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

Fluorescence spectrometric analysis for diagnosing compositional variations in effluent organic matter by chlorination and ozonation

Analyzing the reactivity of organic matter to oxidants such as chlorination and ozonation is crucial for evaluating the effectiveness of water treatment systems and their potential impacts on environmental safety and human health. This study explored the changes in organic substances, specifically bovine serum albumin (BSA), humic acid sodium salt (HA), and effluent organic matter (EfOM) from a wastewater treatment facility during chlorination and ozonation. Four spectrometric techniques were employed: ultraviolet absorbance at 254 nm (UVA254), fluorescent excitation-emission matrix (EEM), synchronous fluorescence two-dimensional correlation spectroscopy (SF-2DCOS), and EEM-parallel factor integrated 2DCOS (EEM-PARAFAC-2DCOS). The findings revealed that ozone possesses superior oxidizing properties compared to chlorine, as evidenced by UVA254 and EEM analyses, resulting in more diverse structural modifications in EfOM. SF-2DCOS and EEM-PARAFAC-2DCOS provided comprehensive details on the direction and sequence of these changes, with EEM-PARAFAC-2DCOS delivering clear and intuitive insights. Protein-like and fulvic-like substances were susceptible to chlorination and ozonation, exhibiting different reaction sequences with each oxidant. Furthermore, variations in protein-like and humic-like components in actual EfOM samples may not align precisely with those in model substances, emphasizing the importance of considering specific organic matter variations in real EfOM samples compared to model substances. This research offered a deeper understanding of the reactivity and transformation of organic matter in wastewater treatment processes through simple and rapid spectroscopic methods, potentially improving the management and mitigation of undesired byproducts.

Exploring optical descriptors for rapid estimation of coastal sediment organic carbon and nearby land-use classifications via machine learning models

This study utilizes ultraviolet and fluorescence spectroscopic indices of dissolved organic matter (DOM) from sediments, combined with machine learning (ML) models, to develop an optimized predictive model for estimating sediment total organic carbon (TOC) and identifying adjacent land-use types in coastal sediments from the Yellow and Bohai Seas. Our results indicate that ML models surpass traditional regression techniques in estimating TOC and classifying land-use types. Penalized Least Squares Regression (PLR) and Cubist models show exceptional TOC estimation capabilities, with PLR exhibiting the lowest training error and Cubist achieving a correlation coefficient 0.79. In land-use classification, Support Vector Machines achieved 85.6 % accuracy in training and 92.2 % in testing. Maximum fluorescence intensity and ultraviolet absorbance at 254 nm were crucial factors influencing TOC variations in coastal sediments. This study underscores the efficacy of ML models utilizing DOM optical indices for near real-time estimation of marine sediment TOC and land-use classification.