Priority control sequence of 34 typical pollutants in effluents of Chinese wastewater treatment plants

Emerging contaminants in Taihu lake Basin: Multi-dimensional prioritization, colloidal adsorption, and trophic-level disruptions

Emerging contaminants (ECs) pose significant threats to aquatic ecosystems across multiple trophic levels. This year-long study in China's Taihu Lake Basin investigated the spatiotemporal distribution, environmental behavior, and ecological impacts of ECs in diverse aquatic systems. Key findings revealed strong spatiotemporal heterogeneity, with industrial areas exhibiting the highest EC concentrations (mean: 1888.28 ng/L), dominated by perfluorinated compound. Seasonal variations showed spring EC levels twice those of other seasons, linked to intensified industrial activity. Colloidal adsorption played a critical role in EC fate, contributing 0.74-45.48 % to pollutant distribution, particularly for antibiotics and bisphenols. A multidimensional prioritization framework integrating concentration, detection rate, colloidal adsorption, toxicity, persistence, and bioaccumulation identified nine priority ECs, including erythromycin, tetrabromobisphenol A (TBBPA), and perfluorooctanoic acid (PFOA). ECs significantly disrupted plankton communities, with zooplankton diversity experiencing stronger suppression than phytoplankton, indicating trophic-level-dependent impacts. This study underscores the need for integrated monitoring strategies and regulatory thresholds to mitigate EC risks in complex aquatic ecosystems.

Using machine learning models to predict the dose-effect curve of municipal wastewater for zebrafish embryo toxicity

Municipal wastewater substantially contributes to aquatic ecological risks. Assessing the toxicity of municipal wastewater through dose-effect curves is challenging owing to the time-consuming, labor-intensive, and costly nature of biological assays. This study developed machine learning models to predict wastewater dose-effect curves for zebrafish embryos. The influent and effluent samples from 176 wastewater treatment plants in China were analyzed to collect water quality data, including information on seven chemical parameters and the toxic effects on zebrafish embryos at eight relative enrichment factors (REFs) of wastewater. Using Spearman's rank correlation coefficient and the max-relevance and min-redundancy algorithm, the parameters of ammonium nitrogen content and toxic effect values at REFs of 2 and 25 (REF2 and REF25), were identified as crucial input features from 15 variables. Decision tree, random forest, and gradient-boosted decision tree (GBDT) models were developed. Among these, GBDT exhibited the best performance, with an average R2 value of 0.91 and an average mean absolute percentage error (MAPE) of 27.91 %. Integrating the dose-effect curve pattern into the machine learning model considerably optimized the GBDT model, reaching a minimum MAPE of 14.74 %. The developed model can accurately determine the dose-effect curves of actual wastewater, reducing at least 75 % of the experimental workload. These findings provide a valuable tool for assessing zebrafish embryo toxicity in municipal wastewater management. This study indicates that combining environmental expertise and machine learning models allows for a scientific assessment of the potential toxic risks in wastewater, providing new perspectives and approaches for environmental policy development.

Which Pollutants Should Be Prioritized for Control in Multipollutant Complex Contaminated Groundwater of Chemical Industrial Parks?

Increasing chemical pollutants in groundwater within chemical industrial parks pose a critical environmental challenge, necessitating innovative strategies to address contaminants with the highest risks to environmental health and ensure sustainable management. Herein, we investigated 277 chemical pollutants from 367 sampling points across 10 rounds, totaling 1,016,590 measured data points. An environmental health prioritization index (EHPI) was proposed and applied to integrate multiple criteria: occurrence, migration, persistence, bioaccumulation, acute and chronic toxicity, and health effects to rank the target pollutants for priority control. Thirty pollutants were classified as the top-priority group and 81 as high-priority, with metals, polycyclic aromatic hydrocarbons, and haloalkanes ranking highest, while emerging contaminants of concern ranked lower. The top 6 pollutants were beryllium, benzo(g,h,i)perylene, nickel, benzo(a)pyrene, chrysene, and arsenic. The EHPI method was compared against five other weighting schemes, including AHP (analytic hierarchy process), entropy, AHP-entropy, AHP-TOPSIS (technique for order preference by similarity to ideal solution), and entropy-TOPSIS. EHPI effectively captured and integrated the results from more simplistic prioritization schemes. Overall, 38 pollutants are recommended for inclusion in the priority control list, focusing on the top-priority group and high detection and exceedance categories. This framework provides critical guidance for focused monitoring, assessment, and control of the highest-risk groundwater pollutants, supporting more effective environmental and human health protection.

