An artificial intelligence-based model for predicting reproductive toxicity of bisphenol analogues mixtures to the rotifer Brachionus calyciflorus.

The joint toxicity effects of mixtures, particularly reproductive toxicity, one of the main causes of aquatic ecosystem degradation, are often overlooked as it is impractical to test all mixtures. This study developed and evaluated the following models to predict the concentration response curve concerning the joint reproductive toxicity of mixtures of three bisphenol analogues (BPA, BPF, BPAF) on the rotifer Brachionus calyciflorus: concentration addition (CA), independent action (IA), and two deep neural network (DNN) models. One applied mixture molecular descriptors as input variables (DNN-QSAR), while the other applied the ratios of chemicals in the mixtures (DNN-Ratio). Descriptors related to molecular mass were found to be of greater importance and exhibited a proportional relationship with toxic effects. The results indicate that the range of correlation coefficients (R2) between predicted and measured values for various mixture rays by CA and IA models is 0.372 to 0.974 and - 0.970 to 0.586, respectively. The R2 values for DNN-Ratio and DNN-QSAR were 0.841 to 0.984 and 0.834 to 0.991, respectively, demonstrating that models developed by DNN significantly outperform traditional models in predicting the joint toxicity of mixtures. Furthermore, DNN-QSAR not only predicts mixture toxicity but also provides accurate toxicity predictions for BPA, BPF, and BPAF, with R2 values of 0.990, 0.616, and 0.887, respectively, while DNN-Ratio yields values of 0.920, 0.355, and - 0.495. The study also found that the joint effects of mixtures are primarily influenced by the total concentration of the mixtures, and an increase in total concentration shifts the joint effects towards addition. This study introduces a novel approach to predict joint toxicity and analyze the influencing factors of joint effects, providing a more comprehensive assessment of the ecological risk posed by mixtures.

Microeukaryotic plankton community dynamics under ecological water replenishment: Insights from eDNA metabarcoding.

Ecological water replenishment (EWR) is an important strategy for river restoration globally, but timely evaluation of its ecological effects at a large spatiotemporal scale to further adjust the EWR schemes is of great challenge. Here, we examine the impact of EWR on microeukaryotic plankton communities in three distinct river ecosystems through environmental DNA (eDNA) metabarcoding. The three ecosystems include a long-term cut-off river, a short-term connected river after EWR, and long-term connected rivers. We analyzed community stability by investigating species composition, stochastic and deterministic dynamics interplay, and ecological network robustness. We found that EWR markedly reduced the diversity and complexity of microeukaryotic plankton, altered their community dynamics, and lessened the variation within the community. Moreover, EWR disrupted the deterministic patterns of community organization, favoring dispersal constraints, and aligning with trends observed in naturally connected rivers. The shift from an isolated to a temporarily connected river appeared to transition community structuring mechanisms from deterministic to stochastic dominance, whereas, in permanently connected rivers, both forces concurrently influenced community assembly. The ecological network in temporarily connected rivers post-EWR demonstrated significantly greater stability and intricacy compared to other river systems. This shift markedly bolstered the resilience of the ecological network. The eDNA metabarcoding insights offer a novel understanding of ecosystem resilience under EWR interventions, which could be critical in assessing the effects of river restoration projects throughout their life cycle.

Effects of Environmental Organic Pollutants on Environment and Human Health: The Latest Updates.

This editorial introduces the Special Issue "Effects of Environmental Organic Pollutants on Environment and Human Health: The Latest Updates" [...].

Health risk assessment and development of human health ambient water quality criteria for PCBs in Taihu Basin, China.

