Influence of effluent quality parameters on Daphnia spp. overgrowth in an urban wastewater treatment plant: a multiyear case study analysis

Academic Editors: Jie Gao and Zhuofan Gao Received: 15 March 2025 Revised: 5 April 2025 Accepted: 7 April 2025 Published: 11 April 2025 Citation: Esperanço, P.; Egito, R.; Oliveira, V.; Amaral, A.L.; Rodrigues, C. Influence of Effluent Quality Parameters on Daphnia spp. Overgrowth in an Urban Wastewater Treatment Plant: A Multiyear Case Study Analysis. Processes 2025,13, 1164. https://doi.org/ 10.3390/pr13041164 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Influence of Effluent Quality Parameters on Daphnia spp. Overgrowth in an Urban Wastewater Treatment Plant: A Multiyear Case Study Analysis Pedro Esperanço 1,2 , Rômulo Egito 3, Verónica Oliveira 1,2,* , António Luís Amaral 1,2,4 and Carla Rodrigues 1,2 1Polytechnic University of Coimbra, Rua da Misericórdia, Lagar dos Cortiços, S. Martinho do Bispo, 3045-093 Coimbra, Portugal; [email protected] (P.E.); [email protected] (A.L.A.); [email protected] (C.R.) 2Research Center for Natural Resources, Environment and Society (CERNAS), Polytechnic University of Coimbra, Bencanta, 3045-601 Coimbra, Portugal 3Public Waters of Alentejo, Rua Dr. Aresta Branco, 7800-310 Beja, Portugal; r[email protected] 4CEB—Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal *Correspondence: ver[email protected] Abstract: Wastewater treatment plants (WWTPs) play a crucial role in treating sewage, which undergoes multiple treatment stages to ensure a safe treated effluent. However, any interference during these stages can compromise the final effluent quality. Such is the case of the overgrowth of the microcrustacean Daphnia spp., known to inhabit WWTPs, but with its presence in the decantation stage negatively impacting effluent clarification and further disinfection. This study aimed to evaluate how the effluent quality parameters influence the occurrence of Daphnia spp. in the secondary decanter of a WWTP. Wastewater monitoring data collected from 2017 to 2022 were analyzed. Firstly, as the COVID-19 pandemic occurred during the studied period, it was assessed whether the quality and load of the raw wastewater changed. Subsequently, an analysis was carried out using multivariate statistical methods for all the steps of WWTP. Comparing the periods before and during the pandemic, the raw wastewater volume decreased by 19.58%, and the BOD, COD, and TSS decreased by 37.78%, 16.86%, and 35.75%, respectively. These were the parameters affected the most. The statistical analysis revealed correlations between the presence of Daphnia spp. and specific effluent quality parameters, including raw wastewater BOD values below 500 mg L −1 , treated effluent BOD values below 13 mg L −1 , and pH levels exceeding 7.3. Additionally, BOD and pH were highlighted as critical parameters influencing their presence or absence. Keywords: wastewater treatment plant; Daphnia spp.; biochemical oxygen demand; wastewater monitoring 1. Introduction Wastewater treatment plants (WWTPs) are facilities with the purpose of reducing the pollutants present in wastewater so that it can be returned to the receiving environment without negatively affecting it [ 1 – 3 ]. For this to happen, wastewater undergoes treatment processes in which the pollutants present are eliminated through physical, biological, and, if necessary, chemical treatments [ 3 , 4 ]. In a general context, wastewater treatment generally comprises four key stages: pre-treatment, primary treatment, secondary treatment, and tertiary treatment [ 3 – 6 ]. During pre-treatment, mechanical methods are employed to remove up to 80% of solid material, including sand, plastics, and food waste [ 7 ]. Following Processes 2025,13, 1164 https://doi.org/10.3390/pr13041164 Processes 2025,13, 1164 2 of 15 this, primary treatment involves sedimentation, where suspended solids are separated, allowing the clarified wastewater to proceed to the secondary treatment phase. In the secondary treatment phase occurs the biological treatment that relies on aerobic microorganisms, commonly known as activated sludge, to degrade the organic matter present in the wastewater [ 1 , 8 ]. Finally, the tertiary treatment, conducted after the secondary sedimentation phase, incorporates advanced processes, such as chlorination, ultraviolet irradiation, and membrane filtration, that are