Chemosphere 120 (2015) 16–22
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Evaluation of toxicity data to green algae and relationship with hydrophobicity Ling Fu, Jin J. Li, Yu Wang, Xiao H. Wang, Yang Wen, Wei C. Qin, Li M. Su ⇑, Yuan H. Zhao ⇑ State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
h i g h l i g h t s Algal toxicity is species-specific chemical sensitivities. Response variables, exposure periods and species sensitivity affect algal toxicity. 48 h Exposure period is the most sensitive for algal growth inhibition test. Algae have very close species sensitivity for baseline compounds. Algae have different species sensitivity for polar narcotics and reactive compounds.
a r t i c l e
i n f o
a b s t r a c t
Article history: Received 14 March 2014 Received in revised form 8 May 2014 Accepted 13 May 2014
The quality of the biological activity data is of great importance for the development of algal quantitative structure–activity relationship (QSAR) models. However, a number of algal QSAR models in the literature were developed based on toxicity data without considering the response endpoints, exposure periods and species sensitivity. In this paper, 2323 algal toxicity data (log 1/EC50) in different toxicity response endpoints for 1081 compounds to 26 algal species within different exposure periods (14 and 15 min; 24, 48, 72, 96, 168 and 192 h) were used to evaluate the quality of the toxicity data to green algae. Analysis of 72 h toxicity to algae showed that the closed test had the same sensitivity as the open test for most of the test compounds, but a significant difference was observed for a few compounds. The overall average difference for all compounds ranges from 0.15 to 0.43 log units between toxicity endpoints (yield–growth rate). The relationships between exposure periods of 24, 48, 72 and 96 h indicated that 48 h exposure period is the most sensitive for algal growth inhibition test, and its sensitivity is 0.25 log units greater than 72 and 96 h exposure periods, respectively. Interspecies relationships showed that some algal species have very close sensitivity (e.g. Pseudokirchneriella subcapitata and Chlorella pyrenoidosa or Chlorella vulgaris and Scenedesmus obliquus, respectively), whereas some species have significantly different sensitivity (e.g. P. subcapitata and S. obliquus). Relationships between toxicity and hydrophobicity demonstrated that no difference was observed for non-polar narcotics within different exposure periods (24, 48, 72, and 96 h) or response variables (yield and growth rate). For polar narcotics, in contrast, algal toxicity is dependent on algal species and is related to the response variables and exposure period. We cannot expect significant QSAR models between algal toxicity and descriptors without considering species sensitivity, exposure periods and response endpoints. Ó 2014 Elsevier Ltd. All rights reserved.
Handling Editor: A. Gies Keywords: Algae Toxicity endpoint Interspecies Response variable Exposure period Hydrophobicity
1. Introduction According to REACH (Registration, Evaluation, Authorization, and Restriction of Chemicals), all the substances from manufacturers or importers must be registered and evaluated for hazardous ⇑ Corresponding authors. Tel.: +86 431 89165610; fax: +86 431 89165606. E-mail addresses: (Y.H. Zhao).
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(L.M.
http://dx.doi.org/10.1016/j.chemosphere.2014.05.040 0045-6535/Ó 2014 Elsevier Ltd. All rights reserved.
Su),
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effects to human and environment. The experimental toxicity data of these substances to aquatic ecosystem are required in order to evaluate their hazard and risk. Acute aquatic toxicity data are usually determined using 96 h LC50 to fish, 48 h EC50 to Daphnia magna and 72 h EC50 to algae (OECD, 2006). Among the organisms used in the ecotoxicity test, algae represent the primary producers in aquatic ecosystems and play an important role in their sustainability. The adverse effect of chemicals on algal populations is likely to disrupt the higher trophic levels. Therefore, studying the
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toxic effects of chemicals on algae provides valuable information regarding the risks that chemicals might pose to aquatic ecosystems (Geis et al., 2000; Ertürk and Saçan, 2012). Currently, there are several toxicity endpoints (or called response variables) used in the evaluation of toxicity to algae, such as growth rate, yield and integral. The response endpoint of growth rate (or called average specific growth rate) is calculated on the basis of the logarithmic increase of biomass during the test period, expressed per day. Yield response endpoint is the biomass at the end of the test minus the starting biomass (OECD, 2011). Optionally the log-biomass integral (area under the growth curve) may be used as an additional endpoint. This option may be attractive in situations with highly irregular growth in inhibited cultures (OECD, 2002). The growth rate endpoint is preferred by most ecotoxicologists to the biomass type endpoint (e.g. yield) because algal growth rate is considered to be more stable, comparable, and ecologically relevant (Chen et al., 2012). However, as indicated in the OECD guideline, the toxicity values calculated by using these response variables are not comparable and this difference must be recognised when using the results of the test. EC50 values based upon growth rate will generally be higher than results based upon yield, due to the mathematical basis of the respective approaches. This should not be interpreted as a difference in sensitivity between the two response variables, simply that the values are different mathematically (OECD, 2011). The interspecies toxicity relationships have the potential to predict the toxicity of compounds from one species to another species. It is also helpful in the interpretation of toxic mechanisms. The toxicity data of chlorophenols on marine algae (Dunaliella tertiolecta) was found to correlate well with the data for Vibrio fischeri, D. magna, freshwater algae (Pseudokirchneriella subcapitata), Tetrahymena pyriformis and fish (Pimephales promelas) (Ertürk and Saçan, 2012). On the other hand, poor interspecies relationships observed between toxicities to algae and T. pyriformis or D. magna for compounds with a wide range of structures suggested that compounds have different toxic mechanisms of action between these species (Zhang et al., 2010). In the meanwhile, algal species vary widely in their response to pesticides with poor interspecies correlation (Boyle, 1984). Algae, lacking a nervous system, seem to be more sensitive than fathead minnow for insecticides (Yeh and Chen, 2006). There is a large body of evidence showing that algae are more sensitive to chemicals than fish (Huang et al., 2007; Kahru and Dubourguier, 2010). Investigations using different algal species as test organisms have demonstrated that algae vary greatly in their response to chemicals. There were differential responses to various pesticides by two species of algae and the sensitivity of various species of algae exposed to chlorothalonil varied by nearly two orders of magnitude (Ma et al., 2002). There are relatively large toxicity databases for fish, D. magna, which represent higher tropic levels, but fewer consistent toxicity data are available for algae, the primary producers (Cronin et al., 2004; Ertürk et al., 2012). The European Commission has stated that the animal tests for assessment should be replaced by the use of quantitative structure–activity relationships (QSARs). Such method has the potential to fill data gaps in algal toxicity for certain chemicals. Some QSAR models predicted algal toxicity with specific descriptors such as the octanol/water partition coefficient, the energy of the lowest unoccupied molecular orbital, the dissociation constant and other parameters (Schmitt et al., 2000; Neuwoehner et al., 2008; Lee and Chen, 2009; Aruoja et al., 2011, 2014; Tebby et al., 2011; Ertürk et al., 2012). However, the number of algal QSARs available in the literature was remarkably small compared to those constructed by using toxicity data on fish, protozoa, bacteria or daphnia (Ertürk and Saçan, 2013). The algal toxicity data contain much more variation and this most likely is due to differences in experimental methods (Aruoja et al., 2011,
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2014). This is a reflection of the lack of reliable algal toxicity data in the literature (Cronin et al., 2004). The quality of QSAR models depends on the quality of the biological activity data. Although the quality of the algal toxicity data was crucial for the development of algal QSAR models, a number of QSAR models developed in the literature were based on the unknown response endpoints, exposure periods and species sensitivity. The variation of algal toxicity data was unknown and little effort has been made on the evaluation of toxicity data to green algae based on experimental uncertainty, response endpoints, exposure periods and species sensitivity. In this paper, 2323 algal toxicity data for 1081 compounds to 26 algal species within different exposure periods (14 and 15 min; 24, 48, 72, 96, 168 and 192 h) and different toxicity response endpoints were compiled from the literature and databases. The compounds were classified into different classes based on the substituted functional groups and mode of action (MOA) of the compounds. The aims of the work were: First, to carry out evaluation of experimental uncertainty of algal toxicity from different sources based on different exposure periods, response endpoints and experimental methods; Second, to investigate the effect of calculation of response endpoints on the algal toxicity; Third, to study the algal interspecies relationship and relationship between algal toxicity and other toxicity endpoints; and Forth, to discuss the algal species sensitivity from relationships between algal toxicity and hydrophobicity for non-polar (baseline) and polar narcotics (less inert compounds). 2. Materials and methods 2.1. Toxicity to green algae A total of 2323 toxicity data for 1081 chemicals to 26 green algal species were mainly compiled from the literature (Faust et al., 2001; Lu et al., 2001; Altenburger et al., 2004; Furusjö et al., 2006; Ma et al., 2006; Tsai and Chen, 2007; Lee and Chen, 2009; Aruoja et al., 2011; Chen et al., 2012; Ertürk et al., 2012; Ertürk and Saçan, 2012, 2013) and two databases, i.e., CHRIP (Chemical Risk Information Platform, http://www.safe.nite.go.jp/ english/db.html) and EU pesticides database (http://ec.europa.eu/ sanco_pesticides/public/?event=activesubstance.selection). The details of the references for each toxicity data expressed as log 1/ EC50 (M) can be found in Supplementary material. If available, the following information was supplied for the toxicity data: toxicity response endpoints (growth rate, yield and/or integral), exposure periods (24, 48, 72, 96, 168 and 192 h) and algal species. Meanwhile, 15 min inhibition of esterase activity to freshwater green algae Chlorella vulgaris, 14 min and 48 h inhibition of photosynthesis to Desmodesmus subspicatus and P. subcapitata, respectively, and toxicity data to freshwater cyanobacteria were also listed in Supplementary material. These cyanobacterium data were used to investigate relationships with the algal toxicity endpoints. The names, SMILES and CAS numbers of all the compounds can be found in Supplementary material. The summary of the toxicity endpoints is listed in Table 1. 2.2. Molecular descriptors and statistical analysis The n-octanol/water partition coefficients (log KOW) were obtained from the KOWWIN programme in the EPI Suite version 4.0 (http://www.epa.gov/oppt/exposure/pubs/episuitedl.htm); where possible measured log KOW values were used in preference to calculated values. The relationship between the log KOW and log 1/EC50 was performed using a least-squares linear regression with the Minitab software (version 14). For each regression, the following descriptive information is provided: number of
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Table 1 Toxicity data (log 1/EC50, M) to different freshwater and marine species. Endpoints
Description
24 h log 1/EC50
