Comparison of water quality between the Lake of Banyoles, Girona, Spain, and the River Seine, Paris, France, considering the bridges along the river where the Olympic Games took place: Evaluation of Suitability for Aquatic Sports Practice

Comparison of water quality between the Lake of Banyoles, Girona, Spain, and the River Seine, Paris, France, considering the bridges along the river where the Olympic Games took place: Evaluation of Suitability for Aquatic Sports Practice | 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 Article Comparison of water quality between the Lake of Banyoles, Girona, Spain, and the River Seine, Paris, France, considering the bridges along the river where the Olympic Games took place: Evaluation of Suitability for Aquatic Sports Practice Anna Mota-Bertran, Marc Saez This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6429020/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 The quality of water in recreational and sports environments plays a crucial role in ensuring the safety and health of participants. Aquatic sports, such as rowing and swimming, are becoming increasingly popular, but poor water quality can pose significant health risks. This study compares the water quality between two iconic locations: Lake Banyoles in Catalonia, a natural environment that has hosted numerous sports events since the 1992 Olympic Games, and the River Seine in Paris, an urban waterway that recently hosted the 2024 Olympic Games. Both locations present different challenges in water quality management due to their distinct environments. Banyoles has a lower population density and a rural setting, whereas the Seine flows through a densely populated city and experiences pollution from urban stormwater and wastewater discharges. Microbiological contamination, particularly from indicator bacteria such as Escherichia coli and enterococci, is used to assess water safety. These bacteria are widely recognized as markers of fecal contamination and indicators of potential health risks. This study analyzed microbiological parameters between 2014 and 2022, using samples from areas frequently used for recreational activities in both locations. The results show that Lake Banyoles consistently presented lower concentrations of Escherichia coli (1.70) and enterococci (1.58) compared to the River Seine, where concentrations were significantly higher, with values of 3.31 and 2.47, respectively. The analysis also highlighted greater variability in bacterial concentrations in the Seine, with higher peaks of contamination. Statistical tests confirmed significant differences between the two locations, with Escherichia coli levels consistently higher in Paris (p < 0.001) over the years. Temporal trends showed a slight improvement in water quality in Banyoles, particularly in Escherichia coli levels, whereas no significant improvement was observed in the Seine. The study highlights the importance of effective water management practices to minimize microbiological risks. It also underscores that urban environments like the Seine face persistent challenges, such as stormwater runoff and inadequate sewage systems, contributing to ongoing water contamination. These findings provide valuable insights for managing water quality in recreational areas, helping decision-makers prioritize health and safety measures to ensure sustainable aquatic sports practices. Earth and environmental sciences/Environmental sciences Health sciences/Health care water quality microbiological contamination Escherichia coli Enterococci water sports Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Water, essential for life, serves as the backdrop for a wide range of recreational and sports activities that enrich human well-being and promote health. In recent years, aquatic sports have gained significant popularity, becoming a key component of leisure and physical culture. However, ensuring the quality of water in these venues is paramount to the safety and health of participants, as poor water quality can pose serious risks to health (Bain et al. 2014 ). Two emblematic places that represent the duality of water sports, Banyoles, in Catalonia, is a rich natural environment and the Seine River in Paris a vital artery of the city that forms a dynamic urban space. The Banyoles Lake, as a natural space, requires and enjoys careful protection and management to guarantee the sustainability of the flora and fauna that inhabit it, aspects that also impact on the practice of water sports. On the other hand, the Seine River represents an urban icon, especially significant because it has been the site of the last Olympic Games, placing a special focus of interest on the quality and safety of water in the development of the aquatic events, highlighting the challenge of maintaining water quality in recreational areas in the middle of a densely populated urban environment. Breen et al. ( 2018 ) underscored the importance of water quality in aquatic sports, emphasizing the need for multidisciplinary approaches to assess and manage the risks associated with water contamination. Their research highlights the complexity of contamination sources and the various environmental factors that influence water quality, stressing the importance of tailored solutions for each location. Microbiological contamination of water, particularly the presence of human pathogens and other pollutants, represents a significant threat to the health of individuals engaging in aquatic activities. The detection of indicator bacteria, such as Escherichia coli and Enterobacter, has become an essential tool for assessing water safety in recreational environments. These microorganisms serve as markers of microbiological water quality, helping to identify potential health hazards. Escherichia coli is a gram-negative, rod-shaped bacterium from the Enterobacteriaceae family and one of the most extensively studied organisms. It replicates rapidly under optimal conditions, with a doubling time of approximately 20 minutes, making it a valuable model for genetic manipulation and the production of industrial enzymes. Since the first sequencing of its genome in 1997, more than 4,800 Escherichia coli genomes have been analysed. Its fast growth and adaptability also make it an ideal organism for studying microbial evolution, as demonstrated by long-term experiments spanning over 50,000 generations (Tenaillon et al. 2016 ). Similarly, the genus Enterobacter, also part of the Enterobacteriaceae family, comprises facultative anaerobic, gram-negative bacilli. First described in 1960, this genus has undergone several taxonomic revisions over the past decades. Enterobacter species are found in various environmental habitats, including soil and water, and can act as either endophytes or phytopathogens in a range of plant species (Hormaeche and Edwards 1960 ; Singh et al. 2018 ). Both Escherichia coli and Enterobacter are pivotal in monitoring water quality and play a crucial role in understanding the risks of microbiological contamination in aquatic sports settings. In many countries, health and environmental authorities have established specific standards and regulations to ensure water quality in recreational areas, such as beaches, water parks, and locations for aquatic sports. However, the implementation of these regulations can vary significantly depending on local characteristics, pollution sources, and water management practices. This variability highlights the need for detailed scientific analysis of water quality at each specific location (WHO, 2022). In this context, the present study aims to provide a detailed comparison of water quality in Lake Banyoles and the Seine River in Paris by evaluating the concentrations of indicator bacteria, such as Escherichia coli and Enterobacter, and to determine which of these places offer safer conditions for water sports. The importance of this fact is already highlighted by Tandyrak et al. (2016) in their studies on the relevance of determining water quality in recreational areas. The conclusions drawn from this work provide authorities, athletes and citizens with information based on scientific evidence, which allows the implementation of actions or decisions aimed at the sustainable management of water quality in recreational and sports spaces to preserve the health and well-being of people. Methods The study focuses on the analysis of microbiological parametres of water quality in each locality between 2014 and 2022, with the aim of also obtaining results derived from their comparison. At Lake Banyoles, samples were taken from zones used for activities such as rowing, open-water swimming, and bathing, with the data provided by the company Aigües de Banyoles. In the Seine, locations with frequent recreational use, such as authorized bathing areas and tourist navigation routes, were selected, with the data supplied by the company Eau de Paris. Microbiological parameters were analysed to determine water quality through the logarithmic concentrations of Escherichia coli and Enterococci as primary indicators of faecal contamination, given their relevance in public health risk assessment. Microbiological parameters were quantified using standard membrane filtration techniques and selective media to isolate these two bacteria. Quality control measures, including triplicate sampling and cross-verifications, were implemented. To achieve the objectives of this study, a comprehensive statistical analysis was performed to compare microbiological concentrations across different locations and over various time periods. The initial comparisons were carried out using Student's t-test for pairwise evaluations and Analysis of