker, C. S. (2002). Measuring dissolved oxygen concentration in aquaculture . Southern Regional Aquaculture Center Stoneville, MS, USA. Jabin, S., Gupta, P., & Sharma, M. (2021). Polyelectrolytes as a Material of Value in Water Treatment: A Review. Asian Journal of Water, Environment and Pollution, 18 (3), 109–115. Jabin, S., & Kapoor, J. K. (2020). Role of Polyelectrolytes in the Treatment of Water and Wastewater BT - Sustainable Green Chemical Processes and their Allied Applications. In Inamuddin & A. Asiri (Eds.), (pp. 289–309). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-42284-4_10 Jabin, S., Kapoor, J. K., Jadoun, S., Chandna, N., & Chauhan, N. P. S. (2023). Synthesis and characterization of polyamine-based polyelectrolytes for wastewater treatment in the sugar industry. Journal of Molecular Structure, 1275 , 134573. https://doi.org/10.1016/J.MOLSTRUC.2022.134573 Kapoor, J. K., Jabin, S., & Bhatia, H. S. (2015). Optimization of coagulation-flocculation process for food industry waste water treatment using polyelectrolytes with inorganic coagulants. J. Indian Chem. Soc, 92 , 1697–1703. Li, H. Y., Xu, J., & Xu, R. Q. (2013). The effect of temperature on the water quality of lake. Advanced Materials Research, 821 , 1001–1004. Muthuraman, G., & Sasikala, S. (2014). Removal of turbidity from drinking water using natural coagulants. Journal of Industrial and Engineering Chemistry, 20 (4), 1727–1731. Nasrazadani, S., & Eureste, E. (2008). Application of FTIR for Quantitative Lime Analysis. www.ntis.gov. Accessed 27 February 2024 Paul, M. J., Coffey, R., Stamp, J., & Johnson, T. (2019). A review of water quality responses to air temperature and precipitation changes 1: Flow, water temperature, saltwater intrusion. JAWRA Journal of the American Water Resources Association, 55 (4), 824–843. Piaskowski, K., Świderska-Dąbrowska, R., & Dąbrowski, T. (2023). Impact of cationic polyelectrolytes on activated sludge morphology and biological wastewater treatment in a Sequential Batch Reactor (SBR). Journal of Water Process Engineering, 52 , 103500. Pires, J. (2015). Simple Analysis of Historical Lime Mortars. Journal of Chemical Education, 92 (3), 521–523. https://doi.org/10.1021/ed500336p Roy, M., & Shamim, F. (2020). Research on the impact of industrial pollution on River Ganga: A Review. International Journal of Prevention and Control of Industrial Pollution, 6 (1), 43–51. Sánchez-Martín, J., Ghebremichael, K., & Beltrán-Heredia, J. (2010). Comparison of single-step and two-step purified coagulants from Moringa oleifera seed for turbidity and DOC removal. Bioresource technology, 101 (15), 6259–6261. Shroff, P., Vashi, R. T., Champaneri, V. A., & Patel, K. K. (2015). Correlation study among water quality parameters of groundwater of Valsad district of south Gujarat (India). Journal of fundamental and applied sciences, 7 (3), 340–349. Singh, K. P., Malik, A., & Sinha, S. (2005). Water quality assessment and apportionment of pollution sources of Gomti river (India) using multivariate statistical techniques—a case study. Analytica Chimica Acta, 538 (1–2), 355–374. Soros, A., Amburgey, J. E., Stauber, C. E., Sobsey, M. D., & Casanova, L. M. (2019). Turbidity reduction in drinking water by coagulation-flocculation with chitosan polymers. Journal of Water and Health, 17 (2), 204–218. Tyagi, C., & Sharma, A. (2016). Optimization of structural and dielectric properties of CdSe loaded poly (diallyl dimethyl ammonium chloride) polymer in a desired frequency and temperature window. Journal of Applied Physics, 119 (1). Voulvoulis, N. (2018). Water reuse from a circular economy perspective and potential risks from an unregulated approach. Current Opinion in Environmental Science & Health, 2 , 32–45. Wang, Y., Yang, J., & Chang, J. (2019). Development of a coupled quantity-quality-environment water allocation model applying the optimization-simulation method. Journal of Cleaner Production, 213 , 944–955. Yadav, S., & Goyal, V. C. (2022). Current Status of Ponds in India: A Framework for Restoration, Policies and Circular Economy. Wetlands, 42 (8), 107. Caption of Figures Additional Declarations No competing interests reported. Supplementary Files GA.png Graphical Abstract Cite Share Download PDF Status: Published Journal Publication published 02 Sep, 2024 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Revision requested 24 Jul, 2024 Reviews received at journal 13 Jun, 2024 Reviewers agreed at journal 23 May, 2024 Reviewers agreed at journal 02 May, 2024 Reviewers invited by journal 27 Apr, 2024 Editor assigned by journal 17 Apr, 2024 Submission checks completed at journal 17 Apr, 2024 First submitted to journal 22 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4150081","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":292145200,"identity":"1fb159f0-d4d3-45ae-85e4-31dcfe9813f7","order_by":0,"name":"Shagufta Jabin","email":"","orcid":"","institution":"Manav Rachna International Institute of Research \u0026 Studies","correspondingAuthor":false,"prefix":"","firstName":"Shagufta","middleName":"","lastName":"Jabin","suffix":""},{"id":292145201,"identity":"a636fbb2-db59-4dbb-b02b-88d4d117334a","order_by":1,"name":"J. K. 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13:22:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4150081/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4150081/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-024-13004-3","type":"published","date":"2024-09-02T16:06:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55322147,"identity":"4483d9bb-9d45-4c61-85ad-9d3025b38f54","added_by":"auto","created_at":"2024-04-25 16:26:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":737247,"visible":true,"origin":"","legend":"\u003cp\u003eView of area and image of collection site of (A) pond of village- Thanthari, district- Palwal, Haryana, India (B) Yamuna river, Okhla New Delhi, India (C) Kheri canal, Faridabad district, Haryana, India\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4150081/v1/61395b374a99845bea61ff0b.png"},{"id":55322143,"identity":"ea9c54f6-c50d-456f-919e-35d0201bbfe3","added_by":"auto","created_at":"2024-04-25 16:26:29","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74241,"visible":true,"origin":"","legend":"\u003cp\u003eMinimum, Maximum, and Mean Turbidity value of Pond Water before and after treatment with PDADMAC (A) March 2023 (B) June 2023 (C) September 2023 (D) December 2023\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4150081/v1/58e2789da8f2b13804cb45f0.jpeg"},{"id":55322146,"identity":"49d28bcf-7a4a-4d15-9b19-52be0944cda9","added_by":"auto","created_at":"2024-04-25 16:26:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":497727,"visible":true,"origin":"","legend":"\u003cp\u003eMinimum, Maximum, and Mean Turbidity value of River Water before and after treatment with PDADMAC (A) March 2023 (B) June 2023 (C) September 2023 (D) December 2023\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4150081/v1/b106d323671f2136b511536d.png"},{"id":55322144,"identity":"5b164cda-025d-4b96-9c18-d2c14dd9e6d5","added_by":"auto","created_at":"2024-04-25 16:26:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":485950,"visible":true,"origin":"","legend":"\u003cp\u003eMinimum, Maximum, and Mean Turbidity value of Canal Water before and after treatment with PDADMAC (A) March 2023 (B) June 2023 (C) September 2023 (D) December 2023\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4150081/v1/044a6d94aac60ab0b55dd3ea.png"},{"id":55322154,"identity":"574871b5-d49c-4e38-9071-7b214876dcc4","added_by":"auto","created_at":"2024-04-25 16:26:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":110708,"visible":true,"origin":"","legend":"\u003cp\u003eX-Ray diffractogram of impurities/sludge after filtration (a) with lime (b) with lime + polyelectrolyte\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4150081/v1/83631dcde8c9bbb8a5d001be.png"},{"id":55322151,"identity":"00ec2ae1-6847-4495-93c1-25392a2f787f","added_by":"auto","created_at":"2024-04-25 16:26:30","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":222525,"visible":true,"origin":"","legend":"\u003cp\u003eScanning electron micrographs of sludge- lime (A - C) ; sludge-lime-polyelectrolyte composite ( D-F).\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4150081/v1/5d09edae00b08d64383e6e8f.jpeg"},{"id":64186085,"identity":"4e4c78a8-a1da-4142-aaff-b8e42baa687a","added_by":"auto","created_at":"2024-09-09 16:24:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3681473,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4150081/v1/ad59a9f2-85f3-4788-806a-2dae54f19762.pdf"},{"id":55322145,"identity":"88de8757-6a9b-4401-a671-26eadee37229","added_by":"auto","created_at":"2024-04-25 16:26:29","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":337171,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical Abstract\u003c/p\u003e","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-4150081/v1/d6061c5ffa9195ae5473675c.