Registration Dossier

Diss Factsheets

Administrative data

Hazard for aquatic organisms

Freshwater

Hazard assessment conclusion:
PNEC aqua (freshwater)
PNEC value:
2.9 mg/L
Assessment factor:
2
Extrapolation method:
sensitivity distribution
PNEC freshwater (intermittent releases):
13.7 mg/L

Marine water

Hazard assessment conclusion:
PNEC aqua (marine water)
PNEC value:
2.9 mg/L
Assessment factor:
2
Extrapolation method:
sensitivity distribution

STP

Hazard assessment conclusion:
PNEC STP
PNEC value:
10 mg/L
Assessment factor:
1
Extrapolation method:
assessment factor

Sediment (freshwater)

Hazard assessment conclusion:
no exposure of sediment expected

Sediment (marine water)

Hazard assessment conclusion:
no exposure of sediment expected

Hazard for air

Air

Hazard assessment conclusion:
no hazard identified

Hazard for terrestrial organisms

Soil

Hazard assessment conclusion:
PNEC soil
PNEC value:
5.7 mg/kg soil dw
Assessment factor:
2
Extrapolation method:
sensitivity distribution

Hazard for predators

Secondary poisoning

Hazard assessment conclusion:
no potential for bioaccumulation

Additional information

7.6.1.1 PNEC derivation for freshwater

 

Approach for PNEC derivation for freshwater

The available ecotoxicity database for the effect of boron on freshwater organisms is large. Therefore, the use of the statistical extrapolation method is preferred for PNEC derivation rather than the use of an assessment factor on the lowest NOEC, as specified by the Guidance document on information requirements and chemical safety assessment Chapter R.10.3.1.3. The PNEC is based on the 50% confidence value of the 5th percentile value of the chronic effect NOEC/EC10 data (HC5-50) and an additional assessment factor taking into account the uncertainty on the HC5-50 (thus PNEC = HC5-50/AF). The advantage of this statistical extrapolation method is that it uses the whole sensitivity distribution of species in a collection of laboratory test data to derive a PNEC instead of taking only the lowest long-term NOEC.

Selection of toxicity data

All reliable Klimisch 1 and 2 chronic toxicity data were used for the derivation of PNEC value for the freshwater compartment. Because no other reliable toxicity data are available, unbounded data for two species (Xenopus laevis and Ambystoma maculatum) are also included in the database and used for PNEC derivation. These unbounded NOEC values represent worst-case estimates for the sensitivity of these species towards B.

Derivation of ‘added’ toxicity values

The added LC10/EC10 or NOEC values are obtained by subtracting the background B-concentrations as observed in the culture/test media (=control) from the measured LC10/EC10 or NOEC values.

Averaging data for same process/species.

The geometric mean of the retained quality-screened toxicity data for a process/species was calculated to avoid over-representation of ecotoxicological data from one particular species or function. This approach is relevant if there is more than one set of data on the same species, (strain if known), endpoint, duration, life stage and testing condition. In this case the greatest weight is attached to the most reliable and relevant datum. When there is more than one set of data with the same reliability rating, it is necessary to look in more detail into the study reports to see whether there is an explanation for the difference. If no explanation can be found and the results are for the same species and endpoints and are not more than one order of magnitude apart, they can be harmonized by a geometric mean. The approach used in this dossier is outlined hereunder: 

- If for one process/species several chronic NOEC or EC10 values based on the same toxicological endpoint are available, these values are averaged by calculating the geometric mean, resulting in the “species mean” NOEC or EC10

- If for one species several chronic NOEC or EC10 values based on different toxicological endpoints are available, the value for the most sensitive endpoint is selected. This value is determined on the basis of the geometric mean if more than one value for the same endpoint is available

Overview of the geometric mean values of freshwater toxicity data

An overview of the ‘added’ geometric mean values (mg B/L) for the most sensitive chronic endpoints is given in the following table. The ‘added’ geometric mean chronic toxicity values for freshwater organisms vary between 6.0 mg B/L (for the higher plant Spirodella polyrhizza) and 69.9 mg B/L (for the amphibian Bufo fowleri).

Species Name

Taxa

Effect Parameter (most sensitive endpoint)

Added geomean NOEC/EC10(mg B/L)