Variance (ANOVA) for assessing differences among groups. Additionally, non-parametric tests were performed to account for potential deviations from normality, and regression analyses were conducted to explore relationships between variables and assess trends in the data.These tests allowed the identification of statistically significant variations in microbiological concentrations between the study sites and across the years of observation. To delve deeper into these findings, post hoc tests were employed following the ANOVA to examine specific interactions and differences more precisely. These post hoc analyses provided detailed insights into how the microbiological concentrations varied not only between sites but also within each site over time. This step was crucial for identifying patterns that might not have been apparent through the primary analyses alone. All statistical analyses were conducted using Jamovi (version 2.3.28), a statistical software. It was chosen for its intuitive interface and robust capabilities, which facilitated the execution of both primary tests and subsequent post hoc analyses. Results Table 1 describes the logarithmic concentrations of the bacteria Escherichia coli and Enterococci in samples collected in Banyoles and Paris. It provides various descriptive statistics to facilitate comparison between the two locations. In Banyoles, 150 samples were analysed for Escherichia coli and 149 for Enterococci, while in Paris, 165 samples were included for both parameters. The averages indicate significantly lower concentrations in Banyoles, with 1.70 for Escherichia coli and 1.58 for Enterococci, compared to Paris, where these values are 3.31 and 2.47, respectively. This shows that Paris exhibits higher concentrations of both bacteria, especially Escherichia coli, which is more than double the levels in Banyoles. Table 1 Descriptive analysis of the logarithmic concentrations of bacterial presence Place Escherichia coli Enterococci N Banyoles 150 149 Paris 165 165 Mean Banyoles 1.70 1.58 Paris 3.31 2.47 Median Banyoles 1.73 1.57 Paris 3.31 2.38 Standard deviation Banyoles 0.37 0.49 Paris 0.58 0.71 Minimum Banyoles 0.85 0.33 Paris 2.04 1.18 Maximum Banyoles 2.97 3.29 Paris 4.54 4.54 25th percentil Banyoles 1.43 1.26 Paris 2.89 1.94 75th percentil Banyoles 1.90 1.83 Paris 3.75 2.93 The median, confirms this trend, as in Banyoles it is 1.73 for Escherichia coli and 1.57 for Enterococci, values very close to the mean, suggesting a homogeneous data distribution. In Paris, the median is also close to the mean, with 3.31 for Escherichia coli and 2.38 for Enterococci, though the values are significantly higher. Regarding variability, standard deviations reveal lower dispersion in Banyoles, with 0.37 for Escherichia coli and 0.49 for Enterococci, compared to higher variability in Paris, with 0.58 for Escherichia coli and 0.71 for Enterococci, particularly for Enterococci. In terms of data range, Banyoles presents more compact values, with minimums of 0.85 for Escherichia coli and 0.33 for Enterococci, and maximums of 2.97 and 3.29, respectively. In Paris, the range is much broader, with minimums of 2.04 for Escherichia coli and 1.18 for Enterococci, and maximums of 4.54 for both bacteria. Percentiles reinforce these observations; in Banyoles, the values between the 25th and 75th percentiles are closer (1.43 to 1.90 for Escherichia coli and 1.26 to 1.83 for Enterococci), indicating a more compact distribution, while in Paris, these percentiles show greater dispersion (2.89 to 3.75 for Escherichia coli and 1.94 to 2.93 for Enterococci). Overall, these results show that bacterial concentrations are lower and more homogeneous in Banyoles, whereas in Paris, they are higher, with greater variability and a wider range of values. Table 2 displays the logarithmic concentrations of Escherichia coli and Enterococci from Banyoles and Paris between 2014 and 2022. The results reveal consistent and significant differences between the two locations, with higher concentrations in Paris throughout the entire period. Table 2 Descriptive analysis of logarithmic concentrations of Escherichia coli and Enterococci between 2014 and 2022 Bacteria Year Place N Mean Median Standard deviation Minimum Maximum 25th percentil 75th percentil Escherichia coli 2014 Banyoles 29 1.93 1.87 0.36 1.29 2.97 1.74 2.11 Paris 17 3.34 3.24 0.44 2.87 4.54 3.01 3.45 2015 Banyoles 23 1.69 1.76 0.35 0.90 2.29 1.42 1.92 Paris 17 3.46 3.63 0.60 2.04 4.10 3.17 3.85 2016 Banyoles 16 1.37 1.39 0.29 0.92 1.78 1.05 1.57 Paris 22 3.32 3.32 0.47 2.66 4.37 2.99 3.50 2017 Banyoles 11 1.91 1.87 0.30 1.52 2.58 1.73 2.01 Paris 17 3.44 3.45 0.67 2.42 4.44 2.91 4.10 2018 Banyoles 9 1.65 1.82 0.31 1.04 1.95 1.37 1.85 Paris 12 3.68 3.74 0.66 2.50 4.54 3.22 4.34 2019 Banyoles 18 1.80 1.83 0.33 1.23 2.26 1.60 2.07 Paris 12 3.42 3.60 0.48 2.56 3.86 3.16 3.81 2020 Banyoles 10 1.74 1.75 0.32 1.37 2.30 1.44 1.89 Paris 24 2.96 2.80 0.53 1.21 3.92 2.52 3.34 2021 Banyoles 18 1.53 1.47 0.36 1.03 2.27 1.24 1.80 Paris 20 3.38 3.25 0.51 2.54 4.40 3.06 3.66 2022 Banyoles 16 1.60 1.62 0.31 0.85 2.27 1.50 1.67 Paris 24 3.16 3.04 0.63 2.26 4.54 2.60 3.70 Enterococci 2014 Banyoles 29 1.79 1.76 0.31 1.25 2.50 1.57 1.97 Paris 17 2.49 2.29 0.60 1.79 4.18 2.10 2.84 2015 Banyoles 22 1.61 1.64 0.35 1.00 2.40 1.33 1.75 Paris 17 2.50 2.40 0.66 1.48 4.54 2.10 2.73 2016 Banyoles 16 0.99 1.03 0.37 0.33 1.64 0.81 1.25 Paris 22 2.52 2.47 0.50 1.71 3.82 2.26 2.68 2017 Banyoles 11 1.70 1.61 0.34 1.21 2.45 1.53 1.80 Paris 17 2.61 2.37 0.78 1.66 4.07 1.90 3.23 2018 Banyoles 9 1.59 1.55 0.38 1.07 2.16 1.35 1.87 Paris 12 2.91 2.72 0.88 1.33 4.13 2.39 3.67 2019 Banyoles 18 1.67 1.63 0.42 1.14 2.28 1.26 2.00 Paris 12 2.56 2.68 0.69 1.34 3.44 2.11 3.13 2020 Banyoles 10 1.71 1.55 0.54 1.10 2.93 1.45 1.97 Paris 24 2.16 2.03 0.68 1.18 3.59 1.68 2.50 2021 Banyoles 18 1.47 1.42 0.47 0.72 2.64 1.33 1.73 Paris 20 2.53 2.48 0.60 1.50 3.66 2.09 2.97 2022 Banyoles 16 2.60 1.37 0.78 0.67 3.29 1.09 1.85 Paris 24 2.28 1.97 0.85 1.25 4.12 1.57 3.05 The general comparison of microbiological pollution levels shows that the Seine River (Paris) consistently exhibits higher levels of Escherichia coli and Enterococci compared to Lake Banyoles, reflecting a greater presence of fecal contamination in the Seine. Regarding Escherichia coli, the average values recorded in the Seine range between 3.16 and 3.68, while in Banyoles they remain much lower, between 1.37 and 1.93. These differences are also reflected in the medians: in the Seine, the medians range from 2.80 to 3.74, while in Banyoles they range from 1.39 to 1.87. This demonstrates that both the average and the median are significantly higher in the Seine, indicating more persistent and elevated contamination in this environment. In the case of Enterococci, the differences follow a similar trend. In the Seine, the average values range between 2.16 and 2.91, with medians between 2.03 and 2.72. In contrast, in Banyoles, the averages range from 0.99 to 2.60, with medians ranging from 1.03 to 1.76. This pattern reinforces the difference between the two locations, where Lake Banyoles exhibits significantly better microbiological quality. Analyzing the evolution over the years, it is observed that in Banyoles, both for Escherichia coli and Enterococci, the averages and medians have tended to slightly decrease between 2014 and 2021, indicating a progressive improvement in water quality. However, in 2022, an increase in Enterococci values is detected, with an average of 2.60 and a median of 1.37. In contrast, in the Seine, both averages and medians remain relatively stable over the years, without showing significant improvement. Regarding data dispersion, it is observed that the Seine exhibits greater variability in the recorded values, reflected in higher standard deviations. This indicates larger fluctuations in contamination levels. Furthermore, the maximum values recorded in the Seine are significantly higher than those in Banyoles, with peaks reaching up to 4.54 for both Escherichia coli and Enterococci, compared to lower maxima of 2.97 and 2.50, respectively, in Banyoles. Figure 1 shows the comparison of Escherichia Coli values ​​between Banyoles and Paris, representing both the mean with its 95% confidence interval and the median. Since the confidence intervals do not overlap, this suggests that there are statistically significant differences between the two locations. According to the graph, Paris presents higher values ​​compared to Banyoles in terms of the concentration of the bacteria studied. Figure 2 illustrates the concentration of Enterococci between Banyoles and Paris, showing both the mean with its 95% confidence interval and the median. Since the confidence intervals do not overlap, it can be concluded that there are statistically significant differences between both locations. According to the graph, Paris presents higher values ​​in the concentration of the studied bacteria compared to Banyoles. The results in Table 3 analyse the differences in the logarithmic concentrations of Escherichia coli and Enterococci between Banyoles and Paris using Student's t-test and the Mann-Whitney U test. For Escherichia coli, Student's t-test yields a value of -29.3 with 313 degrees of freedom (df) and a significance level of pvalue 0.001. This difference is further supported by the Mann-Whitney U test, which is also significant with pvalue < 0.001. Table 3 Analysis of Logarithmic Concentrations of Escherichia coli and Enterococci in Banyoles and Paris using Statisticals tests Bacteria Statistic Degrees of freedom pvalue Escherichia coli t de Student -29.3 a 313 < 0.001 U de Mann-Whitney 148 < 0.001 Enterococci t de Student -12.8 a 312 < 