designed to eliminate residual contaminants, such as pathogens and other harmful microorganisms [ 9 ]. However, the presence of Daphnia spp., commonly known as water fleas, is often significant in secondary decanters, before the treated effluent proceeds to the tertiary treatment, due to the already increased water quality guaranteed by the secondary treatment, and its presence is easily detected by their large size and rapid parthenogenetic reproduction [10]. Daphnia spp. presence comes with both benefits and disadvantages. On one hand, they contribute to controlling algae growth and to removing organic matter and nutrients from wastewater [ 11 , 12 ]. On the other hand, their overgrowth in secondary decanters can pose substantial challenges for the final stages of treatment, mainly when disinfection is needed. Their movement and feeding behavior resuspend solids, leading to increased turbidity and higher solids load, compromising the applied ultraviolet-based tertiary treatment by blocking the radiation from effectively passing through the system to disinfect the final effluent, as reported in this case study. This not only challenges the system’s ability to meet legal discharge limits but also disrupts disinfection processes, as the increased turbidity and particulate matter reduce the effectiveness of disinfection treatments following the decantation step. Understanding why Daphnia spp. occurs in this specific treatment step is crucial for managing their presence and preventing their uncontrolled proliferation, assuring the quality of the final effluent. Daphnia spp. exhibits remarkable adaptability to various environmental conditions, including pollution, temperature changes, photoperiod, salinity, and nutrient levels [ 13 , 14 ]. When under highly stressful conditions, Daphnia spp. populations switch to sexual reproduction, producing dormant eggs that facilitate colonization of new habitats and the re-establishment of their population [ 13 , 15 ]. These characteristics are the primary reason why Daphnia spp. has successfully colonized WWTPs and continues to thrive by repeatedly re-establishing their populations. Indeed, the presence of Daphnia spp. in WWTPs is well-documented. For example, Kumar and Kiran identified Daphnia spp. and other zooplankton in water collected from a sewage-fed tank, demonstrating that nutrient-rich waters coupled with aquatic weeds favored the abundance of zooplankton [ 16 ]. In their study, water quality was assessed from samples taken during the occurrence of Daphnia spp., revealing a water temperature range of 22.5 to 32 ◦ C, an alkaline pH, high dissolved oxygen levels (2.4–4.8 mg L −1 ), low biochemical oxygen demand (BOD) levels (4.8–16.8 mg L −1 ), and nutrient concentrations such as nitrates (14.6–54.4 mg L −1 ) and phosphates (0.28–1.08 mg‘L −1 ) [ 16 ]. Other studies have sought to explore their potential to enhance wastewater treatment efficiency, as they have shown the capacity to remove particulate matter and reduce bacteria and coliform contents [ 17 – 22 ]. Some studies, such as Pau et al., reported the Daphnia magna ability to filter sludge particles from the treated wastewater on secondary decanters, with laboratory tests reporting a 29.4% reduction in the suspended particles and in situ tests reporting a 30.4% reduction [ 19 ]. Tertiary treatment based on Daphnia magna filtration has even been considered by Serra et al., showing a 35% reduction in the concentration of particles with diameters below 30 µ m and a significant inactivation of E. coli by 1.2 log units, although lower than conventional ultraviolet irradiation (inactivation between 1.5 and 4.0 log units) depending on temperature and hydraulic retention time [ 22 ]. However, no research is found on their occurrence when they become problem- Processes 2025,13, 1164 3 of 15 atic. In cases such as the overgrowth reported herein, there is a lack of knowledge of the factors driving their proliferation or of strategies to predict and effectively control their presence. Considering the above, the main objective of this work was to study the occurrence of Daphnia spp. in an urban WWTP secondary decanter. The specific objectives consisted of processing the monitoring data of the WWTP from 2017 to 2022 and identifying the most relevant physicochemical conditions for their presence. To this end, statistical tools were used to identify patterns in the quality of the effluent when Daphnia spp. appeared. It was also sought to identify possible ways of controlling this organism in WWTP without loss of quality in the final treated effluent. 