Laboratory Development of an AI System for the Real-Time Monitoring of Water Quality and Detection of Anomalies Arising from Chemical Contamination

Monitoring water quality is critical for mitigating risks to human health and the environment. It is also essential for ensuring high quality water-based and water-dependent products and services. The monitoring and detection of chemical contamination are often based around a small set of parameters or substances. Conventional monitoring often involves the collection of water samples in the field and subsequent analyses in the laboratory. Such strategies are expensive, time consuming, and focused on a narrow set of potential risks. They also induce a significant time delay between a contamination event and a possible reactive measure. Here, we developed a real-time monitoring system based on Artificial Intelligence (AI) for field deployable sensors. We used data obtained from full-scan UV-spec and fluorescence sensors for validation in this study. This multi-sensor system consists of (a) anomaly detection that uses multivariate statistical methods to detect any anomalous state in an aqueous environment and (b) anomaly identification, using Machine Learning (ML) to classify the anomaly into one of the a priori known categories. For a proof of concept, we tested this methodology on a supply of municipal drinking water and a few representative organic chemical contaminants applied in a laboratory-controlled environment. The outcomes confirm the ability for the multi-sensor system to detect and identify changes in water quality due to incidences of chemical contamination. The method may be applied to numerous other areas where water quality should be measured online and in real time, such as in surface-water, urban runoff, or food and industrial process water.publishedVersio

Assessments as Teaching and Research Tools in an Environmental Problem-Solving Program for In-Service Teachers

This article discusses the use of a scenario-based assessment tool in two environmental geoscience in-service programs for middle school and high school teachers. This tool served both to guide instructional techniques and as a method to evaluate the success of the instructional approach. In each case, participants were assessed before the workshops to reveal misconceptions that could be addressed in program activities and afterwards to reveal shifts in their understanding of concepts and approaches. The researchers noted that this scenario-based assessment was effective in providing guidance in refining instructional techniques and as a method to evaluate the effectiveness of an instructional program. In addition, participating teachers reported significant changes in their teaching as a result of the program. Educational levels: Graduate or professional, Graduate or professional

Optimizing Sensing: From Water to the Web

Where should we place sensors to quickly detect contamination in drinking water distribution networks? Which blogs should we read to learn about the biggest stories on the Web? Such problems are typically NP-hard in theory and extremely challenging in practice. The authors present algorithms that exploit submodularity to efficiently find provably near-optimal solutions to large, complex real-world sensing problems

A Review on the Application of Natural Computing in Environmental Informatics

Natural computing offers new opportunities to understand, model and analyze the complexity of the physical and human-created environment. This paper examines the application of natural computing in environmental informatics, by investigating related work in this research field. Various nature-inspired techniques are presented, which have been employed to solve different relevant problems. Advantages and disadvantages of these techniques are discussed, together with analysis of how natural computing is generally used in environmental research.Comment: Proc. of EnviroInfo 201

A Hidden Resource: Household-led Rural Water Supply in Ethiopia

Self supply as a strategy for WASH is defined as "improvement to water supplies delivered largely or wholly through user investment usually at household level." The two research studies reported on in this paper examined self supply in rural Ethiopia, gaining insights on the performance of existing family wells, factors that affect the decision of families to build their own wells and the way they use them, and elements of the enabling environment that can be targeted to promote self supply

STOP-IT: strategic, tactical, operational protection of water infrastructure against cyberphysical threats

Water supply and sanitation infrastructures are essential for our welfare, but vulnerable to several attack types facilitated by the ever-changing landscapes of the digital world. A cyber-attack on critical infrastructures could for example evolve along these threat vectors: chemical/biological contamination, physical or communications disruption between the network and the supervisory SCADA. Although conceptual and technological solutions to security and resilience are available, further work is required to bring them together in a risk management framework, strengthen the capacities of water utilities to systematically protect their systems, determine gaps in security technologies and improve risk management approaches. In particular, robust adaptable/flexible solutions for prevention, detection and mitigation of consequences in case of failure due to physical and cyber threats, their combination and cascading effects (from attacks to other critical infrastructure, i.e. energy) are still missing. There is (i) an urgent need to efficiently tackle cyber-physical security threats, (ii) an existing risk management gap in utilities’ practices and (iii) an un-tapped technology market potential for strategic, tactical and operational protection solutions for water infrastructure: how the H2020 STOP-IT project aims to bridge these gaps is presented in this paper.Postprint (published version

Heat as a proxy to image dynamic processes with 4D electrical resistivity tomography

Since salt cannot always be used as a geophysical tracer (because it may pollute the aquifer with the mass that is necessary to induce a geophysical contrast), and since in many contaminated aquifer salts (e.g., chloride) already constitute the main contaminants, another geophysical tracer is needed to force a contrast in the subsurface that can be detected from surface geophysical measurements. In this context, we used heat as a proxy to image and monitor groundwater flow and solute transport in a shallow alluvial aquifer (< 10 m deep) with the help of electrical resistivity tomography (ERT). The goal of our study is to demonstrate the feasibility of such methodology in the context of the validation of the efficiency of a hydraulic barrier that confines a chloride contamination to its source. To do so, we combined a heat tracer push/pull test with time-lapse 3D ERT and classical hydrogeological measurements in wells and piezometers. Our results show that heat can be an excellent salt substitution tracer for geophysical monitoring studies, both qualitatively and semi-quantitatively. Our methodology, based on 3D surface ERT, allows to visually prove that a hydraulic barrier works efficiently and could be used as an assessment of such installations

Potential of the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor for the monitoring of terrestrial chlorophyll fluorescence

Global monitoring of sun-induced chlorophyll fluorescence (SIF) is improving our knowledge about the photosynthetic functioning of terrestrial ecosystems. The feasibility of SIF retrievals from spaceborne atmospheric spectrometers has been demonstrated by a number of studies in the last years. In this work, we investigate the potential of the upcoming TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite mission for SIF retrieval. TROPOMI will sample the 675–775 nm spectral window with a spectral resolution of 0.5 nm and a pixel size of 7 km × 7 km. We use an extensive set of simulated TROPOMI data in order to assess the uncertainty of single SIF retrievals and subsequent spatio-temporal composites. Our results illustrate the enormous improvement in SIF monitoring achievable with TROPOMI with respect to comparable spectrometers currently in-flight, such as the Global Ozone Monitoring Experiment-2 (GOME-2) instrument. We find that TROPOMI can reduce global uncertainties in SIF mapping by more than a factor of 2 with respect to GOME-2, which comes together with an approximately 5-fold improvement in spatial sampling. Finally, we discuss the potential of TROPOMI to map other important vegetation parameters at a global scale with moderate spatial resolution and short revisit time. Those include leaf photosynthetic pigments and proxies for canopy structure, which will complement SIF retrievals for a self-contained description of vegetation condition and functioning