Inhibition of growth to Scenedesmus vacuolatus (S. vacuolatus) and a few other algal species with yield and unknown endpoints (a few compounds with growth rate endpoint) Inhibition of growth to Pseudokirchneriella subcapitata (P. subcapitata), Scenedesmus obliquus (S. obliquus) and a few other algal species with growth rate and yield endpoints (a few compounds with unknown endpoint) Inhibition of growth to Dunaliella tertiolecta (D. tertiolecta) (marine green algae) with growth rate and unknown endpoints Inhibition of growth to P. subcapitata, Desmodesmus subspicatus (D. subspicatus) and a few other species with growth rate endpoint (a few compounds with yield or integral or unknown endpoints) Inhibition of growth to D. tertiolecta and a few other algal species with growth rate and unknown endpoints (a few compounds with integral endpoint) Inhibition of growth to P. subcapitata, S. obliquus, Chlorella pyrenoidosa (C. pyrenoidosa), Chlorella vulgaris (C. vulgaris) and a few other algal species with growth rate, unknown endpoints (a few compounds with yield or integral endpoints) Inhibition of growth to D. tertiolecta and a few other algal species with growth rate and unknown endpoints (a few compounds with yield or integral endpoints) Inhibition of growth to D. subspicatus and P. subcapitata with growth rate, integral and unknown endpoints Inhibition of growth to unknown algal species with unknown exposure periods and endpoints Inhibition of growth to freshwater cyanobacteria with exposure periods of 72, 96 or 168 h with growth rate, integral and unknown endpoints Inhibition of esterase activity to C. vulgaris Inhibition of photosynthesis (reduction of dissolved oxygen production) to D. subspicatus Inhibition of photosynthesis (reduction of dissolved oxygen production) to P. subcapitata Inhibition of photosynthesis (reduction of dissolved oxygen production) to S. obliquus
48 h log 1/EC50 48 h log 1/EC50 72 h log 1/EC50 72 h log 1/EC50 96 h log 1/EC50 96 h log 1/EC50 168 h or 192 h log 1/EC50 log 1/EC50 72 h, 96 h or 168 h log 1/EC50 15 min log 1/EC50 14 min log 1/EC50 48 h log 1/EC50 192 h log 1/EC50
observations used in the analysis (N), coefficient of determination (R2), standard error of the estimate (S) and Fisher’s criterion (F). The species sensitivity was evaluated from the Pearson correlation coefficient (R), average residual (AR) and absolute average residual P (AAR) between the species toxicity endpoints (AR = (log 1/EC50 P to species 1 log 1/EC50 to species 2)/N, AAR = |log 1/EC50 to species 1 log 1/EC50 to species 2|/N. 3. Results and discussion 3.1. Evaluation of experimental uncertainty of toxicity to green algae Experimental uncertainty of toxicity to green algae was evaluated from the toxicity residuals (maximum–minimum log 1/ EC50 values) for the overlapped compounds reported by different references. Fig. 1 is the histograms of the average absolute residuals (AAR) for overlapped compounds to a single algal species within the exposure periods of 24, 48, 72 and 96 h, respectively. It shows that the experimental uncertainty of toxicity increases with increase of exposure periods. The uncertainty of 24 h toxicities is close to 48 h toxicities (AAR = 0.23 and 0.19, respectively). The uncertainty of 72 h toxicities (AAR = 0.30) is greater than 24 h or 48 h. The highest AAR is observed in 96 h toxicities to freshwater green algae with AAR = 0.40. These AAR values will be used
Fig. 1. Histrograms of AR and AAR of log 1/EC50 to single algal species.
for the evaluation of interspecies correlation, effect of exposure periods and response endpoints on the toxicity to algal species in the following results and discussion. It is noteworthy that, in theory, the experimental uncertainty of toxicity should be carried out by using same response endpoint (i.e. the endpoints in growth rate, yield and integral, respectively) within the same exposure period to same algal species. However, except toxicity data for 48 h and 72 h log 1/EC50 to freshwater algae, not many toxicity data with same endpoint are available to freshwater and marine green algae in other exposure periods. The AAR values of 24 h and 96 h log 1/EC50 to freshwater green algae in Fig. 1 were calculated based on not only the same response endpoint, but the unknown toxicity endpoints. Inclusion of unknown toxicity endpoints can result in the increase of AAR because of the possible difference of the response variables used in the calculation of AAR (this can be seen from the following results). For example, the AAR of 48 h log 1/EC50 presented in Fig. 1 were calculated from single response endpoint (i.e. either growth rate or yield) with AAR = 0.19. If no consideration was given to the response variables, the uncertainty of the toxicity would significantly increase to AAR = 0.42, which is close to the AAR of 96 h log 1/EC50. It is one of the reasons why the AAR of 96 h log 1/EC50 to freshwater green algae is greater than AAR values of other exposure periods. There are many reasons that can cause the experimental uncertainty of toxicity compiled in this paper. One reason can be attributed to the use of different test methods in the algal toxicity test. Most of the data compiled in this paper are based on the method of OECD, ISO and EPA guidelines (ISO, 2002; OECD, 2002, 2006, 2011; EPA, 2005). However, no information is available for some other toxicity data. It is obvious that the different test methods can result in the difference in toxicity to same algal species within the same exposure period (Aruoja et al., 2011, 2014). The traditional toxicity test is based on the most standard algal protocols, the batch-type test. However, it has been criticized as being unsuitable for assessing the effects of volatile compounds (OECD, 2002; Tsai and Chen, 2007). The exposure concentration might thus be altered significantly because large headspace may cause a significant portion of the volatile compound to partition from the aqueous phase into the headspace until equilibrium is reached (Mayer et al., 2000). Comparison of algal toxicity data obtained from the closed test (or called slightly modified test) with the literature data obtained from the batch-type test showed that
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the EC50 values in closed test were 2–3 times lower than the EC50 obtained from the traditional toxicity test (Chen and Lin, 2006), which will result in 0.3–0.47 greater in log 1/EC50. However, this difference was based on toxicities within 48 h and 72 h periods, respectively. Our result showed that 48 h exposure period is more sensitive than 72 h for algae (see below). Examination of the 16 toxicity data (log 1/EC50) determined within 72 h showed that the closed test had the same sensitivity as the open test when growth rate was used as the endpoint (see 72 h log 1/EC50 to freshwater algae in Supplementary material). The toxicity of two compounds (carbon tetrachloride and o-xylene) obtained in closed test is even lower than the toxicity obtained from open test. The difference in the test methods sometimes can result in significant difference in toxicity. 3.2. Calculation of response endpoints (or variables) There are three response endpoints (growth rate, yield and biomass integral) suggested by the OECD guideline (2002 and 2011). Most of the toxicity values were calculated from the growth rate; some of them were calculated from yield; and a few of them were calculated from the integral method. Inspection of the response endpoints compiled in this paper showed that there was a limited number of data available that could be used to compare the difference between response endpoints, except 48 h exposure period to P. subcapitata, and 72 h and 96 h exposure periods to D. tertiolecta. Eqs. (1)–(3) are the regression equations between growth rate and yield endpoints to P. subcapitata and D. tertiolecta, respectively.
species below. As described in OECD guideline (2006 and 2011), this should not be interpreted as a difference in sensitivity between the two response endpoints, simply that the values are different mathematically. This information on the calculation of response endpoints or variables is very valuable and should be reported in the published literature. Unfortunately, they were not available for many toxicity data in many published papers (see Supplementary material). The information on the calculation of response endpoints in Supplementary material shows that most of the toxicities within 24 h exposure period were calculated from either yield or unknown methods. The toxicities within 48 h exposure contain a number of data calculated from both growth rate and yield endpoints. Most of the toxicities compiled within 72 h and 96 h were either calculated from growth rate or unknown methods (see Table 1). Because there are not enough compounds with known same endpoint and no significant difference was observed between growth rate and yield within 72 h or 96 h exposure periods, the unknown endpoints (NA) were also used in the following analysis. The following analysis on the toxicity data was based upon the yield and unknown endpoints to 24 h exposure period, growth rate and yield respectively to 48 h, growth rate and unknown endpoints to 72 h and 96 h. All the toxicity data calculated by using integral method were not used in the following analysis because very few data were available in the data set. The compounds with a number of available toxicity data for each exposure period to one algal species were extracted and are listed in separate columns in Supplementary material.
P. subcapitata: 3.3. Interspecies correlation and species sensitivity
Log 1=EC50 ð48 h growth rateÞ ¼ 0:934 log 1 =EC50 ð48 h yieldÞ 0:17
ð1Þ
N ¼ 101 R2 ¼ 0:96 S ¼ 0:29 F ¼ 2125 AR ¼ 0:43 AAR ¼ 0:43 D. tertiolecta:
Log1=EC50 ð72 h growth rateÞ ¼ 0:952log 1=EC50 ð72 h yieldÞ 0:01 ð2Þ N ¼ 13 R2 ¼ 0:99 S ¼ 0:10 F ¼ 719 AR ¼ 0:19 AAR ¼ 0:19 Log1=EC50 ð96 h growth rateÞ ¼ 0:976log 1=EC50 ð96 h yieldÞ 0:01 ð3Þ N ¼ 14 R2 ¼ 0:99 S ¼ 0:08 F ¼ 1418 AR ¼ 0:15 AAR ¼ 0:15 Although there are significant relationships between these two response endpoints within the same period, the toxicity values calculated by using these two response endpoints are not comparable. The log 1/EC50 values based upon growth rate are generally lower than results based upon yield. 98% (98/101) of 48 h log 1/ EC50 values calculated by using yield method are greater than that by using growth rate, with AR = 0.43 (residuals range from 0.16 to –2.34) and AAR = 0.43 (Eq. (1)). The difference between growth rate and yield endpoints is much higher than the experimental uncertainty of toxicity within 48 h exposure period (see Fig. 1). On the other hand, the growth rate endpoint is close to the yield endpoint within 72 h and 96 h exposure periods with AR or AAR less than 0.20, which is no greater than the experimental uncertainty of toxicity shown above (Fig. 1). The result indicates that the difference of growth rate and yield endpoints within 48 h is much higher than that within 72 h or 96 h exposure periods. This is likely due to the difference in toxicity sensitivity; the sensitivity for 48 h is greater than 72 h or 96 h exposure period. This can be seen from the relationship between exposure periods to same algal
The interspecies toxicity correlation was carried out for the toxicities within the same exposure period (72 h or 96 h) for freshwater and marine algae. The toxicity data for the other exposure periods were not available. Table 2 lists the interspecies correlation coefficient (R), the number of data points (N), the average residual (AR) and absolute average residual (AAR) between toxicities of any two of freshwater and marine algal species. Significant interspecies correlations were observed between algal species, with Pearson correlation coefficients around or over 0.8. Although the interspecies correlations for 96 h to freshwater species (No. 2–7 in Table 2) are not as significant as for 72 h to freshwater species (No. 1) and 72 h and 96 h to freshwater and marine species (No. 8–9), we cannot expect R values better than these coefficients obtained in Table 2; this is because of higher uncertainty of toxicities in 96 h exposure period to freshwater species (see Fig. 1). The toxicity data used here were the response endpoints calculated from either growth rate or unknown methods. As described above, the toxicity expressed in different response endpoints can sometimes be significantly different. AR reflects the difference of overall sensitivity between two species. Difference of AAR and AR in Table 2 reflects uncertainty
Table 2 Interspecies toxicity correlations within same exposure periods. No.