2. Materials and Methods 2.1. Case Study The analyzed urban WWTP is located in Fátima, Portugal, and comprises a primary inlet channel, which receives raw wastewater that is subsequently distributed across two independent lines (Figure 1). Each line includes an oxidation ditch followed by a secondary decanter. Post-decantation, the treated effluents from both lines converge and undergo ultraviolet (UV) disinfection treatment prior to discharge into the receiving water body. Upon analyzing the WWTP’s treatment stages, the following six data collection points were identified: (1) influent or raw wastewater, (2) secondary sludge line 1, (3) secondary sludge line 2, (4) aerobic zone 1, (5) aerobic zone 2, and (6) final effluent. Processes 2025, 13, x FOR PEER REVIEW 3 of 16 become problematic. In cases such as the overgrowth reported herein, there is a lack of knowledge of the factors driving their proliferation or of strategies to predict and effectively control their presence. Considering the above, the main objective of this work was to study the occurrence of Daphnia spp. in an urban WWTP secondary decanter. The specific objectives consisted of processing the monitoring data of the WWTP from 2017 to 2022 and identifying the most relevant physicochemical conditions for their presence. To this end, statistical tools were used to identify patterns in the quality of the effluent when Daphnia spp. appeared. It was also sought to identify possible ways of controlling this organism in WWTP without loss of quality in the final treated effluent. 2. Materials and Methods 2.1. Case Study The analyzed urban WWTP is located in Fátima, Portugal, and comprises a primary inlet channel, which receives raw wastewater that is subsequently distributed across two independent lines (Figure 1). Each line includes an oxidation ditch followed by a secondary decanter. Post-decantation, the treated effluents from both lines converge and undergo ultraviolet (UV) disinfection treatment prior to discharge into the receiving water body. Upon analyzing the WWTP’s treatment stages, the following six data collection points were identified: (1) influent or raw wastewater, (2) secondary sludge line 1, (3) secondary sludge line 2, (4) aerobic zone 1, (5) aerobic zone 2, and (6) final effluent. Figure 1. Satellite overview of the studied WWTP (Fátima, Portugal) and collection points: (1) raw wastewater, (2) secondary sludge line (1, 3) secondary sludge line (2, 4) aerobic zone (1, 5) aerobic zone 2, and (6) final effluent. These points were chosen to provide a comprehensive assessment of the WWTP performance, covering upstream, within, and downstream locations of the secondary treatment system. Raw wastewater (1) reflects the characteristics of wastewater entering the plant. As the effluent subsequently flows through two independent lines, it is necessary to identify whether the treatment is equally efficient in both lines. For this reason, points (2) to (5) were identified as important for this study. After the secondary decantation, the treated effluent joins up again in just one line, with this monitoring point considered the final effluent (6). From these 6 points it was possible to characterize the raw wastewater, the WWTP treatment influence on the improvement of water quality, and, after all the treatment stages, characterize the treated effluent. Figure 1. Satellite overview of the studied WWTP (Fátima, Portugal) and collection points: (1) raw wastewater, (2) secondary sludge line (1, 3) secondary sludge line (2, 4) aerobic zone (1, 5) aerobic zone 2, and (6) final effluent. These points were chosen to provide a comprehensive assessment of the WWTP performance, covering upstream, within, and downstream locations of the secondary treatment system. Raw wastewater (1) reflects the characteristics of wastewater entering the plant. As the effluent subsequently flows through two independent lines, it is necessary to identify whether the treatment is equally efficient in both lines. For this reason, points (2) to (5) were identified as important for this study. After the secondary decantation, the treated effluent joins up again in just one line, with this monitoring point considered the final effluent (6). From these 6 points it was possible to characterize the raw wastewater, the WWTP treatment influence on the improvement of water quality, and, after all the treatment stages, characterize the treated effluent. Processes 2025,13, 1164 4 of 15 The urban WWTP provided an extensive dataset of analytical parameters obtained through monthly monitoring from 2017 to 2022. From this dataset, 12 parameters were selected for analysis due to their availability through weekly monitoring, resulting in a robust and comprehensive database. The selected parameters include volumetric flow, biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammonium ion (NH 4+ ), nitrate (NO 3− ), total nitrogen (TN), pH, total phosphorus (TP), total and volatile suspended solids (TSS and VSS), and total and volatile solids (TS and VS). In addition, records documenting the occurrence of Daphnia spp. within WWTP were also incorporated, enabling a detailed assessment of the factors influencing their presence and potential impacts on the treatment process. As the problem with Daphnia spp. was identified at an unusual time, during a pandemic period, a study was carried out to identify whether this event influenced the load and quality of the raw wastewater. For this analysis, the following parameters were compared: volumetric flow, BOD, COD, pH, TN, TP, and TSS. All monthly values monitored at the WWTP were averaged between June 2017 and February 2020, a period prior to COVID19. These values were next compared with the period during the COVID-19 pandemic, between March 2020 and February 2022. Statistical tools were then used to enlighten the occurrence patterns of Daphnia spp. in the WWTP, relating to the selected parameters (monitored at 6 different points in the WWTP). A total of 23 sample points were chosen due to their representativeness of the WWTP treatment stages and data availability from 2017 to 2022, namely: Flow, Biochemical Oxygen Demand-Raw Wastewater (BOD-RW), Biochemical Oxygen DemandEffluent (BOD-E), Chemical Oxygen Demand-Raw Wastewater (COD-RW), Chemical Oxygen Demand-Effluent (COD-E), Ammonium Ion-Effluent (NH4-E), Nitrate-Effluent (NO3-E), Nitrogen-Raw Wastewater (TN-RW), Nitrogen-Effluent (TN-E), pH-A, pH-E, Phosphorus-Raw Wastewater (TP-RW), Phosphorus-Effluent (TP-E), Total Suspended Solids-Raw Wastewater (TSS-RW), Total Suspended Solids-Effluent (TSS-E), Total Suspended Solids-Aerobic Zone 1 (TSS-AZ1), Volatile Suspended Solids-Aerobic Zone 1 (VSSAZ1), Total Solids-Secondary Sludge 1 (TS-SS1), Volatile Solids-Secondary Sludge 1 (VSSS1), Total Suspended Solids-Aerobic Zone 2 (TSS-AZ2), Volatile Suspended Solids-Aerobic Zone 2 (VSS-AZ2), Total Solids-Secondary Sludge 2 (TS-SS2), and Volatile Solids-Secondary Sludge 2 (VS-SS2). The selected WWTP points include essential parameters commonly monitored in WWTPs, allowing us to track how the raw wastewater content in organic matter (BOD, COD, and TSS), nutrients (TN and TP), and pH fluctuate as they enter the biological treatment and ultimately impact the final effluent. By including measurements at different treatment stages, the assessment of the treatment performance and operational stability can be carried out. The content on TSS and vs. on both the anaerobic zones and decanted sludge, 1 and 2, are of extreme importance to evaluate if both streams are operating at similar levels. 2.2. Statistical Analysis Pearson’s correlation matrix, logistic regression, and Principal Component Analysis (PCA) were computed using the R program (R Core Team 4.3.1, 2023), adopting a significance level of <0.05 for statistical differences [23]. 2.2.1. Pearson’s Correlation Matrix The first approach consisted of submitting the selected parameters to a Pearson’s correlation matrix in order to investigate how they are correlated and to identify and remove collinear parameters. A correlation coefficient above 0.7 was adopted as the cut-off value for removing colinear parameters [24]. Processes 2025,13, 1164 5 of 15 2.2.2. Principal Component Analysis (PCA) The subset of the retained parameters was next subjected to a statistical procedure, PCA [ 25 – 27 ], to reduce the dimensionality of the dataset and identify possible clusters regarding the absence and presence of Daphnia spp. This second approach assumes that the occurrence of Daphnia spp. can be related to various analytical parameters, so the scenarios of ‘absence’ and ‘presence’ of the microcrustacean would form two distinct clusters within a multivariate space. The analytical parameters were first log-normalized for standardization purposes and to avoid possible data non-normality effects. In addition, Student’s t-tests were carried out to check for statistical differences between the absence and presence of Daphnia spp. (categorical parameters) using the first three axes of the PCA (response parameters) of the analytical parameters. 