0.001 U de Mann-Whitney 3500 < 0.001 ᵃ The Levene's test is significant (p < 0.05), suggesting a violation of the assumption of equal variances. Hₐ µ Banyoles ≠ µ Paris For Enterococci, Student's t-test shows a value of -12.8 with 312 df and pvalue < 0.001, complemented by a significant Mann-Whitney U test result (pvalue < 0.001). It is important to note that for both variables, Levene's Test is significant (pvalue < 0.05), indicating a violation of the assumption of equal variances. This justifies the use of both Student's t-test and the Mann-Whitney U test to confirm the robustness of the results. In both cases, the data support the alternative hypothesis that the means for Banyoles and Paris are significantly different, with notable variations in logarithmic concentrations between these two locations. Table 4 presents the coefficients of the statistical model analysing the effects of several predictor variables and their interactions on the dependent variable, Escherichia coli. The model's intercept shows an estimated value of 3.34 with a standard error of 0.11, indicating that the expected value of Escherichia coli for the reference group (year 2014 and Banyoles location) is 3.34, with a highly significant result. Table 4 Regression model coefficients for Escherichia coli: Effects of location and year Predictor Estimation SE t pvalue Intercept a 3.34 0.11 29.69 < 0.001 Place : Banyoles-Paris -1.41 0.14 -9.97 < 0.001 Year : 2015 − 2014 0.12 0.16 0.76 0.451 2016 − 2014 -0.02 0.15 -0.13 0.896 2017 − 2014 0.10 0.16 0.62 0.536 2018 − 2014 0.34 0.18 1.92 0.055 2019 − 2014 0.08 0.18 0.48 0.629 2020 − 2014 -0.38 0.15 -2.62 0.009 2021 − 2014 0.04 0.15 0.26 0.794 2022 − 2014 -0.18 0.15 -1.20 0.231 Place*year : (Banyoles – Paris) ✻ (2015–2014) -0.36 0.21 -1.76 0.079 (Banyoles – Paris) ✻ (2016–2014) -0.54 0.21 -2.59 0.010 (Banyoles – Paris) ✻ (2017–2014) -0.12 0.23 -0.52 0.603 (Banyoles – Paris) ✻ (2018–2014) -0.62 0.25 -2.47 0.014 (Banyoles – Paris) ✻ (2019–2014) -0.21 0.22 -0.95 0.342 (Banyoles – Paris) ✻ (2020–2014) 0.19 0.23 0.87 0.388 (Banyoles – Paris) ✻ (2021–2014) -0.44 0.21 -2.13 0.034 (Banyoles – Paris) ✻ (2022–2014) -0.15 0.21 -0.71 0.477 a Represents reference level Regarding the location variable “Place”, Banyoles has significantly higher Escherichia coli values compared to Paris, with a difference of -1.41units, a highly significant effect. For the years, no significant differences in Escherichia are observed compared to 2014 between 2015 and 2019, except for 2018, which shows a slight marginal increase in Escherichia coli values. The year 2020 exhibits a significant decrease, implying a considerable reduction in Escherichia coli values compared to 2014, while 2021 and 2022 show no significant differences. Additionally, the interactions between location and year reveal additional effects on Escherichia coli depending on their combination. Specifically, significant interactions are observed in 2016 and 2018, with a greater decrease in Escherichia coli in Paris compared to Banyoles. In 2021, an additional reduction in Paris relative to Banyoles is also observed, whereas in other years, the interactions are not significant, indicating that the differences between locations are less pronounced in those years. In summary, the model suggests that Paris consistently exhibits lower Escherichia coli values compared to Banyoles, with variations over time. The years 2016, 2018, and 2021 show significant interactions between location and year. Figure 3 shows the evolution of Escherichia coli values in Paris and Banyoles during the period from 2014 to 2022. The data are grouped by location, with error bars reflecting the variability associated with the measurements for each year. The results indicate a clear difference between the two locations, as Escherichia coli values in Paris are significantly higher than those in Banyoles throughout all the analyzed years. Furthermore, the distribution of values demonstrates a certain degree of interannual stability, with minor variations over time. The error bars highlight the dispersion of the data within each year, emphasizing the levels of consistency in the measurements. Table 5 shows the results of a statistical analysis exploring the effects of various predictors and their interactions on a dependent variable. The intercept has an estimate of 2.49, and this effect is statistically significant, with a p-value of less than 0.001. Table 5 Regression model coefficients for Enterococci: Effects of location and year Predictor Estimation SE t pvalue Intercept a 2.49 0.14 17.34 < 0.001 Place : Banyoles-Paris -0.70 0.18 -3.88 < 0.001 Year : 2015 − 2014 0.01 0.20 0.07 0.947 2016 − 2014 0.03 0.19 0.15 0.884 2017 − 2014 0.12 0.20 0.58 0.560 2018 − 2014 0.42 0.22 1.87 0.063 2019 − 2014 0.07 0.22 0.31 0.757 2020 − 2014 -0.33 0.19 -1-75 0.081 2021 − 2014 0.04 0.20 0.22 0.830 2022 − 2014 -0.21 0.19 -1.11 0.269 Place*year : (Banyoles – Paris) ✻ (2015–2014) -0.19 0.26 -0.71 0.478 (Banyoles – Paris) ✻ (2016–2014) -0.82 0.27 -3.08 0.002 (Banyoles – Paris) ✻ (2017–2014) -0.21 0.29 -0.72 0.473 (Banyoles – Paris) ✻ (2018–2014) -0.62 0.32 -1.94 0.053 (Banyoles – Paris) ✻ (2019–2014) -0.19 0.29 -0.67 0.505 (Banyoles – Paris) ✻ (2020–2014) 0.25 0.29 0.88 0.380 (Banyoles – Paris) ✻ (2021–2014) -0.36 0.26 -1.35 0.177 (Banyoles – Paris) ✻ (2022–2014) 0.02 0.26 0.06 0.949 a Represents reference level Regarding the main effect of "Place" (comparing Banyoles with Paris), the estimate is -0.70, indicating that the expected value of the dependent variable in Banyoles is 0.70 units lower than in Paris, assuming 2014 as the reference year. This effect is also statistically significant, with a p-value of less than 0.001. As for the main effect of "Year" (comparing different years to the reference year, 2014), no statistically significant differences are observed in any year, except for 2018, where there is slight evidence of an increase (estimate of 0.42 and p-value of 0.063), and for 2020, where a possible decrease is observed (estimate of -0.33 and p-value of 0.081). However, neither result reaches the threshold for significance. The analysis of the interaction between "Place" and "Year" reveals that the effect of Banyoles compared to Paris varies significantly in some years. Specifically, in 2016, the effect of Banyoles is significantly more negative compared to Paris, with an estimate of -0.82 and a p-value of 0.002. A marginally significant interaction is also detected in 2018, with an estimate of -0.62 and a p-value of 0.053, suggesting a trend toward a more negative effect for Banyoles that year. For the other years, no significant interactions are observed, indicating that in these cases, the difference between Banyoles and Paris relative to 2014 does not vary significantly. In summary, the results show that, on average, Banyoles exhibits lower values in the dependent variable compared to Paris, and this effect is more pronounced in specific years such as 2016. The effects of the years themselves are generally not significant. The Fig. 4 shows the estimated marginal means of the dependent variable by place (Paris and Banyoles) and year, along with the 95% confidence intervals. These means represent the adjusted values of the model, taking into account the effects of the other variables. In general, a clear difference can be observed between Paris and Banyoles in all years, with Paris consistently showing higher values in the dependent variable than Banyoles. This difference is evident from the marginal means, which range from around 2.3–2.7 for Paris and between 1.3–1.8 for Banyoles, depending on the year. In 2014, which is the reference year, the estimated mean for Paris is 2.63 (95% CI: 2.43–2.82), while for Banyoles it is 1.70 (95% CI: 1.52–1.89). This difference between locations is consistent across all years, and the confidence intervals do not overlap, indicating a statistically significant difference between the two locations. Some trends can be identified over the years. In Paris, the marginal values slightly decrease in certain years (for example, 2016 and 2020) but tend to recover afterward. In Banyoles, fluctuations are also observed, with the lowest values in 2016 (1.34, 95% CI: 1.13–1.55) and 2020 (1.37, 95% CI: 1.15–1.60). These declines may reflect specific temporal effects on the dependent variable. Discussion The results of this study reflect a clear difference in the concentration of Escherichia coli and Enterococci in water samples collected in Banyoles and Paris between 2014 and 2022. The data obtained provide consistent evidence of superior microbiological quality in Lake Banyoles compared to the Seine River, highlighting significant differences in the level of fecal contamination between the two environments. These results are consistent with previous studies indicating higher microbiological contamination in urban rivers of densely populated areas with a lack of sanitation systems (Ali Gh et al., 2024). The higher average concentrations in Paris (Escherichia coli: 3.31; Enterococci: 2.47) compared to Banyoles (1.70 and 1.58, respectively) reflect the direct influence of urban discharges and wastewater on the Seine. This reality has also been documented in other urban rivers, where wastewater management plays a key role in water quality (Kanungo et al., 2024 ). In Banyoles, the lower population density and strict water control due to the frequency of sporting events explain the significantly lower concentrations and the more homogeneous distribution of the data. This is consistent with research on protected lake ecosystems, which exhibit fewer fluctuations in microbiological parameters due to their lower exposure to anthropogenic contamination