Bayesian Optimization-Enhanced Reinforcement learning for Self-adaptive and multi-objective control of wastewater treatment

Controllers of wastewater treatment plants (WWTPs) often struggle to maintain optimal performance due to dynamic influent characteristics and the need to balance multiple operational objectives. In this study, Reinforcement Learning (RL) algorithms across different activated sludge process configurations was tested, and a novel approach that integrates RL with Bayesian Optimization (BO) to enhance the control of critical operational parameters in activated sludge processes was developed. This study extended the application of advanced machine learning techniques to complex WWTP control problems, moving beyond simplified benchmarks. The integration of BO with RL avoided sub-optimal performance and accelerated convergence to optimal control policies in controlling the A2O process, resulting in a significant 46% reduction in operational costs and a 12% decrease in energy consumption while maintaining compliance with effluent discharge standards. This approach offers a practical pathway for WWTPs to enhance treatment efficiency, reduce operational costs, and contribute to sustainable wastewater management practices.

A genetically encoded fluorescent whole-cell biosensor for real-time detecting estrogenic activities in water samples

Real-time monitoring of estrogenic activity in the aquatic environment is a challenging task. Current biosensors face difficulties due to their limited response speed and environmental tolerance, especially for detecting wastewater, the major source of estrogenic compounds in aquatic environments. To address these difficulties, this study developed a single fluorescent protein (FP) -based whole-cell bacterial biosensor named ER-Light, which was achieved by inserting the sensing domain of the estrogen receptor (ER) into the FP Citrine and expressing it in the periplasm of Escherichia coli. As designed, ER-Light enables the detection of net estrogenic activity in mixtures, represented by estradiol equivalent concentration (EEQ). ER-Light detects EEQ in 40 s with a detection limit of 4.55 × 10-7 μM and a maximum working range of 1.1 × 10-4 μM, demonstrating sufficient response speed, sensitivity, and working range. In addition, the ER-Light can survive and tolerate wastewater effluent. Satisfactory recoveries (91.0 % to 102.1 %) eliminated concerns about the matrix effect of wastewater. EEQs (Not detected-2.9 ×10-5 µM) measured by ER-Light from the effluent of 9 wastewater treatment plants validate its practicality in detecting wastewater. This is the first attempt to integrate ER into FP-based biosensors for environment monitoring. Our findings provide valuable design rules for real-time detection of bioactivity effects in the environment, contributing to the safeguarding of ecological and human health.

Tracing contaminants of emerging concern and their transformations in the whole treatment process of a municipal wastewater treatment plant using nontarget screening and molecular networking strategies

This study employed nontarget screening with high-resolution mass spectrometry and molecular network strategy to characterize the occurrence and tranformation of contaminants of emerging concern (CECs) through a wastewater treatment plant in Guangzhou. We detected 70,631 compounds in positive mode and 14,423 in negative mode in influent, from which 94.5 % of these compounds were successfully eliminated after treatment. Among them, 510 chemicals were identified, with pharmaceuticals being the largest category excluding natural products, accounting for 146 compounds. And 29 CECs were semiquantified with concentrations ranging from 2.80 ng/L (Fluconazole) to 10,351 ng/L (Nicotine). The removal efficiency varied: 60 compounds were easily removable (>90 % removal), 17 were partially removable (40-90 % removal), and 44 were non-degradable (<40 % removal). Additionally, we tentatively identified transformation products (TPs) of CECs using a molecular network analysis, revealing over 20,000 compound pairs sharing common fragments, with 191 compounds potentially linked to 47 level 1 compounds, suggesting their role as TPs of CECs. These findings illuminated the actual treatment efficiency of wastewater treatment plants for CECs and the potential TPs, offering valuable insights for future improvements in wastewater management practices.

