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Journal of Chemistry
Volume 2014 (2014), Article ID 728254, 9 pages
http://dx.doi.org/10.1155/2014/728254
Research Article

Prediction and Evaluation of the Mixture Toxicity of Twelve Phenols and Ten Anilines to the Freshwater Photobacterium Vibrio qinghaiensis sp.-Q67

College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541004, China

Received 18 April 2014; Revised 1 July 2014; Accepted 1 July 2014; Published 16 July 2014

Academic Editor: Maria N. D.S. Cordeiro

Copyright © 2014 Lingyun Mo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Twelve substituted phenols and ten substituted anilines were chosen to investigate if the dose addition and independent action models can be used to evaluate the mixture toxicity of phenolic and aniline derivatives (PADs). Their photobacterium toxicity to the freshwater luminescent bacterium Vibrio qinghaiensis sp.-Q67 showed that the two-parameter Weibull or Logit function could be effectively applied to describe the dose-response relationships. The joint toxicity of three equivalent-effect concentration ratio (EECR) mixtures and twelve uniform design concentration ratio (UDCR) mixtures could be well evaluated using the dose addition (DA) or the independent action (IA) model within 95% confidence intervals.

1. Introduction

Phenol and aniline derivatives (PADs) are important chemical materials and intermediates in synthesis of other compounds, such as dyes, pharmaceuticals, polymers, and synthetic resins. However, they are also the major carcinogenic pollutants in aquatic environments and extremely toxic to organisms [1, 2]. Many of PADs are priority pollutants in the water [3].

Phenol and aniline derivatives usually occur as mixture contaminants in the aquatic environments [4]. However, most of the toxicity data obtained are information about pure and individual chemicals, not related to mixtures. Therefore, the reliability is questionable to evaluate the multicomponent mixture toxicity from the toxicity data about single chemicals. Several mixtures in different toxicity tests were studied by some researchers to get better results in the predictive evaluation [5].

In order to evaluate the combined toxicity, it is required firstly to assume the relationships between the toxicity of individual chemicals and their mixtures quantitatively. Essentially, two different basic models are available for this purpose, that is, the independent action (IA) and the dose addition (DA) [5]. Other early analysis methods of mixture toxicity included concepts such as toxic units, mixture toxicity index, and additive index. On these two different basic models, the similarity parameter was built [4, 5]. Joint toxicity studies on chemical mixtures have been slowly progressing over the past decades, with preliminary researches involving two-component mixtures in equieffect concentration (most of which are EC50) [4, 6]. Since the year 2000, the nonlinear simulation of the dose-effect curve and the recurrent points have been used to estimate the low effects concentration, and the dose addition and independent action for multicomponent mixture predictions [5, 7, 8]. The mixture toxicity of nonpolar narcotic chemicals could be predicted by two QSAR approaches, that is, the forward stepwise multilinear regression (MLR) and the radial basis function neural networks (RBFNNs) from molecular descriptors which are calculated and defined as composite descriptors according to the fractional concentrations of the mixture components [9]. In order to enable the prioritization of ionic liquids with a favourable cytotoxicity profile, a Classification and Regression Tree (CART) classifier has been recently proposed due to its simplicity, reliability, good predictive capability, and transparent chemical interpretation based on structural molecular fragments [10, 11]. Toxicological evaluation of chemical mixtures is now considered to have truly entered into the field of multicomponent mixtures [4, 6, 1215].

In this work, the joint toxicities of 22 species of phenol and aniline derivatives (PADs) and the toxicities of their mixtures to the freshwater photoluminescent bacterium Vibrio qinghaiensis sp.-Q67 (Q67) were measured through a microplate toxicity test procedure [1619]. To seek how the mixture toxicity changes with changing concentrations of individual chemicals in the mixture effectively, the compounds were mixed in equieffect concentrations ratio (EECR) or in uniform design concentration ratio (UDCR).

2. Materials and Methods

2.1. Materials

The PADs are common in water environment and all have a high solubility. Some physical-chemical properties of 22 tested PADs are listed in Table 1. Three EECR mixtures and twelve UDCR mixtures of the test agents were made by the stock solutions, which were prepared with pure agents in distilled water, kept in critically cleaned glass containers, and stored at 4°C.

tab1
Table 1: Some physical-chemical properties of 22 PADs.