2.2.3. Logistic Regression The third approach aimed to understand the relative contribution of the analytical parameters that best explain the occurrence of Daphnia spp. in the urban WWTP. In this case, a logistic regression was applied, with the response parameter being binary (0 = absence and 1 = presence) and the categorical parameters being the analytical parameters [ 28 , 29 ]. Once again, the analytical parameters were log-normalized to standardize the parameters in terms of units. The explanatory performance of the logistic model was assessed using the Wald test (chi-squared) and the Akaike criterion (AIC) [ 30 ]. For this set of analyses, a significance level of <0.05 was adopted to assess significant statistical differences. 2.2.4. Classification Tree Finally, a classification tree analysis was performed using the parameters identified by the logistic regression as most influential to Daphnia spp. presence. The most predictive parameter determines the first split (branches), with subsequent branches added until meeting the stopping criteria. Unlike regression models, which fit a single relationship across the entire domain, classification trees adapt to changing relationships within different input regions. Model performance is evaluated using confusion matrices and receiver operating characteristic (ROC) curves that quantify true/false positives and negatives, enabling the calculation of sensitivity (true positive rate), specificity (true negative rate), and accuracy (correct classification rate). A well-balanced model exhibits high and similar sensitivity and specificity, minimizing false classifications [31]. 3. Results 3.1. Effect of the COVID-19 Pandemic This first assessment of the quality of the raw wastewater is essential because the first case of the presence of Daphnia spp. was identified in December 2020, 10 months after the declaration of the COVID-19 global pandemic. Between the date of this first appearance and June 2022, approximately 30 interferences in the treatment were identified caused by the presence of the microcrustacean. The first sign of the appearance of microcrustaceans was an increase in turbidity in the final treated effluent. After that, WWTP technicians carried out a visual inspection, observing red clouds in the secondary decanter and registering their occurrence. Table 1shows the variations in the raw inflow between the period before (June 2017–February 2020) and during (March 2020–February 2022) the pandemic. Processes 2025,13, 1164 6 of 15 Table 1. Variation in raw wastewater quality caused by COVID-19 (mean ±standard deviation). Parameters Before COVID-19 During COVID-19 Variation (%) p-Value Raw wastewater flow (m3·day−1)2640 ±278 2036 ±270 −19.6% 0.002 1 BOD (mg O2·L−1)651 ±129 405 ±101 −37.8% <0.001 1 COD (mg O2·L−1)1026 ±210 854 ±274 −16.8% 0.048 1 Nitrogen (mg N·L−1)83.5 ±28.7 90.5 ±32.3 +8.38% 0.467 Phosphorus (mg P·L−1)9.40 ±3.34 10.7 ±2.01 +13.8% 0.219 TSS (mg·L−1)437 ±110 281 ±131 −35.7% 0.004 1 pH 7.28 ±0.04 7.43 ±0.11 +1.99% <0.001 1 1indicates significant (p< 0.01) or marginally significant (p< 0.05) differences. 3.2. Pearson’s Correlation Matrix The Pearson’s correlation matrix shows a series of highly correlated parameters (Figure 2). In fact, 10 of the parameters analyzed presented correlation coefficients (r) greater than 0.7, leading to the following being removed: COD_RW, COD_E, TP_RW, TP_E, TSS_A, TSS_E, VSS_AZ1, TS_SS1, VSS_AZ2, and TS_SS2. Such high correlations are due to the WWTP operating structure, with the raw wastewater following a single treatment line through the initial stages (grading/sampling and desanding/degreasing) before being split into two separate biological reactor lines and secondary decantation stages, as seen in Figure 1. This division results in non-correlated parameters between both streams (VSS_AZ1, TS_SS1, VSS_AZ2, and TS_SS2) and could further be affecting the correlation of the COD and TP parameters