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Composition of the training and test sets used to compute and validate the classification models of the EC sensor.
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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 sub...
Contexts in source publication
Context 1
... composition of the dataset used for the calculation of the PLS-DA classification models of target B01, B08, and B15 is reported in Table 1. ...Context 2
... composition of the dataset used for the calculation of the PLS-DA classification models of target B01, B08, and B15 is reported in Table 1. For explanatory purpose, the signatures obtained during the training step for each of the three target compounds are presented in Figure 5. Here, for the sake of clarity, are presented only the experiments performed in water, soapy water, and artificial waste water. ...Context 3
... explanatory purpose, the signatures obtained during the training step for each of the three target compounds are presented in Figure 5. Here, for the sake of clarity, are presented only the experiments performed in water, soapy water, and artificial waste water. The peak positions and intensity of each target using each electrode are given in Supplementary Information Table S1. For explanatory purpose, the signatures obtained during the training step for each of the three target compounds are presented in Figure 5. Here, for the sake of clarity, are presented only the experiments performed in water, soapy water, and artificial waste water. ...Context 4
... explanatory purpose, the signatures obtained during the training step for each of the three target compounds are presented in Figure 5. Here, for the sake of clarity, are presented only the experiments performed in water, soapy water, and artificial waste water. The peak positions and intensity of each target using each electrode are given in Supplementary Materials Table S1. ...Similar publications
Purpose of Review
Unfortunately, improvised explosive devices (IED) are now used outside the battlefield and their associated injuries are now seen in civilian hospitals. Recent terror attacks in which IEDs were used in the United States and Europe have resulted in a large number of civilian casualties. Despite the rise in these attacks across the...
Citations
... Clandestine laboratory investigation is one of the most dangerous tasks undertaken by law enforcement due to the presence of hazardous chemical compounds [2]. A "bomb factory" cannot be immediately distinguished from a clandestine laboratory preparing drugs of abuse, despite the presence in the scientific literature of analytical approaches to spot bomb factories [6][7][8][9][10]. ...
Illicit drug production in clandestine laboratories involves the use of large quantities of different chemicals that can be obtained for legitimate purposes. The identification of these chemicals, including reagents, catalyzers and solvents, is crucial for forensic investigations. From a legal point of view, a drug precursor is a material that is specific and critical to the production of a finished chemical and that constitutes a significant portion of the final molecular structure of the drug. In this study, a gas chromatography quartz-enhanced photoacoustic spectroscopy (GC-QEPAS) sensor—in conjunction with a deep learning model—was evaluated for its effectiveness in the detection and identification of interesting compounds for the production of amphetamine, methamphetamine, 3,4-methylenedioxymethamphetamine (MDMA), phenylcyclohexyl piperidine (PCP), and cocaine. The GC-QEPAS sensor includes a gas sampler, a fast GC for separation, and a QEPAS detector, which excites molecules exiting the GC column using a quantum cascade laser to provide the infra-red (IR) spectrum. The on-site capability of the GC-QEPAS system offers significant advantages over the current instruments employed in this field, including rapid analysis, which is crucial in field operations. This allows law enforcement to quickly identify specimens of interest on site. The system’s performance was validated by taking into account the limit of detection, repeatability, and within-laboratory reproducibility. The results showed excellent repeatability and reproducibility for both the GC and QEPAS modules. The deep learning model, a multilayer perceptron neural network, was trained using IR spectra and retention times, achieving very high classification accuracy in the testing conditions. This study demonstrated the efficacy of the GC-QEPAS sensor combined with a deep learning model for the reliable identification of drug precursors, providing a robust tool for law enforcement during criminal investigations in clandestine laboratories.
... In recent years several sensor prototypes for monitoring the composition of wastewater in the context of WasteWater Treatment Plants (WWTP) systems have been proposed in the literature Bourelly et al., 2020;Betta et al., 2019;Molinara et al., 2020;Bria et al., 2020;De Vito et al., 2018; Sewage monitoring system for tracking synthetic drug laboratories, 2022; Hoes et al., 2009;Lim, 2012;Lepot et al., 2017;Ji et al., 2020;Drenoyanis et al., 2019;Pisa et al., 2019;Desmet et al., 2017). The sensors presented are based on different technologies such as electrochemical sensors, optical sensors, based on mass or ion spectrometry, etc. and can be mounted inside wells with the aim of detecting the presence or concentration of certain pollutants. ...
... It does not include any pollutant detection system. In Desmet et al. (2017), a system for detecting explosive precursors is presented, i.e., those substances that terrorists could use to manufacture rudimentary bombs. In this work, sensors functionalized with gold, palladium, and platinum are used, and voltammetry is used to detect substances. ...