Polychlorinated biphenyls (PCBs) are a class of typical persistent organic pollutants (POPs) with carcinogenicity and extensively found in diverse environmental mediums. The Taihu Basin is one of the most economically developed regions in China, and it has also caused a lot of historical legacy and unconscious emissions of PCBs, posing a threat to the health of people in the region. This study counted the concentrations of PCBs in five environmental media (water, soil, air, dust, and food) in the Taihu Basin from 2000 to 2020 and used Monte Carlo simulation to simulate the multi-channel exposure of PCBs in people of different ages (children, teenagers, and adults), and evaluated their noncarcinogenic and carcinogenic health risks. Finally, the human health ambient water quality standards (AWQC) for PCBs were obtained using regional exposure parameters and bioaccumulation factors. The results showed that the pollution of PCBs in the Taihu Basin was relatively serious in China. The concentration of PCBs in dust is higher than other environmental media. And exposure to water and food is the main exposure pathway for PCBs in the population of the region. Besides, PCBs pose no noncarcinogenic risk to people in this region, but their carcinogenic risk to residents exceeds the safety threshold. Among the three population groups, adults have the highest risk of cancer, and prevention measures need to be taken by controlling the intake of related foods and the concentration of PCBs in water. The following human health AWQC values of the PCBs in Taihu Basin is 3.2 × 10-9 mg/L.

The application of human-derived cell lines in neurotoxicity studies of environmental pollutants.

As industrial and societal advancements progress, an increasing number of environmental pollutants linked to human existence have been substantiated to elicit neurotoxicity and developmental neural toxicity. For research in this field, human-derived neural cell lines have become excellent in vitro models. This study examines the utilization of immortalized cell lines, specifically the SH-SY5Y human neuroblastoma cell line, and neural cells derived from human pluripotent stem cells, in the investigation of neurotoxicity and developmental neural toxicity caused by environmental pollutants. The study also explores the culturing techniques employed for these cell lines and provides an overview of the standardized assays used to assess various biological endpoints. The environmental pollutants involved include a variety of organic compounds, heavy metals, and microplastics. The utilization of cell lines derived from human sources holds significant significance in elucidating the neurotoxic effects of environmental pollutants and the underlying mechanisms. Finally, we propose the possibility of improving the in vitro model of the human nervous system and the toxicity detection methods.

BDE-209-induced genotoxicity, intestinal damage and intestinal microbiota dysbiosis in zebrafish (Danio Rerio).

The environmental presence of polybrominated diphenyl ethers (PBDEs) is ubiquitous due to their wide use as brominated flame retardants in industrial products. As a common congener of PBDEs, decabromodiphenyl ether (BDE-209) can pose a health risk to animals as well as humans. However, to date, few studies have explored BDE-209's toxic effects on the intestinal tract, and its relevant mechanism of toxicity has not been elucidated. In this study, adult male zebrafish were exposed to BDE-209 at 6 µg/L, 60 µg/L and 600 µg/L for 28 days, and intestinal tissue and microbial samples were collected for analysis to reveal the underlying toxic mechanisms. Transcriptome sequencing results demonstrated a dose-dependent pattern of substantial gene differential expression in the group exposed to BDE-209, and the differentially expressed genes were mainly concentrated in pathways related to protein synthesis and processing, redox reaction, and steroid and lipid metabolism. In addition, BDE-209 exposure caused damage to intestinal structure and barrier function, and promoted intestinal oxidative stress, inflammatory response, apoptosis and steroid and lipid metabolism disorders. Mechanistically, BDE-209 induced intestinal inflammation by increasing the levels of TNF-α and IL-1ß and activating the NFκB signaling pathway, and might induce apoptosis through the p53-Bax/Bcl2-Caspase3 pathway. BDE-209 also significantly inhibited the gene expression of rate-limiting enzymes such as Sqle and 3ßhsd (p < 0.05) to inhibit cholesterol synthesis. In addition, BDE-209 induced lipid metabolism disorders through the mTOR/PPARγ/RXRα pathway. 16S rRNA sequencing results showed that BDE-209 stress reduced the richness and diversity of intestinal microbiota, and reduced the abundance of probiotics (e.g., Bifidobacterium and Faecalibacterium). Overall, the results of this study help to clarify the intestinal response mechanism of BDE-209 exposure, and provide a basis for evaluating the health risks of BDE-209 in animals.

Environmental DNA unveiling the fish community structure and diversity features in the Yangtze River basin.

