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Keywords:

  • Chlorophenols;
  • Marine algae;
  • Toxicity;
  • Interspecies toxicity relationships;
  • Hydrophobicity

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSION
  7. Acknowledgements
  8. REFERENCES

The toxicity of phenol and 13 chlorinated phenols to the marine alga Dunaliella tertiolecta is presented for the first time. The newly generated marine algal toxicity data was found to correlate strongly with the widely used hydrophobicity parameterthe logarithm of the n-octanol–water partition coefficient (log KOW). Interspecies relationships using the new marine algal toxicity data of chlorophenols with the previously published data on bacterium (Vibrio fischeri), protozoan (Tetrahymena pyriformis), daphnid (Daphnia magna), freshwater alga (Pseudokirchneriella subcapitata), and fish (Pimephales promelas) revealed promising results that could be exploited in extrapolations using freshwater data to predict marine algal toxicity. Environ. Toxicol. Chem. 2012; 31: 1113–1120. © 2012 SETAC


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSION
  7. Acknowledgements
  8. REFERENCES

Chlorophenols represent a group of commercially produced substituted phenols obtained by chlorinating phenol or hydrolyzing chlorobenzenes 1. They are used as intermediates in the synthesis of dyes, pigments, phenolic resins, pesticides, and herbicides. Some chlorophenols are also used as fungicides, mold inhibitors, disinfectants, and wood preservatives 2.

Chlorophenols have been detected in both terrestrial and aquatic food chains owing to their ubiquitous use for domestic, agricultural, and industrial purposes 3. As such, the toxicological effects of these important contaminants on different organisms have been of major environmental and scientific interest 4, 5.

Among the organisms used in ecotoxicity testing, 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 6. Therefore, studying the toxic effects of chemicals on algae provides valuable information regarding the risks that new or existing chemicals might pose to aquatic ecosystems.

The freshwater environment has been considered the part of the hydrosphere most vulnerable to chemical substances. Consequently, regulatory schemes have focused primarily on the protection of freshwater communities 7. As a result, a substantial amount of information is available on the toxicity of chemicals to freshwater organisms, but there are relatively fewer data on the effect of chemicals on marine organisms 8, 9, in particular on aquatic plants and algae 10, 11. Once chemicals are released into the environment they can cause biological effects on nontarget organisms in marine environments in countries such as Turkey, which is surrounded by sea on three sides. Therefore, determination of the toxicity of chemicals to nontarget species such as marine algae will be beneficial to understand the impact of chemicals on marine ecosystems 12. Ideally, to assess the potential impact of chemicals entering marine ecosystems, any hazard or risk assessment should be based on data generated using a range of ecologically relevant marine species, such as algae, invertebrates, and fish 10. For this reason, more toxicity data are needed on marine organisms, as recommended by expert groups 11 and regulatory schemes 13.

The generation of new marine toxicity data is not only required to increase the knowledge base in this field of ecotoxicity, but may also serve to test the extrapolation possibilities using freshwater data, or data retrieved from other test systems, to predict marine toxicity. In fact, interspecies extrapolation possibilities using toxicity data have been investigated using different approaches, such as quantitative structure–activity relationships 14–16, species sensitivity distributions 9, and interspecies correlation estimation 17. These interspecies toxicity relationships have the potential to predict the toxicity of compounds to other species 18; however, not much attention has been directed to marine algae.

In the present study, we experimentally determined the toxicities of 14 environmentally significant phenolic compounds to the marine alga Dunaliella tertiolecta in a static algal growth inhibition assay. The inhibitory concentration of the test substance that decreases the growth by 50% (IC50) at the end of 48, 72, and 96 h was calculated and reported together with the IC20, no-observed-effect concentration, and lowest-observed-effect concentration of the chemical. We investigated the relationship between the newly generated marine algal toxicity data of chlorophenols and their corresponding hydrophobicity values, expressed by the logarithm of the n-octanol–water partition coefficient (log Kow). Additionally, we developed several interspecies relationships using the marine algal toxicity data and the literature toxicity data on chlorophenols to organisms from different trophic levels, including a bacterium (Vibrio fischeri), a protozoan (Tetrahymena pyriformis), a daphnid (Daphnia magna), a freshwater alga (Pseudokirchneriella subcapitata), and a fish (Pimephales promelas).

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSION
  7. Acknowledgements
  8. REFERENCES

Chemicals and chemical analysis

All chlorophenols used in the present study were purchased from Sigma-Aldrich. Phenol was obtained from Merck. The majority of the chemicals had purity ≥98%. No further purification was undertaken.

Stock solutions were prepared below water solubility limits of each compound using artificial seawater, which was prepared according to standard methods 19. The stock solutions of tetrachlorophenols and pentachlorophenol were prepared in dimethyl sulfoxide. For the tests using these compounds, an additional solvent control containing the maximal dimethyl sulfoxide concentration (0.1% v/v) was employed. Statistical analysis using t tests revealed no significant difference (p > 0.05) between the growth in controls with and without dimethyl sulfoxide. The inhibitory concentration of these chemicals was calculated taking the growth in solvent controls into account.

The nominal concentration of each chemical was measured by gas chromatography (Agilent, 6890N equipped with an automatic sampler, split/splitless injection port, and flame ionization detector) at the beginning of the experiments. The maximal test concentration of each chemical was analyzed in a separate test vessel without algae at the beginning and at the end of the experiments to check whether there was a significant chemical loss from volatilization, adsorption on the test vessel, and so forth, during the experiment.