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of Poly (diallyl dimethyl ammonium chloride) and Lime for Surface Water Treatment (Pond, River, and Canal water): Seasonal Variations and Correlation Analyses","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eStudied physicochemical properties of surface water across seasons.\u003c/li\u003e\n \u003cli\u003eEmployed PDADMAC polyelectrolyte as a coagulation aid.\u003c/li\u003e\n \u003cli\u003eAnalyzed using XRD and SEM peaks to explore sludge-lime-polyelectrolyte interaction.\u003c/li\u003e\n \u003cli\u003eUtilized SPSS for parameter analysis.\u003c/li\u003e\n \u003cli\u003eIdentified a significant correlation between polyelectrolyte dosage, initial turbidity, and turbidity removal percentage.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eWater quality is a critical concern influenced by a myriad of factors including geological and morphological attributes, vegetation, and human activities such as industrialization and urbanization (S\u0026aacute;nchez-Mart\u0026iacute;n et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Soros et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Turbidity, a measure of suspended and colloidal matter in water, is a key indicator of water quality, affecting both aesthetics and functionality (Asthana et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). High turbidity levels not only diminish water purity but also introduce unpleasant tastes and odors, impacting water treatment processes and increasing treatment costs (Muthuraman and Sasikala \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Moreover, turbid water inhibits respiratory processes and reduces visibility, underscoring the importance of minimizing turbidity levels in surface water (Frieder et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gautam \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hargreaves and Tucker \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Paul et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn India, the pollution of rivers has reached alarming levels due to rapid urbanization and industrial growth, posing significant threats to aquatic ecosystems and human health (Roy and Shamim \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Pollution stems from various sources including industrial and sewage waste discharge, agricultural runoff, and solid waste deposition (Singh et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The influx of untreated and treated suspensions further exacerbates water quality degradation, necessitating effective suspension removal strategies (Voulvoulis \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePolyelectrolytes have emerged as crucial agents for water treatment, offering enhanced flocculation capabilities and versatility in controlling properties such as charge units and molecular weight (Yadav and Goyal \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). When used in conjunction with metal coagulants, polyelectrolytes aid in destabilizing suspended particles and enhancing flocculation processes (Jabin et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Poly diallyl dimethyl ammonium chloride (PDADMAC), a high-charge density cationic polyelectrolyte, is particularly effective as a secondary coagulant for suspension removal (Jabin and Kapoor \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, surface water samples were collected from various sources in India, including ponds, rivers, and canals, to assess turbidity levels and water quality parameters. Pond water, sourced from Thanthari, Palwal district, Haryana, river water from the Yamuna River, and canal water from the Kheri canal in Greater Faridabad, Haryana, were analyzed. The Yamuna River, a major tributary of the Ganga River, faces substantial pollution from treated and untreated waste, highlighting the urgency of effective water treatment measures. A view of the area and collection site of all three sources of surface water has been shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe objective of this study was to evaluate the efficacy of lime in conjunction with PDADMAC for turbidity reduction in surface water. Additionally, correlation analyses were conducted to understand the relationships among various water quality parameters including initial turbidity, electrical conductivity, total dissolved solids, pH, polyelectrolyte dosage, and percentage removal of turbidity. By elucidating these correlations, the study aims to provide insights into effective water treatment strategies and facilitate informed decision-making for water quality management.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eWater samples were collected throughout the year of 2023 across four distinct seasons: March (spring), June (summer), September (autumn), and December (winter). Physicochemical parameters including pH, electrical conductivity (E.C.), turbidity, total dissolved solids (TDS), and dissolved oxygen were determined using standard methods outlined by the American Public Health Association (APHA, 1995) (Association \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1926\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe samples were collected in clean polyethylene bottles rinsed with de-ionized water to avoid contamination and stored in a refrigerator before analysis. This study provides insights into the quality of surface water and the impacts of anthropogenic activities on water resources.\u003c/p\u003e \u003cp\u003eInorganic coagulant lime was sourced from CDH (India) and prepared in distilled water to obtain a concentration of 10 mg/L. Polydiallyl dimethyl ammonium chloride (PDADMAC), obtained from Sigma Aldrich, was utilized as a coagulant aid.\u003c/p\u003e \u003cp\u003eWater pH was measured using a pH meter (Anna HI 8314, USA), while electrical conductivity was assessed with a Philips conductivity bridge and dip-type cell at a temperature of 27\u0026deg;C\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u0026deg;C. Turbidity measurements were conducted using a turbidity meter (Hanna HI93703, U.S.A.), and TDS values were calculated using a tubular TDS meter.\u003c/p\u003e \u003cp\u003eThe turbidity, temperature, E.C., TDS, and pH values of pond water, river water, and canal water across different seasons are presented in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, respectively, along with a summary of basic statistical analysis. These tables offer comprehensive insights into the variations of key water quality parameters across different seasons and water sources.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 \u003cem\u003eRaw pond water characteristics\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eThe E.C. of pond water exhibited notable variations throughout the investigation, reaching its peak in September and its lowest point in December. This fluctuation in E.C. mirrored the trends observed in TDS. Additionally, the pH levels of pond water displayed seasonal variations. In March, June, and December 2023, the mean pH was recorded at 8, whereas in September 2023, it decreased to 7.0. This pH variation is significant as it influences subsequent turbidity treatment processes.\u003c/p\u003e \u003cp\u003eThe turbidity levels of pond water also varied across different seasons. In March, June, September, and December 2023, the turbidity measurements were recorded at 175 NTU, 189 NTU, 96 NTU, and 145 NTU, respectively. These fluctuations in turbidity levels underscore the dynamic nature of water quality in ponds and emphasize the importance of continuous monitoring and treatment measures.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of raw pond water with a summary of basic statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurbidity(NTU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e183.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e175.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e175.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature(\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE.C(mho)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS(mg. L-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurbidity(NTU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e179.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e198.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e189.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e189.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature(\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE.C(mho)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS(mg. L-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e499.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e520.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e511.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e512.