Water Quality Assessment of Inland Waters in Cartagena de Indias – Colombia, Using Multivariate Statistics

Water Quality Assessment of Inland Waters in Cartagena de Indias – Colombia, Using Multivariate Statistics | 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 Water Quality Assessment of Inland Waters in Cartagena de Indias – Colombia, Using Multivariate Statistics Henry Adolfo Lambis Miranda, Ildefonso Baldiris Navarro, Maicol Ahumedo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6239670/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Worldwide, industrial growth, overpopulation, and a lack of awareness about the need to protect essential resources are posing serious risks to natural water sources. The aesthetic value and ecological integrity of urban coastal waters are vital resources that the tourism industry depends on, and they are being jeopardized for the benefit of nearby populations. In this research the water quality of four inland waters in Cartagena de Indias were analyzed during the period 2008–2022. The study was focused on water parameters as electrical conductivity (EC), dissolved oxygen (DO), biochemical oxygen demand (BOD 5 ), chemical oxygen demand (COD), salinity (Sal), pH, total suspended solids (TSS), total and fecal coliforms (TC), chlorophyll (Chla) and total phosphorus (TP). Descriptive and multivariable statistics were used to clarify the behavior of data. Capability analysis was applied to know if the water bodies may handle the amount of entering pollutants. Principal components analysis detected four components that explain 73.9% of the variance of data. PCA was also used to know the possible pollution sources and main contributors to contamination. Two trophic state indexes showed the level of contamination presented by waters. Marine and Freshwater Ecology Applied Statistics Water quality Cartagena de Indias Capability Index PCA TSI Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. INTRODUCTION One of the most vulnerable natural resources nowadays is water, which is facing numerous difficulties worldwide as a result of urbanization, industrialization, growing urbanization, and global warming, etc (Sanae et al.,2024). The lack of public awareness is leading underdeveloped countries to face difficult problems such as poor water quality, health diseases, fish kills, and others. The principal effect of these perturbances in shallow waters is an alteration in aquatic biodiversity, which may change the species distribution and the dynamics of water communities (Martins et al.,2023). In addition, as a result the intensification of human activities, levels of nitrogen, phosphorus, emerging pollutants and pathogens in water may increase and become a threat to humans and ecosystems (Lencha et al., 2021 ). Tourism in developing nations is the fastest growing sector of the blue economy, where tourism activity is growing at 3–4.5% per year (Naveed Arif et al., 2022 ), but in part it depends on the wellbeing of the water resources. Cartagena de Indias is the main Caribbean port for Colombia and also a tourist destination; Millions of visitors come to enjoy the natural resources associated with water and the old city's history. Consequently, a satisfactory tourism experience depends in part on the quality of water and marine ecosystems (Gonzalez et al., 2023). One of the anthropogenic factors that directly affects the quality of water is eutrophication, which is an increase in the concentration of phytoplankton due to an oversupply of nutrients (nitrogen and phosphorus) in water caused by man activities. This process may lead to oxygen depletion and may be lethal for aquatic life. As a result of eutrophication, the water turbidity increases, toxic algae might grow and immersed macrophytes vanish (Zamora-Lopez et al., 2023). A eutrophication descriptor used worldwide is the trophic state index (TSI) proposed by Carlson (Mourão et al., 2020 ). This index classifies the body of water into oligotrophic, mesotrophic, eutrophic or hypereutrophic (Garcia-Avila et al., 2023). Cartagena de Indias has a large inland water system with several coastal lagoons and channels connecting them, the system drains into the Caribbean Sea (Baldiris-Navarro et al, 2019 ), which means that these inland systems may impact negatively on the water used by tourists. For this reason, a rigorous monitoring and evaluation program must be implemented to ensure good water quality and prevent associated diseases. Multivariate statistics have proven to be a useful tool in decision making to improve water quality around the world (Fraga et al. 2020 ). Capability Index is a statistical process control tool that may help decide whether a body of water may regenerate itself according to the range of values of some properties, such as chlorophyll a (Silva et al., 2022 ). The Kruskal Wallis test, Wilcoxon test, and boxplots may be useful in indicating which water sampling stations have different behavior in data. Principal component analysis is a multivariate statistical technique that may be used to identify which water properties have a greater impact on its quality. These analyses are possible if data is at hand, which is the case of this paper. The aim of this research was to evaluate the water quality and trophic status of four inland water in Cartagena de Indias using multivariate statistical techniques. 2. METHODOLOGY 2.1 Study area The study zone is in Cartagena de Indias, which is located on the Colombian Caribbean, at coordinates 10 ° 26 'north latitude and 75 ° 33' west longitude (Duarte-Restrepo et al., 2021 ) (Tosic et al., 2019 ). Sample points are located in Cabrero lagoon, Juan Angola Channel, Juan Polo lagoon and a point in Cienaga de la Virgen (see Fig. 1 ). The area is rich in biodiversity, with a variety of marine, brackish-water, and freshwater mollusk species, including bivalves, scaphopods, and gastropods, inhabiting the coastal lagoon and the adjacent marine areas. The flora in this region primarily consists of mangroves that are situated along the edges of the lagoons, with the red mangrove (Rhizophora mangle) being the dominant species. Nevertheless, there exist various types of decorative vegetation such as almendron ( Terminalia cattapa ), uvita de playa ( Coccoloba uvifera ), and Payandé (Phitecellobium dulcis) can be observed (Villate et al., 2020). In Cartagena, the dry season extends from December to April and is characterized by strong northeasterly winds and infrequent rains. The rainy season runs from may to november and is characterized by light winds, variable directions and strong rains. All these water bodies merge with the Caribbean Sea in the Cartagena Bay. 2.2 Analytical Procedures Surface water samples were collected monthly at four different points in Cartagena (see Fig. 1 ). Electrical conductivity (EC) and salinity (Sal) (SM-2520-B) were measured in situ using portable multi parameter Hach 5465011 Sension. For Biochemical oxygen demand, chlorophyll, total phosphorus and coliforms samples were preserved and taken to the laboratory. Biochemical oxygen demand (BOD 5 ) was measured by Winkler method (SM 4500-O G), chlorophyll-a (Chl) by spectrophotometry (SM-10150), total phosphorus (TP) by ascorbic method (SM 4500-P B, E) and total and fecal coliforms (TC) were measured by multiple-tube method (SM 9222B) (Rice et al., 2017 ). 2.3 Determination of the trophic state and trophic state indices Trophic state refers to the nutrient enrichment level of a water body, impacting its ecological balance. Trophic state indices (TSIs) are tools used to assess this state by integrating various parameters like chlorophyll-a, water clarity, and total phosphorus. This study employs chlorophyll-a and total phosphorus indices, which are instrumental in categorizing water bodies into groups such as oligotrophic (low nutrients) to eutrophic (high nutrients), aiding in understanding ecosystem health and potential issues like algal blooms (Sherjah et al., 2022 ) (Saetang et al., 2021). Two TSIs were calculated using equations exposed by Carlson, the mathematical formulas are: TSI-Chla = 9.81 ln (Chla) + 30.6 (1) TSI-TP = 14.42 ln (TP) + 4.15 (2) Where Chla and TP are chlorophyll-a and total phosphorus concentration, respectively. Then, the Chla concentration and TP concentration were combined to calculate the TSI index value (Mourão et al., 2020 ) using the equation: TSI = (TSI-Chla + TSI-TP)/2 (3) 2.4 Data analysis In this research were used some multivariate techniques that may help to clarify the state of inland waters in Cartagena, Colombia. Process capability analysis is a statistical technique used to assess a process's ability to meet specified limits, such as those set by customers or designers (Pereira, 2021 ). In this research, it was used to evidence if the different water bodies may accomplish with the specifications of the Colombian water laws. This analysis is crucial for understanding the competence of water quality management processes, especially in scenarios where water quality degradation is observed. A cp value less than 1.0 indicates that the process variation exceeds the specification limits, suggesting that the process is not capable of consistently maintain within the specified environmental limits (Avramova et al., 2024 ). PCA aims to reduce the complexity of datasets while preserving data covariance, allowing for visualization through scatterplots with minimal information loss. It identifies components that capture the most information in the data, ordered by how well they approximate the data in a least squares sense. PCA is crucial for dimensionality reduction, outlier detection, and providing insights into the structure of data. Other techniques applied include Kruskal-Wallis, Wilcoxon rank sum test, box plots and descriptive statistics, which facilitate the interpretation of water properties (Mustafa et al., 2023 ). 