Voluntary Risk assessment of copper, copper II sulphate pentahydrate, copper(I)oxide, copper(II)oxide, dicopper chloride trihydroxide




НазваниеVoluntary Risk assessment of copper, copper II sulphate pentahydrate, copper(I)oxide, copper(II)oxide, dicopper chloride trihydroxide
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F
igure 3 47 :
Overview of the SSD for the different freshwater eco-regions as a function of the DOC/pH content.

Influence of frequency distribution on the HC5-50


- HC5-50 for best fitting distributions using A/D versus K/S goodness-of-fit approaches


Goodness-of-fit statistics (Go-F) were used to select the best fitting distribution among all distributions tested. The influence of the choice of the Go-F, i.e. the Anderson-Darling (A/D) versus the Kolmogorov-Smirnov (K/S) approaches, on the selected frequency distribution and estimated HC5-50 value has been assessed for the different scenarios is summarized in Table 3 -17.


Table 3 17: Summary of the HC5-50 for the best fitting distributions using the A/D and the K/S Go-F approaches



Scenario

HC5-50 (µg/l)

A/D

K/S

Ditch in The Netherlands

22.1

beta

22.1

beta

River Otter in the United Kingdom

7.8

log-normal

7.8

log-normal

River Teme in the United Kingdom

17.6

beta

17.6

beta

River Rhine in The Netherlands

8.2

log-normal

8.2

log-normal

River Ebro in Spain

9.3

beta

9.3

beta

Lake Monate in Italy

10.6

log-normal

10.6

log-normal

Acidic lake in Sweden

11.5

beta

11.5

beta


Table 3 -17 shows that, as expected, the choice of the Go-F, i.e. the Anderson-Darling (A/D) versus the Kolmogorov-Smirnov (K/S) approaches, did not influences the estimated HC5-50 value. The K/S and A/D values provide information on the goodness of fits.


- HC5 and HC5--50 for best fitting approach (using A/D) versus the conventional log-normal approach


A summary of the HC5 and HC5-50 for the best fitting (using the A/D goodness-of-fit approach) and the ‘conventional’ log-normal distributions derived for the different selected scenarios is provided in Table 3 -18 .

Table 3 18: Summary of the HC5 and HC5-50 for the best fitting and log-normal distributions derived for the different selected scenarios

Scenario

HC5 and HC5-50 (µg/l)

Log-normal distribution

Best fitting distribution (A/D based approach)

Ditch in The Netherlands

27.7 - 27.2

log-normal

25.1 - 22.1

beta

River Otter in the United Kingdom

7.9 -7.8

log-normal

7.9 - 7.8

log-normal

River Teme in the United Kingdom

22.3 - 21.9

log-normal

20.0 - 17.6

beta

River Rhine in The Netherlands

8.3 - 8.2

log-normal

8.3 - 8.2

log-normal

River Ebro in Spain

10.8- 10.6

log-normal

10.4 - 9.3

beta

Lake Monate in Italy

10.8 - 10.6

log-normal

10.8 - 10.6

log-normal

Acidic lake in Sweden

11.3 - 11.1

log-normal

12.2 - 11.5

beta


Table 3 -18 demonstrates that the use of the conventional log-normal frequency distribution is often the best fitting distribution to the toxicity data. In all other cases very similar HC5 and HC5-50 values are observed between the conventional log-normal and the best fitting distribution (maximum factor of difference 1.1).


- Goodness-of-fit statistics (using A/D & K/S) for the best fitting and the log-normal frequency distributions


A summary of the goodness-of-fit statistics (using A/D & K/S) for the best fitting and the log-normal frequency distributions is provided in Table 3 -19. These statistics measure how good the distribution fits the data points, i.e. the lower the goodness-of-fit statistic the better the fit.


Table 3 19 : Goodness-of-fit statistics (according to Andersen-Darling (A/D) and Kolmogorov-Smirnov (K/S)) for the best fitting and log-normal frequency distributions.