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurbidity(NTU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature(\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE.C(mho)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS(mg. L-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurbidity(NTU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e145.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e143.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature(\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE.C(mho)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS(mg. L-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Raw river water characteristics\u003c/h2\u003e \u003cp\u003eUpon evaluation of the raw water from the Yamuna, it was observed that the turbidity levels peaked at 249 NTU in June, while reaching their lowest point at 94 NTU in March. Concurrently, the temperature of the water samples exhibited fluctuations ranging from 19\u0026deg;C to 38\u0026deg;C, with the highest temperatures recorded in June and the lowest in March.\u003c/p\u003e \u003cp\u003eSimilarly, the E.C. demonstrated its highest values in June and its lowest in March. The elevated E.C. values in June indicate the presence of a significant amount of dissolved inorganic substances in the ionized water. TDS serves as an indicator of the overall salinity of water, with the highest TDS recorded in June at 849 mg/L and the lowest in September at 595 mg/L.\u003c/p\u003e \u003cp\u003eThe pH levels of the water samples ranged from 7.4 to 8.6, showcasing variations in acidity and alkalinity. These findings highlight the dynamic nature of water quality parameters in the Yamuna, emphasizing the importance of continued monitoring and management strategies to ensure water safety and purity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of raw river water with a summary of basic statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurbidity(NTU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature(\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE.C(mho)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS(mg. L-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurbidity(NTU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e236.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e268.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e249.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e246.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature(\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE.C(mho)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS(mg. L-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e850.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurbidity(NTU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e110.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature(\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE.C(mho)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS(mg. L-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e136.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurbidity(NTU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e172.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e158.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e157.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature(\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE.C(mho)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS(mg. L-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Raw canal water characteristics\u003c/h2\u003e \u003cp\u003eThe turbidity levels in canal water were notably higher compared to other surface water sources. In June 2023, the average turbidity reached 333 NTU, whereas it decreased to a minimum of 198 NTU in December 2023. Concurrently, the temperature of the water samples exhibited fluctuations ranging from 22\u0026deg;C to 39\u0026deg;C, with June 2023 recording the highest temperatures and December 2023 the lowest. Similarly, the E.C. values fluctuated between 0.5 to 1.4 mho, mirroring the trend observed in TDS. Notably, the E.C. peaked in December 2023 and was at its lowest in June 2023, demonstrating seasonal variations in water quality parameters.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of raw canal water with a summary of basic statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeasons\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurbidity(NTU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e240.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e262.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e248.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e247.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature(\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE.C(mho)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS(mg. L-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurbidity(NTU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e312.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e350.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e333.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e335.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.705\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature(\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE.C(mho)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS(mg. L-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e209.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurbidity(NTU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e205.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e204.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature(\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.414\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE.C(mho)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS(mg. L-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e312.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurbidity(NTU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e215.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e198.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature(\u003csup\u003eo\u003c/sup\u003eC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE.C(mho)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS(mg. L-1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Removal of turbidity using polyelectrolyte\u003c/h2\u003e \u003cp\u003eTo eliminate turbidity, a combination of PDADMAC and lime was employed across all types of surface water. Initially, the raw water underwent filtration using a stainless sieve followed by filter paper with a pore size of 7\u0026ndash;8\u0026micro;m. Subsequently, a jar test procedure was conducted, a widely adopted laboratory method for assessing coagulation and flocculation processes at a bench scale. The objective was to ascertain the optimal operating conditions, including pH, temperature, and chemical dosage, for effective water treatment. Previous studies have demonstrated the efficacy of this method in determining the optimal doses of polyelectrolyte for turbidity removal.\u003c/p\u003e \u003cp\u003eFor this experiment, a conventional jar test apparatus, featuring the Phipps and Bird six-paddle stirrer with an illuminated base, was utilized within 2 L square Plexiglas containers. After a 30-minute sedimentation period, a 10 ml aliquot was extracted from the mid-depth of the beaker, and the residual turbidity was measured (Chiavola et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Haghiri et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This process was repeated for all types of water samples. Variations in results were observed due to disparities in the physicochemical properties of water quality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 X-Ray Diffraction (XRD) analysis\u003c/h2\u003e \u003cp\u003eThe studies of X-ray diffraction were carried out using a Rigaku D/Max-2500 X-ray diffractometer. This instrument utilized a Cu-Kα X-ray tube with a wavelength of 1.540538 angstroms, operating at a current of 20 mA and an input voltage of 40 kV. Throughout the study, two diffractograms were generated to investigate the interactions within the sludge-lime-polymer system-one for sludge with lime, and another for sludge with lime and polyelectrolyte. These analyses aimed to provide insights into the complex interactions occurring within the composite material.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Scanning Electron Microscopy (SEM) analysis\u003c/h2\u003e \u003cp\u003eMaterials were imaged using an SEM apparatus manufactured by Jeol (Japan), specifically the JSM 6510Lv model. Operating at a 15 kV accelerating voltage, this SEM enabled detailed visualization of the specimens. A typical SEM allows for scanning areas ranging from 1 cm to 5 \u0026micro;m, offering magnifications between 20x and 30,000x with a spatial resolution of 50\u0026ndash;100 nm. In SEM analysis, a focused beam of electrons interacts with the specimen, producing images that unveil the surface topography of the sample.