sources (Yuan et al., 2023 ). The temporal analysis reveals progressive improvements in water quality in Banyoles, a trend that aligns with other studies linking environmental conservation programs to enhanced microbiological quality. Mahy and Luizi ( 2023 ) indicate in their study that improvements in waste management and the use of protected areas reduce fecal contamination. These actions are similar to water quality management practices in bio-mineral pools, which use bio-filtration and UV treatment to control organic and bacterial contamination, thereby improving microbiological safety and reducing health risks for users. In contrast, the lack of a downward trend in the Seine highlights the persistent impact of urban fecal discharges. Previous studies had already identified this issue in the Seine, emphasizing the role of stormwater and combined sewer systems as recurrent sources of bacterial contamination (Mouchel et al., 2020 ). This is consistent with recent research on fecal contamination in aquatic ecosystems, which has identified rainfall and urban drainage systems as key factors in increasing fecal coliform levels. In this context, predictive models of fecal contamination have proven useful for assessing contamination risk and improving water quality management. However, reducing fecal discharges into water through improved wastewater treatment systems remains the most critical factor in minimizing public health risks (Aghalari et al., 2020 ). The spike in Enterococci in Banyoles in 2022 could be associated with extreme weather events or an increase in industrial or agricultural activities in the area. This aligns with studies that have documented the effect of heavy rainfall on the microbiological contamination of lake systems, as it increases the transport of sediments and fecal materials from surrounding areas. As observed in the study conducted on seven triathlons held during the summer of 1993 and 1994, precipitation can increase the concentration of coliforms and E. coli, thereby raising fecal contamination levels in recreational waters (Medema et al., 1997 ). The results obtained in this study are consistent with existing scientific knowledge, but some limitations should be considered. For example, the lack of data on weather conditions or other physicochemical parameters (such as nutrients, conductivity, or temperature) limits a full understanding of the factors influencing microbiological concentrations. Future studies should include these parameters to achieve a more comprehensive and definitive analysis. Additionally, it could also be interesting to investigate the impact of implementing specific water management measures in both locations and compare them with similar environments. Abbreviations ANOVA Analysis of Variance CI Confidence Interval df Degrees of Freedom E. coli Escherichia coli WHO World Health Organization Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Data availability The datasets generated and/or analysed during the current study are not publicy available due to confidentiality agreements with the local water authorities involved in the data collection. These entities have restricted the public sharing of raw water data due to legal and privacy constraints. However, the data are available from the corresponding author on reasonable request and permission from data providers. Competing interests Not applicable. Funding Not applicable. Authors' contributions AM and MS conceived the original idea for the paper and defined the study characteristics. AM conducted the bibliographic search and drafted the introduction. MS and AM selected and performed the methods and statistical analysis. All authors contributed to writing and final editing, as well as reviewing and approving the manuscript. Acknowledgements We sincerely thank Eau de Paris and Aigües de Banyoles for providing us with the data related to the studied water parameters. Without their collaboration, this study would not have been possible References Aghalari, Z., Dahms, H., Sillanpää, M., Sosa-Hernandez, J. E. & Parra-Saldívar, R. Effectiveness of wastewater treatment systems in removing microbial agents: A systematic review. Globalization Health . 16 (1), 13 (2020). Ali Gh, Chaudhari, M., Shah, P. & Shrivastav, P. Temporal changes in water quality in Leh Ladakh region: Impact of urbanization. Environ. Res. Technol. 7 (4), 637–664. 10.35208/ert.1431710 (2024). Bain, R. et al. Fecal contamination of drinking-water in low-and middle-income countries: a systematic review and meta-analysis. PLoS Med. 11 10.1371/journal.pmed.1001644 (2014). Breen, B., Curtis, J. & Hynes, S. Water quality and recreational use of public waterways. J. Environ. Econ. Policy . 7 , 1–15. 10.1080/21606544.2017.1335241 (2018). Hormaeche, E. & Edwards, P. A proposed genus Enterobacter. Int. Bull. Bacteriol. Nomencl Taxon . 10 , 71–74 (1960). Kanungo, J., Sahoo, T., Bal, M. & Behera, I. D. Performance of bioremendiation strategy in waste lubricating oil pollutants: a review. Geomicrobiol J. 41 (4), 360–373. 10.1080/01490451.2023.2245395 (2024). Mahy, J. & Luizi, F. Review on the management of water quality for biomineral swimming polls in Western Europe. Environ. Monit. Assess. 195 (7), 872. 10.1007/s10661-023-11502-4 (2023). Medema, G. J., van Asperen, I. A. & Havelaar, A. H. Assessment of the exposure of swimmers to Microbiological Contaminants in fresh waters. Water Sci. Technol. 35 , 157–163. 10.2166/wst.1997.0727 (1997). Mouchel, J-M. et al. Bathing activities and microbiological river water quality in the Paris area: a long-term perspective. In: The Handbook of Environmental Chemistry. (2020). Singh, N. K. et al. Multi-drug resistant Enterobacter bugandensis species isolated from the International Space Station and comparative genomic analyses with human pathogenic strains. BMC Microbiol. 18 , 175. 10.1186/s12866-018-1325-2 (2018). Tadyrak, R., Parszuto, K. & Grochowska, J. Water quality of lake Elk as a factor connected with tourism. Leisure and recreation on an urban area. Quaestiones Geographicae . 35 (3), 51–55. 10.1515/quageo-2016-0026 (2016). Tenaillon, O. et al. Tempo and mode of genome evolution in a 50,000-generation experiment. Nature 536 (7615), 165–170. 10.1038/nature18959 (2016). WHO-. Guidelines for drinking-water quality: Fourth edition incorporating the first and second addenda [Internet] PMID: 35417116 (World Health Organization, 2022). Yuan, Y. et al. Asynchronous onset of anthropogenic soil erosion in monsoonal China during the Holoncene. Palaeogeography, palaeoclimatology, palaecology. ; 624 :111653. (2023). 10.1016/j.palaeo.2023.111653 Additional Declarations No competing interests reported. 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-6429020","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":449468822,"identity":"e50db2b3-8eda-42da-a6e3-724ea4a96320","order_by":0,"name":"Anna Mota-Bertran","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYPACGwaGA0CKh2gNBxjSSNdymAQt/A3ciY8/1JxP7Du/gPHB2zYitEgc4N1scODY7cSZNx4wG84lRgvDAd5tEgfYbiduuHGATZqXGC3yYC3/zoG0sP8mSosBSMvBtgOJG843sDETpcXwMNAvZ/uSjWfeYGyWnHOOCC1yx3s3Pqj4Zifbd/7wwQ9vyojQwsAMY0gkNhCjHhnwHyBVxygYBaNgFIwUAACCST9JjW/IjQAAAABJRU5ErkJggg==","orcid":"","institution":"University of Girona","correspondingAuthor":true,"prefix":"","firstName":"Anna","middleName":"","lastName":"Mota-Bertran","suffix":""},{"id":449468823,"identity":"0e594299-8cab-44be-b015-6c3e40a1209a","order_by":1,"name":"Marc Saez","email":"","orcid":"","institution":"University of Girona","correspondingAuthor":false,"prefix":"","firstName":"Marc","middleName":"","lastName":"Saez","suffix":""}],"badges":[],"createdAt":"2025-04-11 14:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6429020/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6429020/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82143803,"identity":"154d2c27-d858-478e-9f2e-bc8db47290d7","added_by":"auto","created_at":"2025-05-07 06:42:40","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91325,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of median and mean of Escherichia Coli between places\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6429020/v1/c1688d85706558983a81d843.jpeg"},{"id":82139329,"identity":"50b87d2c-0a0f-4cf4-b989-58dab171dff3","added_by":"auto","created_at":"2025-05-07 06:26:40","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77119,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of median and mean of Enterococci between places\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6429020/v1/1d897bddf1700e4ccbd9b1bc.jpeg"},{"id":82139332,"identity":"b63bae81-fa9e-4750-85b6-8447bf31d60e","added_by":"auto","created_at":"2025-05-07 06:26:40","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":133810,"visible":true,"origin":"","legend":"\u003cp\u003eInterannual comparison of Escherichia coli between Paris and Banyoles\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6429020/v1/0640907af64891fb1da41342.jpeg"},{"id":82139333,"identity":"040d12f7-9aa3-4baa-8d6b-2d30e96437da","added_by":"auto","created_at":"2025-05-07 06:26:40","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":137124,"visible":true,"origin":"","legend":"\u003cp\u003eInterannual comparison of Enterococci between Paris and Banyoles\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6429020/v1/b8ac8adaac54a4cfafc550f5.jpeg"},{"id":84664978,"identity":"ba320d53-f92c-4b7e-8afe-25bd1abca3a4","added_by":"auto","created_at":"2025-06-16 05:33:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1460779,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6429020/v1/46f4d3e2-e721-40a6-9793-e865fbdf07e3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of water