Fish, as top predators in aquatic ecosystems, play an important role in maintaining the structure and functioning of these ecosystems, making their diversity a topic of great interest. This study focused on the Yangtze River Basin to investigate the fish community structure and diversity using environmental DNA (eDNA) technology. The results showed that a total of 71616 fish operational taxonomic units (OTUs) and 90 fish belonging to 23 families were detected, with the Cyprinidae family being the dominant group, followed by the Cobitidae, Amblycipitidae, etc. Compared to historical traditional morphological fish surveys, the quantity of fish detected using eDNA was relatively low, but the overall distribution pattern of fish communities was generally consistent. The highest fish Shannon-Wiener diversity index in the Yangtze River Basin sites reaches 2.60 with an average value of 1.25. The fish diversity index was higher in the downstream compared to the middle and upstream regions, and there were significant differences among different sampling sites. Significant environmental factors influencing α-diversity included chlorophyll-a, chemical oxygen demand, dissolved oxygen, total nitrogen, and elevation. Non-metric multidimensional scaling (NMDS) analysis revealed significant differences in fish community composition between the upstream and middle/lower reaches of the Yangtze River, while the composition of fish communities in the middle and lower reaches was more similar. Redundancy analysis (RDA) indicated that total organic carbon (TOC) was positively correlated with fish community distribution in the upstream, while water temperature and NO3-N were negatively correlated with fish distribution in the upstream. NH3-N and CODMn were negatively correlated with fish distribution in the middle and downstream regions, indicating a relatively severe water pollution in these areas. Additionally, fish communities in the Yangtze River displayed a typical distance decay pattern.

A review of the toxic effects of ammonia on invertebrates in aquatic environments.

Aquatic invertebrates are the organisms most susceptible to ammonia toxicity. However, the toxic effects of ammonia on invertebrates are still poorly understood. This study reviews the research progress in ammonia toxicology for the period from 1986 to 2023, focusing on the effects on invertebrates. Through examining the toxic effects of ammonia at different levels of organization (community, individual, tissue and physiology, and molecular) as well as the results from omics studies, we determined that the most significant effects were on the reproductive capacity of invertebrates and the growth of offspring, although different populations show variation in their tolerance to ammonia, and tissues have varied potential to respond to ammonia stress. A multicomponent analysis is an in-depth technique employed in toxicological studies, as it can be used to explore the enrichment pathways and functional genes expressed under ammonia stress. This study comprehensively discusses ammonia toxicity from multiple aspects in order to provide new insights into the toxic effects of ammonia on aquatic invertebrates.

QSAR-QSIIR-based prediction of bioconcentration factor using machine learning and preliminary application.

Bioconcentration factor (BCF) is one of the important parameters for developing human health ambient water quality criteria (HHAWQC) for chemical pollutants. Traditional experimental method to obtain BCF is time-consuming and costly. Therefore, prediction of BCF by modeling has attracted much attention. QSAR (Quantitative Structure-Activity Relationship) model based on molecular descriptor is often used to predict BCF, however, in order to improve the accuracy of prediction, previous models are only applicable for prediction for a single category of substance and a single species, and cannot meet the needs of BCF prediction of pollutants lacing toxicity data. In this study, optimized 17 traditional molecular descriptor and five kinds of bioactivity descriptor were selected from more than 200 molecular descriptor and 25 kinds of biological activity descriptors. A QSAR-QSIIR (Quantitative Structure In vitro-In vivo Relationship) model suitable for multiple chemical substances and whole species is constructed by using optimized 4-MLP machine learning algorithm with selected molecular and bioactivity descriptors. The constructed model significantly improves the prediction accuracy of BCF. The R2 of verification set and test set are 0.8575 and 0.7924, respectively, and the difference between predicted BCF and measured BCF is mostly less than 1.5 times. Then, BCF of BTEX in Chinese common aquatic products is predicted using the constructed QSAR-QSIIR model, and the HHAWQC of BTEX in China are derived using the predicted BCF, which provides a valuable reference for establishment of China's BTEX water quality standards.