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result and Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Result of different surface water with basic statistics\u003c/h2\u003e\n\u003cp\u003eSixteen water samples were collected from various surface water sources over the course of one year. The optimization of lime dosage for four different samples is summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eOptimum lime dosages in different surface water samples in different seasons\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWater Sample\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSeason\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDosage of Lime (mg/L)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTurbidity of Raw water (NTU)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePond Water\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eMarch 2023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e175\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRiver Water\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCanal Water\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e248\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePond Water\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eJune\u003c/p\u003e\n\u003cp\u003e2023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e189\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRiver Water\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e249\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCanal Water\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e333\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePond Water\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eSeptember 2023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e96\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRiver Water\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e110\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCanal Water\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e205\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePond Water\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eDecember 2023\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e145\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRiver Water\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e158\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCanal Water\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e198\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe optimal lime dosage varied across different water sources and seasons, with specific dosages identified for each scenario. In March, the optimal lime dosage was determined to be 7.5 mg/L for pond water and 10 mg/L for river water. Conversely, canal water consistently required 5 mg/L of lime dosage throughout all seasons due to its consistently high turbidity compared to other surface water sources. Notably, an inverse relationship was observed between turbidity levels and optimal lime dosage, with higher turbidity necessitating lower lime dosages. This trend was consistent across all four seasons.\u003c/p\u003e\n\u003cp\u003eTo address residual lime concentration in treated water and further reduce turbidity, cationic polyelectrolyte PDADMAC was employed in conjunction with lime for turbidity removal from various water samples. Enhanced performance was achieved when the coagulant aid was added to the water sample after proper lime mixing, in contrast to the simultaneous addition of polyelectrolyte and lime.\u003c/p\u003e\n\u003cp\u003ePDADMAC, characterized by its quaternary ammonium salt nature and synthetic polyelectrolyte properties, demonstrated efficacy in turbidity removal. Its high charge density and molecular weight facilitated the flocculation of negatively charged suspended particles through adsorption and charge neutralization mechanisms. This effectiveness was particularly notable in high turbidity water compared to low turbidity water (Kapoor et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Piaskowski et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe treatment results for pond water across four different seasons are depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e(A-B). Following treatment, the average residual turbidity of pond water ranged from 4.76 NTU to 9.0 NTU for March, June, September, and December 2023, respectively. Interestingly, the lowest residual turbidity was observed when the raw water exhibited the highest turbidity levels. This underscores PDADMAC's superior performance in highly turbid water conditions compared to situations with lower turbidity levels.\u003c/p\u003e\n\u003cp\u003eThe efficacy of PDADMAC in turbidity removal from river water is illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e (A-D) across the months of March, June, September, and December 2023. Notably, the combined action of lime and PDADMAC proved notably more effective at higher initial turbidity levels compared to lower turbidity conditions. This heightened effectiveness can be attributed to the presence of ample colloidal suspensions during periods of maximum turbidity, facilitating adsorption and charge neutralization processes. June 2023 exhibited the highest turbidity levels in river water, as depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e (B).\u003c/p\u003e\n\u003cp\u003eIn canal water, the use of polyelectrolyte as a coagulant aid in the flocculation process resulted in reduced lime dosage and residual turbidity, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e (A-B). Given that canal water exhibited the highest raw turbidity in June compared to other surface water sources, the removal of turbidity peaked during this period (97.99%), as illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Following jar testing, minimal changes in pH (within \u0026plusmn;\u0026thinsp;0.1) were observed across all treated water samples.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e provides a summary of the optimal polyelectrolyte dosage alongside the percentage of turbidity removal across various surface water sources. Polyelectrolyte dosage emerged as a critical factor in turbidity removal, with the highest removal rates achieved at minimal polyelectrolyte dosages across all water samples. In the presence of suspended solids, low molecular mass polymers primarily react with soluble organics, while high molecular mass polymers, at low dosages, preferentially interact with suspended solids (Kapoor et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Piaskowski et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, PDADMAC, being a high molecular weight polyelectrolyte, operates optimally at a dosage of 2 mg/L for highly turbid canal and river water in June. It is crucial to maintain an optimal dosage of coagulant aid to prevent excessive adsorption, which could lead to poor adsorption site accessibility.\u003c/p\u003e\n\u003cp\u003eMoreover, the optimal dosage of polyelectrolyte was identified as 4 mg/L in river water for the March, June, and December seasons. This optimized dosage strategy aims to balance effective turbidity removal while minimizing excess polyelectrolyte adsorption, ensuring optimal treatment outcomes.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe optimum dosage of polyelectrolyte in the removal of turbidity in different surface\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTypes of surface water\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSeasons\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOptimum dosage of polyelectrolyte (mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTurbidity of raw water (ntu)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePercentage of removal of turbidity (%)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003epond Water\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emarch\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e175\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e97.28\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ejune\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e189\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e97.04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eseptember\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e90.62\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edecember\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e145\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e97.09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eriver