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A new Dataset for Detection of Illegal or Suspicious Spilling in Wastewater through Low-cost Real-time Sensors
... The effectiveness of the proposed classifier is tested against a set of state-of-the-art baselines on a dataset created in collaboration with Sensichips s.r.l. and made available to the scientific community [9]. Results show that the proposed methodology outperforms the baseline methods and its effectiveness allows for practical usage of the developed methodology. ...
... In fact, the composition of WW is not stable over time, for example, due to atmospheric events such as rain. In detail, all the samples were acquired between 2019 and 2021 in two different laboratories in Poland and in Italy and were recently made public [9]. Table 2 reports the substances used. ...
The detection of contaminants in several environments (e.g., air, water, sewage systems) is of paramount importance to protect people and predict possible dangerous circumstances. Most works do this using classical Machine Learning tools that act on the acquired measurement data. This paper introduces two main elements: a low-cost platform to acquire, pre-process, and transmit data to classify contaminants in wastewater; and a novel classification approach to classify contaminants in wastewater, based on deep learning and the transformation of raw sensor data into natural language metadata. The proposed solution presents clear advantages against state-of-the-art systems in terms of higher effectiveness and reasonable efficiency. The main disadvantage of the proposed approach is that it relies on knowing the injection time, i.e., the instant in time when the contaminant is injected into the wastewater. For this reason, the developed system also includes a finite state machine tool able to infer the exact time instant when the substance is injected. The entire system is presented and discussed in detail. Furthermore, several variants of the proposed processing technique are also presented to assess the sensitivity to the number of used samples and the corresponding promptness/computational burden of the system. The lowest accuracy obtained by our technique is 91.4%, which is significantly higher than the 81.0% accuracy reached by the best baseline method.
Water pollution is a growing concern that affects ecosystems, human health, and industrial processes. Traditional water quality monitoring systems are often expensive and require expert analysis. This paper proposes a low-cost sensing platform integrated with Natural Language Generation (NLG) techniques to automatically detect and describe wastewater pollution. The system employs low-cost IoT sensors to measure key water quality parameters, including pH, turbidity, dissolved oxygen, and chemical contaminants. The collected sensor data is processed using machine learning models to identify pollution levels, and an NLG module translates the findings into human-readable reports. The proposed system provides an affordable, scalable, and real-time wastewater pollution detection solution, bridging the gap between raw sensor data and actionable insights for policymakers, industries, and environmental agencies.
In the framework of indoor air monitoring, this paper proposes an Internet of Things ready solution to detect and classify contaminants. It is based on a compact and low–power integrated system including both sensing and processing capabilities. The sensing is composed of a sensor array on which electrical impedance measurements are performed through a microchip, named SENSIPLUS, while the processing phase is mainly based on Machine Learning techniques, embedded in a low power and low resources micro controller unit, for classification purposes. An extensive experimental campaign on different contaminants has been carried out and raw sensor data have been processed through a lightweight Multi Layer Perceptron for embedded implementation. More complex and computationally costly Deep Learning techniques, as Convolutional Neural Network and Long Short Term Memory, have been adopted as a reference for the validation of Multi Layer Perceptron performance. Results prove good classification capabilities, obtaining an accuracy greater than 75% in average. The obtained results, jointly with the reduced computational costs of the solution, highlight that this proposal is a proof of concept for a pervasive IoT air monitoring system.
Electroanalytical techniques are useful for detection and identification because the instrumentation is simple and can support a wide variety of assays. One example is cyclic square wave voltammetry (CSWV), a practical detection technique for different classes of compounds including explosives, herbicides/pesticides, industrial compounds, and heavy metals. A key barrier to the widespread application of CSWV for chemical identification is the necessity of a high performance, generalizable classification algorithm. Here, machine and deep learning models were developed for classifying samples based on voltammograms alone. The highest performing models were Long Short-Term Memory (LSTM) and Fully Convolutional Networks (FCNs), depending on the dataset against which performance was assessed. When compared to other algorithms, previously used for classification of CSWV and other similar data, our LSTM and FCN-based neural networks achieve higher sensitivity and specificity with the area under the curve values from receiver operating characteristic (ROC) analyses greater than 0.99 for several datasets. Class activation maps were paired with CSWV scans to assist in understanding the decision-making process of the networks, and their ability to utilize this information was examined. The best-performing models were then successfully applied to new or holdout experimental data. An automated method for processing CSWV data, training machine learning models, and evaluating their prediction performance is described, and the tools generated provide support for the identification of compounds using CSWV from samples in the field.