3. RESULTS AND DISCUSSION A brief overview of the threshold values pertaining to water quality for secondary contact in Colombia is presented in Table 1 (Baldiris-Navarro et al., 2018 ). For PCA analysis pH, COD and OD were added to the dataset. Box and whisker plots were used to the compare experimental data obtained from the water bodies: Cabrero lagoon, Juan Angola channel, Juan Polo lagoon and Cienaga de la Virgen. Table 1 shows the Threshold values for the different variables according to Colombian environmental law. Table 1 Threshold values for water quality Variable Threshold value Chla 2,7–10 ug/L EC 2–50 ms/cm BOD 5 < 5 mg/L TP < 0,15 mg/L Sal 5–35 mg/L TC < 5000 MPV/100 mL FC < 200 MPV/100 mL The descriptive statistic parameters for water properties are outlined in Table 2 . Due to the variability of environmental data and to establish normality within the dataset, the application of the natural logarithm was employed to transform the values of total coliforms and fecal coliforms in all statistical analyses. Table 2 Summary of descriptive statistic parameters Chl EC BOD 5 TP Sal TC FC Min 1,33 9,24 0,60 0,05 5,82 0,59 0,59 Median 13,31 52,15 5,19 0,15 33,45 5,20 4,36 Mean 15,94 47,37 6,30 0,18 30,65 5,08 4,48 Max 63,00 66,80 20,49 0,69 45,50 14,65 14,65 Chlorophyll is a green pigment found in plants, microalgae and some bacteria. In water quality, chlorophyll is used as an indicator of the presence of algae in water. Therefore, chlorophyll levels in water are an important measure to assess water quality and determine the possible presence of algal blooms and associated eutrophication (Mozafari et al., 2023 ). According to Table 2 . in Cabrero lagoon the chlorophyll variable presented an average of 16.68 ug/L, with a range that oscillated between 3.06–34.92 ug/L. In the Cienaga de la Virgen an average of 11.42 ug/L was obtained with a range that varied between 1.33–39.01 ug/L. In the Juan Angola channel, the average was 17.01 ug/L, with a range of 2.18–63 ug/L. Finally, in Juan Polo lagoon, the average chlorophyll was 18.63 ug/L, with a range of 3.06–33.09 ug/L. The chlorophyll concentration presented a mean value of approximately 15,9 ug/L, with a standard deviation of 11,9 ug/L. Nevertheless, concentration is above the upper limit of capability, established at 10 ug/L. Consequently, the percentage of out-of-range concentrations reaches 82,38%. With the Cp value equal to 0.1 as indicated in Fig. 2 . This evidences the deficiency in biological metabolization capacity of all the water bodies which results in the accumulation of incoming nutrients, leading to increased levels of chlorophyll and subsequently elevated algae concentrations. The Kruskal-Wallis test suggests a notable variation in chlorophyll concentrations across the different sampling locations (p-value = 0.017) (see Fig. 3 ). Furthermore, the Wilcoxon rank sum test reveals that Juan Polo lagoon exhibited the highest chlorophyll levels, likely attributed to the discharge of sewage from the local community, excessive growth of algae in water, known as an algae bloom, may deplete oxygen in water and produce harmful toxins for humans and animals. Similar but not so high values were found by (Pérez-Martín, 2023 ) for chlorophyll in mar menor, where high values of this parameter were related with elevated densities of algae. In aquatic environments, electrical conductivity (EC) serves as an indirect indicator of the concentration of dissolved salts, thus playing a crucial role in evaluating water quality. Elevated levels of conductivity could suggest the existence of impurities like agricultural salts, brackish groundwater, or effluents from sewage systems. In the Cabrero lagoon, the EC presented a mean of 44.81 ms/cm, with a range between 19.26–56.50 ms/cm. In the Cienaga de la Virgen a mean of 48.32 ms/cm was obtained with a range between 14.33–62.20 ms/cm. In the Juan Angola channel, the mean was 42.69 ms/cm, with a range of 9.24–61.70 ms/cm. Finally, in the Juan Polo lagoon, the average electrical conductivity was 53.67 ms/cm, with a range of 17.23–66.80 ms/cm. Juan Polo presented the highest EC followed by Cienaga, probably cause by the entrance of wastewater and sea water to these stations. The analysis conducted indicates that there is a significant difference in electrical conductivity (EC) values among the different sampling points, with a p-value of 0.001541 according to the Kruskal-Wallis test. Juan Polo showed the highest values for this variable, The lagoon is filled with pollutants by the surrounding community, consequently elevating the EC of water. The 5-day Biochemical Oxygen Demand (BOD 5 ) is a measure of the amount of dissolved oxygen in water that is consumed by microorganisms over a 5-day period. The BOD 5 is used as an indicator of the amount of organic matter present in the water. A high BOD 5 indicates organic matter present in water, which may negatively affect its quality by reducing the oxygen level in the water. In the Cabrero lagoon the BOD 5 variable presented an average of 4.42 mg/L, with a range between 1.54–7.86 mg/L. In the Cienaga de la Virgen a mean of 5.66 mg/L was obtained with a range that varied between 0.6–14.14 mg/L. In the Juan Angola channel, the average was 6.96 mg/L, with a range of 2.1–20.49 mg/L. Finally, in the Juan Polo lagoon, the mean of BOD 5 was 8.18 mg/L, with a range of 1.03–14.88 mg/L. The concentration of BOD 5 exhibited a mean value of approximately 6.3 mg/L, with a standard deviation of 3.73 mg/L. It can be seen in Fig. 4 that the critical point value stands at 0.22, indicating that the ecosystem lacks the ability to sustain the necessary conditions for this particular variable, potentially due to elevated levels of wastewater influx (see Fig. 4 ). The Kruskal-Wallis test of BOD 5 data showed (p-value = 0.013). that there is a significant difference in the behavior of BOD 5 between the sampling stations. Juan Polo and Juan Angola exhibited the highest level of BOD 5 in the water samples. Phosphorus is an essential nutrient for the growth of plants and aquatic organisms, but its excess in water might cause eutrophication, reduce the level of oxygen and harm aquatic life. Phosphorus levels in the Cabrero lagoon exhibited an average concentration of 0.14 mg/L, showing a fluctuation between 0.07–0.24 mg/L. Within the Cienaga de la Virgen, an average phosphorus content of 0.12 mg/L was recorded, ranging from 0.05–0.25 mg/L. Moving on to the Juan Angola channel, the mean phosphorus concentration was measured at 0.24 mg/L, with values ranging from 0.10–0.55 mg/L. Lastly, in the Juan Polo lagoon, the average phosphorus concentration stood at 0.18 mg/L, with a range of 0.15–0.39 mg/L. The phosphorus concentration presented a central tendency around 0,181 mg/L, with a standard deviation of 0,117 mg/L. However, the capability analysis indicates that the mean concentration is above the upper limit established at 0,15 mg/L. Consequently, the percentage of concentrations out of range reaches 66,45% and the Cp is 0.28, which indicate that the water in not capable to biotransform the phosphorus load. The analysis of phosphorus levels reveals a noteworthy disparity in values across distinct sampling sites. When analyzing the phosphorus concentrations at Juan Polo and Juan Angola, no statistical difference was established. This indicates that these two sites demonstrated the highest levels of this specific variable. Elevated phosphorus levels may create anoxic conditions, altering plant species composition, and ultimately harming fish and aquatic life. These values are similar to those reported by (Ngadi et al., 2023 ) in Marchica lagoon (Moroccan northern Mediterranean). Salinity refers to the quantity of dissolved salts in water. Low salinity is most