Water Pollution Monitoring Research Articles

Porous silica nanospheres: a nanoplatform towards protein immobilization and cogent design of biosensors for aromatic water pollutant detection

A rational approach for exploiting the physicochemical properties of porous silica nanospheres (pSN) affecting biomolecule immobilization and aromatic water pollutant monitoring has been demonstrated.

A Water Pollution Monitoring System Based on the Internet of Things

The author aims to propose an implementation scheme for the overall design of a water environment online monitoring system based on the Internet of Things. The design of the system is mainly divided into three main parts: The sensor node acquisition part, the sink node transceiver part, and the upper computer monitoring and control part. The sensor node collection section consists of electrochemical probes (temperature, pH, conductivity, dissolved oxygen, turbidity), hardware conditioning circuits, and Zigbee sending nodes. The measured parameters are identified and converted into electrical signals through chemical probes, amplified and processed by conditioning circuits, and finally converted into digital signals and encoded into data packets through the sending node, which are uploaded to the aggregation node. The receiving and transmitting part of the aggregation node consists of a ZigBee receiving node and a serial/USB communication module. The receiving node is responsible for receiving data packets sent through the sending node, and achieving communication and transmission with the upper computer through a serial P/USB communication module. The upper computer monitoring and control part processes the received data packets in real-time through the upper computer interface software, and displays them in an orderly and purposeful manner through chart analysis.

ОЦІНКА МОЖЛИВОСТІ РЕГУЛЮВАННЯ ВОДНОГО РЕЖИМУ ОКРЕМИХ ТЕРИТОРІЙ ЧОРНОБИЛЬСЬКОЇ ЗОНИ ВІДЧУЖЕННЯ

The characteristics of the natural features of the Chernobyl Exclusion Zone (CEZ), in particular the nature of the relief, soil cover, main plant groups, and landscape characteristics, are provided. The hydrographic network of the region is detailed, represented by rivers, lakes, ponds, dams of the Pripyat and Uzh rivers, the reservoir of the Kyiv HPP, reclamation channels, water mirrors in front of filtration dams. It was noted that the main processes that form the current level of radioactive contamination of water systems in the CEZ are natural processes of radioactive decay, vertical sinking and geochemical fixation of radionuclides. Analysis of water pollution monitoring data in rivers and canals of the exclusion zone shows that by regulating water levels in most areas of the catchment areas of the CEZ, it is impossible to significantly influence the reduction of water pollution in them compared to the effects of their natural self-purification. Therefore, it is impractical to continue large-scale works on water flow regulation by hydrotechnical structures in order to minimize the removal of radioactive substances beyond the exclusion zone. It is advisable to leave in operation only those systems that allow maintaining increased moisture in peatlands, reducing the risks of fires. The studies carried out on the reclamation system “Buryakivka” in the basin of the Sakhan River, as an experimental training ground for the regulation of the regime of floodplains of the CEZ, are covered in detail. The use of the method of remote sensing of the Earth (DSS) to control the water regime and monitor the transformation of reclamation systems into wetlands is substantiated. In particular, spectral analysis of image pixels in different parts of the spectrum using vegetation indices. A positive nature protection effect from the increase in the area of wetlands in the Exclusion Zone was noted in the context of the prospects of climate change and in the context of the probability of increasing cases of dry periods in the territory of Polissia.

Use of Neural Networks and Computer Vision for Spill and Waste Detection in Port Waters: An Application in the Port of Palma (MaJorca, Spain)

Water quality and pollution is the main environmental concern for ports and adjacent coastal waters. Therefore, the development of Port Environmental Management systems often relies on water pollution monitoring. Computer vision is a powerful and versatile tool for an exhaustive and systematic monitoring task. An investigation has been conducted at the Port of Palma de Mallorca (Spain) to assess the feasibility and evaluate the main opportunities and difficulties of the implementation of water pollution monitoring based on computer vision. Experiments on surface slicks and marine litter identification based on random image sets have been conducted. The reliability and development requirements of the method have been evaluated, concluding that computer vision is suitable for these monitoring tasks. Several computer vision techniques based on convolutional neural networks were assessed, finding that Image Classification is the most adequate for marine pollution monitoring tasks due to its high accuracy rates and low training requirements. Image set size for initial training and the possibility to improve accuracy through retraining with increased image sets were considered due to the difficulty in obtaining port spill images. Thus, we have found that progressive implementation can not only offer functional monitoring systems in a shorter time frame but also reduce the total development cost for a system with the same accuracy level.

