Search Results (57)

The escalating global demand for fresh water, driven by urbanization and industrial growth, underscores the need for sustainable water management, particularly in the water-intensive construction sector. Although prior studies have primarily concentrated on treated wastewater, the practical viability of utilizing untreated wastewater has not been thoroughly investigated—especially in developing nations where treatment expenses frequently impede actual implementation, even for non-structural uses. While prior research has focused on treated wastewater, the potential of untreated or partially treated wastewater from diverse industrial sources remains underexplored. This study investigates the feasibility of incorporating wastewater from textile, sugar mill, service station, sewage, and fertilizer industries into concrete paver block production. The novelty lies in a dual approach, combining experimental analysis with XGBoost-based machine learning (ML) models to predict the impact of key physicochemical parameters—such as Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), and Hardness—on mechanical properties like compressive strength (CS), water absorption (WA), ultrasonic pulse velocity (UPV), and dynamic modulus of elasticity (DME). The ML models showed high predictive accuracy for CS (R² = 0.92) and UPV (R² = 0.97 direct, 0.99 indirect), aligning closely with experimental data. Notably, concrete pavers produced with textile (CP-TXW) and sugar mill wastewater (CP-SUW) attained 28-day compressive strengths of 47.95 MPa and exceeding 48 MPa, respectively, conforming to ASTM C936 standards and demonstrating the potential to substitute fresh water for non-structural applications. These findings demonstrate the viability of using untreated wastewater in concrete production with minimal treatment, offering a cost-effective, sustainable solution that reduces fresh water dependency while supporting environmentally responsible construction practices aligned with SDG 6 (Clean Water and Sanitation) and SDG 12 (Responsible Consumption and Production). Additionally, the model serves as a practical screening tool for identifying and prioritizing viable wastewater sources in concrete production, complementing mandatory laboratory testing in industrial applications. Full article

Non-point source (NPS) pollution from agriculture accounts for more than 20% of the total pollution load in the Republic of Korea, with the highest nutrient balance among OECD countries. Rice paddy fields are among the most important NPSs because of their large area, intensive fertilizer use, intensive use of irrigation water, and subsequent drainage. Therefore, the use of controlled drainage in paddy fields (Test) was evaluated for reduction in the discharged volumes and pollutant loads in drainage and stormwater runoff in comparison to plots using traditional drainages (Control). The results show that the loads were highly variable and that the reductions in the annual load of biochemical oxygen demand (BOD), suspended solid (SS), total nitrogen (T-N), total phosphorus (T-P), and total organic carbon (TOC) in the Test compared to that of the Control were 31.0 ± 28.9%, 83.5 ± 11.8%, 65.4 ± 12.2%, 69.1 ± 21.7%, and 64.9 ± 12.9%, respectively. It was shown that discharge in the post-harrowing and transplanting drainage (HD) was predominantly responsible for the total loads; therefore, the load reduction in HD was evaluated further at additional sites. The reduction at all studied sites was highly variable and as follows: 30.0 ± 33.6%, 70.9 ± 24.6%, 32.2 ± 45.5%, 45.7 ± 37.0%, and 27.0 ± 71.5%, for BOD, SS, T-N, T-P, and TOC, respectively. It was also demonstrated that controlled drainage contributed significantly to reducing the loads and volume of stormwater runoff from paddy fields. Correlations between paddy field conditions and multiple regression showed that the loads were significantly related to paddy water quality. The results of this study strongly suggest that controlled drainage is an excellent alternative for reducing the discharge of NPS pollutants from paddy fields. It is also suggested that the best discharge control would be achieved by combinations of various discharge mitigation alternatives, such as the management of irrigation, drainage, and fertilization, as well as drainage treatment, supported by more field tests, identification of the fates of pollutants, effects of rainfall, and climate changes. Full article