A 1-ml sample was extracted in 0.5 ml methylene chloride in 2-ml vials capped with Teflon-lined septum caps. The injector and detector temperatures (250 and 300°C, respectively) were held constant during the analysis. The HP-5MS capillary column used for separation was 30 m long and had a 0.25 mm inner diameter and a 0.25 µm film thickness. The gas chromatography oven was programmed for an initial temperature of 35°C for 5 min and then increased to 220°C at 8°C/min. Helium was used as the carrier gas at a constant flow rate of 33.3 cm/s, and the injector was operated in splitless mode.

Algal growth inhibition assay

Algal growth inhibition tests were performed in batch cultures according to standard procedures 19, 20 using the marine alga D. tertiolecta, which is a recommended ecotoxicity test species 19, 21. The inoculum was prepared with algae harvested from 4- to 5-d-old cultures in the exponential growth phase. Each milliliter of inoculum contained approximately 104 cells. Experiments were carried out in a temperature-controlled growth chamber (18 ± 0.5°C). Continuous illumination (30 µmol photons m−2s−1 at the level of test solutions) 19 was provided from a rack of cool-white fluorescent tubes, horizontally arranged above a light-reflecting platform on which the test vessels were located.

After range finding assays, definitive experiments were carried out in three replicates using five concentrations of the test chemical. A total of 100 ml of test medium with algae was dispensed into sterile 500-ml borosilicate Erlenmayer flasks. Autoclaved magnetic stirrers were added to the test vessels to prevent algal attachment to the glass surface, and each vessel was stirred gently prior to sampling.

Bioassays were carried out in filtered (GF/C glass microfiber Whatman filters) seawater and enriched with modified f/2 medium 22. Seawater was taken from the Sea of Marmara, near the coast of Samatya in Istanbul and stored in a freezer at –24°C in a plastic container after filtration. Natural seawater characterization (Table 1) was made based on the standard procedures 19. Additionally, the concentration of environmentally significant heavy metals (Table 1) in the seawater was measured using inductive coupled plasma (Perkin Elmer Optima 2100 DV).

Table 1. Natural seawater characterization
ParameterMeasurementaMetalConcentration (µg/L)
  • a

    Mean value ± standard deviation.

    BDL = below detection limit.

pH8.3 ± 0.1Al46 ± 12
Salinity23 ± 3‰PbBDL
Conductivity30 ± 2 ms/cmZn40 ± 18
Chloride11.5 ± 0.9 g/LCdBDL
Alkalinity0.15 ± 0.05 g/L CaCO3Cu6 ± 2
Nitrate0.44 ± 0.1 mg/LCr2 ± 1
Phosphate0.71 ± 0.2 mg/LNi4 ± 3
Sulfate1.2 ± 0.06 g/LCoBDL

The Organisation for Economic Co-operation and Development (OECD) 20 recommends that at the end of 72 h the algal population in the controls should increase by at least a factor of 16, which corresponds to a specific growth rate of 0.92 d−1, the increase in pH between the beginning and end of the test should not exceed 1.5 units, and the coefficient of variation should be ≤10% among the controls. The validity of the tests was assessed based on these criteria.

In the present study, 3,5-dichlorophenol was used as a reference toxicant as recommended in the OECD guidelines 20. The toxicity measurement of 3,5-dichlorophenol was repeated twice, once at the beginning of the study and once 6 months later, to compare the response of algal cells.

Measurement of algal growth

The growth response of D. tertiolecta exposed to each of the studied chemicals was determined by daily measurements of optical density at 680 nm (OD680) with a spectrophotometer (Schimadzu, UV-1208) over 96 h. The scanning of various wavelengths in the spectrophotometer showed that 680 nm corresponded to the maximal chlorophyll a absorption for D. tertiolecta, as was also reported by Janssen et al 23.

The response variables, the yield, and the average specific growth rate, were calculated as recommended in the OECD guidelines 20. We verified that there is a linear relationship between algal cell counts and optical density (r2 = 0.97). Therefore, optical density was used as a surrogate measure for the calculation of response variables for D. tertiolecta to express biomass increase during the test.

The growth response of algae in the presence of phenol and seven chlorophenols (chlorophenols and dichlorophenols) was also determined by daily measurements of in vivo chlorophyll fluorescence intensity based on the visible light emitted by chlorophyll a after excitation at 430 nm and emission at 663 nm with a spectrofluorimeter (Perkin-Elmer LS 55). It was found that IC50 values obtained either by spectrophotometry or by spectrofluorimetry did not differ significantly (p > 0.05), and their 95% confidence intervals overlapped (results not shown). Therefore, only the IC50 and IC20 values determined using OD680 values were reported.

Calculation of inhibitory concentrations

To obtain a concentration–response relationship, nonlinear regression was used because this procedure handles data irregularities better than other techniques 20. Percentage of inhibition relative to the controls against the test substance concentration was fitted using polynomial regression to calculate the IC50 and IC20 with associated confidence intervals. Calculations for all compounds were carried out using the growth inhibition data obtained from three replicates of one bioassay (n = 1). Curve fitting was carried out in SPSS statistical analysis software (SPSS 10.1 statistical package), and the roots of the polynomials were calculated in Scientific Workplace software 3.0 (MacKichan Software). The no-observed-effect concentration and lowest-observed-effect concentration for each compound were calculated using Dunnett's test in ToxCalc 5.0.32 (Tidepool Scientific Software).

