On this page an updated list of scientific publications developed by our experts on the SYSTEM project findings and results is available. If you are interested in knowing more, click on the title of each publication to read the abstract and get access to the full text. 

The use of organic peroxides for the preparation of homemade explosives (HMEs) is common amongst terrorists due to inexpensive precursor chemicals and simple synthetic procedures. Triacetone triperoxide (TATP) is the most notable peroxide explosive, and has been deployed in several terrorist attacks as explosive filling of improvised explosive devices (IEDs). Forensic identification of TATP in pre-blast and post-blast residues, including on-site analysis, poses significant analytical challenges and induces demand for practicable and sensitive detection techniques. This work presents a concept suitable for laboratory and on-site identification of TATP residues in liquid samples (aqueous TATP synthetic waste) and in gas phase. It is based on TATP enrichment from the aqueous or gas phase using different types of passive samplers (polydimethylsiloxane (PDMS) sampling rods and activated carbon sampling tubes (ACST)) and subsequent identification of the explosive by gas chromatography-mass spectrometry (GC-MS) or GC with positive chemical ionisation and tandem MS (GC-PCI-MS/MS) analytical techniques. Additionally, investigation of the stability of TATP in aqueous solutions and of the stability of enriched TATP in passive samplers under different storage conditions, as well as development of TATP re-extraction procedures from passive samplers have been performed in this study. The practical use of passive samplers was demonstrated during and after TATP production processes. Moreover, post-blast sampling of TATP under different conditions of controlled blasting events was investigated using the passive sampling concept.

Hehet, P., Pütz, M., Kämmerer, B., Umlauf, G., Geiss, O., Caetano, J. G. N., Karaghiosoff, K., Wende, M. Determination of triacetone triperoxide (TATP) traces using passive samplers in combination with GC-MS and GC-PCI-MS/MS methods. Forensic Sci Int. 2023 Jul;348:111673. doi: 10.1016/j.forsciint.2023.111673. Epub 2023 Mar 29. PMID: 37031011.

Online monitoring of organic micropollutants (OMPs) in the aquatic environment at high temporal resolution is an upcoming technique that provides insights into their dynamics and has the potential to bring water research and management to a new level. An online monitoring setup was developed to quantify OMPs in wastewater treatment plant (WWTP) influent and effluent using automated and continuous sampling, sample preparation, online solid-phase extraction-liquid chromatography-tandem mass spectrometry analysis and data evaluation. This online monitoring setup provided high selectivity and sensitivity (limit of quantification down to 1 ng/L) as well as a stable performance during one week of constant operation whilst using a high sampling frequency of 10 min (>1000 samples). Custom automated data evaluation enabled quantification within seconds after each measurement and results were comparable to those from a commercial software. Additionally, an alarm tool was included in the evaluation application, which automatically notified the user in case a substance exceeded a predefined threshold. The online monitoring setup was applied to WWTP influent and effluent, where 57 substances were monitored over a period of one week and two days, respectively. High temporal resolution enabled the observation of periodic patterns of pharmaceuticals as well as pollution by OMPs originating from point and diffuse sources, while dynamics of OMPs in WWTP effluent were less pronounced. These new insights into the dynamics of OMPs in WWTP influent, which would not be observable using 24 h composite samples, will be a starting point for new stormwater and wastewater research and management strategies.

Köke, N., Solano, F., Knepper, T.P., Frömel, T. Unraveling the dynamics of organic micropollutants in wastewater: Online LC-MS/MS analysis at high temporal resolution. Environ Pollut. 2022 Oct 1;310:119807. doi: 10.1016/j.envpol.2022.119807. Epub 2022 Jul 20. PMID: 35870533.

Over the last decade, a market for new psychoactive substances (NPS) has emerged, thus increasing the heterogeneity and complexity of narcotic products. Synthetic cannabinoid receptor agonists (SCRAs) represent a predominant group of NPS in the EU, consistent with continuous and persistent prevalence of SCRA use noted in toxicology cases. The continuous emergence of new relevant substances poses a great challenge for analytical laboratories. The key issues concern the speed of SCRAs appearing on and disappearing from the drug market, as well as keeping track on the concerning toxicological effects, potency and chemical compositions of newly emerging products. Common data sources for monitoring illicit drug market and drug use include seizure data, drug-related medical emergencies or deaths, results of drug consumer surveys as well as toxicological examinations of human matrices including hair analysis. Wastewater-based epidemiology (WBE) is a rapidly developing approach for this application area, with potential to complement and extend the existing monitoring tools.

Hehet, P., Wende, M., Pütz, M., Baumer, C., Uhl, M., Scheufler, F. Comparative study of wastewater and hair analysis to assess synthetic cannabinoid receptor agonists (SCRA) use. Poster presented at: GTFI-SoHT 2022 conference; 8-10 June 2022; Verona, Italy.

