Calibration of an in-situ fluorescence-based sensor platform for reliable BOD5 measurement in wastewater

Improving monitoring of dissolved organic matter from the wastewater treatment plant to the receiving environment: A new high-frequency in situ fluorescence sensor capable of analyzing 29 pairs of Ex/Em wavelengths

A high-frequency, in situ fluorescence probe, called Fluocopée®, has been developed in order to better monitor variations in both the quality and quantity of dissolved organic matter within various aquatic environments (e.g. wastewater, receiving environments) thanks to a wide choice of 29 measured Excitation/Emission wavelength pairs. This advance pave the way to new measurement possibilities in comparison with existing probes, which are usually only able to measure 1-4 fluorophores. The qualification tests of the Fluocopée® probe indicate a high level of accuracy for the measurements of tyrosine, tryptophan and humic acids solutions. Good repeatability and reproducibility are also observed. For the first time, this tool has been deployed in an urban watershed (Bougival, Seine River, downstream of Paris) and in the settled effluent from a wastewater treatment plant (Seine aval, Achères, France). This new high-frequency in situ probe offers great application potential, including organic matter quality and quantity monitoring at drinking and wastewater treatment plants (treatment optimization) and in continental and marine waters (the fate of organic matter in biogeochemical cycles).

Online control of UV and UV/H2O2 processes targeted for the removal of contaminants of emerging concern (CEC) by a fluorescence sensor

This study assessed the online and real-time monitoring of contaminants of emerging concern (CEC) using a microbial/tryptophan-like fluorescence sensor in a quaternary AOP (advanced oxidation process) pilot plant installed downstream of a tertiary municipal wastewater treatment plant (WWTP). Real-time fluorescence measurements were validated with lab-scale tryptophan-like fluorescence. Changes in water quality induced by different UV or UV/H2O2 doses were detected by the fluorescence sensor allowing real-time control of processes. The removal of CEC was discussed considering their photo-susceptibility and reactivity with •OH and then classified into three groups based on their reactivity and removal efficiency (RE). Linear models of CEC removal developed using real-time fluorescence removal as a surrogate parameter resulted very accurate (overall R2≥0.90) for most of CEC. Furthermore, real-time fluorescence data were successfully used to predict i) pseudo-observed first-order degradation rate constants of CEC (R2=0.99), and ii) UV doses during both UV and UV/H2O2 processes (R2>0.90). The findings of this study demonstrated that fluorescence sensors can be employed in operational relevant environment to monitor a broad range of CEC and control UV doses during UV-AOPs. Therefore, the implementation of fluorescence sensors is expected for optimizing costs, energy consumption and efficiency of quaternary wastewater treatments.

Optical measurements of dissolved organic matter as proxies for CODMn and BOD5 in plateau lakes

The presence of organic matter in lakes profoundly impacts drinking water supplies, yet treatment processes involving coagulants and disinfectants can yield carcinogenic disinfection by-products. Traditional assessments of organic matter, such as chemical oxygen demand (CODMn) and biochemical oxygen demand (BOD5), are often time-consuming. Alternatively, optical measurements of dissolved organic matter (DOM) offer a rapid and reliable means of obtaining organic matter composition data. Here we employed DOM optical measurements in conjunction with parallel factor analysis to scrutinize CODMn and BOD5 variability. Validation was performed using an independent dataset encompassing six lakes on the Yungui Plateau from 2014 to 2016 (n = 256). Leveraging multiple linear regressions (MLRs) applied to DOM absorbance at 254 nm (a254) and fluorescence components C1-C5, we successfully traced CODMn and BOD5 variations across the entire plateau (68 lakes, n = 271, R2 > 0.8, P < 0.0001). Notably, DOM optical indices yielded superior estimates (higher R2) of CODMn and BOD5 during the rainy season compared to the dry season and demonstrated increased accuracy (R2 > 0.9) in mesotrophic lakes compared to oligotrophic and eutrophic lakes. This study underscores the utility of MLR-based DOM indices for inferring CODMn and BOD5 variability in plateau lakes and highlights the potential of integrating in situ and remote sensing platforms for water pollution early warning.

