Role of non-fluorescent chromophores in inner filter effect correction and PARAFAC decomposition

Seasonal studies of aquatic humic substances from Amazon rivers: characterization and interaction with Cu (II), Fe (II), and Al (III) using EEM-PARAFAC and 2D FTIR correlation analyses

Aquatic humic substances (AHS) are defined as an important components of organic matter, being composed as small molecules in a supramolecular structure and can interact with metallic ions, thereby altering the bioavailability of these species. To better understand this behavior, AHS were extracted and characterized from Negro River, located near Manaus city and Carú River, that is situated in Itacoatiara city, an area experiencing increasing anthropogenic actions; both were characterized as blackwater rivers. The AHS were characterized by 13C nuclear magnetic ressonance and thermochemolysis GC-MS to obtain structural characteristics. Interaction studies with Cu (II), Al (III), and Fe (III) were investigated using fluorescence spectroscopy applied to parallel factor analysis (PARAFAC) and two-dimensional correlation spectroscopy with Fourier transform infrared spectroscopy (2D-COS FTIR). The AHS from dry season had more aromatic fractions not derived from lignin and had higher content of alkyls moities from microbial sources and vegetal tissues of autochthonous origin, while AHS isolated in the rainy season showed more metals in its molecular architecture, lignin units, and polysacharide structures. The study showed that AHS composition from rainy season were able to interact with Al (III), Fe (III), and Cu (II). Two fluorescent components were identified as responsible for interaction: C1 (blue-shifted) and C2 (red-shifted). C1 showed higher complexation capacities but with lower complexation stability constants (KML ranged from 0.3 to 7.9 × 105) than C2 (KML ranged from 3.1 to 10.0 × 105). 2D-COS FTIR showed that the COO- and C-O in phenolic were the most important functional groups for interaction with studied metallic ions.

Adsorption of humic acid from different organic solid waste compost to phenanthrene, is fluorescence excitation or quenching?

Humic acid (HA) from different organic solid waste (OSW) compost has been shown good adsorption properties for phenanthrene. However, the raw material of HA can affect its structure, resulting in differences in adsorption capacity. Therefore, this study focused on the adsorption characteristics of phenanthrene by HA from different OSW compost. In this work, chicken manure (CM), rice straw (RS) and lawn waste (LW) were selected as sources of composted HA. The adsorption mechanism of HA from different OSW compost were revealed through analytical techniques including three-dimensional fluorescence spectroscopy (EEM), two-dimensional correlation spectroscopy (2DCOS), and Fourier-transform infrared spectroscopy (FTIR). The results suggested that HA from LW compost had a better adsorption affinity for phenanthrene because of its more complex fluorescent component, where C1 as a simple component determined the adsorption process specifically. Furthermore, after HA from LW compost adsorbed phenanthrene, the increase in aromatic -COOH and -NH was the main reason for fluorescence quenching. These results indicated that HA from LW compost had better adsorption effect for phenanthrene. The results of this study were expected to provide a selection scheme for the control of phenanthrene pollution and environmental remediation.

Insight into the dynamics of dissolved organic matter components under latitude change perturbation

Dissolved organic matter (DOM) which can help the transportation of nutrients and pollutants plays essential role in the aquatic ecosystems. However, the dynamics of individual DOM component under the change of latitude have not been elucidated to date. The composition and dynamics of DOM were assessed in this study. Two individual parallel factor analysis (PARAFAC) components were found in each sampling site in Heilongjiang. To further characterize the inner change of the identified PARAFAC components, two-latitude correlation spectroscopy (2DCOS) technique was applied to the excitation loadings data. Interestingly, not all the fluorophore in a PARAFAC component change in the same direction as the overall change of a component. From upstream to downstream, the peak A1 in PARAFAC component C1 showed a downward trend, but peak A2 presented an upward trend. In PARAFAC component C2, the peak T2 and peak T3 showed an inverse changing trend under latitude perturbation. Furthermore, basic nutrients parameters in Heilongjiang were also characterized in each sampling sites. The relationships between DOM and nutrients showed that component C1 made a significant contribution to chemical oxygen demand (COD) and biochemical oxygen demand (BOD5). The evolutions of DOM peak A1 and peak A2 were accompanied by the changing of Total phosphorus (TP). The findings in this study could make a contribution to explore the fate of DOM in high humic-like substance containing river.