The problem of detecting illegal pollutants in wastewater is of fundamental importance for public health and security. The availability of distributed, low–cost and low–power monitoring systems, particularly enforced by IoT communication mechanisms and low-complexity machine learning algorithms, would make it feasible and easy to manage in a widespread manner. Accordingly, an End-to-End IoT-ready node for the sensing, local processing, and transmission of the data collected on the pollutants in the wastewater is presented here. The proposed system, organized in sensing and data processing modules, can recognize and distinguish contaminants from unknown substances typically present in wastewater. This is particularly important in the classification stage since distinguishing between background (not of interest) and foreground (of interest) substances drastically improves the classification performance, especially in terms of false positive rates. The measurement system, i.e., the sensing part, is represented by the so-called Smart Cable Water based on the SENSIPLUS chip, which integrates an array of sensors detecting various water-soluble substances through impedance spectroscopy. The data processing is based on a commercial Micro Control Unit (MCU), including an anomaly detection module, a classification module, and a false positive reduction module, all based on machine learning algorithms that have a computational complexity suitable for low-cost hardware implementation.
An extensive experimental campaign on different contaminants has been carried out to train machine-learning algorithms suitable for low-cost and low-power MCU. The corresponding dataset has been made publicly available for download. The obtained results demonstrate an excellent classification ability, achieving an accuracy of more than 95% on average, and are a reliable ”proof of concept” of a pervasive IoT system for distributed monitoring.
... The main disadvantage of this approach is that the data are not easy to read due to the number of sensors or the not perfect correlation between the sensor value and the pollutant. The adopted methods mainly focus on the application of electrodes of different metals [6] or covered by sensing films [7], and optical sensors [8]. Measurements from sensors are usually based on Electrochemical Impedance Spectroscopy (EIS) [9]- [11], a frequency domain technique that evaluates the response of an electrochemical cell to a low amplitude sinusoidal perturbation. ...
... To extract the information from such amount of data particularly useful is the Machine Learning (ML) or Deep Learning (DL) approach. In literature ML applications are reported in [12], Artificial Neural Network and Principal Component Regres-sion are used to estimate nitrate concentration in groundwater, whereas in [6] partial least square discriminant analysis is applied to detect explosive precursors in wastewater. Also, DL solutions have been proposed, e.g. in [13] Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) were adopted for detection and classification of chemicals in seawater. ...
... They are trying to propose new emerging sensors able to reliable detect pollutants saving money, size and energy consumption, new network technologies, new communication standards, and, finally, new methods for the data analysis. Many researchers are exploiting the advantages offered by Artificial Intelligence and Machine Learning (ML) [3,4,13,15]. ML techniques are often preprocessed using Principal Component Analysis (PCA). ...
The problem of detecting pollutants in water with non-invasive and low-cost sensors is an open question. In this paper, we propose a system for the detection and classification of pollutants based on the improvement of a previous proposal, focused on geometric cones. The solution is based on a classifier suitable to be implemented aboard the so-called Smart Cable Water (SCW) sensor, a multi-sensor based on SENSIPLUS® technology developed by Sensichips s.r.l. The SCW endowed with six interdigitated electrodes is a smart-sensor covered by specific sensing materials that allow differentiating between different water contaminants. Using the PCA or LDA decomposition, we obtain a data compression that makes data suitable for the “edge computing” paradigm with a reduction from a 10-dimensional space to a 3-dimensional space. We defined an ad-hoc classifier to distinguish contaminants represented by points in the 3-dimensional space. We used an evolutionary algorithm to learn the classifier’s parameters. Finally, we compared the performance of our system with that achieved by the old classification system based only on PCA, as well as those achieved by other machine learning algorithms. The proposed system achieved the best accuracy of 87%, outperforming the other state-of-the-art systems compared. The novelty of the system proposed lies in the usage of an evolutionary algorithm for the optimization of the parameters of a novel PCA-based classification algorithm for the detection of water pollutants.
... Researchers' interest in exploring methods for the detection, localization, and quantification of pollutants in wastewater networks (WWNs) and water distribution systems (WDSs) has grown considerably in recent years, especially regarding sensor technology [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Questionable policies regarding waste disposal are prevalent in today's industry [17,18]. ...