The Application of Reference Dose Prediction Model to Human Health Water Quality Criteria and Risk Assessment.

Oral reference dose (RfD) is a key parameter for deriving the human health ambient water quality criteria (AWQC) for non-carcinogenic substances. In this study, a non-experimental approach was used to calculate the RfD values, which explore the potential correlation between toxicity and physicochemical characteristics and the chemical structure of pesticides. The molecular descriptors of contaminants were calculated using T.E.S.T software from EPA, and a prediction model was developed using a stepwise multiple linear regression (MLR) approaches. Approximately 95% and 85% of the data points differ by less than 10-fold and 5-fold between predicted values and true values, respectively, which improves the efficiency of RfD calculation. The model prediction values have certain reference values in the absence of experimental data, which is beneficial to the advancement of contaminant health risk assessment. In addition, using the prediction model constructed in this manuscript, the RfD values of two pesticide substances in the list of priority pollutants are calculated to derive human health water quality criteria. Furthermore, an initial assessment of the health risk was performed by the quotient value method based on the human health water quality criteria calculated by the prediction model.

Environmental DNA sequencing reveals the regional difference in diversity and community assembly mechanisms of eukaryotic plankton in coastal waters.

The diversity and community assembly mechanisms of eukaryotic plankton in coastal waters is so far not clear. In this study, we selected the coastal waters of Guangdong-Hong Kong-Macao Greater Bay Area, which is a highly developed region in China, as the research area. By use of high-throughput sequencing technologies, the diversity and community assembly mechanisms of eukaryotic marine plankton were studied in which a total of 7,295 OTUs were obtained, and 2,307 species were annotated by doing environmental DNA survey of 17 sites consist of surface and bottom layer. Ultimately, the analysis reveals that the species abundance of bottom layer is, by and large, higher than that in the surface layer. In the bottom, Arthropoda is the first largest group, accounting for more than 20% while Arthropoda and Bacillariophyta are dominant groups in surface waters accounting for more than 40%. It is significant of the variance in alpha-diversity between sampling sites, and the difference of alpha-diversity between bottom sites is greater than that of surface sites. The result suggests that the environmental factors that have significant influence on alpha-diversity are total alkalinity and offshore distance for surface sites, and water depth and turbidity for bottom sites. Likewise, the plankton communities obey the typical distance-decay pattern. Analysis about community assembly mechanisms reveals that, overall, dispersal limitation is the major pattern of community formation, which accounts for more than 83% of the community formation processes, suggesting that stochastic processes are the crucial assembly mechanism of the eukaryotic plankton community in the study area.

Chinese Technical Guideline for Deriving Water Quality Criteria for Protection of Freshwater Organisms.

In recent years, China has determined the national goal of "developing national environmental criteria", thereby promoting the rapid development of environmental quality criteria research in China. In 2017, the Ministry of Ecology and Environment of China (MEEC, formerly the Ministry of Environmental Protection of China) issued the technical guideline for deriving water quality criteria (WQC) for protection of freshwater organisms (HJ 831-2017), and in 2022, they organized the guideline revision and issued an updated version (HJ 831-2022). The primary contents of the revision included the following. The minimum toxicity data requirements were upgraded from 6 to 10, and the species mean toxicity value was replaced by the same effect toxicity value for the criteria calculation. It is now required that the tested organisms must be distributed in China's natural fresh waters, and the toxicity data of non-native model species will no longer be used. The list of freshwater invasive species in China that cannot be used as test species was added into the guideline. The acute/chronic ratio (ACR) method for the criteria derivation and the extreme value model were deleted, and the provisions for testing the toxicity data distribution were also deleted. The exposure time of the toxicity test of various tested organisms was refined, and the priority of the toxicity data was clearly specified. This paper introduces the framework and specific technical requirements of HJ 831-2022 in detail, including data collection, pre-processing of toxicity data, criteria derivation, fitting models, and quality control. This introduction is helpful for international peers to understand the latest research progress of China's WQC.