Which emerging micropollutants deserve more attention in wastewater in the post-COVID-19 pandemic period? Based on distribution, risk, and exposure analysis

Aggravated accumulation of emerging micropollutants (EMs) in aquatic environments, especially after COVID-19, raised significant attention throughout the world for safety concerns. This article reviews the sources and occurrence of 25 anti-COVID-19 related EMs in wastewater. It should be pointed out that the concentration of anti-COVID-19 related EMs, such as antivirals, plasticizers, antimicrobials, and psychotropic drugs in wastewater increased notably after the pandemic. Furthermore, the ecotoxicity, ecological, and health risks of typical EMs before and after COVID-19 were emphatically compared and analyzed. Based on the environmental health prioritization index method, the priority control sequence of typical EMs related to anti-COVID-19 was identified. Lopinavir (LPV), venlafaxine (VLX), di(2-ethylhexyl) phthalate (DEHP), benzalkonium chloride (BAC), triclocarban (TCC), di-n-butyl phthalate (DBP), citalopram (CIT), diisobutyl phthalate (DIBP), and triclosan (TCS) were identified as the top-priority control EMs in the post-pandemic period. Besides, some insights into the toxicity and risk assessment of EMs were also provided. This review provides direction for proper understanding and controlling the EMs pollution after COVID-19, and is of significance to evaluate objectively the environmental and health impacts induced by COVID-19.

Quality evaluation parameter and classification model for effluents of wastewater treatment plant based on machine learning

With the growing consensus of emerging pollutants and biological toxicity risks in wastewater treatment plant (WWTP) effluents, traditional water quality management based on general chemical parameters no longer meets the new challenges. Here, a first-hand dataset containing 9 conventional parameters, 22 mental and inorganic ions, 25 biotoxicity parameters, and 54 emerging pollutants from effluents of 176 municipal WWTPs across China were measured. Four clustering algorithms and five classification algorithms were applied to 65 well-performing models to determine a novel evaluation parameter system. A total of 14 parameters were selected by semi-supervised machine learning, including TN, TP, NH4+-N, NO2--N, Se, SO42-, Caenorhabditis elegans body width, 72 hpf zebrafish embryo hatching rate, tetracycline, acetaminophen, gemfibrozil (Lopid), PFBA, PFHxA, and HFPO-DA. These parameters were then used to construct a Healthy Effluent Quality Index model (HEQi). The application efficiency of HEQi was compared with other common methods such as the Water Quality Index (WQI), Fuzzy Synthesized Evaluation (FSE), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in classifying 176 effluents. Results implicated that under the new evaluation criteria, the major task in North and Northeast China remains to reduce the conventional parameters, especially NO2--N. However, it is necessary to strengthen the removal of biotoxicity and emerging pollutants in parts of Central and Eastern China. This study offers new methodological tools and scientific insights for improving water quality assessment and safe discharge of wastewater.

Effect driven prioritization of contaminants in wastewater treatment plants across China: A data mining-based toxicity screening approach

A diversity of contaminants of emerging concern (CECs) are present in wastewater effluent, posing potential threats to receiving waters. It is urgent for a holistic assessment of the occurrence and risk of CECs related to wastewater treatment plants (WWTP) on national and regional scales. A data mining-based risk prioritization method was developed to collect the reported contaminants and their respective concentrations in municipal and industrial WWTPs and their receiving waters across China over the past 20 years. A total of 10,781 chemicals were reported in 8336 publications, of which 1037 contaminants were reported with environmental concentrations. While contaminant categories varied across WWTP types (municipal vs. industrial) and regions, pharmaceuticals and cyclic hydrocarbons were the most studied CECs. Contaminant composition in receiving water was closer to that in municipal than industrial WWTPs. Publications on legacy pesticides and polycyclic aromatic hydrocarbons in WWTP decreased recently compared to the past, while pharmaceuticals and perfluorochemicals have received increasing attention, showing a changing concern over time. Detection frequency, concentration, removal efficiency, and toxicity data were integrated for assessing potential risks and prioritizing CECs on national and regional scales using an environmental health prioritization index (EHPi) approach. Among 666 contaminants in municipal WWTP effluent, trichlorfon and perfluorooctanesulfonic acid were with the highest EHPi scores, while 17ɑ-ethinylestradiol and bisphenol A had the highest EHPi scores among 304 contaminants in industrial WWTPs. The prioritized contaminants varied across regions, suggesting a need for tailoring regional measures of wastewater treatment and control.