likely caused by a lack of nutrients in the water, which may affect aquatic life and human water use. Salinity in Cabrero lagoon presented an average of 29,04 o/oo, with a range that oscillated between 12,00–37,10 o/oo. In the Cienaga de la Virgen an average of 31,34 o/oo was obtained with a range that varied between 8,30–40,70 o/oo. In the Juan Angola channel, the average was 27,54 o/oo, with a range of 5,82 − 41,70 o/oo. Finally, in the Juan Polo lagoon the average was 34,67 o/oo, with a range of 10,10–45,50 o/oo. The Juan polo point showed the highest mean for salinity probably caused for the proximity of the Caribbean Sea. Total coliforms are a group of bacteria used as indicators of water quality and the possible presence of pathogens. The presence of total coliforms in the water indicates possible fecal contamination and may indicate an increased risk of waterborne diseases. Total coliforms in Cabrero lagoon exhibited an average of 5.28 ln MPV/100 mL, ranging from 0.69 to 11.16 ln MPV/100 ml. Within the Cienaga de la Virgen, an average of 3.50 ln MPV/100 mL was recorded, with a range spanning from 0.69 to 11.31 ln MPV/100 mL. The Juan Angola channel displayed an average of 7.51 ln MPV/100 mL, with values ranging from 2.48 to 14.65 ln MPV/100 mL. Finally, the Juan Polo lagoon showed average total coliforms of 4.02 ln MPV/100 mL, with a range of 0.59 to 10.80 ln MPV/100 mL. The concentration of total coliforms presented a central tendency around 5.1 with a standard deviation of 3.41. The concentration is above the upper limit of capability. Consequently, the percentage of concentrations out of range reaches 21.97% and a Cp of 0.42. The system is not capable of handle the daily load of coliforms. Juan Angola and Juan Polo had the highest total coliforms concentrations probably caused by anthropogenic activities near water (Borbolla-Vazquez et al., 2020 ). Fecal coliforms are a subgroup of coliform bacteria found in the intestines and feces of warm-blooded animals, including humans. The presence of fecal coliforms in water may indicate the presence of pathogens that may cause disease in humans. In Cabrero lagoon fecal coliforms presented an average of 4,37 with a range between 0.59–11.16. In Ciénaga de la Virgen, an average of 3.09 ln MPV/100 mL was obtained with a range between 0.69–11.26. In Juan Angola channel, the average was 6.79, with a range of 0.69–14.65. Finally, in Juan Polo lagoon, the average of fecal coliforms was 3.67 ln MPV/100 mL, with a range between 0.59–7.6. The concentration of fecal coliforms presented a central tendency around 4,48 ln MPV/100 mL with a standard deviation of 3.27, according to data. However, the concentration is above the upper capability limit, established at 5,3. Consequently, the percentage of concentrations out of range reaches 48,66% and a Cp of 0.27. Kruskal-Wallis indicates that there is a significant difference in the values of fecal coliforms among the sampling locations (p-value < 0.05). Wilcoxon results show a significant difference in fecal coliform values between Juan Angola and Cienaga, with a p-value of 0.005. Furthermore, upon examination of the p-values associated with Juan Polo, Cabrero, and Cienaga, it was determined that there exists no statistically significant variance among these designated sampling locations (p > 0.05). Juan Angola experiences the highest surge of fecal coliform originating from the nearby community, which is linked to the pollution caused by human and animal fecal material, similar results were reported in India by (Fulke et al., 2024 ). Principal component analysis Principal Component Analysis (PCA) is a statistical technique used to reduce the complexity of a data set and to identify patterns and relationships between variables. In water quality, PCA is used to identify the main factors influencing water quality and to make more informed decisions about the management and conservation of water resources. In addition, it can also be used for continuous monitoring of water quality. In order to assess the appropriateness of a given dataset for Principal Component Analysis, researchers conduct Kaiser-Meyer-Olkin (KMO) and Bartlett tests of sphericity. A KMO value approaching unity suggests the dataset's suitability for PCA, whereas a value below 0.5 suggests otherwise. Bartlett's test, on the other hand, scrutinizes whether the correlation matrix resembles an identity matrix; a significance level below 0.05 indicates the presence of significant relationships among the variables. In this paper, the dataset achieved a KMO value of 0.64 and a significance level of 2,2E-16, which indicates that it is suitable for principal component analysis (Angello et al., 2020 ). Table 3 Eigenvalues and variances of the dimensions of water bodies Eigenvalue Variance Percent Cumulative Variance Percent PC1 3,528 32,07 32,07 PC2 2,141 19,46 51,54 PC3 1,362 12,38 63,92 PC4 1,101 10,00 73,93 The principal component analysis for the inland waters in this study, the examination of the selected variables reveals their relationship levels in the PC1, PC2, PC3 and PC4, which together explain 73.93% of the accumulated variability in data (see Table 3 ). The analysis shows a direct relationship between variables that may be seen in Fig. 8 for PC1 and PC2. The first component (PC1) accounting 32% of the total variance, showed high positive loadings of salinity, electrical conductivity and chemical oxygen demand and also showed moderate negative loadings of fecal coliforms and total coliforms. This factor may be attributed to the physical and chemical properties of the water and anthropogenic pollution sources. The second factor (PC2) explained 19.46% of the total variance. The variable chlorophyll, BOD 5 and phosphorus showed moderates negative loads. This factor containing organic variables and nutrients indicates water pollution associated to influences from domestic sources, solid waste and industrial discharges. PC3 has a strong correlation with dissolved oxygen probably for the high variation of this parameter. PC4 depends on pH and total suspended solids. This factor may be accredited to the properties of freshwater and sea water entering the ecosystem, as well as to the natural erosion processes occurring within the basin. These two factors are mainly derived from runoff containing a significant number of solids and pollutants from specific sources such as car wash facilities and residential areas. Trophic state indices The TSI-Chla is a useful index to assess water quality in terms of its trophic state. Reference values vary depending on the type of water body and TSI-Chla values above 50 indicate high trophic status, while values below 30 indicate low trophic status. TSI-Chla values between 30 and 50 indicate a moderate trophic state. This indicator can indicate the presence of nutrients and algae blooms, which may negatively affect water quality and aquatic life (Lin et al., 2022 ). Analyzing the data, it was found that in the Cabrero lagoon the TSI-Chla variable presented an average of 56.7, with a range that oscillated between 41.57–6546. In the Ciénaga de la Virgen an average of 52,54 was obtained with a range that varied between 33,40–66,54. In the Juan Angola channel, the mean was 53.2, with a range of 38.25–71.24. Finally, in the Juan Polo lagoon, the average TSI-Chla was 57.77 with a range of 41.57–64.93 (see Fig. 9 ). All mean values exceeded 50, suggesting a eutrophic condition, where water clarity diminishes and only fishing activities are recommended, the outcomes exhibit resemblance to those reported by (Tibebe et al., 2019 ) in Lake Tana, Ethiopia. The TSI-TP is an indicator that refers to the concentration of total phosphorus in the water column. According to Data analysis it was determined that in Cabrero lagoon the TSI-TP presented an average of 74.67, with a range between 65.41–83.18. In Ciénaga de la Virgen a mean of 71.64 was obtained with a range that varied between 60.56–83.77. In Juan Angola channel, the average was 81.90, with a range of 70.56–95.14. Finally, in the Juan Polo lagoon, the average TSI-TP was 80 with a range of 76.40–98.41 (see Fig. 9 ). The analysis of information shows that the process of eutrophication exerts influence over these aquatic environments, potentially leading to the emergence of algal blooms within the ecosystem. The waters are unsuitable for several uses and may become a threat for human health. These results are similar to those reported by (El-Serehy et al., 2018 ) in lakes in the Suez Canal. Calculated TSI values for Cabrero lagoon ranged between 58.8 and 73.6, in Juan Polo lagoon had values between 59 and 81, Juan Angola channel showed values between 56 and 80 and Cienaga de la Virgen showed a range between 47 and 75. This value indicates eutrophic conditions in all the water bodies. This indicates that water as a natural resource is under too much pressure and this may lead to a critical deterioration of water and the beauty of natural scenarios which attract millions of visitors every year to the city of Cartagena de Indias. Conclusions Based on the data and examined indicators, all inland waters in this investigation exhibit eutrophication, likely stemming from communal practices such as improper disposal of wastewater and solid waste. Addressing this issue may involve implementing public awareness campaigns aimed at both children and adults. Principal component analysis (PCA) indicated that wastewater discharges, tides and anthropogenic activities are affecting significatively the water quality in the city. Analysis using statistical process control has revealed that the aquatic environments are incapable of managing the substantial influx of nutrients and organic matter, emphasizing the need for intervention to sustain water quality suitable for tourism purposes; failure to do so may threaten the city's prospects in this lucrative sector. The results of the current investigation could be applied towards the management and reduction of eutrophication in forthcoming times to safeguard biodiversity in the aquatic ecosystems in Cartagena de Indias, Colombia. 