quality between the Lake of Banyoles, Girona, Spain, and the River Seine, Paris, France, considering the bridges along the river where the Olympic Games took place: Evaluation of Suitability for Aquatic Sports Practice","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWater, essential for life, serves as the backdrop for a wide range of recreational and sports activities that enrich human well-being and promote health. In recent years, aquatic sports have gained significant popularity, becoming a key component of leisure and physical culture. However, ensuring the quality of water in these venues is paramount to the safety and health of participants, as poor water quality can pose serious risks to health (Bain et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTwo emblematic places that represent the duality of water sports, Banyoles, in Catalonia, is a rich natural environment and the Seine River in Paris a vital artery of the city that forms a dynamic urban space. The Banyoles Lake, as a natural space, requires and enjoys careful protection and management to guarantee the sustainability of the flora and fauna that inhabit it, aspects that also impact on the practice of water sports. On the other hand, the Seine River represents an urban icon, especially significant because it has been the site of the last Olympic Games, placing a special focus of interest on the quality and safety of water in the development of the aquatic events, highlighting the challenge of maintaining water quality in recreational areas in the middle of a densely populated urban environment.\u003c/p\u003e \u003cp\u003eBreen et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) underscored the importance of water quality in aquatic sports, emphasizing the need for multidisciplinary approaches to assess and manage the risks associated with water contamination. Their research highlights the complexity of contamination sources and the various environmental factors that influence water quality, stressing the importance of tailored solutions for each location.\u003c/p\u003e \u003cp\u003eMicrobiological contamination of water, particularly the presence of human pathogens and other pollutants, represents a significant threat to the health of individuals engaging in aquatic activities. The detection of indicator bacteria, such as Escherichia coli and Enterobacter, has become an essential tool for assessing water safety in recreational environments. These microorganisms serve as markers of microbiological water quality, helping to identify potential health hazards.\u003c/p\u003e \u003cp\u003eEscherichia coli is a gram-negative, rod-shaped bacterium from the Enterobacteriaceae family and one of the most extensively studied organisms. It replicates rapidly under optimal conditions, with a doubling time of approximately 20 minutes, making it a valuable model for genetic manipulation and the production of industrial enzymes. Since the first sequencing of its genome in 1997, more than 4,800 Escherichia coli genomes have been analysed. Its fast growth and adaptability also make it an ideal organism for studying microbial evolution, as demonstrated by long-term experiments spanning over 50,000 generations (Tenaillon et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, the genus Enterobacter, also part of the Enterobacteriaceae family, comprises facultative anaerobic, gram-negative bacilli. First described in 1960, this genus has undergone several taxonomic revisions over the past decades. Enterobacter species are found in various environmental habitats, including soil and water, and can act as either endophytes or phytopathogens in a range of plant species (Hormaeche and Edwards \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1960\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBoth Escherichia coli and Enterobacter are pivotal in monitoring water quality and play a crucial role in understanding the risks of microbiological contamination in aquatic sports settings. In many countries, health and environmental authorities have established specific standards and regulations to ensure water quality in recreational areas, such as beaches, water parks, and locations for aquatic sports. However, the implementation of these regulations can vary significantly depending on local characteristics, pollution sources, and water management practices. This variability highlights the need for detailed scientific analysis of water quality at each specific location (WHO, 2022).\u003c/p\u003e \u003cp\u003eIn this context, the present study aims to provide a detailed comparison of water quality in Lake Banyoles and the Seine River in Paris by evaluating the concentrations of indicator bacteria, such as Escherichia coli and Enterobacter, and to determine which of these places offer safer conditions for water sports. The importance of this fact is already highlighted by Tandyrak et al. (2016) in their studies on the relevance of determining water quality in recreational areas.\u003c/p\u003e \u003cp\u003eThe conclusions drawn from this work provide authorities, athletes and citizens with information based on scientific evidence, which allows the implementation of actions or decisions aimed at the sustainable management of water quality in recreational and sports spaces to preserve the health and well-being of people.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe study focuses on the analysis of microbiological parametres of water quality in each locality between 2014 and 2022, with the aim of also obtaining results derived from their comparison.\u003c/p\u003e \u003cp\u003eAt Lake Banyoles, samples were taken from zones used for activities such as rowing, open-water swimming, and bathing, with the data provided by the company Aig\u0026uuml;es de Banyoles. In the Seine, locations with frequent recreational use, such as authorized bathing areas and tourist navigation routes, were selected, with the data supplied by the company Eau de Paris.\u003c/p\u003e \u003cp\u003eMicrobiological parameters were analysed to determine water quality through the logarithmic concentrations of Escherichia coli and Enterococci as primary indicators of faecal contamination, given their relevance in public health risk assessment. Microbiological parameters were quantified using standard membrane filtration techniques and selective media to isolate these two bacteria. Quality control measures, including triplicate sampling and cross-verifications, were implemented.\u003c/p\u003e \u003cp\u003eTo achieve the objectives of this study, a comprehensive statistical analysis was performed to compare microbiological concentrations across different locations and over various time periods. The initial comparisons were carried out using Student's t-test for pairwise evaluations and Analysis of Variance (ANOVA) for assessing differences among groups. Additionally, non-parametric tests were performed to account for potential deviations from normality, and regression analyses were conducted to explore relationships between variables and assess trends in the data.These tests allowed the identification of statistically significant variations in microbiological concentrations between the study sites and across the years of observation.\u003c/p\u003e \u003cp\u003eTo delve deeper into these findings, post hoc tests were employed following the ANOVA to examine specific interactions and differences more precisely. These post hoc analyses provided detailed insights into how the microbiological concentrations varied not only between sites but also within each site over time. This step was crucial for identifying patterns that might not have been apparent through the primary analyses alone.\u003c/p\u003e \u003cp\u003eAll statistical analyses were conducted using Jamovi (version 2.3.28), a statistical software. It was chosen for its intuitive interface and robust capabilities, which facilitated the execution of both primary tests and subsequent post hoc analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e describes the logarithmic concentrations of the bacteria Escherichia coli and Enterococci in samples collected in Banyoles and Paris. It provides various descriptive statistics to facilitate comparison between the two locations. In Banyoles, 150 samples were analysed for Escherichia coli and 149 for Enterococci, while in Paris, 165 samples were included for both parameters. The averages indicate significantly lower concentrations in Banyoles, with 1.70 for Escherichia coli and 1.58 for Enterococci, compared to Paris, where these values are 3.31 and 2.47, respectively. This shows that Paris exhibits higher concentrations of both bacteria, especially Escherichia coli, which is more than double the levels in Banyoles.\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\u003eDescriptive analysis of the logarithmic concentrations of bacterial presence\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlace\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEscherichia coli\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnterococci\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMedian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eStandard deviation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMinimum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eMaximum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e25th percentil\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e75th percentil\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.93\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 median, confirms this trend, as in Banyoles it is 1.73 for Escherichia coli and 1.57 for Enterococci, values very close to the mean, suggesting a homogeneous data distribution. In Paris, the median is also close to the mean, with 3.31 for Escherichia coli and 2.38 for Enterococci, though the values are significantly higher. Regarding variability, standard deviations reveal lower dispersion in Banyoles, with 0.37 for Escherichia coli and 0.49 for Enterococci, compared to higher variability in Paris, with 0.58 for Escherichia coli and 0.71 for Enterococci, particularly for Enterococci.