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The environmental pollution caused by toxic chemicals such as pesticides has become a global problem. The mixture of dichlorvos (DIC), dimethoate (DIM), aldicarb (ALD) poses potential risks to the environment and human health. To fully explore the interaction of complex mixtures on Caenorhabditis elegans behavioral toxicity endpoint. This study created a synergistic-antagonistic heatmap (SAHmap) based on the combination index to systematically describe the toxicological interaction prospect of the mixture system. It was shown that the three pesticides and their binary as well as ternary mixture rays have significant concentration-response relationship on three behavioral endpoints of nematodes, From the perspective of synergistic-antagonistic heatmaps, all the mixture rays in the DIC-DIM mixture system showed strong synergism on the three behavioral and lethal endpoints. In the ternary mixture system, the five mixture rays showed different interaction between the behavioral endpoint and the lethal endpoint, and showed slight synergism to two behavioral endpoints as a whole. The emergence of synergism should arouse our attention to these hazardous chemicals. In addition, the use of SAHmap and the significant linear correlation among three behavioral endpoints further improved the efficiency of the study on the behavioral toxicity of pesticide mixtures to Caenorhabditis elegans.

Some personal care products (PCPs) and their chemical components showed a hormetic effect in the freshwater photobacterium Vibrio qinghaiensis sp. -Q67 (Q67) after long-term exposure. However, how hormesis transfers between chemical components and PCP mixture, and which chemical component plays a major role remain unknown. To this end, according to the seven compounds detected in one skin lotion (SK5) and their concentration ratios, many mixture rays were constructed to simulate the SK5. Of these seven compounds, three presented monotonic concentration-response curves (CRC) to Q67 at 0.25 and 12 h (called a S-shaped compound). The other four compounds showed hormetic CRCs after 12 h and monotonic CRCs at 0.25 h (called a J-shaped compound). Based on their mixture ratios, we designed one ternary mixture ray of all S-shaped compounds, one quaternary mixture ray of all J-shaped compounds, and four quaternary mixture rays of one J-shaped and three S-shaped compounds. It was shown that SK5 could be approximately simulated by the mixture ray of the seven compounds detected in SK5 and only the mixture rays containing at least one hormesis-inducing compound produced hormesis to Q67 at 12 h. Based on the concentration ratios of various compounds and comparison of four hormetic characteristic parameters to those of various mixture rays, it was found that the compound betaine (BET) is a key compound affecting the hormesis of mixtures. Additionally, we studied the hormesis mechanism of BET on Q67 via quorum sensing (QS). This preliminarily indicated that the autoinducer-2 triggered the QS pathway. This study elucidated the transfer pattern of hormesis into mixtures, which would be an efficient method to identifying the potential components that affect hormesis transfer in mixtures. We expect that this study will provide new insights into hormesis and its mixtures.

At present, the toxicity prediction of mixtures mainly focuses on the concentration addition (CA) and independent action (IA) based on individual toxicants to predict the toxicity of multicomponent mixtures. This process of predicting the toxicity of multicomponent mixtures based on single substances or low component mixtures is called down-to-top method in this study. However, due to the particularity of some toxicants, we have to use the top-to-down idea to obtain or eliminate the toxicity of some components from mixtures. For example, the toxicity of toxicants is obtained from the toxicity of a mixture with, especially toxic, cosolvent added. In the study, two top-to-down methods, the inverse CA (ICA) and inverse IA (IIA) models, were proposed to eliminate the effects of a certain component from multicomponent mixtures. Furthermore, taking the eight binary mixtures consisting of different shapes of cosolvents (isopropyl alcohol (IPA) having hormesis and dimethyl sulfoxide (DMSO)) and toxicants (two ionic liquids and two pesticides) as an example, combined with the interaction evaluated by CA and IA model, the influence of different shapes of components on top-to-down toxicity prediction was explored. The results showed that cosolvent IPA having hormesis may cause unpredictable effects, even at low concentrations, and should be used with caution. For DMSO, most of the toxicant's toxicity obtained by ICA and IIA models were almost in accordance with those observed experimentally, which showed that ICA and IIA could effectively eliminate the effects of cosolvent, even if toxic cosolvent, from the mixture. Ultimately, a frame of cosolvent use and toxicity correction for the hydrophobic toxicant were suggested based on the top-to-down toxicity prediction method. The proposed methods improve the existing framework of mixture toxicity prediction and provide a new idea for mixture toxicity evaluation and risk assessment.

