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.
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.
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.
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
This publication is also available in the following versions:
- Cap a un mètode d’avaluació d’impacte per a la protecció de dades: Donant sentit a les obligacions del RGPD (Catalan)
- Vers une méthode pour l’analyse d’impact relative à la protection des données : Comprendre et interpréter les obligations du RGPD (French)
- Entwicklung einer Methode für die Datenschutz-Folgenabschätzung: Erläuterung und Auslegung der Anforderungen der DSGVO (German)
- Προς μία μέθοδο για την Εκτίμηση Αντικτύπου σχετικά με την Προστασία Δεδομένων: Κατανοώντας τις απαιτήσεις του ΓΚΠΔ (Greek)
- Em direção a um método para avaliações de impacto sobre a proteção de dados: entendendo as exigências do RGPD (Portuguese)
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.
Despite being a scientific publication, a specific section shows possibilities on how to improve the water quality of receiving water bodies in compliance with the Water framework Directive (in terms of emission standards, imission goals).
Sokac, M., Veliskova, Y. (in press). Impact of Combined Sewer Overflows Events on Recipient Water Quality. In M. Nasr, A. Negm (Eds.), Cost-efficient Wastewater Treatment Technologies: Volume-II Engineered Systems. Springer International Publishing.
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.
Cerro, G., Ferrigno, L., Molinara, M., Gerevini, L., Bourelly, C., Simmarano, R. A preliminary metrological characterization of a water contaminant detection system based on a multi-sensor microsystem. Poster accepted at: AISEM XX (The Italian Association of Sensors and Microsystems; TBD.
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.
Cerro, G., Ferrigno, L., Molinara, M., Gerevini, L., Bourelly, C., Simmarano, R. A preliminary metrological characterization of an air contaminant detection system based on a multi-sensor microsystem. Poster accepted at: AISEM XX (The Italian Association of Sensors and Microsystems; TBD.
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.
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