Illegal discharges of pollutants into sewage networks are a growing problem in large European cities. Such events often require restarting wastewater treatment plants, which cost up to a hundred thousand Euros. A system for localization and quantification of pollutants in utility networks could discourage such behavior and indicate a culprit if it happens. We propose an enhanced algorithm for multisensor data fusion for the detection, localization, and quantification of pollutants in wastewater networks. The algorithm processes data from multiple heterogeneous sensors in real-time, producing current estimates of network state and alarms if one or many sensors detect pollutants. Our algorithm models the network as a directed acyclic graph, uses adaptive peak detection, estimates the amount of specific compounds, and tracks the pollutant using a Kalman filter. We performed numerical experiments for several real and artificial sewage networks, and measured the quality of discharge event reconstruction. We report the correctness and performance of our system. We also propose a method to assess the importance of specific sensor locations. The experiments show that the algorithm’s success rate is equal to sensor coverage of the network. Moreover, the median distance between nodes pointed out by the fusion algorithm and nodes where the discharge was introduced equals zero when more than half of the network nodes contain sensors. The system can process around 5000 measurements per second, using 1 MiB of memory per 4600 measurements plus a constant of 97 MiB, and it can process 20 tracks per second, using 1.3 MiB of memory per 100 tracks.
... When performing an environmental monitoring activity, sensing technologies and data analysis are the main components to address. Deep reviews are available as sensing technology regards [5], [6]: the typical set-ups involve the application of metal electrodes [7] or the adoption of spe-cific sensing films [8]. Data flowing from sensing technologies are generally processed through normalization techniques and feed machine learning [9] and deep learning [10] methods, used to classify pollutants. ...
Water monitoring systems continuously working
ensure real–time pollutant detection capabilities according to
their sensitivity and specificity. It is necessary to balance such
features because, although being able to sense several substances
is a desired feature, the reduction of false positives is a primary
goal a classification system should have. High false positive makes
the system unusable. The current solution enables a 24/7 service
with a sampling rate equal to 0.6 Hz. Our goal is to limit false
positives to 1 per day, thus achieving 99.99% accuracy at least. In
this paper, we add a false positive reduction module to our pre-
existent system, aiming to manage false positive boosters as sensor
drift and signal oscillations. Obtained results, using a Multi Layer
Perceptron classifier, confirm the false positive reduction while
keeping high true positive rates.
... In recent years, several sensor prototypes for monitoring wastewater composition at points that are further from the WWTP [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] have been proposed and studied. These sensors (electrochemical sensors, optical sensors, mass spectrometry, ion spectrometry, etc.) can be mounted within manholes and main sewer lines, and aim at detecting the presence or concentration of certain pollutants. ...
Harsh pollutants that are illegally disposed in the sewer network may spread beyond the sewer network—e.g., through leakages leading to groundwater reservoirs—and may also impair the correct operation of wastewater treatment plants. Consequently, such pollutants pose serious threats to water bodies, to the natural environment and, therefore, to all life. In this article, we focus on the problem of identifying a wastewater pollutant and localizing its source point in the wastewater network, given a time-series of wastewater measurements collected by sensors positioned across the sewer network. We provide a solution to the problem by solving two linked sub-problems. The first sub-problem concerns the detection and identification of the flowing pollutants in wastewater, i.e., assessing whether a given time-series corresponds to a contamination event and determining what the polluting substance caused it. This problem is solved using random forest classifiers. The second sub-problem relates to the estimation of the distance between the point of measurement and the pollutant source, when considering the outcome of substance identification sub-problem. The XGBoost algorithm is used to predict the distance from the source to the sensor. Both of the models are trained using simulated electrical conductivity and pH measurements of wastewater in sewers of a european city sub-catchment area. Our experiments show that: (a) resulting precision and recall values of the solution to the identification sub-problem can be both as high as 96%, and that (b) the median of the error that is obtained for the estimation of the source location sub-problem can be as low as 6.30 m.
... Nowadays, many research activities have been addressing the problem of pollution monitoring by using the emerging sensing technologies, as well as the new possibilities of data analysis offered by Artificial Intelligence and Machine Learning [3,4,15,17]. Among the others, in [7] the authors used Artificial Neural Network and Principal Component Regression techniques to estimate nitrate concentration in groundwater, whereas a pattern recognition solution based on partial least square discriminant analysis (PLS-DA) was presented in [15]. ...
... Nowadays, many research activities have been addressing the problem of pollution monitoring by using the emerging sensing technologies, as well as the new possibilities of data analysis offered by Artificial Intelligence and Machine Learning [3,4,15,17]. Among the others, in [7] the authors used Artificial Neural Network and Principal Component Regression techniques to estimate nitrate concentration in groundwater, whereas a pattern recognition solution based on partial least square discriminant analysis (PLS-DA) was presented in [15]. More recentely, also Deep Learning (DL) architectures have been used to detect and classify chemicals in seawater [7,14,15]. ...