Scenario

Goodness-of-fit statistic (A/D)

Goodness-of-fit statistic (K/S)







Best fitting

Log-normal

Best fitting

Log-normal

Rivers

Small ditches (The Netherlands)

0.22

0.30

0.42

0.54

Medium rivers (United Kingdom)

  • River Otter

  • River Teme

0.25

0.25

0.25

0.30

0.38

0.42

0.38

0.47

Large rivers (Germany) – River Rhine

0.25

0.25

0.46

0.46




Mediterranean river (Spain) – River Ebro

0.18

0.40

0.41

0.60

Lakes

Oligotrophic systems (Italy) – Lake Monate

0.22

0.22

0.40

0.40

Acidic system (Sweden)

0.26

0.39

0.46

0.61


Estimation of the 50th , 5th and 95th % confidence limit on the HC5.


According to the TGD (2003) the PNEC should be derived from the HC5 at 50th % conficence limit (µg/l) and considering the application of an additional assessment factor. Table 3 -20 provides a summary of the HC5-50 (i.e at 50th % confidence limit together with 5th and 95th confidence limits) derived from the best fitting distribution selected according to the A/D goodness-of-fit statistics.

Table 3 20: HC5-50 (i.e. at 50th % confidence limit together with 5th and 95th confidence limits) derived from the best fitting distribution and log normal distribuiotn.. All values in µg/l..

Scenario

HC5-50 (µg/l) using the best fitting distribution

HC5-50 (µg/l) using the log normal distribution

Ditch in The Netherlands

22.1 (19.8-24.2)

beta

27.2 (16.1-39.9)

log-normal

River Otter in the United Kingdom

7.8 (4.4-11.7)

log-normal

7.8 (4.4-11.7)

log-normal

River Teme in the United Kingdom

17.6 (15.9-19.2)

beta

21.9 (13.4-31.4)

log-normal

River Rhine in The Netherlands

8.2 (4.7-12.1)

log-normal

8.2 (4.7-12.1)

log-normal

River Ebro in Spain

9.3 (8.6-10.0)

beta

10.6 (6.1-15.8)

log-normal

Lake Monate in Italy

10.6 (7.0-14.4)

log-normal

10.6 (7.0-14.4)

log-normal

Acidic lake in Sweden

11.5 (11.1-12.0)

beta

11.1 (6.9-15.7)

log-normal



Reduction of intra-species variability

Figure 3 -48 and Table 3 -21 shows the original (non-normalised) and BLM normalised (to typical conditions) intra-species variability (expressed as the ratio between the highest and lowest NOEC from a specific species, i.e. max/min) thereby demonstrating the reduction in intra-species variability introduced by the BLM. The data from Dreissenia (2 datapoints) were excluded from the analysis due to uncertainty in the DOC level of the testwater.






Figure 3 48 : The intra-species variability (expressed as max/min ratios) of the NOECs expressed as dissolved µg Cu/l test medium and BLM-normalised to an EU typical case freshwater environment, using the chronic bioavailability models.

The chronic BLM, developed for D. magna was applied to the other invertebrates through normalising the NOECs, gathered in the copper ecotoxicity database and characterised by varying physico-chemical test conditions, to an EU typical case pH, hardness, DOC. The data show that the chronic copper D. magna BLM drastically reduced the observed intra-species variability for all invertebrate species in the copper effects data base. The max/min ratios for the normalised invertebrate NOEC data show to be all below a factor of 9.4, while originally a considerably higher intra-species variability (up to 31 for C. dubia) was observed for the non-normalised NOEC data. Similar results were obtained with the fish species. Normalisation of the chronic fish data using the developed chronic fish BLM resulted in max/min ratios all below a factor of 8.0 while originally a considerably higher intra-species variability (up to 21 for O. mykiss) was observed for the non-normalised NOEC data. On average, the BLM-normalisation resulted in an average reduction in intra-species variability (expressed as max/min ratios) of 61% for the invertebrates C. dubia, H. azteca, B. calyciflorus, D. magna, D. pulex and 45% for the fish O. mykiss, P. fluviatilis, P. promelas and S. fontanilis, all gill breathers (the data for O. kisutch were not considered here as the intraspecies variability was very low before and after normalisation). In contrast to the gill breathers (i.e. invertebrates/fish), the water/organism inter-phase of algae is the cell wall. The copper toxicity model developed for algae is explained by a (rapid) equilibrium binding of the metal to the cell wall, followed by (slow) transportation of the metal to the plasma-membrane or the cytoplasm, where the toxic effect is elicited (see chapter 2.5.2.3).