In the hazard assessment of mixtures, the mixture predicted no-effect concentration (mPNEC) is always derived by the concentration addition (CA) model (mPNECCA) to assess the risk of mixtures combined with exposure assessment. However, the independent action (IA) model, which is also widely used as the CA model in the prediction and evaluation of mixture toxicity, is always used to calculate the population fraction showing a predefined effect, not mPNEC, and this limits the application of IA model in the mixture risk assessment. In this study, we explored the process of mPNEC derived by the IA method (mPNECIA) based on the species sensitivity distribution (SSD) and compared mPNECIA with mPNECCA. Taking two common pesticides, dimethoate (DIM) and dichlorvos (DIC), exposed in the actual water environment as an example, their SSD models were constructed separately using nine distribution functions after toxicity data screening and quality testing. For both DIC and DIM, all different nine models had passed the Kolmogorov-Smirnov test. Then, the PNECs of two pesticides were derived based on SSD models. Finally, mPNECIA with different concentration ratios was derived and compared to mPNECCA based on 81 combinations of nine SSD models. Most mPNEC values derived by IA model were more conservative than those by CA. It is worth noting that the mPNECIA is more conservative than mPNECCA for the commonly used log-logit distribution (function 7), log-normal distribution (8), and log-Weibull distribution (9). This study provides a new direction for the application of IA in the risk assessment and enriches the framework of mixture risk assessment.

Previous studies demonstrated long-term stimulation of some commercial personal care products (PCPs) on freshwater luminescent bacteria Vibrio qinghaiensis sp .-Q67 (Q67). However, whether a certain component can affect mixture's hormetic effect is still unknown. In this paper, two of ingredients in PCPs, 2-phenoxyethanol (PhE) and polyethylene glycol 400 (PEG400), were selected as object compounds to explore the relationship between concentration-response (CR) of mixtures and that of a single component. It was found that PEG400 has monotonic CR (MCR) on Q67 both at the short-term (0.25 h) and long-term (12 h) exposures while PhE has MCR at 0.25 h and hormetic CR (HCR) at 12 h. Here, the concentration-response curves (CRCs) of PEG400 at 0.25 and 12 h are overlapped each other and the CRCs of PEG400 are on the right of PhE. If the pEC 50 is taken as a toxic index, the toxicities of PEG400 at two times are basically the same, and those of PhE are the same, too, but PhE is twice as toxic as PEG400. For the mixtures of PEG400 and PhE, all rays except R1 have MCRs at 0.25 h while all rays have HCRs at 12 h where the higher the mixture ratio of PhE is, the more negative the maximum stimulation effect is. More importantly, the E min values of all rays are more negative (1.79–3.17-fold) than that of PhE worked alone, which implies that the introduction of PEG400 significantly enhances stimulative effect of PhE. At 0.25 h, all binary mixture rays but R1 produce a low-concentration additive action and high-concentration synergism. At 12 h, all rays display additive action, antagonism, additive action, and synergism in turn when the concentration changes from low to high. The overall findings suggested toxicological interactions should be considered in the risk assessment of PCPs and their potential impacts on ecological balances.