Water\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emach\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e92.09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ejune\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e249\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e97.14\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eseptember\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e96.36\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edecember\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e158\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e94.87\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003ecanal Water\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003emarch\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e248\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e97.81\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ejune\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e333\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e97.91\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eseptember\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e205\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e95.70\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003edecember\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e198\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e96.78\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 X-ray powder diffraction (XRD) analysis\u003c/h2\u003e\n\u003cp\u003eThe XRD spectra obtained for the samples containing sludge with lime and sludge with lime with polyelectrolyte provide valuable insights into the structural composition and interaction of these components.\u003c/p\u003e\n\u003cp\u003eThe XRD spectra obtained for the sludge with lime combination reveal distinctive diffraction peaks corresponding to the crystalline phases present in the sample. Typically, the XRD pattern for sludge with lime exhibits peaks corresponding to the crystalline phases of lime, including calcium hydroxide (Ca(OH)\u003csub\u003e2\u003c/sub\u003e), calcium carbonate (CaCO\u003csub\u003e3\u003c/sub\u003e), and calcium oxide (CaO). These peaks align well with the characteristic peaks of calcite at 2\u0026theta;\u0026thinsp;~\u0026thinsp;29.20\u0026deg;, indicative of the (111) plane, as observed in Card No. 5-586 from the International Center for Diffraction Data (ICDD) (Galv\u0026aacute;n-Ruiz et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Pires \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, the intensity of the peak at 39.02\u0026deg; corresponding to the (200) plane is comparatively low due to the overlay of sludge samples onto the lime (Nasrazadani and Eureste \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e), Fi 5 (A). These peaks facilitate the identification of crystalline phases formed upon the addition of lime to the sludge, providing insights into the degree of crystallinity and phase composition of the sample. Moreover, shifts or changes in peak intensity suggest potential interactions or reactions between the sludge and lime components.\u003c/p\u003e\n\u003cp\u003eIn contrast, the XRD spectra obtained for the sludge with lime plus polyelectrolyte combination exhibit alterations in peak intensity, position, or appearance compared to the sludge with lime alone. These changes indicate potential modifications in the crystalline structure or phase composition induced by the addition of polyelectrolyte. Specifically, a sharp peak near 2\u0026theta;\u0026thinsp;~\u0026thinsp;30\u0026deg; becomes more intense and undergoes a slight shift in angle after interaction with polyelectrolyte, while a peak observed at 2\u0026theta;\u0026thinsp;~\u0026thinsp;17 disappears upon the addition of polyelectrolyte. Furthermore, a new peak emerges at 2\u0026theta;\u0026thinsp;~\u0026thinsp;31.68\u0026deg;, characteristic of PDADMAC, suggesting an increase in the semicrystalline nature of the sample following the addition of polyelectrolyte (Tyagi and Sharma \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). The disappearance of the peak at 2\u0026theta;\u0026thinsp;~\u0026thinsp;34\u0026deg; from the spectra indicates the dominance of polyelectrolyte in the material over lime, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e (B). These alterations in peak characteristics signify interactions between the polyelectrolyte and the sludge-lime matrix, potentially leading to the formation of new crystalline phases or changes in crystallographic parameters. Overall, XRD analysis provides valuable insights into the structural modifications induced by the incorporation of polyelectrolyte into the sludge-lime composite, shedding light on its influence on the overall properties of the material.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Scanning electron microscopy (SEM) analysis:\u003c/h2\u003e\n\u003cp\u003eSEM analysis was conducted on sludge samples (after filtration) treated with lime alone and in combination with polyelectrolyte. The impact of lime as a standalone metal coagulant on sludge was examined at magnification scales of 500X (Figure A), 25KX (Figure B), and 50KX (Figure C). Similarly, sludge treated with lime in conjunction with polyelectrolyte was studied at the same magnification scales of 500X (Figure D), 25KX (Figure E), and 50KX (Figure F).\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e (A) presents the original sludge particles treated with lime alone. These particles exhibit smaller size with an unevenly dispersed arrangement on the surface and strong adhesion to water. Figures\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e (B) and 6 (C) depict the unadjusted spaces observable on the surface without uniform consistency. These observations provide insight into the structural changes induced by the application of lime as a solo metal coagulant.\u003c/p\u003e\n\u003cp\u003eFollowing the addition of polyelectrolyte, molecules permeate and occupy spaces within the structure, indicating the formation of a sludge-lime-polyelectrolyte composite (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eE and F). Analysis of the sludge treated with lime and polyelectrolyte confirms the development of dense and smooth floc structures during the treatment process (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD). Under SEM examination, strong bonding within the sludge-lime-polyelectrolyte composite is evident, showcasing unique and well-defined features (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eD-F).\u003c/p\u003e\n\u003cp\u003ePolyelectrolyte plays a crucial role in enlarging sludge particles and facilitating strong aggregation through charge neutralization mechanisms (Figure E and F). This phenomenon enhances the separation of water from sludge, leading to improved water purification. Furthermore, the processes of adsorption and charge neutralization transform destabilized particles into larger aggregates and flocs, aiding in effective water cleaning.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4 SPSS Pearson correlations\u003c/h2\u003e\n\u003cp\u003eMonitoring water quality is facilitated through correlation studies among various parameters (Chekkala et al. \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shroff et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). A correlation matrix for different variables is constructed using SPSS to establish correlations between different aspects of surface water. The degree of correlation between variables is assessed through the Pearson correlation coefficient. This coefficient, denoted as r, indicates the strength and direction of linear relationships between pairs of continuous variables. The analysis determines whether variables are strongly correlated with each other. Essentially, the Pearson Correlation assesses whether there is statistical evidence supporting a linear relationship among the parameters.\u003c/p\u003e\n\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n\u003ch2\u003e3.4.1 Analysis of pond water\u003c/h2\u003e\n\u003cp\u003eTo analyze the influence of certain factors on pond water characteristics, the Statistical Package for Social Science (SPSS) software was employed. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e presents the Pearson correlation coefficient, illustrating the relationship between Turbidity, Temperature, E.C., TDS, pH, percentage removal of Turbidity, and Initial Turbidity.\u003c/p\u003e\n\u003cp\u003eThe correlation analysis of pond water revealed several significant relationships. There was a strong positive linear correlation between the percentage removal of turbidity and initial turbidity (r\u0026thinsp;=\u0026thinsp;0.887), which was statistically significant (p\u0026thinsp;=\u0026thinsp;0.000). Additionally, the dosage of polyelectrolyte exhibited a strong negative correlation with the percentage of removal (r = -0.881), also statistically significant.