Table 3 -21 demonstrates that the algae bioavailability model developed for R. subcapitata also allows to understand the variability observed in the ecotoxicity of other algae species such as C. vulgaris and C. reinhardti. On average, the BLM-normalisation resulted in an average reduction in intra-species variability (expressed as max/min ratios) of 69% for the freshwater algae.

It must be emphasized that some toxicity values from the database resulted from tests where a large pH variation was noticed (pH variation > 1 unit). The toxicity database revealed a large pH variation in toxicity data from Jop et al. (1991) on C. dubia (pH between 6.3-7.6), from Spehar & Fiandt (1985) on P. promelas (pH between 6.0-8.1) and from McKim & Benoit (1971) on S. fontanilis. The use of the average pH for BLM calculation could therefore introduce additional uncertainty in the BLM calculation and therefore in the reduction in intra-species variability. The influence of the rejection of such data on the HC5 derivation is provided in the chapter on sensitivity analysis and the effect on reduction in the Table herebelow. A further increase in intra-species reduction is observed for the toxicity data on the fathead minnow, i.e. from 42% to 56%.

Table 3 21: Comparison of the intra-species variability (expressed as max/min ratios,) before and after BLM normalisation of the NOEC data (n= number of individual datapoints, the variability includes the variability among endpoints)




Non-normalised

Normalised

Variability reduction

Invertebrates

Ceriodaphnia dubia (n=14)

Daphnia magna (n=9)

Daphnia pulex (n=9)

Hyalella azteca (n=5)

Brachionus calyciflorus (n=4)

Clistoronia magnifica (n=2)



30.5

14.4

10.0

2.7

12.6

1.5



9.4

4.6

2.4

2.4

2.5

1.6



+69%

+68%

+76%

+13%

+80%

No reduction

Fish

Oncorhynchus kisutch (n=5)

Oncorhynchus mykiss (n=7)

Perca fluviatilis (n=2)

Pimephales notatus (n=3)

Pimephales promelas (n=14)

Salvelinus fontanilis (n=14)



1.6

20.5

4.8

1.6

13.8

7.0



3.0

2.5

2.4

1.3

8.0 (6.0*)

5.5



no reduction

+88%

+51%

+22%

+42% (56%*)

+21%

Algae

Chlamydomonas reinhardtii (n=4)

Chlorella vulgaris (n=17)

Pseudokircherniella subcapitata (n=12)



8.1

16.5

10.4



1.4

2.5

6.2



+82%

+84%

+40%

*: after removal of the Spehar & Fiandt (1985) data for P. promelas where a pH variation of > 1 unit was noticed


Figure 3 -49 finally compares the selected high quality copper NOECs, (from Table 3 -7, Table 3 -8 and Table 3 -9), with the NOECs predicted by the BLM at the physico-chemistry of the respective tests. The figure integrates the variability related to physico-chemistry of the test media (normalized by BLM) as well as intra and interlaboratory variability due to differences in test set-up (eg strains, exposure regimes …). The individual NOECs in the database varied with a factor of 232 and can be predicted with the BLM tool with predicted/observed ratios ranging between 0.2 to 3.3. All ratio’s are below the factor 5 set as ctriteria for observed/predicted ratio’s in the TCNES document on read across-species.



Figure 3 49 : Observed NOECs versus BLM predicted NOECs (by endpoint) at the physico-chemistry of the ecotoxicity tests. This graph includes all NOECs retained in the ecotox database.

The figures therefore demonstrate that the application of the chronic BLMs drastically reduce the uncertainty associated with the effects assessment, further demonstrating its importance for setting an ecologically more relevant PNEC.