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Appl Sci Eng Prog. https://doi.org/10.14416/j.asep.2021.01.004 Pereira P (2021) Process capability indexes: Trends and developments in the manufacturing of blood components. Transfus Apher Sci 60(6):103314. https://doi.org/10.1016/j.transci.2021.103314 Avramova T, Vasileva D, Peneva T (2024) An overview of the basic concepts and terms related to manufacturing process capability evaluation. In AIP Conf. Proc. (Vol. 3104, No. 1). AIP Publishing Mustafa SFZ, Deris M, Manan MA, Beddu TSB, Mohd Kamal S, Mohamad NL, Yavari D, Qazi S, Hanafiah S, Omar Abu Nassar Z, Yeoh S, Sheriff KL, Wan Mohtar I, Isa WHM, Yusoff MH, M. S., Aziz A, H (2023) Modelling of similarity characteristics of polycyclic aromatic hydrocarbons (PAHs) in Sungai Perak, Malaysia via rough set theory and principal component analysis (PCA). Chem Phys Lett 828:140721. https://doi.org/10.1016/j.cplett.2023.140721 Baldiris-Navarro I, Sanchez-Aponte J, Gonzalez-Delgado A, Acosta-Jimenez JC, Jimenez AR (2018) Multivariable statistical evaluation of water quality in Juan polo coastal lagoon (Colombian Caribbean). Contemp Eng Sci 11(27):1339–1348. https://doi.org/10.12988/ces.2018.83125 Mozafari Z, Noori R, Siadatmousavi SM, Afzalimehr H, Azizpour J (2023) Satellite-Based Monitoring of Eutrophication in the Earth’s Largest Transboundary Lake. GeoHealth , 7 (5), e2022GH000770. https://doi.org/10.1029/2022GH000770 Pérez-Martín MÁ (2023) Understanding Nutrient Loads from Catchment and Eutrophication in a Salt Lagoon: The Mar Menor Case. Water 15(20):3569. https://doi.org/10.3390/w15203569 Ngadi H, Layachi M, Azizi G, Baghour M, Esseffar S, Loukili H, Moumen A (2023) Evaluation of the water quality and the eutrophication risk in Ramsar site on Moroccan northern Mediterranean (Marchica lagoon): A multivariate statistical approach. Mar Pollut Bull 194:115373. https://doi.org/10.1016/j.marpolbul.2023.115373 Borbolla-Vazquez J, Ugalde-Silva P, León-Borges J, Díaz-Hernández JA (2020) Total and faecal coliforms presence in cenotes of Cancun; Quintana Roo. Mexico BioRisk 15:31–43. https://doi.org/10.3897/biorisk.15.58455 Fulke AB, Panigrahi J, Eranezhath S, Karthi J, Dora GU (2024) Environmental variables and its association with faecal coliform at Madh Island beaches of megacity Mumbai, India. Environ Pollut 341:122885. https://doi.org/10.1016/j.envpol.2023.122885 Angello ZA, Tränckner J, Behailu BM (2020) Spatio-temporal evaluation and quantification of pollutant source contribution in little akaki river, Ethiopia: conjunctive application of factor analysis and multivariate receptor model. Pol J Environ Stud 30(1):23–34 Lin CY, Tsai MS, Tsai JT, Lu CC (2022) Prediction of Carlson Trophic State Index of Small Inland Water from UAV-Based Multispectral Image Modeling. Appl Sci 13(1):451 Tibebe D, Kassa Y, Melaku A, Lakew S (2019) Investigation of spatio-temporal variations of selected water quality parameters and trophic status of Lake Tana for sustainable management, Ethiopia. Microchem J 148:374–384. https://doi.org/10.1016/j.microc.2019.04.085 El-Serehy HA, Abdallah HS, Al-Misned FA, Al-Farraj SA, Al-Rasheid KA (2018) Assessing water quality and classifying trophic status for scientifically based managing the water resources of the Lake Timsah, the lake with salinity stratification along the Suez Canal. Saudi J Biol Sci 25(7):1247–1256. https://doi.org/10.1016/j.sjbs.2018.05.022 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-6239670","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":429532802,"identity":"3e6eab68-582a-4f54-96ef-6c45965d2547","order_by":0,"name":"Henry Adolfo Lambis Miranda","email":"","orcid":"https://orcid.org/0000-0002-6261-4062","institution":"Fundación Universitaria Tecnológico COmfenalco","correspondingAuthor":false,"prefix":"","firstName":"Henry","middleName":"Adolfo Lambis","lastName":"Miranda","suffix":""},{"id":429533006,"identity":"19014e72-778e-4ef6-8e60-d6f9c2c37089","order_by":1,"name":"Ildefonso Baldiris Navarro","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABNElEQVRIie2SMUvDQBTH3xG4LqddIwnmKzwJVERMvkokoEuEgCCCg0PhusT9pAU/QxGKY0qhXeIeUCRSKA4Z4lIUKpikXcSEdhS833Rw9+O9/3sHIJH8QagKJGQIDEAJAfDHpVOnwEqhzlKhaxQolfLEViXWKdtaOwlT/0U3usFc833L2L9rT9/eHyxoNjyEj8vfjeljHPbwnOHz40AT6O71xrR1cBu5sBOkSIKoIouDI4YOQ/VsoDEMiaDQMre4Ahh7qBBeoZxmpWIIb1YotqCNufnFr8GuVbxlFYg9WijHgjJzSvgIUK1R9MjPs+SNxSfmIUPXFdS7IDd8wtRo5g8rshjdTj9JF45tCPf1iS2sI6FM7rNPfrXb7Lj9pGJilRT7hXJZ4WZC/nWyTV9KJBLJv+AbOoBkECBwtvYAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-9189-3710","institution":"Universidad de Cartagena","correspondingAuthor":true,"prefix":"","firstName":"Ildefonso","middleName":"Baldiris","lastName":"Navarro","suffix":""},{"id":429533007,"identity":"71e519b1-0ed6-41e9-b837-bd4177dbf430","order_by":2,"name":"Maicol Ahumedo","email":"","orcid":"https://orcid.org/0000-0002-1805-3261","institution":"Universidad de 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Indias.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6239670/v1/99ca5a0ef8c7036738281dce.jpeg"},{"id":78756339,"identity":"bbfa89ae-a996-4d44-a87e-9d849e0142c7","added_by":"auto","created_at":"2025-03-18 12:56:53","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157243,"visible":true,"origin":"","legend":"\u003cp\u003eChlorophyll Behavior in water bodies.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6239670/v1/146bb0afcf62b278f613b6a0.jpeg"},{"id":78756338,"identity":"98d821d7-e721-416c-b8c4-cb20370f1c94","added_by":"auto","created_at":"2025-03-18 12:56:53","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":301620,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots for parameters in different water bodies\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6239670/v1/f1588dac74be4f6941321596.jpeg"},{"id":78757965,"identity":"34f8c4d1-b5c0-4ed7-98d8-911ff8ef7291","added_by":"auto","created_at":"2025-03-18 13:20:53","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":171338,"visible":true,"origin":"","legend":"\u003cp\u003eBOD\u003csub\u003e5\u003c/sub\u003e Behavior in water bodies.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6239670/v1/e974d38777bbbf2c52191c04.jpeg"},{"id":78757620,"identity":"fdf279a4-c736-467d-99bf-3dfeb86b73b2","added_by":"auto","created_at":"2025-03-18 13:12:53","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":179606,"visible":true,"origin":"","legend":"\u003cp\u003ePhosphorus Behavior in water bodies.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6239670/v1/6c5a7f6aaa42e1fd1d63a1d9.jpeg"},{"id":78756529,"identity":"a57deef5-c539-499e-9608-bcf843b4054a","added_by":"auto","created_at":"2025-03-18 13:04:53","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":172394,"visible":true,"origin":"","legend":"\u003cp\u003eTotal Coliforms Behavior in water bodies.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6239670/v1/9667b6e61b3633827d49b792.jpeg"},{"id":78756348,"identity":"0293297b-1895-4b92-a5b8-383d4be1fa34","added_by":"auto","created_at":"2025-03-18 12:56:53","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":164093,"visible":true,"origin":"","legend":"\u003cp\u003eFecal Coliforms Behavior in water bodies.