Correlation studies

The interspecies toxicity relationships as well as the correlation between hydrophobicity and toxicity were investigated by using the linear regression analysis. Decimal logarithm of the reciprocal IC50 values in mmol/L (log[IC50]−1), denoted as pT, was used in the regression analysis.

The literature toxicity data were retrieved from the following references: 30-min toxicity data on V. fischeri (pTVf) from Warne et al. 24; 40-h toxicity data on T. pyriformis (pTTp) from Enoch et al. 5; 24-h toxicity data on D. magna (pTDm) from Briens et al. 25; 96-h toxicity data of P. subcapitata (pTPs) from Rijksinstituut Voor Volksgezondheid En Milieu–National Institute of Public Health and the Environment (RIVM) 26; and 96-h toxicity data on P. promelas (pTPp) from Papa et al. 27. Log KOW data of chlorophenols were extracted from the expert database of EPISuite software 28.

The relationships were evaluated by the number of compounds put into analysis (n), square of correlation coefficient (r2), standard error of the estimate (se), and Fischer statistics (F). For the interspecies toxicity relationships, an observation was classified as an outlier if the value of its standardized residual was >3, to be able to cover the differences in test design, the distinct characteristics of media used (e.g., salinity, pH), differences in species sensitivity, and interlaboratory variances. As for the relationship between hydrophobicity and toxicity, an observation was classified as an outlier if the value of its standardized residual was >2. All statistical analyses were carried out in SPSS.

RESULTS AND DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSION
  7. Acknowledgements
  8. REFERENCES

Toxicity of chlorophenols to D. tertiolecta

All bioassays concurred with the validity criteria provided in the Materials and Methods section. At the end of 72 h, the mean specific growth rate of algae was found to be 1.07 (± 0.11) d−1, which is higher than the minimum specific growth rate (0.92 d−1) recommended by the OECD for this exposure duration 20. For all test durations, the coefficient of variation of controls was ≤10% throughout the tests. The starting pH of the bioassays was 8.4 (± 0.1). The pH values recorded in the controls at the end of 48, 72, and 96 h were 8.9 (± 0.1), 9.6 (± 0.1), and 9.9 (± 0.1), respectively.

The gas chromatography analysis revealed that the tested concentration (100 mg/L) of 2-chlorophenol was decreased by 5% at the end of 96 h. As stated by Riedl and Altenburger 29, volatile substances are characterized by a Henry's constant of H ≥ 1 Pa m3/mol. Based on this criterion, among the chemicals studied, only 2-chlorophenol can be classified as a volatile substance, with a Henry's constant of 1.13 Pa m3/mol. The Henry's constant values of the chlorophenols were retrieved from EPISuite software 28. It seems that the decrease in the concentration of 2-chlorophenol during the test is because of the volatility of this compound. It should be noted that because none of the test concentrations changed by more than 20% the nominal concentrations were used to calculate the toxic potency of each compound as recommended by the OECD 20. The experimental IC50 and IC20 values and the associated confidence intervals for 48-, 72-, and 96-h exposures based on yield and average specific growth rate are presented in Table 2, together with no-observed-effect concentration and lowest-observed-effect concentration for each test chemical.

Table 2. Compounds tested, 50 and 20% inhibitory concentration values calculated at the end of 48, 72, and 96 h, no-observed-effect concentration and lowest-observed-effect concentration, and toxic class for both response variables
CompoundReponse variableToxica class48 h72 h96 hNOEC/LOEC (mg/L)c
IC50 (mg/l)bIC20 (mg/L)IC50 (mg/L)IC20 (mg/L)IC50 (mg/L)IC20 (mg/L)
  • a

    Toxic class based on 72-h growth inhibition.

  • b

    For all compounds, the inhibitory concentrations were obtained from three replicates of one bioassay (n = 1), except 3,5–dichlorophenol, where the average inhibitory concentration of two separate bioassays (n = 2) was reported.

  • c

    NOEC and LOEC values based on 72-h growth inhibition.

  • d

    95% confidence intervals.

  • e

    For all tests, the number of control replicates was three, except 2,3–dichlorophenol, where one control replicate was removed from analysis because of exceptionally low algal growth.

    NOEC = no-observed-effect concentration; LOEC = lowest-observed-effect concentration; IC50 = 50% inhibitory concentration; IC20 = 20% inhibitory concentration; SGR = average specific growth rate; P = phenol; CP = chlorophenol; DCP = dichlorophenol; TCP = trichlorophenol; TeCP = tetrachlorophenol; PCP = pentachlorophenol.