Environmental impact of toxic and corrosive synthesis waste generated by the clandestine production of amphetamine-type stimulants (ATS) is a known problem, which can even result in a malfunction of wastewater treatment plants (WWTPs), e. g. in case of illegal discharge into the sewage system of large amounts of highly acidic chemical waste which is generated in clandestine labs converting pre-precursors to the most prevalent ATS precursor benzyl methyl ketone (BMK). ATS synthesis-specific substances, precursor chemicals, intermediates and route-specific by-products may also support wastewater-based epidemiology (WBE) studies to explain abnormally high loads of drugs in wastewater by distinguishing whether these high loads were caused by consumption or disposal of synthesis waste into the sewage system. Although some of these synthesis-specific substances can be detected in traces in the final form of consumption of the product, these substances are removed from the drug product to a large extent during cleaning steps, e.g. the frequently applied steam distillation step to purify the amphetamine raw base after clandestine Leuckart synthesis. In contrast, these synthesis-specific by-products are very prominent in chemical synthesis wastes, whereby their detection in wastewater would prove a disposal of synthesis wastes instead of excretion after drug product consumption. As a prerequisite, such substances need to exhibit a certain chemical and biological stability in wastewater and, therefore, lab-scale experiments were performed in a mixture of WWTP effluent and activated sludge. 14 selected synthesis-specific substances, all related to the production of ATS, comprised pre-precursors (e.g. α-phenylacetoacetonitrile (APAAN) or α-phenylacetoacetamide (APAA)), precursors (e.g. BMK), intermediates (e.g. N-formylamphetamine (NFA)), synthesis by-products (e.g. N,N-di-(β-phenylisopropyl)amine (DPIA)) and final products (e.g. amphetamine (AMPH)). Stability of test substances was evaluated by targeted HPLC-MS/MS analysis, while HPLC-HRMS techniques were used for the identification of transformation products (TPs) of substances that have undergone primary degradation. All substances were detectable for five days minimum and seven out of 14 substances underwent at least primary degradation. A total of three TPs were identified: TP164 was formed by oxidation of ephedrine (EPHE) and was further transformed after maximum formation, while TP180-1 and TP180-2 were formed by reduction of APAA and both remained stable. This is the first study investigating the stability of ATS synthesis-specific substances in wastewater demonstrating sufficient stability for wastewater monitoring studies.

Köke, N., Greif, M., Rößler, T., Pütz, M., Frömel, T., Knepper, T. P. (2022). Stability of selected substances related to the clandestine production of amphetamine-type stimulants in wastewater – identification of transformation products, Talanta Open, 2022, 100104, ISSN 2666-8319, https://doi.org/10.1016/j.talo.2022.100104.

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.

De Stefano, C., Ferrigno, L. et al (2022). Evolutionary Computation to Implement an IoT-Based System for Water Pollution Detection. SN Computer Science. 3. 10.1007/s42979-021-00986-x.

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.

Chachuła, K., Słojewski, T.M., Nowak, R. Multisensor Data Fusion for Localization of Pollution Sources in Wastewater Networks. Sensors 2022, 22, 387. https://doi.org/10.3390/s22010387. 

We present and evaluate an IoT-enabled sensing and actuating system for localizing illegal industrial harsh discharges of polluting wastewater in sewer networks. The special conditions of the sewer environment bring special challenges for the design of an IoT system and of its real-time algorithm for anomaly detection and localization in wastewater networks. The proposed design fulfills these requirements by using a new IoT architecture pattern, which we generalize and name Hop-by-hop Anomaly Detection and Actuation (HADA). The distributed anomaly detection and localization algorithm makes predictions over previous sensor measurements, while taking into account seasonality effects of wastewater and noise of the sensors. Based on simulations in a large network with three common illegal industrial wastewater pollutants, the advantages and limitations of the proposed wastewater anomaly localization system are discussed. The IoT system, including its anomaly detection and localization algorithm, was implemented using in a low-power microcontroller and tested in flowing wastewater with different harsh industrial waste.

Solano, F. D., Krause, S., Wöllgens, C. An Internet-of-Things Enabled Smart System for Wastewater Monitoring. IEEE Access, vol. 10, 2022, pp. 4666-4685. DOI: 10.1109/ACCESS.2022.3140391.

The established approaches of suspect and nontarget screening (NTS) using liquid chromatography–high-resolution mass spectrometry (LC-HRMS) are usually applied in the field of environmental and bioanalytical analysis. Herein, these approaches were employed on a forensic-toxicological application by analyzing different production waste samples from controlled amphetamine synthesis via Leuckart route to evaluate the suitability of this methodology for identification of route-specific organic substances in such waste samples. For analysis, two complementary LC techniques were used to cover a broad polarity spectrum. After data processing and peak picking using the enviMass software and further manual data restriction, 17 features were tentatively identified as suspects, three of which were subsequently identified with reference substances. All suspects had been previously identified in studies, in which gas chromatography–mass spectrometry (GC-MS) was successfully applied for synthesis marker assessment in waste and amphetamine samples. Remaining features with high signal intensity and assigned sum formula were selected for the attempt of structure elucidation. Seven potential synthesis markers were tentatively identified, which were not yet reported, except the sum formula of one compound, and which were partly also detected in real case waste samples afterward. The innovative application of the NTS approach using LC-HRMS for the analysis of aqueous amphetamine synthesis waste samples showed its suitability as extension to GC-MS analysis as it was possible to successfully identify seven new potential marker compounds, which are specific either for the conversion of the pre-precursors α-phenylacetoacetonitrile and α-phenylacetoacetamide to benzyl methyl ketone or for the subsequent Leuckart synthesis route after their conversion.

Greif, M, Köke, N, Pütz, M, Rößler, T, Knepper, TP, Frömel, T. Nontarget screening of production waste samples from Leuckart amphetamine synthesis using liquid chromatography – high-resolution mass spectrometry as a complementary method to GC-MS impurity profiling. Drug Test Anal. 2022; 1- 12. doi:10.1002/dta.3224

Identifying the source of a dangerous or toxic substance in sewage networks is a complicated and difficult task in practice, due to the accident manager’s efforts to conceal the event and avoid legal sanctions. The question is whether it is possible to locate the source of pollution on the basis of known data (monitored time course of pollution). The problem defined in this way is called “inverse problem”. From a hydrodynamic point of view, the solution of the inverse problem is not simple and often not even unambiguous, even in the case of a simple section of pipeline or water flow. Determining the source of pollution is problematic due to the high degree of uncertainty in solving this issue. In the case of a sewer network, the situation is even more complicated due to its network structure and topology. The paper describes the proposed procedure of practical solution of the inverse problem for the sewer network. To solve the inverse problem defined in this way, our own numerical model was developed. Part of the paper is a brief description of this model, as well as of its results, numerical tests and sensitivity analysis of the method used for errors in the input data. Finally, the solution of the inverse problem in the sewer network environment is proposed.