Ecosystem risk-based prioritization of micropollutants in wastewater treatment plant effluents across China

Identifying priority pollutants in wastewater treatment plant (WWTP) effluents is crucial for optimizing monitoring efforts, improving regulations, and developing targeted mitigation strategies. Despite the presence of numerous trace organic pollutants in WWTP effluents, a comprehensive prioritization scheme is lacking, hindering effective control. This study screened 216 micropollutants, including pharmaceuticals, pesticides, and industrial chemicals, which had been detected in effluents from 46 WWTPs across China. A multi-criteria prioritization method was developed, considering exposure potential based on median concentrations and detection frequencies, as well as hazard potential determined by persistence, bioaccumulation, in vitro toxicity, and in vivo toxicity. Pollutants with low exposure or hazard potential were filtered out, and a priority index was calculated to rank the remaining 59 substances. The top 15 priority pollutants included regulated persistent organic pollutants like perfluorooctanoic acid and their alternatives such as perfluorobutane sulfonate, pesticide transformation products, and emerging contaminants such as bisphenol A, which are not currently regulated in WWTP effluents. This study provides a systematic approach to identify priority pollutants and generates a guiding framework for monitoring, regulation, and control of both well-recognized and overlooked contaminants in WWTP effluents.

Ecological implications and drivers of emerging contaminants in Dongting Lake of Yangtze River Basin, China: A multi-substance risk analysis

Emerging contaminants (ECs) are increasingly recognized as a global threat to biodiversity and ecosystem health. However, the cumulative risks posed by ECs to aquatic organisms and ecosystems, as well as the influence of anthropogenic activities and natural factors on these risks, remain poorly understood. This study assessed the mixed risks of ECs in Dongting Lake, a Ramsar Convention-classified Typically Changing Wetland, to elucidate the major EC classes, key risk drivers, and magnitude of anthropogenic and natural impacts. Results revealed that ECs pose non-negligible acute (30% probability) and chronic (70% probability) mixed risks to aquatic organisms in the freshwater lake ecosystem, with imidacloprid identified as the primary pollutant stressor. Redundancy analysis (RDA) and structural equation modeling (SEM) indicated that cropland and precipitation were major drivers of EC contamination levels and ecological risk. Cropland was positively associated with EC concentrations, while precipitation exhibited a dilution effect. These findings provide critical insights into the ecological risk status and key risk drivers in a typical freshwater lake ecosystem, offering data-driven support for the control and management of ECs in China.

Functional analysis, diversity, and distribution of the ean cluster responsible for 17β-estradiol degradation in sphingomonads

17β-estradiol (E2) is a natural endocrine disruptor that is frequently detected in surface and groundwater sources, thereby threatening ecosystems and human health. The newly isolated E2-degrading strain Sphingomonas colocasiae C3-2 can degrade E2 through both the 4,5-seco pathway and the 9,10-seco pathway; the former is the primary pathway supporting the growth of this strain and the latter is a branching pathway. The novel gene cluster ean was found to be responsible for E2 degradation through the 4,5-seco pathway, where E2 is converted to estrone (E1) by EanA, which belongs to the short-chain dehydrogenases/reductases (SDR) superfamily. A three-component oxygenase system (including the P450 monooxygenase EanB1, the small iron-sulfur protein ferredoxin EanB2, and the ferredoxin reductase EanB3) was responsible for hydroxylating E1 to 4-hydroxyestrone (4-OH-E1). The enzymatic assay showed that the proportion of the three components is critical for its function. The dioxygenase EanC catalyzes ring A cleavage of 4-OH-E1, and the oxidoreductase EanD is responsible for the decarboxylation of the ring A-cleavage product of 4-OH-E1. EanR, a TetR family transcriptional regulator, acts as a transcriptional repressor of the ean cluster. The ean cluster was also found in other reported E2-degrading sphingomonads. In addition, the novel two-component monooxygenase EanE1E2 can open ring B of 4-OH-E1 via the 9,10-seco pathway, but its encoding genes are not located within the ean cluster. These results refine research on genes involved in E2 degradation and enrich the understanding of the cleavages of ring A and ring B of E2.IMPORTANCESteroid estrogens have been detected in diverse environments, ranging from oceans and rivers to soils and groundwater, posing serious risks to both human health and ecological safety. The United States National Toxicology Program and the World Health Organization have both classified estrogens as Group 1 carcinogens. Several model organisms (proteobacteria) have established the 4,5-seco pathway for estrogen degradation. In this study, the newly isolated Sphingomonas colocasiae C3-2 could degrade E2 through both the 4,5-seco pathway and the 9,10-seco pathway. The novel gene cluster ean (including eanA, eanB1, eanC, and eanD) responsible for E2 degradation by the 4,5-seco pathway was identified; the novel two-component monooxygenase EanE1E2 can open ring B of 4-OH-E1 through the 9,10-seco pathway. The TetR family transcriptional regulator EanR acts as a transcriptional repressor of the ean cluster. The cluster ean was also found to be present in other reported E2-degrading sphingomonads, indicating the ubiquity of the E2 metabolism in the environment.