Exposure period and species
N
R
AR
AAR
1 2 3 4 5 6 7 8 9
72 h 96 h 96 h 96 h 96 h 96 h 96 h 72 h 96 h
8 43 47 56 64 45 46 18 15
0.96 0.86 0.71 0.80 0.82 0.83 0.73 0.92 0.98
0.19 0.57 0.13 0.48 0.31 0.15 0.30 0.42 0.45
0.37 0.69 0.75 0.68 0.59 0.52 0.66 0.46 0.45
log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50
(P. subcapitata–D. subspicatus) (P. subcapitata–S. obliquus) (P. subcapitata–C. pyrenoidosa) (P. subcapitata–C. vulgaris) (S. obliquus–C. pyrenoidosa) (S. obliquus–C. vulgaris) (C. pyrenoidosa–C. vulgaris) (P. subcapitata–D. tertiolecta) (P. subcapitata–D. tertiolecta)
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of the toxicity between two species. The AR values in Table 2 show that D. subspicatus is a more sensitive species than P. subcapitata based upon the 72 h log 1/EC50 (No. 1 in Table 2). Based upon the 96 h log 1/EC50, P. subcapitata has similar sensitivity with C. pyrenoidosa because their AR (No. 3 in Table 2) is not greater than the uncertainty of toxicity (Fig. 1) within 96 h exposure period. A similar situation was observed between C. vulgaris and S. obliquus species (No. 6 in Table 2). The order of sensitivity to the freshwater and marine algal species is D. subspicatus > P. subcapitata P C. pyrenoidosa > D. tertiolecta P C. vulgaris P S. obliquus. This result indicates that the toxicity is algal species-related. The large difference of AAR and AR between species in Table 2 reflects great uncertainty of toxicity within 72 h and 96 h exposure periods, which were in agreement with what we observed in the uncertainty of toxicity (see Fig. 1). On the other hand, the difference between AAR and AR not only reflects the great experimental uncertainty of toxicities, but also indicates that there could be species-specific chemical sensitivities (see results below). Caution should be taken when toxicity data from different algal species were used in the development of QSAR models.
3.4. Relationship between exposure periods The exposure period relationships were carried out between 48 h, 72 h and 96 h to same freshwater algal species (P. subcapitata) and same marine algal species (D. tertiolecta), respectively. The correlation between 24 h and 48 h was conducted between S. vacuolatus and P. subcapitata (both freshwater species) because no data was available for the toxicities to P. subcapitata within 24 h exposure period. Table 3 listed the R, AR and AAR between four exposure periods (Eqs. (1)–(4), and (5), (6)). There are significant correlations between four exposure periods, with R2 greater than 0.80. It indicates that it is possible to predict toxicities to algal species from one exposure period to another exposure period. The AR values in Table 3 indicate that 48 h exposure period is the most sensitive to algal species. There are about 0.25 and 0.09 log units difference between toxicities of 48 h and 72 h or 96 h exposure periods to P. subcapitata and D. tertiolecta, respectively. The decrease in toxicity with increase of exposure periods probably results from the acclimation of algae to chemicals (Ertürk and Saçan, 2012). No great difference was observed in toxicities between 72 h and 96 h to either freshwater or marine algal species (see Eqs. (4) and (6) in Table 3). The toxicities within these two exposure periods can be used together, but not with the toxicities within 48 h. 24 h exposure period seems also less sensitive than 48 h. However, this result was based on the comparison of toxicities between different algal species. As described above, the toxicity is species-dependent. Hence, this result could be wrong if S. vacuolatus and P. subcapitata had significantly different species sensitivity.
3.5. Relationships between algal toxicities and other toxicity endpoints There are four other toxicity endpoints compiled in this paper, 14 min and 48 h inhibition of photosynthesis (based on the dissolved oxygen production) of freshwater green algae D. subspicatus and P. subcapitata, respectively, 15 min inhibition of esterase activity (based on the disappearance of fluorescein diacetate) in freshwater green algae C. vulgaris and growth inhibition of freshwater cyanobacteria. Table 3 (Eqs. (7)–(10)) listed the regression equations for these toxicity endpoints with 24 h, 48 h and 72 h log 1/EC50 to different algal species. Significant relationships were observed between toxicities expressed as 48 h growth rate and toxicities expressed as 48 h inhibition of photosynthesis to P. subcapitata (Eq. (7) in Table 3). Very low AR and AAR values indicate that the inhibition of photosynthesis is closely related with the inhibition of growth and the dissolved oxygen production can be used to replace the variable of growth rate. Table 3 also lists the regression equations for 72 h and 24 h toxicities (log 1/EC50) with toxicities expressed as 14 min inhibition of photosynthesis and 15 min inhibition of esterase activity, respectively (Eqs. (8) and (9)). Although there were significant relationships between these toxicity endpoints, the number of compounds was limited for both regression equations. It remains a question whether or not we can use 14 min inhibition of photosynthesis and 15 min inhibition of esterase activity to predict algal toxicities expressed as inhibition of growth, such as growth rate. Furthermore, their sensitivity seems too low to be good surrogates for the toxicity to growth of green algae, with AR = 1.31 and 1.12, respectively, more than 10 times less sensitive than the algal growth toxicity (see Eqs. (8) and (9)). The cyanobacteria obtain their energy through photosynthesis as well. Although often called blue-green algae, that name is a misnomer as cyanobacteria are prokaryotic and algae are eukaryotic. Relationship between toxicities to algae and cyanobacteria (Eq. (10) in Table 3) shows that cyanobacteria are more sensitive than algal species. It indicates that cyanobacteria are much valuable species in the evaluation of hazardous effects to human and environment. However, this regression is based on toxicity values to different cyanobacterial species within different exposure periods. Because of the limited number of overlapped compounds used in the analysis, more work needs to be carried out on the relationship between toxicities to algae and cyanobacteria.
3.6. Relationship between algal toxicity and hydrophobicity Although the interspecies toxicity correlation is useful in the comparison of toxicities of compounds between species within different exposure periods, it is limited by the number of compounds containing toxicity data to both species. To overcome this problem, relationship between toxicity and hydrophobicity was employed to
Table 3 Correlation between algal toxicities within exposure periods of 24, 48, 72 and 96 h, and relationships with other toxicity endpoints. No. Species (endpoints)
Equations
1 2 3 4 5 6 7 8 9 10
log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50
S. vacuolatus–P. subcapitata (yield–yield) P. subcapitata–P. subcapitata (growth rate–growth rate) P. subcapitata–P. subcapitata (growth rate–growth rate) P. subcapitata–P. subcapitata (growth rate–growth rate) D. tertiolecta–D. tertiolecta (growth rate–growth rate) D. tertiolecta–D. tertiolecta (growth rate–growth rate) P. subcapitata–P. subcapitata (growth rate–inhibition of photosynthesis) P. subcapitata–D. subspicatus (growth rate–inhibition of photosynthesis) S. vacuolatus–C. vulgaris (yield–inhibition of esterase activity) P. subcapitata–Cyanobacteria (growth rate–inhibition of growth)
(24 h) = 0.990 log 1/EC50 (48 h) 0.34 (48 h) = 1.02 log 1/EC50 (72 h) + 0.17 (48 h) = 1.05 log 1/EC50 (96 h) + 0.09 (72 h) = 1.09 log 1/EC50 (96 h) 0.32 (48 h) = 1.00 log 1/EC50 (72 h) + 0.07 (72 h) = 1.00 log 1/EC50 (96 h) + 0.04 (48 h) = 0.983 log 1/EC50 (48 h) 0.11 (72 h) = 0.551 log 1/EC50 (14 min) + 3.02 (24 h) = 0.974 log 1/EC50 (15 min) + 1.18 (72 h) = 0.798 log 1/EC50 0.21
N
R2
S
14 25 18 44 31 31 36 8 8 14
0.94 0.83 0.89 0.82 0.99 1.00 0.96 0.89 0.95 0.80
0.57 184 0.38 0.50 108 0.24 0.45 130 0.26 0.63 185 0.06 0.09 3120 0.09 0.04 12 483 0.04 0.29 844 0.18 0.32 49 1.31 0.57 103 1.12 0.54 47 1.49
F
AR
AAR 0.46 0.41 0.38 0.45 0.09 0.04 0.22 1.39 1.12 1.49
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investigate similarity and difference between species within different exposure periods. We first examine the non-polar narcotics. It is well known that aliphatic and aromatic compounds, such as alkanes, alcohols, ethers, ketones, alkyl benzenes and their halogenated derivatives, are non-polar narcotics (Verhaar et al., 1992). Significant correlations between log KOW and the toxicity have been found for the non-polar narcotics to many species. Fig. 2 is the plot of hydrophobicity expressed as log KOW against toxicity to S. vacuolatus within 24 h exposure period and P. subcapitata within 48 h, 72 h and 96 h, respectively. Other toxicity endpoints were not shown in Fig. 2, because no data were available for the non-polar narcotics in this paper. The trend lines in Fig. 2 show that 24 h log 1/EC50 to S. vacuolatus is slightly lower than other endpoints for non-polar narcotics. No significant difference was observed between exposure periods (i.e. 48 h, 72 h and 96 h), and response endpoints (i.e. 48 h growth rate and yield) for these non-polar narcotics. Similar distribution between five endpoints indicates that it is possible to construct a single combined model for these endpoints to non-polar narcotics.
log 1=EC50 ¼ 0:940 log K OW þ 1:15
21
Fig. 3. Plots of toxicity against log KOW for polar narcotics (e.g. alkyl and chlorinated phenols and anilines).