Sokáč, M., Velísková, Y. Riešenie inverznej úlohy – lokalizácia zdroja znečistenia v stokovej sieti [Solution of inverse task – localisation of pollution source in sewer network]. In: Botyanszk , L. (eds.), 2021. Hydrological Processes in the Soil Plant Atmosphere System. IH SAS, E-Book, Bratislava, pp. 153-163. ISBN: 978-80-89139-50-7.

The illegal production of amphetamines and its consumption represent an urgent problem in Europe in terms of health consequences for consumers and environmental damage caused by the disposal of synthesis waste. In order to counteract this problem, the EU project “Synergy of integrated Sensors and Technologies for urban sEcured environMent” (SYSTEM) aims at developing sensor systems for the detection of characteristic substances associated with the production of synthetic drugs. The various devices that have been manufactured for this are intended to support the identification of illegal laboratories by monitoring air emissions from specific target areas and thereby detecting substances characteristic of the production process. A prototype of these developed devices is the active sampling system called “Dyna Sampler”, which is intended to implement fast mobile sampling. The Dyna Sampler pumps a stream of air through its system, which can be directed to a polydimethylsiloxane string sampler for sampling, which is then analysed by gas chromatography-mass spectrometry. Active sampling using the Dyna Sampler is to be compared to passive sampling using a polydimethylsiloxane string by diffusion and evaluated with regard to its applicability in air monitoring of illegal amphetamine production. In this work, the advantages of active sampling using the Dyna Sampler are worked out and shown. With active sampling, it is possible to detect gas space concentrations of an analyte that are up to ten times lower than with passive sampling. In addition, sampling using the Dyna Sampler was successfully tested in a simulated experiment of a real scenario of amphetamine synthesis. The results show that the use of the Dyna sampler is possible in the further course of the SYSTEM project. However, further tests in the field are necessary to determine whether the detection limit of this sampling system is sufficient for this purpose.

Gaar, S. (2021). Investigation of an active sampling concept compared to a passive sampling concept and evaluation of their suitability for the gas phase monitoring of clandestine synthetic drug manufacturing processes within the EU-project SYSTEM (Unpublished bachelor’s dissertation). Hamm-Lippstadt University of Applied Sciences, Hamm, Germany.

Experiments focused on pollution transport and dispersion phenomena in conditions of low flow (low water depth and velocities) in sewers with bed sediment and deposits are presented. Such conditions occur very often in sewer pipes during dry weather flows. Experiments were performed in laboratory conditions. To simulate real hydraulic conditions in sewer pipes, sand of fraction 0.6–1.2 mm was placed on the bottom of the pipe. In total, we performed 23 experiments with 4 different thicknesses of sand sediment layers. The first scenario is without sediment, the second is with sediment filling 3.4% of the pipe diameter (sediment layer thickness = 8.5 mm), the third scenario represents sediment filling 10% of the pipe diameter (sediment layer thickness = 25 mm) and sediment fills 14% of the pipe diameter (sediment layer thickness = 35 mm) in the last scenario. For each thickness of the sediment layer, a set of tracer experiments with different flow rates was performed. The discharge ranges were from (0.14–2.5)·10−3 m3·s−1, corresponding to the range of Reynolds number 500–18,000. Results show that in the hydraulic conditions of a circular sewer pipe with the occurrence of sediment and deposits, the value of the longitudinal dispersion coefficient Dx decreases almost linearly with decrease of the flow rate (also with Reynolds number) to a certain limit (inflexion point), which is individual for each particular sediment thickness. Below this limit the value of the dispersion coefficient starts to rise again, together with increasing asymmetricity of the concentration distribution in time, caused by transient (dead) storage zones.

Sokáč, M., Velísková, Y. Impact of Sediment Layer on Longitudinal Dispersion in Sewer Systems. Water 2021, 13, 3168. https://doi.org/10.3390/w13223168

Wireless sensor networks (WSNs) are fundamental to the ever-evolving technologies associated with the broader Internet of Things (IoT). In this paper, we consider the problem of coverage and cost function minimization in a sewer network. The problem of sensor coverage in a sewer network is presented as a mixed-integer programming problem. This approach guarantees that we obtain the optimal solution to the problem under consideration. To solve this problem we used a CPLEX solver. The study is performed for a practically relevant network within selected scenarios determined by realistic data sets.

Sikorski, A., Kozdrowski, S., Solano, F. D. IoT Device Deployment for Optimal Wastewater Network Coverage. 2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 2021, pp. 1-6. DOI: 10.23919/SoftCOM52868.2021.9559098.

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.

Bria, A. et al. A False Positive Reduction System For Continuous Water Quality Monitoring. 2021 IEEE International Conference on Smart Computing (SMARTCOMP), 2021, pp. 311-316. DOI: 10.1109/SMARTCOMP52413.2021.00065.

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.

De Stefano C., Ferrigno L., Fontanella F., Gerevini L., Molinara M. (2021) A Novel Evolutionary Approach for IoT-Based Water Contaminant Detection. In: Castillo P.A., Jiménez Laredo J.L. (eds) Applications of Evolutionary Computation. EvoApplications 2021. Lecture Notes in Computer Science, vol 12694. Springer, Cham. https://doi.org/10.1007/978-3-030-72699-7_49

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.

Buras, M.P., Solano Donado, F. Identifying and Estimating the Location of Sources of Industrial Pollution in the Sewage Network. Sensors 2021, 21, 3426. https://doi.org/10.3390/s21103426.