Modular wastewater treatment units in large residential complexes in India’s crowded cities often lack stringent monitoring due to cost constraints and limited technical manpower. Although these plants must meet effluent standards, testing often requires sending samples to external labs, causing delays and added costs. As a result, they are rarely monitored, risking improper effluent discharge. Quick, cost-effective assessments of effluent quality could significantly improve plant operation and maintenance. Addressing the special challenges faced by such wastewater treatment systems, artificial intelligence (AI)-based soft sensors and virtual instruments have been developed to forecast effluent quality with the help of a water quality parameter that is inexpensively, easily, and immediately measurable with a hand-held device. In this study, advanced artificial neural network (ANN)-based soft sensors were developed to enhance the monitoring and management of effluent quality in five modular wastewater treatment plants in Bangalore. The models serve as virtual instruments for the measurement of total suspended solids (TSS), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP), using the wastewater turbidity as the input parameter. By using these AI models, operators can better anticipate and manage water quality, ultimately contributing to more efficient and effective wastewater treatment operations. This innovative approach represents a significant advancement in wastewater treatment technology providing a practical and efficient solution to streamline monitoring and enhance overall plant performance. Full article

Universities and other institutes of higher education could be considered as key actors in the implementation of sustainability pillars, such as the adoption of sustainable practices in wastewater management. However, the adoption of such practices is still an emerging issue. This paper discusses the design and operation of the first combined Oxylag and high rate algal pond (COHRAP) constructed at the university campus in Tunisia for irrigation. Performance was evaluated based on the removal efficiencies of nutrients, chemical oxygen demand (COD), biochemical oxygen demand (BOD), heavy metals, coliforms, and biomass productivity. The potential reuse of sludge and algal biomass is discussed based on the Tunisian national standard regulation for sludge reuse in agriculture (NT 106.20) and the European regulation (EC, 2019/1009) for fertilizer products. Effluent phytotoxicity is tested on the germination and growth on Zea mays L. The results indicate that the COHRAP performance was globally satisfactory; however, biomass productivity (1.4 g m⁻²d⁻¹) was low, indicating the need for adjustments in the operational parameters. Despite the effluent limitations for TSS and Hg, no phytotoxic effect was observed. Regarding the heavy metal content in sludge and algal biomass, the results obtained were in compliance with NT 106.20 and EC, 2019/1009), respectively. The energy consumption of COHRAP is 1.05 kWh/m³ resulting in operational costs of 0.29 euros/m³. This study revealed that COHRAP could be a sustainable option to treat wastewater from university campuses with resource recovery. Such a choice can be improved by the implementation of an algae recovery step. Full article

Water quality is a crucial determinant in decision-making processes aimed at optimizing resource allocation across various industries. Pollutant impurities that hinder the sufficient supply of water have a deleterious impact on the quality and are damaging to living species, especially aquatic life. Various chemical and biological treatments are used to reduce water pollution levels. A technology involving a mixture of anaerobic and aerobic beneficial microbes is becoming popular for its eco-friendly characteristics. Effective Microorganism (EM) technology utilizes naturally existing microorganisms that can purify and restore the environment. The study investigated the application of Effective Microorganism-Activated Solution (EMAS), TeMo Decomposer (TeMo), and Lactic Acid Bacteria (LAB) to enhance water quality. Additionally, microbial testing will be carried out to identify bacteria present in each EM. EM-based rehabilitation of polluted and degraded water bodies significantly contributes to the restoration of aquatic habitats and ecosystems. This study aimed to assess the water quality at Tasik Alumni, Kampus Pauh, Perlis, Malaysia. Four sampling points in Tasik Alumni were chosen to reflect the water quality status of the lake. The sampling was conducted once at four points locations in Tasik Alumni. Seven water quality measures, including pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammoniacal nitrogen (NH₃-N), total suspended solid (TSS), and turbidity, were analysed ex-situ and categorised according to Water Quality Index (WQI) and National Water Quality Standard (NWQS) classifications. The Tasik Alumni was categorised as mildly contaminated. The results clearly showed the efficiency of this technique in restoring and conserving water quality in a degraded or polluted lake. Full article