PYieldNot classified165.4 (148.4–182.2)d83.1 (67.6–103.7)180.0 (161.2–197.4)92.1 (67.7–119.3)187.2 (161.2–212.0)102.5 (67.3–142.1)60/120
 SGRNot classified203.7 (187.2–220.1)153.4 (133.4–179.7)217.6 (210.0–224.0)183.6 (176.4–188.8)244.2 (236.8–250.6)193.7 (177.4–206.6)120/180
2CPYieldHarmful46.5 (39.5–54.1)17.0 (13.3–22.6)58.6 (51.8–64.2)29.0 (22.2–37.3)62.4 (60.1–64.7)32.1 (27.8–36.6)<20/20
 SGRHarmful71.8 (67.2–76.0)42.9 (35.4–50.3)79.5 (75.4–79.7)57.0 (51.2–59.5)78.2 (77.0–79.3)57.6 (56.6–58.6)20/40
3-CPYieldHarmful15.9 (14.2–19.1)5.1 (4.1–6.2)30.5 (25.0–37.7)13.3 (10.0–18.5)41.3 (31.9–49.9)19.6 (12.4–30.0)10/20
 SGRHarmful26.5 (23.5–30.2)11.0 (8.9–13.7)48.0 (42.5–53.4)25.4 (20.2–32.2)54.4 (49.0–61.0)33.7 (26.7–43.8)10/20
4-CPYieldHarmful20.1 (17.8–22.5)6.4 (5.2–7.8)27.4 (23.8–32.2)11.6 (9.2–14.9)30.4 (28.0–33.0)12.4 (10.7–14.3)10/20
 SGRHarmful44.6 (38.0–54.9)17.1 (14.0–22.1)54.4 (51.8–57.1)29.6 (24.9–34.6)57.0 (56.3–59.3)34.5 (32.7–37.1)10/20
2,3-DCPeYieldHarmful17.2 (14.4–19.8)8.4 (5.9–12.2)19.8 (19.2–20.4)11.4 (10.4–12.4)21.5 (21.1–21.8)14.1 (13.3–14.8)<10/10
 SGRHarmful22.7 (22.0–23.4)16.1 (14.5–17.8)24.7 (24.3–25.1)19.3 (18.3–20.3)25.5 (24.9–26.1)20.8 (19.5–22.1)10/15
2,4-DCPYieldHarmful9.7 (8.4–11.3)4.1 (3.4–5.2)10.2 (9.3–11.1)4.5 (4.0–5.1)11.7 (10.8–12.7)5.5 (4.9–6.3)2.5/5
 SGRHarmful14.8 (13.5–15.9)8.0 (6.7–9.5)15.7 (15.3–16.1)9.2 (8.7–9.9)16.7 (16.4–16.9)10.7 (10.2–11.2)5/10
2,6-DCPYieldHarmful60.9 (54.0–68.6)24.5 (19.9–30.5)82.0 (72.9–89.1)39.2 (30.0–50.1)91.7 (85.6–97.4)53.0 (42.6–63.5)20/40
 SGRNot classified88.6 (78.4–101.7)48.7 (32.2–55.5)110.8 (97.7–128.7)73.4 (65.3–87.9)126.9 (110.9–141.3)86.1 (73.0–105.0)40/60
3,5-DCPYieldToxic3.2 (2.7–4.0)1.2 (1.0–1.6)5.6 (4.7–6.5)2.3 (1.6–3.4)6.7 (5.9–7.3)3.7 (2.6–4.9)<2/2
 SGRToxic5.6 (4.6–6.7)2.6 (1.9–3.7)8.0 (7.7–8.3)5.8 (5.3–6.3)8.3 (8.1–8.4)6.5 (6.1–6.8)2/4
2,3,4-TCPYieldToxic2.6 (2.3–3.1)1.0 (0.8–1.2)4.1 (3.5–4.6)1.5 (1.1–2.1)4.4 (3.7–5.1)1.7 (0.9–2.7)1/2
 SGRToxic5.2 (4.8–5.5)2.6 (2.1–3.2)6.2 (6.0–6.3)4.3 (4.0–4.6)6.3 (6.2–6.4)4.6 (4.3–4.9)1/2
2,4,6-TCPYieldHarmful35.3 (32.3–37.9)21.1 (15.3–26.6)40.8 (39.5–41.9)31.3 (27.9–34.0)42.0 (41.2–42.8)32.9 (30.8–34.7)20/30
 SGRHarmful41.5 (40.5–42.1)32.0 (30.0–33.5)43.2 (42.9–43.7)35.4 (34.4–36.3)43.6 (43.3–43.9)35.9 (35.3–36.5)20/30
2,4,5-TCPYieldToxic2.0 (1.7–2.3)0.8 (0.7–1.0)2.5 (2.2–2.9)0.9 (0.8–1.1)3.7 (3.1–4.4)1.6 (1.2–2.2)<1/1
 SGRToxic3.1 (2.7–3.4)1.4 (1.2–1.6)5.2 (4.9–5.5)2.7 (2.3–3.1)6.1 (5.9–6.3)4.2 (3.7–4.6)1/2
2,3,4,5-TeCPYieldVery toxic0.6 (0.5–0.7)0.2 (0.1–0.3)0.8 (0.7–0.9)0.2 (0.1–0.3)0.9 (0.8–1.0)0.3 (0.2–0.4)0.1/0.5
 SGRToxic0.94 (0.86–1.03)0.40 (0.35–0.47)1.39 (1.28–1.50)0.65 (0.54–0.78)1.60 (1.55–1.65)0.92 (0.84–1.01)0.1/0.5
2,3,5,6-TeCPYieldToxic1.1 (1.0–1.3)0.4 (0.3–0.5)1.8 (1.4–2.2)0.4 (0.2–0.7)2.9 (2.2–3.4)1.0 (0.6–1.7)0.1/0.5
 SGRToxic2.6 (2.4–2.8)1.6 (1.3–1.8)4.9 (4.3–5.4)2.8 (1.8–3.4)6.0 (5.8–6.1)4.2 (3.9–4.6)1/2
PCPYieldVery toxic0.25 (0.21–0.29)0.13 (0.09–0.17)0.29 (0.27–0.31)0.14 (0.11–0.18)0.33 (0.32–0.35)0.19 (0.16–0.22)0.1/0.2
 SGRVery toxic0.36 (0.34–0.37)0.23 (0.20–0.26)0.39 (0.38–0.40)0.28 (0.27–0.29)0.41 (0.40–0.42)0.31 (0.30–0.32)0.1/0.2