Since their first appearance in 2008, synthetic cannabinoid receptor agonists (SCRAs) remain the most popular new psychoactive substances (NPS) in the EU. Following consumption, these drugs and their metabolites are urinary excreted and enter the sewage system enabling the application of wastewater-based epidemiology (WBE). Knowing the fate of target analytes in sewage water is essential for successful application of WBE. This study investigates the stability of several chemically diverse SCRAs and selected human metabolites under sewage conditions utilizing a combination of liquid chromatography–tandem mass spectrometry and high-resolution mass spectrometry (HRMS). Target analytes included SCRAs with indole (5F-PB-22, PB-22 pentanoic acid), indazole (AMB-FUBINACA, 5F-ADB, 5F-ADB dimethylbutanoic acid), carbazole (MDMB-CHMCZCA, EG-018), and γ-carboline (Cumyl-PeGaClone) chemical core structures representing most of the basic core structures that have occurred up to now. Stability tests were performed using wastewater effluent containing 5% activated sludge as inoculum to monitor degradation processes and formation of transformation products (TPs). The majority of investigated SCRAs, excluding the selected human metabolites, was recalcitrant to microbial degradation in sewage systems over a period of 29 days. Their stability was rather controlled by physico-chemical processes like sorption and hydrolysis. Considering a typical hydraulic in-sewer retention time of 24 h, the concentration of AMB-FUBINACA decreased by 90% thus representing the most unstable SCRA investigated in this study. Among the 10 newly identified TPs, three could be considered as relevant markers and should be included into future WBE studies to gain further insight into use and prevalence of SCRAs on the drug market.

Hehet P, Köke N, Zahn D, et al. Synthetic cannabinoid receptor agonists and their human metabolites in sewage water: Stability assessment and identification of transformation products. Drug Test Anal. 2021;13(10):1758-1767. https://doi.org/10.1002/dta.3129

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.

Chachuła, K.; Nowak, R.; Solano, F. Pollution Source Localization in Wastewater Networks. Sensors 2021, 21, 826. https://doi.org/10.3390/s21030826

Illicit production of amphetamine-type stimulants is an ongoing phenomenon, which causes health and environmental problems. Especially during production of amphetamine, large quantities of toxic waste are generated, which the perpetrator groups illegally disposed of in various ways. Doing so at dump sites, several tons of process waste are often found. From a German perspective, the Netherlands, which is currently growing into being the largest European ATS producer, is having major problems. Dutch perpetrator groups have increased to illegally dump the process waste in the neighbouring countries (i.e., Belgium and Germany), thus creating a new area of responsibility for the German police and the German forensic experts Institutes. At a crime scene such as a dump site, portable analysis techniques are used to map the process waste used for the corresponding synthesis step. The scope of this work concerns the development of a Raman-based portable analysis procedure (SERS) to characterize/identify chemical wastes from Leuckart amphetamine production during crime scene work. The results offer a promising perspective for using such a procedure to differentiate process waste from illegal amphetamine synthesis. Its use in sampling at a dump site can be of assistance when summarizing the process waste of a synthesis step, so that a suitable sampling scenario can be derived. Compared to IMS and MS, Raman spectrometers offer the advantage of being immediately usable as they do not need a prior time-consuming sample preparation.

Schanzmann, H. (2021). Development of rapid examination methods for the characterization of process waste and the illicit manufacture of synthetic drugs using portable analysis techniques (Unpublished master’s dissertation). Hamm-Lippstadt University of Applied Sciences, Hamm, Germany.

This paper deals with question how the bed sediment or deposits impact transport processes in conditions of flow with low velocity and water depth. This is often a problem especially in case of flow in sewer network. For this reason, there were performed several tests in laboratory flume having the shape of a pipe with circular cross-section. To simulate the hydraulic condition in sewer pipe with sediments and deposits, some sand was inserted in the pipe with various layer thickness and granularity. It was used a sand of fraction 0.6–1.2 mm. In total, 4 sets of experiments with different layer thickness were performed: with layer thickness of 0 mm (no sediments), 8.5 mm (3.4% of the pipe diameter), 25 mm (10%) and 35 mm (14%) of sand sediment. For each thickness of the sediment layer a set of tracer experiment was performed with different discharges ranging approximately (0.14–2.5) l s-1. Results of the tracer experiments show, that the value of the longitudinal dispersion coefficient Dx in the hydraulic conditions of circular sewer pipe with sediment and deposits decreases when the Reynolds number is decreasing too. The value of Dx reaches its minimal value in the range of the Reynolds number between 4500 up to 10 000. With Reynolds number below this range the value of Dx start to rise.

Sokáč, M., Velísková, Y. (2020). Impact of roughness changes on contaminant transport in sewers. Acta Hydrologica Slovaca, 21(2), 145-151. 2644-6291 (printed until 2018). DOI: https://doi.org/10.31577/ahs-2020-0021.02.0018.

Modern water quality monitoring system enables detailed observation of water quality parameters. Measured data of the pollution concentration time course can be consequently used for determination of the pollution source position. Paper deals with the solution of inverse problem, where the pollution source and its position is determined from the pollution concentration time courses obtained in the monitored watercourse profile located downstream. The main objective of this paper is to introduce the simple method for solution of pollution spreading inverse task and to analyse the accuracy of this method application. For this aim, a software tool was developed. Two different analytical solutions equation for this tool were used. For the method verification, data from a field tracer experiment were used. The experiment was performed on a lowland channel with extensive vegetation coverage. The test results show, that the proposed procedure is feasible, the numeric solution is reliable, stable and fast. Results of tests have also indicated the impact of used analytical solution equation and also the software tool ability to fit the specific conditions in the real streams.

Sokáč, M., Velísková, Y. (2020). Pollution source localisation in a simple river branch. IOP Conference Series: Earth and Environmental Science, Volume 612, The 6th International Conference on Water Resource and Environment. https://doi.org/10.1088/1755-1315/612/1/012037.