This study investigates the operational efficiency of the lab-scale oxidation ditch (OD) functioning in simultaneous nitrification and denitrification modes, focusing on forecasting biochemical oxygen demand (BOD₅) concentrations over a five-day horizon. This forecasting capability aims to optimize the operational regime of aeration tanks by adjusting the specific load on organic pollutants through active sludge dosage modulation. A comprehensive statistical analysis was conducted to identify trends and seasonality alongside significant correlations between the forecasted values and various time lags. A total of 20 time lags and the “month” feature were selected as significant predictors. These models employed include Multi-head Attention Gated Recurrent Unit (MAGRU), long short-term memory (LSTM), Autoregressive Integrated Moving Average–Long Short-Term Memory (ARIMA–LSTM), and Prophet and gradient boosting models: CatBoost and XGBoost. Evaluation metrics (Mean Squared Error (MSE), Mean Absolute Error (MAE), Symmetric Mean Absolute Percentage Error (SMAPE), and Coefficient of Determination (R²)) indicated similar performance across models, with ARIMA–LSTM yielding the best results. This architecture effectively captures short-term trends associated with the variability of incoming wastewater. The SMAPE score of 1.052% on test data demonstrates the model’s accuracy and highlights the potential of integrating artificial neural networks (ANN) and machine learning (ML) with mechanistic models for optimizing wastewater treatment processes. However, residual analysis revealed systematic overestimation, necessitating further exploration of significant predictors across various datasets to enhance forecasting quality. Full article

Sludge management is a very relevant aspect in the operation of Wastewater Treatment Plants (WWTPs). In activated sludge systems, it is common to have daily (or continuous) monitoring of total suspended solids in the aeration tank (MLSS). If such control is not properly performed, it can cause solids to wash out in the secondary sedimentation tank or significantly impact BOD (Biochemical Oxygen Demand) and nitrogen removal. There are many commercially available systems which can provide real-time monitoring of solids (mainly optical or ultrasound sensors). Even though commercially available (usually with a high cost), there are still issues related to the use of such sensors. The most important one is the progressive accumulation of solids, which cause measurement errors. In this work, the authors investigated the application of a low-cost US sensor for MLSS (mixed-liquor suspended solids) monitoring in two full-scale activated sludge WWTPs. The tested sensor was similar to a previously described device, which had been previously employed in a pilot-scale UASB reactor in Brazil. The main differences were related to an integrated treatment and acquisition system which allowed real-time treatment of the US wave as well as data acquisition at a predefined time. The values generated by the sensor were compared with a commercial optical sensor installed in the same WWTP and double-checked with periodic gravimetric tests. The results at a Leeuwarden WWTP showed that the measurements of the US sensor, the optical sensor, and gravimetric test did not present significant differences during the test period at a significance level of 5%. Absolute errors were on average 0.04% (US sensor) and 0.03% (optic sensor) of MLSS compared to the gravimetric test. Although the use of the tested US sensor for monitoring solids in WWTP is promising, there are still several improvements that need to be made to the sensor. These include implementing a more precise calibration frequency, establishing a cleaning routine, and preventing sensor fouling. Furthermore, the sensor still needs a more thorough cost–benefit analysis, which would help assess the practicality of implementing this technology in various WWTPs. Full article

The discharge of industrial effluents containing toxic heavy metals in water sources has serious consequences for human health and the environment, and biosorption appears to be an environmentally-friendly and cost-effective alternative that can be used for wastewater treatment. The use of different types of agricultural waste as biosorbents for the removal of toxic heavy metals, although an alternative, is quite difficult to apply in practice because these wastes have many other uses. Based on these considerations, in this study, soybean biomass (SB), soybean waste biomass obtained from oil extraction (SBW), and biochar obtained from soybean waste pyrolysis (BC-SBW) were tested as biosorbents for removing Pb(II) and Cd(II) ions, in batch systems. Under optimal conditions (pH = 5.4, 4.0 g biosorbent/L, room temperature (25 ± 1 °C), contact time = 180 min), the biosorption capacity increases in the order SB < SBW < BC-SBW for both metal ions (Pb(II) ions (69.43 mg/g < 99.81 mg/g < 116.83 mg/g) and Cd(II) ions (25.63 mg/g < 36.12 mg/g < 49.10 mg/g)), indicating that BC-SBW has the highest efficiency in removing toxic heavy metals. In addition, experiments on wastewater samples have shown that, in addition to significantly reducing the content of heavy metals, BC-SBW also significantly improved other quality indicators (such as pH, TSM (total suspended matter), COD (chemical oxygen demand), BOD5 (biochemical oxygen demand)), compared to the other biosorbents (SB and SBW). Quantitative evaluation of the biosorption performance of each biosorbent (SB, SBW, and BC-SBW) shows that BC-SBW has a real chance of being used on an industrial scale for wastewater treatment. All these aspects allowed the development of a circular approach for the use of soybean biomass in the removal processes of toxic heavy metals. This approach minimizes the shortcomings of using biomass as a biosorbent and increases the chance of using these materials in industrial practice. Full article