Dunaliella tertiolecta revealed dose-dependent responses to chemicals tested in this study. The response of algae to phenol is provided as an example in Figure 1. Based on average specific growth rate, which is the scientifically preferred response variable 20, the IC50 and associated confidence intervals for 48 and 96 h did not overlap, which might suggest that the toxicity of phenol and chlorophenols to D. tertiolecta tended to decrease between these durations (Table 2). This could be attributed to the increase in pH of the test medium because of the fixation of CO2 during photosynthesis. This, in turn, affects the uptake, bioconcentration, and toxicity of phenolic compounds 30. Weak acids such as chlorophenols tend to ionize at a pH greater than their acid dissociation constants (pKa), and the degree of ionization is enhanced as the (pH – pKa) difference increases. The decrease in toxicity of weak acids has been attributed to the fact that the un-ionized form of the molecule contributes to the toxicity more than the ionized form, because the neutral molecule is more bioavailable than the corresponding charged molecule 16, 31, 32. For example, Escher and Hermens 33 pointed out that the toxicity of 2,4,5-trichlorophenol to Scenedesmus vacuolatus decreased by tenfold as the pH of the test medium increased from 6.4 to 8.1. Fahl et al. 32 demonstrated that the toxicity of sulfonylurea herbicides, which are weak acids like chlorophenols, was lower at pH 6 than at pH 5 to the freshwater alga Chlorella fusca. In another study, Lee and Chen 34 determined the toxicity of benzoic acids to P. subcapitata and also found that a pH increase led to a reduction in the toxicity of these chemicals. It is, therefore, likely that the pH increase caused by algal growth rendered the chlorophenols less toxic to D. tertiolecta as exposure duration increased from 48 to 96 h. The influence of pH on toxicity would be minimal at the end of 48-h exposure, so the toxicity estimates for this exposure duration can be used to denote the toxicity of chlorophenols toward D. tertiolecta, if the influence of pH on toxicity should be kept to a minimum.

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Figure 1. Response of Dunaliella tertiolecta to phenol. Bars represent experimental errors.

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Another factor that can be related to the decrease in toxicity of chlorophenols with increasing endpoint duration might be the adaptation or acclimation of algae to the test compounds. Throughout the tests, at higher test concentrations, a lag period was observed before the algal cells resumed growth. Similar observations were also reported by Olivier et al. 35, who stated that the Chlorella VT-1 adapted to chlorophenol compounds and that, after a lag period, the cultures began to grow rapidly. The authors suggested that the lag phase indicates some form of detoxification that is required before the algae can resume growth. Scragg et al. 36 suggested, in the same context, that resistant cells survive and initiate the later growth. Another observation was highlighted by Scragg 37, that naphthalene inhibited the growth of approximately 98% of Chlamydomonas angulosa algal cells on addition, but after 3 d the growth was restored and matched the controls. We also observed later growth of D. tertiolecta cells after a lag phase in the presence of relatively high concentrations of each test chemical. In conclusion, together with the influence of pH on toxicity as discussed above, algal adaptation to chlorophenols might have a role in rendering these compounds less toxic at the end of a 96-h exposure compared with a 48-h exposure. Based on the IC50 values, the least toxic compound was found to be phenol, whereas the most toxic compound was pentachlorophenol regardless of exposure duration or response variable (Table 2). The toxicity of the chemicals based on IC20 values also followed the same toxicity pattern to D. tertiolecta. As expected, the IC50 values based on average specific growth rates were found to be higher than those based on yield because of the mathematical basis of the two approaches 20.

The European Union Technical Guidance Document (EU TGD) 13 classifies the toxicity of chemicals to aquatic organisms according to the EC50 values (effective concentration that reduces the measured endpoint by 50% and the endpoint encompasses lethality, immobilization, growth inhibition, etc.). Within this scheme, the compounds with EC50 values <1 mg/L are classified as very toxic. Compounds with EC50 values between 1 and 10 mg/L are categorized as toxic, and those with EC50 values between 10 and 100 mg/L are classified as harmful. Substances with EC50 values above 100 mg/L are not classified. The toxic class of each chemical based on the toxicity at the end of a 72-h exposure is provided in Table 2. The IC50 values calculated by the yield method revealed that six chemicals were classified as harmful, four chemicals as toxic, and two chemicals as very toxic, and one chemical (phenol) was not classified according to this toxicity scheme because it had an EC50 value higher than 100 mg/L. When the IC50 values calculated by the average growth rate method were used for classification, the toxic class of 2,6-dichlorophenol and 2,3,4,5-tetrachlorophenol was reduced from harmful to not classified and from very toxic to toxic, respectively. No matter which response variable was selected for toxicity classification, 3,5-dichlorophenol and chlorophenols having at least three chlorine atoms (with the exception of 2,4,6-trichlorophenol) were found to be either toxic or very toxic to D. tertiolecta. Although environmental factors (e.g., pH, suspended matter, temperature, and presence of other chemicals) may enhance or decrease the acute or chronic toxicity of these chemicals, their release to the environment may severely disrupt algal populations. Moreover, if algal growth is affected, the biomass at higher tropic levels can be impacted as well 12. On the other hand, although the typical chlorophenol concentrations reported in the aquatic environment are not higher 1 than the no-observed-effect concentration or IC20 values reported in the present study, long-term effects of continuous low-level exposure to chemicals might also have unexpected consequences on the ecosystem 12.