ð4Þ
2
N ¼ 76 R ¼ 0:93 S ¼ 0:34 F ¼ 942 Fig. 3 is the plot of log KOW against toxicity to different species within different exposure periods for primary amines, anilines and phenols with alkyl and chlorine substituents. These compounds were well recognized as polar narcotics. The phenols with four to five chlorine substituents were excluded from the compounds because they were classified as weak acid respiratory uncouplers (Netzeva et al., 2008). The trend lines in Fig. 3 show that there are significant relationships between toxicities and log KOW for polar narcotics. In comparison with equations to nonpolar narcotics (Fig. 2), the correlation coefficients for polar narcotics are all lower than those for the non-polar narcotics. This is in agreement with the results presented by other authors to other species (Cronin and Dearden, 1995; Dearden et al., 2000). Fig. 3 shows that there is slight difference between 48 h toxicity endpoints to P. subcapitata expressed as growth rate and yield for the polar narcotics (see Lines 1 and 2). No great difference can be observed between 48 h, 72 h and 96 h to marine green algae D. tertiolecta (Lines 4, 6 and 10). Differences can be observed between 48 h, 72 h and 96 h exposure periods to the same species P. subcapitata (Lines 1, 5 and 7); and between 48 h and 96 h exposure periods to the same species S. obliquus (Lines 3 and 8). Large differences
can be found between P. subcapitata and S. obliquus species within 48 h exposure period (Lines 1 and 3). The above results indicate that algal toxicity is species-specific chemical sensitivities. We cannot expect significant QSAR models between algal toxicity and descriptors without considering the species sensitivity, exposure periods and response endpoints.
4. Conclusions Evaluation on the toxicity data to green algae shows that toxicities can vary greatly if we ignore the difference between response variables (or toxicity endpoints), species sensitivity and exposure periods. Comparison of algal toxicity obtained from the closed test with the data obtained from the batch-type (or open) test within 72 h showed that the closed test had the same sensitivity as the open test for most of compounds, but significant differences were observed for a few compounds. There is no difference between response variables of growth rate and yield for non-polar narcotics, and slight differences for polar narcotics. The overall average difference for all compounds ranges from 0.15 to 0.43 log units between toxicity endpoints (yield–growth rate). The relationships between exposure periods of 24, 48, 72 and 96 h indicate that 48 h exposure period is the most sensitive for algal growth inhibition test; a slight difference was observed between 72 h and 96 h. The overall difference is about 0.25 log units between toxicities of 48 and 72 or 96 h exposure periods to P. subcapitata. However, no difference was observed for non-polar narcotics within 24, 48, 72 and 96 h endpoints. Interspecies relationship showed that some algae have very close species sensitivity (e.g. P. subcapitata and C. pyrenoidosa or C. vulgaris and S. obliquus, respectively). On the other hand, some algae have significantly different species sensitivity (e.g. P. subcapitata and S. obliquus). Caution should be taken when toxicity data from different algal species are used in the development of QSAR models. The information of calculation of response endpoints (or variables), exposure periods, and algal species is very valuable and should be reported in the published literature. We cannot expect significant QSAR models between algal toxicity and descriptors without considering species sensitivity, exposure periods and response endpoints. Acknowledgement
Fig. 2. Plots of toxicity against log KOW for non-polar narcotics (e.g. alkanes, alcohols, ethers, ketones, benzenes and their chlorides).
This work is supported by the National Natural Science Foundation of China (21377022 and 21107012).
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Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.chemosphere. 2014.05.040. References Altenburger, R., Walter, H., Grote, M., 2004. What contributes to the combined effect of a complex mixture? Environ. Sci. Technol. 38, 6353–6362. Aruoja, V., Sihtmäe, M., Dubourguier, H.-C., Kahru, A., 2011. Toxicity of 58 substituted anilines and phenols to algae Pseudokirchneriella subcapitata and bacteria Vibrio fischeri: comparison with published data and QSARs. Chemosphere 84, 1310–1320. Aruoja, V., Moosus, M., Kahru, A., Sihtmäe, M., Maran, U., 2014. Measurement of baseline toxicity and QSAR analysis of 50 non-polar and 58 polar narcotic chemicals for the alga Pseudokirchneriella subcapitata. Chemosphere 96, 23–32. Boyle, T.P., 1984. The effect of environmental contaminants on aquatic algae. In: Shubert, L.E. (Ed.), Algae as Ecological Indicators. Academic Press, New York, pp. 237–256. Chen, C.Y., Lin, J.H., 2006. Toxicity of chlorophenols to Pseudokirchneriella subcapitata under air-tight test environment. Chemosphere 62, 503–509. Chen, C.Y., Kuo, K.L., Fan, J.W., 2012. Toxicity of propargylic alcohols on green alga— Pseudokirchneriella subcapitata. J. Environ. Monit. 14, 181–186. Cronin, M.T.D., Dearden, J.C., 1995. QSAR in toxicology. 1. Prediction of aquatic toxicity. Quant. Struct. – Act. Relat. 14, 1–7. Cronin, M.T.D., Netzeva, T.I., Dearden, J.C., Edwards, R., Worgan, A.D.P., 2004. Assessment and modeling of the toxicity of organic chemicals to Chlorella vulgaris: development of a novel database. Chem. Res. Toxicol. 17, 545–554. Dearden, J.C., Cronin, M.T.D., Zhao, Y.H., Raevsky, O.A., 2000. QSAR studies of compounds acting by polar and non-polar narcosis: an examination of the role of polarisability and hydrogen bonding. Quant. Struct. – Act. Relat. 19, 3–9. EPA (Environmental Protection Agency), 2005. Environmental Effects Testing Guidelines, Subpart B – Aquatic Guidelines, 797.1050 Algal Acute Toxicity Test. Ertürk, M.D., Saçan, M.T., 2012. First toxicity data of chlorophenols on marine alga Dunaliella tertiolecta: correlation of marine algal toxicity with hydrophobicity and interspecies toxicity relationships. Environ. Toxicol. Chem. 31, 1113–1120. Ertürk, M.D., Saçan, M.T., 2013. Assessment and modeling of the novel toxicity data set of phenols to Chlorella vulgaris. Ecotoxicol. Environ. Saf. 90, 61–68. Ertürk, M.D., Saçan, M.T., Novic, M., Minovski, N., 2012. Quantitative structure– activity relationships (QSARs) using the novel marine algal toxicity data of phenols. J. Mol. Graph. Model. 38, 90–100. Faust, M., Altenburger, R., Backhaus, T., Blanck, H., Boedeker, W., Gramatica, P., Hamer, V., Scholze, M., Vighi, M., Grimme, L.H., 2001. Predicting the joint algal toxicity of multi-component s-triazine mixtures at low-effect concentrations of individual toxicants. Aquat. Toxicol. 56, 13–32. Furusjö, E., Svenson, A., Rahmberg, M., Andersson, M., 2006. The importance of outlier detection and training set selection for reliable environmental QSAR predictions. Chemosphere 63, 99–108.
Geis, S.W., Fleming, K.L., Korthals, E.T., Searle, G., Reynolds, L., Karner, D.A., 2000. Modifications to the algal growth inhibition test for use as a regulatory assay. Environ. Toxicol. Chem. 19, 36–41. Huang, C.P., Wang, Y.J., Chen, C.Y., 2007. Toxicity and quantitative structure–activity relationships of nitriles based on Pseudokirchneriella subcapitata. Ecotoxicol. Environ. Saf. 67, 439–446. ISO (International Organization for Standardization), 2002. Water quality-algal growth inhibition test (ISO 8692). Kahru, A., Dubourguier, H.C., 2010. From ecotoxicology to nanoecotoxicology. Toxicology 269, 105–119. Lee, P.Y., Chen, C.Y., 2009. Toxicity and quantitative structure–activity relationships of benzoic acids to Pseudokirchneriella subcapitata. J. Hazard. Mater. 165, 156– 161. Lu, G.H., Yuan, X., Zhao, Y.H., 2001. QSAR study on the toxicity of substituted benzenes to algae (Scenedesmus obliqnus). Chemosphere 44, 437–440. Ma, J., Zheng, R., Xu, L., Wang, S., 2002. Differential sensitivity of two green algae, Scenedesmus obliqnus and Chlorella pyrenoidosa, to 12 pesticides. Ecotoxicol. Environ. Saf. 52, 57–61. Ma, J., Wang, S., Wang, P., Ma, L., Chen, X., Xu, R., 2006. Toxicity assessment of 40 herbicides to the green alga Raphidocelis subcapitata. Ecotoxicol. Environ. Saf. 63, 456–462. Mayer, P., Nyholm, N., Verbruggen, E., Hermens, J., Tolls, J., 2000. Algal growth inhibition test in filled, closed bottles for volatile and sorptive materials. Environ. Toxicol. Chem. 19, 2551–2556. Netzeva, T.H., Pavan, M., Worth, A.P., 2008. Review of (quantitative) structure– activity relationships for acute aquatic toxicity. QSAR Comb. Sci. 27, 77–90. Neuwoehner, J., Junghans, M., Koller, M., Escher, B.I., 2008. QSAR analysis and specific endpoints for classifying the physiological modes of action of biocides in synchronous green algae. Aquat. Toxicol. 90, 8–18. OECD (Organization for Economic Co-operation and Development), 2002. Guideline for testing of chemicals. Proposal for updating guideline 201. Freshwater alga and cyanobacteria, Growth Inhibition Test. Paris, France. OECD, 2006. Guideline 201 for testing of chemicals. Alga, Growth Inhibition Test. Paris, France. OECD, 2011. Guideline 201 for testing of chemicals. Alga, Growth Inhibition Test. Paris, France. Schmitt, H., Altenburger, R., Jastorff, B., Schüürmann, G., 2000. Quantitative structure–activity analysis of the algae toxicity of nitroaromatic compounds. Chem. Res. Toxicol. 13, 441–450. Tebby, C., Mombelli, E., Pandard, P., Péry, A.R.R., 2011. Exploring an ecotoxicity database with the OECD (Q)SAR Toolbox and DRAGON descriptors in order to prioritise testing on algae, daphnids, and fish. Sci. Total Environ. 409, 3334– 3343. Tsai, K.P., Chen, C.Y., 2007. An algal toxicity database of organic toxicants derived by a closed-system technique. Environ. Toxicol. Chem. 26, 1931–1939. Verhaar, H.J.M., van Leeuwen, C.J., Hermens, J.L.M., 1992. Classifying environmental pollutants. 1: structure-activity relationships for prediction of aquatic toxicity. Chemosphere 25, 471–491. Yeh, H.J., Chen, C.Y., 2006. Toxicity assessment of pesticides to Pseudokirchneriella subcapitata under air-tight test environment. J. Hazard. Mater. A131, 6–12. Zhang, X.J., Qin, H.W., Su, L.M., Qin, W.C., Zou, M.Y., Sheng, L.X., Zhao, Y.H., Abraham, M.H., 2010. Interspecies correlations of toxicity to eight aquatic organisms: theoretical considerations. Sci. Total Environ. 408, 4549–4555.
