Characteristics of Dissolved Organic Matter and Its Role in Lake Eutrophication at the Early Stage of Algal Blooms—A Case Study of Lake Taihu, China

An Intelligent Early Warning System for Harmful Algal Blooms: Harnessing the Power of Big Data and Deep Learning

Harmful algal blooms (HABs) pose a significant ecological threat and economic detriment to freshwater environments. In order to develop an intelligent early warning system for HABs, big data and deep learning models were harnessed in this study. Data collection was achieved utilizing the vertical aquatic monitoring system (VAMS). Subsequently, the analysis and stratification of the vertical aquatic layer were conducted employing the "DeepDPM-Spectral Clustering" method. This approach drastically reduced the number of predictive models and enhanced the adaptability of the system. The Bloomformer-2 model was developed to conduct both single-step and multistep predictions of Chl-a, integrating the " Alert Level Framework" issued by the World Health Organization to accomplish early warning for HABs. The case study conducted in Taihu Lake revealed that during the winter of 2018, the water column could be partitioned into four clusters (Groups W1-W4), while in the summer of 2019, the water column could be partitioned into five clusters (Groups S1-S5). Moreover, in a subsequent predictive task, Bloomformer-2 exhibited superiority in performance across all clusters for both the winter of 2018 and the summer of 2019 (MAE: 0.175-0.394, MSE: 0.042-0.305, and MAPE: 0.228-2.279 for single-step prediction; MAE: 0.184-0.505, MSE: 0.101-0.378, and MAPE: 0.243-4.011 for multistep prediction). The prediction for the 3 days indicated that Group W1 was in a Level I alert state at all times. Conversely, Group S1 was mainly under an Level I alert, with seven specific time points escalating to a Level II alert. Furthermore, the end-to-end architecture of this system, coupled with the automation of its various processes, minimized human intervention, endowing it with intelligent characteristics. This research highlights the transformative potential of integrating big data and artificial intelligence in environmental management and emphasizes the importance of model interpretability in machine learning applications.

Characteristics of dissolved organic matter during the cyanobacterial degradation in Chaohu Lake, China

The actual DOM in Chaohu Lake was used to feed cyanobacterial to explore the changes of microbial communities, fluorescence spectral characteristics and molecular composition of DOM during the degradation of cyanobacteria. It is found that cyanobacterial grow periodically depending on the concentration of nutrients with the decreasing concentration of nutrient salts. Both Bacteroidetes and Actinobacteria have strong correlation with algae growth. Bacteroidetes has a positive correlation with algae growth, relationship on the contrary, Actinobacteria has a negative relationship. The humus-like components in the four groups are similar, but the protein-like component (C3) shows periodic changes with the life process of cyanobacteria. The average molecular weight of each sample detected by Orbitrap high-resolution mass spectrometer increases slightly and the DOM increase aromaticity in the end. In this study, the molecule of Carboxyl-Rich Alicyclic Molecules (CRAM) is difficult to be done by photodegradation and biodegradation in the early periods, but some molecules of CRAM are selectively degraded by microorganisms in the final period. The growth of cyanobacterial lead to increasing the concentration of protein-like and carbohydrate-like molecule of DOM in the water. In the final stage, the molecule group of CHO disappear significantly and the molecule group of heteroatomic group increase.

Improving algal bloom detection using spectroscopic analysis and machine learning: A case study in a large artificial reservoir, South Korea

The prediction of algal blooms using traditional water quality indicators is expensive, labor-intensive, and time-consuming, making it challenging to meet the critical requirement of timely monitoring for prompt management. Using optical measures for forecasting algal blooms is a feasible and useful method to overcome these problems. This study explores the potential application of optical measures to enhance algal bloom prediction in terms of prediction accuracy and workload reduction, aided by machine learning (ML) models. Compared to absorption-derived parameters, commonly used fluorescence indices such as the fluorescence index (FI), humification index (HIX), biological index (BIX), and protein-like component improved the prediction accuracy. However, the prediction accuracy was decreased when all optical indices were considered for computation due to increased noise and uncertainty in the models. With the exception of chemical oxygen demand (COD), this study successfully replaced biochemical oxygen demand (BOD), dissolved organic carbon (DOC), and nutrients with selected fluorescence indices, demonstrating relatively analogous performance in either training or testing data, with consistent and good coefficient of determination (R2) values of approximately 0.85 and 0.74, respectively. Among all models considered, ensemble learning models consistently outperformed conventional regression models and artificial neural networks (ANNs). However, there was a trade-off between accuracy and computation efficiency among the ensemble learning models (i.e., Stacking and XGBoost) for algal bloom prediction. Our study offers a glimpse of the potential application of spectroscopic measures to improve accuracy and efficiency in algal bloom prediction, but further work should be carried out in other water bodies to further validate our proposed hypothesis.