The widespread use of pesticides results in their frequent detection in water bodies and other environmental media. Pesticide residues may cause certain risks to the environment and human health, and reliable predicted no effect concentrations (PNEC) must be obtained when assessing environmental risks. Species sensitivity distribution (SSD) is an important method for the derivation of chemical PNECs. Construction of the SSD model requires sufficient toxicity data to various species including at least eight families in three phyla, suitable nonlinear fitting functions and assessment factors (AFs) with certain uncertainty. However, most chemicals could not collect sufficient species toxicity data, while some chemicals had sufficient species toxicity data but could not find suitable fitting functions, thus hindering the construction of effective SSD models. To this end, the established QSAR models were applied to predict toxicity of chemicals to specific species to fill in the toxicity data gaps required for SSD and selecting multiple nonlinear functions to optimize the SSD model. Combined with QSAR and SSD methods, a new method of PNEC derivation was developed and successfully applied to the derivation of PNEC for 35 pesticides. Three QSAR models were used to predict the toxicities of six pesticides with few toxicity data. Nine two-parameter nonlinear functions were used to fit the toxicity-cumulative probability data one by one to determine the optimal SSD models. The hazardous concentrations at the cumulative probability of 5% and 10%, i. e, HC

Studies have shown that the mixture toxicity of compounds with similar modes of action (MOAs) is usually predicted by the concentration addition (CA) model. However, due to the lack of toxicological information on compounds, more evidence is needed to determine whether the above conclusion is generally applicable. In general, the same type of compounds with similar chemical structures have similar MOAs, so whether the toxicities of the mixture of these compounds are additive needs to be further studied. In this paper, three types of pesticides with similar chemical structures (three organophosphoruses, two carbamates and two neonicotinoids) that may have similar MOAs were selected and five binary mixture systems were constructed. For each system, five mixture rays with different concentration ratios were designed by the direct equipartition ray design (EquRay) method. The mortality of Caenorhabditis elegans was regarded as the endpoint for the toxicity exposure to single pesticides and binary mixtures. The combined toxicities were evaluated simultaneously using the CA model, isobologram and combination index. The structural similarity of the same type of pesticides was quantitatively analyzed according to the MACCS molecular fingerprint and the slope of dose-response curve at pEC50. The results show that the toxicities of neonicotinoid mixtures and carbamate mixtures are almost antagonistic. The entire mixture system of dichlorvos and dimethoate produced synergism, and four of the five mixture rays of dimethoate and methamidophos induced antagonism, while among the mixture rays of dichlorvos and methamidophos, different concentrations showed different interaction types. The results of structural similarity analysis show that the size of structural similarity showed a certain quantitative relationship with the toxicity interaction of mixtures, that is, the structural similarity of the same type of pesticides may show an additive action in a certain range.

Carbamate pesticides (CMs) and organophosphorus pesticides (OPs) have been widely used in agriculture and toxicologically affect non-target organisms. Although there are many reports about their toxicities, the combined behavioral toxicities of CM/OP mixtures on Caenorhabditis elegans have rarely been studied. In this study, body bend inhibition (BBI), head thrash inhibition (HTI), and swimming speed inhibition (SSI) by CMs and OPs were chosen as the toxicity endpoints. The locomotion behavioral toxicities of individual pesticides (carbofuran (CAR), methomyl (MET), chlorpyrifos (CPF), and triazophos (TAP)) and their binary mixtures on C. elegans were determined systematically and the toxicological interaction profiles of various CM/OP mixture rays constructed using the combination index. It was shown that four pesticides and their binary mixture rays have significant inhibitory effects on the locomotion behavior of C. elegans; that is, they produce locomotion behavioral toxicities and the toxicity of two OPs is higher than those of two CMs. The toxicological interactions in the binary CM and OP mixtures are different from each other. For example, one mixture ray (CAR-MET-R1) in the CM system on the SSI endpoint exhibits synergism at all concentration levels, another ray (CAR-MET-R3) displays low-dose synergism and high-dose additive action on BBI and HTI endpoints, and weak synergism at high-dose on SSI, and other rays perform additive action. Two rays (CPF-TAP-R1 and CPF-TAP-R2) in the OP mixture system display low-dose additive action and high-dose antagonism on the three endpoints. Another ray (CPF-TAP-R3) shows the additive action at all concentration levels. It can be concluded that it is not sufficient to evaluate the combined toxicity of binary CM/OP mixtures using only one concentration ratio ray and that it is necessary to examine multiple concentration ratios.

