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

Common issues of data science on the eco-environmental risks of emerging contaminants

Data-driven approaches (e.g., machine learning) are increasingly used to replace or assist laboratory studies in the study of emerging contaminants (ECs). In the past ten years, an increasing number of models or approaches have been applied to ECs, and the datasets used are continuously enriched. However, there are large knowledge gaps between what we have found and the natural eco-environmental meaning. For most published reviews, the contents are organized by the types of ECs, but the common issues of data science, regardless of the type of pollutant, are not sufficiently addressed. To close or narrow the knowledge gaps, we highlight the following issues ignored in the field of data-driven EC research. Complicated biological and ecological data and ensemble models revealing mechanisms and spatiotemporal trends with strong causal relationships and without data leakage deserve more attention in the future. In addition, the matrix influence, trace concentration, and complex scenario have often been ignored in previous works. Therefore, an integrated research framework related to natural fields, ecological systems, and large-scale environmental problems, rather than relying solely on laboratory data-related analysis, is urgently needed. Beyond the current prediction purposes, data science can inspire the discovery of scientific questions, and mutual inspiration among data science, process and mechanism models, and laboratory and field research is a critical direction. Focusing on the above urgent and common issues related to data, frameworks, and purposes, regardless of the type of pollutant, data science is expected to achieve great advancements in addressing the eco-environmental risks of ECs.

Applications of artificial intelligence for chemical analysis and monitoring of pharmaceutical and personal care products in water and wastewater: A review

Specifying and interpreting the occurrence of emerging pollutants is essential for assessing treatment processes and plants, conducting wastewater-based epidemiology, and advancing environmental toxicology research. In recent years, artificial intelligence (AI) has been increasingly applied to enhance chemical analysis and monitoring of contaminants in environmental water and wastewater. However, their specific roles targeting pharmaceuticals and personal care products (PPCPs) have not been reviewed sufficiently. This review aims to narrow the gap by highlighting, scoping, and discussing the incorporation of AI during the detection and quantification of PPCPs when utilising chemical analysis equipment and interpreting their monitoring data for the first time. In the chemical analysis of PPCPs, AI-assisted prediction of chromatographic retention times and collision cross-sections (CCS) in suspect and non-target screenings using high-resolution mass spectrometry (HRMS) enhances detection confidence, reduces analysis time, and lowers costs. AI also aids in interpreting spectroscopic analysis results. However, this approach still cannot be applied in all matrices, as it offers lower sensitivity than liquid chromatography coupled with tandem or HRMS. For the interpretation of monitoring of PPCPs, unsupervised AI methods have recently presented the capacity to survey regional or national community health and socioeconomic factors. Nevertheless, as a challenge, long-term monitoring data sources are not given in the literature, and more comparative AI studies are needed for both chemical analysis and monitoring. Finally, AI assistance anticipates more frequent applications of CCS prediction to enhance detection confidence and the use of AI methods in data processing for wastewater-based epidemiology and community health surveillance.

Characterizing PFASs in aquatic ecosystems with 3D hydrodynamic and water quality models

Understanding how per- and polyfluoroalkyl substances (PFASs) enter aquatic ecosystems is challenging due to the complex interplay of physical, chemical, and biological processes, as well as the influence of hydraulic and hydrological factors and pollution sources at the catchment scale. The spatiotemporal dynamics of PFASs across various media remain largely unknown. Here we show the fate and transport mechanisms of PFASs by integrating monitoring data from an estuarine reservoir in Singapore into a detailed 3D model. This model incorporates hydrological, hydrodynamic, and water quality processes to quantify the distributions of total PFASs, including the major components perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS), across water, particulate matter, and sediments within the reservoir. Our results, validated against four years of field measurements with most relative average deviations below 40%, demonstrate that this integrated approach effectively characterizes the occurrence, sources, sinks, and trends of PFASs. The majority of PFASs are found in the dissolved phase (>95%), followed by fractions sorbed to organic particles like detritus (1.0-3.5%) and phytoplankton (1-2%). We also assess the potential risks in both the water column and sediments of the reservoir. The risk quotients for PFOS and PFOA are <0.32 and < 0.00016, respectively, indicating an acceptable risk level for PFASs in this water body. The reservoir also exhibits substantial buffering capacity, even with a tenfold increase in external loading, particularly in managing the risks associated with PFOA compared to PFOS. This study not only enhances our understanding of the mechanisms influencing the fate and transport of surfactant contaminants but also establishes a framework for future research to explore how dominant environmental factors and processes can mitigate emerging contaminants in aquatic ecosystems.