The telltale fluorescence fingerprints of sewer flows for interpreting the low influent concentration in wastewater treatment plant

Low degradability of wastewater treatment plant (WWTP) influents negatively affects its ability to effectively remove pollutants through wastewater treatment processes. Proactive assessment of urban sewer system performance is highly valued in the selection of targeted countermeasures for this occurrence. In this study, a fluorescence spectrum interpretation approach was developed to identify the causes of low biodegradability of WWTP influent by using parallel factor analysis (PARAFAC) and fluorescence regional integration (FRI) of excitation-emission matrix spectroscopy. Statistical analysis was also used to further interpret the PARAFAC- and FRI-derived data. The urban sewer catchment served by a WWTP in Wuhan City, China, was used as the test site to demonstrate the effectiveness of this approach. The results showed that electronics manufacturing industrial wastewater and groundwater input into the urban sewer would significantly decrease the biodegradability of the WWTP influents, and these sources were characterized by much lower fluorescence peak intensities, especially for protein-like substances, including tryptophan-like T and tyrosine-like B1 and B2. The potential conversion of high freshness T into low freshness B2 within the sewer may also contribute to this undesirable scenario. The ratio of peak T to peak B2 and the ratio of the FRI fraction of region I to that of region II can be used together to determine the predominance of industrial wastewater and groundwater. T/B2 < 1.3 indicates the entry of industrial wastewater or groundwater into urban sewers, and I/II > 0.5 further confirms the input of industrial wastewater. Accordingly, the low biodegradability of the WWTP influents in our study site is mostly due to the inflow of industrial wastewater rather than groundwater infiltration into the urban sewers. Therefore, actions should be focused on the surveillance of industrial wastewater rather than widespread sewer inspection and repairs. In this way, this methodology is cost-effective in aiding targeted countermeasures to improve the urban sewer system performance.

Artificial neural network implementation for dissolved organic carbon quantification using fluorescence intensity as a predictor in wastewater treatment plants

Although spectroscopic methods provide a fast and cost-effective means of monitoring dissolved organic carbon (DOC) in natural and engineered water systems, the prediction accuracy of these methods is limited by the complex relationship between optical properties and DOC concentration. In this study, we developed DOC prediction models using multiple linear/log-linear regression and feedforward artificial neural network (ANN) and investigated the effectiveness of spectroscopic properties, such as fluorescence intensity and UV absorption at 254 nm (UV₂₅₄), as predictors. Optimum predictors were identified based on correlation analysis to construct models using single and multiple predictors. We compared the peak-picking and parallel factor analysis (PARAFAC) methods for selecting appropriate fluorescence wavelengths. Both methods had similar prediction capability (p-values >0.05), suggesting PARAFAC was not necessary for choosing fluorescence predictors. Fluorescence peak T was identified as a more accurate predictor than UV₂₅₄. Combining UV₂₅₄ and multiple fluorescence peak intensities as predictors further improved the prediction capability of the models. The ANN models outperformed the linear/log-linear regression models with multiple predictors, achieving higher prediction accuracy (peak-picking: R² = 0.8978, RMSE = 0.3105 mg/L; PARAFAC: R² = 0.9079, RMSE = 0.2989 mg/L). These findings suggest the potential to develop a real-time DOC concentration sensor based on optical properties using an ANN for signal processing.

Low-Cost Water Quality Sensors for IoT: A Systematic Review

In many countries, water quality monitoring is limited due to the high cost of logistics and professional equipment such as multiparametric probes. However, low-cost sensors integrated with the Internet of Things can enable real-time environmental monitoring networks, providing valuable water quality information to the public. To facilitate the widespread adoption of these sensors, it is crucial to identify which sensors can accurately measure key water quality parameters, their manufacturers, and their reliability in different environments. Although there is an increasing body of work utilizing low-cost water quality sensors, many questions remain unanswered. To address this issue, a systematic literature review was conducted to determine which low-cost sensors are being used for remote water quality monitoring. The results show that there are three primary vendors for the sensors used in the selected papers. Most sensors range in price from US$6.9 to US$169.00 but can cost up to US$500.00. While many papers suggest that low-cost sensors are suitable for water quality monitoring, few compare low-cost sensors to reference devices. Therefore, further research is necessary to determine the reliability and accuracy of low-cost sensors compared to professional devices.

A case study: The deployment of a novel in situ fluorimeter for monitoring biological contamination within the urban surface waters of Kolkata, India

The quality and health of many of our vital freshwater systems are poor. To tackle this with ever increasing pressures from anthropogenic and climatic changes, we must improve water quality monitoring and devise and implement more appropriate water quality parameters. Recent research has highlighted the potential for Peak T fluorescence (tryptophan-like fluorescence, TLF) to monitor microbial activity in aquatic systems. The VLux TPro (Chelsea Technologies Ltd., UK), an in situ real-time fluorimeter, was deployed in different urban freshwater bodies within Kolkata (West Bengal, India) during March 2019. This study is the first to apply this technology in surface waters within a densely populated urban area. Spot-sampling was also undertaken at 13 sampling locations enabling physicochemical analysis, bacterial enumeration and determination of nutrient (nitrate and phosphate) concentrations. This case study has demonstrated the ability of an in situ fluorimeter, VLux TPro, to successfully identify both biological contamination events and potential elevated microbial activity, related to nutrient loading, in complex surface freshwaters, without the need for expensive and time-consuming laboratory analysis.