This data article aimed to investigate the quality of surface water in Kalingarayan Canal for heavy metal pollution, Tamil Nadu. Eight heavy metals like Fe, Cu, Mn, Cr, Zn, Cd, Pb, and Ni were analyzed in the water, for a period of three years, spanning the time frame between January 2014 to December 2016. Eight stations were selected along the Kalingarayan Canal, and water samples were collected on a monthly basis from these stations. The pH of the samples was in the alkaline state (6.88–8.90), whereas conductance was in the range of 394–4276 µs/cm. The average concentration of heavy metals in the surface water ranges from 0.040 to 10.75, 0.030 to 0.890, 0.02 to 0.91, 0.00 to 1.96, 0.00 to 0.01, 0.00 to 0.053, 0.01 to 0.12 and 0.110 to 3.40 mg/L for the metals Fe, Mn, Zn, Cu, Cd, Ni, Pb and Cr respectively. The dominance of various heavy metals in the surface water follows the sequence: Fe > Cr > Cu > Zn > Mn > Pb > Ni > Cd respectively. The canal is affected by anthropogenic activities and industrialization in terms of heavy metals.
Although all countries are intensifying their efforts against terrorism and increasing their mutual cooperation, terrorist bombing is still one of the greatest threats to society. The discovery of hidden bomb factories is of primary importance in the prevention of terrorism activities. Criminals preparing improvised explosives (IE) use chemical substances called precursors. These compounds are released in the air and in the waste water during IE production. Tracking sources of precursors by analyzing air or wastewater can then be an important clue for bomb factories’ localization. We are reporting here a new multiplex electrochemical sensor dedicated to the on-site simultaneous detection of three explosive precursors, potentially used for improvised explosive device preparation (hereafter referenced as B01, B08, and B15, for security disclosure reasons and to avoid being detrimental to the security of the counter-explosive EU action). The electrochemical sensors were designed to be disposable and to combine ease of use and portability in a screen-printed eight-electrochemical cell array format. The working electrodes were modified with different electrodeposited metals: gold, palladium, and platinum. These different coatings giving selectivity to the multi-sensor through a “fingerprint”-like signal subsequently analyzed using partial least squares-discriminant analysis (PLS-DA). Results are given regarding the detection of the three compounds in a real environment and in the presence of potentially interfering species.
Remotely sensed data can reinforce the abilities of water resources researchers and decision makers to monitor waterbodies more effectively. Remote sensing techniques have been widely used to measure the qualitative parameters of waterbodies (i.e., suspended sediments, colored dissolved organic matter (CDOM), chlorophyll-a, and pollutants). A large number of different sensors on board various satellites and other platforms, such as airplanes, are currently used to measure the amount of radiation at different wavelengths reflected from the water's surface. In this review paper, various properties (spectral, spatial and temporal, etc.) of the more commonly employed spaceborne and airborne sensors are tabulated to be used as a sensor selection guide. Furthermore, this paper investigates the commonly used approaches and sensors employed in evaluating and quantifying the eleven water quality parameters. The parameters include: chlorophyll-a (chl-a), colored dissolved organic matters (CDOM), Secchi disk depth (SDD), turbidity, total suspended sediments (TSS), water temperature (WT), total phosphorus (TP), sea surface salinity (SSS), dissolved oxygen (DO), biochemical oxygen demand (BOD) and chemical oxygen demand (COD).
Purpose
The purpose of this paper is to report thick film environmental and chemical sensor arrays designed for deployment in both subterranean and submerged aqueous applications.
Design/methodology/approach
Various choices of materials for reference electrodes employed in these different applications have been evaluated and the responses of the different sensor types are compared and discussed.
Findings
Results indicate that the choice of binder materials is critical to the production of sensors capable of medium term deployment (e.g. several days) as the binders not only affect the tradeoff between hydration time and drift but also have a significant bearing on device sensitivity and stability. Sensor calibration is shown to remain an issue with long‐term deployments (e.g. several weeks) but this can be ameliorated in the medium term with the use of novel device fabrication and packaging techniques.