Grain shape is a factor affecting the stigma exsertion rate in rice.

Stigma exsertion rate (SER) is an index of outcrossing ability in rice and is a key trait of male sterile lines (MSLs) in hybrid rice. In this study, it was found that the maintainer lines carrying gs3 and gs3/gw8 showed higher SER. Single-segment substitution lines (SSSLs) carrying gs3, gw5, GW7 or gw8 genes for grain shape and gene pyramiding lines were used to reveal the relationship between grain shape and SER. The results showed that the grain shape regulatory genes had pleiotropic effects on SER. The SERs were affected by grain shapes including grain length, grain width and the ratio of length to width (RLW) not only in low SER background, but also in high SER background. The coefficients of determination (R2) between grain length and SER, grain width and SER, and grain RLW and SER were 0.78, 0.72, and 0.91 respectively. The grain RLW was the most important parameter affecting SER, and a larger grain RLW was beneficial to stigma exsertion. The pyramiding line PL-gs3/GW7/gw8 showed the largest grain RLW and the highest SER, which will be a fine breeding resource. Further research showed that the grain shape regulatory genes had pleiotropic effects on stigma shape, although the R2 values between grain shape and stigma shape, and stigma shape and SER were lower. Our results demonstrate that grain shape is a factor affecting SER in rice, in part by affecting stigma shape. This finding will be helpful for breeding MSLs with high SER in hybrid rice.

New insights into the mechanism of ammonia toxicity: Focus on Cactus.

The NF-κB signaling pathway is the most critical pathway in innate immunity. IκB (Cactus) is the primary cytoplasmic inhibitor of NF-κB (Dorsal). In this study, we found that ammonia exposure could significantly induce the expression of Cactus, in a dose-dependent manner in different tissues, with the highest expression in the gill of Corbicula fluminea. The expression pattern-related elements (Tube and Dorsal) in the NF-κB signaling pathway were also analyzed, showing significant up-regulation in 48 h. There was an inhibitory effect between up-regulated Cactus and Dorsal in 72 h, which may regulate Dorsal as a negative feedback pathway function to control the expression of pro-inflammatory cytokines (IL-1ß, IL-6, and TNF-α). Besides, through molecular docking simulation, we found that the Cactus could be directly activated by NH3, complementing the regulatory mechanism of the Cactus. To further test our hypothesis, the levels of pro-inflammatory cytokines decreased after adding PDTC (the antioxidant of Cactus/IκB), suggesting that PDTC can prevent the degradation of Cactus, inhibit Dorsal translocating into the nucleus, and activate the pro-inflammatory cytokines. This revealed the inhibitory effect of Cactus on activating Dorsal/NF-κB factors in the NF-κB signaling pathway. Thus, we suggested that the Cactus is an essential regulator of ammonia-activated inflammation in C. fluminea, which was reported to be activated only by bacteria and immune stimulators. Our study provides a new perspective on the mechanism of ammonia toxicity in invertebrates.

Reconstruction of the High Stigma Exsertion Rate Trait in Rice by Pyramiding Multiple QTLs.

Asian cultivated rice is a self-pollinating crop, which has already lost some traits of natural outcrossing in the process of domestication. However, male sterility lines (MSLs) need to have a strong outcrossing ability to produce hybrid seeds by outcrossing with restorer lines of male parents in hybrid rice seed production. Stigma exsertion rate (SER) is a trait related to outcrossing ability. Reconstruction of the high-SER trait is essential in the MSL breeding of rice. In previous studies, we detected eighteen quantitative trait loci (QTLs) for SER from Oryza sativa, Oryza glaberrima, and Oryza glumaepatula using single-segment substitution lines (SSSLs) in the genetic background of Huajingxian 74 (HJX74). In this study, eleven of the QTLs were used to develop pyramiding lines. A total of 29 pyramiding lines with 2-6 QTLs were developed from 10 SSSLs carrying QTLs for SER in the HJX74 genetic background. The results showed that the SER increased with increasing QTLs in the pyramiding lines. The SER in the lines with 5-6 QTLs was as high as wild rice with strong outcrossing ability. The epistasis of additive by additive interaction between QTLs in the pyramiding lines was less-than-additive or negative effect. One QTL, qSER3a-sat, showed minor-effect epistasis and increased higher SER than other QTLs in pyramiding lines. The detection of epistasis of QTLs on SER uncovered the genetic architecture of SER, which provides a basis for using these QTLs to improve SER levels in MSL breeding. The reconstruction of the high-SER trait will help to develop the MSLs with strong outcrossing ability in rice.