This Policy Brief proposes a template for a report from a process of data protection impact assessment (DPIA) in the European Union (EU). Grounded in the previously elaborated framework (cf. Policy Brief No. 1/2017) and method for impact assessment (cf. Policy Brief No. 1/2019), the proposed template conforms to the requirements of Articles 35–36 of the General Data Protection Regulation (GDPR) and reflects best practices for impact assessment, offering at the same time five novel aspects. First, it aims at comprehensiveness to arrive at the most robust advice for decision making. Second, it aims at efficiency, that is, to produce effects with the least use of resources. Third, it aims at exploring and accommodating the perspectives of various stakeholders, although the perspective of individuals dominates; it, therefore, fosters fundamental rights thinking by, for example, requiring justification for each choice, hence going beyond a mere ‘tick-box’ exercise. Fourth, it aims at adhering to the legal design approach to guide the assessors in a practical, easy and intuitive manner throughout the 11-step assessment process, providing necessary explanations for each step, while being structured in expandable and modifiable tables and fields to fill in. Fifth, it assumes its lack of finality as it will need to be revised as experience with its use grows. The template is addressed predominantly to assessors entrusted by data controllers to perform the assessment process, yet it may also assist data protection authorities (DPA) in the EU to develop (tailored down) templates for DPIA for their own jurisdictions.

Kloza, D., Calvi, A., Casiraghi, S., Vazquez Maymir, S., Ioannidis, N., Tanas, A., & Van Dijk, N. (2020). Data protection impact assessment in the European Union: developing a template for a report from the assessment process. d.pia.lab Policy Brief, 2020(1), 1-52

In this article, we design and evaluate several algorithms for the computation of the optimal Rice coding parameter. We conjecture that the optimal Rice coding parameter can be bounded and verify this conjecture through numerical experiments using real data. We also describe algorithms that partition the input sequence of data into sub-sequences, such that if each sub-sequence is coded with a different Rice parameter, the overall code length is minimised. An algorithm for finding the optimal partitioning solution for Rice codes is proposed, as well as fast heuristics, based on the understanding of the problem trade-offs.

Solano Donado, F. On the Optimal Calculation of the Rice Coding Parameter. Algorithms 2020, 13(8), 181; https://doi.org/10.3390/a13080181

Water pollution causes an ever-increasing number of diseases and represents a worldwide concern, both for governments and researchers, as well as public opinion. This pollution also regards drinkable water, with two billion people plagued by this problem. Therefore, it is crucial to find reliable and low-cost technologies for a continuous and diffused monitoring of water. In this paper, we present a novel approach that allows the detection of water contaminants by using an ad-hoc classification system that can be implemented aboard low-cost sensors. To this aim, we first project the input data from the sensors into a 3-D space by using the PCA algorithm, then we use an ad-hoc devised classifier to distinguish the contaminants in the transformed space. We used an evolutionary algorithm to learn the parameters of the classifiers. The experiments were performed on a large dataset containing data from four contaminants, with the phosphoric and sulphuric acids, among the others. The results obtained confirm the effectiveness of the proposed approach.

De Stefano, C., Ferrigno, L., Fontanella, F., Gerevini, L., Scotto di Freca, A. (2020). A novel PCA-based approach for building on-board sensor classifiers for water contaminant detection. Pattern Recognition Letters, 135, 375-381. https://doi.org/10.1016/j.patrec.2020.05.015

This policy brief lays the foundations for a method for data protection impact assessment (DPIA) in the European Union (EU). First, as a prerequisite, it proposes a generic method for impact assessment, which is intended to be used – when tailored to the particular context – in multiple domains of practice, such as environment, technology development or regulation (Section 2). Next, building on this generic method and interpreting the requirements of the General Data Protection Regulation (GDPR), this policy brief lays the foundations for a specific method for the process of DPIA in the EU, which is also intended to be adapted to the context of use (Section 3). In particular, the policy brief aims to clarify two crucial aspects of this specific method, which have thus far proved to be the most contentious. These aspects are the appraisal techniques (that is, the necessity and proportionality assessment, and risk appraisal), and stakeholder involvement (including public participation) in decision-making. Section 4 summarises the findings and calls for further guidance, clarification and tailoring down. This policy brief is addressed predominantly to policy-makers who develop methods for impact assessment, practitioners who tailor these methods to the context in which they are used and assessors who conduct the assessment process in accordance with these methods.

Kloza, D., Van Dijk, N., Casiraghi, S., Vazquez Maymir, S., Roda, S., Tanas, A., & Konstantinou, I. (2019). Towards a method for data protection impact assessment: Making sense of GDPR requirements. d.pia.lab Policy Brief, 1(2019), 1-8

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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.

G. Betta, G. Cerro, M. Ferdinandi, L. Ferrigno and M. Molinara, “Contaminants detection and classification through a customized IoT-based platform: A case study,” in IEEE Instrumentation & Measurement Magazine, vol. 22, no. 6, pp. 35-44, Dec. 2019, doi: 10.1109/MIM.2019.8917902.

This chapter analyses the impacts of discontinuous pollution sources on the water quality of receiving water bodies and offers the possibility to solve such type of water management problem by numerical modelling way. Typical examples of such pollution sources are the combined sewer overflows (CSOs), but generally also different types of storm water management in urban areas. There were designed and performed numerical simulations for four feasible alternatives of storm sewer management (different mixing ratio, different size of storm tanks) in the town Banská Bystrica at the Hron River (Slovak republic). The model MIKE11 was used for numerical simulations of water quality. Results of each modelled alternatives were analysed. Simulation was performed in two alternatives – for Qa and Q355, whereas Qa is the yearly average discharge and Q355 is a discharge, which exceeded 355 days in a year. Results of the study show practical implications and impacts on the receiving water body quality depending on the type of the storm water management.