Originality/value
The reported results indicate that is possible through careful choice of materials and fabrication methods to achieve near stable thick film reference electrodes that are suitable for use in solid state chemical sensors in a variety of different application areas.
A multi-sensor water quality monitoring system incorporating an UV/Vis spectrometer and a turbidimeter was used to monitor the Chemical Oxygen Demand (COD), Total Suspended Solids (TSS) and Oil & Grease (O&G) concentrations of the effluents from the Chinese restaurant on campus and an electrocoagulation-electroflotation (EC-EF) pilot plant. In order to handle the noise and information unbalance in the fused UV/Vis spectra and turbidity measurements during the calibration model building, an improved boosting method, Boosting-Iterative Predictor Weighting-Partial Least Squares (Boosting-IPW-PLS), was developed in the present study. The Boosting-IPW-PLS method incorporates IPW into boosting scheme to suppress the quality-irrelevant variables by assigning small weights, and builds up the models for the wastewater quality predictions based on the weighted variables. The monitoring system was tested in the field with satisfactory results, underlying the potential of this technique for the online monitoring of water quality.
Water pollution caused by human activities poses a serious global threat to human health. Sensor technologies enabling water monitoring are an important tool that can help facing this problem. In this work, we propose an embedded IoT-ready system based on a proprietary sensor technology for the detection and recognition of six water contaminants. The system architecture is composed of two layers: (i) a sensing layer based on the SENSIPLUS chip, a proprietary Micro-Analytical Sensing Platform with six interdigitated electrodes metalized through different materials; and (ii) a data collection, communication, and classification layer with both hardware and software components. Being classification the most computationally and resource intensive operation, we evaluated nine machine learning solutions of different complexity and analyzed the trade-off between recognition accuracy, processing time, and memory usage to find a solution suitable to be implemented on an edge node. The highest average accuracy of 95.4% was achieved with K-nearest neighbor classification without constraints on processing time and memory usage, which confirms the potentiality of the system. When such constraints are taken into consideration, the best performance dropped to 86.4% offered by Multi Layer Perceptron.
Internet of Things (IoT) is involving more and more fields where monitoring actions and fast and reliable data communication are simultaneously needed. Inside the general class of monitoring applications, those related to pollutant detection and classification are currently faced by many researchers and companies. Several approaches are being proposed in the literature, but lots of open issues and challenges are still to be handled before deploying a commonly considered optimum system. This contribution proposes a novel low-cost and highly flexible platform which is intended to tackle such challenges adopting ad-hoc hardware and software techniques. The proposed solution is applied to air and water contaminant detection case studies. The paper provides the reader with an innovative system in the field of pollution monitoring and focuses the attention on limitations, challenges and possible improvements needed to obtain reliable contaminant detection and, consequently, improve life quality.
Nowadays, an increasing concern about people and environmental health and safety is spreading all over the world, and technologies such as efficient monitoring systems are furthered. In the field of air quality, filtering systems, especially based on activated carbons (ACs), are commonly used. In most cases, their state of health is not monitored, and their time of life is based on statistical and a priori evaluations. This paper proposes a novel microsensor platform for the real-time monitoring of AC filters based on the impedance measurements during gas exposition. A metrological characterization of the novel proposed instrument is provided in this paper, by comparing its output to a reference RLC meter. A calibration and adjustment procedure is developed in order to analyze the device measurement capabilities and obtain correction coefficients. Experiments with different gas typologies are presented, and the analysis of filters impedance dynamics is provided. IEEE
This paper proposes the realization and preliminary characterization of a smart sensor platform for the online monitoring of the residual life of activated carbon air based filters. The platform performs the measurement to give reliable information about the filter maintenance or early warning for the presence of dangerous gases both in industrial and military scenarios. A preliminary feasibility study has been carried out to evaluate the activated carbon filter electrical impedance sensitivity to chemicals adsorption. After a detailed presentation of the employed hardware and software solutions, a description of the experimental setup is given and a preliminary proof of the assumed relationship is provided.