A rapid approach with machine learning for quantifying the relative burden of antimicrobial resistance in natural aquatic environments

The massive use and discharge of antibiotics have led to increasing concerns about antimicrobial resistance (AMR) in natural aquatic environments. Since the dose-response mechanisms of pathogens with AMR have not yet been fully understood, and the antibiotic resistance genes and bacteria-related data collection via field sampling and laboratory testing is time-consuming and expensive, designing a rapid approach to quantify the burden of AMR in the natural aquatic environment has become a challenge. To cope with such a challenge, a new approach involving an integrated machine-learning framework was developed by investigating the associations between the relative burden of AMR and easily accessible variables (i.e., relevant environmental variables and adjacent land-use patterns). The results, based on a real-world case analysis, demonstrate that the quantification speed has been reduced from 3-7 days, which is typical for traditional measurement procedures with field sampling and laboratory testing, to approximately 0.5 hours using the new approach. Moreover, all five metrics for AMR relative burden quantification exceed the threshold level of 85%, with F1-score surpassing 0.92. Compared to logistic regression, decision trees, and basic random forest, the adaptive random forest model within the framework significantly improves quantification accuracy without sacrificing model interpretability. Two environmental variables, dissolved oxygen and resistivity, along with the proportion of green areas were identified as three key feature variables for the rapid quantification. This study contributes to the enrichment of burden analyses and management practices for rapid quantification of the relative burden of AMR without dose-response information.

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.

A study on identifying synergistic prevention and control regions for PM2.5 and O3 and exploring their spatiotemporal dynamic in China

Air pollutants, notably ozone (O3) and fine particulate matter (PM2.5) give rise to evident adverse impacts on public health and the ecotope, prompting extensive global apprehension. Though PM2.5 has been effectively mitigated in China, O3 has been emerging as a primary pollutant, especially in summer. Currently, alleviating PM2.5 and O3 synergistically faces huge challenges. The synergistic prevention and control (SPC) regions of PM2.5 and O3 and their spatiotemporal patterns were still unclear. To address the above issues, this study utilized ground monitoring station data, meteorological data, and auxiliary data to predict the China High-Resolution O3 Dataset (CHROD) via a two-stage model. Furthermore, SPC regions were identified based on a spatial overlay analysis using a Geographic Information System (GIS). The standard deviation ellipse was employed to investigate the spatiotemporal dynamic characteristics of SPC regions. Some outcomes were obtained. The two-stage model significantly improved the accuracy of O3 concentration prediction with acceptable R2 (0.86), and our CHROD presented higher spatiotemporal resolution compared with existing products. SPC regions exhibited significant spatiotemporal variations during the Blue Sky Protection Campaign (BSPC) in China. SPC regions were dominant in spring and autumn, and O3-controlled and PM2.5-dominated zones were detected in summer and winter, respectively. SPC regions were primarily located in the northwest, north, east, and central regions of China, specifically in the Beijing-Tianjin-Hebei urban agglomeration (BTH), Shanxi, Shaanxi, Shandong, Henan, Jiangsu, Xinjiang, and Anhui provinces. The gravity center of SPC regions was distributed in the BTH in winter, and in Xinjiang during spring, summer, and autumn. This study can supply scientific references for the collaborative management of PM2.5 and O3.

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.

Antibiotic resistant genes profile in the surface water of subtropical drinking water river-reservoir system

To comprehensively understand antibiotic resistant genes (ARGs) profile in the subtropical drinking water river-reservoir system, this study selected Dongzhen river-reservoir system in Mulan Creek as object to investigate the spatial-temporal characteristics of ARGs diversity, bacterial host and resistance mechanism, and to analyze the key environmental factors driving ARGs profile variation. The results indicated that a total of 440 ARGs were detected in the target system, and the ARGs distribution pattern in the reservoir was attributed to autologous evolution or the comprehensive influence of feeding river system. The predominant bacterial host at different sites showed similar variations to dominated ARGs, and Proteobacteria, Actinobacteria and Bacteroidetes harbored most ARGs at phylum level, which showed the highest proportions of 74%, 37% and 35%, respectively. Antibiotic efflux was the primary resistance mechanism in all samples from wet season (45%-60%), yet the samples from dry season exhibited multiple resistance mechanisms, including inactivation (37%-52%), efflux (44%), and target alteration (43%). The total relative abundances of ARGs in the target system ranged from 0.89 × 10-2 to 1.71 × 10-2, and seasonal variation had a more significant influence on ARGs abundance than spatial variation (R = 0.68, P < 0.01). Environmental factors analysis indicated that the concentrations of nitrite nitrogen and total organic carbon were significant factors explaining ARGs number and various resistance mechanism proportions (P < 0.01), accounting for 48.7% and 61.1% of the variation, respectively; ammonia nitrogen concentration, total organic carbon concentration, temperature and pH were the significant influence factors on the relative abundance of ARGs (P < 0.05), with standardized regression weights of 0.700, 1.414, 1.447, and 1.727, respectively. In summary, in the surface water of the target system, ARGs diversity was primarily driven by ARGs horizontal transfer and antibiotics biosynthesis. Nutrients mainly promoted ARGs abundance by providing abundant energy, rather than increasing bacterial reproductive capacity.