Sokac, M., Veliskova, Y. (2021). Impact of Combined Sewer Overflows Events on Recipient Water Quality. In: The Handbook of Environmental Chemistry. Springer, Berlin, Heidelberg. https://doi.org/10.1007/698_2021_782

Analytical solutions of the one-dimensional (1D) advection–dispersion equations, describing the substance transport in streams, are often used because of their simplicity and computational speed. Practical computations, however, clearly show the limits and the inaccuracies of this approach. These are especially visible in cases where the streams deform concentration distribution of the transported substance due to hydraulic and morphological conditions, e.g., by transient storage zones (dead zones), vegetation, and irregularities in the stream hydromorphology. In this paper, a new approach to the simulation of 1D substance transport is presented, adapted, and tested on tracer experiments available in the published research, and carried out in three small streams in Slovakia with dead zones. Evaluation of the proposed methods, based on different probability distributions, confirmed that they approximate the measured concentrations significantly better than those based upon the commonly used Gaussian distribution. Finally, an example of the application of the proposed methods to an iterative (inverse) task is presented.

Sokac, M., Velísková, Y. & Gualtieri, C. (2019). Application of Asymmetrical Statistical Distributions for 1D Simulation of Solute Transport in Streams. In Water 2019, 11(10), 2145; https://doi.org/10.3390/w11102145

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.

Bria, A., Cerro, G., Ferdinandi, M., Marrocco, C., Molinara, M. (2020). An IoT-ready solution for automated recognition of water contaminants. Pattern Recognition Letters. 135. 10.1016/j.patrec.2020.04.019.

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 ightweight 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.

M. Molinara, M. Ferdinandi, G. Cerro, L. Ferrigno and E. Massera, “An End to End Indoor Air Monitoring System Based on Machine Learning and SENSIPLUS Platform,” in IEEE Access, vol. 8, pp. 72204-72215, 2020, doi: 10.1109/ACCESS.2020.2987756.

Nowadays, the problem of pollution in water is a very serious issue. Before applying solutions to reduce it to an acceptable level, it is crucial to monitor it with non-invasive and low cost solutions, as those offered by sensor technologies. Accordingly, here an IoT sensor solution to detect and classify pollution is proposed. The sensing and data pre-processing is represented by SENSIPLUS Technology. In terms of classification, nine machine learning algorithms are proposed. They differ by computational burden, accuracy, processing time, memory usage. The best trade-off among all these parameters is a challenging task. Best classification performance at the moment has been obtained by K-Nearest Neighbours (KNN) algorithm, without any limitation on processing and memory.

Bourelly, C., Gerevini, L., Cicalini, M., Manfredini, G. A preliminary characterization of a water contaminant detection system based on a multi-sensor microsystem. Sensors and Microsystems: Proceedings of the AISEM 2020 Regional Workshop (AISEM XX), 2021, pp. 223-229. DOI: 10.1007/978-3-030-69551-4_30. 

Monitoring the air quality is a crucial task for the human health. In this framework, an IoT ready proposal is here reported to perform both the sensing and the classification of possible air contaminants. In terms of sensing stage, the solution includes a sensor array whose electrical impedance is measured by using the SENSIPLUS microchip. The system is characterized by very compact sizes and low power requirements. Several contaminants are considered in the experimental phase and Multilayer Perceptron (MLP) algorithm has processed sensor data to classify contaminants. The obtained accuracy with our Multi Layer Perceptron implementation is lower bounded by 75%, as an average. We are testing other algorithm, lighter than MLP, in order to find a solution that can be perform classification directly on MCU without decrease too much accuracy.

Gerevini, L., Bourelly, C. et al., A preliminary characterization of an air contaminant detection system based on a multi-sensor microsystem. Sensors and Microsystems: Proceedings of the AISEM 2020 Regional Workshop (AISEM XX), 2021, pp. 215-222. DOI: 10.1007/978-3-030-69551-4_30. 

The environment surrounds any type of human activity and its quality has an effect on people’s lives and health. At the same time, all human activities have an impact on the environment and therefore the environment could also become the means of ”revealing” behaviors that could endanger the normal life of citizens.
The project presented in this paper aims to create a network of innovative ”learning sensors” for indoor and outdoor use, able to detect abnormal and dangerous substances in water and in air and at the same time able to learn from the context.

Ferrigno, L., Gerevini, L.,  Molinara, M., Bourelly, C. , Fantasia, G.,  Frau, J.,  Loprevite, M., Morello, D., Nardone, R., Pinelli, S., Teolis, G., Vitelli, M. (2020). Pollution Tracker, a preliminary proposal for an end-to-end environmental monitoring system.  Paper presented at the 6th Italian Conference on ICT for Smart Cities and Communities, online.

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.

Ferrigno, L., Gerevini, L.,  Molinara, M., Bourelly, C. , Cerro, G. (2020). Anomaly detection in water quality monitoring: a preliminary solution.  Paper presented at the 6th Italian Conference on ICT for Smart Cities and Communities, online.

Wastewater-based epidemiology  (WBE) is an indirect approach to estimate illicit drug consumption at the population level through the presence of drugs and their metabolites in the wastewater produced.
This methodology, however, requires detailed knowledge about the fate of illicit drugs and drug metabolites during their residence time in the sewage system, specifically their stability and the transformation products that may eventually be formed from them.
Synthetic cannabinoid receptor agonists (SCRAs), the most prevalent group of new psychoactive substances (NPS), were selected as analytes for this biotransformation study. The stability of SCRAs and their relevant human metabolites in wastewater was investigated with HPLC-MS/MS. If a primary degradation was observed, the samples were analyzed further with HPLC-HRMS to identify possible transformation products (TPs).