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6239670/v1/4ac66fe10befc79de88b4158.jpeg"},{"id":78757621,"identity":"3f77c6ba-a842-4f3c-9ba3-3d31d09ffe31","added_by":"auto","created_at":"2025-03-18 13:12:53","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":185916,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6239670/v1/5baafd16f45b48c4f2d5720f.jpeg"},{"id":78756533,"identity":"8afa864e-820d-45d6-b8a5-497ef6dbad14","added_by":"auto","created_at":"2025-03-18 13:04:53","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":162283,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot TSI-Chla and TSI-TP\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6239670/v1/469b9029c37ae7fdf5bc60f0.jpeg"},{"id":78757984,"identity":"eeec6c9e-068c-42a8-86ce-c09eedccaff8","added_by":"auto","created_at":"2025-03-18 13:20:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2429021,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6239670/v1/3a43830a-7ba8-4b79-b157-8fe7137f3322.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eWater Quality Assessment of Inland Waters in Cartagena de Indias – Colombia, Using Multivariate Statistics\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eOne of the most vulnerable natural resources nowadays is water, which is facing numerous difficulties worldwide as a result of urbanization, industrialization, growing urbanization, and global warming, etc (Sanae et al.,2024). The lack of public awareness is leading underdeveloped countries to face difficult problems such as poor water quality, health diseases, fish kills, and others. The principal effect of these perturbances in shallow waters is an alteration in aquatic biodiversity, which may change the species distribution and the dynamics of water communities (Martins et al.,2023). In addition, as a result the intensification of human activities, levels of nitrogen, phosphorus, emerging pollutants and pathogens in water may increase and become a threat to humans and ecosystems (Lencha et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTourism in developing nations is the fastest growing sector of the blue economy, where tourism activity is growing at 3\u0026ndash;4.5% per year (Naveed Arif et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but in part it depends on the wellbeing of the water resources. Cartagena de Indias is the main Caribbean port for Colombia and also a tourist destination; Millions of visitors come to enjoy the natural resources associated with water and the old city's history. Consequently, a satisfactory tourism experience depends in part on the quality of water and marine ecosystems (Gonzalez et al., 2023).\u003c/p\u003e \u003cp\u003eOne of the anthropogenic factors that directly affects the quality of water is eutrophication, which is an increase in the concentration of phytoplankton due to an oversupply of nutrients (nitrogen and phosphorus) in water caused by man activities.\u003c/p\u003e \u003cp\u003eThis process may lead to oxygen depletion and may be lethal for aquatic life. As a result of eutrophication, the water turbidity increases, toxic algae might grow and immersed macrophytes vanish (Zamora-Lopez et al., 2023). A eutrophication descriptor used worldwide is the trophic state index (TSI) proposed by Carlson (Mour\u0026atilde;o et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This index classifies the body of water into oligotrophic, mesotrophic, eutrophic or hypereutrophic (Garcia-Avila et al., 2023).\u003c/p\u003e \u003cp\u003eCartagena de Indias has a large inland water system with several coastal lagoons and channels connecting them, the system drains into the Caribbean Sea (Baldiris-Navarro et al, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which means that these inland systems may impact negatively on the water used by tourists. For this reason, a rigorous monitoring and evaluation program must be implemented to ensure good water quality and prevent associated diseases.\u003c/p\u003e \u003cp\u003eMultivariate statistics have proven to be a useful tool in decision making to improve water quality around the world (Fraga et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Capability Index is a statistical process control tool that may help decide whether a body of water may regenerate itself according to the range of values of some properties, such as chlorophyll a (Silva et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The Kruskal Wallis test, Wilcoxon test, and boxplots may be useful in indicating which water sampling stations have different behavior in data. Principal component analysis is a multivariate statistical technique that may be used to identify which water properties have a greater impact on its quality. These analyses are possible if data is at hand, which is the case of this paper. The aim of this research was to evaluate the water quality and trophic status of four inland water in Cartagena de Indias using multivariate statistical techniques.\u003c/p\u003e"},{"header":"2. METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe study zone is in Cartagena de Indias, which is located on the Colombian Caribbean, at coordinates 10 \u0026deg; 26 'north latitude and 75 \u0026deg; 33' west longitude (Duarte-Restrepo et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (Tosic et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Sample points are located in Cabrero lagoon, Juan Angola Channel, Juan Polo lagoon and a point in Cienaga de la Virgen (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe area is rich in biodiversity, with a variety of marine, brackish-water, and freshwater mollusk species, including bivalves, scaphopods, and gastropods, inhabiting the coastal lagoon and the adjacent marine areas. The flora in this region primarily consists of mangroves that are situated along the edges of the lagoons, with the red mangrove (Rhizophora mangle) being the dominant species. Nevertheless, there exist various types of decorative vegetation such as almendron (\u003cem\u003eTerminalia cattapa\u003c/em\u003e), uvita de playa (\u003cem\u003eCoccoloba uvifera\u003c/em\u003e), and Payand\u0026eacute; (Phitecellobium dulcis) can be observed (Villate et al., 2020). In Cartagena, the dry season extends from December to April and is characterized by strong northeasterly winds and infrequent rains. The rainy season runs from may to november and is characterized by light winds, variable directions and strong rains. All these water bodies merge with the Caribbean Sea in the Cartagena Bay.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 \u003cem\u003eAnalytical Procedures\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eSurface water samples were collected monthly at four different points in Cartagena (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Electrical conductivity (EC) and salinity (Sal) (SM-2520-B) were measured in situ using portable multi parameter Hach 5465011 Sension. For Biochemical oxygen demand, chlorophyll, total phosphorus and coliforms samples were preserved and taken to the laboratory. Biochemical oxygen demand (BOD\u003csub\u003e5\u003c/sub\u003e) was measured by Winkler method (SM 4500-O G), chlorophyll-a (Chl) by spectrophotometry (SM-10150), total phosphorus (TP) by ascorbic method (SM 4500-P B, E) and total and fecal coliforms (TC) were measured by multiple-tube method (SM 9222B) (Rice et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Determination of the trophic state and trophic state indices\u003c/h2\u003e \u003cp\u003eTrophic state refers to the nutrient enrichment level of a water body, impacting its ecological balance. Trophic state indices (TSIs) are tools used to assess this state by integrating various parameters like chlorophyll-a, water clarity, and total phosphorus. This study employs chlorophyll-a and total phosphorus indices, which are instrumental in categorizing water bodies into groups such as oligotrophic (low nutrients) to eutrophic (high nutrients), aiding in understanding ecosystem health and potential issues like algal blooms (Sherjah et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (Saetang et al., 2021). Two TSIs were calculated using equations exposed by Carlson, the mathematical formulas are:\u003c/p\u003e \u003cp\u003eTSI-Chla\u0026thinsp;=\u0026thinsp;9.81 ln (Chla)\u0026thinsp;+\u0026thinsp;30.6 (1)\u003c/p\u003e \u003cp\u003eTSI-TP\u0026thinsp;=\u0026thinsp;14.42 ln (TP)\u0026thinsp;+\u0026thinsp;4.15 (2)\u003c/p\u003e \u003cp\u003eWhere Chla and TP are chlorophyll-a and total phosphorus concentration, respectively. Then, the Chla concentration and TP concentration were combined to calculate the TSI index value (Mour\u0026atilde;o et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) using the equation:\u003c/p\u003e \u003cp\u003eTSI = (TSI-Chla\u0026thinsp;+\u0026thinsp;TSI-TP)/2 (3)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data analysis\u003c/h2\u003e \u003cp\u003eIn this research were used some multivariate techniques that may help to clarify the state of inland waters in Cartagena, Colombia. Process capability analysis is a statistical technique used to assess a process's ability to meet specified limits, such as those set