The freshwater photoluminescent bacterium Vibrio qinghaiensis sp.-Q67 (Q67) was used as the test organism that was kindly provided by East China Normal University, China. The culture condition and media have been described in detail in some of our earlier works [1619]. To prepare the culture medium, the following chemicals were dissolved into 1000 mL distilled water: 5.0 g tryptone, 5.0 g yeast extract, 3.0 g glycerin, 35.8 mg Na2HPO4·12H2O, 13.6 mg KH2PO4, 33.0 mg CaCl2, 1.54 g NaCl, 0.61 g MgCl2·6H2O, 1.34 g NaHCO3, 0.25 g MgSO4·7H2O. The solution was adjusted to pH 8.8–9.0 and turned into a solid culture medium after adding 2% agar. 50 mL culture medium was put into a 100 mL conical flask, which was then obturated by using a brown paper and sterilized at 121°C for 20 min through high pressure steam. For each experiment, the photoluminescent bacteria Q67 were inoculated from a stock culture medium into a fresh culture medium, which was then maintained and cultured for 24 h at 22 ± 1°C. Finally, the bacteria Q67 were further cultured in a liquid medium at 22 ± 1°C by shaking (120 r/min) for 18 h [16, 1820].

2.2. Methods

A Veritas luminometer with a 96-well microplate was bought from the Turner BioSystems Inc., USA, and used to examine the toxicity of PADs and their mixtures. Regarding our earlier microplate toxicity test procedure, twelve concentration gradients were planned for each of the twelve phenols and ten anilines [16, 17]. 100 μL Milli-Q water was injected into twelve wells of the first row of the microplate, which were regarded as twelve controls (without any toxicant). Twelve different toxicant solutions were injected into twelve wells of the second row of the microplate and then filled with Milli-Q water to the total volume of 100 μL. To obtain a chemiluminescence inhibition (response) from 1% to the maximum, the twelve toxicant solutions were prepared after a proper dilution factor. Similarly, some other toxicant solutions were prepared and injected into twelve wells of the third or fourth row of the microplate. The fifth row of the microplate was used as a test duplication of the second row. Finally, each well of the microplate was filled with 100 μL bacterial Q67 suspension. All microplate tests were triplicated. Considering the sum of all component concentrations as the total concentration of a PADs mixture, the microplate toxicity test with the PADs mixture was conducted in the same manner as the procedure of a single PAD toxicant.

In each test, the photoluminescent bacteria Q67 were exposed to different treatments and controls, and the relative light unit (RLU) was measured. An inhibition ratio of the luminous intensity of the bacterium ( or ) was applied to express the PADs toxicity to the photoluminescent bacteria and can be calculated by the following equation: where is a mean RLUs value of a treatment and a mean RLUs value of all untreated controls.

2.3. Mixture Design

The test mixtures were prepared by both the EECR and UDCR methods. According to the measured results for the 22 PAD toxic pollutants, the best nonlinear simulation was performed and the given effect (%) was expressed as a concentration . For the three EECR mixtures, the EC1, EC10, and EC50 percentages of the total mixture concentration () were designated as the concentration ratios of each PAD in the mixtures, that is, EE22-01, EE22-10, and EE22-50, respectively. The concentrations percentages of different PAD compounds in the mixtures were showed in Table 2.

tab2
Table 2: The effect concentration of 22 PADs in 15 mixtures (%).