\u003c/p\u003e\n\u003cp\u003eFurthermore, the turbidity of raw water displayed a strong positive correlation with the percentage of removal. Higher initial turbidity corresponded to a greater percentage of turbidity removal. Moreover, the E.C. showed a significant positive correlation with TDS (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.977), indicating a direct relationship between the two parameters. Lastly, there was a direct correlation observed between temperature and the initial turbidity of raw water (r\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.570).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePearson Correlation Coefficient for Pond Water\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eParameter\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTemp\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eE.C.\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTDS\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003epH\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDosage\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e% removal of turbidity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eInitial Turbidity\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eTemp\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.293\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.372\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.612\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.164\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.334\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.570\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.270\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.543\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.206\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.021\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eE.C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.293\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.977\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.431\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.784\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.596\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.453\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.270\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.095\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.078\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eTDS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.372\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.977\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.293\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.699\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.501\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.350\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.156\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.270\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.048\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.183\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003epH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.612\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.431\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.293\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.796\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.821\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.905\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.095\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.270\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eDosage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.164\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.784\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.699\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.796\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.881\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.879\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.543\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e% removal\u003c/p\u003e\n\u003cp\u003eof turbidity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.334\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.596\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.501\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.821\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.881\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.887\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.206\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.048\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eInitial turbidity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.570\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.453\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.350\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.905\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.879\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.887\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.021\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.078\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.183\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Correlation is significant at the 0.05 level (2-tailed).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e**. Correlation is significant at the 0.01 level (2-tailed).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n\u003ch2\u003e3.4.2 Analysis of river water\u003c/h2\u003e\n\u003cp\u003eThe correlation analysis of river water variables was conducted using IBM SPSS software, and the Pearson correlation coefficients are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. The Pearson correlation coefficient indicates the strength and direction of the relationship between different parameters.\u003c/p\u003e\n\u003cp\u003eThe correlation matrix revealed several significant relationships. Initial turbidity exhibited a strong positive correlation (r\u0026thinsp;=\u0026thinsp;0.740) with the temperature of river water, which was statistically significant. Furthermore, the dosage of polyelectrolyte displayed a strong negative linear relationship with initial turbidity (r = -0.879), which was also statistically significant (p\u0026thinsp;=\u0026thinsp;0.000). Additionally, initial turbidity showed a strong negative correlation (r = -0.914) with E.C., also statistically significant (p\u0026thinsp;=\u0026thinsp;0.000).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab7\" style=\"width: 702px;\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCorrelation coefficient values for river water\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth style=\"width: 81px;\" align=\"left\"\u003e\n\u003cp\u003eParameter\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 106px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003eTemp\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003eE.C\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003eTDS\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003epH\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003eDosage\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e% removal of turbidity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003eInitial Turbidity\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 81px;\" rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eTemp\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.783\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e0.121\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e-0.190\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e-0.628\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e0.687\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e.740\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e.655\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e.482\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 81px;\" rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eE.C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.783\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e0.323\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e-0.091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e-0.879\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e0.825\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e0.914\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e0.222\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e0.737\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 81px;\" rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eTDS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.121\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.323\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e0.293\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e-0.415\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e-0.138\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e0.381\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.655\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.222\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e0.270\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e0.611\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e0.146\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 