Predicting Antibiotic Resistance and Assessing the Risk Burden from Antibiotics: A Holistic Modeling Framework in a Tropical Reservoir.

Predicting the hotspots of antimicrobial resistance (AMR) in aquatics is crucial for managing associated risks. We developed an integrated modeling framework toward predicting the spatiotemporal abundance of antibiotics, indicator bacteria, and their corresponding antibiotic-resistant bacteria (ARB), as well as assessing the potential AMR risks to the aquatic ecosystem in a tropical reservoir. Our focus was on two antibiotics, sulfamethoxazole (SMX) and trimethoprim (TMP), and on Escherichia coli (E. coli) and its variant resistant to sulfamethoxazole-trimethoprim (EC_SXT). We validated the predictive model using withheld data, with all Nash-Sutcliffe efficiency (NSE) values above 0.79, absolute relative difference (ARD) less than 25%, and coefficient of determination (R2) greater than 0.800 for the modeled targets. Predictions indicated concentrations of 1-15 ng/L for SMX, 0.5-5 ng/L for TMP, and 0 to 5 (log10 MPN/100 mL) for E. coli and -1.1 to 3.5 (log10 CFU/100 mL) for EC_SXT. Risk assessment suggested that the predicted TMP could pose a higher risk of AMR development than SMX, but SMX could possess a higher ecological risk. The study lays down a hybrid modeling framework for integrating a statistic model with a process-based model to predict AMR in a holistic manner, thus facilitating the development of a better risk management framework.

An integrated modelling framework for multiple pollution source identification in surface water.

Pollution source identification is vital in water safety management. An integrated simulation-optimization modelling framework comprising a process-based hydrodynamic water quality model, artificial neural network surrogate model and particle swarm optimization (PSO) was proposed to achieve rapid, accurate and reliable pollution source identification. In this study, the hydrodynamics and water quality processes in a straight lab-based flume were simulated to test pollution source identification under steady flow conditions. Additionally, the pollution source identification in the unsteady flow conditions was examined using a real-life estuary, specifically the Yangtze River estuary. First, we developed two process-based models to simulate hydrodynamics and water quality in the flume and estuary. Then, the data generated from the process-based models were used to develop surrogate models. Three typical artificial neural networks (ANNs) algorithms: backpropagation (BP), radial basis function (RBF) and general regression neural networks (GRNN) were selected to develop surrogates for process-based models (PBMs), and they were coupled with PSO algorithm to achieve the hybrid modelling framework for pollution source identification. Our results showed that hybrid PBM-ANNs-PSO models could be applied to identify the pollution source and quantify release intensity in spatial distribution when the discharge type was assumed as the point source with a continuous release. Multiple-performance criteria metrics, in terms of the coefficient of determination, root-mean-square error, mean absolute error, evaluated the model performance as "Excellent prediction". The BP-PSO models consistently appear to be the top-performing source identification model within the developed models, with most cases of relative error (RE) values lower than 5%. The new insights from the hybrid modelling framework would provide useful information for the local government agency to make reasonable decisions regarding pollution source identification issues.

Insights into effects of discharge ratio on flow characteristics and pollutant transport in a Y-shaped open channel confluence with emergent rigid vegetation.

Turbulence generated within the vegetated confluence system is important for water quality and river management. In this study, we conducted a series of experiments to explore the extent to which emergent rigid vegetation in the confluence channel influences hydrodynamic characteristics and contaminant transport. First, a series of tests with increasing discharge ratios (from 0.35, 0.5, and 1) was conducted to quantify the effects of the discharge ratio on hydrodynamic conditions within the vegetated confluence. Then, tests with different discharge ratios were also set up to explore how contaminants released locations and modes (line and point source) influence the transport and mixing of contaminants. The results showed that increasing the discharge ratio induced larger momentum in the confluence area. The increase in discharge ratio rendered the circulation stronger, and its position came earlier in the non-vegetative area. In addition, the dimensionless turbulent kinetic energy peaked near the interface of the non/vegetated zone. With the increase in the discharge ratio, the dimensionless turbulent kinetic energy was found to be smaller. In the contaminants transport tests, the results revealed larger discharge ratio could speed up contaminants transport and mixing. The applications from this study would be helpful to pollutant transport management in natural confluences.