... Among the others, in [7] the authors used Artificial Neural Network and Principal Component Regression techniques to estimate nitrate concentration in groundwater, whereas a pattern recognition solution based on partial least square discriminant analysis (PLS-DA) was presented in [15]. More recentely, also Deep Learning (DL) architectures have been used to detect and classify chemicals in seawater [7,14,15]. ...
Nowadays, the problem of pollution in water is a very serious issue to be faced and it is really important to be able to monitoring it with non-invasive and low-cost solutions, like those offered by smart sensor technologies. In this paper, we propose an improvement of an our innovative classification system, based on geometrical cones, to detect and classify pollutants, belonging to a given set of substances, spilled into waste water. The solution is based on an ad-hoc classifier that can be implemented aboard the Smart Cable Water (SCW) sensor, based on SENSIPLUS technology developed by Sensichips s.r.l. The SCW is a smart-sensor endowed with six interdigitated electrodes, covered by specific sensing materials that allow detecting between different water contaminants. In order to develop an algorithm suitable to apply the “edge computing” paradigm we first compress the input data from a 10-dimensional space to a 3-D space by using the PCA decomposition techniques. Then we use an ad-hoc classifier to classify between the different contaminants in the transformed space. To learn the classifier’s parameters we used the evolutionary algorithms. The obtained results have been compared with the old classification system and other, more classical, machine learning approaches.
... In recent years, several project initiatives [6,7] and prototypes of sensor systems [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] for wastewater monitoring have been proposed and studied. These include the design of sensors (electrochemical sensors, optical sensors, mass spectrometry, ion spectrometry, etc.) for manholes, main sewer lines, water bodies, and basins at the WWTP for estimating the presence or concentration of specific pollutants at the point and time of measurement. ...
In December 2016, the wastewater treatment plant of Baarle-Nassau, Netherlands, failed. The failure was caused by the illegal disposal of high volumes of acidic waste into the sewer network. Repairs cost between 80,000 and 100,000 EUR. A continuous monitoring system of a utility network such as this one would help to determine the causes of such pollution and could mitigate or reduce the impact of these kinds of events in the future. We have designed and tested a data fusion system that transforms the time-series of sensor measurements into an array of source-localized discharge events. The data fusion system performs this transformation as follows. First, the time-series of sensor measurements are resampled and converted to sensor observations in a unified discrete time domain. Second, sensor observations are mapped to pollutant detections that indicate the amount of specific pollutants according to a priori knowledge. Third, pollutant detections are used for inferring the propagation of the discharged pollutant downstream of the sewage network to account for missing sensor observations. Fourth, pollutant detections and inferred sensor observations are clustered to form tracks. Finally, tracks are processed and propagated upstream to form the final list of probable events. A set of experiments was performed using a modified variant of the EPANET Example Network 2. Results of our experiments show that the proposed system can narrow down the source of pollution to seven or fewer nodes, depending on the number of sensors, while processing approximately 100 sensor observations per second. Having considered the results, such a system could provide meaningful information about pollution events in utility networks.
... see the extensive reviews in [1]- [3]. The proposed methods mainly focus on the application of electrodes of different metals [4] or covered by sensing films [5], and optical sensors [6]. Measurements from sensors are usually based on Electrochemical Impedance Spectroscopy (EIS) [7]- [9], a frequency domain technique that evaluates the response of an electrochemical cell to a low amplitude sinusoidal perturbation. ...
... After measurement acquisition, data analysis for contaminants classification is principally based on machine learning methods. For example, in [10], Artificial Neural Network and Principal Component Regression are used to estimate nitrate concentration in ground water, whereas in [4] partial least square discriminant analysis is applied to detect explosive precursors in wastewater. Also deep learning solutions have been proposed, e.g. in [11] Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) were adopted for detection and classification of chemicals in sea water. ...
In smart city framework, the water monitoring through an efficient, low-cost, low-power and IoT-oriented sensor technology is a crucial aspect to allow, with limited resources, the analysis of contaminants eventually affecting wastewater. In this sense, common interfering substances, as detergents, cannot be classified as dangerous contaminants and should be neglected in the classification. By adopting classical machine learning approaches having a finite set of possible responses, each alteration of the sensor baseline is always classified as one out of the predetermined substances. Consequently, we developed an anomaly detection system based on one-class classifiers, able to discriminate between a recognized set of substances and an interfering source. In this way, the proposed detection system is able to provide detailed information about the water status and distinguish between harmless detergents and dangerous contaminants.