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Evaluation of toxicity data to green algae and relationship with hydrophobicity Ling Fu, Jin J. Li, Yu Wang, Xiao H. Wang, Yang Wen, Wei C. Qin, Li M. Su ⇑, Yuan H. Zhao ⇑ State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, Jilin 130117, PR China
h i g h l i g h t s Algal toxicity is species-specific chemical sensitivities. Response variables, exposure periods and species sensitivity affect algal toxicity. 48 h Exposure period is the most sensitive for algal growth inhibition test. Algae have very close species sensitivity for baseline compounds. Algae have different species sensitivity for polar narcotics and reactive compounds.
a r t i c l e
i n f o
a b s t r a c t
Article history: Received 14 March 2014 Received in revised form 8 May 2014 Accepted 13 May 2014
The quality of the biological activity data is of great importance for the development of algal quantitative structure–activity relationship (QSAR) models. However, a number of algal QSAR models in the literature were developed based on toxicity data without considering the response endpoints, exposure periods and species sensitivity. In this paper, 2323 algal toxicity data (log 1/EC50) in different toxicity response endpoints for 1081 compounds to 26 algal species within different exposure periods (14 and 15 min; 24, 48, 72, 96, 168 and 192 h) were used to evaluate the quality of the toxicity data to green algae. Analysis of 72 h toxicity to algae showed that the closed test had the same sensitivity as the open test for most of the test compounds, but a significant difference was observed for a few compounds. The overall average difference for all compounds ranges from 0.15 to 0.43 log units between toxicity endpoints (yield–growth rate). The relationships between exposure periods of 24, 48, 72 and 96 h indicated that 48 h exposure period is the most sensitive for algal growth inhibition test, and its sensitivity is 0.25 log units greater than 72 and 96 h exposure periods, respectively. Interspecies relationships showed that some algal species have very close sensitivity (e.g. Pseudokirchneriella subcapitata and Chlorella pyrenoidosa or Chlorella vulgaris and Scenedesmus obliquus, respectively), whereas some species have significantly different sensitivity (e.g. P. subcapitata and S. obliquus). Relationships between toxicity and hydrophobicity demonstrated that no difference was observed for non-polar narcotics within different exposure periods (24, 48, 72, and 96 h) or response variables (yield and growth rate). For polar narcotics, in contrast, algal toxicity is dependent on algal species and is related to the response variables and exposure period. We cannot expect significant QSAR models between algal toxicity and descriptors without considering species sensitivity, exposure periods and response endpoints. Ó 2014 Elsevier Ltd. All rights reserved.
Handling Editor: A. Gies Keywords: Algae Toxicity endpoint Interspecies Response variable Exposure period Hydrophobicity
1. Introduction According to REACH (Registration, Evaluation, Authorization, and Restriction of Chemicals), all the substances from manufacturers or importers must be registered and evaluated for hazardous ⇑ Corresponding authors. Tel.: +86 431 89165610; fax: +86 431 89165606. E-mail addresses: (Y.H. Zhao).
[email protected]
(L.M.
http://dx.doi.org/10.1016/j.chemosphere.2014.05.040 0045-6535/Ó 2014 Elsevier Ltd. All rights reserved.
Su),
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effects to human and environment. The experimental toxicity data of these substances to aquatic ecosystem are required in order to evaluate their hazard and risk. Acute aquatic toxicity data are usually determined using 96 h LC50 to fish, 48 h EC50 to Daphnia magna and 72 h EC50 to algae (OECD, 2006). Among the organisms used in the ecotoxicity test, algae represent the primary producers in aquatic ecosystems and play an important role in their sustainability. The adverse effect of chemicals on algal populations is likely to disrupt the higher trophic levels. Therefore, studying the
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toxic effects of chemicals on algae provides valuable information regarding the risks that chemicals might pose to aquatic ecosystems (Geis et al., 2000; Ertürk and Saçan, 2012). Currently, there are several toxicity endpoints (or called response variables) used in the evaluation of toxicity to algae, such as growth rate, yield and integral. The response endpoint of growth rate (or called average specific growth rate) is calculated on the basis of the logarithmic increase of biomass during the test period, expressed per day. Yield response endpoint is the biomass at the end of the test minus the starting biomass (OECD, 2011). Optionally the log-biomass integral (area under the growth curve) may be used as an additional endpoint. This option may be attractive in situations with highly irregular growth in inhibited cultures (OECD, 2002). The growth rate endpoint is preferred by most ecotoxicologists to the biomass type endpoint (e.g. yield) because algal growth rate is considered to be more stable, comparable, and ecologically relevant (Chen et al., 2012). However, as indicated in the OECD guideline, the toxicity values calculated by using these response variables are not comparable and this difference must be recognised when using the results of the test. EC50 values based upon growth rate will generally be higher than results based upon yield, due to the mathematical basis of the respective approaches. This should not be interpreted as a difference in sensitivity between the two response variables, simply that the values are different mathematically (OECD, 2011). The interspecies toxicity relationships have the potential to predict the toxicity of compounds from one species to another species. It is also helpful in the interpretation of toxic mechanisms. The toxicity data of chlorophenols on marine algae (Dunaliella tertiolecta) was found to correlate well with the data for Vibrio fischeri, D. magna, freshwater algae (Pseudokirchneriella subcapitata), Tetrahymena pyriformis and fish (Pimephales promelas) (Ertürk and Saçan, 2012). On the other hand, poor interspecies relationships observed between toxicities to algae and T. pyriformis or D. magna for compounds with a wide range of structures suggested that compounds have different toxic mechanisms of action between these species (Zhang et al., 2010). In the meanwhile, algal species vary widely in their response to pesticides with poor interspecies correlation (Boyle, 1984). Algae, lacking a nervous system, seem to be more sensitive than fathead minnow for insecticides (Yeh and Chen, 2006). There is a large body of evidence showing that algae are more sensitive to chemicals than fish (Huang et al., 2007; Kahru and Dubourguier, 2010). Investigations using different algal species as test organisms have demonstrated that algae vary greatly in their response to chemicals. There were differential responses to various pesticides by two species of algae and the sensitivity of various species of algae exposed to chlorothalonil varied by nearly two orders of magnitude (Ma et al., 2002). There are relatively large toxicity databases for fish, D. magna, which represent higher tropic levels, but fewer consistent toxicity data are available for algae, the primary producers (Cronin et al., 2004; Ertürk et al., 2012). The European Commission has stated that the animal tests for assessment should be replaced by the use of quantitative structure–activity relationships (QSARs). Such method has the potential to fill data gaps in algal toxicity for certain chemicals. Some QSAR models predicted algal toxicity with specific descriptors such as the octanol/water partition coefficient, the energy of the lowest unoccupied molecular orbital, the dissociation constant and other parameters (Schmitt et al., 2000; Neuwoehner et al., 2008; Lee and Chen, 2009; Aruoja et al., 2011, 2014; Tebby et al., 2011; Ertürk et al., 2012). However, the number of algal QSARs available in the literature was remarkably small compared to those constructed by using toxicity data on fish, protozoa, bacteria or daphnia (Ertürk and Saçan, 2013). The algal toxicity data contain much more variation and this most likely is due to differences in experimental methods (Aruoja et al., 2011,
17
2014). This is a reflection of the lack of reliable algal toxicity data in the literature (Cronin et al., 2004). The quality of QSAR models depends on the quality of the biological activity data. Although the quality of the algal toxicity data was crucial for the development of algal QSAR models, a number of QSAR models developed in the literature were based on the unknown response endpoints, exposure periods and species sensitivity. The variation of algal toxicity data was unknown and little effort has been made on the evaluation of toxicity data to green algae based on experimental uncertainty, response endpoints, exposure periods and species sensitivity. In this paper, 2323 algal toxicity data for 1081 compounds to 26 algal species within different exposure periods (14 and 15 min; 24, 48, 72, 96, 168 and 192 h) and different toxicity response endpoints were compiled from the literature and databases. The compounds were classified into different classes based on the substituted functional groups and mode of action (MOA) of the compounds. The aims of the work were: First, to carry out evaluation of experimental uncertainty of algal toxicity from different sources based on different exposure periods, response endpoints and experimental methods; Second, to investigate the effect of calculation of response endpoints on the algal toxicity; Third, to study the algal interspecies relationship and relationship between algal toxicity and other toxicity endpoints; and Forth, to discuss the algal species sensitivity from relationships between algal toxicity and hydrophobicity for non-polar (baseline) and polar narcotics (less inert compounds). 2. Materials and methods 2.1. Toxicity to green algae A total of 2323 toxicity data for 1081 chemicals to 26 green algal species were mainly compiled from the literature (Faust et al., 2001; Lu et al., 2001; Altenburger et al., 2004; Furusjö et al., 2006; Ma et al., 2006; Tsai and Chen, 2007; Lee and Chen, 2009; Aruoja et al., 2011; Chen et al., 2012; Ertürk et al., 2012; Ertürk and Saçan, 2012, 2013) and two databases, i.e., CHRIP (Chemical Risk Information Platform, http://www.safe.nite.go.jp/ english/db.html) and EU pesticides database (http://ec.europa.eu/ sanco_pesticides/public/?event=activesubstance.selection). The details of the references for each toxicity data expressed as log 1/ EC50 (M) can be found in Supplementary material. If available, the following information was supplied for the toxicity data: toxicity response endpoints (growth rate, yield and/or integral), exposure periods (24, 48, 72, 96, 168 and 192 h) and algal species. Meanwhile, 15 min inhibition of esterase activity to freshwater green algae Chlorella vulgaris, 14 min and 48 h inhibition of photosynthesis to Desmodesmus subspicatus and P. subcapitata, respectively, and toxicity data to freshwater cyanobacteria were also listed in Supplementary material. These cyanobacterium data were used to investigate relationships with the algal toxicity endpoints. The names, SMILES and CAS numbers of all the compounds can be found in Supplementary material. The summary of the toxicity endpoints is listed in Table 1. 2.2. Molecular descriptors and statistical analysis The n-octanol/water partition coefficients (log KOW) were obtained from the KOWWIN programme in the EPI Suite version 4.0 (http://www.epa.gov/oppt/exposure/pubs/episuitedl.htm); where possible measured log KOW values were used in preference to calculated values. The relationship between the log KOW and log 1/EC50 was performed using a least-squares linear regression with the Minitab software (version 14). For each regression, the following descriptive information is provided: number of
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Table 1 Toxicity data (log 1/EC50, M) to different freshwater and marine species. Endpoints
Description
24 h log 1/EC50