\u003c/p\u003e \u003cp\u003eIn terms of data range, Banyoles presents more compact values, with minimums of 0.85 for Escherichia coli and 0.33 for Enterococci, and maximums of 2.97 and 3.29, respectively. In Paris, the range is much broader, with minimums of 2.04 for Escherichia coli and 1.18 for Enterococci, and maximums of 4.54 for both bacteria. Percentiles reinforce these observations; in Banyoles, the values between the 25th and 75th percentiles are closer (1.43 to 1.90 for Escherichia coli and 1.26 to 1.83 for Enterococci), indicating a more compact distribution, while in Paris, these percentiles show greater dispersion (2.89 to 3.75 for Escherichia coli and 1.94 to 2.93 for Enterococci).\u003c/p\u003e \u003cp\u003eOverall, these results show that bacterial concentrations are lower and more homogeneous in Banyoles, whereas in Paris, they are higher, with greater variability and a wider range of values.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the logarithmic concentrations of Escherichia coli and Enterococci from Banyoles and Paris between 2014 and 2022. The results reveal consistent and significant differences between the two locations, with higher concentrations in Paris throughout the entire period.\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\u003eDescriptive analysis of logarithmic concentrations of Escherichia coli and Enterococci between 2014 and 2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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=\"left\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlace\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\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 \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25th percentil\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e75th percentil\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"17\" rowspan=\"18\"\u003e \u003cp\u003e\u003cb\u003eEscherichia coli\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"17\" rowspan=\"18\"\u003e \u003cp\u003e\u003cb\u003eEnterococci\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanyoles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3.05\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 general comparison of microbiological pollution levels shows that the Seine River (Paris) consistently exhibits higher levels of Escherichia coli and Enterococci compared to Lake Banyoles, reflecting a greater presence of fecal contamination in the Seine. Regarding Escherichia coli, the average values recorded in the Seine range between 3.16 and 3.68, while in Banyoles they remain much lower, between 1.37 and 1.93. These differences are also reflected in the medians: in the Seine, the medians range from 2.80 to 3.74, while in Banyoles they range from 1.39 to 1.87. This demonstrates that both the average and the median are significantly higher in the Seine, indicating more persistent and elevated contamination in this environment.\u003c/p\u003e \u003cp\u003eIn the case of Enterococci, the differences follow a similar trend. In the Seine, the average values range between 2.16 and 2.91, with medians between 2.03 and 2.72. In contrast, in Banyoles, the averages range from 0.99 to 2.60, with medians ranging from 1.03 to 1.76. This pattern reinforces the difference between the two locations, where Lake Banyoles exhibits significantly better microbiological quality.\u003c/p\u003e \u003cp\u003eAnalyzing the evolution over the years, it is observed that in Banyoles, both for Escherichia coli and Enterococci, the averages and medians have tended to slightly decrease between 2014 and 2021, indicating a progressive improvement in water quality. However, in 2022, an increase in Enterococci values is detected, with an average of 2.60 and a median of 1.37. In contrast, in the Seine, both averages and medians remain relatively stable over the years, without showing significant improvement.\u003c/p\u003e \u003cp\u003eRegarding data dispersion, it is observed that the Seine exhibits greater variability in the recorded values, reflected in higher standard deviations. This indicates larger fluctuations in contamination levels. Furthermore, the maximum values recorded in the Seine are significantly higher than those in Banyoles, with peaks reaching up to 4.54 for both Escherichia coli and Enterococci, compared to lower maxima of 2.97 and 2.50, respectively, in Banyoles.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the comparison of Escherichia Coli values ​​between Banyoles and Paris, representing both the mean with its 95% confidence interval and the median. Since the confidence intervals do not overlap, this suggests that there are statistically significant differences between the two locations. According to the graph, Paris presents higher values ​​compared to Banyoles in terms of the concentration of the bacteria studied.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the concentration of Enterococci between Banyoles and Paris, showing both the mean with its 95% confidence interval and the median. Since the confidence intervals do not overlap, it can be concluded that there are statistically significant differences between both locations. According to the graph, Paris presents higher values ​​in the concentration of the studied bacteria compared to Banyoles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e analyse the differences in the logarithmic concentrations of Escherichia coli and Enterococci between Banyoles and Paris using Student's t-test and the Mann-Whitney U test. For Escherichia coli, Student's t-test yields a value of -29.3 with 313 degrees of freedom (df) and a significance level of pvalue 0.001. This difference is further supported by the Mann-Whitney U test, which is also significant with pvalue\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\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\u003eAnalysis of Logarithmic Concentrations of Escherichia coli and Enterococci in Banyoles and Paris using Statisticals tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDegrees of freedom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epvalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEscherichia coli\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003et de Student\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-29.3\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eU de Mann-Whitney\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEnterococci\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003et de Student\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12.8\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eU de Mann-Whitney\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eᵃ The Levene's test is significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting a violation of the assumption of equal variances.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eHₐ \u0026micro;\u003csub\u003eBanyoles\u003c/sub\u003e\u0026thinsp;\u0026ne;\u0026thinsp;\u0026micro; \u003csub\u003eParis\u003c/sub\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor Enterococci, Student's t-test shows a value of -12.8 with 312 df and pvalue\u0026thinsp;\u0026lt;\u0026thinsp;0.001, complemented by a significant Mann-Whitney U test result (pvalue\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIt is important to note that for both variables, Levene's Test is significant (pvalue\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating a violation of the assumption of equal variances. This justifies the use of both Student's t-test and the Mann-Whitney U test to confirm the robustness of the results.\u003c/p\u003e \u003cp\u003eIn both cases, the data support the alternative hypothesis that the means for Banyoles and Paris are significantly different, with notable variations in logarithmic concentrations between these two locations.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the coefficients of the statistical model analysing the effects of several predictor variables and their interactions on the dependent variable, Escherichia coli. The model's intercept shows an estimated value of 3.34 with a standard error of 0.11, indicating that the expected value of Escherichia coli for the reference group (year 2014 and Banyoles location) is 3.34, with a highly significant result.