between the raw wastewater and treated effluent. Processes 2025, 13, x FOR PEER REVIEW 6 of 16 Table 1. Variation in raw wastewater quality caused by COVID-19 (mean ± standard deviation). Parameters Before COVID-19 During COVID-19 Variation (%) p-Value Raw wastewater flow (m3·day−1) 2640 ± 278 2036 ± 270 −19.6% 0.002 1 BOD (mg O2·L–1) 651 ± 129 405 ± 101 −37.8% <0.001 1 COD (mg O2·L–1) 1026 ± 210 854 ± 274 −16.8% 0.048 1 Nitrogen (mg N·L–1) 83.5 ± 28.7 90.5 ± 32.3 +8.38% 0.467 Phosphorus (mg P·L–1) 9.40 ± 3.34 10.7 ± 2.01 +13.8% 0.219 TSS (mg·L–1) 437 ± 110 281 ± 131 −35.7% 0.004 1 pH 7.28 ± 0.04 7.43 ± 0.11 +1.99% <0.001 1 1 indicates significant (p < 0.01) or marginally significant (p < 0.05) differences. 3.2. Pearson’s Correlation Matrix The Pearson’s correlation matrix shows a series of highly correlated parameters (Figure 2). In fact, 10 of the parameters analyzed presented correlation coefficients (r) greater than 0.7, leading to the following being removed: COD_RW, COD_E, TP_RW, TP_E, TSS_A, TSS_E, VSS_AZ1, TS_SS1, VSS_AZ2, and TS_SS2. Such high correlations are due to the WWTP operating structure, with the raw wastewater following a single treatment line through the initial stages (grading/sampling and desanding/degreasing) before being split into two separate biological reactor lines and secondary decantation stages, as seen in Figure 1. This division results in non-correlated parameters between both streams (VSS_AZ1, TS_SS1, VSS_AZ2, and TS_SS2) and could further be affecting the correlation of the COD and TP parameters between the raw wastewater and treated effluent. Figure 2. Pearson’s correlation matrix with the measured analytical parameters. The circle’s size corresponds to the correlation’s magnitude, varying from − 1 to 1 (bar on the right side). Red indicates negative correlations, while blue indicates positive correlations. 3.3. Principal Component Analysis The first three principal components of the PCA explained around 60% of the total variability of the data: PC 1 (27.9%), PC 2 (16.4%), and PC 3 (14.7%). A high degree of Processes 2025,13, 1164 7 of 15 overlap between the two groups (absence and presence of Daphnia spp.) is apparent, with only a slight tendency towards segregation, as observed by the distance of the centroids (Figure 3a–c). Processes 2025, 13, x FOR PEER REVIEW 7 of 16 Figure 2. Pearson’s correlation matrix with the measured analytical parameters. The circle’s size corresponds to the correlation’s magnitude, varying from −1 to 1 (bar on the right side). Red indicates negative correlations, while blue indicates positive correlations. 3.3. Principal Component Analysis The first three principal components of the PCA explained around 60% of the total variability of the data: PC 1 (27.9%), PC 2 (16.4%), and PC 3 (14.7%). A high degree of overlap between the two groups (absence and presence of Daphnia spp.) is apparent, with only a slight tendency towards segregation, as observed by the distance of the centroids (Figure 3a–c). Processes 2025, 13, x FOR PEER REVIEW 8 of 16 Figure 3. PCA biplots demonstrate a pattern of high overlap between the absence (red) and presence (blue) groups of Daphnia spp., with a slight tendency towards segregation (distance between centroids). (a) PC 1 × PC 2; (b) PC 1 × PC 3; (c) PC 2 × PC 3. The Daphnia spp. presence pattern, though not statistically significant as next determined, was more related to lower PC1 and PC3 values. Analyzing Figure 3b, it is also clear that the higher values of PC1 and PC3 (and hence of lower Daphnia spp. occurrence) correspond to the direction of the BOD_RW and BOD_E (in both cases corroborated by the logistic regression) and NH4_E and are opposite to the direction of VS_SS1 (in these two later cases just shortly failing to be considered as significant drivers by the logistic regression). It is also possible to verify in Figure 3a that larger values of VS_SS1, TN_E, and TN_RW correspond to a greater distinction between the presence and absence of Daphnia spp. Finally, Figure 3c suggests that higher values of VS_SS1, VS_SS2, TSS_AZ1, and TSS_AZ2 are associated with a more pronounced distinction between the presence and absence of Daphnia spp. However, in these two later figures (Figure 3b,c), the fact that PC2 does not provide for any distinction between Daphnia presence or absence limits their use in this analysis. 