Smart Water Quality Prediction Using Atom Search Optimization with Fuzzy Deep Convolutional Network

Smart solutions for monitoring water pollution are becoming increasingly prominent nowadays with the advance in the Internet of Things (IoT), sensors, and communication technologies. IoT enables connections among different devices with the capability to gather and exchange information. Additionally, IoT extends its ability to address environmental issues along with the automation industry. As water is essential for human survival, it is necessary to integrate some mechanisms for monitoring water quality. Water quality monitoring (WQM) is an efficient and cost-effective system intended to monitor the quality of drinking water that exploits IoT techniques. Therefore, this study developed a new smart water quality prediction using atom search optimization with the fuzzy deep convolution network (WQP-ASOFDCN) technique in the IoT environment. The WQP-ASOFDCN technique seamlessly monitors the water quality parameters using IoT devices for data collection purposes. Data pre-processing is carried out at the initial stage to make the input data compatible for further processing. For water quality prediction, the F-DCN model was utilized in this study. Furthermore, the prediction performance of the F-DCN approach was improved by using the ASO algorithm for the optimal hyperparameter tuning process. A sequence of simulations was applied to validate the enhanced water quality prediction outcomes of the WQP-ASOFDCN method. The experimental values denote the better performance of the WQP-ASOFDCN approach over other approaches in terms of different measures.

Surface Functionalization Strategies of Polystyrene for the Development Peptide-Based Toxin Recognition.

The development of a robust surface functionalization method is indispensable in controlling the efficiency, sensitivity, and stability of a detection system. Polystyrene (PS) has been used as a support material in various biomedical fields. Here, we report various strategies of polystyrene surface functionalization using siloxane derivative, divinyl sulfone, cyanogen bromide, and carbonyl diimidazole for the immobilization of biological recognition elements (peptide developed to detect ochratoxin A) for a binding assay with ochratoxin A (OTA). Our objective is to develop future detection systems that would use polystyrene cuvettes such as immobilization support of biological recognition elements. The goal of this article is to demonstrate the proof of concept of this immobilization support. The results obtained reveal the successful modification of polystyrene surfaces with the coupling agents. Furthermore, the immobilization of biological recognition elements, for the OTA binding assay with horseradish peroxidase conjugated to ochratoxin A (OTA-HRP) also confirms that the characteristics of the functionalized peptide immobilized on polystyrene retains its ability to bind to its ligand. The presented strategies on the functionalization of polystyrene surfaces will offer alternatives to the possibilities of immobilizing biomolecules with excellent order- forming monolayers, due to their robust surface chemistries and validate a proof of concept for the development of highly efficient, sensitive, and stable future biosensors for food or water pollution monitoring.

Guest Editorial: Emerging Trends and Challenges in Internet-of-Underwater-Things

Internet-of-Underwater-Things (IoUT) is a network of smart interconnected underwater objects. Recently, IoUT has shown its applicability in numerous scenarios such as marine life monitoring, off-shore exploration, underwater lost-treasure discovery, sports, assisted navigation, location awareness, environmental monitoring (e.g., monitoring of water quality, water pollution, water pressure or water temperature), water-based disasters (e.g., tsunami or nuclear accident), defense systems (e.g., surveillance systems or submarine detection) and several other applications. Even though IoUT systems are key enabler in underwater communication, they face challenges due to unreliable transmission medium, unstable radio signals, limited range, low bandwidth, inborn noise, low transmission rate, slow propagation speed, node mobility, lower resources and limited battery capacity. These challenges cause issues in channel modeling, optimal routing, security, privacy, communication overhead, congestion control, packet error rate, packet latency, energy consumption etc. In this respect, this Special Issue (SI) aims to address some of the aforementioned challenges.