Sensitivity analysis

  • Impact of the incorporation of the Q2 datapoints on the HC5

Some Q2 ecotoxicty data were identified by the Netherlands and a sensitivity analysis on these data was requested (more details in Appendix ZB). Comparison of sensitivity with similar species was performed and the potential impact on incorporating these Q2 datapoints on the HC5-50 was investigated (see Appendix env V). The following Q2 nominal chronic toxicity values were assessed:


* Chlorella fusca: a chronic nominal NOEC value of 8.0 µg/l was derived from Wong (1985). The test was performed in Bristol’s medium where the following characteristics were noticed: a pH: 6.8; DOC: 0.5 mg/l; Ca: 9.0 mg/l; Mg: 7.4 mg/l. This medium contains 0.06 mg/l CuSO4.5H2O, resulting in 15 µg Cu/l. The total NOEC value equals therefore 23 µg Cu/l.

* Chlorella pyrenoidesa: a chronic nominal NOEC value of 6.3 µg/l was derived from Stauber & Florence (1989). The test was performed in modified MBL medium where the following characteristics were noticed: a pH: 7.2; DOC: 0.5 mg/l; Ca: 10.0 mg/l; Mg: 6.6 mg/l. This medium contains 2.3 µg Cu/l. The total NOEC value equals therefore 8.6 µg Cu/l.

* Polypedilum nubifer: a chronic nominal NOEC value of 6.7 µg/l was derived from Hatakeyama (1988). The test was performed in underground water where the following characteristics were noticed: a pH: 7.95; DOC: 1.3 mg/l; Ca: 20.4 mg/l; Mg: 4.1 mg/l. No background Cu were reported, therefore a default background of 0.5 µg Cu/l was used in the analysis, resulting therefore in a total NOEC of 7.2 µg Cu/l.


The sensitivity analysis showed that the impact of the incorporation of the Q2 data on the HC5-50 value was very limited. Indeed, very similar HC5-50 were noticed for the river Otter eco-region values (7.4 and 7.8 µg/l) and for the river Teme (21.9 and 20.9 µg/l) (Figure 3 -50 and Figure 3 -51)




Figure 3 50: Effect of incorporation of Q2 datapoints on the HC5-50 derivation for the river Otter.





Figure 3 51 : Effect of incorporation of Q2 datapoints on the HC5-50 derivation for the river Teme.



  • Impact of the incorporation of the datapoints with large pH variations on the HC5-50


The toxicity database revealed a large pH variation in toxicity data from Jop et al. (1991) on C. dubia (pH between 6.3-7.6), from Spehar & Fiandt (1985) on P. promelas (pH between 6.0-8.1) and from McKim & Benoit (1971) on S. fontanilis. The use of the average pH for BLM calculation could therefore introduce additional uncertainty in the BLM calculation and therefore in the reduction in intra-species variability. The influence of the rejection of such data on the HC5-50 derivation is shown in Figure 3 -52 and Figure 3 -53. The analysis, performed for the river Teme and Otter eco-regions, clearly showed that removal of the NOEC toxicity data derived from testing with large pH variation did not affect the HC5-50.





Figure 3 52 :Effect of inclusion of datapoints with high pH variation on the HC5-50 derivation for the river Otter




Figure 3 53 :Effect of inclusion of datapoints with high pH variation on the HC5-50 derivation for the river Teme


- Impact of removal of default DOC assumptions on the HC5-50 derivation


Normalisation of the individual NOEC datapoints suggests that reliable assumptions concerning the DOC concentrations are available. For natural waters, the DOC concentrations may however severely fluctuate among rivers or lakes. The following studies were performed in natural waters of unknown origin or in natural waters where reliable DOC estimated could not be made:

    1. Jop et al., 1995: river water of unknown origin and unknown DOC concentration,

    2. Belanger & Cherry, 1990: Amy Bayou river with unknown DOC concentration,

    3. Van Leuwen et al., 1988: Lake Ijssel with unknown DOC concerntration,

    4. Deaver & Rodgers, 1996: spring water of unknown origin and unknown DOC concentration,

    5. Sauter et al., 1976: well water of unknown origin and unknown DOC concentration,

    6. Solbe & Cooper, 1976: well water of unknown origin and unknown DOC concentration,

    7. Seim et al., 1984: well water of unknown origin and unknown DOC concentration,

    8. Horning & Neiheisel, 1979: spring water of unknown origin and unknown DOC concentration,

    9. Mount & Stephan, 1969: spring water of unknown origin and unknown DOC concentration,

    10. Mount, 1968: spring water of unknown origin and unknown DOC concentration,

    11. Pickering et al., 1977: spring water of unknown origin and unknown DOC concentration,

    12. Scudder et al., 1988: groundwater of unknown origin and unknown DOC concentration,


Exclusion of the data from the above mentioned studies resulted in very similar HC5-50 for the scenario of the river Otter (8.1 µg/l versus 7.8 µg/l) data for the river Teme (22.3 µg/l versus 21.9 µg/l), see Figure 3 -54 and Figure 3 -55.



Figure 3 54 :Effect of exclusion of datapoints with default DOC assumptions for natural waters on the HC5-50 derivation for the river Otter



Figure 3 55 :Effect of exclusion of datapoints with default DOC assumptions for natural waters on the HC5-50 derivation for the river Teme


- Impact of the introduction of more conservative DOC assumptions on the HC5-50 derivation


Normalisation of the toxicity data using the BLM’s suggests that reliable assumptions exists on the DOC content of the test media. In case no reliable DOC assumptions could be made, default values from Santore et al. (2002) were used. For artificial/reconstituted media an assumption of 0.5 mg/l DOC was proposed and for well waters a DOC concentration of 1.3 mg/l DOC. However from the Nickel risk assessment other assumptions, based on measured data, were proposed for reconstituted waters, i.e. 0.3 mg/l DOC, and for the well water from the studies performed by Nebeker et al. (1984), i.e. 1.1 mg/l.


Moreover, for natural waters where the TOC concentrations only were reported, a DOC/TOC ratio of 0.8 was assumed. The impact of assuming a lower and higher DOC/TOC ratio of 0.5 and 1 mg/L on the HC5-50 was also investigated.


Incorporation of such DOC estimates (0.3 mg/l for artificial/reconstituted waters; 1.1 mg/l for well waters from the study of Nebeker et al. (1984); DOC/TOC ratio of 1 for natural waters) resulted in similar to higher HC5-50 for both investigated scenarios: (1) the river Otter (8.8 µg/l and 7.7 µg/l versus 7.8 µg/l) and (2) the river Teme (24.8 µg/l and 21.9 µg Cu/l versus 21.9 µg/l), see Figure 3 -56 and Figure 3 -57.





Figure 3 56 : Effect of incorporation of more conservative DOC assumptions on the HC5-50 derivation for the river Otter. Original DOC assumptions: 0.5 mg/l for reconstituted waters; DOC/TOC= 0.8; low DOC assumptions: 0.3 mg/l for reconstituted waters (cfr. University of Ghent) and 1.1 mg/l for well waters from the study of Nebeker et al. (1984); DOC/TOC= 0.5: High DOC assumptions: 1.0 mg/l for reconstituted waters (cfr. Santore et al., 2002); DOC/TOC= 1.0.




Figure 3 57 :Effect of incorporation of more conservative DOC assumptions on the HC5-50 derivation for the river Teme Original DOC assumptions: 0.5 mg/l for reconstituted waters; DOC/TOC= 0.8; low DOC assumptions: 0.3 mg/l for reconstituted waters (cfr. University of Ghent) and 1.1 mg/l for well waters from the study of Nebeker et al. (1984); DOC/TOC= 0.5: High DOC assumptions: 1.0 mg/l for reconstituted waters (cfr. Santore et al., 2002); DOC/TOC= 1.0.


  • Impact of the use of the long-term NOEC for Dreissenia polymorpha (Kraak et al., 1994) on the HC5-50 derivation.