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression model coefficients for Escherichia coli: Effects of location and year\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epvalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBanyoles-Paris\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYear\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u0026thinsp;\u0026minus;\u0026thinsp;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u0026thinsp;\u0026minus;\u0026thinsp;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u0026thinsp;\u0026minus;\u0026thinsp;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u0026thinsp;\u0026minus;\u0026thinsp;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u0026thinsp;\u0026minus;\u0026thinsp;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u0026thinsp;\u0026minus;\u0026thinsp;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u0026thinsp;\u0026minus;\u0026thinsp;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u0026thinsp;\u0026minus;\u0026thinsp;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace*year\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e(Banyoles \u0026ndash; Paris) ✻ (2015\u0026ndash;2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Banyoles \u0026ndash; Paris) ✻ (2016\u0026ndash;2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Banyoles \u0026ndash; Paris) ✻ (2017\u0026ndash;2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Banyoles \u0026ndash; Paris) ✻ (2018\u0026ndash;2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Banyoles \u0026ndash; Paris) ✻ (2019\u0026ndash;2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.342\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Banyoles \u0026ndash; Paris) ✻ (2020\u0026ndash;2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Banyoles \u0026ndash; Paris) ✻ (2021\u0026ndash;2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Banyoles \u0026ndash; Paris) ✻ (2022\u0026ndash;2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003e Represents reference level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding the location variable \u0026ldquo;Place\u0026rdquo;, Banyoles has significantly higher Escherichia coli values compared to Paris, with a difference of -1.41units, a highly significant effect. For the years, no significant differences in Escherichia are observed compared to 2014 between 2015 and 2019, except for 2018, which shows a slight marginal increase in Escherichia coli values. The year 2020 exhibits a significant decrease, implying a considerable reduction in Escherichia coli values compared to 2014, while 2021 and 2022 show no significant differences.\u003c/p\u003e \u003cp\u003eAdditionally, the interactions between location and year reveal additional effects on Escherichia coli depending on their combination. Specifically, significant interactions are observed in 2016 and 2018, with a greater decrease in Escherichia coli in Paris compared to Banyoles. In 2021, an additional reduction in Paris relative to Banyoles is also observed, whereas in other years, the interactions are not significant, indicating that the differences between locations are less pronounced in those years.\u003c/p\u003e \u003cp\u003eIn summary, the model suggests that Paris consistently exhibits lower Escherichia coli values compared to Banyoles, with variations over time. The years 2016, 2018, and 2021 show significant interactions between location and year.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the evolution of Escherichia coli values in Paris and Banyoles during the period from 2014 to 2022. The data are grouped by location, with error bars reflecting the variability associated with the measurements for each year. The results indicate a clear difference between the two locations, as Escherichia coli values in Paris are significantly higher than those in Banyoles throughout all the analyzed years.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, the distribution of values demonstrates a certain degree of interannual stability, with minor variations over time. The error bars highlight the dispersion of the data within each year, emphasizing the levels of consistency in the measurements.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the results of a statistical analysis exploring the effects of various predictors and their interactions on a dependent variable. The intercept has an estimate of 2.49, and this effect is statistically significant, with a p-value of less than 0.001.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression model coefficients for Enterococci: Effects of location and year\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003epvalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBanyoles-Paris\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYear\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u0026thinsp;\u0026minus;\u0026thinsp;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.947\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u0026thinsp;\u0026minus;\u0026thinsp;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u0026thinsp;\u0026minus;\u0026thinsp;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u0026thinsp;\u0026minus;\u0026thinsp;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u0026thinsp;\u0026minus;\u0026thinsp;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u0026thinsp;\u0026minus;\u0026thinsp;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1-75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u0026thinsp;\u0026minus;\u0026thinsp;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u0026thinsp;\u0026minus;\u0026thinsp;2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace*year\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e(Banyoles \u0026ndash; Paris) ✻ (2015\u0026ndash;2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Banyoles \u0026ndash; Paris) ✻ (2016\u0026ndash;2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Banyoles \u0026ndash; Paris) ✻ (2017\u0026ndash;2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Banyoles \u0026ndash; Paris) ✻ (2018\u0026ndash;2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Banyoles \u0026ndash; Paris) ✻ (2019\u0026ndash;2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Banyoles \u0026ndash; Paris) ✻ (2020\u0026ndash;2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Banyoles \u0026ndash; Paris) ✻ (2021\u0026ndash;2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Banyoles \u0026ndash; Paris) ✻ (2022\u0026ndash;2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003e Represents reference level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRegarding the main effect of \"Place\" (comparing Banyoles with Paris), the estimate is -0.70, indicating that the expected value of the dependent variable in Banyoles is 0.70 units lower than in Paris, assuming 2014 as the reference year. This effect is also statistically significant, with a p-value of less than 0.001. As for the main effect of \"Year\" (comparing different years to the reference year, 2014), no statistically significant differences are observed in any year, except for 2018, where there is slight evidence of an increase (estimate of 0.42 and p-value of 0.063), and for 2020, where a possible decrease is observed (estimate of -0.33 and p-value of 0.081). However, neither result reaches the threshold for significance.\u003c/p\u003e \u003cp\u003eThe analysis of the interaction between \"Place\" and \"Year\" reveals that the effect of Banyoles compared to Paris varies significantly in some years. Specifically, in 2016, the effect of Banyoles is significantly more negative compared to Paris, with an estimate of -0.82 and a p-value of 0.002. A marginally significant interaction is also detected in 2018, with an estimate of -0.62 and a p-value of 0.053, suggesting a trend toward a more negative effect for Banyoles that year. For the other years, no significant interactions are observed, indicating that in these cases, the difference between Banyoles and Paris relative to 2014 does not vary significantly.\u003c/p\u003e \u003cp\u003eIn summary, the results show that, on average, Banyoles exhibits lower values in the dependent variable compared to Paris, and this effect is more pronounced in specific years such as 2016. The effects of the years themselves are generally not significant.\u003c/p\u003e \u003cp\u003eThe Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the estimated marginal means of the dependent variable by place (Paris and Banyoles) and year, along with the 95% confidence intervals. These means represent the adjusted values of the model, taking into account the effects of the other variables.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn general, a clear difference can be observed between Paris and Banyoles in all years, with Paris consistently showing higher values in the dependent variable than Banyoles. This difference is evident from the marginal means, which range from around 2.3\u0026ndash;2.7 for Paris and between 1.3\u0026ndash;1.8 for Banyoles, depending on the year.\u003c/p\u003e \u003cp\u003eIn 2014, which is the reference year, the estimated mean for Paris is 2.63 (95% CI: 2.43\u0026ndash;2.82), while for Banyoles it is 1.70 (95% CI: 1.52\u0026ndash;1.89). This difference between locations is consistent across all years, and the confidence intervals do not overlap, indicating a statistically significant difference between the two locations.\u003c/p\u003e \u003cp\u003eSome trends can be identified over the years. In Paris, the marginal values slightly decrease in certain years (for example, 2016 and 2020) but tend to recover afterward. In Banyoles, fluctuations are also observed, with the lowest values in 2016 (1.34, 95% CI: 1.13\u0026ndash;1.55) and 2020 (1.37, 95% CI: 1.15\u0026ndash;1.60). These declines may reflect specific temporal effects on the dependent variable.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of this study reflect a clear difference in the concentration of Escherichia coli and Enterococci in water samples collected in Banyoles and Paris between 2014 and 2022. The data obtained provide consistent evidence of superior microbiological quality in Lake Banyoles compared to the Seine River, highlighting significant differences in the level of fecal contamination between the two environments. These results are consistent with previous studies indicating higher microbiological contamination in urban rivers of densely populated areas with a lack of sanitation systems (Ali Gh et al., 2024).