Regardless of the exposure duration, the results revealed that the toxicity of chlorophenols generally increased with an increasing number of chlorine atoms on the aromatic ring, which is consistent with previous reports 2, 4. Additionally, the -ortho-substituted chlorophenols were less toxic than the -meta- and -para-substituted congeners. As stated by Boyd et al. 4, this has been ascribed to the shielding of the OH group by -ortho-substituted chlorine atom(s). Among mono-chlorophenols, 2-chlorophenol was found to be less toxic than 3-chlorophenol or 4-chlorophenol. Among the dichloro-substituted phenols, 2,6-dichlorophenol was the least toxic compound. The simultaneous occurrence of chlorine atoms substituted at the 2 and 6 positions is known to result in a lower toxicity of these congeners 1, 25, 31. Consistent with the -ortho effect mentioned above, 2,4,6-trichlorophenol was found to be less toxic than 2,3,4-trichlorophenol or 2,4,5-trichlorophenol, and 2,3,5,6-tetrachlorophenol was less toxic than 2,3,4,5-tetrachlorophenol.

The toxicity of 3,5-dichlorophenol, the reference toxicant, was determined twice during the study, and it was found that the 95% confidence intervals overlapped for the two IC50 values. This indicates that the algal response did not change significantly during the course of the study. Additionally, the 72-h IC50 of 3,5-dichlorophenol determined in the present study (5.6 ± 0.9 mg/L based on yield and 8.0 ± 0.3 mg/L based on average specific growth rate) was comparable to the toxicity of this compound (6.4 ± 2.4 mg/L) to the freshwater alga Desmodesmus subspicatus determined in a 72-h algal growth inhibition ring test with 18 laboratories participating 38.

Correlation of marine algal toxicity with hydrophobicity

From a regulatory perspective, the EU TGD 13 proposed several equations employing the hydrophobicity parameter, the logarithm of the n-octanol–water partition coefficient (log KOW), for the use of these relationships in aquatic risk assessment. Although there is a benchmark equation to be used in risk assessment for freshwater algae (reported toxicity to P. subcapitata) regarding nonpolar narcotics, no equation is available for polar narcotics. As for marine algae, no equation has been developed for either nonpolar or polar narcotics. It was underscored in the technical guidance document 13 that only the experimental data that were generated according to OECD test guidelines or comparable methods were used in the reported models. The toxicological assays in this study were conducted according to OECD guidelines 20 and standard methods 19, so the experimental data generated in the present study would meet the quality criteria set forth in the document. Therefore, it was of interest to investigate whether a statistically sound relationship between log KOW and marine algal toxicity data of chlorophenols could be developed.

As stated by Qin et al. 15, previous studies have shown that toxicities of both nonpolar and polar narcotics usually correlate well with log KOW. Chlorophenols are mainly polar narcotic chemicals 5 and the toxicity of these compounds to marine algae is likely to correlate well with log KOW. To test this, we carried out a linear regression analysis between the marine algal toxicity data and log KOW values of the chlorophenols. The log KOW values ranged from 1.46 for phenol to 5.12 for pentachlorophenol. The linear regression analysis revealed that, for all compounds in the analysis (n = 14), there is indeed a strong correlation between hydrophobicity and marine algal toxicity determined at the end of 48, 72, and 96 h (r2 = 0.84, r2 = 0.85, r2 = 0.87, respectively). However, the toxicity of 2,4,6-trichlorophenol was overestimated, and the standardized residuals of 2,4,6-trichlorophenol for 48-, 72-, and 96-h models were 2.75, 2.71, and 2.70, respectively. This classified 2,4,6-trichlorophenol as an outlier. Removal of 2,4,6-trichlorophenol from the analysis significantly increased the correlation between the hydrophobicity and marine algal toxicity (Table 3).

Table 3. Correlation of hydrophobicity with marine algal toxicity of chlorophenols
Exposure duration (h)Equation No.Relationshipanr2seF
  • a

    Equations were obtained after the removal of 2,4,6-trichlorophenol from analysis. KOW= n-octanol–water partition coefficient.

Models using toxicity data based on yield
 481pT = 0.95 (± 0.07) log KOW – 1.63 (± 0.22)130.950.22205.01
 722pT = 0.94 (± 0.06) log KOW – 1.73 (± 0.22)130.950.22208.42
 963pT = 0.92 (± 0.06) log KOW – 1.75 (± 0.20)130.950.21225.54
Models using toxicity data based on average specific growth rate
 481'pT = 0.93 (± 0.06) log KOW – 1.79 (± 0.20)130.960.21235.27
 722'pT = 0.91 (± 0.06) log KOW – 1.83 (± 0.18)130.960.18277.20
 963'pT = 0.90 (± 0.06) log KOW – 1.85 (± 0.18)130.960.19262.60