Originally the short term (2 days exposure; endpoint filtration rate) NOEC value of 16 µg/l for the bivalve Dreissenia polymorpha was used in the database. Carefull assessment of all Kraak et al. studies revealed that a long term (9-11 weeks exposure; endpoint filtration rate) NOEC of 13.0 µg/l was available for the same species. Replacement of the short term NOEC by the long term NOEC data from the above mentioned study for the bivalve D. polymorpha resulted in very similar HC5-50 for the scenario of the river Otter (7.7 µg/l versus 7.8 µg/l) data for the river Teme (21.6 µg/l versus 21.9 µg/l), see Figure 3 -58 and Figure 3 -59.





Figure 3 58 :Effect of incorporation of long term NOEC for Dreissenia polymorpha on the HC5-50 derivation for the river Otter




Figure 3 59 :Effect of incorporation of long term NOEC for Dreissenia polymorpha on the HC5-50 derivation for the river Teme


- Impact of not including amphibians in the database

In view of responding to the TCNES question on the non-inclusion of amphibians, the data from US EPA Ecotox were evaluated (Table 3 -22). Almost all tests gathered from literature on the effect of copper towards amphibians were performed on early life stages, in laboratory water (with low DOC concentration), with 4 days exposure times and according to standard guidelines. Most of the studies report however EC50 values and test concentrations were not measured. The results clearly show that amphibians (data were found for Xenopus laevis, Rana pipiens, Rana ridibunda, Rana hexadactyla, Rana Sphenocephala and Rana tigrina) are not sensitive towards copper. Indeed LC50 and NOEC values respectively between 39 and 1,250 µg/l and between 40 and 100,000 µg/l were found.

Table 3 22: EPA Ecotox data on amphibian

Organism

Age/size of organisms

Test substance (& purity)

Exposure time

Endpoint


LC50/NOEC

(µg/l)

Testtype


Physico-chemical

conditions

Medium


Reference


Xenopus laevis

larvae

CuSO4 (ACS grade)

4 days

development

100 (EC50)

renewal

24°C; pH: 7.0-8.0

Laboratory water

Fort & Stoves, 1996

Xenopus laevis

larvae

CuSO4 (ACS grade)

4 days

development

380 (EC50)

renewal

24°C; pH: 7.0-8.0

Laboratory water

Fort & Stoves, 1996

Xenopus laevis

larvae

CuSO4 (ACS grade)

4 days

development

920 (EC50)

renewal

24°C; pH: 7.0-8.0

Laboratory water

Fort & Stoves, 1996

Xenopus laevis

larvae

CuSO4 (ACS grade)

4 days

development

950 (EC50)

renewal

24°C; pH: 7.0-8.0

Laboratory water

Fort & Stoves, 1996

Xenopus laevis

embryos

CuSO4 (ACS grade)

4 days

development

740 (EC50)

renewal

24°C; pH: 7.0-7.5; H: 102-110 mg/l

Laboratory water

Fort et al., 1996

Xenopus laevis

embryos

CuSO4 (ACS grade)

4 days

development

880 (EC50)

renewal

24°C; pH: 7.0-7.5; H: 102-110 mg/l

Laboratory water

Fort et al., 1996

Xenopus laevis

larvae

CuSO4 (ACS grade)

4 days

mortality

1,080 (LC50)

renewal

24°C; pH: 7.0-8.0

Laboratory water

Fort & Stoves, 1996

Xenopus laevis

larvae

CuSO4 (ACS grade)

4 days

mortality

1,250 (LC50)

renewal

24°C; pH: 7.0-8.0

Laboratory water

Fort & Stoves, 1996

Xenopus laevis

larvae

CuSO4 (ACS grade)

4 days

mortality

150 (LC50)

renewal

24°C; pH: 7.0-8.0

Laboratory water

Fort & Stoves, 1996

Xenopus laevis

larvae

CuSO4 (ACS grade)

4 days

mortality

420 (LC50)

renewal

24°C; pH: 7.0-8.0

Laboratory water

Fort & Stoves, 1996

Xenopus laevis

embryos

CuSO4 (ACS grade)

4 days

mortality

890 (LC50)

renewal

24°C; pH: 7.0-7.5; H: 102-110 mg/l

Laboratory water

Fort et al., 1996

Xenopus laevis

embryo

CuSO4 (ACS grade)

