An environmentally friendly surrogate method for measuring the soluble chemical oxygen demand in wastewater: use of three-dimensional excitation and emission matrix fluorescence spectroscopy in wastewater treatment monitoring

Exploring relationships among landfill leachate parameters through multivariate analysis for monitoring purposes

Elucidating the properties of landfill leachate and the relationships among leachate parameters is crucial for efforts to determine appropriate landfill leachate monitoring activity and management strategies. This study investigated the physical, chemical and optical parameters of leachate in an old Japanese landfill over a 13-month period. The parameters were explored based on their relationships with the maximum fluorescence (Fmax) of three components (microbial humic-like C1, terrestrial humic-like C2 and protein-like C3) deconvoluted from excitation-emission matrix fluorescence spectroscopy coupled with parallel factor analysis. Dissolved organic carbon (DOC), chemical oxygen demand (COD), Cl- and SO42- concentrations and pH ranged from 2.6 to 38.2 mg C L-1, 9 to 324 mg L-1, 14 to 972 mg L-1, 26 to 1554 mg L-1 and 6.9 to 11.6, respectively. Linear regression analysis suggested that the Fmax values of C2 and C3 represented DOC, whereas the Fmax value of C2 alone could serve as a COD indicator. Hierarchical cluster analysis and principal component analysis were employed to successfully categorise leachate samples based on their locations. Higher dissolved organic matter levels were observed in leachate within the old disposal area, whereas elevated levels of inorganic components such as SO42- and Cl- were found in leachate collected from the extended disposal area and at a treatment facility. Statistical analysis provides crucial tools for assessing and managing various areas of a landfill, supporting targeted and effective waste management strategies.

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.

Research on the chemical oxygen demand spectral inversion model in water based on IPLS-GAN-SVM hybrid algorithm

Spectral collinearity and limited spectral datasets are the problems influencing Chemical Oxygen Demand (COD) modeling. To address the first problem and obtain optimal modeling range, the spectra are preprocessed using six methods including Standard Normal Variate, Savitzky-Golay Smoothing Filtering (SG) etc. Subsequently, the 190-350 nm spectral range is divided into 10 subintervals, and Interval Partial Least Squares (IPLS) is used to perform PLS modeling on each interval. The results indicate that it is best modeled in the 7th range (238~253 nm). The values of Mean Square Error (MSE), Mean Absolute Error (MAE) and R2score of the model without pretreatment are 1.6489, 1.0661, and 0.9942. After pretreatment, the SG is better than others, with MSE and MAE decreasing to 1.4727, 1.0318 and R2score improving to 0.9944. Using the optimal model, the predicted COD for three samples are 10.87 mg/L, 14.88 mg/L, and 19.29 mg/L. To address the problem of the small dataset, using Generative Adversarial Networks for data augmentation, three datasets are obtained for Support Vector Machine (SVM) modeling. The results indicate that, compared to the original dataset, the SVM's MSE and MAE have decreased, while its accuracy has improved by 2.88%, 11.53%, and 11.53%, and the R2score has improved by 18.07%, 17.40%, and 18.74%.

Molecular-level insights into the transformation and degradation pathways of dissolved organic matter during full-scale swine wastewater treatment

The rapid development of swine farming has resulted in the generation of a large amount of swine wastewater (SW), and dissolved organic matter (DOM) has a crucial role in determining the efficiency and safety of SW treatment. In this study, the transformation and influential mechanisms of DOM on the quality of SW effluent during full-scale SW treatment in actual engineering were systematically investigated using multispectral analysis and the Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) technique. The results showed that S-containing, reduced, saturated, and less aromatic molecules were preferentially removed in the C-AF, while C-S preferentially removed reduced, unsaturated, and aromatic molecules, as well as molecules with large molecular weights. And in the two-stage A/O, the degradation of organic matter and DOM transformation occurred mainly in the A/O-1, with the A/O-2 acting as a supplement to further enhance the humification of DOM. Furthermore, the AOP preferentially removed lignin-like and highly unsaturated compounds, replacing them with a new generation of substances such as proteins and tannins with low aromaticity and unsaturation. More deeply, oxygen addition reactions dominate in both A/O and AOP. Specifically, the most common types of reactions in the A/O were the corresponding potential precursor-product pairs based on methyl to carboxylic acid (-H2 + O2) and alcohol to carboxylic acid (-H2 + O), while tri-hydroxylation (+O3) and di-hydroxylation (+H2O2) reactions were predominant in the AOP. Finally, the study's findings might suggest improving the actual engineering by prioritizing the AOP before the A/O-2 and using the C-S for safeguard treatment of the A/O-2 effluent. It is reliable that this kind of adjustment guarantees safe drainage indications and raises each process unit's efficiency in purifying.