Perfluoroalkyl acids in representative edible aquatic species from the lower Yangtze River: Occurrence, distribution, sources, and health risk.

Perfluoroalkyl acid (PFAA) exposure poses a potential hazard to wildlife and humans. Food consumption is one of the main routes of PFAA exposure for the general population, with aquatic organisms being the major contributors. To evaluate the risk of coastal residents' intake of wild aquatic organisms, 14 PFAAs were detected in crucian carp and oriental river prawn from 18 sampling sites from the lower reaches of Yangtze River. The total PFAA (∑PFAA) concentrations ranged from 5.9 to 51.3 ng/g wet weight (ww) in the muscle of crucian carp and river prawn, suggesting the potential risk to human and wildlife. Perfluorooctanesulfonate (PFOS), perfluorooctanoic acid (PFOA) and long-chain PFAAs (C ≥ 10) were the main pollutants in the tissues of crucian carp and river prawn, which are known for their higher bioaccumulation capacity. The ∑PFAA concentration in all the samples showed an increasing trend from upstream to downstream and was higher in the south bank, owing to population density, prevailing winds, background pollution and industrial emission. Principal component analysis-multiple linear regression and Pearson correlation analysis showed that WWTP effluent, industrial pollution and surface runoff ware the main sources of PFAAs in the aquatic organisms and industrial pollution highest contributor, suggesting better regulation is needed to manage them. The assessment of risk to human health and wild life suggested a low risk for most residents of cities along the Yangtze River except for resident of Nantong, where frequent consumption of wild aquatic organisms may cause potential risk to human health, especially for traditional eaters and middle-aged people.

Advancing prediction of emerging contaminants in a tropical reservoir with general water quality indicators based on a hybrid process and data-driven approach.

Monitoring and predicting the occurrence and dynamic distributions of emerging contaminants (ECs) in the aquatic environment has always been a great challenge. This study aims to explore the potential of fully utilizing the advantages of combining traditional process-based models (PBMs) and data-driven models (DDMs) with general water quality indicators in terms of improving the accuracy and efficiency of predicting ECs in aquatic ecosystems. Two representative ECs, namely Bisphenol A (BPA) and N, N-diethyltoluamide (DEET), in a tropical reservoir were chosen for this study. A total of 36 DDMs based on different input datasets using Artificial Neural Networks (ANN) and Random Forests (RF) were examined in three case studies. The models were applied in prognosis validation based on easily accessible data on water quality indicators. Our results revealed that all the models yielded good fits when compared to the observed data. These new insights into the advantages using the combination of traditional PBMs and DDMs with general water quality datasets help to overcome the constraints in terms of model accuracy and efficiency as well as technical and budget limitations due to monitoring surveys and laboratory experiments in the study of fate and transport of ECs in aquatic environments.

Source, fate, transport and modelling of selected emerging contaminants in the aquatic environment: Current status and future perspectives.

The occurrence of emerging contaminants (ECs), such as pharmaceuticals and personal care products (PPCPs), perfluoroalkyl and polyfluoroalkyl substances (PFASs) and endocrine-disrupting chemicals (EDCs) in aquatic environments represent a major threat to water resources due to their potential risks to the ecosystem and humans even at trace levels. Mathematical modelling can be a useful tool as a comprehensive approach to study their fate and transport in natural waters. However, modelling studies of the occurrence, fate and transport of ECs in aquatic environments have generally received far less attention than the more widespread field and laboratory studies. In this study, we reviewed the current status of modelling ECs based on selected representative ECs, including their sources, fate and various mechanisms as well as their interactions with the surrounding environments in aquatic ecosystems, and explore future development and perspectives in this area. Most importantly, the principles, mathematical derivations, ongoing development and applications of various ECs models in different geographical regions are critically reviewed and discussed. The recommendations for improving data quality, monitoring planning, model development and applications were also suggested. The outcomes of this review can lay down a future framework in developing a comprehensive ECs modelling approach to help researchers and policymakers effectively manage water resources impacted by rising levels of ECs.

Multi-class secondary metabolites in cyanobacterial blooms from a tropical water body: Distribution patterns and real-time prediction.