As discussed in the present study, chlorine atoms located at the 2 and 6 positions relative to the –OH group probably led to the lower toxicity of this compound compared with the congeners with equal numbers of chlorine atoms attached to the aromatic ring. Consistent with this explanation, the toxicity of 2,6-dichlorophenol was overestimated by the models (Eqn. 1–3 in Table 3), but the standardized residuals of this compound were lower than the cutoff value to identify 2,6-dichlorophenol as an outlier (standardized residuals of 2,6-dichlorophenol in 48-, 72-, and 96-h models were 1.25, 1.31, and 1.30, respectively). Interestingly, the toxicities of two other compounds used in the present study that have chlorine atoms substituted simultaneously at the 2 and 6 positions (2,4,5,6-tetrachlorophenol and pentachlorophenol) had standardized residuals lower than one (results not shown), indicating that the models (Eqn. 1–3 in Table 3) predicted the toxicities of these compounds well. Czaplicka 1 stated that the toxicity of chlorophenols increases if chlorine atoms are substituted at the 3, 4, and 5 positions. Based on this information, it is possible that the presence of chlorine atoms at 4 and 5 positions in 2,4,5,6-tetrachlorophenol and at 3, 4, and 5 positions in pentachlorophenol counteracts the lowering effect of 2 and 6 positions in the toxicity of these molecules toward D. tertiolecta. Additionally, as stated by Schüürman et al. 39, the high lipophilicity of pentachlorophenol (log KOW of 5.12) leads to high narcotic-type membrane perturbations in the organisms, and this might be the reason why pentachlorophenol is most toxic to D. tertiolecta.

In conclusion, the simple equations describing the relationship between hydrophobicity and marine algal toxicity (Eqn. 1–3 and Eqn. 1'–3' in Table 3) reported in the present study have the potential to be used as a tool to carry out the preliminary risk assessments of polar narcotics to marine algae. These equations that employ log KOW directly describe the mechanistic understanding of the toxicity, as do the proposed models in EU TGD 13. It should be noted that these equations are valid in the log KOW range of 1.46 to 5.12. It should also be noted that the proposed equations were not presented as quantitative structure–activity relationship models because they were not validated as recommended by OECD 40. This is due mainly to the limited number of data, which did not allow a reasonable division of the data set into training and test sets. Therefore, we strongly recommend that more chemicals be tested using the marine alga D. tertiolecta to have a large data set and to obtain externally validated quantitative structure–activity relationship models.

Interspecies toxicity relationships

In the present study, we developed several interspecies relationships (Table 4) using the toxicity data of chlorophenols to D. tertiolecta, V. fischeri, T. pyriformis, D. magna, P. subcapitata, and P. promelas. Among the interspecies toxicity relationships developed in the present study, the one between D. tertiolecta and V. fischeri is the only relationship constructed between two species representative of marine environments (Eqn. 4–6 and Eqn. 4'–6' in Table 4), whereas the other organisms represent freshwater ecosystems. The slopes and intercepts of each relationship were significant at p ≤ 0.05. The linear regression line of the relationships reported in Equations 7 to 9 (and Eqn. 7'–9') and in Equations 13 to 15 (and Eqn. 13'–15') passed through the origin; therefore, no intercept was reported for these equations. In all relationships, there was no outlier.

Table 4. Statistical summary of the interspecies toxicity relationshipsa
Test duration (present study/literature study)Equation no.Relationshipa,bnr2seF
  • a

    Coefficients in parenthesis indicate the standard error of the estimate.

  • b

    The literature toxicity data were retrieved from the following references: 30-min toxicity data on V. fischeri (pTVf) from Warne et al. 24; 40-h toxicity data on T. pyriformis (pTTp) from Enoch et al. 5; 24-h toxicity data on D. magna (pTDm) from Briens et al. 25; 96-h toxicity data of P. subcapitata (pTPs) from RIVM 26; and 96-h toxicity data on P. promelas (pTPp) from Papa et al. 27.