81px;\" rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003epH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e-0.190\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e-0.091\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e0.293\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.240\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e-0.352\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e-0.218\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.482\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.737\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e0.270\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e0.181\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e0.418\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 81px;\" rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eDosage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e-0.628\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e-0.879\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e-0.415\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e0.240\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e-0.825\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e-0.977\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.009\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e0.110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e0.371\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 81px;\" rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e% removal\u003c/p\u003e\n\u003cp\u003eof turbidity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.687\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.825\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e-0.138\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e-0.352\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e-0.825\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e0.851\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e0.611\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e0.181\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 81px;\" rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eInitial turbidity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.740\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.914\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e0.381\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e-0.218\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e-0.977\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e0.851\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e0.146\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e0.418\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 106px;\" align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 36px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 48px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 140px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 97.8195px;\" align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 650.819px;\" colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003e*. Correlation is significant at the 0.05 level (2-tailed).\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 650.819px;\" colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003e**. Correlation is significant at the 0.01 level (2-tailed).\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMoreover, the correlation matrix indicated a strong negative linear relationship between the dosage of polyelectrolyte and initial turbidity (r = -0.977), with statistical significance (p\u0026thinsp;=\u0026thinsp;0.000). Furthermore, the percentage removal of turbidity showed a strong positive linear relation with initial turbidity (r\u0026thinsp;=\u0026thinsp;0.851), which was statistically significant (p\u0026thinsp;=\u0026thinsp;0.000). Additionally, there was a statistically significant relationship between the percentage of removal and the dosage of polyelectrolyte (p\u0026thinsp;=\u0026thinsp;0.000), with both parameters negatively correlated (r = -0.825) with each other.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n\u003ch2\u003e3.4.3 Analysis of canal water\u003c/h2\u003e\n\u003cp\u003eThe correlation analysis for various variables was conducted using SPSS software, revealing the relationships between different parameters. Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e presents the Pearson correlation coefficients for different parameters of canal water.\u003c/p\u003e\n\u003cp\u003eThe correlation matrix indicated several significant findings. Initial turbidity demonstrated a statistically significant (p\u0026thinsp;=\u0026thinsp;0.002) strong positive correlation (r\u0026thinsp;=\u0026thinsp;0.712) with the temperature of canal water. Furthermore, initial turbidity exhibited a strong negative correlation with E.C. (r = -0.917) and TDS (r = -0.945), both of which were statistically significant (p\u0026thinsp;=\u0026thinsp;0.000). However, there was no significant correlation observed between initial turbidity and pH.\u003c/p\u003e\n\u003cp\u003eMoreover, the dosage of polyelectrolyte displayed a strong negative linear relationship with initial turbidity (r = -0.910), which was statistically significant (p\u0026thinsp;=\u0026thinsp;0.000). Additionally, the correlation matrix showed a strong positive linear relationship between the percentage removal of turbidity and initial turbidity (r\u0026thinsp;=\u0026thinsp;0.690), which was statistically significant (p\u0026thinsp;=\u0026thinsp;0.003). Furthermore, there was a negative correlation (r = -0.584) observed between the dosages of polyelectrolyte and the percentage of removal of turbidity.\u003c/p\u003e\n\u003cp\u003eThe correlation analysis across different surface water types revealed consistent trends. Initial turbidity showed a strong correlation with temperature across all surface water types, and the dosage of polyelectrolyte exhibited a strong correlation with initial turbidity across all surface water types.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab8\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCorrelation coefficient values for canal water\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eParameter\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTemp\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eE.C\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTDS\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003epH\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDosage\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e% removal of turbidity\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eInitial Turbidity\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eTemp\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.621\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.765\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.152\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.692\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.712\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.574\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.963\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eE.C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.621\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.970\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.266\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.973\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.680\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.917\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.319\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eTDS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.765\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.970\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.222\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.948\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.574\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.945\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.409\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003epH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.152\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.266\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.222\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.478\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.241\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.574\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.319\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.409\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.644\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.061\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.369\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eDosage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.692\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.973\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.948\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.584\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.910\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.644\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e% removal\u003c/p\u003e\n\u003cp\u003eof