A pipeline for monitoring water pollution: The example of heavy metals in Lombardy waters

Time-dependent geolocalized analysis of pollution data allows to better understand their dynamics over time and could suggest strategies to restore a good ecological status of contaminated area. This research analyzes concentrations of pollutants in surface waters and groundwater monitored by the Regional Environment Protection Agency of Lombardy from 2017 to 2020. Lombardy is one of the richest and populous region of Europe, providing an interesting example of the impact of environmental pollutants due to anthropogenic and industrial activities, not only for Italy but also for all Europe. Results show that groundwater displays more sites with heavy metals above the legal limit with respect to surface waters, including As, Ni, Cr and Zn. Furthermore, the spatio-temporal analysis of the data clearly shows that the introduction of more restrictive laws is a proper policy to improve the ecological status of the water.

Metal-doped carbon dots as robust nanomaterials for the monitoring and degradation of water pollutants

The contamination of the environment by domestic and industrial discharges is a relevant and persistent problem that needs novel solutions. Innovations in the detection, adsorption, and removal or in-situ degradation of toxic components are urgently required. Various effective techniques and materials have been proposed to address this problem, in which carbon dots (CDs) stand out because of their unique properties and low-cost and abundant nature. Their combination with different metals results in the enhancement of their innate properties. Metal-doped CDs have shown excellent results and competitive advantages in recent times. Considering the above useful critiques and CDs notable potentialities, this review discusses different approaches in detail to sense, adsorb, and photodegrade different pollutants in water samples. It was found that altering the electronic structure of CDs via metal doping has a great potential to enhance the optical, electrical, chemical, and magnetic capabilities of CDs, which in turn is beneficial for wastewater treatment.

Ultrasensitive graphene sensor for nitrate detection using triethylamine as a probe molecule

Nitrate anions are the main form of nitrogen in the environment and threaten the ecosystem and human health when their concentration is beyond 0.3 mg L−1. Here, we designed and fabricated a novel nitrate sensor based on graphene field-effect transistors using triethylamine (TEA) as the probe molecule. The sensor uses graphene grown by chemical vapor deposition as the channel material. Triethylamine is modified on the graphene surface to detect nitrate anions. The sensitivity of the proposed TEA/graphene sensor reaches 1.3 nM (0.111 μg L−1), which is higher than that of mainstream sensors. This easy-to-fabricate and low-cost sensor also shows high specificity for other anions, such as Cl−, CO32–, and SO42−. Our study provides an excellent technical means for the rapid, accurate, and real-time monitoring of water pollution caused by nitrate anions.

Design, Modeling, Control, and Experiments for Multiple AUVs Formation

The multiple autonomous underwater vehicle (AUV) formation plays an important role in underwater missions, such as oceanographic sampling and water pollution monitoring. This article presents the mechatronic design, modeling, formation control, and experiments of multiple AUVs. The structure of the AUV and a simplified mathematical model for tracking control are described. To achieve formation control, we formulate a control framework for the multiple AUVs. The upper layer is a formation algorithm based on a novel leader-follower control law. The bottom layer is a dynamic controller based on active disturbance rejection control (ADRC). The formation algorithm is in charge of calculating reference values for the followers to maintain a desired pattern with the leader. The stability and convergence properties of the algorithm have been analyzed using the Lyapunov stability method. Meanwhile, an ADRC approach-based dynamic controller is established to track the reference values. Numerical simulations are carried out to analyze formation control and validate the control framework. The multiple AUVs can switch and maintain the formation between the one-line pattern and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> pattern. Finally, extensive formation field experiments involving the one-line pattern and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> pattern show the good motion ability of the self-designed AUVs and also verify the feasibility of the proposed control approach. Note to Practitioners—The motivation of the article is to design a practical formation control approach for multiple AUVs and verify the control approach in the field. Although there have been a lot of prior research studies on multiple AUVs, how to design a formation control approach subjected to communication bandwidth constraints and how to develop multiple AUVs and verify the effectiveness of the control method in the field are worthy of intense investigation. Hence, this article builds the mechatronic design and dynamic model of the AUV and proposes a novel leader-follower formation control approach based on the dynamics and kinematic model of AUV. Besides, the stability of formation control for multiple AUVs is proven by the Lyapunov theorem. Multiple AUVs can switch and maintain formation between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula> pattern and one-line pattern with a smaller error. Finally, the performance of the proposed formation control strategy is experimentally verified using three self-made AUVs inside a large reservoir. The proposed method is suitable for multiple AUVs missions involving underwater surveillance, underwater pipeline inspection in the ocean.