3.4. T Test Regarding Student’s t-test, it did not register any significant difference in the three principal components (axes) of the PCA (environmental parameters) between the “absence” and “presence” groups of Daphnia (Axis 1 (PC 1): t = 1.439, p = 0.162; Axis 2 (PC 2): t = −0.840, p = 0.415; Axis 3 (PC 3): t = 1.401, p = 0.173). These results corroborate the pattern of high overlap between the absence and presence of Daphnia spp. found by the PCA (Figure 4a–c). Figure 3. PCA biplots demonstrate a pattern of high overlap between the absence (red) and presence (blue) groups of Daphnia spp., with a slight tendency towards segregation (distance between centroids). (a) PC 1 ×PC 2; (b) PC 1 ×PC 3; (c) PC 2 ×PC 3. Processes 2025,13, 1164 8 of 15 The Daphnia spp. presence pattern, though not statistically significant as next determined, was more related to lower PC1 and PC3 values. Analyzing Figure 3b, it is also clear that the higher values of PC1 and PC3 (and hence of lower Daphnia spp. occurrence) correspond to the direction of the BOD_RW and BOD_E (in both cases corroborated by the logistic regression) and NH4_E and are opposite to the direction of VS_SS1 (in these two later cases just shortly failing to be considered as significant drivers by the logistic regression). It is also possible to verify in Figure 3a that larger values of VS_SS1, TN_E, and TN_RW correspond to a greater distinction between the presence and absence of Daphnia spp. Finally, Figure 3c suggests that higher values of VS_SS1, VS_SS2, TSS_AZ1, and TSS_AZ2 are associated with a more pronounced distinction between the presence and absence of Daphnia spp. However, in these two later figures (Figure 3b,c), the fact that PC2 does not provide for any distinction between Daphnia presence or absence limits their use in this analysis. 3.4. T Test Regarding Student’s t-test, it did not register any significant difference in the three principal components (axes) of the PCA (environmental parameters) between the “absence” and “presence” groups of Daphnia (Axis 1 (PC 1): t = 1.439, p= 0.162; Axis 2 (PC 2): t = − 0.840, p= 0.415; Axis 3 (PC 3): t = 1.401, p= 0.173). These results corroborate the pattern of high overlap between the absence and presence of Daphnia spp. found by the PCA (Figure 4a–c). Processes 2025, 13, x FOR PEER REVIEW 9 of 16 Figure 4. Boxplot graphs of the T-test results show no significant differences between the “absence” and “presence” groups of Daphnia for PC 1 (a), PC 2 (b), and PC 3 (c), used as proxies for analytical parameters. 3.5. Logistic Regression Logistic regression identified three parameters with different levels of influence on the occurrence of Daphnia spp. The biochemical oxygen demand of both the raw wastewater (BOD_RW) and the effluent (BOD_E) had a moderate negative effect (i.e., marginally significant p-value) on the response parameter, i.e., the presence of Daphnia spp. was related to low levels of BOD_RW (<500 mg/L) and BOD_E (<13 mg/L) (Figure 5a,b). Figure 4. Boxplot graphs of the T-test results show no significant differences between the “absence” and “presence” groups of Daphnia for PC 1 (a), PC 2 (b), and PC 3 (c), used as proxies for analytical parameters. Processes 2025,13, 1164 9 of 15 3.5. Logistic Regression Logistic regression identified three parameters with different levels of influence on the occurrence of Daphnia spp. The biochemical oxygen demand of both the raw wastewater (BOD_RW) and the effluent (BOD_E) had a moderate negative effect (i.e., marginally significant p-value) on the response parameter, i.e., the presence of Daphnia spp. was related to low levels of BOD_RW (<500 mg/L) and BOD_E (<13 mg/L) (Figure 5a,b). Processes 2025, 13, x FOR PEER REVIEW 10 of 16 Figure 5. Graphs of the logistic regression model showing the influence of biochemical oxygen deficiency (a,b) and pH (c) on the occurrence of Daphnia (0 = absence/1 = presence). (Dots represent the observational data; blue line depicts the predict mean; shadow indicates 95% confidence intervals). These relationships produced close AIC and Wald (chi-squared) values (Table 2), which shows the similar effects of these variables. Figure 5. Graphs of the logistic regression model showing the influence of biochemical oxygen deficiency (a,b) and pH (c) on the occurrence of Daphnia (0 = absence/1 = presence). (Dots represent the observational data; blue line depicts the predict mean; shadow indicates 95% confidence intervals).