Real-time, in situ accurate monitoring of nitrogen contaminants in wastewater over a long-term period is critical for swift feedback control, enhanced nitrogen removal efficiency, and reduced energy consumption of wastewater treatment processes. Existing nitrogen sensors suffer from high cost, low stability, and short life times, posing hurdles for their mass deployment to capture a complete picture within heterogeneous systems. Tackling this challenge, this study presents solidstate ion-selective membrane (S-ISM) nitrogen sensors for ammonium (NH 4 + ) and nitrate (NO 3 − ) in wastewater that were coupled to a wireless data transmission gateway for real-time remote data access. Lab-scale test and continuous-flow field tests using real municipal wastewater indicated that the S-ISM nitrogen sensors possessed excellent accuracy and precision, high selectivity, and multiday stability. Importantly, autocorrections of the sensor readings on the cloud minimized temperature influences and assured accurate nitrogen concentration readings in remote-sensing applications. It was estimated that real-time, in situ monitoring using wireless S-ISM nitrogen sensors could save 25% of electric energy under normal operational conditions and reduce 22% of nitrogen discharge under shock conditions.

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.

Sensor reading drifting caused by sensor property deterioration is a major problem of long-term continuous monitoring in wastewater and hinders wide-range application of online wastewater management. This study aims to tackle this problem by developing denoising data processing algorithm (DDPA) for a typical electrochemical sensor, solid-state ion-selective membrane (S-ISM) sensor. Based on data mining and electrochemical principles, DDPA was designed by combining digital filter and outlier analysis to differentiate actual sensor readings from background noise when the S-ISM sensitivity declined over time. The sensor sensitivity was raised from 21 mV/dec to 55 mV/dec after the reading processing, without compromising the detection limit (7 × 10–6 mol/L). Furthermore, long-term accuracy of S-ISM sensors in wastewater was enhanced by adding hydrophobic polytetrafluoroethylene (PTFE) into polymer matrix. The sensitivity (57 mV/dec) of PTFE-loaded S-ISM sensors was the near-theoretical value on the first day and still higher than 35 mV/dec after 24 days in wastewater, providing an excellent stable baseline for DDPA. Combination of sensor material enhancement (adding PTFE) with sensor reading processing (using DDPA) assured the stable and high sensitivity (55 mV/dec after 24 days) and high detection limit (<5 × 10–5 mol/L) for wastewater monitoring. The study demonstrates a new route toward long-term accurate wastewater monitoring and smart wastewater sensor networks by establishing a strong correlation between multiorder derivatives of sensor readings and electrochemical responses with DDPA as an efficient data analysis approach.

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Long-term accurate and continuous monitoring of nitrate (NO3 –) concentration in wastewater and groundwater is critical for determining treatment efficiency and tracking contaminant transport. Current nitrate monitoring technologies, including colorimetric, chromatographic, biometric, and electrochemical sensors, are not feasible for continuous monitoring. This study addressed this challenge by modifying NO3 – solid-state ion-selective electrodes (S-ISEs) with poly­(tetrafluoroethylene) (PTFE, (C2F4) n ). The PTFE-loaded S-ISE membrane polymer matrix reduces water layer formation between the membrane and electrode/solid contact, while paradoxically, the even more hydrophobic PTFE-loaded S-ISE membrane prevents bacterial attachment despite the opposite approach of hydrophilic modifications in other antifouling sensor designs. Specifically, an optimal ratio of 5% PTFE in the S-ISE polymer matrix was determined by a series of characterization tests in real wastewater. Five percent of PTFE alleviated biofouling to the sensor surface by enhancing the negative charge (−4.5 to −45.8 mV) and lowering surface roughness (R a: 0.56 ± 0.02 nm). It simultaneously mitigated water layer formation between the membrane and electrode by increasing hydrophobicity (contact angle: 104°) and membrane adhesion and thus minimized the reading (mV) drift in the baseline sensitivity (“data drifting”). Long-term accuracy and durability of 5% PTFE-loaded NO3 – S-ISEs were well demonstrated in real wastewater over 20 days, an improvement over commercial sensor longevity.