by customers or designers (Pereira, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In this research, it was used to evidence if the different water bodies may accomplish with the specifications of the Colombian water laws. This analysis is crucial for understanding the competence of water quality management processes, especially in scenarios where water quality degradation is observed. A cp value less than 1.0 indicates that the process variation exceeds the specification limits, suggesting that the process is not capable of consistently maintain within the specified environmental limits (Avramova et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). PCA aims to reduce the complexity of datasets while preserving data covariance, allowing for visualization through scatterplots with minimal information loss. It identifies components that capture the most information in the data, ordered by how well they approximate the data in a least squares sense. PCA is crucial for dimensionality reduction, outlier detection, and providing insights into the structure of data. Other techniques applied include Kruskal-Wallis, Wilcoxon rank sum test, box plots and descriptive statistics, which facilitate the interpretation of water properties (Mustafa et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cp\u003eA brief overview of the threshold values pertaining to water quality for secondary contact in Colombia is presented in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (Baldiris-Navarro et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For PCA analysis pH, COD and OD were added to the dataset. Box and whisker plots were used to the compare experimental data obtained from the water bodies: Cabrero lagoon, Juan Angola channel, Juan Polo lagoon and Cienaga de la Virgen. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the Threshold values for the different variables according to Colombian environmental law.\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\u003eThreshold values for water quality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThreshold value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,7\u0026ndash;10 ug/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;50 ms/cm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBOD\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5 mg/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0,15 mg/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;35 mg/L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5000 MPV/100 mL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;200 MPV/100 mL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe descriptive statistic parameters for water properties are outlined in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Due to the variability of environmental data and to establish normality within the dataset, the application of the natural logarithm was employed to transform the values of total coliforms and fecal coliforms in all statistical analyses.\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\u003eSummary of descriptive statistic parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBOD\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5,82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13,31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33,45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5,20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4,36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15,94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47,37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30,65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5,08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4,48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMax\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66,80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20,49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45,50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14,65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14,65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eChlorophyll is a green pigment found in plants, microalgae and some bacteria. In water quality, chlorophyll is used as an indicator of the presence of algae in water. Therefore, chlorophyll levels in water are an important measure to assess water quality and determine the possible presence of algal blooms and associated eutrophication (Mozafari et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). According to Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. in Cabrero lagoon the chlorophyll variable presented an average of 16.68 ug/L, with a range that oscillated between 3.06\u0026ndash;34.92 ug/L. In the Cienaga de la Virgen an average of 11.42 ug/L was obtained with a range that varied between 1.33\u0026ndash;39.01 ug/L. In the Juan Angola channel, the average was 17.01 ug/L, with a range of 2.18\u0026ndash;63 ug/L. Finally, in Juan Polo lagoon, the average chlorophyll was 18.63 ug/L, with a range of 3.06\u0026ndash;33.09 ug/L.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe chlorophyll concentration presented a mean value of approximately 15,9 ug/L, with a standard deviation of 11,9 ug/L. Nevertheless, concentration is above the upper limit of capability, established at 10 ug/L. Consequently, the percentage of out-of-range concentrations reaches 82,38%. With the Cp value equal to 0.1 as indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. This evidences the deficiency in biological metabolization capacity of all the water bodies which results in the accumulation of incoming nutrients, leading to increased levels of chlorophyll and subsequently elevated algae concentrations. The Kruskal-Wallis test suggests a notable variation in chlorophyll concentrations across the different sampling locations (p-value\u0026thinsp;=\u0026thinsp;0.017) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Furthermore, the Wilcoxon rank sum test reveals that Juan Polo lagoon exhibited the highest chlorophyll levels, likely attributed to the discharge of sewage from the local community, excessive growth of algae in water, known as an algae bloom, may deplete oxygen in water and produce harmful toxins for humans and animals. Similar but not so high values were found by (P\u0026eacute;rez-Mart\u0026iacute;n, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) for chlorophyll in mar menor, where high values of this parameter were related with elevated densities of algae.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn aquatic environments, electrical conductivity (EC) serves as an indirect indicator of the concentration of dissolved salts, thus playing a crucial role in evaluating water quality. Elevated levels of conductivity could suggest the existence of impurities like agricultural salts, brackish groundwater, or effluents from sewage systems. In the Cabrero lagoon, the EC presented a mean of 44.81 ms/cm, with a range between 19.26\u0026ndash;56.50 ms/cm. In the Cienaga de la Virgen a mean of 48.32 ms/cm was obtained with a range between 14.33\u0026ndash;62.20 ms/cm. In the Juan Angola channel, the mean was 42.69 ms/cm, with a range of 9.24\u0026ndash;61.70 ms/cm. Finally, in the Juan Polo lagoon, the average electrical conductivity was 53.67 ms/cm, with a range of 17.23\u0026ndash;66.80 ms/cm. Juan Polo presented the highest EC followed by Cienaga, probably cause by the entrance of wastewater and sea water to these stations.\u003c/p\u003e \u003cp\u003eThe analysis conducted indicates that there is a significant difference in electrical conductivity (EC) values among the different sampling points, with a p-value of 0.001541 according to the Kruskal-Wallis test. Juan Polo showed the highest values for this variable, The lagoon is filled with pollutants by the surrounding community, consequently elevating the EC of water.\u003c/p\u003e \u003cp\u003eThe 5-day Biochemical Oxygen Demand (BOD\u003csub\u003e5\u003c/sub\u003e) is a measure of the amount of dissolved oxygen in water that is consumed by microorganisms over a 5-day period. The BOD\u003csub\u003e5\u003c/sub\u003e is used as an indicator of the amount of organic matter present in the water. A high BOD\u003csub\u003e5\u003c/sub\u003e indicates organic matter present in water, which may negatively affect its quality by reducing the oxygen level in the water. In the Cabrero lagoon the BOD\u003csub\u003e5\u003c/sub\u003e variable presented an average of 4.42 mg/L, with a range between 1.54\u0026ndash;7.86 mg/L. In the Cienaga de la Virgen a mean of 5.66 mg/L was obtained with a range that varied between 0.6\u0026ndash;14.14 mg/L. In the Juan Angola channel, the average was 6.96 mg/L, with a range of 2.1\u0026ndash;20.49 mg/L. Finally, in the Juan Polo lagoon, the mean of BOD\u003csub\u003e5\u003c/sub\u003e was 8.18 mg/L, with a range of 1.03\u0026ndash;14.88 mg/L.