Inhibition of growth to Scenedesmus vacuolatus (S. vacuolatus) and a few other algal species with yield and unknown endpoints (a few compounds with growth rate endpoint) Inhibition of growth to Pseudokirchneriella subcapitata (P. subcapitata), Scenedesmus obliquus (S. obliquus) and a few other algal species with growth rate and yield endpoints (a few compounds with unknown endpoint) Inhibition of growth to Dunaliella tertiolecta (D. tertiolecta) (marine green algae) with growth rate and unknown endpoints Inhibition of growth to P. subcapitata, Desmodesmus subspicatus (D. subspicatus) and a few other species with growth rate endpoint (a few compounds with yield or integral or unknown endpoints) Inhibition of growth to D. tertiolecta and a few other algal species with growth rate and unknown endpoints (a few compounds with integral endpoint) Inhibition of growth to P. subcapitata, S. obliquus, Chlorella pyrenoidosa (C. pyrenoidosa), Chlorella vulgaris (C. vulgaris) and a few other algal species with growth rate, unknown endpoints (a few compounds with yield or integral endpoints) Inhibition of growth to D. tertiolecta and a few other algal species with growth rate and unknown endpoints (a few compounds with yield or integral endpoints) Inhibition of growth to D. subspicatus and P. subcapitata with growth rate, integral and unknown endpoints Inhibition of growth to unknown algal species with unknown exposure periods and endpoints Inhibition of growth to freshwater cyanobacteria with exposure periods of 72, 96 or 168 h with growth rate, integral and unknown endpoints Inhibition of esterase activity to C. vulgaris Inhibition of photosynthesis (reduction of dissolved oxygen production) to D. subspicatus Inhibition of photosynthesis (reduction of dissolved oxygen production) to P. subcapitata Inhibition of photosynthesis (reduction of dissolved oxygen production) to S. obliquus
48 h log 1/EC50 48 h log 1/EC50 72 h log 1/EC50 72 h log 1/EC50 96 h log 1/EC50 96 h log 1/EC50 168 h or 192 h log 1/EC50 log 1/EC50 72 h, 96 h or 168 h log 1/EC50 15 min log 1/EC50 14 min log 1/EC50 48 h log 1/EC50 192 h log 1/EC50
observations used in the analysis (N), coefficient of determination (R2), standard error of the estimate (S) and Fisher’s criterion (F). The species sensitivity was evaluated from the Pearson correlation coefficient (R), average residual (AR) and absolute average residual P (AAR) between the species toxicity endpoints (AR = (log 1/EC50 P to species 1 log 1/EC50 to species 2)/N, AAR = |log 1/EC50 to species 1 log 1/EC50 to species 2|/N. 3. Results and discussion 3.1. Evaluation of experimental uncertainty of toxicity to green algae Experimental uncertainty of toxicity to green algae was evaluated from the toxicity residuals (maximum–minimum log 1/ EC50 values) for the overlapped compounds reported by different references. Fig. 1 is the histograms of the average absolute residuals (AAR) for overlapped compounds to a single algal species within the exposure periods of 24, 48, 72 and 96 h, respectively. It shows that the experimental uncertainty of toxicity increases with increase of exposure periods. The uncertainty of 24 h toxicities is close to 48 h toxicities (AAR = 0.23 and 0.19, respectively). The uncertainty of 72 h toxicities (AAR = 0.30) is greater than 24 h or 48 h. The highest AAR is observed in 96 h toxicities to freshwater green algae with AAR = 0.40. These AAR values will be used
Fig. 1. Histrograms of AR and AAR of log 1/EC50 to single algal species.
for the evaluation of interspecies correlation, effect of exposure periods and response endpoints on the toxicity to algal species in the following results and discussion. It is noteworthy that, in theory, the experimental uncertainty of toxicity should be carried out by using same response endpoint (i.e. the endpoints in growth rate, yield and integral, respectively) within the same exposure period to same algal species. However, except toxicity data for 48 h and 72 h log 1/EC50 to freshwater algae, not many toxicity data with same endpoint are available to freshwater and marine green algae in other exposure periods. The AAR values of 24 h and 96 h log 1/EC50 to freshwater green algae in Fig. 1 were calculated based on not only the same response endpoint, but the unknown toxicity endpoints. Inclusion of unknown toxicity endpoints can result in the increase of AAR because of the possible difference of the response variables used in the calculation of AAR (this can be seen from the following results). For example, the AAR of 48 h log 1/EC50 presented in Fig. 1 were calculated from single response endpoint (i.e. either growth rate or yield) with AAR = 0.19. If no consideration was given to the response variables, the uncertainty of the toxicity would significantly increase to AAR = 0.42, which is close to the AAR of 96 h log 1/EC50. It is one of the reasons why the AAR of 96 h log 1/EC50 to freshwater green algae is greater than AAR values of other exposure periods. There are many reasons that can cause the experimental uncertainty of toxicity compiled in this paper. One reason can be attributed to the use of different test methods in the algal toxicity test. Most of the data compiled in this paper are based on the method of OECD, ISO and EPA guidelines (ISO, 2002; OECD, 2002, 2006, 2011; EPA, 2005). However, no information is available for some other toxicity data. It is obvious that the different test methods can result in the difference in toxicity to same algal species within the same exposure period (Aruoja et al., 2011, 2014). The traditional toxicity test is based on the most standard algal protocols, the batch-type test. However, it has been criticized as being unsuitable for assessing the effects of volatile compounds (OECD, 2002; Tsai and Chen, 2007). The exposure concentration might thus be altered significantly because large headspace may cause a significant portion of the volatile compound to partition from the aqueous phase into the headspace until equilibrium is reached (Mayer et al., 2000). Comparison of algal toxicity data obtained from the closed test (or called slightly modified test) with the literature data obtained from the batch-type test showed that
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the EC50 values in closed test were 2–3 times lower than the EC50 obtained from the traditional toxicity test (Chen and Lin, 2006), which will result in 0.3–0.47 greater in log 1/EC50. However, this difference was based on toxicities within 48 h and 72 h periods, respectively. Our result showed that 48 h exposure period is more sensitive than 72 h for algae (see below). Examination of the 16 toxicity data (log 1/EC50) determined within 72 h showed that the closed test had the same sensitivity as the open test when growth rate was used as the endpoint (see 72 h log 1/EC50 to freshwater algae in Supplementary material). The toxicity of two compounds (carbon tetrachloride and o-xylene) obtained in closed test is even lower than the toxicity obtained from open test. The difference in the test methods sometimes can result in significant difference in toxicity. 3.2. Calculation of response endpoints (or variables) There are three response endpoints (growth rate, yield and biomass integral) suggested by the OECD guideline (2002 and 2011). Most of the toxicity values were calculated from the growth rate; some of them were calculated from yield; and a few of them were calculated from the integral method. Inspection of the response endpoints compiled in this paper showed that there was a limited number of data available that could be used to compare the difference between response endpoints, except 48 h exposure period to P. subcapitata, and 72 h and 96 h exposure periods to D. tertiolecta. Eqs. (1)–(3) are the regression equations between growth rate and yield endpoints to P. subcapitata and D. tertiolecta, respectively.
species below. As described in OECD guideline (2006 and 2011), this should not be interpreted as a difference in sensitivity between the two response endpoints, simply that the values are different mathematically. This information on the calculation of response endpoints or variables is very valuable and should be reported in the published literature. Unfortunately, they were not available for many toxicity data in many published papers (see Supplementary material). The information on the calculation of response endpoints in Supplementary material shows that most of the toxicities within 24 h exposure period were calculated from either yield or unknown methods. The toxicities within 48 h exposure contain a number of data calculated from both growth rate and yield endpoints. Most of the toxicities compiled within 72 h and 96 h were either calculated from growth rate or unknown methods (see Table 1). Because there are not enough compounds with known same endpoint and no significant difference was observed between growth rate and yield within 72 h or 96 h exposure periods, the unknown endpoints (NA) were also used in the following analysis. The following analysis on the toxicity data was based upon the yield and unknown endpoints to 24 h exposure period, growth rate and yield respectively to 48 h, growth rate and unknown endpoints to 72 h and 96 h. All the toxicity data calculated by using integral method were not used in the following analysis because very few data were available in the data set. The compounds with a number of available toxicity data for each exposure period to one algal species were extracted and are listed in separate columns in Supplementary material.
P. subcapitata: 3.3. Interspecies correlation and species sensitivity
Log 1=EC50 ð48 h growth rateÞ ¼ 0:934 log 1 =EC50 ð48 h yieldÞ 0:17
ð1Þ
N ¼ 101 R2 ¼ 0:96 S ¼ 0:29 F ¼ 2125 AR ¼ 0:43 AAR ¼ 0:43 D. tertiolecta:
Log1=EC50 ð72 h growth rateÞ ¼ 0:952log 1=EC50 ð72 h yieldÞ 0:01 ð2Þ N ¼ 13 R2 ¼ 0:99 S ¼ 0:10 F ¼ 719 AR ¼ 0:19 AAR ¼ 0:19 Log1=EC50 ð96 h growth rateÞ ¼ 0:976log 1=EC50 ð96 h yieldÞ 0:01 ð3Þ N ¼ 14 R2 ¼ 0:99 S ¼ 0:08 F ¼ 1418 AR ¼ 0:15 AAR ¼ 0:15 Although there are significant relationships between these two response endpoints within the same period, the toxicity values calculated by using these two response endpoints are not comparable. The log 1/EC50 values based upon growth rate are generally lower than results based upon yield. 98% (98/101) of 48 h log 1/ EC50 values calculated by using yield method are greater than that by using growth rate, with AR = 0.43 (residuals range from 0.16 to –2.34) and AAR = 0.43 (Eq. (1)). The difference between growth rate and yield endpoints is much higher than the experimental uncertainty of toxicity within 48 h exposure period (see Fig. 1). On the other hand, the growth rate endpoint is close to the yield endpoint within 72 h and 96 h exposure periods with AR or AAR less than 0.20, which is no greater than the experimental uncertainty of toxicity shown above (Fig. 1). The result indicates that the difference of growth rate and yield endpoints within 48 h is much higher than that within 72 h or 96 h exposure periods. This is likely due to the difference in toxicity sensitivity; the sensitivity for 48 h is greater than 72 h or 96 h exposure period. This can be seen from the relationship between exposure periods to same algal
The interspecies toxicity correlation was carried out for the toxicities within the same exposure period (72 h or 96 h) for freshwater and marine algae. The toxicity data for the other exposure periods were not available. Table 2 lists the interspecies correlation coefficient (R), the number of data points (N), the average residual (AR) and absolute average residual (AAR) between toxicities of any two of freshwater and marine algal species. Significant interspecies correlations were observed between algal species, with Pearson correlation coefficients around or over 0.8. Although the interspecies correlations for 96 h to freshwater species (No. 2–7 in Table 2) are not as significant as for 72 h to freshwater species (No. 1) and 72 h and 96 h to freshwater and marine species (No. 8–9), we cannot expect R values better than these coefficients obtained in Table 2; this is because of higher uncertainty of toxicities in 96 h exposure period to freshwater species (see Fig. 1). The toxicity data used here were the response endpoints calculated from either growth rate or unknown methods. As described above, the toxicity expressed in different response endpoints can sometimes be significantly different. AR reflects the difference of overall sensitivity between two species. Difference of AAR and AR in Table 2 reflects uncertainty
Table 2 Interspecies toxicity correlations within same exposure periods. No.