Reference dose prediction by using CDK molecular descriptors: A non-experimental method.

Reference dose (RfD) is an estimate of a daily dose that individual can be exposed chronically without obvious deleterious effects during a lifetime. In the area of toxicology, researchers always use the traditional approach by employing NOAEL/LOAEL or the benchmark dose (BMD) and other dose-response approaches to estimate RfD. These methods have, despite their typicalness, certain limitations. In this study, we present a novel method of the estimation of reference dose without experiments. The information of the organic chemicals is available from the Integrated Risk Information System (IRIS) of USEPA. Molecular descriptors for each molecular structure were calculated by an integrated platform, and the chemicals were classified into four categories based on molecular similarity: 128 contained benzene rings, 47 were heteroaromatics, 104 contained halogen substituents and 44 were halogenated aliphatic hydrocarbons. The predictive model of RfD was constructed by the multiple linear stepwise regression (MLR) method. Approximately 95% and 82% of the data points differ by less than 10-fold and 5-fold between the predicted values and the true values respectively. The non-experimental method improves the estimation efficiency and has a certain reference value to predict.

Impairment of the gut health in Danio rerio exposed to triclocarban.

Triclocarban (TCC) is the principal component in personal and health care products because it is a highly effective, broad-spectrum, and safe antibacterial agent. TCC has recently been discovered in aquatic creatures and has been shown to constitute a health danger to aquatic animals. Although several studies have looked into the toxicological effects of TCC on a variety of aquatic animals from algae to fish, the possible gut-toxicity molecular pathway in zebrafish has never been thoroughly explored. We investigated the gut-toxic effects of TCC on zebrafish by exposing them to different TCC concentrations (100 and 1000 µg/L) for 21 days. We discovered for the first time that the MAPK and TLR signaling pathways related to gut diseases were significantly altered, and inflammation (up-regulation of TNF-α, IL-6, and IL-1ß) caused by TCC was confirmed to be largely mediated by the aryl hydrocarbon receptor (AHR) and its related cytokines. This was found using the results of qPCR, a transcriptome analysis, and molecular docking (AHR, AHRR, CYP1A1 and CYP1B1). Furthermore, high-throughput 16S rDNA sequencing demonstrated that TCC exposure reduced the bacterial diversity and changed the gut microbial composition, with the primary phyla Fusobacteria and Proteobacteria, as well as the genera Cetobacterium and Rhodobacteraceae, being the most affected. TCC exposure also caused damage to the gut tissue, including an increase in the number of goblet cells and a reduction in the height of the columnar epithelium and the thickness of the muscular layer, as shown by hematoxylin and eosin staining. Our findings will aid in understanding of the mechanism TCC-induced aquatic toxicity in aquatic species.

Research on freshwater water quality criteria, sediment quality criteria and ecological risk assessment of triclosan in China.