Background One of the major challenges in environmental science is how to assess the toxicity and risk of complex pollutant mixtures. However, only a few studies have pointed out that there is a significant difference between the toxicities of chemicals on genetically modified strains and wild-type organisms and there are few reports of the differences in the toxicity of chemical mixtures. Therefore, six chemicals, two substituted phenols (4-chlorophenol and 4-nitrophenol), two pesticides (dichlorvos and glyphosate) and two ionic liquids (1-butylpyridinium chloride and 1-butylpyridinium bromide), were selected to construct a six-component mixture system, the lethality of various mixtures on the genetically modified Caenorhabditis elegans strain mtl-2::GFP (MTL-2) at 12 and 24 h were determined, and differences in toxicity to other strains were compared. Results Although the toxicity of 4-chlorophenol on MTL-2 was not significantly different from that on N2 wild-type Caenorhabditis elegans (N2), the toxicities of the other five chemicals on MTL-2 were greater than those on N2. The toxicities of six single chemicals and nine mixture rays on MTL-2 increased with time, which is consistent with the effect on N2 and on the genetically modified strain sod-3::GFP Caenorhabditis elegans (SOD-3). The toxicological interactions of various mixtures in MTL-2 at 12 h were half antagonistic (ANT) and half additive (ADD), while at 24 h, they were mainly synergistic (SYN). The toxicological interactions of various mixtures in MTL-2 change from ANT/ADD to primarily SYN with time, which is different from the change from ANT to ADD in SOD-3 and from SYN to ADD in N2. Conclusions The toxicity and toxicity interactions of chemical mixtures on different Caenorhabditis elegans strains are different. Therefore, it is necessary to examine the effect of genetic factors on the toxicological interaction of mixtures to avoid underestimating or overestimating the mixture risk.

Background The weak magnetic field (WMF) can enhance the ability to remove target pollutants in wastewater, which drives us to consider whether WMF could give rise to the hormesis or not. In our previous study, it was found that polymyxin B sulfate (POL) can induce weak hormesis on Vibrio qinghaiensis sp.-Q67 (Q67). To this end, we set up four different WMF treatments during Q67 culture and POL exposure process, having no WMF in all cases (NW), adding WMF all the time (AW), exerting WMF only during the bacterial culture (BW), and exerting WMF only in POL exposure period (EW). Results It was shown that the concentration–response curves (CRCs) of POL in four WMF treatments at the exposure times of 6, 9, and 12 h are non-monotonic hormetic curves where the maximum stimulative effects (Emin) of POL in BW and EW are obviously larger than those in AW and NW. The maximum Emin is 26.8% occurring in EW and 20.7% in BW at 6 h, while the max Emin is 14.6% in NW at 9 h, it means that stimulations of POL in BW and EW are earlier and stronger than those in NW. These findings first indicated that WMF can enhance the hormesis of POL. Conclusions This study showed that WMF as a key factor may influence the maximum stimulation effect of hormesis. The characteristic of biphasic (hormetic effect) challenges the traditional classical threshold model that is close to chemical risk assessment. But the mechanism of hormesis even now is inconclusive. WMF as a novelty and neglected factor has the potential to support the further development of hormesis mechanism.