A tailored and rapid approach for ozonation catalyst design

Catalytic ozonation is widely employed in advanced wastewater treatment owing to its high mineralization of refractory organics. The key to high mineralization is the compatibility between catalyst formulation and wastewater quality. Machine learning can greatly improve experimental efficiency, while fluorescence data can provide additional wastewater quality information on the composition and concentration of organics, which is conducive to optimizing catalyst formulation. In this study, machine learning combined with fluorescence spectroscopy was applied to develop ozonation catalysts (Mn/γ-Al₂O₃ catalyst was used as an example). Based on the data collected from 52 different catalysts, a machine-learning model was established to predict catalyst performance. The correlation coefficient between the experimental and model-predicted values was 0.9659, demonstrating the robustness and good generalization ability of the model. The range of the catalyst formulations was preliminarily screened by fluorescence spectroscopy. When the wastewater was dominated by tryptophan-like and soluble microbial products, the impregnation concentration and time of Mn(NO₃)₂ were less than 0.3 mol L^-1 and 10 h, respectively. Furthermore, the optimized Mn/γ-Al₂O₃ formulation obtained by the model was impregnation with 0.155 mol L^-1 Mn(NO₃)₂ solution for 8.5 h and calcination at 600 °C for 3.5 h. The model-predicted and experimental values for total organic carbon removal were 54.48% and 53.96%, respectively. Finally, the improved catalytic performance was attributed to the synergistic effect of oxidation (•OH and ¹O₂) and the Mn/γ-Al₂O₃ catalyst. This study provides a rapid approach to catalyst design based on the characteristics of wastewater quality using machine learning combined with fluorescence spectroscopy.

Development of an accurate optical sensor for the in situ real-time determination of the chemical oxygen demand of seawater

Chemical oxygen demand (COD) is an important indicator for monitoring the quality of seawater. The COD of seawater reflects the levels of organic pollutants in the water. Methods that are commonly used to measure the COD of seawater have high accuracy, good repeatability, and low costs. However, using them for the in situ real-time monitoring of the COD of seawater is unfavorable because they require complex procedures and a long measurement time and may cause pollution to the environment. This paper reports on an optical sensor that accurately determines the COD of seawater in situ. The COD determination is based on the absorption of ultraviolet and visible lights with different wavelengths by organic matter in the water. Single-point LEDs emitting lights with different wavelengths (254, 265, 280, and 546 nm) were used as sources of excitation lights, and photodiodes were used as receiving devices. The optical system, circuit system, and mechanical structure of the sensor were efficiently integrated. The inversion of the COD of seawater was obtained after turbidity correction using the multiple linear regression algorithm. The maximum measurement error, detection limit, and repeatability of the sensor were 5%, 0.05 mg/l, and 0.62%, respectively. Moreover, the R2 values for correlations between COD values and absorbance values measured at three wavelengths (254, 265, and 280 nm) were above 0.99. Overall, the sensor is suitable for the in situ real-time monitoring of the COD of seawater. It requires a short measurement time and generates no pollution.

Characterization of DBP precursor removal by magnetic ion exchange resin using spectroscopy and high-resolution mass spectrometry