Cyanobacterial blooms that produce toxins occur in freshwaters worldwide and yet, the occurrence and distribution patterns of many cyanobacterial secondary metabolites particularly in tropical regions are still not fully understood. Moreover, predictive models for these metabolites by using easily accessible water quality indicators are rarely discussed. In this study, we investigated the co-occurrence and spatiotemporal trends of 18 well-known and less-studied cyanobacterial metabolites (including [D-Asp3] microcystin-LR (DM-LR), [D-Asp3] microcystin-RR (DM-RR), microcystin-HilR (MC-HilR), microcystin-HtyR (MC-HtyR), microcystin-LA (MC-LA), microcystin-LF (MC-LF), microcystin-LR (MC-LR), microcystin-LW (MC-LW), microcystin-LY (MC-LY), microcystin-RR (MC-RR) and microcystin-WR (MC-WR), Anatoxin-a (ATX-a), homoanatoxin-a (HATX-a), cylindrospermospin (CYN), nodularin (NOD), anabaenopeptin A (AptA) and anabaenopeptin B (AptB)) in a tropical freshwater lake often plagued with blooms. Random forest (RF) models were developed to predict MCs and CYN and assess the relative importance of 22 potential predictors that determined their concentrations. The results showed that 11 MCs, CYN, ATX-a, HATX-a, AptA and AptB were found at least once in the studied water body, with MC-RR and CYN being the most frequently occurring, intracellularly and extracellularly. AptA and AptB were detected for the first time in tropical freshwaters at low concentrations. The metabolite profiles were highly variable at both temporal and spatial scales, in line with spatially different phytoplankton assemblages. Notably, MCs decreased with the increase of CYN, possibly revealing interspecific competition of cyanobacteria. The rapid RF prediction models for MCs and CYN were successfully developed using 4 identified drivers (i.e., chlorophyll-a, total carbon, rainfall and ammonium for MCs prediction; and chloride, total carbon, rainfall and nitrate for CYN prediction). The established models can help to better understand the potential relationships between cyanotoxins and environmental variables as well as provide useful information for making policy decisions.

Microplastics in equatorial coasts: Pollution hotspots and spatiotemporal variations associated with tropical monsoons.

Microplastics (MP < 5 mm) are eroding oceanic health and coastal development at a planetary scale. Coastlines in Southeast Asia (SEA) are plagued with plastic litters, but how MP are dispersed within SEA region is poorly understood, which can vary dramatically under the tropical climate. We systematically quantified MP in equatorial Singapore, to assess how prevailing Monsoons and other factors impact MP distributions in beaches and mangroves. Data highlighted spatial preponderance differed broadly by seasons (p < 0.05) and were strongly modulated by wind speediness (p < 0.05; r = 0.6-0.7) and promoted transboundary migrations of MP. Conversely, an inverse relationship existed between sediment MP and rainfall (r = -0.54) possibly due to re-entrainment of surficial MP. Elevated concentrations in mangrove's compartments (p < 0.05) suggest effective repository hotspots. Coastal MP consisted assorted morphologies and commonest polymers including 34% polypropylene (PP), 26% polyethelene (PE), and 23% Low Density PE. Further comparisons revealed coastal MP in Singapore accelerated by two orders of magnitude since 2014, implying cumulative pollution which is not reversible. We synthesized the first seasonal coastal MP report in SEA which is useful for source apportionment, prediction study, and mitigation planning under tropical circumstances.

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.

Modelling the spatial and seasonal distribution, fate and transport of floating plastics in tropical coastal waters.

A coupled high-resolution hydrodynamic-particle tracking model was developed to study the spatiotemporal distribution and pathways of floating plastics in the coastal waters of equatorial Singapore. The coupled model was first calibrated and validated against the field measurements and then applied to explore impact of various prevailing wind and hydrodynamic conditions on fate and transport of the plastics. The results highlighted that the wind effect on the hydrodynamics is negligible, but it influences the transmissions of floating plastics significantly in the Singapore's coastal waters. The spatial and seasonal hotspots of plastic waste were identified, which were consistent with field observations when the windage ranged from 3% to 5%. A further evaluation of the predicted trajectories showed that plastic wastes released from the land could be transported approximately 70 km seaward within 72 h when the windage was 5%. Furthermore, it was also found that the effects of climate change and increasing plastic usage would aggravate plastic pollution and accelerate its transport. The established model can provide new insights into the spatiotemporal distribution and fate of plastic waste in the tropical coastal waters, which is useful to assist regulators in making policy decisions in response to the future climate change and plastic usage.