To avoid the significant range limitation of the concentration percentages in EECR mixtures, 12 UDCR mixtures (denoted as UD22-01, UD22-02, UD22-03, UD22-04, UD22-05, UD22-06, UD22-07, UD22-08, UD22-09, UD22-10, UD22-11, and UD22-12) were designed using the former 11 lines in the uniform design table (U-table), U13(1322) (Table 2). The uniform design (UD) experiment is planned in terms of the uniform design table (U-table). Like orthogonal design, UD offers lots of experimental tables to the users for convenient utilization. U-tables of form (), where the subscript refers to the number of the mixture experiments and the superscript to the number of pesticides (factors) in the experiment (mixture), are purposely chosen to mimic the tables of orthogonal designs, (), except that the number of levels equals the number of experiments () [16]. In light of U13(1322) given in Table 2, twelve 22-component mixtures and a control group were designed. For each mixture design, proper concentrations were selected based on the results of some preexperiments and no uniform dilution factors were applied. As an effective experimental design method, the uniform design (UD) can save time and effort in investigating the changes of concentration in three-dimension space. When the factors in the uniform design (the numbers of mixture components) and the levels of the factors (the concentration levels) are large, it is very necessary and efficient to use the uniform design (UD) to investigate the joint effect of a multiple-component mixture because the UD can reduce significantly the mixture experiment efforts.

By using the computer program APTox, the dose-response curves (DRCs) were predicted based on both of the DA and IA models within a confidence interval of 95%; the root mean square error (RMSEx) andthe correlation coefficient () of the models and the mixture values were also calculated [16, 20]. Mathematically, the DA model is expressed as where is the number of mixture components, the concentration of the th component that provokes % effect when applied singly, and the concentration of the th component in the mixture [16, 20]. The IA model can be formulated mathematically as where and are the total concentration and total effect of the mixture, respectively, and is the effect of the th component with the concentration of in the mixture [16, 20].

3. Results and Discussion

3.1. Photobacterial Toxicity of Single PADs

Based on the nonlinear least-squares fitting the experimental concentration-response data of the 22 species of PADs, the best dose-response curves (DRCs) were obtained. The DRCs of the 22 PADs to the photobacterium could be well described by the two-parameter function Weibull or Logit. According to the experimental results of the 12 concentration points designed with the microplate method and the experience rules that concentration points should be 5-6 times the parameter values of the DRCs model, the two-parameter model is markedly more effective than the three-parameter model in fitting the experimental data. Therefore, all DRCs simulations in the present paper have applied only two parameters with the Weibull and Logit model in the data analysis.

The Weibull and Logit functions can be expressed in (2) and (3), respectively,

From the nonlinear least-squares fitting to the experimental data, the best simulated DRCs of the 22 PADs were obtained (Table 3). The results indicated that the dose-response curves (DRCs) of the 22 PADs on the luminescent bacterium Q67 could be described by the two-parameter Weibull or Logit models. The RMSE, , and parameters of the fitting models are also calculated and given in Table 3. By substituting the and values into the Weibull or Logit functions, the values of the concentration () that inhibited the chemiluminescence by (%) at each effect level were calculated. In Table 3, the pEC50, that is, the negative logarithm of the EC50 (−logEC50), for 22 PADs are also given in comparison with classical toxic indices. Both of the Weibull and Logit functions could well describe the concentration-toxicity data of 22 PADs with and , which indicated the high stability and good calibration ability of the fitting of the two models [19].

tab3
Table 3: Parameters of the concentration-response functions for the individual PADs.

A believable concentration-response examination of individual chemicals is indispensable to evaluate mixture toxicities. As a typical example for the concentration-inhibition ratio relationship of the single substance, the experimental and fitting results for o-methylaniline (PAD16) are illustrated in Figure 1(a). The photobacterial toxicity relationship for PAD16 was a nonsymmetrical and sigmoidal curve, which was sharper in the high inhibition section than in the low inhibition section, which was a general character of the 22 PADs tested. Additionally, Figure 1(a) reveals the relation between EC1 and EC50 in the low inhibition section. For the estimations, was chosen as a lower limit of the effect level, since the obtained test data commonly tolerated the interpolative approximation of inhibitions down to this level.

fig1
Figure 1: The concentration-inhibition ratio relationship of the single substance. (a) o-Methylaniline; (b) 22 PADs (●: observed; ○: controlled; —: fitted by Weibull model).