Current Chinese surface water environmental quality standard GB3838-2002 for ammonia fails to take water quality factors and native organism distributions in different basins into consideration. In this study, ammonia toxicity tests were performed using three aquatic organisms native to the Shaying River Basin (China). Published ammonia toxicity data with pH and temperature, and toxicity data acquired in this study were used to establish water quality criteria. The final criterion maximum concentration (CMC) and criterion continuous concentration (CCC) for the Shaying River Basin were 5.09 and 1.36 (mg total ammonia nitrogen (TAN))/L (pH 7 and 20 °C), respectively. In addition, based on the corresponding relationship between ammonia toxicity and temperature and pH, the ecological risk assessment of ammonia was conducted in different seasons for the Shaying River using a tiered approach of both hazard quotient (HQ) and the joint probability (JPC) methods. Two methods gave consistent results: the ecological risks of ammonia to aquatic species in the Shaying River Basin were severe and the risk could be ranked as wet season > flat season > dry season. It is therefore indicating that monitoring, evaluation, and early warning of ammonia pollution need to be taken to prevent and control the risks posed by ammonia pollution, especially for wet season (because of high temperatures and pH) or flat season (because of high pH values). We hope the present work could provide valuable information to manage and control ammonia pollution in the Shaying River Basin.

The chemical mixtures in various environmental media not only have concentration diversity but also mixture-ratio diversity. It is impossible to experimentally determine the toxicities of all mixtures; therefore, it is necessary to develop effective methods based on models to predict mixture toxicity. In this study, a new approach (BNNmix) based on the back-propagation neural network (BPNN) was developed and used to predict the toxicities of seven-component mixtures (consisting of two substituted phenols, two pesticides, two ionic liquids, and one heavy metal) on Caenorhabditis elegans. We found that the combined toxicities of various mixtures used in the experiments were neither global concentration-additive nor global response-additive, which implied that it was impossible to accurately predict the toxicities of such mixtures by using common models such as concentration addition (CA) and response addition (independent action, IA). Using the BNNmix approach to estimate or predict the toxicities of the mixtures under test, it was found that the predictive toxicities of various mixtures with different mixture ratios and concentrations were almost in accordance with those observed experimentally. Unlike the CA and IA models, the BNNmix approach can predict not only the toxicities of mixtures having toxicological interactions but also those with global concentration or response additivities.

Hormesis describes a specific phenomenon in a biphasic concentration-response curve: low concentrations stimulate a response, while high concentrations suppress it. Hormesis could be influenced by several environmental factors, e.g. pH. In this study, the concentration-response/bioluminescence inhibition profiles (CRPs) of six components in personal care products to Vibrio qinghaiensis sp.-Q67 were measured at five different pH levels. When the exposure lasted for 0.25 h, CRPs of the six components at various pH levels were S-shaped, except ascorbic acid 2-glucoside (AA2G) at pH 10.5. When it lasted for 12 h, the CRPs were J-shaped, except AA2G at pH 6.5, 7.5, and 9.5. To rationally explain these changes in hormesis expressed by J-shaped CRP, four characteristic parameters, the minimum effect (Emin) and its corresponding concentration (ECmin), the median effective concentration (EC50), and the zero effect concentration point (ZEP, where the effect is 0 and the concentration is ZEP), were used to quantify the J-shaped CRP. The results indicated that these parameters vary with pH. Additionally, ZEP showed an excellent linear relationship with EC10 (R2 = 0.9994) at all pH levels, indicating that EC10 could replace the no-observed effective concentration (NOEC) in ecological risk assessment. Furthermore, to elucidate the possible mechanism of hormesis, the binding of the components to the luciferase receptors was analyzed using molecular docking technology. The results showed that the components displaying hormesis bind more easily to the α subunit of luciferase than to the β subunit.

Water quality criteria (WQC) for an increasing number of emerging chemicals need to be developed to protect human health and biological safety. Existing species sensitivity distribution (SSD) methods can only be used to help establish WQC for ecological protection, and cannot be extended to the protection of human beings from various hazards. In this study, a novel procedure called Conlecs is proposed to derive the concentration limits (ConLs) of pesticides outside the criteria for human drinking water (CHDW) using the existing criteria of pesticides and SSD integrated with the toxicity prediction achieved through robust QSAR models. Optimal SSD models of four pesticides (within the CHDW) and two pesticides (outside the CHDW) on 12 species were first constructed, and the existing ConLs of four pesticides within the CHDW were then utilized to select the most suitable species for the optimal proportions to avoid human hazards (PHH), allowing the ConLs of two pesticides outside the CHDW to be derived.