β-Cyclodextrin-Based Supramolecular Imprinted Fiber Array for Highly Selective Detection of Parabens.

A novel high-throughput array analytical platform based on derived β-cyclodextrin supramolecular imprinted polymer (SMIP) fibers was constructed to achieve selective enrichment and removal of parabens. SMIP fiber arrays have abundant imprinting sites and introduce the host–guest inclusion effect of the derived β-cyclodextrin, which is beneficial to significantly improve the adsorption ability of fiber for parabens. Upon combination with HPLC, a specific and sensitive recognition method was developed with a low limit of detection (0.003–0.02 µg/L, S/N = 3) for parabens analysis in environmental water. This method has a good linearity (R > 0.9994) in the linear range of 0.01–200 µg/L. The proposed SMIP fiber array with high-throughput adsorption capacity has great potential in monitoring water pollution, which also provides a reliable reference for the analysis of more categories of pharmaceutical and personal care product pollutants.

Smartphone-based spectrometry system as a prescreening assessment of copper and iron for real time control of water pollution

Due to anthropogenic actions, the presence of pollutants in water bodies, such as toxic metals, are increasingly negatively affecting water quality, biodiversity and sustainable goals worldwide. Therefore, decentralization of water pollution monitoring with low-cost devices, such as using smartphones, suggests an innovative green technology for in situ and real-time control. In this study, a Handheld Smartphone Spectrophotometry System (HSSS) was developed to estimate copper and iron concentration water samples. The system mainly comprises a portable commercial spectrometer (GoSpectro) that can measure the spectrum of light in the visible region. The HSSS LOD and LOQ for copper were equal to 0.589 and 1.784 mg/L, respectively, and 0.479 and 1.450 mg/L, respectively for iron. In addition, the results of copper and iron concentrations in samples with unknown concentrations using HSSS were close to the Benchtop Spectrophometer (BS). Finally, HSSS performance showed to be a new green technology for water quality management with potential applications for monitoring water resources and also providing further possibilities to measure other pollutants by the same technique, in addition to metals.

Evaluation of water pollution monitoring for heavy metal contamination: A case study of Agodi Reservoir, Oyo State, Nigeria.

Heavy metals affect the suitability of aquatic environment for all purposes; hence, this study evaluated heavy metal contamination in Agodi Reservoir Oyo State, Nigeria. Atomic Absorption Spectrophotometry was used to determine heavy metal concentrations. Heavy Metal Pollution Index (HPI), Water Quality Index (WQI), Pollution Index (PI), Comprehensive Pollution Index (CPI), Contamination Index (CI), Single-Factor Pollution Index (SFPI), Heavy metal Evaluation Index (HEI), and Human Health Risk Assessments (HRA) were used to determine the extent of heavy metal pollution and their impact on the aquatic environment. The order of heavy metal concentrations in both wet and dry seasons was Fe > Mn > Zn > Cu > Cd > Ni > Cr > Pb > Co and Mn > Fe > Zn > Cu > Co > Ni > Cd > Cr > Pb, respectively. WQI for both wet (3182.6) and dry (3649.5) seasons classified the reservoir as "unsuitable for aquatic life." Also, the CPI rated the reservoir to be "severely polluted" in both dry (311.2) and wet (268.7) seasons. Similarly, HEI (951.3 and 2059.7) and Cd (942.3 and 2050.7) rated the reservoir as "highly polluted" in wet and dry seasons, respectively. The Hazard Quotient (HQ ingestion) was in the order of Mn > Cu > Cd > Zn > Fe > Co > Ni > Cr > Pb in the dry season while the order was Cu > Mn > Cd > Fe > Zn > Ni > Pb > Co > Cr in the wet season. The HQ ingestion revealed that Cr (0.00), Ni (0.33; 022), and Pb (0.00; 0.06) were the only metals with HQ values lesser than 1 (HQ < 1) while the values of HQ (dermal) were less 1 (HQ < 1) indicating that there was no health risk in association with the domestic use of the water. The pollution level of the reservoir means that urgent attention is needed from different agencies for the conservation, management, and sustainable development of the reservoir.