Hehet, P., Köke, N., Frömel, T., Zahn, D., Pütz, M., Knepper, T. P. Stability test of selected synthetic cannabinoids and some of their human metabolites in sewage water and identification of transformation products. Poster presented at: Late Summer Workshop Haltern am See; 22-25 September 2019; Haltern am See, Germany.

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

C. Bourelly et al., “A Preliminary Solution for Anomaly Detection in Water Quality Monitoring,” 2020 IEEE International Conference on Smart Computing (SMARTCOMP), Bologna, Italy, 2020, pp. 410-415, doi: 10.1109/SMARTCOMP50058.2020.00086.

This paper describes the dispersion process in sewer pipes, which is from the hydraulic point of view a prismatic stream channel with relatively constant roughness of streambed. In such hydraulic conditions should the effect of “dead zones” not occur, but this effect was observed during the field experiments. The reason for this was the presence of bed sediments and deposits, which form together with other small obstacles irregularities in the sewer pipe such dead zones. Dead zones are areas with small flow velocities, which act as a zone with transient (temporary) storage, where the pollution is accumulated and released gradually later. This process modifies the dispersion process in sewer systems and causes irregularities in the transport process. Field experiments were performed in a straight sewer section and also in the part with directional changes of sewer line, both under dry weather flow conditions, i.e. with relatively low pipe filling, discharges and velocities. Sewer pipes had a low slope, so a lot of deposits and sediments were present. Paper presents the results of field experiments and analyse the impacts of sediments and deposits in sewer system on the transport and dispersion process, which is reflected in the value of dispersion coefficient. In the case of sewer pipelines, the most important is the longitudinal dispersion coefficient DL. Comparing the values of DL in the straight part and in the part with directional changes, DL values in the straight part were higher than from the section with directional changes. The maximal value of the dimensionless longitudinal dispersion coefficient p reached 25.2 in a straight section; in the part with the directional changes p it was up to 39.3. This result indicates that in the straight part of the sewer line, the longitudinal dispersion of the substance is dominant in the total dispersion process, whereas in the sewer part with directional changes, the transversal (lateral) dispersion contributes to the whole mixing process (dispersion process is also influenced by the velocity gradient in the transverse direction). Within the measured channel section, there were three directional changes – angles of 90o, 135o and 105o. The influence of the direction change angle to the longitudinal dispersion coefficient within the performed measurements has not been clearly determined yet.

Velísková, Yvetta & Sokac, M.. (2019). Dispersion Process in Sewer Pipes with Sediments and Deposits. IOP Conference Series: Earth and Environmental Science. 362. 012107. 10.1088/1755-1315/362/1/012107.

Hydrodynamic dispersion is an important pollution transport phenomenon. Dispersion, from hydrodynamic point of view, is the spreading of mass from highly concentrated areas to less concentrated areas in flowing fluid. Mass dispersion with advection is basic motion mechanics of particles, transported in water. The main characteristics of dispersion are dispersion coefficients in relevant directions. The dispersion rate is described by the value of the dispersion coefficient in the advection – dispersion equation. Morphological irregularities, such as small cavities existing in sand or gravel beds, side arms and embankments, bigger obstacles, bank vegetation and uprooted trees, can produce recirculating flows which occur on different scales on both the riverbanks and the riverbed. These irregularities act as dead zones for the current flowing in the main stream direction. Dead zones significantly modify velocity profiles in the main channel and affect dispersive mass transport within the river by collecting and separating part of the solute from the main current. Subsequently, the solute is slowly released and incorporated back to the main current in the stream, creating a significant distortion of the tracer concentration time course. Strong influence of these impacts raises the question of the adequacy using standard solutions, for modelling the dispersion of pollution or other substances carried by the stream. Paper describe the observed effect of “dead zones”, which theoretically should not occur in conditions of sewer system (prismatic channel) and discuss its cause. The dead zones effect becomes evident especially in case of low discharges (dry weather flows). The reasons can be lower sewer construction quality (irregular slopes, sewer settlement due to the ground consolidation), but also obstacles, sediments and deposits in sewer pipes. The effect of dead zones was observed during field experiments, performed in a straight sewer sections under dry weather flow conditions, i.e. with relatively low pipe filling, discharges and velocities. Paper describes also approaches how to consider the dead zones phenomenon in numerical models, simulating waste water quality in sewer networks and shows results of the dead zones parameter estimation.

Velísková, Y., Sokáč, M. Hydrodynamic Dispersion in Sewer: Determination of Dead Zones Parameters. In 16th International Symposium on Water Management and Hydraulic Engineering, WMHE2019; Skopje; North Macedonia; 5 September 2019 through 7 September 2019, ISBN: 978-608-4510-33-8.

Majority of existing simulation models is composed for simulate the pollution spreading (concentration) only downstream from the pollutant source. Consequence of this is that these models strictly require all the initial and boundary conditions (discharges, location and concentration of the pollutant). However, opposite problems may occur in practice: pollution concentration time courses in specific cross-section profile along a watercourse are known (e.g. based on the on-line monitoring), but pollution source location, as well as the pollution amount, are unknown. Such task is called „inverse“ task. The objective of this task is to determine the location of pollution source as well as the total pollution amount and time course of pollution efflux. A unique pollution source identification in river systems is very unlikely because of the river system tree structure and its branching but result of inverse task can be a selection of regions with high probability of source location. Paper presents a modelling study, focused on localisation of the pollution source of the rivers. The study is based on a real field experiment, however on a single river section without branching.

Sokac, M.. (2019). Localization of the pollution source in the river system based on pollution dispersion. 10.5593/sgem2019/3.1/S12.043.

