WaterSpy: A High Sensitivity, Portable Photonic Device for Pervasive Water Quality Analysis

Application of Machine Learning Methods for an Analysis of E-Nose Multidimensional Signals in Wastewater Treatment

The work represents a successful attempt to combine a gas sensors array with instrumentation (hardware), and machine learning methods as the basis for creating numerical codes (software), together constituting an electronic nose, to correct the classification of the various stages of the wastewater treatment process. To evaluate the multidimensional measurement derived from the gas sensors array, dimensionality reduction was performed using the t-SNE method, which (unlike the commonly used PCA method) preserves the local structure of the data by minimizing the Kullback-Leibler divergence between the two distributions with respect to the location of points on the map. The k-median method was used to evaluate the discretization potential of the collected multidimensional data. It showed that observations from different stages of the wastewater treatment process have varying chemical fingerprints. In the final stage of data analysis, a supervised machine learning method, in the form of a random forest, was used to classify observations based on the measurements from the sensors array. The quality of the resulting model was assessed based on several measures commonly used in classification tasks. All the measures used confirmed that the classification model perfectly assigned classes to the observations from the test set, which also confirmed the absence of model overfitting.

A thermoelectrically stabilized aluminium acoustic trap combined with attenuated total reflection infrared spectroscopy for detection of Escherichia coli in water

Acoustic trapping is a non-contact particle manipulation method that holds great potential for performing automated assays. We demonstrate an aluminium acoustic trap in combination with attenuated total reflection Fourier-transform infrared spectroscopy (ATR-FTIR) for detection of E. coli in water. The thermal conductivity of aluminium was exploited to thermo-electrically heat and hold the acoustic trap at the desired assay temperature of 37 °C. Systematic characterisation and optimisation of the acoustic trap allowed high flow rates while maintaining high acoustic trapping performance. The ATR element serves not only as a reflector for ultrasound standing wave generation but also as a sensing interface. The enzyme conversion induced by alkaline phosphatase-labelled bacteria was directly monitored in the acoustic trap using ATR-FTIR spectroscopy. Sequential injection analysis allowed automated liquid handling, including non-contact bacteria retention, washing and enzyme-substrate exchange within the acoustic trap. The presented method was able to detect E. coli concentrations as low as 1.95 × 106 bacteria per mL in 197 min. The demonstrated ultrasound assisted assay paves the way to fully automated bacteria detection devices based on acoustic trapping combined with ATR-FTIR spectroscopy.

Design of a Real-Time Salinity Detection System for Water Injection Wells Based on Fuzzy Control

Salinity is an important index of water quality in oilfield water injection engineering. To address the need for real-time measurement of salinity in water flooding solutions during oilfield water injection, a salinity measurement system that can withstand a high temperature environment was designed. In terms of the polarization and capacitance effects, the system uses an integrator circuit to collect information and fuzzy control to switch gears to expand the range. Experimental results show that the system can operate stably in a high-temperature environment, with an accuracy of 0.6% and an uncertainty of 0.2% in the measurement range of 1–10 g/L.

Polarimetric Balanced Detection: Background-Free Mid-IR Evanescent Field Laser Spectroscopy for Low-Noise, Long-term Stable Chemical Sensing

In this work, we introduce polarimetric balanced detection as a new attenuated total reflection (ATR) infrared (IR) sensing scheme, leveraging unequal effective thicknesses achieved with laser light of different polarizations. We combined a monolithic widely tunable Vernier quantum cascade laser (QCL-XT) and a multibounce ATR IR spectroscopy setup for analysis of liquids in a process analytical setting. Polarimetric balanced detection enables simultaneous recording of background and sample spectra, significantly reducing long-term drifts. The root-mean-square noise could be improved by a factor of 10 in a long-term experiment, compared to conventional absorbance measurements obtained via the single-ended optical channel. The sensing performance of the device was further evaluated by on-site measurements of ethanol in water, leading to an improved limit of detection (LOD) achieved with polarimetric balanced detection. Sequential injection analysis was employed for automated injection of samples into a custom-built ATR flow cell mounted above a zinc sulfide multibounce ATR element. The QCL-XT posed to be suitable for mid-IR-based sensing in liquids due to its wide tunability. Polarimetric balanced detection proved to enhance the robustness and long-term stability of the sensing device, along with improving the LOD by a factor of 5. This demonstrates the potential for new polarimetric QCL-based ATR mid-IR sensing schemes for in-field measurements or process monitoring usually prone to a multitude of interferences.

Urban Hydroinformatics: Past, Present and Future

Hydroinformatics, as an interdisciplinary domain that blurs boundaries between water science, data science and computer science, is constantly evolving and reinventing itself. At the heart of this evolution, lies a continuous process of critical (self) appraisal of the discipline’s past, present and potential for further evolution, that creates a positive feedback loop between legacy, reality and aspirations. The power of this process is attested by the successful story of hydroinformatics thus far, which has arguably been able to mobilize wide ranging research and development and get the water sector more in tune with the digital revolution of the past 30 years. In this context, this paper attempts to trace the evolution of the discipline, from its computational hydraulics origins to its present focus on the complete socio-technical system, by providing at the same time, a functional framework to improve the understanding and highlight the links between different strands of the state-of-art hydroinformatic research and innovation. Building on this state-of-art landscape, the paper then attempts to provide an overview of key developments that are coming up, on the discipline’s horizon, focusing on developments relevant to urban water management, while at the same time, highlighting important legal, ethical and technical challenges that need to be addressed to ensure that the brightest aspects of this potential future are realized. Despite obvious limitations imposed by a single paper’s ability to report on such a diverse and dynamic field, it is hoped that this work contributes to a better understanding of both the current state of hydroinformatics and to a shared vision on the most exciting prospects for the future evolution of the discipline and the water sector it serves.