Smart and Secure Dyeing Industrial Water Pollution Monitoring Using IoT

Textile industries are responsible for one of the major environmental pollution because they release undesirable dye effluents. Therefore, environmental legislation commonly obligates textile factories to treat these effluents before discharge into the water bodies. The main aim of our project is to control the parameters causing pollution and to reduce the effect of these parameters without affecting the natural or industrial environment. The industrial waste is continuously sensed from a pH sensor. If anyone parameter exceeds its standard level, this information will send to the pollution control board through the IoT module. Another important step is these parameters can be monitor through the internet by using a web page (cloud). These systems find the amount of pH present in the industrial waste during Treatment. Thus our project will monitor and control pollution efficiently and the data can be transferred through cloud communication. Cayenne is a GUI to the user and IoT is used for outside world interaction for information transfer.

Hybridization of long short-term memory with Sparrow Search Optimization model for water quality index prediction

Water quality (WQ) analysis is a critical stage in water resource management and should be handled immediately in order to control pollutants that could have a negative influence on the ecosystem. The dramatic increase in population, the use of fertilizers and pesticides, and the industrial revolution have resulted in severe effects on the WQ environment. As a result, the prediction of WQ greatly helped to monitor water pollution. Accurate prediction of WQ is the foundation of managing water environments and is of high importance for protecting water environment. WQ data presents in the form of multi-variate time-sequence dataset. It is clear that the accuracy of predicting WQ will be enhanced when the multi-variate relation and time sequence dataset of WQ are fully utilized. This article presents the Water Quality Prediction utilising Sparrow Search Optimization with Hybrid Long Short-Term Memory (WQP-SSHLSTM) model. The presented WQP-SSHLSTM model intends to examine the data and classify WQ into distinct classes. To achieve this, the presented WQP-SSHLSTM model undergoes data scaling process to scale the input data into uniform format. Followed by, a hybrid long short-term memory-deep belief network (LSTM-DBN) technique is employed for the recognition and classification of WQ. Moreover, Sparrow search optimization algorithm (SSOA) is utilized as a hyperparameter optimizer of the proposed DBN-LSTM model. For demonstrating the enhanced outcomes of the presented WQP-SSHLSTM model, a sequence of experiments has been performed and the outcomes are reviewed under distinct prospects. The WQP-SSHLSTM model has achieved 99.84 percent accuracy, which is the maximum attainable. The simulation outcomes ensured the enhanced outcomes of the WQP-SSHLSTM model on recent methods.

Ecologically based methods for promoting safer nanosilver for environmental applications

Nanosilver, widely employed in consumer products as biocide, has been recently proposed as sensor, adsorbent and photocatalyst for water pollution monitoring and remediation. Since nanosilver ecotoxicity still pose limitations to its environmental application, a more ecological exposure testing strategy should be coupled to the development of safer formulations. Here, we tested the environmental safety of novel bifunctionalized nanosilver capped with citrate and L-cysteine (AgNPcitLcys) as sensor/sorbent of Hg2+ in terms of behaviour and ecotoxicity on microalgae (1–1000 µg/L) and microcrustaceans (0.001–100 mg/L), from the freshwater and marine environment, in acute and chronic scenarios. Acute toxicity resulted poorly descriptive of nanosilver safety while chronic exposure revealed stronger effects up to lethality. Low dissolution of silver ions from AgNPcitLcys was observed, however a nano-related ecotoxicity is hypothesized. Double coating of AgNPcitLcys succeeded in mitigating ecotoxicity to tested organisms, hence encouraging further research on safer nanosilver formulations. Environmentally safe applications of nanosilver should focus on ecologically relevant exposure scenarios rather than relying only on acute exposure data.

Viral Eco-Genomic Tools: Development and Implementation for Aquatic Biomonitoring.