Exposure period and species
N
R
AR
AAR
1 2 3 4 5 6 7 8 9
72 h 96 h 96 h 96 h 96 h 96 h 96 h 72 h 96 h
8 43 47 56 64 45 46 18 15
0.96 0.86 0.71 0.80 0.82 0.83 0.73 0.92 0.98
0.19 0.57 0.13 0.48 0.31 0.15 0.30 0.42 0.45
0.37 0.69 0.75 0.68 0.59 0.52 0.66 0.46 0.45
log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50
(P. subcapitata–D. subspicatus) (P. subcapitata–S. obliquus) (P. subcapitata–C. pyrenoidosa) (P. subcapitata–C. vulgaris) (S. obliquus–C. pyrenoidosa) (S. obliquus–C. vulgaris) (C. pyrenoidosa–C. vulgaris) (P. subcapitata–D. tertiolecta) (P. subcapitata–D. tertiolecta)
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of the toxicity between two species. The AR values in Table 2 show that D. subspicatus is a more sensitive species than P. subcapitata based upon the 72 h log 1/EC50 (No. 1 in Table 2). Based upon the 96 h log 1/EC50, P. subcapitata has similar sensitivity with C. pyrenoidosa because their AR (No. 3 in Table 2) is not greater than the uncertainty of toxicity (Fig. 1) within 96 h exposure period. A similar situation was observed between C. vulgaris and S. obliquus species (No. 6 in Table 2). The order of sensitivity to the freshwater and marine algal species is D. subspicatus > P. subcapitata P C. pyrenoidosa > D. tertiolecta P C. vulgaris P S. obliquus. This result indicates that the toxicity is algal species-related. The large difference of AAR and AR between species in Table 2 reflects great uncertainty of toxicity within 72 h and 96 h exposure periods, which were in agreement with what we observed in the uncertainty of toxicity (see Fig. 1). On the other hand, the difference between AAR and AR not only reflects the great experimental uncertainty of toxicities, but also indicates that there could be species-specific chemical sensitivities (see results below). Caution should be taken when toxicity data from different algal species were used in the development of QSAR models.
3.4. Relationship between exposure periods The exposure period relationships were carried out between 48 h, 72 h and 96 h to same freshwater algal species (P. subcapitata) and same marine algal species (D. tertiolecta), respectively. The correlation between 24 h and 48 h was conducted between S. vacuolatus and P. subcapitata (both freshwater species) because no data was available for the toxicities to P. subcapitata within 24 h exposure period. Table 3 listed the R, AR and AAR between four exposure periods (Eqs. (1)–(4), and (5), (6)). There are significant correlations between four exposure periods, with R2 greater than 0.80. It indicates that it is possible to predict toxicities to algal species from one exposure period to another exposure period. The AR values in Table 3 indicate that 48 h exposure period is the most sensitive to algal species. There are about 0.25 and 0.09 log units difference between toxicities of 48 h and 72 h or 96 h exposure periods to P. subcapitata and D. tertiolecta, respectively. The decrease in toxicity with increase of exposure periods probably results from the acclimation of algae to chemicals (Ertürk and Saçan, 2012). No great difference was observed in toxicities between 72 h and 96 h to either freshwater or marine algal species (see Eqs. (4) and (6) in Table 3). The toxicities within these two exposure periods can be used together, but not with the toxicities within 48 h. 24 h exposure period seems also less sensitive than 48 h. However, this result was based on the comparison of toxicities between different algal species. As described above, the toxicity is species-dependent. Hence, this result could be wrong if S. vacuolatus and P. subcapitata had significantly different species sensitivity.
3.5. Relationships between algal toxicities and other toxicity endpoints There are four other toxicity endpoints compiled in this paper, 14 min and 48 h inhibition of photosynthesis (based on the dissolved oxygen production) of freshwater green algae D. subspicatus and P. subcapitata, respectively, 15 min inhibition of esterase activity (based on the disappearance of fluorescein diacetate) in freshwater green algae C. vulgaris and growth inhibition of freshwater cyanobacteria. Table 3 (Eqs. (7)–(10)) listed the regression equations for these toxicity endpoints with 24 h, 48 h and 72 h log 1/EC50 to different algal species. Significant relationships were observed between toxicities expressed as 48 h growth rate and toxicities expressed as 48 h inhibition of photosynthesis to P. subcapitata (Eq. (7) in Table 3). Very low AR and AAR values indicate that the inhibition of photosynthesis is closely related with the inhibition of growth and the dissolved oxygen production can be used to replace the variable of growth rate. Table 3 also lists the regression equations for 72 h and 24 h toxicities (log 1/EC50) with toxicities expressed as 14 min inhibition of photosynthesis and 15 min inhibition of esterase activity, respectively (Eqs. (8) and (9)). Although there were significant relationships between these toxicity endpoints, the number of compounds was limited for both regression equations. It remains a question whether or not we can use 14 min inhibition of photosynthesis and 15 min inhibition of esterase activity to predict algal toxicities expressed as inhibition of growth, such as growth rate. Furthermore, their sensitivity seems too low to be good surrogates for the toxicity to growth of green algae, with AR = 1.31 and 1.12, respectively, more than 10 times less sensitive than the algal growth toxicity (see Eqs. (8) and (9)). The cyanobacteria obtain their energy through photosynthesis as well. Although often called blue-green algae, that name is a misnomer as cyanobacteria are prokaryotic and algae are eukaryotic. Relationship between toxicities to algae and cyanobacteria (Eq. (10) in Table 3) shows that cyanobacteria are more sensitive than algal species. It indicates that cyanobacteria are much valuable species in the evaluation of hazardous effects to human and environment. However, this regression is based on toxicity values to different cyanobacterial species within different exposure periods. Because of the limited number of overlapped compounds used in the analysis, more work needs to be carried out on the relationship between toxicities to algae and cyanobacteria.
3.6. Relationship between algal toxicity and hydrophobicity Although the interspecies toxicity correlation is useful in the comparison of toxicities of compounds between species within different exposure periods, it is limited by the number of compounds containing toxicity data to both species. To overcome this problem, relationship between toxicity and hydrophobicity was employed to
Table 3 Correlation between algal toxicities within exposure periods of 24, 48, 72 and 96 h, and relationships with other toxicity endpoints. No. Species (endpoints)
Equations
1 2 3 4 5 6 7 8 9 10
log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50 log 1/EC50
S. vacuolatus–P. subcapitata (yield–yield) P. subcapitata–P. subcapitata (growth rate–growth rate) P. subcapitata–P. subcapitata (growth rate–growth rate) P. subcapitata–P. subcapitata (growth rate–growth rate) D. tertiolecta–D. tertiolecta (growth rate–growth rate) D. tertiolecta–D. tertiolecta (growth rate–growth rate) P. subcapitata–P. subcapitata (growth rate–inhibition of photosynthesis) P. subcapitata–D. subspicatus (growth rate–inhibition of photosynthesis) S. vacuolatus–C. vulgaris (yield–inhibition of esterase activity) P. subcapitata–Cyanobacteria (growth rate–inhibition of growth)
(24 h) = 0.990 log 1/EC50 (48 h) 0.34 (48 h) = 1.02 log 1/EC50 (72 h) + 0.17 (48 h) = 1.05 log 1/EC50 (96 h) + 0.09 (72 h) = 1.09 log 1/EC50 (96 h) 0.32 (48 h) = 1.00 log 1/EC50 (72 h) + 0.07 (72 h) = 1.00 log 1/EC50 (96 h) + 0.04 (48 h) = 0.983 log 1/EC50 (48 h) 0.11 (72 h) = 0.551 log 1/EC50 (14 min) + 3.02 (24 h) = 0.974 log 1/EC50 (15 min) + 1.18 (72 h) = 0.798 log 1/EC50 0.21
N
R2
S
14 25 18 44 31 31 36 8 8 14
0.94 0.83 0.89 0.82 0.99 1.00 0.96 0.89 0.95 0.80
0.57 184 0.38 0.50 108 0.24 0.45 130 0.26 0.63 185 0.06 0.09 3120 0.09 0.04 12 483 0.04 0.29 844 0.18 0.32 49 1.31 0.57 103 1.12 0.54 47 1.49
F
AR
AAR 0.46 0.41 0.38 0.45 0.09 0.04 0.22 1.39 1.12 1.49
L. Fu et al. / Chemosphere 120 (2015) 16–22
investigate similarity and difference between species within different exposure periods. We first examine the non-polar narcotics. It is well known that aliphatic and aromatic compounds, such as alkanes, alcohols, ethers, ketones, alkyl benzenes and their halogenated derivatives, are non-polar narcotics (Verhaar et al., 1992). Significant correlations between log KOW and the toxicity have been found for the non-polar narcotics to many species. Fig. 2 is the plot of hydrophobicity expressed as log KOW against toxicity to S. vacuolatus within 24 h exposure period and P. subcapitata within 48 h, 72 h and 96 h, respectively. Other toxicity endpoints were not shown in Fig. 2, because no data were available for the non-polar narcotics in this paper. The trend lines in Fig. 2 show that 24 h log 1/EC50 to S. vacuolatus is slightly lower than other endpoints for non-polar narcotics. No significant difference was observed between exposure periods (i.e. 48 h, 72 h and 96 h), and response endpoints (i.e. 48 h growth rate and yield) for these non-polar narcotics. Similar distribution between five endpoints indicates that it is possible to construct a single combined model for these endpoints to non-polar narcotics.
log 1=EC50 ¼ 0:940 log K OW þ 1:15
21
Fig. 3. Plots of toxicity against log KOW for polar narcotics (e.g. alkyl and chlorinated phenols and anilines).