Tryptophan-like fluorescence (TLF) is a key indicator of water contamination, particularly of microbial origin and biodegradable organic compounds. This study introduces an Internet of Things (IoT)-enabled portable device (IoT-TLF-PD) for real-time monitoring of microbial quality and biochemical oxygen demand (BOD₅). The device was tested using surface water (S1), secondary wastewater (S2), and final wastewater effluents (S3). Results showed significant correlations between TLF intensity, Escherichia coli (E. coli) counts, and BOD₅, with R² values of 0.77 (S1), 0.61 (S2), and 0.76 (S3) for BOD₅, and 0.60 (S2) to 0.68 (S3) for E. coli. Considering various water samples, a strong correlation was found between E. coli and BOD₅ with TLF intensity normalized by total organic carbon (TOC) concentration (TLF intensity/TOC). The R² value for E. coli was 0.92, and for BOD₅, it was 0.77. This indicates the necessity of accounting for organic matter concentration when interpreting TLF intensity in water quality assessments. The study confirmed the potential of the IoT-TLF-PD to serve as a cost-effective, real-time indicator for assessing water quality, especially for detecting microbial contamination. This technology offers a valuable tool for environmental monitoring and water management. Full article

Assessing regional water quality and evaluating the associated risks to human health posed by aquatic contaminants are paramount for conserving and managing surface water resources and formulating effective local policy decisions. This study utilizes 17 water quality parameters collected monthly from nine national monitoring stations in Nanning City, China, from January 2021 to March 2023, i.e., water temperature (WT), pH, dissolved oxygen (DO), permanganate index (COD_Mn), chemical oxygen demand (COD), five-day biochemical oxygen demand (BOD₅), ammonia nitrogen (NH₃-N), total phosphorus (TP), copper (Cu), zinc (Zn), fluoride (F^-), selenium (Se), arsenic (As), mercury (Hg), cadmium (Cd), chromium (Cr), and lead (Pb). Analyses were performed utilizing the Water Quality Index (WQI) and multiple stepwise regression to ascertain seasonal and spatial variations in water quality and to identify key water quality parameters. Human health risks were evaluated, focusing on eight heavy metals. The results indicated that the average WQI for the designated area was 94.1, with individual monitoring stations displaying WQIs ranging from 93.22 to 96.44, categorizing the water quality as “excellent”. The WQI exhibited seasonal fluctuations, peaking in spring and winter while decreasing in summer and autumn. All measured parameters met national standards for Class I and II surface waters. The stepwise regression analysis facilitated the construction of minimized WQI models (WQI_min) derived from three different training and testing datasets, with a WQI_min model incorporating six key parameters, i.e., DO, COD_Mn, NH₃-N, Hg, WT, and Se. The concentration of heavy metals in the water exhibited a sequence of Zn (3.35 µg/L) > Cr (2.00 µg/L) > Cu (1.36 µg/L) > As (1.29 µg/L) > Se (0.32 µg/L) > Pb (0.17 µg/L) > Cd (0.03 µg/L) > Hg (0.01 µg/L), with all metals adhering to the Class I standard. Children are more susceptible to the adverse effects of heavy metal contamination than adults, exhibiting a total environmental health risk of 1.28 times greater. Cr was identified as the predominant contributor to the aggregate health risk, accounting for over 80% of the risk in adults and children, followed by As (19.1%). Future protection efforts are recommended to prioritize the control of Cr and As concentrations in Nanning City. Full article

Biogas has improved due to technological advancements, environmental awareness, policy support, and research innovation, making it a more cost-effective and environmentally friendly renewable energy source. The Generalized Linear Model (GLM) was employed to examine the relationship between purchased and generated energy from 2007 to 2023. Metrics such as deviance, log likelihood, and dispersion phi were examined to assess model fit. The Mann–Kendall test was utilized to detect trends in energy datasets. Biochemical Oxygen Demand (BOD5) and Chemical Oxygen Demand (COD) reduction was significant, exceeding 97% from 2014 to 2023. However, treated sewage displayed limited susceptibility to biological degradation, with COD to BOD5 ratios increasing from 2.28 to 6.59 for raw sewage and from 2.33 to 7.05 for treated sewage by 2023. Additionally, the efficiency of sewage purification processes was calculated, and multivariate regression analysis was conducted on gas composition data. Principal Coordinate Ordination (PCO) and k-means clustering were used for dimensionality reduction and biogas component clustering, respectively. This research showed that biogas from the waste water treatment process can be used, particularly in methane production. Technological advancements have made biogas production more efficient, enhancing energy generation within a circular economy framework. Full article