The nonlinear fitting with the regression model was depicted in Figure 1(b). The obtained concentration-response curves showed significant discrepancy in position and shape. In the strict mathematical term, however, the intersections among the curves obviously indicated that the curves did not occur in parallel with each other. It was very obvious, as the ratio between EC1 and EC50 was used as a gradient indicator in the low effect sections of the dose-response curves. The ratio was diverse for the substance examined and varied from 0.00357 of p-chlorophenol (PAD03) to 0.04138 of o-chloroaniline (PAD20). In Table 3, for the extreme case PAD03, the EC50 was more than 280 times greater than the EC1, but the EC50 was only 24 times higher than the EC1 for PAD20. The toxic dose range of PAD03 was the widest among the 22 compounds. The most toxic PAD was p-nitroaniline (PAD17, ) and the least toxic PAD was aniline (PAD13, ). The toxicity order of the 22 PADs was p-nitroaniline (PAD17, ) > 2,4-dichlorophenol (PAD05, 3.66) > o-nitroaniline (PAD18, 3.65) > p-chlorophenol (PAD03, 3.43) > p-nitrophenol (PAD11, 3.29) > o-nitrophenol (PAD12, 3.20) > m-nitrophenol (PAD10, 3.16) > 2,3-dimethylphenol (PAD02, 3.08) > p-chloroaniline (PAD22, 2.93) > o-cresol (PAD08, 3.00) > p-cresol (PAD09, 2.99) > m-nitroaniline (PAD19, 2.93) > m-chloroaniline (PAD21, 2.89) > m-cresol (PAD07, 2.83) > o-chlorophenol (PAD04, 2.81) > o-chloroaniline (PAD20, 2.78) > 3,5-dihydroxytoluene (PAD01, 2.77) > p-methylaniline (PAD15, 2.52) > phenol (PAD06, 2.47) > m-methylaniline (PAD14, 2.42) > o-methylaniline (PAD16, 2.36) > aniline (PAD13, 2.04).

3.2. Photobacterial Toxicity of PADs Mixtures

The concentration-response relationships of three EECR mixtures and twelve UDCR mixtures were determined on the Veritas luminometer in the same procedure as described above. The three EECR mixtures (EE22-01, EE22-10, and EE22-50) and 12 UDCR mixtures (UD22-01UD22-12) consisted of all 22 PADs. The experimental data of the mixture toxicities and the predictions of the DA and IA models are illustrated in Figures 2 and 3. The dependence of the inhibition of photobacterial luminosity on the total concentration of the mixture constituent () can be expressed by the concentration-response equations.

728254.fig.002
Figure 2: The concentration-inhibition ratio relationship of three PADs mixtures constructed by the EECR method (●: observed; ○: controlled; —: predicted by DA; : predicted by IA).
728254.fig.003
Figure 3: The concentration-inhibition ratio relationship of twelve PADs mixtures constructed by the UDCR method (●: observed; ○: controlled; —: predicted by DA; : predicted by IA).

The fitting parameters of the Weibull or Logit models, , , RSME, and , were calculated for the toxicity prediction of the 15 PADs mixtures (Table 4). The Weibull or Logit functions could well fit the concentration-toxicity data of the 15 PADs mixtures with and , which indicated the high stability and good calibration ability of the fitting of the two models. The values of the 15 PADs mixtures varied from 2.64 for UD22-03 to 2.94 for UD22-09, which were within the range from the least toxic PAD (2.04 for PAD13) to the most toxic PAD (3.68 for PAD17), indicating the absence of strong synergistic or antagonistic interactions within the mixture [13].

tab4
Table 4: The t-DRC model as well as statistics for toxicity predictions by DA and IA models of 15 PAD mixtures.

A mixture concentration can be reflected only within a certain space of a direction for the equieffect concentrations ratio (EECR) method; that is, it cannot describe the whole situation and can only represent some alike directions of space concentrations because of the existing relationship among the effect concentrations (), even though different EECR mixtures were measured. Instead, the PAD mixtures can be designed by the uniform design concentration ratio (UDCR) method to investigate the toxicity rule at the various space concentrations of PADs [16, 19]. To study the toxicity interaction among different PADs in the mixtures, the joint toxicities of all 15 PADs mixtures were also evaluated according to the DA and IA models (Table 4). Based upon the optimal nonlinear Weibull or Logit models for the 22 individual PADs, the 20 points varying between 1% and 99% for the inhibition concentrations of the DA model () and the inhibition concentrations of the IA model () were determined by the APTox computer program.

