Fate and Transport Modelling of Emerging Pollutants from Watersheds to Oceans: A Review

Critical review on toxic contaminants in surface water ecosystem: sources, monitoring, and its impact on human health

Surface water pollution is a critical and urgent global issue that demands immediate attention. Surface water plays a crucial role in supporting and sustaining life on the earth, but unfortunately, till now, we have less understanding of its spatial and temporal dynamics of discharge and storage variations at a global level. The contamination of surface water arises from various sources, classified into point and non-point sources. Point sources are specific, identifiable origins of pollution that release pollutants directly into water bodies through pipes or channels, allowing for easier identification and management, e.g., industrial discharges, sewage treatment plants, and landfills. However, non-point sources originate from widespread activities across expansive areas and present challenges due to its diffuse nature and multiple pathways of contamination, e.g., agricultural runoff, urban storm water runoff, and atmospheric deposition. Excessive accumulation of heavy metals, persistent organic pollutants, pesticides, chlorination by-products, pharmaceutical products in surface water through different pathways threatens food quality and safety. As a result, there is an urgent need for developing and designing new tools for identifying and quantifying various environmental contaminants. In this context, chemical and biological sensors emerge as fascinating devices well-suited for various environmental applications. Numerous chemical and biological sensors, encompassing electrochemical, magnetic, microfluidic, and biosensors, have recently been invented by hydrological scientists for the detection of water pollutants. Furthermore, surface water contaminants are monitored through different sensors, proving their harmful effects on human health.

Role of extracellular polymeric substances in the aggregation and biological response of micro(nano)plastics with different functional groups and sizes

In this work, the effects of extracellular polymeric substances (EPS) on the aggregation and biological responses of different micro(nano)plastics (MNPs, <1000 µm) were investigated. EPS increased the colloidal stability of PS MPs in NaCl or CaCl₂. For the three PS NPs (PS-NH₂, PS-COOH, and PS-naked), EPS also enhanced their colloidal stabilities in the presence of NaCl. However, the effect of CaCl₂ on the colloidal stabilities of PS NPs in the presence of EPS depended on their surface functional groups. In CaCl₂, both Derjaguin-Landau-Verwey-Overbeek theory and molecular bridging explained the interaction between MNPs (both NPs and MPs) and EPS. Laser Direct Infrared and scanning electron microscope imaging showed that opalescent EPS corona formed on PS MPs via intermolecular-bridging by Ca²⁺, and the critical coagulation concentrations (70 mM in NaCl, 1.5 mM in CaCl₂) in EPS were much lower than that for PS NPs (1000 mM for NaCl; 65 mM for CaCl₂). PS-NH₂ NPs showed the highest increase in the growth of bacteria (Bacillus subtilis), followed by PS MPs and PS-naked NPs, while PS-COOH NPs had no significant effect. Biological response of PS NPs was unaffected by EPS, while EPS further enhanced the positive effects of PS MPs on bacterial growth.

A comprehensive review of various approaches for treatment of tertiary wastewater with emerging contaminants: what do we know?

In the last few decades, environmental contaminants (ECs) have been introduced into the environment at an alarming rate. There is a risk to human health and aquatic ecosystems from trace levels of emerging contaminants, including hospital wastewater (HPWW), cosmetics, personal care products, endocrine system disruptors, and their transformation products. Despite the fact that these pollutants have been introduced or detected relatively recently, information about their characteristics, actions, and impacts is limited, as are the technologies to eliminate them efficiently. A wastewater recycling system is capable of providing irrigation water for crops and municipal sewage treatment, so removing ECs before wastewater reuse is essential. Water treatment processes containing advanced ions of biotic origin and ECs of biotic origin are highly recommended for contaminants. This study introduces the fundamentals of the treatment of tertiary wastewater, including membranes, filtration, UV (ultraviolet) irradiation, ozonation, chlorination, advanced oxidation processes, activated carbon (AC), and algae. Next, a detailed description of recent developments and innovations in each component of the emerging contaminant removal process is provided.

A data-driven binary-classification framework for oil fingerprinting analysis

A marine oil spill is one of the most challenging environmental issues, resulting in severe long-term impacts on ecosystems and human society. Oil dispersants are widely applied as a treating agent in oil spill response operations. The usage of dispersants significantly changes the behaviors of dispersed oil and consequently challenges the oil fingerprinting analysis. In this study, machine learning was first introduced to analyze oil fingerprinting by developing a data-driven binary classification framework. The modeling integrated dimensionality reduction algorithms (e.g., principal component analysis, PCA) to distinguish. Five groups of biomarkers, including terpanes, steranes, triaromatic steranes (TA-steranes), monoaromatic steranes (MA-steranes), and diamantanes, were selected. Different feature spaces were created from the diagnostic index of biomarkers, and six ML algorithms were applied for comparative analysis and optimizing the modeling process, including k-nearest neighbor (KNN), support vector classifier (SVC), random forest classifier (RFC), decision tree classifier (DTC), logistic regression classifier (LRC), and ensemble vote classifier (EVC). Hyperparameter optimization and cross-validation through GridSearchCV were applied to prevent overfitting and increase the model accuracy. Model performance was evaluated by model score and F-score through confusion matrices. The results indicated that the RFC algorithm from the diamantanes dataset performed the best. It delivered the highest F-score (0.871) versus the lowest F-score (0.792) from the EVC algorithm from the TA-steranes dataset by PCA with a variance of 95%. Therefore, diamantanes were recommended as the most suitable biomarker for distinguishing WCO and CDO to aid oil fingerprinting under the conditions in this study. The results proved the proposed method as a potential analysis tool for oil spill source identification through ML-aided oil fingerprinting. The study also showed the value of ML methods in oil spill response research and practice.

A comprehensive modelling approach to understanding the fate, transport and potential risks of emerging contaminants in a tropical reservoir

We developed a comprehensive integrated water quality modeling approach towards a better understanding of the fate and transport of emerging contaminants and comprehensive assessment of their potential risks in a tropical reservoir. Two representative emerging contaminants, namely Bisphenol A (BPA) and N, N-diethyltoluamide (DEET), were selected for this study. Unlike the traditional water quality modeling approach, the target emerging contaminants were modelled in four multi-compartments and coupled to a 3D-dimensional eutrophication model to investigate their interactions with other water quality state variables. First, the integrated model was calibrated and validated in four multi-compartments against an observed dataset in 2014. Subsequently, the correlation analysis between emerging contaminants and general water quality parameters were conducted. The potential ecological risks in this reservoir were also assessed via the trophic state index (TSI) and coupled to a species sensitivity distribution (SSD)-Risk Quotient (RQ) method. Finally, the model was applied to describe the dynamics of the two emerging contaminants and examine the direct and indirect influences of other environmental factors on their multi-compartment distributions in the aquatic environment. The comprehensive approach provides new insights into dynamic modeling of the fate and transport of emerging contaminants, their interactions with other state variables as well as an assessment of their potential risks in aquatic ecosystems.