A smartphone-assisted ratiometric colorimetric and fluorescent probe for triple-mode determination of nitrite based on MnO2 nanoparticles and carbon quantum dots

A triple-mode colorimetric and fluorescent sensing scheme based on manganese dioxide nanoparticles (MnO₂NPs) and carbon quantum dots (CQDs) were developed to determine nitrite. MnO₂NPs can oxidize 3,3',5,5'-tetramethylbenzidine (TMB) into a blue oxidation product (TMBox), which is further oxidized into a yellow diimine derivative by nitrite. The ratio of absorbance at 652 nm to 452 nm was monitored as signal response for UV-vis detection mode. A "turn-off" CQDs fluorescence probe was also constructed for fluorescent detection mode. Smartphone tool kit was used to capture the color of sample for smartphone-based measurement. Various analytical performance under different detection modes were obtained and compared. The proposed methods were applied to food samples with satisfactory recoveries (83.3-106 %). The results were validated with AOAC standard spectrophotometric method. The current triple-mode detection were accurate, convenient, low-cost and fast for analyzing nitrite in foods and water samples on-site.

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.

CFSA-AGD: An accurate crosstalk fluorescence spectroscopic decomposition method for identifying and quantifying FDOMs in aquatic environments

Fluorescent substances exist in various aquatic environments and other environmental media. It is a critical task to identify the components accurately and quantify their contents precisely. Based on the Crosstalk Fluorescence Spectroscopy Analysis (CFSA) model, a fluorescence spectroscopic decomposition using the Alternating Gradient Descent (AGD) algorithm is developed. By reducing the residual error of the model through alternating iterations, the CFSA-AGD method achieves unsupervised model training and automatic spectroscopic decomposition without extra experimental operations such as dilution or absorbance measurement, exempting from tedious modeling process. The objectives of this work are to validate that the CFSA-AGD method can comprehensively address the decomposition of fluorescence spectral crosstalk. Furthermore, the novel method is applied to the spectroscopic decomposition of natural FDOMs in aquatic environments as a standard tool. The spectral data analyzing the performance of this method is verified and compared with the conventional methods through the experiment on standard samples. The results indicate that CFSA-AGD has higher spectroscopic decomposition accuracy and gives more abundant information on the characteristic spectra with less residual error than parallel factor analysis. This means that the fluorescence spectra of natural FDOMs can be decomposed into the characteristic fluorescence emission spectra of single components with higher accuracy and the characteristic fluorescence absorption spectra that cannot be obtained by the conventional methods. Meanwhile, it improves the analytical precision of the contents (from R² ≥ 0.9778 to R² ≥ 0.9920) and reduces the ultimate residual error by two orders of magnitude (from 1.42 × 10^-1 to 4.68 × 10^-3) when the method is used to estimate the measured fluorescence spectra.

Prediction of water quality based on SVR by fluorescence excitation-emission matrix and UV-Vis absorption spectrum

As a result of economic development, the pollution of freshwater resources in urban areas of China is becoming more and more serious. Therefore, it is urgent and necessary to develop a real-time monitoring method for the water quality of urban streams and rivers. In this study, a novel method (CFFA) Combined by peak-picking method, Fluorescence spectral indexes, Fluorescence regional integration, and Absorption spectral indexes were designed to extract wide-ranging information from the combination of the excitation-emission matrix (EEM) and absorption spectrum (Abs) of water samples. More than 600 freshwater samples were collected at 180 sections of 60 rivers in the Yangzhou urban region from April 2018 and May 2019. The CFFA inputs form was applied to establish the prediction models of water quality indexes (COD_Cr, COD_Mn, NH₃-N, TP, TN, and BOD₅) based on ε-Support Vector Regression (ε-SVR). To examine the performance of the prediction models, contrastive analysis among CFFA and the other three input models was carried out. Results show that CFFA input models have shorter modeling time, lower RMSE and MAPE, and higher R² in both training and testing sets, and each constituent part of CFFA is important to the precise prediction on the basis of the ablating analysis. Our study highlights that SVR models with the CFFA input trained by numerous and various water samples could effectively predict multiple indexes for real-time water quality monitoring.

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.