Water monitoring technologies are widely used for contaminants detection in wide variety of water ecology applications such as water treatment plant and water distribution system. A tremendous amount of research has been conducted over the past decades to develop robust and efficient techniques of contaminants detection with minimum operating cost and energy. Recent developments in spectroscopic techniques and biosensor approach have improved the detection sensitivities, quantitatively and qualitatively. The availability of in-situ measurements and multiple detection analyses has expanded the water monitoring applications in various advanced techniques including successful establishment in hand-held sensing devices which improves portability in real-time basis for the detection of contaminant, such as microorganisms, pesticides, heavy metal ions, inorganic and organic components. This paper intends to review the developments in water quality monitoring technologies for the detection of biological and chemical contaminants in accordance with instrumental limitations. Particularly, this review focuses on the most recently developed techniques for water contaminant detection applications. Several recommendations and prospective views on the developments in water quality assessments will also be included.
Contamination of groundwater is one of the major health concerns in the rapidly urbanizing and industrializing world. Since groundwater is one of the most important resources for the domestic, industrial, and agricultural purposes, the quality and quantity is of prime importance. Nitrite which is a reduced form of nitrate ion is one of the potential contaminants in the groundwater. The detection of nitrite ion is one of the laborious works and also it gets easily oxidized to nitrate ion and hence modeling approaches for the nitrite concentration will be one of the resilient quantification techniques. In the present study, the effective performance of the linear and non-linear models such as multiple linear regression (MLR), principal component regression (PCR), artificial neural network (ANN), and the integrated technique of principal components and artificial neural network (PC-ANN) is evaluated in the prediction of the nitrite concentration. The MLR and PCR showed better results either in generation step or in the validation step but not both. ANN shows better results in both generation and validation steps but the results in the validation steps, though good but accuracy is comparatively lower than the generation step. In the case of PC-ANN, the prediction of the model is found to be good both in the generation and in the validation steps. The Nash–Sutcliffe efficiency test clearly illustrates better performance of PC-ANN in comparison with other models in the present study for the quantification of nitrite concentration in groundwater.
This paper presents a low cost and holistic approach to the water quality monitoring problem for drinking water distribution systems as well as for consumer sites. Our approach is based on the development of low cost sensor nodes for real time and in-pipe monitoring and assessment of water quality on the fly. The main sensor node consists of several in-pipe electrochemical and optical sensors and emphasis is given on low cost, lightweight implementation, and reliable long time operation. Such implementation is suitable for large scale deployments enabling a sensor network approach for providing spatiotemporally rich data to water consumers, water companies, and authorities. Extensive literature and market research are performed to identify low cost sensors that can reliably monitor several parameters, which can be used to infer the water quality. Based on selected parameters, a sensor array is developed along with several microsystems for analog signal conditioning, processing, logging, and remote presentation of data. Finally, algorithms for fusing online multisensor measurements at local level are developed to assess the water contamination risk. Experiments are performed to evaluate and validate these algorithms on intentional contamination events of various concentrations of escherichia coli bacteria and heavy metals (arsenic). Experimental results indicate that this inexpensive system is capable of detecting these high impact contaminants at fairly low concentrations. The results demonstrate that this system satisfies the online, in-pipe, low deployment-operation cost, and good detection accuracy criteria of an ideal early warning system.
An electrochemical impedance immunosensor for the detection of Escherichia coli was developed by immobilizing anti-E. coli antibodies at an Au electrode. The immobilization of antibodies at the Au electrode was carried out through a stable acyl amino ester intermediate generated by 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC) and N-hydrosuccinimide (NHS), which could condense antibodies reproducibly and densely on the self-assembled monolayer (SAM). The surface characteristics of the immunosensor before and after the binding reaction of antibodies with E. coli were characterized by atomic force microscopy (AFM). The immobilization of antibodies and the binding of E. coli cells to the electrode could increase the electro-transfer resistance, which was directly detected by electrochemical impedance spectroscopy (EIS) in the presence of Fe(CN)63−/Fe(CN)64− as a redox probe. A linear relationship between the electron-transfer resistance and the logarithmic value of E. coli concentration was found in the range of E. coli cells from 3.0×103 to 3.0×107cfumL−1 with the detection limit of 1.0×103cfumL−1. With preconcentration and pre-enrichment steps, it was possible to detect E. coli concentration as low as 50cfu/mL in river water samples.
