Forward-Looking Roadmaps for Long-Term Continuous Water Quality Monitoring: Bottlenecks, Innovations, and Prospects in a Critical Review

Abstract

Long-term continuous monitoring (LTCM) of water quality can bring far-reaching influences on water ecosystems by providing spatiotemporal data sets of diverse parameters and enabling operation of water and wastewater treatment processes in an energy-saving and cost-effective manner. However, current water monitoring technologies are deficient for long-term accuracy in data collection and processing capability. Inadequate LTCM data impedes water quality assessment and hinders the stakeholders and decision makers from foreseeing emerging problems and executing efficient control methodologies. To tackle this challenge, this review provides a forward-looking roadmap highlighting vital innovations toward LTCM, and elaborates on the impacts of LTCM through a three-hierarchy perspective: data, parameters, and systems. First, we demonstrate the critical needs and challenges of LTCM in natural resource water, drinking water, and wastewater systems, and differentiate LTCM from existing short-term and discrete monitoring techniques. We then elucidate three steps to achieve LTCM in water systems, consisting of data acquisition (water sensors), data processing (machine learning algorithms), and data application (with modeling and process control as two examples). Finally, we explore future opportunities of LTCM in four key domains, water, energy, sensing, and data, and underscore strategies to transfer scientific discoveries to general end-users.

Machine Learning Algorithm Integrated with Real-Time In Situ Sensors and Physiochemical Principle-Driven Soft Sensors toward an Anaerobic Digestion-Data Fusion Framework

“…Our study has breakthroughs at two distinct levels. First, at the mechanistic level, a new pathway of AD data acquisition was explored to capture physiochemical patterns/variations accurately in a real-time in situ mode via the deployment of a novel solid-state mm-sized sensor array (MEA) technology capable of monitoring multiple liquid-phase AD parameters including pH, temperature, oxidation–reduction potential (ORP), conductivity, and ammonium (NH 4 + ). ,,, For the parameters (e.g., volatile fatty acid, alkalinity) unable to measure in a real-time in situ mode, we adopted ADM1 as the soft sensor for data generation with full physiochemical backbones compared to previous data-driven soft-sensor methods. , Second, at the data level, the MLA interpretability was enhanced with the integrated data acquisition technology consisting of robust MEA Physical Sensors (MAPS) and ADM1-based Soft Sensor (ADSS), in which different ML models were analyzed with posthoc interpretation methods to extract physical meanings via the assessment of feature/AD parameter correlations and lab AD experiments (Figure a–c). With the close loop of the MAPS-MLA-ADSS-Experiment strategy, a feasible path was elucidated to conquer the long-standing interpretability problem in MLA and complexity problem in ADM1 by first “pouring” physical meanings into MLA features through MAPS and ADSS, then “brew” those meanings out of the MLA prediction results.…”

Enhanced Long-Term Antifouling Ability and Enrichment Effect of a Vertically Ordered Mesoporous Silica Film via Covalent Linkage of Chondroitin Sulfate for In Situ Detection of Cu2+ in Real Environmental Samples

“…1 Thus, the design of a reliable and stable sensing platform for the longterm monitoring of pollutants in the environment can effectively provide an effective security guarantee for human health and safety. 2,3 Heavy metal ions (HMIs), extremely dangerous contamination in water, are non-biodegradable, non-biocompatible, and can enter organisms by the alimentary chain. Therefore, HMIs could pose significant environmental and health risks.…”

“…The accuracy and reproducibility of the electrochemical detection will be affected to some extent. 2 As a result, the practical application of electrochemical sensors will be extremely restricted. 13 Therefore, it is crucial to effectively exploit a stable and antifouling electrochemical interface, which could promote the application of electrochemical sensing platforms in real and complex matrices.…”

“…Compared with traditional detection methods, electrochemical sensors have gained widespread attention due to their advantages of high sensitivity, fast analysis, and unnecessity for cumbersome devices and additional chromogenic reagents. , Most importantly, electrochemical sensors are unique in that they can be used for both labeled and unlabeled detection, and they are unaffected by the color of the detection matrix and the tiny sample sizes. , However, nonspecific adsorption among multiple complex environments in which actual samples exist is one of the urgent problems to be solved in electrochemical sensing. The accuracy and reproducibility of the electrochemical detection will be affected to some extent . As a result, the practical application of electrochemical sensors will be extremely restricted .…”

“…As a result of continued industrialization, many pollutants from industrial waste have been released into the natural environment and can be a threat to the health of human beings . Thus, the design of a reliable and stable sensing platform for the long-term monitoring of pollutants in the environment can effectively provide an effective security guarantee for human health and safety. , Heavy metal ions (HMIs), extremely dangerous contamination in water, are non-biodegradable, non-biocompatible, and can enter organisms by the alimentary chain. Therefore, HMIs could pose significant environmental and health risks. − As one of the common HMIs, Cu 2+ , is highly toxic to the human body after accumulating a certain concentration owing to its easy accumulation .…”