66 stages

development

50 (NOEC)

renewal

24°C; pH: 7.0-8.0

Laboratory water

Fort & Stoves, 1996

Xenopus laevis

embryo

CuSO4 (ACS grade)

4 days

growth

1,000 (NOEC)

renewal

24°C; pH: 7.0-8.0

Laboratory water

Fort & Stoves, 1996

Xenopus laevis

larvae

CuSO4 (ACS grade)

4 days

growth

150 (NOEC)

renewal

24°C; pH: 7.0-8.0

Laboratory water

Fort & Stoves, 1996

Xenopus laevis

larvae

CuSO4 (ACS grade)

4 days

growth

40 (NOEC)

renewal

24°C; pH: 7.0-8.0

Laboratory water

Fort & Stoves, 1996

Xenopus laevis

larvae

CuSO4 (ACS grade)

4 days

mortality

500 (NOEC)

renewal

24°C; pH: 7.0-8.0

Laboratory water

Fort & Stoves, 1996

Rana pipiens


tadpole

Cu (not specified)

7 days

mortality

67 (LC50) (0% mortality at 19 µg/l)

static

20°C

Laboratory water

Redick & La Point, 2004

Rana pipiens


tadpole

Cu (not specified)

7 days

growth

71 (NOEC)

static

20°C

Laboratory water

Redick & La Point, 2004

Rana ridibunda

adults

CuCl2

30 days

growth

100,000 (NOEC)

Not reported

Not reported

Laboratory water

Papadimitriou & Loumbourdis, 2002

Rana hexadactyla

20 mm

CuSO4

4 days

mortality

39 (LC50)

renewal

15°C; pH: 6.1; H: 20 mg/l

Laboratory water

Khangarot et al., 1985

Rana pipiens


eggs

CuSO4

4 days

mortality

60 (LC50)

renewal

22°C; pH: 7.2-7.8; H: 100 mg/l

Laboratory water

Birge & Black, 1979

Rana sphenocephala

tadpole

CuSO4

4 days

mortality

230 (LC50)

static

22°C; pH: 8.3; H: 171 mg/l

Laboratory water

Bridges et al., 2002

Rana tigrana

larvae

CuSO4

4 days

mortality

389 (LC50)

static

26.5°C; pH: 7.5; H: 240 mg/l

Laboratory water

Khangarot et al., 1981
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Voluntary Risk assessment of copper, copper II sulphate pentahydrate, copper(I)oxide, copper(II)oxide, dicopper chloride trihydroxide iconA unique form for copper infiltration – wrought wire infiltrant

Voluntary Risk assessment of copper, copper II sulphate pentahydrate, copper(I)oxide, copper(II)oxide, dicopper chloride trihydroxide iconSupplemental information Demonstration of proton-coupled electron transfer in the copper-containing nitrite reductases1

Voluntary Risk assessment of copper, copper II sulphate pentahydrate, copper(I)oxide, copper(II)oxide, dicopper chloride trihydroxide iconA note on the efficacy of ethylenediaminetetra-acetic acid disodium salt as a stripping agent for corrosion products of copper

Voluntary Risk assessment of copper, copper II sulphate pentahydrate, copper(I)oxide, copper(II)oxide, dicopper chloride trihydroxide iconParadise: or the garden of Eden with the countries circumjacent inhabited by the patriarchs (London: [1720?]) 1 map, colour, copper engraving 31. 5 X 46. 5 cm

Voluntary Risk assessment of copper, copper II sulphate pentahydrate, copper(I)oxide, copper(II)oxide, dicopper chloride trihydroxide iconMeasurement of the isotopic fractionation of 15N14N16O, 14N15N16O and 14N14N18O in the uv photolysis of nitrous oxide

Voluntary Risk assessment of copper, copper II sulphate pentahydrate, copper(I)oxide, copper(II)oxide, dicopper chloride trihydroxide iconStudy of serum ferritin, HbA1c, nitric oxide, uric acid levels in type 2 diabetes mellitus”

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