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe concentration of BOD\u003csub\u003e5\u003c/sub\u003e exhibited a mean value of approximately 6.3 mg/L, with a standard deviation of 3.73 mg/L. It can be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e that the critical point value stands at 0.22, indicating that the ecosystem lacks the ability to sustain the necessary conditions for this particular variable, potentially due to elevated levels of wastewater influx (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The Kruskal-Wallis test of BOD\u003csub\u003e5\u003c/sub\u003e data showed (p-value\u0026thinsp;=\u0026thinsp;0.013). that there is a significant difference in the behavior of BOD\u003csub\u003e5\u003c/sub\u003e between the sampling stations. Juan Polo and Juan Angola exhibited the highest level of BOD\u003csub\u003e5\u003c/sub\u003e in the water samples.\u003c/p\u003e \u003cp\u003ePhosphorus is an essential nutrient for the growth of plants and aquatic organisms, but its excess in water might cause eutrophication, reduce the level of oxygen and harm aquatic life. Phosphorus levels in the Cabrero lagoon exhibited an average concentration of 0.14 mg/L, showing a fluctuation between 0.07\u0026ndash;0.24 mg/L. Within the Cienaga de la Virgen, an average phosphorus content of 0.12 mg/L was recorded, ranging from 0.05\u0026ndash;0.25 mg/L. Moving on to the Juan Angola channel, the mean phosphorus concentration was measured at 0.24 mg/L, with values ranging from 0.10\u0026ndash;0.55 mg/L. Lastly, in the Juan Polo lagoon, the average phosphorus concentration stood at 0.18 mg/L, with a range of 0.15\u0026ndash;0.39 mg/L.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe phosphorus concentration presented a central tendency around 0,181 mg/L, with a standard deviation of 0,117 mg/L. However, the capability analysis indicates that the mean concentration is above the upper limit established at 0,15 mg/L. Consequently, the percentage of concentrations out of range reaches 66,45% and the Cp is 0.28, which indicate that the water in not capable to biotransform the phosphorus load. The analysis of phosphorus levels reveals a noteworthy disparity in values across distinct sampling sites. When analyzing the phosphorus concentrations at Juan Polo and Juan Angola, no statistical difference was established. This indicates that these two sites demonstrated the highest levels of this specific variable. Elevated phosphorus levels may create anoxic conditions, altering plant species composition, and ultimately harming fish and aquatic life. These values are similar to those reported by (Ngadi et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in Marchica lagoon (Moroccan northern Mediterranean).\u003c/p\u003e \u003cp\u003eSalinity refers to the quantity of dissolved salts in water. Low salinity is most likely caused by a lack of nutrients in the water, which may affect aquatic life and human water use. Salinity in Cabrero lagoon presented an average of 29,04 o/oo, with a range that oscillated between 12,00\u0026ndash;37,10 o/oo. In the Cienaga de la Virgen an average of 31,34 o/oo was obtained with a range that varied between 8,30\u0026ndash;40,70 o/oo. In the Juan Angola channel, the average was 27,54 o/oo, with a range of 5,82\u0026thinsp;\u0026minus;\u0026thinsp;41,70 o/oo. Finally, in the Juan Polo lagoon the average was 34,67 o/oo, with a range of 10,10\u0026ndash;45,50 o/oo. The Juan polo point showed the highest mean for salinity probably caused for the proximity of the Caribbean Sea.\u003c/p\u003e \u003cp\u003eTotal coliforms are a group of bacteria used as indicators of water quality and the possible presence of pathogens. The presence of total coliforms in the water indicates possible fecal contamination and may indicate an increased risk of waterborne diseases. Total coliforms in Cabrero lagoon exhibited an average of 5.28 ln MPV/100 mL, ranging from 0.69 to 11.16 ln MPV/100 ml. Within the Cienaga de la Virgen, an average of 3.50 ln MPV/100 mL was recorded, with a range spanning from 0.69 to 11.31 ln MPV/100 mL. The Juan Angola channel displayed an average of 7.51 ln MPV/100 mL, with values ranging from 2.48 to 14.65 ln MPV/100 mL. Finally, the Juan Polo lagoon showed average total coliforms of 4.02 ln MPV/100 mL, with a range of 0.59 to 10.80 ln MPV/100 mL.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe concentration of total coliforms presented a central tendency around 5.1 with a standard deviation of 3.41. The concentration is above the upper limit of capability. Consequently, the percentage of concentrations out of range reaches 21.97% and a Cp of 0.42. The system is not capable of handle the daily load of coliforms. Juan Angola and Juan Polo had the highest total coliforms concentrations probably caused by anthropogenic activities near water (Borbolla-Vazquez et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFecal coliforms are a subgroup of coliform bacteria found in the intestines and feces of warm-blooded animals, including humans. The presence of fecal coliforms in water may indicate the presence of pathogens that may cause disease in humans. In Cabrero lagoon fecal coliforms presented an average of 4,37 with a range between 0.59\u0026ndash;11.16. In Ci\u0026eacute;naga de la Virgen, an average of 3.09 ln MPV/100 mL was obtained with a range between 0.69\u0026ndash;11.26. In Juan Angola channel, the average was 6.79, with a range of 0.69\u0026ndash;14.65. Finally, in Juan Polo lagoon, the average of fecal coliforms was 3.67 ln MPV/100 mL, with a range between 0.59\u0026ndash;7.6.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe concentration of fecal coliforms presented a central tendency around 4,48 ln MPV/100 mL with a standard deviation of 3.27, according to data. However, the concentration is above the upper capability limit, established at 5,3. Consequently, the percentage of concentrations out of range reaches 48,66% and a Cp of 0.27. Kruskal-Wallis indicates that there is a significant difference in the values of fecal coliforms among the sampling locations (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Wilcoxon results show a significant difference in fecal coliform values between Juan Angola and Cienaga, with a p-value of 0.005. Furthermore, upon examination of the p-values associated with Juan Polo, Cabrero, and Cienaga, it was determined that there exists no statistically significant variance among these designated sampling locations (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Juan Angola experiences the highest surge of fecal coliform originating from the nearby community, which is linked to the pollution caused by human and animal fecal material, similar results were reported in India by (Fulke et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePrincipal component analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePrincipal Component Analysis (PCA) is a statistical technique used to reduce the complexity of a data set and to identify patterns and relationships between variables. In water quality, PCA is used to identify the main factors influencing water quality and to make more informed decisions about the management and conservation of water resources. In addition, it can also be used for continuous monitoring of water quality. In order to assess the appropriateness of a given dataset for Principal Component Analysis, researchers conduct Kaiser-Meyer-Olkin (KMO) and Bartlett tests of sphericity. A KMO value approaching unity suggests the dataset's suitability for PCA, whereas a value below 0.5 suggests otherwise. Bartlett's test, on the other hand, scrutinizes whether the correlation matrix resembles an identity matrix; a significance level below 0.05 indicates the presence of significant relationships among the variables. In this paper, the dataset achieved a KMO value of 0.64 and a significance level of 2,2E-16, which indicates that it is suitable for principal component analysis (Angello et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\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\u003eEigenvalues and variances of the dimensions of water bodies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEigenvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariance Percent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCumulative Variance Percent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32,07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19,46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51,54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63,92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePC4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10,00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73,93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe principal component analysis for the inland waters in this study, the examination of the selected variables reveals their relationship levels in the PC1, PC2, PC3 and PC4, which together explain 73.93% of the accumulated variability in data (see Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The analysis shows a direct relationship between variables that may be seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e for PC1 and PC2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe first component (PC1) accounting 32% of the total variance, showed high positive loadings of salinity, electrical conductivity and chemical oxygen demand and also showed moderate negative loadings of fecal coliforms and total coliforms. This factor may be attributed to the physical and chemical properties of the water and anthropogenic pollution sources. The second factor (PC2) explained 19.46% of the total variance. The variable chlorophyll, BOD\u003csub\u003e5\u003c/sub\u003e and phosphorus showed moderates negative loads. This factor containing organic variables and nutrients indicates water pollution associated to influences from domestic sources, solid waste and industrial discharges. PC3 has a strong correlation with dissolved oxygen probably for the high variation of this parameter. PC4 depends on pH and total suspended solids. This factor may be accredited to the properties of freshwater and sea water entering the ecosystem, as well as to the natural erosion processes occurring within the basin. These two factors are mainly derived from runoff containing a significant number of solids and pollutants from specific sources such as car wash facilities and residential areas.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTrophic state indices\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe TSI-Chla is a useful index to assess water quality in terms of its trophic state. Reference values vary depending on the type of water body and TSI-Chla values above 50 indicate high trophic status, while values below 30 indicate low trophic status. TSI-Chla values between 30 and 50 indicate a moderate trophic state. This indicator can indicate the presence of nutrients and algae blooms, which may negatively affect water quality and aquatic life (Lin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalyzing the data, it was found that in the Cabrero lagoon the TSI-Chla variable presented an average of 56.7, with a range that oscillated between 41.57\u0026ndash;6546. In the Ci\u0026eacute;naga de la Virgen an average of 52,54 was obtained with a range that varied between 33,40\u0026ndash;66,54. In the Juan Angola channel, the mean was 53.2, with a range of 38.25\u0026ndash;71.24. Finally, in the Juan Polo lagoon, the average TSI-Chla was 57.77 with a range of 41.57\u0026ndash;64.93 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). All mean values exceeded 50, suggesting a eutrophic condition, where water clarity diminishes and only fishing activities are recommended, the outcomes exhibit resemblance to those reported by (Tibebe et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in Lake Tana, Ethiopia.\u003c/p\u003e \u003cp\u003eThe TSI-TP is an indicator that refers to the concentration of total phosphorus in the water column. According to Data analysis it was determined that in Cabrero lagoon the TSI-TP presented an average of 74.67, with a range between 65.41\u0026ndash;83.18. In Ci\u0026eacute;naga de la Virgen a mean of 71.64 was obtained with a range that varied between 60.56\u0026ndash;83.77. In Juan Angola channel, the average was 81.90, with a range of 70.56\u0026ndash;95.14. Finally, in the Juan Polo lagoon, the average TSI-TP was 80 with a range of 76.40\u0026ndash;98.41 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The analysis of information shows that the process of eutrophication exerts influence over these aquatic environments, potentially leading to the emergence of algal blooms within the ecosystem. The waters are unsuitable for several uses and may become a threat for human health. These results are similar to those reported by (El-Serehy et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in lakes in the Suez Canal.\u003c/p\u003e \u003cp\u003eCalculated TSI values for Cabrero lagoon ranged between 58.8 and 73.6, in Juan Polo lagoon had values between 59 and 81, Juan Angola channel showed values between 56 and 80 and Cienaga de la Virgen showed a range between 47 and 75. This value indicates eutrophic conditions in all the water bodies. This indicates that water as a natural resource is under too much pressure and this may lead to a critical deterioration of water and the beauty of natural scenarios which attract millions of visitors every year to the city of Cartagena de Indias.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eBased on the data and examined indicators, all inland waters in this investigation exhibit eutrophication, likely stemming from communal practices such as improper disposal of wastewater and solid waste. Addressing this issue may involve implementing public awareness campaigns aimed at both children and adults. Principal component analysis (PCA) indicated that wastewater discharges, tides and anthropogenic activities are affecting significatively the water quality in the city. Analysis using statistical process control has revealed that the aquatic environments are incapable of managing the substantial influx of nutrients and organic matter, emphasizing the need for intervention to sustain water quality suitable for tourism purposes; failure to do so may threaten the city's prospects in this lucrative sector. The results of the current investigation could be applied towards the management and reduction of eutrophication in forthcoming times to safeguard biodiversity in the aquatic ecosystems in Cartagena de Indias, Colombia.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSanae B, Mohammed abbou, Nisrine B, Youness I, Nariman G, Mustapha O, Zakia T, R (2024) Assessment of surface water quality: Case study of Oued Fez catchment areas (Morocco). 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Saudi J Biol Sci 25(7):1247\u0026ndash;1256. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.sjbs.2018.05.022\u003c/span\u003e\u003cspan address=\"10.1016/j.sjbs.2018.05.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Universidad de Cartagena","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Water quality, Cartagena de Indias, Capability Index, PCA, TSI","lastPublishedDoi":"10.21203/rs.3.rs-6239670/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6239670/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWorldwide, industrial growth, overpopulation, and a lack of awareness about the need to protect essential resources are posing serious risks to natural water sources. The aesthetic value and ecological integrity of urban coastal waters are vital resources that the tourism industry depends on, and they are being jeopardized for the benefit of nearby populations. In this research the water quality of four inland waters in Cartagena de Indias were analyzed during the period 2008\u0026ndash;2022. The study was focused on water parameters as electrical conductivity (EC), dissolved oxygen (DO), biochemical oxygen demand (BOD\u003csub\u003e5\u003c/sub\u003e), chemical oxygen demand (COD), salinity (Sal), pH, total suspended solids (TSS), total and fecal coliforms (TC), chlorophyll (Chla) and total phosphorus (TP). Descriptive and multivariable statistics were used to clarify the behavior of data. Capability analysis was applied to know if the water bodies may handle the amount of entering pollutants. Principal components analysis detected four components that explain 73.9% of the variance of data. PCA was also used to know the possible pollution sources and main contributors to contamination. Two trophic state indexes showed the level of contamination presented by waters.\u003c/p\u003e","manuscriptTitle":"Water Quality Assessment of Inland Waters in Cartagena de Indias – Colombia, Using Multivariate Statistics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-18 12:56:48","doi":"10.21203/rs.3.rs-6239670/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90f8b210-d896-4f03-bb8a-de0008b5cb2e","owner":[],"postedDate":"March 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45747489,"name":"Marine and Freshwater Ecology"},{"id":45747490,"name":"Applied Statistics"}],"tags":[],"updatedAt":"2025-03-18T12:56:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-18 12:56:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6239670","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6239670","identity":"rs-6239670","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}