\u003c/p\u003e \u003cp\u003eThe higher average concentrations in Paris (Escherichia coli: 3.31; Enterococci: 2.47) compared to Banyoles (1.70 and 1.58, respectively) reflect the direct influence of urban discharges and wastewater on the Seine. This reality has also been documented in other urban rivers, where wastewater management plays a key role in water quality (Kanungo et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Banyoles, the lower population density and strict water control due to the frequency of sporting events explain the significantly lower concentrations and the more homogeneous distribution of the data. This is consistent with research on protected lake ecosystems, which exhibit fewer fluctuations in microbiological parameters due to their lower exposure to anthropogenic contamination sources (Yuan et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe temporal analysis reveals progressive improvements in water quality in Banyoles, a trend that aligns with other studies linking environmental conservation programs to enhanced microbiological quality. Mahy and Luizi (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) indicate in their study that improvements in waste management and the use of protected areas reduce fecal contamination. These actions are similar to water quality management practices in bio-mineral pools, which use bio-filtration and UV treatment to control organic and bacterial contamination, thereby improving microbiological safety and reducing health risks for users.\u003c/p\u003e \u003cp\u003eIn contrast, the lack of a downward trend in the Seine highlights the persistent impact of urban fecal discharges. Previous studies had already identified this issue in the Seine, emphasizing the role of stormwater and combined sewer systems as recurrent sources of bacterial contamination (Mouchel et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This is consistent with recent research on fecal contamination in aquatic ecosystems, which has identified rainfall and urban drainage systems as key factors in increasing fecal coliform levels. In this context, predictive models of fecal contamination have proven useful for assessing contamination risk and improving water quality management. However, reducing fecal discharges into water through improved wastewater treatment systems remains the most critical factor in minimizing public health risks (Aghalari et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe spike in Enterococci in Banyoles in 2022 could be associated with extreme weather events or an increase in industrial or agricultural activities in the area. This aligns with studies that have documented the effect of heavy rainfall on the microbiological contamination of lake systems, as it increases the transport of sediments and fecal materials from surrounding areas. As observed in the study conducted on seven triathlons held during the summer of 1993 and 1994, precipitation can increase the concentration of coliforms and E. coli, thereby raising fecal contamination levels in recreational waters (Medema et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results obtained in this study are consistent with existing scientific knowledge, but some limitations should be considered. For example, the lack of data on weather conditions or other physicochemical parameters (such as nutrients, conductivity, or temperature) limits a full understanding of the factors influencing microbiological concentrations. Future studies should include these parameters to achieve a more comprehensive and definitive analysis. Additionally, it could also be interesting to investigate the impact of implementing specific water management measures in both locations and compare them with similar environments.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eANOVA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of Variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003edf\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDegrees of Freedom\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eE. coli\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEscherichia coli\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWHO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicy available due to confidentiality agreements with the local water authorities involved in the data collection. These entities have restricted the public sharing of raw water data due to legal and privacy constraints. However, the data are available from the corresponding author on reasonable request and permission from data providers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAM and MS conceived the original idea for the paper and defined the study characteristics. AM conducted the bibliographic search and drafted the introduction. MS and AM selected and performed the methods and statistical analysis. All authors contributed to writing and final editing, as well as reviewing and approving the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank Eau de Paris and Aigües de Banyoles for providing us with the data related to the studied water parameters. Without their collaboration, this study would not have been possible\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAghalari, Z., Dahms, H., Sillanp\u0026auml;\u0026auml;, M., Sosa-Hernandez, J. E. \u0026amp; Parra-Sald\u0026iacute;var, R. Effectiveness of wastewater treatment systems in removing microbial agents: A systematic review. \u003cem\u003eGlobalization Health\u003c/em\u003e. \u003cb\u003e16\u003c/b\u003e (1), 13 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli Gh, Chaudhari, M., Shah, P. \u0026amp; Shrivastav, P. 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(2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.palaeo.2023.111653\u003c/span\u003e\u003cspan address=\"10.1016/j.palaeo.2023.111653\" 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":false,"hideJournal":true,"highlight":"","institution":"","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, microbiological contamination, Escherichia coli, Enterococci, water sports","lastPublishedDoi":"10.21203/rs.3.rs-6429020/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6429020/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe quality of water in recreational and sports environments plays a crucial role in ensuring the safety and health of participants. Aquatic sports, such as rowing and swimming, are becoming increasingly popular, but poor water quality can pose significant health risks. This study compares the water quality between two iconic locations: Lake Banyoles in Catalonia, a natural environment that has hosted numerous sports events since the 1992 Olympic Games, and the River Seine in Paris, an urban waterway that recently hosted the 2024 Olympic Games. Both locations present different challenges in water quality management due to their distinct environments. Banyoles has a lower population density and a rural setting, whereas the Seine flows through a densely populated city and experiences pollution from urban stormwater and wastewater discharges. Microbiological contamination, particularly from indicator bacteria such as Escherichia coli and enterococci, is used to assess water safety. These bacteria are widely recognized as markers of fecal contamination and indicators of potential health risks.\u003c/p\u003e\n\u003cp\u003eThis study analyzed microbiological parameters between 2014 and 2022, using samples from areas frequently used for recreational activities in both locations. The results show that Lake Banyoles consistently presented lower concentrations of Escherichia coli (1.70) and enterococci (1.58) compared to the River Seine, where concentrations were significantly higher, with values of 3.31 and 2.47, respectively. The analysis also highlighted greater variability in bacterial concentrations in the Seine, with higher peaks of contamination. Statistical tests confirmed significant differences between the two locations, with Escherichia coli levels consistently higher in Paris (p \u0026lt; 0.001) over the years.\u003c/p\u003e\n\u003cp\u003eTemporal trends showed a slight improvement in water quality in Banyoles, particularly in Escherichia coli levels, whereas no significant improvement was observed in the Seine. The study highlights the importance of effective water management practices to minimize microbiological risks. It also underscores that urban environments like the Seine face persistent challenges, such as stormwater runoff and inadequate sewage systems, contributing to ongoing water contamination. These findings provide valuable insights for managing water quality in recreational areas, helping decision-makers prioritize health and safety measures to ensure sustainable aquatic sports practices.\u003c/p\u003e","manuscriptTitle":"Comparison of water quality between the Lake of Banyoles, Girona, Spain, and the River Seine, Paris, France, considering the bridges along the river where the Olympic Games took place: Evaluation of Suitability for Aquatic Sports Practice","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 06:26:35","doi":"10.21203/rs.3.rs-6429020/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":"8b99c23b-5dd1-4bc2-82a7-8035006a950a","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47828086,"name":"Earth and environmental sciences/Environmental sciences"},{"id":47828087,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2025-06-16T05:08:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 06:26:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6429020","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6429020","identity":"rs-6429020","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}