Linear alkylbenzene sulfonate threats to surface waters at the national scale: A neglected traditional pollutant

Freshwater biodiversity and ecosystem services might decline due to exposure to chemicals. However, researchers have devoted much attention to the potential risks of emerging contaminants, while placing less effort on historical pollutants, such as the surfactant, linear-alkylbenzene-sulfonate (LAS), which is a major component of widely used synthetic detergents worldwide. In this study, a multilevel risk assessment approach was used to assess risks posed by LAS to aquatic organisms, on a wide spatial scale, based on various assessment endpoints. Additionally, bottom-up approaches were used to assess contributions of LAS source discharges to aquatic environments. Concentrations of LAS in surface waters of China ranged from less than the limit of detection to 14,200 μg/L. The predicted no effect concentration (PNEC) based on adverse effects on reproduction is 15 μg/L, which is slightly less than the PNEC based on other endpoints. 99% of surface waters in Chaohu Lake and the Hai River (Ch: Haihe) were predicted to pose a risk to growth of aquatic organisms, with a protection threshold of 5% of species (HC₅). Discharges of LAS were estimated using activity data and emission factors for 280 major cities in the basin. Rural domestic sources were the main source of LAS to surface waters. These outcomes provided a process for developing comprehensive management and control approaches to help researchers and policymakers effectively manage water resources affected by increasing concentrations of LAS.

A comparative study of data-driven models for runoff, sediment, and nitrate forecasting

Effective prediction of qualitative and quantitative indicators for runoff is quite essential in water resources planning and management. However, although several data-driven and model-driven forecasting approaches have been employed in the literature for streamflow forecasting, to our knowledge, the literature lacks a comprehensive comparison of well-known data-driven and model-driven forecasting techniques for runoff evaluation in terms of quality and quantity. This study filled this knowledge gap by comparing the accuracy of runoff, sediment, and nitrate forecasting using four robust data-driven techniques: artificial neural network (ANN), long short-term memory (LSTM), wavelet artificial neural network (WANN), and wavelet long short-term memory (WLSTM) models. These comparisons were performed in two main tiers: (1) Comparing the machine learning algorithms' results with the model-driven approach; In order to simulate the runoff, sediment, and nitrate loads, the Soil and Water Assessment Tool (SWAT) model was employed, and (2) Comparing the machine learning algorithms with each other; The wavelet function was utilized in the ANN and LSTM algorithms. These comparisons were assessed based on the substantial statistical indices of coefficient of determination (R-Squared), Nash-Sutcliff efficiency coefficient (NSE), mean absolute error (MAE), and root mean square error (RMSE). Finally, to prove the applicability and efficiency of the proposed novel framework, it was successfully applied to Eagle Creek Watershed (ECW), Indiana, U.S. Results demonstrated that the data-driven algorithms significantly outperformed the model-driven models for both the calibration/training and validation/testing phases. Furthermore, it was found that the coupled ANN and LSTM models with wavelet function led to more accurate results than those without this function.

Combining knowledge graph with deep adversarial network for water quality prediction

Water quality prediction is an important research focus in smart water and can provide the support to control and reduce water pollution. However, existing water quality prediction models are mainly data-driven and only rely on various sensor data. This paper proposes a new water quality prediction modeling approach integrating data and knowledge. We develop a water quality prediction framework that combines knowledge graph and deep adversarial networks. The knowledge extraction and management compound extracts the water quality knowledge graph from different knowledge sources by using the deep adversarial joint model. The fusing parameter importance learning compound calculates the contribution of parameters in water quality prediction by taking into account both knowledge and data levels of correlation. Finally, a water quality prediction model combining weighted CNN-LSTM with adversarial learning predicts the values of total nitrogen based on real-time monitoring data. The experimental results on monitoring data from the Juhe River of China show that the proposed model can greatly improve the effect of water quality prediction.

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.