Nowadays water monitoring represents one of the most challenging global aims for the protection of people and environment health. In this paper we propose the application of an integrated system for the detection and recognition of contaminants in water. It is based on a two layer architecture: a sensing layer based on SENSIPLUS chip, and a data collection and classification layer, hereafter referred as SENSIPLUS Deep Machine (SDM). The SDM includes: a Micro Controller Unit (MCU), an optional host controller (e.g. laptop, smartphone, etc.) and different software components for data communication, analysis, and classification/regression based on machine learning techniques. Although the SDM classification/regression module can be potentially developed with any machine learning solution, in this paper we adopted an Artificial Neural Network with only one hidden layer to have a lightweight solution suitable to run (for inference) on ultra low power MCU. Aiming at further minimizing the network complexity, two alternative training sessions have been pursued: the first one using raw sensors’ data and the second one applying a feature space dimensionality reduction through the Principal Component Analysis technique. Comparable and positive results (higher than 82% as average accuracy) have been obtained, confirming the validity and potentiality of the proposed system.

M. Ferdinandi et al., “A Novel Smart System for Contaminants Detection and Recognition in Water,” 2019 IEEE International Conference on Smart Computing (SMARTCOMP), Washington, DC, USA, 2019, pp. 186-191, doi: 10.1109/SMARTCOMP.2019.00051.

Wastewater-based epidemiology (WBE) is a progressing approach to estimate illicit drug use at the population level. Transformation processes during residence time in sewers need to be examined to address target residues for chemical analysis. Moreover, the stability data are an important factor for correct calculation of drug concentration via wastewater-based approaches. Synthetic cannabinoids, being the most prevalent group of new psychoactive substances (NPS), were selected as target analytes for our biotransformation study as part of the ongoing EU-project “SYSTEM“.

Hehet, P., Köke, N., Knepper, T. P., Pütz, M. Biotransformation of synthetic cannabinoids and selected human metabolites in sewage water. Poster presented at: XI Symposium of GTFCh (Society of Toxicological and Forensic Chemistry); 11-13 April 2019; Mosbach, Germany.

Within the framework of the 3-year project SYSTEM: “SYnergy of integrated Sensors and Technologies for urban sEcured environment” co-funded within the EU Horizon 2020 Programme, one major purpose is the development and application of sensitive and robust on-line  measurement techniques. The main objective of the transnational European initiative is to develop and test a customised sensing system for hazardous substances detection in complementary utility networks and public environments. For ensuring greater protection of citizens, the innovative monitoring and observing of fused data sources will be tested in seven urban areas.

The SYSTEM Consortium, composed by 22 partner organizations from Belgium, Germany, Italy, Poland, Slovak Republic, Sweden, and the United Kingdom, includes four law enforcement authorities, three utility network operators, five scientific/academic partners, two industrial partners, four small and medium enterprises, two research foundation/no profit organizations, one association, and one municipality. Additional law enforcement authorities, utility network operators and municipalities have already provided their commitment to support testing and demonstration of innovative technologies. As associated partner Sciex is responsible for design and maintenance of an Online-SPE-LC-MS/MS system.
As wastewater influent is one of the harshest matrices, a crucial step is automated filtration in combination with sufficient enrichment for target analytes. The latter compose of markers of clandestinely produced synthetic drugs including pre-precursor compounds as well as designer drugs and stable transformation products thereof.

Besides first results obtained during pilot tests, the design and planned outcome will be presented.

Knepper, T. P., Pütz, M. Online-Analytik von Drogen und deren Vorläufersubstanzen in Kläranlagen-Zuläufen. Oral presentation at: 8th Berliner LC-MS/MS Symposium; 2 April 2019; Berlin, Germany.

The challenge to detect contaminants inside water solutions is addressed in this paper, through the use of an integrated, low-cost, smart and IoT platform, namely SENSIPLUS. In particular, the complete process from the sensing phase to classification and results analysis is provided with further investigations about the limitations of the current proposal and the description of a further processing technique that promises to improve classification accuracy. The classification is performed by adopting machine learning techniques, particularly Artificial Neural Network, that well fits the implementation on a low-cost microcontroller, as the one SENSIPLUS platform uses.

Bourelly C., Ferdinandi M., Molinara M., Ferrigno L., Simmarano R. (2020) Chemicals Detection in Water by SENSIPLUS Platform: Current State and Ongoing Progress. In: Di Francia G. et al. (eds) Sensors and Microsystems. AISEM 2019. Lecture Notes in Electrical Engineering, vol 629. Springer, Cham. https://doi.org/10.1007/978-3-030-37558-4_57

This work aims at analysing the sensitivity and the discriminative capability of the SENSIPLUS® platform for the detection of chemical substances in wastewater. To this aim, a measurements campaign has been carried out through a preliminary measurement setup where clean tap water has been used. The statistical procedure Principal Component Analysis has been adopted for the evaluation of the dataset principal components and to analyse the discriminative capability of the adopted sensors. Obtained results show a data separation between two different chemicals typologies.

Bourelly, C., Cerro, G., Ferdinandi, M., Molinara, M., Ferrigno, L. A sensitivity analysis of the SENSIPLUS® platform for chemicals’ detection in water through electrical impedance measurements. Poster presented at: AISEM XX (The Italian Association of Sensors and Microsystems; 11-13 February 2019; Naples, Italy.

The work deals with a technique adopted to calibrate in laboratory chemiresistor gas sensing film based on graphene that work at room temperature installed on a micro sensor board for applications in open air and IOT scenario. From the study in controlled environment the beginning of poisoning due to chemisorption can be estimated for the sensing layer and is possible to avoid harmful exposure to the analite during the calibration.

Massera E. et al. (2020) A Fast Gas Sensing Layer Working at Room Temperature for IOT in Air Quality Scenario. In: Di Francia G. et al. (eds) Sensors and Microsystems. AISEM 2019. Lecture Notes in Electrical Engineering, vol 629. Springer, Cham. https://doi.org/10.1007/978-3-030-37558-4_33