In Figures 2 and 3, the comparisons of the experimental data with the results calculated using the DA and IA models are illustrated. The DA and IA models could well predict the toxicity effects of all 15 PADs mixtures within 95% confidences of the experimental DRCs. The calculated DRC model parameters () indicated that the difference between the predicted results of these two models was insignificant in the present experiment.

All of the predicted curves are very similar to each other, but the inhibition effects evaluated by DA model were slightly lower than those predicted by IA model in all cases except for the EE22-01 mixture (Figures 2 and 3), which might be related to a very small synergistic or antagonistic effect [15, 19]. With regard to the existing quantitative structure-activity relationship (QSAR) method for the prediction of mixture toxicity, the independent action (IA) and the dose addition (DA) are two ideal and prominent reference models of additive behavior for the analysis of joint activity, but the two approaches do not consider the interactions of constituents in a mixture, which can result in complex and substantial changes in the apparent properties of its components, causing synergistic or antagonistic effects [9]. The result of the present work corresponded with the pharmacological presumption that the combined toxicities of the rigorously similar-acting chemicals could be well evaluated using the DA model [6, 13]. This finding was well in accordance with the result of a previous experimental study on phenylurea mixtures; that is, the identical evaluations of the DA and IA models showed that the DA model can be used to predict the toxicities of the similar-acting chemicals exactly [6]. Assuming the inherent variance of natural periphyton and epipsammon communities, the joint inhibitions of these 22 rigorously congeneric phenylureas having an analogous toxicological action mechanism could be well evaluated by the DA and IA models. It was found that the RMSEDA and RMSEIA, the and were almost identical for both the DA and IA models (Table 4). As shown in Figures 2 and 3, both concepts created nearly the same concentration-response curves in all cases, the obtained data points of three EECR and twelve UDCR mixtures scattered around the solid lines of the DA model and the dotted lines of the IA model. Thus, the effects of 15 PADs mixtures could be well predicted using both DA and IA models. For these 15 various PADs mixtures, the values for the toxic effect calculations using IA model were lower than or identical to the values for the toxic effect calculations using DA model (Table 4).

The polar and narcotic PADs show a fairly broad range of toxicities for numerous aquatic organisms that have been comprehensively studied [2124]. Their toxicity was related to the property of being water-repellent, weak H bonding acceptor ability, and strong H bonding donor ability [23]. The mixture toxicities of two aniline compounds to Photobacterium phosphoreum could be principally described by the simple summing of the two-component toxicities [25]. The quantitative structure-activity relationship could be well used to express the PADs toxicities to Scenedesmus obliquus. Additionally, the toxicity of mixtures could be successfully predicted by the ideal based on structural factors of individual chemicals in two-component mixtures [26]. The data of the present study indicated that the toxicity inhibitions of the PADs mixtures were greater than that of the most toxic PAD compound. The DA model can be well utilized to evaluate the toxic effects of multicomponent mixtures of benzene derivatives, regardless of the inhibition levels and the PADs concentration ratios [19]. The PADs toxic evaluation in water systems is no more limited to individual pure chemicals. Both of the DA and IA approaches may be used to evaluate the toxic effects of PADs mixtures.

4. Conclusions

Assessment research of the individual toxicity of the 22 PADs confirmed that these compounds elicited the common effect on the inhibition luminescence of the photobacterium Q67 at concentrations below those that were lethal to the organisms. Based on the nonlinear least-squares fitting the experimental dose-response data of the 22 species of PADs, the best dose-response curves (DRCs) obtained for the 22 PADs to the photobacterium can be accurately expressed using the two-parameter Weibull or Logit model. The toxic inhibitions of multicomponent PADs mixtures can be well evaluated using the DA and IA models based on the concentration-response curves of individual components. Both of these models were proved to be suitable methods for the toxic evaluation of PADs mixtures in the aquatic ecosystem, when the models for the evaluation of multicomponent PADs mixtures were used with the EECR or UDCR mixture design.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

The paper has greatly benefited from insightful comments by the editor and anonymous reviewer. This research was financially assisted by National Natural Science Foundation of China (NSFC21207024, NSFC51268008) and the Guangxi Provincial Natural Science Foundation (2011GXNSFA018059).