Enteric viruses (EVs) occurrence within aquatic environments varies and leads to significant risk on public health of humans, animals, and diversity of aquatic taxa. Early and efficacious recognition of cultivable and fastidious EVs in aquatic systems are important to ensure the sanitary level of aquatic water and implement required treatment strategies. Herein, we provided a comprehensive overview of the conventional and up-to-date eco-genomic tools for aquatic biomonitoring of EVs, aiming to develop better water pollution monitoring tools. In combination with bioinformatics techniques, genetic tools including cloning sequencing analysis, DNA microarray, next-generation sequencing (NGS), and metagenomic sequencing technologies are implemented to make informed decisions about the global burden of waterborne EVs-associated diseases. The data presented in this review are helpful to recommend that: (1) Each viral pollution detection method has its own merits and demerits; therefore, it would be advantageous for viral pollution evaluation to be integrated as a complementary platform. (2) The total viral genome pool extracted from aquatic environmental samples is a real reflection of pollution status of the aquatic eco-systems; therefore, it is recommended to conduct regular sampling through the year to establish an updated monitoring system for EVs, and quantify viral peak concentrations, viral typing, and genotyping. (3) Despite that conventional detection methods are cheaper, it is highly recommended to implement molecular-based technologies to complement aquatic ecosystems biomonitoring due to numerous advantages including high-throughput capability. (4) Continuous implementation of the eco-genetic detection tools for monitoring the EVs in aquatic ecosystems is recommended.

The Geochemical Drivers of Bacterial Community Diversity in the Watershed Sediments of the Heihe River (Northern China)

The city of Zhangye (Gansu Region, China) has been subjected to several changes related to the development of new profitable human activities. Unfortunately, this growth has led to a general decrease in water quality due to the release of several toxic wastes and pollutants (e.g., heavy metals) into the Heihe River. In order to assess the environmental exposure and the potential threat to human health, microbiological diversity for the monitoring of water pollution by biotic and abiotic impact factors was investigated. In particular, we analysed samples collected on different sites using 454 pyrotag sequencing of the 16S ribosomal genes. Then, we focused on alpha-diversity indices to test the hypothesis that communities featuring lower diversity show higher resistance to the disturbance events. The findings report that a wide range of environmental factors such as pH, nutrients and chemicals (heavy metals (HMs)), affected microbial diversity by stimulating mutualistic relationships among bacteria. Furthermore, a selection in bacterial taxa related to the different concentrations of polluting compounds was highlighted. Supporting the hypothesis, our investigation highlights the importance of microbial communities as sentinels for ecological status diagnosis.

Monitoring of natural water pollution by fluorescence spectroscopy

Introduction. Environmental monitoring of the state of water bodies is an important component of environmental protection. A promising direction for assessing the quality of water bodies is to determine their generalized indicators. Fluorescence spectroscopy can serve as a source of important information about the state of water bodies and sources of their pollution. Purpose of the study - to assess the water pollution of the Moskva River and its tributaries using the fluorescence spectroscopy method. To reveal the influence of oil products on the fluorescence spectra of river water. Materials and methods. The object of the study was water from the Moskva River and its tributaries: Pakhra, Kotlovka, Setun. Water samples from the Moskva River were taken in different parts of the city, which are characterized by varying degrees of pollution. Water fluorescence spectra were measured with SM2203 spectrofluorimeter (ZAO Solar, Belarus), total organic and inorganic carbon, on a TOC-VCHP total carbon analyzer (Shimadzu, Japan). Results. The nature of the fluorescence spectra of the Moskva River and its tributaries in the absence of significant anthropogenic pollution are characterized by relative constancy. Bacterial pollution and pollution with aromatic compounds of anthropogenic origin, including oil products, leads to an increase in the fluorescence intensity in the short-wavelength part of the spectrum. The correlation coefficient between the optical density at a wavelength of 254 nm and the content of dissolved organic carbon (DOC) for the studied samples was 0.66, and between the fluorescence intensity at a wavelength of 400 nm and DOC was 0.74. Limitations. The method cannot be used for the quantitative determination of individual substances; it is intended for a semi-quantitative assessment of water pollution. Conclusion. Measurement of fluorescence spectra and total organic carbon makes it possible to give a general assessment of the contamination of a water body (which is especially important for a sample of unknown composition) and to choose the optimal scheme for its targeted analysis. High dissolved organic carbon (DOC) values and high fluorescence intensity at 320 nm are highly likely to indicate chemical contamination of water, while DOC values characteristic of a water body and high fluorescence intensity at 320 nm indicate bacterial contamination.