Deriving convincing human health ambient water quality criteria for benzo[a]pyrene and providing basis for the water quality management: The impacts of national bioaccumulation factors and probabilistic modeling

Freshwater environment risk assessment and derivation of human health ambient water quality criteria for SCCPs in China

Short-chain chlorinated paraffins (SCCPs, CxH2x+2-yCly, where x = 10 to 13 and y = 3 to x) are a complex family of emerging contaminants that have been identified as persistent, bioaccumulative and toxic (PBT) substances. Because of their persistence, high toxicity, bioaccumulation and long-distance mobility, SCCPs were added to the list of controlled POPs in the Stockholm Convention in 2017. China is the largest producer and user of SCCPs all over the world, thus SCCPs have been widely distributed in the freshwater environment. But the ecological risk and human health risk of SCCPs in China were still less evaluated. This study collected and screened the exposure data of SCCPs in freshwater environment in China, and then evaluated the freshwater and sediments ecological risk of SCCPs by risk quotient (RQ) method and the human health risk by margin of exposure (MOE) method. Our results showed that the pollution of SCCPs in the freshwater environment in China was not optimistic. The ecological risks and human health risks had occurred, and even there were high ecological risks occurred in some regions in China. Besides, because of the absence of the concentration threshold of SCCPs to protect human health in freshwater at present, we derived the human health ambient water quality criteria (AWQC) values of SCCPs based on exposure data and bioaccumulation factor (BAF) in China. The human health AWQC values of SCCPs ranged from 14.99 ng L-1 to 154.54 ng L-1 in different regions in China, and the national human health AWQC value was determined as 24.08 ng L-1. This study will provide the scientific references for the management and control of SCCPs in China, especially for the formulation of standard of water environmental quality for SCCPs.

Dynamic changes of DNA methylation induced by benzo(a)pyrene in cancer

Benzo(a)pyrene (BaP), the earliest and most significant carcinogen among polycyclic aromatic hydrocarbons (PAHs), has been found in foods, tobacco smoke, and automobiles exhaust, etc. Exposure to BaP induced DNA damage directly, or oxidative stress-related damage, resulting in cell apoptosis and carcinogenesis in human respiratory system, digestive system, reproductive system, etc. Moreover, BaP triggered genome-wide epigenetic alterations by methylation, which might cause disturbances in regulation of gene expression, and thereby induced cancer. It has been proved that BaP reduced genome-wide DNA methylation, and activated proto-oncogene by hypomethylation in the promoter region, but silenced tumor suppressor genes by promoter hypermethylation, resulting in cancer initiation and progression. Here we summarized the changes in DNA methylation in BaP exposure, and revealed the methylation of DNA plays a role in cancer development.

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.

Evaluating pesticide mixture risks in French Mediterranean coastal lagoons waters

To assess the risk of pesticide mixtures in lagoon waters, this study adopted a multi-step approach using integrative passive samplers (POCIS) and concentration addition (CA) toxicological models. Two French Mediterranean lagoons (Thau and Or) were monitored for a range of 68 pesticides continuously over a period of a year (2015-16). The findings revealed mixtures of dissolved pesticides with varying composition and levels over the year. The Or site contained more pesticides than Thau site (37 vs 28 different substances), at higher concentrations (0.1-58.6 ng.L^-1 at Or vs <0.1-9.9 at Thau) and with overall higher detection frequencies. All samples showed a potential chronic toxicity risk, depending on the composition and concentrations of co-occurring pesticides. In 74 % of the samples, this pesticide risk was driven by a few single substances (ametryn, atrazine, azoxystrobin, carbendazim, chlorotoluron, irgarol, diuron and metolachlor) and certain transformation products (e.g. DPMU and metolachlor OA/ESA). Individually, these were a threat for the three taxa studied (phytoplankton, crustaceans and fish). Yet even a drastic reduction of these drivers alone (up to 5 % of their current concentration) would not eliminate the toxicity risks in 56 % of the Or Lagoon samples, due to pesticide mixtures. The two CA-based approaches used to assess the combined effect of these mixtures, determined chronic potential negative impacts for both lagoons, while no acute risk was highlighted. This risk was seasonal, indicating the importance of monitoring in key periods (summer, winter and spring) to get a more realistic picture of the pesticide threat in lagoon waters. These findings suggest that it is crucial to review the current EU Water Framework Directive's risk-assessment method, as it may incorrectly determine pesticide risk in lagoons.