References

  1. H. Gao, S. H. Zhang, D. Q. Xiong, N. Liu, W. M. Gong, and Q. Wang, “Study on actue toxicities of phenol and aniline to two marine organisms,” Marine Environmental Science, vol. 25, p. 33, 2006. View at Google Scholar
  2. K. W. Thomulka, C. G. Abbas, D. A. Young, and J. H. Lange, “Evaluating median effective concentrations of chemicals with bioluminescent bacteria,” Bulletin of Environmental Contamination and Toxicology, vol. 56, no. 3, pp. 446–452, 1996. View at Publisher · View at Google Scholar · View at Scopus
  3. W. M. Zhou, D. Q. Fu, and Z. G. Sun, “List for priority pollutants in waters,” Environmental Monitoring in China, vol. 6, no. 4, pp. 1–3, 1990. View at Google Scholar
  4. R. Altenburger, H. Schmitt, and G. Schüürmann, “Algal toxicity of nitrobenzenes: combined effect analysis as a pharmacological probe for similar modes of interaction,” Environmental Toxicology and Chemistry, vol. 24, no. 2, pp. 324–333, 2005. View at Publisher · View at Google Scholar
  5. T. Backhaus, R. Altenburger, W. Boedeker, M. Faust, M. Scholze, and L. H. Grimme, “Predictability of the toxicity of a multiple mixture of dissimilarly acting chemicals to Vibrio fischeri,” Environmental Toxicology and Chemistry, vol. 19, no. 9, pp. 2348–2356, 2000. View at Publisher · View at Google Scholar · View at Scopus
  6. T. Backhaus, Å. Arrhenius, and H. Blanck, “Toxicity of a mixture of dissimilarly acting substances to natural algal communities: predictive power and limitations of independent action and concentration addition,” Environmental Science and Technology, vol. 38, no. 23, pp. 6363–6370, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Scholze, W. Boedeker, M. Faust, T. Backhaus, R. Altenburger, and L. H. Grimme, “A general best-fit method for concentration-response curves and the estimation of low-effect concentrations,” Environmental Toxicology and Chemistry, vol. 20, no. 2, pp. 448–457, 2001. View at Publisher · View at Google Scholar · View at Scopus
  8. R. Altenburger, M. Nendza, and G. Schüürmann, “Mixture toxicity and its modeling by quantitative structure-activity relationships,” Environmental Toxicology and Chemistry, vol. 22, no. 8, pp. 1900–1915, 2003. View at Publisher · View at Google Scholar
  9. F. Luan, X. Xu, H. Liu, and M. N. D. S. Cordeiro, “Prediction of the baseline toxicity of non-polar narcotic chemical mixtures by QSAR approach,” Chemosphere, vol. 90, no. 6, pp. 1980–1986, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. M. Cruz-Monteagudo, E. Ancede-Gallardo, M. Jorge, and M. N. Dias Soeiro Cordeiro, “Chemoinformatics profiling of ionic liquids—automatic and chemically interpretable cytotoxicity profiling, virtual screening, and cytotoxicophore identification,” Toxicological Sciences, vol. 136, no. 2, pp. 548–565, 2013. View at Publisher · View at Google Scholar
  11. M. Cruz-Monteagudo and M. N. D. S. Cordeiro, “Chemoinformatics profiling of ionic liquids–uncovering structure-cytotoxicity relationships with network-like similarity graphs,” Toxicological Sciences, vol. 138, no. 1, pp. 191–204, 2014. View at Publisher · View at Google Scholar
  12. M. Faust, R. Altenburger, T. Backhaus et al., “Joint algal toxicity of 16 dissimilarly acting chemicals is predictable by the concept of independent action,” Aquatic Toxicology, vol. 63, no. 1, pp. 43–63, 2003. View at Publisher · View at Google Scholar · View at Scopus
  13. Å. Arrhenius, F. Grönvall, M. Scholze, T. Backhaus, and H. Blanck, “Predictability of the mixture toxicity of 12 similarly acting congeneric inhibitors of photosystem II in marine periphyton and epipsammon communities,” Aquatic Toxicology, vol. 68, no. 4, pp. 351–367, 2004. View at Publisher · View at Google Scholar