turbidity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.680\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.574\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.478\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.584\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.690\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.963\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.061\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eInitial turbidity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePearson correlation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.712\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.917\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.945\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.241\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026minus;\u0026thinsp;.910\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.690\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSig(2-tailed)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.369\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\" align=\"left\"\u003e\n\u003cp\u003e*. Correlation is significant at the 0.05 level (2-tailed).\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\" align=\"left\"\u003e\n\u003cp\u003e**. Correlation is significant at the 0.01 level (2-tailed).\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe results from the SPSS correlation analysis revealed a robust correlation between initial turbidity and various variables. Specifically, there was a notable correlation observed between initial turbidity and temperature, dosage of polyelectrolyte, and percentage of turbidity removal. Moreover, in pond and canal water, E.C. and TDS were found to be strongly correlated with each other, whereas they exhibited a moderate correlation in river water.\u003c/p\u003e \u003cp\u003eFurther insights into the interaction of impurities with lime and polyelectrolyte were gained through XRD studies of soil samples. Additionally, SEM analysis confirmed that treating sludge with lime and polyelectrolyte resulted in the formation of dense flocs, aiding in water purification via charge neutralization.\u003c/p\u003e \u003cp\u003eThe pH of water emerged as a crucial factor, determining the electrical charges of organic and inorganic colloids. Notably, the performance of lime in conjunction with polyelectrolyte was significantly more effective at high initial turbidity levels compared to low turbidity levels. The fluctuating turbidity levels of water at different stages further complicated the treatment process.\u003c/p\u003e \u003cp\u003eOverall, the study underscored the efficacy of PDADMAC as a coagulant aid, even at very low dosages, for turbidity removal across diverse water sources. The addition of PDADMAC alongside lime led to an enhancement in flocculation size, indicating improved treatment efficiency. Thus, the findings support the successful application of polyelectrolyte as a coagulant aid for turbidity removal in various surface water contexts.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical and Disclosures:\u0026nbsp;\u003c/strong\u003eThe submitted work is original and is not published or submitted elsewhere in any form or language.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful to the School of Engineering and Technology, Manav Rachna International Institute of Research \u0026amp; Studies for providing lab facilities for practical work. The corresponding author Sapana Jadoun is grateful for the support National Research and Development Agency of Chile (ANID) for the project FONDECYT project 3200850.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is not supported by any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eAll authors contributed to the study\u0026apos;s conception and design. Lab work, data collection, and analysis were performed by Dr. Shagufta jabin, \u0026nbsp;Jitander Kumar Kapoor guided her during the work. Dr. Anupama Chadha and Dr. Anjali Gupta have analyzed SPSS. The first draft of the manuscript was written by Dr. Shagufta Jabin. Dr. Sapana Jadoun has done the review, editing, and finalizing of the manuscript for publication. All authors read and approved the final manuscript\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u0026nbsp;\u003c/strong\u003eConsent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish:\u003c/strong\u003e The Author confirms that the work described has not been published before and is not under consideration for publication elsewhere. The work has been approved by all co-authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availibility statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will ve made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAssociation, A. P. H. 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Wetlands, \u003cem\u003e42\u003c/em\u003e(8), 107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCaption of Figures\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Surface water, SPSS software, Polyelectrolyte, Turbidity, Different water","lastPublishedDoi":"10.21203/rs.3.rs-4150081/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4150081/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the basic characteristics of various surface water sources, including pond water, river water, and canal water, across four distinct seasons. The research endeavours to assess the impact of a cationic polyelectrolyte, specifically poly diallyl dimethyl ammonium chloride (PDADMAC), utilized as a coagulation aid in conjunction with lime for water treatment purposes. Employing a conventional jar test apparatus, turbidity removal from diverse water samples is examined. Furthermore, the samples undergo characterization utilizing X-ray diffraction (XRD) and Scanning Electron Microscopy (SEM) techniques. The study also conducts correlation analyses on various parameters such as electrical conductivity (EC), pH, total dissolved solids (TDS), turbidity of raw water, polyelectrolyte dosage, and percentage of turbidity removal across different water sources. Utilizing the Statistical Package for Social Science (SPSS) software, these analyses aim to establish robust relationships among initial turbidity, temperature, percentage of turbidity removal, dosage of coagulant aid, electrical conductivity, and total dissolved solids (TDS) in pond water, river water, and canal water. By elucidating these correlations, the study contributes to a deeper understanding of the effectiveness of PDADMAC and lime in water treatment processes across diverse environmental conditions. This research not only enhances our comprehension of surface water treatment methodologies but also provides valuable insights for optimizing water treatment strategies to address the challenges posed by varying water sources and seasonal fluctuations.\u003c/p\u003e","manuscriptTitle":"Assessment of Poly (diallyl dimethyl ammonium chloride) and Lime for Surface Water Treatment (Pond, River, and Canal water): Seasonal Variations and Correlation Analyses","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-25 16:26:09","doi":"10.21203/rs.3.rs-4150081/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-24T17:33:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-13T09:48:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254622301842682187765411338124971396047","date":"2024-05-23T11:31:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321458766179550742345608571793223501074","date":"2024-05-02T22:18:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-27T18:00:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-17T04:32:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-17T04:32:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2024-03-22T13:21:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"92f0a5f5-2c90-4032-906a-556f8e2f4146","owner":[],"postedDate":"April 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-09T16:16:08+00:00","versionOfRecord":{"articleIdentity":"rs-4150081","link":"https://doi.org/10.1007/s10661-024-13004-3","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2024-09-02 16:06:05","publishedOnDateReadable":"September 2nd, 2024"},"versionCreatedAt":"2024-04-25 16:26:09","video":"","vorDoi":"10.1007/s10661-024-13004-3","vorDoiUrl":"https://doi.org/10.1007/s10661-024-13004-3","workflowStages":[]},"version":"v1","identity":"rs-4150081","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4150081","identity":"rs-4150081","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}