ð4Þ
2
N ¼ 76 R ¼ 0:93 S ¼ 0:34 F ¼ 942 Fig. 3 is the plot of log KOW against toxicity to different species within different exposure periods for primary amines, anilines and phenols with alkyl and chlorine substituents. These compounds were well recognized as polar narcotics. The phenols with four to five chlorine substituents were excluded from the compounds because they were classified as weak acid respiratory uncouplers (Netzeva et al., 2008). The trend lines in Fig. 3 show that there are significant relationships between toxicities and log KOW for polar narcotics. In comparison with equations to nonpolar narcotics (Fig. 2), the correlation coefficients for polar narcotics are all lower than those for the non-polar narcotics. This is in agreement with the results presented by other authors to other species (Cronin and Dearden, 1995; Dearden et al., 2000). Fig. 3 shows that there is slight difference between 48 h toxicity endpoints to P. subcapitata expressed as growth rate and yield for the polar narcotics (see Lines 1 and 2). No great difference can be observed between 48 h, 72 h and 96 h to marine green algae D. tertiolecta (Lines 4, 6 and 10). Differences can be observed between 48 h, 72 h and 96 h exposure periods to the same species P. subcapitata (Lines 1, 5 and 7); and between 48 h and 96 h exposure periods to the same species S. obliquus (Lines 3 and 8). Large differences
can be found between P. subcapitata and S. obliquus species within 48 h exposure period (Lines 1 and 3). The above results indicate that algal toxicity is species-specific chemical sensitivities. We cannot expect significant QSAR models between algal toxicity and descriptors without considering the species sensitivity, exposure periods and response endpoints.
4. Conclusions Evaluation on the toxicity data to green algae shows that toxicities can vary greatly if we ignore the difference between response variables (or toxicity endpoints), species sensitivity and exposure periods. Comparison of algal toxicity obtained from the closed test with the data obtained from the batch-type (or open) test within 72 h showed that the closed test had the same sensitivity as the open test for most of compounds, but significant differences were observed for a few compounds. There is no difference between response variables of growth rate and yield for non-polar narcotics, and slight differences for polar narcotics. The overall average difference for all compounds ranges from 0.15 to 0.43 log units between toxicity endpoints (yield–growth rate). The relationships between exposure periods of 24, 48, 72 and 96 h indicate that 48 h exposure period is the most sensitive for algal growth inhibition test; a slight difference was observed between 72 h and 96 h. The overall difference is about 0.25 log units between toxicities of 48 and 72 or 96 h exposure periods to P. subcapitata. However, no difference was observed for non-polar narcotics within 24, 48, 72 and 96 h endpoints. Interspecies relationship showed that some algae have very close species sensitivity (e.g. P. subcapitata and C. pyrenoidosa or C. vulgaris and S. obliquus, respectively). On the other hand, some algae have significantly different species sensitivity (e.g. P. subcapitata and S. obliquus). Caution should be taken when toxicity data from different algal species are used in the development of QSAR models. The information of calculation of response endpoints (or variables), exposure periods, and algal species is very valuable and should be reported in the published literature. We cannot expect significant QSAR models between algal toxicity and descriptors without considering species sensitivity, exposure periods and response endpoints. Acknowledgement
Fig. 2. Plots of toxicity against log KOW for non-polar narcotics (e.g. alkanes, alcohols, ethers, ketones, benzenes and their chlorides).
This work is supported by the National Natural Science Foundation of China (21377022 and 21107012).
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Appendix A. Supplementary material Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.chemosphere. 2014.05.040. References Altenburger, R., Walter, H., Grote, M., 2004. What contributes to the combined effect of a complex mixture? Environ. Sci. Technol. 38, 6353–6362. Aruoja, V., Sihtmäe, M., Dubourguier, H.-C., Kahru, A., 2011. Toxicity of 58 substituted anilines and phenols to algae Pseudokirchneriella subcapitata and bacteria Vibrio fischeri: comparison with published data and QSARs. Chemosphere 84, 1310–1320. Aruoja, V., Moosus, M., Kahru, A., Sihtmäe, M., Maran, U., 2014. Measurement of baseline toxicity and QSAR analysis of 50 non-polar and 58 polar narcotic chemicals for the alga Pseudokirchneriella subcapitata. Chemosphere 96, 23–32. Boyle, T.P., 1984. The effect of environmental contaminants on aquatic algae. In: Shubert, L.E. (Ed.), Algae as Ecological Indicators. Academic Press, New York, pp. 237–256. Chen, C.Y., Lin, J.H., 2006. Toxicity of chlorophenols to Pseudokirchneriella subcapitata under air-tight test environment. Chemosphere 62, 503–509. Chen, C.Y., Kuo, K.L., Fan, J.W., 2012. Toxicity of propargylic alcohols on green alga— Pseudokirchneriella subcapitata. J. Environ. Monit. 14, 181–186. Cronin, M.T.D., Dearden, J.C., 1995. QSAR in toxicology. 1. Prediction of aquatic toxicity. Quant. Struct. – Act. Relat. 14, 1–7. Cronin, M.T.D., Netzeva, T.I., Dearden, J.C., Edwards, R., Worgan, A.D.P., 2004. Assessment and modeling of the toxicity of organic chemicals to Chlorella vulgaris: development of a novel database. Chem. Res. Toxicol. 17, 545–554. Dearden, J.C., Cronin, M.T.D., Zhao, Y.H., Raevsky, O.A., 2000. QSAR studies of compounds acting by polar and non-polar narcosis: an examination of the role of polarisability and hydrogen bonding. Quant. Struct. – Act. Relat. 19, 3–9. EPA (Environmental Protection Agency), 2005. Environmental Effects Testing Guidelines, Subpart B – Aquatic Guidelines, 797.1050 Algal Acute Toxicity Test. Ertürk, M.D., Saçan, M.T., 2012. First toxicity data of chlorophenols on marine alga Dunaliella tertiolecta: correlation of marine algal toxicity with hydrophobicity and interspecies toxicity relationships. Environ. Toxicol. Chem. 31, 1113–1120. Ertürk, M.D., Saçan, M.T., 2013. Assessment and modeling of the novel toxicity data set of phenols to Chlorella vulgaris. Ecotoxicol. Environ. Saf. 90, 61–68. Ertürk, M.D., Saçan, M.T., Novic, M., Minovski, N., 2012. Quantitative structure– activity relationships (QSARs) using the novel marine algal toxicity data of phenols. J. Mol. Graph. Model. 38, 90–100. Faust, M., Altenburger, R., Backhaus, T., Blanck, H., Boedeker, W., Gramatica, P., Hamer, V., Scholze, M., Vighi, M., Grimme, L.H., 2001. Predicting the joint algal toxicity of multi-component s-triazine mixtures at low-effect concentrations of individual toxicants. Aquat. Toxicol. 56, 13–32. Furusjö, E., Svenson, A., Rahmberg, M., Andersson, M., 2006. The importance of outlier detection and training set selection for reliable environmental QSAR predictions. Chemosphere 63, 99–108.
Geis, S.W., Fleming, K.L., Korthals, E.T., Searle, G., Reynolds, L., Karner, D.A., 2000. Modifications to the algal growth inhibition test for use as a regulatory assay. Environ. Toxicol. Chem. 19, 36–41. Huang, C.P., Wang, Y.J., Chen, C.Y., 2007. Toxicity and quantitative structure–activity relationships of nitriles based on Pseudokirchneriella subcapitata. Ecotoxicol. Environ. Saf. 67, 439–446. ISO (International Organization for Standardization), 2002. Water quality-algal growth inhibition test (ISO 8692). Kahru, A., Dubourguier, H.C., 2010. From ecotoxicology to nanoecotoxicology. Toxicology 269, 105–119. Lee, P.Y., Chen, C.Y., 2009. Toxicity and quantitative structure–activity relationships of benzoic acids to Pseudokirchneriella subcapitata. J. Hazard. Mater. 165, 156– 161. Lu, G.H., Yuan, X., Zhao, Y.H., 2001. QSAR study on the toxicity of substituted benzenes to algae (Scenedesmus obliqnus). Chemosphere 44, 437–440. Ma, J., Zheng, R., Xu, L., Wang, S., 2002. Differential sensitivity of two green algae, Scenedesmus obliqnus and Chlorella pyrenoidosa, to 12 pesticides. Ecotoxicol. Environ. Saf. 52, 57–61. Ma, J., Wang, S., Wang, P., Ma, L., Chen, X., Xu, R., 2006. Toxicity assessment of 40 herbicides to the green alga Raphidocelis subcapitata. Ecotoxicol. Environ. Saf. 63, 456–462. Mayer, P., Nyholm, N., Verbruggen, E., Hermens, J., Tolls, J., 2000. Algal growth inhibition test in filled, closed bottles for volatile and sorptive materials. Environ. Toxicol. Chem. 19, 2551–2556. Netzeva, T.H., Pavan, M., Worth, A.P., 2008. Review of (quantitative) structure– activity relationships for acute aquatic toxicity. QSAR Comb. Sci. 27, 77–90. Neuwoehner, J., Junghans, M., Koller, M., Escher, B.I., 2008. QSAR analysis and specific endpoints for classifying the physiological modes of action of biocides in synchronous green algae. Aquat. Toxicol. 90, 8–18. OECD (Organization for Economic Co-operation and Development), 2002. Guideline for testing of chemicals. Proposal for updating guideline 201. Freshwater alga and cyanobacteria, Growth Inhibition Test. Paris, France. OECD, 2006. Guideline 201 for testing of chemicals. Alga, Growth Inhibition Test. Paris, France. OECD, 2011. Guideline 201 for testing of chemicals. Alga, Growth Inhibition Test. Paris, France. Schmitt, H., Altenburger, R., Jastorff, B., Schüürmann, G., 2000. Quantitative structure–activity analysis of the algae toxicity of nitroaromatic compounds. Chem. Res. Toxicol. 13, 441–450. Tebby, C., Mombelli, E., Pandard, P., Péry, A.R.R., 2011. Exploring an ecotoxicity database with the OECD (Q)SAR Toolbox and DRAGON descriptors in order to prioritise testing on algae, daphnids, and fish. Sci. Total Environ. 409, 3334– 3343. Tsai, K.P., Chen, C.Y., 2007. An algal toxicity database of organic toxicants derived by a closed-system technique. Environ. Toxicol. Chem. 26, 1931–1939. Verhaar, H.J.M., van Leeuwen, C.J., Hermens, J.L.M., 1992. Classifying environmental pollutants. 1: structure-activity relationships for prediction of aquatic toxicity. Chemosphere 25, 471–491. Yeh, H.J., Chen, C.Y., 2006. Toxicity assessment of pesticides to Pseudokirchneriella subcapitata under air-tight test environment. J. Hazard. Mater. A131, 6–12. Zhang, X.J., Qin, H.W., Su, L.M., Qin, W.C., Zou, M.Y., Sheng, L.X., Zhao, Y.H., Abraham, M.H., 2010. Interspecies correlations of toxicity to eight aquatic organisms: theoretical considerations. Sci. Total Environ. 408, 4549–4555.