Models using toxicity data based on yield
  Vibrio fischeri    
48 h/30 min4pT = 1.13 (± 0.11) pTVf – 0.49 (± 0.20)140.890.3298.72
72 h/30 min5pT = 1.12 (± 0.10) pTVf – 0.61 (± 0.19)140.900.29114.26
96 h/30 min6pT = 1.11 (± 0.10) pTVf – 0.66 (± 0.18)140.910.28121.72
  Tetrahymena pyriformis    
48 h/40 h7pT = 1.02 (± 0.14) pTTp130.840.4056.00
72 h/40 h8pT = 1.00 (± 0.14) pTTp130.830.4152.19
96 h/40 h9pT = 0.98 (± 0.14) pTTp130.810.4246.95
  Daphnia magna    
48 h/24 h10pT = 1.55 (± 0.21) pTDm – 1.10 (± 0.36)130.830.3655.65
72 h/24 h11pT = 1.57 (± 0.18) pTDm – 1.27 (± 0.32)130.870.3272.64
96 h/24 h12pT = 1.56 (± 0.18) pTDm – 1.33 (± 0.31)130.870.3175.56
  Pseudokirchneriella subcapitata    
48 h/96 h13pT = 0.88 (± 0.14) pTPs110.810.4137.29
72 h/96 h14pT = 0.88 (± 0.14) pTPs110.810.3939.39
96 h/96 h15pT = 0.88 (± 0.14) pTPs110.810.3938.78
  Pimephelas promelas    
48 h/96 h16pT = 1.23 (± 0.12) pTPp – 4.46 (± 0.55)70.960.26111.38
72 h/96 h17pT = 1.21 (± 0.12) pTPp – 4.50 (± 0.58)70.950.2896.78
96 h/96 h18pT = 1.21 (± 0.12) pTPp – 4.52 (± 0.59)70.950.2894.73
Models using toxicity data based on average specific growth rate
  Vibrio fischeri    
48 h/30 min4'pT = 1.12 (± 0.11) pTVf – 0.66 (± 0.19)140.900.30107.81
72 h/30 min5'pT = 1.08 (± 0.10) pTVf – 0.71 (± 0.18)140.900.29110.38
96 h/30 min6'pT = 1.07 (± 0.10) pTVf – 0.74 (± 0.18)140.900.29107.62
  Tetrahymena pyriformis    
48 h/40 h7'pT = 1.01 (± 0.13) pTTp130.850.3862.32
72 h/40 h8'pT = 0.96 (± 0.14) pTTp130.820.4049.56
96 h/40 h9'pT = 0.94 (± 0.14) pTTp130.800.4244.24
  Daphnia magna    
48 h/24 h10'pT = 1.55 (± 0.19) pTDm – 1.31 (± 0.33)130.850.3364.89
72 h/24 h11'pT = 1.54 (± 0.16) pTDm – 1.41 (± 0.28)130.890.2887.62
96 h/24 h12'pT = 1.52 (± 0.16) pTDm – 1.41 (± 0.28)130.890.2887.34
  Pseudokirchneriella subcapitata    
48 h/96 h13'pT = 0.89 (± 0.13) pTPs110.840.3647.85
72 h/96 h14'pT = 0.89 (± 0.12) pTPs110.850.3551.32
96 h/96 h15'pT = 0.90 (± 0.12) pTPs110.850.3553.14
  Pimephelas promelas    
48 h/96 h16'pT = 1.22 (± 0.11) pTPp – 4.65 (± 0.51)70.960.25128.35
72 h/96 h17'pT = 1.19 (± 0.11) pTPp – 4.56 (± 0.54)70.960.26108.71
96 h/96 h18'pT = 1.19 (± 0.11) pTPp – 4.57 (± 0.53)70.960.25113.77

We obtained strong interspecies toxicity relationships between marine algae and five aquatic organisms in terms of regression parameters for both response variables (Table 4). Despite the differences in test design, the distinct characteristics of media used (e.g., salinity, pH), differences in species sensitivity, and interlaboratory variances, the marine algal toxicity bioassays conducted here produced results consistent with those reported in the literature for different species. The strong correlation of marine algal toxicity data with the previously published chlorophenol toxicity on other test organisms indicates an implicit validation of the relationships developed in the present study.

The slopes of the relationships between D. tertiolecta and other aquatic organisms were close to unity, except that of D. magna (Eqn. 10–12 and Eqn. 10'–12' in Table 4) and, to some extent, that of P. promelas (Eqn. 16–18 and Eqn. 16'–18' in Table 4). This finding indicates a one-to-one toxicity relationship of marine algae with bacteria, protozoa, and freshwater algae, probably as a result of similar toxic modes of action of chlorophenols toward these organisms. The large intercepts (>1) are probably because of differences in the complexity of test organisms (unicellular vs. complex), which were clearly reflected in the interspecies toxicity relationships between marine algae and daphnia and between marine algae and fish.

Extraction of any freshwater or marine data for structurally similar compounds or compounds with a uniform mode of action is also of interest, to assess whether extrapolation from freshwater to marine ecotoxicity data sets is viable 11. Evidence is presented in this article that the differences between freshwater and marine responses are generally not great for chlorophenols. Given the structural similarity and polar narcotic mode of action of chlorophenols, it can be concluded that a sound basis exists for using freshwater toxicity data to extrapolate to marine effects. Further studies with diverse data sets (in terms of mode of action and structure) will be necessary to understand fully the differences in the responses of organisms representing freshwater and marine ecosystems.

CONCLUSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSION
  7. Acknowledgements
  8. REFERENCES

The present article provides the first data on the toxicity of chlorophenols to the marine alga D. tertiolecta. The results suggest that the toxicity of chlorophenols increases with increasing chlorination. However, the position of the chlorine atom on the aromatic ring is also important in the toxicity of chlorophenols. The -ortho-substituted chlorine resulted in a lower toxicity compared with -meta- and -para-substituted congeners.

The toxicity of chlorophenols tended to decrease between 48 h and 96 h. This probably results from an increased pH of the test medium, which led to an increased formation of ionized chlorophenols, which in turn contributed to the toxicity less than the corresponding neutral form. Another factor that might be linked to the decrease in toxicity is the acclimation of algae to the test chemicals. Despite the changes in toxicity with varying exposure durations, the toxicity data for marine algae was found to correlate well with the data for five aquatic species regardless of test duration.

The simple equations developed in the present study using the hydrophobicity parameter, log KOW, can be used for preliminary risk assessment and have the potential to fill the information gap present for marine algae in regulatory schemes. Additionally, evidence is provided in the present study that there is a sound basis for extrapolating freshwater data to predict the toxicity of polar narcotic chemicals to marine algae. Although promising interspecies relationships were obtained, more chemicals, preferably covering different modes of action, should be tested using the marine alga D. tertiolecta to build validated quantitative structure–activity relationship models and to improve our understanding of the differences in responses between various aquatic organisms.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSION
  7. Acknowledgements
  8. REFERENCES

The financial support of Boğaziçi University Research Funds (projects 09Y105P and 5564) is appreciated.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. MATERIALS AND METHODS
  5. RESULTS AND DISCUSSION
  6. CONCLUSION
  7. Acknowledgements
  8. REFERENCES
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