Rural and semi-urban areas in arid/semi-arid regions are facing severe water scarcity and a series of environmental challenges nowadays, specifically due to rapid urbanization and economic development, climate change, population growth, increasing water demand, influxes of refugees caused by war and regional political conflict, etc. To solve the emerging problems, the safe reuse of treated wastewater in agriculture can provide an additional water resource for countries with high water scarcity. The aim of this study was to investigate the treatment performance and effectiveness of small decentralized wastewater treatment (DWWT) technologies treating high-strength wastewater with concentrations far beyond the European Union testing ranges of parameters such as five-day biochemical oxygen demand (BOD₅ > 500 mg/L), chemical oxygen demand (COD > 1000 mg/L), or total suspended solids (TSS > 700 mg/L). Four (4) commercially available DWWT technologies with a design capacity of 4–8 PE (population equivalent) were selected and operated with various wastewater compositions in Leipzig, Germany. The technologies were (i) the moving bed biofilm reactor (MBBR), (ii) the sequencing batch reactor (SBR), (iii) the membrane bioreactor (MBR) and (iv) the aerated vertical-flow constructed wetland (AVFCW). This study results clearly demonstrated that the EU-certified small DWWT technologies are quite capable of treating high-strength wastewater and can provide high-quality treated water for safe reuse in rural communities of arid and semi-arid regions. During operation with high-strength wastewater with a mean inflow BOD₅, COD and TSS concentrations of 1532 ± 478, 2547 ± 830 and 546 ± 176 mg/L, a low mean BOD₅ (<10 mg/L), COD (<70 mg/L) and TSS (<15 mg/L) in the outflow of the four systems showed removal efficiency of BOD₅ (>99%), COD (>97%) and TSS (>97%), and met the maximum allowable limit value of water quality class A for reuse in agriculture according to Jordanian and Omani standard. The MBR showed almost a complete removal of Escherichia coli (E. coli) in a range of 6.1–6.9-log removal in the outflow during all three experimental phases and performed best for BOD₅, COD, TSS and pathogen removal when treating high-strength wastewater if properly maintained to prevent potential fouling and clogging of the membrane. Before the final permitting process, long-term monitoring under local temperature and climatic conditions as well as guidelines based on local needs (e.g., in Jordan, Oman, etc.) should be developed to guarantee a minimum level of performance standards of such small DWWT technologies and requirements for operation and maintenance (O&M). Full article

Microplastics’ spreading in the ocean is currently causing significant damage to organisms and ecosystems around the world. To address this oceanic issue, there is a current focus on marine degradable plastics. Polycaprolactone (PCL) is a marine degradable plastic that is attracting attention. To further improve the biodegradability of PCL, we selected a completely new protein that has not been used before as a functional filler to incorporate it into PCL, aiming to develop an environmentally friendly biocomposite material. This novel protein is derived from the mucus bubbles of the violet sea snail (VSS, Janthina globosa), which is a strong bio-derived material that is 100% degradable in the sea environment by microorganisms. Two types of PCL/bubble composites, PCL/b1 and PCL/b5, were prepared with mass ratios of PCL to bubble powder of 99:1 and 95:5, respectively. We investigated the thermal properties, mechanical properties, biodegradability, surface structure, and crystal structure of the developed PCL/bubble composites. The maximum biochemical oxygen demand (BOD) degradation for PCL/b5 reached 96%, 1.74 times that of pure PCL (≈55%), clearly indicating that the addition of protein fillers significantly enhanced the biodegradability of PCL. The surface morphology observation results through scanning electron microscopy (SEM) definitely confirmed the occurrence of degradation, and it was found that PCL/b5 underwent more significant degradation compared to pure PCL. The water contact angle measurement results exhibited that all sheets were hydrophobic (water contact angle > 90°) before the BOD test and showed the changes in surface structure after the BOD test due to the newly generated indentations on the surface, which led to an increase in surface toughness and, consequently, an increase in surface hydrophobility. A crystal structure analysis by wide-angle X-ray scattering (WAXS) discovered that the amorphous regions were decomposed first during the BOD test, and more amorphous regions were decomposed in PCL/b5 than in PCL, owing to the addition of the bubble protein fillers from the VSS. The differential scanning calorimeter (DSC) and thermal gravimetric analysis (TGA) results suggested that the addition of mucus bubble protein fillers had only a slight impact on the thermal properties of PCL. In terms of mechanical properties, compared to pure PCL, the mucus-bubble-filler-added composites PCL/b1 and PCL/b5 exhibited slightly decreased values. Although the biodegradability of PCL was significantly improved by adding the protein fillers from mucus bubbles of the VSS, enhancing the mechanical properties at the same time poses the next challenging issue. Full article