Triclosan (TCS) is a broad-spectrum antimicrobial agent commonly used in pharmaceuticals and personal care products (PPCPs). The widespread use of TCS makes it frequently detected in various environmental mediums. In view of the high detection frequency of TCS in the aquatic environment and sediments, and its toxic effects on aquatic species, it is critical and necessary to derive Chinese TCS water quality criteria (WQC) and sediment quality criteria (SQC) for protecting Chinese aquatic organisms, and perform the ecological risk assessment. In fact, former research had derived the WQC of TCS mainly based on acute and chronic toxicity data. As an endocrine disrupting chemical (EDC), TCS poses adverse effects on the growth, development and reproduction of aquatic organisms at much lower concentration. Considering nonlethal endpoints are sensitive endpoints for EDCs, TCS long-term water quality criteria (LWQC) was derived based on reproduction and growth related endpoints. In this work, the acute toxicity data of 19 aquatic organisms and the chronic toxicity data of 15 aquatic organisms were obtained through collection and screening. The best fitting model of species sensitivity distribution (SSD) models including Normal, Log-Normal, Logistic and Log-Logistic of toxicity data was selected to derive WQC. The short-term and long-term WQC of TCS for Chinese aquatic organisms were 6.22 µg/L and 0.25 µg/L, respectively. Furthermore, through the phase-equilibrium partitioning method, SQC was derived based on WQC. SQC-low (SQC-L) and SQC-high (SQCH) were 0.13 mg/kg and 3.26 mg/kg, respectively. Moreover, the exposure concentration (EPC) data of TCS in Chinese rivers and sediments were collected. And through the hazard quotient (HQ) method and the joint probability curve (JPC) method we found that there were certain TCS ecological risks in Chinese rivers and sediments. Our work will provide a valuable reference for protecting aquatic organisms and minimizing TCS ecological risk in China.

Health risk assessment and development of human health ambient water quality criteria for PBDEs in China.

Polybrominated diphenyl ethers (PBDEs) are not only a class of highly efficient brominated flame retardants (BFRs) but also a class of typical persistent organic pollutants (POPs) that are persistent and widely distributed in various environmental media. This study examined the concentrations of PBDEs in five environmental media (water, soil, air, dust, and food) and two human body media (human milk and blood) in China from 2010 to 2020. In addition, this study conducted multi-pathway exposure health risk assessments of populations of different ages in urban, rural, key regions, and industrial factories using the Monte-Carlo simulation. Finally, the human health ambient water quality criteria (AWQC) of eight PBDEs were derived using Chinese exposure parameters and bioaccumulation factors (BAFs). The results showed that the eastern and southeastern coastal regions of China were heavily polluted by PBDEs, and the variation trends of the ΣPBDEs concentrations in the different exposure media were not consistent. PBDEs did not pose a risk to urban and rural residents in ordinary regions, but the hazard indexes (HIs) for residents in key regions and occupational workers exceeded the safety threshold. Dust exposure was the primary exposure pathway for urban and rural residents in ordinary regions, but for residents in key regions and occupational workers, dietary exposure was the primary exposure pathway. BDE-209 was found to be the most serious individual PBDE congener in China. The following human health AWQC values of the PBDEs were derived: drinking water exposure: 0.233-65.2 µg·L-1; and drinking water and aquatic products exposure: 8.51 × 10-4-1.10 µg·L-1.

Prediction of chemical reproductive toxicity to aquatic species using a machine learning model: An application in an ecological risk assessment of the Yangtze River, China.

The endocrine disrupting chemicals (EDCs) have been at the forefront of environmental issues for over 20 years and are a principle factor considered in every ecological risk assessment, but this kind of risk assessment faces difficulties. The expense, time cost of in vivo tests, and lack of toxicity data are key limiting factors for the ability to conduct ecological risk assessments of EDCs to aquatic species. In this study, a machine learning model named the support vector machine (SVM) was used to predict the reproductive toxicity of EDCs, and the performance of the models was evaluated. The results showed that the SVM model provided more accurate toxicity prediction data compared with the interspecies correlation estimation (ICE) model developed by previous study to predict the reproductive toxicity. The application of the predicted toxicity data was an important supplement to the observed data for the ecological risk assessment of EDCs in the Yangtze River, where estrogens and phenolic compounds have been found at some sampling sites in the middle and lower reaches. The results showed that the ecological risk of estrone, 17ß-estradiol, and ethinyl estradiol were significant. This study revealed the application potential of machine learning models for the prediction of reproductive toxicity effects of EDCs. This can provide reliable alternative toxicity data for the ecological risk assessments of EDCs.