Diurnal and seasonal variations of pCO2 and fluorescent dissolved organic matter (FDOM) in different polluted lakes

This study aimed to assess pollution and daily-to-seasonal dynamics of the partial pressure of CO2 (pCO2) and CO2 degassing flux concerning the fluorescent dissolved organic matter (FDOM) from tropical lakes. A membrane-enclosed pCO2 sensor and water quality multimeter analyzer was deployed to continuously record daily and seasonal variations in pCO2 and CO2 degassing flux in three lakes in Savar, Dhaka. During both wet and dry seasons, all lake water was supersaturated with CO2 in contrast to the atmospheric equilibrium (~400 μatm). The pCO2 values in the lake water during the dry season were relatively low in comparison, and the pCO2 levels in the wet season were much higher due to external inputs of organic matter from watersheds and direct inputs of CO2 from soils or wetlands. The estimated water-to-air CO2 degassing flux in the different levels of polluted lakes varies with the pollution context. Study areas calculated the carbon flux and three lakes released respectively 86.75×107g CO2 year-1, 13.8×107g CO2 year-1, and 9.17×107g CO2 year-1. Three-dimensional excitation-emission matrix (3D-EEM) fluorescence spectroscopy combined with parallel factor (PARAFAC) analysis was used to investigate the distributions of fluorescent components in DOM. EEM-PARAFAC analysis identified humic-like, fulvic-like, protein-like, and more tyrosine-like FDOM components and their environmental dynamics. Terrestrial DOM may provide inputs to the terrestrial humic-like component in the lake water. In contrast, the biological activity of plankton-derived FDOM is the most likely source for the autochthonous humic-like component. FDOM and DO concentrations have negative correlations with pCO2, indicating that when the FDOM and DO level is decreased, the amount of pCO2 values increases.

Fluorescence-based indicators predict the performance of conventional drinking water treatment processes: Evaluation based on the changes in the compositions of dissolved organic matter

This study investigated the treatability of dissolved organic matter (DOM) by the selected lab-scale drinking water treatment processes using fluorescence excitation-emission matrix (EEM) analysis. The fluorescence ratio Peak 3/Peak 2 was established from well-defined fluorescence peak intensity of humic-like components (Ex/Em: 225 nm/425 nm) and protein-like components (Ex/Em: 230 nm/345 nm). Peak 3/Peak 2 predicted the aromatic characteristics of DOM and their origins in the different natural surface water feeding the different drinking water treatment plants. The drinking water treatment processes confirmed the treatability of DOM using Peak 3/Peak 2 and was well-confirmed by specific UV₂₆₀ absorbance relative to dissolved organic carbon (DOC) (SUVA) and fluorescence-based indices. Peak 3/Peak 2 was demonstrated to have a strong correlation with SUVA and DOC removal for the water after treatment by coagulation, adsorption, and chlorination. Compared to the humification index and fluorescence index, Peak 3/Peak 2 is better for indicating the DOM composition in terms of treatability. These findings can broaden the use of fluorescence spectroscopy in water treatment applications, by developing the fluorescence ratio to evaluate the performance of drinking water treatment plants.

Adaptations of microbial communities and dissolved organics to seasonal pressures in a mesotrophic coastal Mediterranean lake

In lake ecosystems, changes in eukaryotic and prokaryotic microbes and the concentration and availability of dissolved organic matter (DOM) produced within or supplied to the system by allochthonous sources are components that characterize complex processes in the microbial loop. We address seasonal changes of microbial communities and DOM in the largest Croatian lake, Vrana. This shallow lake is connected to the Adriatic Sea and is impacted by agricultural activity. Microbial community and DOM structure were driven by several environmental stressors, including drought, seawater intrusion, and heavy precipitation events. Bacterial composition of different lifestyles (free-living and particle-associated) differed and only a part of the particle-associated bacteria correlated with microbial eukaryotes. Oscillations of cyanobacterial relative abundance along with chlorophyll a revealed a high primary production season characterized by increased levels of autochthonous DOM that promoted bacterial processes of organic matter degradation. From our results, we infer that in coastal freshwater lakes dependent on precipitation-evaporation balance, prolonged dry season coupled with heavy irrigation impact microbial communities at different trophic levels even if salinity increases only slightly and allochthonous DOM inputs decrease. These pressures, if applied more frequently or at higher concentrations, could have the potential to overturn the trophic state of the lake. This article is protected by copyright. All rights reserved.

Transparent Exopolymer Particles in Drinking Water Treatment-A Brief Review

Transparent exopolymer particles (TEP) have been described as a class of particulate acidic polysaccharides, which are commonly found in various surface waters. Due to their unique physicochemical characteristics, they have recently been receiving increasing attention on their effects in water treatment. Currently, TEP are commonly known as clear, gel-like polysaccharides. This review first introduced the definition of TEP in water treatment and the relationship between TEP and algal organic matter (AOM). Further, in the review, the authors attempt to offer a holistic view and critical analysis concerning the research on TEPs in source water reservoirs, water plants and membrane treatment processes. It was clearly demonstrated in this review that the formation of TEP in source water reservoirs is largely related to water quality and phytoplankton, and the seasonal water stratification may indirectly affect the formation of TEP. In the waterworks, the relationship between TEP and water treatment process is mutual and there is limited research on this relationship. Finally, the mechanism of TEP-induced membrane fouling and the effect of alleviating TEP-induced membrane fouling is discussed in this review. The TEP removed by ultrafiltration can be recombined after membrane, and the recombination mechanism may be an important way to reduce reverse osmosis membrane contamination.

The importance of organic carbon as a coadjutant in the transport of pollutants

Dissolved organic carbon (DOC) is a physicochemical parameter widely used in the evaluation of surface water quality; however, its role as an agent of transport and transference of pollutants sometimes is still disregarded. The heterogeneous composition of DOC, predominantly composed of humin, humic and fulvic acids, renders it an inherent capacity to bind to organic and inorganic pollutants. This is an important feature when the knowledge of present and future conditions of aquatic environments is of concern. Some authors concluded that DOC is a controlling agent of mobility of metals, phosphorus, herbicides, and pesticides, among others. Nevertheless, some physical and chemical conditions in the water column and in the sediment can immobilize the contaminants and make the DOC less soluble, which will hamper the formation of DOC-pollutant complexes. This mini review is intended to present the importance of DOC quantification and some information on its association with water contaminants, which could render them unavailable for uptake.