Soymilk coagulation process is the most important step in tofu making. An experimental setup was developed to control the coagulation temperature and heating rate by ohmic heating and to perform impedance measurement in time-sharing mode. Soymilk coagulation process was characterized by electrical impedance spectroscopy. Frequency 10kHz was chosen to analyze the coagulation process. Normalized conductivity was useful in determination of the endpoint of soymilk coagulation process. The coagulation process was considered as two successive first-order reactions. The rate constant in the first stage was 10 times higher than that in the second stage, both rate constants increased with coagulation temperature. The activation energy in the second stage was three times greater than that in the first stage. The successive reaction process was elucidated by using soymilk coagulation mechanism. The use of impedance measurement to analyze the coagulation process provides a basis for the control of soymilk coagulation process.
In this review solid-state sensors for on-line monitoring of such water quality parameters as pH, dissolved oxygen (DO), conductivity, turbidity, dissolved organic carbon (DOC) and dissolved metal ions on the high spatial resolution were analysed. It has been established that the next generation of wireless sensor networks can be successfully coupled with accurate, inexpensive, robust and miniature solid-state sensors based on thin- or thick-film semiconductor sensing electrode (SE), which can overcome bio-fouling difficulties. In the development of highly sensitive and selective nanostructured SEs, the control of their structures and morphologies plays an increasing importance as a critical factor determining their properties. Even for nano-scaled complex oxide SEs structural factor remains a complicated concept. Doping semiconductor SE by another nano-oxide has been proven to be a very effective way to modernise suitable properties of the SE toward better selectivity and higher antifouling resistance. Results, mainly obtained during studies of the doped complex oxide SEs of the various chemical sensors have been used for showing the optimisation of the sensors' characteristics. Examples of the different sensors and their improved characteristics have been discussed.
A novel planar interdigital sensor-based sensing system has been developed for detection of dangerous marine biotoxins in seafood. Our main objective is to sense the presence of dangerous contaminated acid in mussels and other seafood by observing the change of reactive impedance of the planar interdigital sensors. Initial studies were conducted with three peptide derivatives namely sarcosine, proline and hydroxylproline. These three chemicals are structurally closely related to our target molecule. The proline molecule is arguably the most important amino acid in peptide conformation, contains the basic structural similarity to the domoic acid. Three novel interdigital sensors have been designed and fabricated. All sensors have the same effective area but having different sensor configurations. The initial results show that sensors respond very well to the chemicals and it is possible to discriminate the different chemicals from the output of the sensor. The sensors were also being tested with three seafood products. Results from the analysis have shown that one configuration has better sensitivity compared to other configurations. The sensor with the best sensitivity was chosen for experiment using proline and mussels. The changes in sensor sensitivity were analyzed with mussels before and after adding the proline. The presence of proline on the mussel surface and also injected proline to the mussels was very clearly detected by the sensor. Further experiment was conducted with small amount of domoic acid (0.5–5.0 μg) injected to a mussel and it was found that the chosen sensor was able to detect small amount of domoic acid (1.0 μg) injected into the mussel sample. The result shows that the sensor was able to detect approximately 12.6 μg/g of domoic acid in mussel meat. The outcomes from the experiments provide chances of opportunity for further research in developing a low cost miniature type of sensors for reliable sensing system for commercial use.
A chemical sensor array (consisting of 8 conducting polymers) was used to continuously monitor for the presence or absence of industrial pollutants in the headspace of wastewater generated from an on-line flow-cell. A domestic wastewater (Cranfield University sewage works) was dosed with diesel to stimulate the presence of an intermittent discharge in a wastewater influent. Response patterns between the sensors were used to detect for the presence of organic compounds in the wastewater. Correlations between the sensor response patterns or fingerprints were also analysed using principal component analysis. The results clearly demonstrate that a chemical sensor array can rapidly identify the presence of organic compounds (such as diesel) in a wastewater matrix and could be further developed to monitor for industrial pollutants at the inlet of a sewage works.
A novel smart system for contaminants detection and recognition in water
- M Ferdinandi
- M Molinara
- G Cerro
- L Ferrigno
- C Marrocco
- A Bria