  14. M. Junghans, T. Backhaus, M. Faust, M. Scholze, and L. H. Grimme, “Application and validation of approaches for the predictive hazard assessment of realistic pesticide mixtures,” Aquatic Toxicology, vol. 76, no. 2, pp. 93–110, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. W. Y. Huang, F. Liu, S. S. Liu, H. L. Ge, and H. H. Chen, “Predicting mixture toxicity of seven phenolic compounds with similar and dissimilar action mechanisms to Vibrio qinghaiensis sp.nov.Q67,” Ecotoxicology and Environmental Safety, vol. 74, no. 6, pp. 1600–1606, 2011. View at Publisher · View at Google Scholar
  16. S. S. Liu, X. Q. Song, H. L. Liu, Y. H. Zhang, and J. Zhang, “Combined photobacterium toxicity of herbicide mixtures containing one insecticide,” Chemosphere, vol. 75, no. 3, pp. 381–388, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Zhang, S. S. Liu, and H. L. Liu, “Effect of ionic liquid on the toxicity of pesticide to Vibrio-qinghaiensis sp.-Q67,” Journal of Hazardous Materials, vol. 170, no. 2-3, pp. 920–927, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. J. Zhang, S. S. Liu, R. N. Dou, and H. L. Liu, “Evaluation on the toxicity of ionic liquid mixture with antagonism and synergism to Vibrio qinghaiensis sp.-Q67,” Chemosphere, vol. 82, no. 7, pp. 1024–1029, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. L. Y. Mo, S. S. Liu, Y. N. Zhu, H. L. Liu, H. Y. Liu, and Z. S. Yi, “Combined toxicity of the mixtures of phenol and aniline derivatives to Vibrio qinghaiensis sp.-Q67,” Bulletin of Environmental Contamination and Toxicology, vol. 87, no. 4, pp. 473–479, 2011. View at Publisher · View at Google Scholar
  20. S. S. Liu, J. Zhang, Y. H. Zhang, and L. T. Qin, “Aptox: Assessment and prediction on toxicity of chemical mixtures,” Acta Chimica Sinica, vol. 70, no. 14, Article ID 1511-1517, pp. 1511–1517, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. H. Könemann and A. Musch, “Quantitative structure-activity relationships in fish toxicity studies—part 2: the influence of pH on the QSAR of chlorophenols,” Toxicology, vol. 19, no. 3, pp. 223–228, 1981. View at Publisher · View at Google Scholar
  22. M. T. D. Cronin and T. W. Schultz, “Structure-toxicity relationships for phenols to Tetrahymena pyriformis,” Chemosphere, vol. 32, no. 8, pp. 1453–1468, 1996. View at Publisher · View at Google Scholar · View at Scopus
  23. E. U. Ramos, W. H. J. Vaes, P. Mayer, and J. L. M. Hermens, “Algal growth inhibition of Chlorella pyrenoidosa by polar narcotic pollutants: toxic cell concentrations and QSAR modeling,” Aquatic Toxicology, vol. 46, no. 1, pp. 1–10, 1999. View at Publisher · View at Google Scholar
  24. G. H. Lu, C. Wang, and X. L. Guo, “Prediction of toxicity of phenols and anilines to algae by quantitative structure-activity relationship,” Biomedical and Environmental Sciences, vol. 21, no. 3, pp. 193–196, 2008. View at Publisher · View at Google Scholar
  25. X. Yuan, L. M. Su, and L. L. Song, “Evaluation and prediction of joint toxicity of aniline and its derivatives to photobacterium phosphoreum,” Chemical Research in Chinese Universities, vol. 20, p. 32, 2004. View at Google Scholar
  26. C. Wang, G. H. Lu, Z. Y. Tang, and X. L. Guo, “Quantitative structure-activity relationships for joint toxicity of substituted phenols and anilines to Scenedesmus obliquus,” Journal of Environmental Sciences, vol. 20, no. 1, pp. 115–119, 2008. View at Publisher · View at Google Scholar