The characteristics of dissolved organic matter (DOM) play an important role in the formation and speciation of carcinogenic disinfection byproducts. This study investigated changes in the characteristics and reactivity of DOM caused by the magnetic ion exchange resins, MIEX® DOC and MIEX® GOLD, using fluorescence excitation-emission matrix (EEM) with parallel factor (PARAFAC) analysis and Orbitrap mass spectrometry (Orbitrap MS) with unknown screening analysis. A five-component PARAFAC model was developed and validated from 208 EEMs of raw and MIEX®-treated water samples. The two resins exhibited preferential removal of the humic-like components (67-87% removal) and successfully removed protein-like components to a lesser extent (5-61% removal). Unknown screening analysis indicated removal of most condensed aromatic structures and lignin-like features that had high O/C values and refractory characteristics of lipid-like features by MIEX® treatments. MIEX® preferentially removed DOM molecules with more oxidized and shorter CH₂ chains. The two resins had similar performance in trihalomethanes formation potential removal, but MIEX® GOLD achieved greater haloacetonitriles formation potential removal owing to its larger pore opening. Over 100 CHOCl DBP features were commonly found in all the samples while tens of CHOCl DBPs were uniquely formed in the samples with and without pre-treatments by MIEX®. Treatments by MIEX® before chlorination resulted in more intermediate CHOCl DBPs formed after chlorination compared to chlorinated raw waters. By optical spectroscopic analysis together with Orbitrap MS molecular characterization, we were able to confirm both quantitative and qualitative changes in DOM properties by MIEX® treatment related to DBP formation.

A review on fluorescence spectroscopic analysis of water and wastewater

In recent years, the application of fluorescence spectroscopy has been widely recognized in water environment studies. The sensitiveness, simplicity, and efficiency of fluorescence spectroscopy are proved to be a promising tool for effective monitoring of water and wastewater. The fluorescence excitation-emission matrix (EEMs) and synchronous fluorescence spectra have been widely used analysis techniques of fluorescence measurement. The presence of organic matter in water and wastewater defines the degree and type of pollution in water. The application of fluorescence spectroscopy to characterize dissolved organic matter (DOM) has made the water quality assessment simple and easy. With the recent advances in this technology, components of DOM are identified by employing parallel factor analysis (PARAFAC), a mathematical trilinear data modeling with EEMs. The majority of wastewater studies indicated that the fluorescence peak of EX/EM at 275 nm/340 nm is referred to tryptophan region (Peak T1). However, some researchers identified another fluorescence peak in the region of EX/EM at 225-237 nm/340-381 nm, which described the tryptophan region and labeled it as Peak T2. Generally, peak T is a protein-like component in the water sample, where T1 and T2 signals were derived from the <0.20μm fraction of pollution. Therefore, a more advanced approach, such as an online fluorescence spectrofluorometer, can be used for the online monitoring of water. The results of various waters studied by fluorescence spectroscopy indicate that changes in peak T intensity could be used for real-time wastewater quality assessment and process control of wastewater treatment works. Finally, due to its effective use in water quality assessment, the fluorescence technique is proved to be a surrogate online monitoring tool and early warning equipment.

Fluorescence excitation/emission matrices as a tool to monitor the removal of organic micropollutants from wastewater effluents by adsorption onto activated carbon

Monitoring the removal of organic micropollutants (OMPs) in advanced wastewater treatment facilities requires expensive and time-consuming analytical methods that cannot be installed online. Spectroscopic techniques such as fluorescence excitation/emission spectroscopy were demonstrated to offer the potential for monitoring OMPs removal in conventional wastewater treatment plants or ozonation pilots but their application to activated carbon (AC) adsorption processes was only investigated at lab scale and not in real treatment facilities. In this study, indexes from fluorescence emission/excitation matrices (EEMs) were used to find correlations with the removal of 28 OMPs from a large-scale AC pilot in fluidized bed employed for wastewater advanced treatment, as well as from batch experiments. Differences in OMPs removal could be observed depending on the operational conditions (i.e. pilot or batch experiments, contact time, type of AC) and the physico-chemical properties of the molecules. 7 PARAFAC components were derived from the fluorescence EEMs of 60 samples obtained before and after adsorption. Positive correlations were obtained between the removal of fluorescence indexes and most OMPs, and correlation coefficients were much higher than the ones obtained with UV₂₅₄, confirming the interesting potential of fluorescence spectroscopy to accurately monitor adsorption performances at the industrial scale. The highest correlation coefficients were obtained for OMPs having the best removals while the ones that were refractory to adsorption, as well as to interactions with DOM, exhibited weak correlations. These results suggest that interactions between OMPs and fluorescing DOM and their subsequent co-adsorption onto AC were at the origin of the correlations found. Lower correlations were also found for the most biodegradable OMPs, which indicated that the occurrence of biological effects could make the monitoring of these compounds more challenging.