Electrospun of functionalized mesoporous UiO-66 as the selective coating of solid phase microextraction Arrow for the determination of nine alkylphenols

A solid-phase microextraction (SPME) Arrow and high-performance liquid chromatography-UV detector (HPLC-UV, detection at 225 nm) based method was developed for the selective determination of nine alkylphenols (APs) in milk. The functionalized mesoporous UiO-66 (4-meso-UiO-66) was utilized as the new coating material, which was synthesized by post-modification of pore-expanded UiO-66-NH2 by an esterification reaction with 4-pentylbenzoic acid. It was fully characterized by X-ray photoelectron spectroscopy (XPS), fourier transformation infrared spectrometry, nitrogen sorption-desorption test, scanning electron microscopy, transmission electron microscopy, and X-ray diffractometer. The characterization results showed the ester groups and benzene rings were introduced into the 4-meso-UiO-66, and the mesoporous structure was predominant in the 4-meso-UiO-66. The extraction mechanism of 4-meso-UiO-66 to APs is the synergistic effect of Zr-O electrostatic interaction and the size exclusion effect resulting from XPS, selectivity test, and nitrogen sorption-desorption test. The electrospinning technique was utilized to fabricate the 4-meso-UiO-66 coated SPME Arrow and polyacrylonitrile (PAN) was used as the adhesive. The mass rate of 4-meso-UiO-66 to PAN and the electrospinning time were evaluated. The extraction and desorption parameters were also studied. The linear range of this method was 0.2-1000 μg L-1 with a coefficient of determination greater than 0.9989 under the optimal conditions. The detection limits were 0.05-1 μg L-1, the inter-day and intra-day precision (RSD) were 2.8-11.5%, and the recovery was 83.6%-112%. The reusability study showed that the extraction performance of this new SPME Arrow could be maintained after 80 adsorption-desorption cycles. This method showed excellent applicability for the selective determination of APs in milk.

Correlations between China's socioeconomic status, disease burdens, and pharmaceuticals and personal care product levels in wastewater

The presence of pharmaceutical and personal care products (PPCPs) in domestic wastewater can potentially indicate socioeconomic status and disease burdens. However, current knowledge is limited to the correlation between specific pharmaceuticals and diseases. This study aims to explore the associations between socioeconomic status, disease burdens, and PPCP levels in domestic wastewater at a national level. Samples from 171 wastewater influents across China were used to measure PPCPs, and the per capita consumption of PPCPs was calculated. Results showed that the 31 targeted PPCPs were widely present in wastewater with varying occurrence characteristics. The mean consumption levels of different PPCPs varied greatly, ranging from 0.03 to 110723.15 µg/d/capita. While there were no significant regional differences in the overall pattern of PPCP consumption, 22 PPCPs showed regional variations between Northern China and Southern China. PPCPs with similar usage purposes exhibited similar distribution patterns. Disease burden (70.1%) was the main factor affecting most PPCP consumption compared to socioeconomic factors (26.4%). Through correlation analyses, specific types of PPCPs were identified that were highly associated with socioeconomic status and disease burdens, such as hypertension-bezafibrate, brucellosis-quinolones, sulfonamides, hepatitis-triclosan, triclocarban, socioeconomic development-fluoxetine, and people's living standards-gemfibrozil. Despite some uncertainties, this study provides valuable insights into the relationship between PPCPs in domestic wastewater and socioeconomic status and human health.