This work proposes an approach to the formation of receptor elements for the rapid diagnosis of the state of surface waters according to two indicators: the biochemical oxygen demand (BOD) index and toxicity. Associations among microorganisms based on the bacteria P. yeei and yeast S. cerevisiae, as well as associations of the yeasts O. polymorpha and B. adeninivorans, were formed to evaluate these indicators, respectively. The use of nanocomposite electrically conductive materials based on carbon nanotubes, biocompatible natural polymers—chitosan and bovine serum albumin cross-linked with ferrocenecarboxaldehyde, neutral red, safranin, and phenosafranin—has made it possible to expand the analytical capabilities of receptor systems. Redox polymers were studied by IR spectroscopy and Raman spectroscopy, the contents of electroactive components were determined by atomic absorption spectroscopy, and electrochemical properties were studied by electrochemical impedance and cyclic voltammetry methods. Based on the proposed kinetic approach to modeling individual stages of bioelectrochemical processes, the chitosan–neutral red/CNT composite was chosen to immobilize the yeast association between O. polymorpha (k_s = 370 ± 20 L/g × s) and B. adeninivorans (320 ± 30 L/g × s), and a bovine serum albumin (BSA)–neutral composite was chosen to immobilize the association between the yeast S. cerevisiae (k_s = 130 ± 10 L/g × s) and the bacteria P. yeei red/CNT (170 ± 30 L/g × s). After optimizing the composition of the receptor systems, it was shown that the use of nanocomposite materials together with associations among microorganisms makes it possible to determine BOD with high sensitivity (with a lower limit of 0.6 mg/dm³) and detect the presence of a wide range of toxicants of both organic and inorganic origin. Both receptor elements were tested on water samples, showing a high correlation between the results of biosensor analysis of BOD and toxicity and the results of standard analytical methods. The results obtained show broad prospects for creating sensitive and portable bioelectrochemical sensors for the early warning of environmentally hazardous situations based on associations among microorganisms and nanocomposite materials. Full article

Biofilms based on bacteria Pseudomonas veronii (Ps. veronii) and Escherichia coli (E. coli) and yeast Saccharomyces cerevisiae (S. cerevisiae) were used for novel biosensor creation for rapid biochemical oxygen demand (BOD) monitoring. Based on the electrochemical measurement results, it was shown that the endogenous mediator in the matrix of E. coli and Ps. veronii biofilms and ferrocene form a two-mediator system that improves electron transport in the system. Biofilms based on Ps. veronii and E. coli had a high biotechnological potential for BOD assessment; bioreceptors based on such biofilms had high sensitivity (the lower limits of detectable BOD₅ concentrations were 0.61 (Ps. veronii) and 0.87 (E. coli) mg/dm³) and high efficiency of analysis (a measurement time 5–10 min). The maximum biosensor response based on bacterial biofilms has been observed in the pH range of 6.6–7.2. The greatest protective effect was found for biofilms based on E. coli, which has high long-term stability (151 days for Ps. veronii and 163 days for E. coli). The results of the BOD₅ analysis of water samples obtained using the developed biosensors had a high correlation with the results of the standard 5-day method (R² = 0.9820, number of tested samples is 10 for Ps. veronii, and R² = 0.9862, number of tested samples is 10 for E. coli). Thus, biosensors based on Ps. veronii biofilms and E. coli biofilms could be a novel analytical system to give early warnings of pollution. Full article