Monitoring dissolved organic matter in wastewater and drinking water treatments using spectroscopic analysis and ultra-high resolution mass spectrometry

Dissolved organic matter (DOM) plays a critical role in determining the quality of wastewater and the safety of drinking water. This is the first review to compare two types of popular DOM monitoring techniques, including absorption spectroscopy and fluorescence excitation-emission matrices (EEMs) coupled with parallel factor analysis (PARAFAC) vs. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS), for the applications in wastewater and drinking water treatments. The optical techniques provide a series of indices for tracking the quantity and quality of chromophoric and fluorescent DOM, while FT-ICR-MS is capable of identifying thousands of DOM compounds in wastewater and drinking water at the molecule level. Both types of monitoring techniques are increasingly used in studying DOM in wastewater and drinking water treatments. They provide valuable insights into the variability of DOM composition in wastewater and drinking water. The complexity and diversity of DOM highlight the challenges for effective water treatments. Different effects of various treatment processes on DOM are also assessed, which indicates that the information on DOM composition and its removal is key to optimize the treatment processes. Considering notable progress in advanced treatment processes and novel materials for removing DOM, it is important to continuously utilize these powerful monitoring tools for assessing the responses of different DOM constituents to a series of treatment processes, which can achieve an effective removal of DOM and the quality of treated water.

Temperature, turbidity, and the inner filter effect correction methodology for analyzing fluorescent dissolved organic matter in urban sewage

Dissolved organic matter (DOM) will be increasingly monitored by means of in situ fluorescence spectroscopy devices in order to supervise wastewater treatment plant efficiency, due to their ease of implementation and high-frequency measurement capacity. However, fluorescence spectroscopy measurements are reported to be sensitive to the sample matrix effects of temperature, the inner filter effect (IFE), and turbidity. Matrix effect estimation tests and signal correction have been developed for DOM (tyrosine-like, tryptophan-like, and humic substances-like fluorescent compounds) fluorescence measurements in unfiltered urban sewage samples. All such tests are conducted in temperature, absorbance, and turbidity ranges representative of urban sewage. For all fluorophores studied, an average of 1% fluorescence intensity decrease per degree (°C) of temperature increase could be observed. Protein-like fluorescent compound signals were found to be significantly affected by turbidity (0 to 210 NTU) and IFE (absorbance 254 nm > 0.200). Only temperature needs to be corrected for humic substances-like fluorescent compounds since other effects were not observed over the studied ranges of absorbance and turbidity. The fluorescence intensity correction method was applied first to each matrix effect separately and then combined by using a sequential mathematical correction methodology. An efficient methodology for determining the matrix effect correction equations for DOM fluorescence analysis into unfiltered urban sewage samples has been highlighted and could be used for in situ fluorescence measurement devices.

Quantification of bacteria in water using PLS analysis of emission spectra of fluorescence and excitation-emission matrices

Bacterial contamination of drinking water is a considerable concern for public health. Tryptophan-like fluorescence (TLF) has been widely suggested to enable fast and inexpensive monitoring and quantification of bacterial contamination of water. Typically, TLF is determined at a certain excitation (ex)/emission (em) wavelengths pair. The aim of this study was to assess fluorescence spectroscopy supported with partial least squares (PLS) algorithms as a tool for a rapid evaluation of the microbial quality of water, by comparing the use of a single ex/em wavelengths pair, of the spectrum of emission obtained at a single excitation wavelength to that of whole excitation-emission matrices (EEMs). For that, laboratory-grown Escherichia coli, Bacillus subtilis and Pseudomonas aeruginosa were studied as the model systems, as well as 90 groundwater samples from 6 different wells in Israel. The groundwater samples were characterized for fluorescence emission, coliforms, fecal coliforms, fecal streptococci and heterotrophic plate counts. The PLS analysis of emission spectra and, especially, of EEMs was capable of meaningfully reducing the detection limit of microorganisms in model systems, as compared with the single ex/em wavelengths pair-based determination commonly used, reaching a detection threshold as low as 10 CFU/ml. Use of PLS-analyzed EEMs becomes beneficial also in terms of correlation and similarity between the actual and predicted bacterial concentrations. Similarly, improved detection of bacteria was also achieved in groundwater samples. Furthermore, at a level of >10⁴ CFU/ml, use of EEMs coupled with PLS enabled discrimination between E. coli, B. subtilis and P. aeruginosa.