Micropterus salmoides

Pisces - Chordata

Mortality

36.8

Oncorhynchus mykiss

Pisces - Chordata

Mortality

19.2

Brachydanio rerio

Pisces - Chordata

Growth

6.3

Daphnia magna

Crustacea

Reproduction

13.9

Spirodella polyrhiza

Higher plants

Growth rate

6.0

Hyalella azteca

Crustacea

Reproduction

6.3

Xenopus laevis

Amphibian - Chordata

Mortality, growth, reproduction

9.42

Pimephales promelas

Pisces - Chordata

Mortality

21.3

Ictalurus punctatus

Pisces - Chordata

Mortality

13.5

Carassius auratus

Pisces - Chordata

Mortality

30.3

Chironomus riparius

Insecta

Emergence

20.1

Brachionus calyciflorus

Rotifera

Reproduction

24.6

Rana pipiens

Amphibian - Chordata

Mortality

45.1

Selenastrum capricornutum

Chlorophyta

Growth rate

17.53

Bufo fowleri

Amphibian - Chordata

Mortality

69.9

Ambystoma maculatum

Amphibian – Chordata

Malformation

24.5

Lampsilis silliquoidea

Mollusc

Biomass

30.0

[2] Unbounded NOEC values

[3] This value was derived from the same experiment as the acute value 

Generation of species sensitivity distribution and HC5-50 calculation

The use of statistical extrapolation is preferred for PNEC derivation rather than the use of an assessment factor on the lowest NOEC. In accordance with the Technical Guidance the PNEC value should be based on the 50% confidence level of the 5th percentile value.

Using the above listed chronic toxicity data, a species sensitivity distribution (SSD) and HC5-50 have been calculated using best fitting approaches. Using the software @Risk (Palisade, USA), the Logistic distribution on the log-transformed toxicity data was selected as the best fitting curve (using the Anderson-Darling goodness-of-fit statistics). This results in a long term added HC5-50 value of 4,6 mg B/L (confidence limits between 1,9 and 6,2 mg B/L). The ETx software (RIVM, The Netherlands), using the normal distribution on the log-transformed long term freshwater toxicity data resulted in a long term added HC5-50 value of 5,7 mg B/L (confidence limits between 3,2 and 8,3 mg B/L

Derivation of an Assessment Factor (Uncertainty analysis)

The ECHA Guidance document (Chapter R.10) recommends that the PNEC be derived using an assessment factor combined with the 50% confidence value of the 5th percentile value of the SSD (thus PNEC = HC5-50/AF). Appropriate AF’s are to be judged on a case-by-case basis and range from 1 to 5. Based on the available data, the following points have to be considered when determining the size of the assessment factor:

  • The overall quality of the database and the end-points covered, e.g., if all the data are generated from “true” chronic studies (e.g., covering all sensitive life stages);

The boron database covered the ecologically relevant endpoints including mortality, growth, reproduction and condition. For all trophic levels, sensitive life stages and reasonably chronic exposures were achieved. For algae, exposure times of 3-4 days were tested therefore covering several generation times. Very sensitive life stages of invertebrates were tested, including neonates exposed until reproduction occurs, i.e. exposed for 14 to 42 days. For fish very sensitive life stages were also included in the database, e.g. embryos and sac fry exposed for between 7 days up to 87 days. Higher plants were exposed to boron between 7 days up to 16 weeks.

It is concluded that the endpoints addressed in the boron database for aquatic species are of appropriate duration and of high quality.

  • The diversity and representativeness of the taxonomic groups covered by the database;

The boron database meets the requirement of 10-15 different NOEC values covering at least 8 taxonomic groups. Indeed, 55 individual high quality NOEC or EC10 values and 17 different species (fish, amphibians, invertebrates, higher plants and algae) were compiled from the database.

The ECHA Guidance (R.10.3.1.3) sets forth requirements about the diversity of species which should be in the data set. These requirements are:

    1. fish. Available chronic data include 6 fish species (Pimephales promelas, Carassius auratus, Oncorhynchus mykiss, Micropterus salmoides, Brachydanio rerio, Ictalurus punctatus).

    2. another family in the phylum Chordata. Available chronic fish data include several fish families. Chronic data on 4 amphibian species are also available (Xenopus laevis, Rana pipiens, Bufo fowleri, Ambystoma maculatum).

    3. a crustacean. Available chronic data include 2 crustacean species (Daphnia magna, Hyalella azteca)

    4. an insect. A chronic study of the midge Chironomus ripariusis available.

    5. a family in a phylum other than Arthropoda or Chordata. Chronic data the mollusc Lamsilis silliquoideais available.

    6. a family in any order of insect or any phylum not already represented. A chronic toxicity data is available for the rotifer Brachionus calyciflorus.

    7. algae. Available data include 1 unicellular green algal species (Selenastrum capricornutum).

    8. higher plants. Available data include 1 higher plant (Spirodella polyrhiza).

It is concluded that the chronic boron dataset covers the appropriate range of taxonomic groups as mentioned in the ECHA Guidance (R.10.3.1.3).

  • Statistical uncertainties around the 5th percentile estimate, e.g., reflected in the goodness-of-fit or the size of confidence interval around the 5th percentile;

The Normal distribution function (as recommended by Aldenberg and Jaworska, 2000) on log-transformed toxicity data using the ETX software (RIVM) calculates an added HC5-50 value of 5.7 mg B/L, with confidence limits ranging between 3.2 and 8.3 mg B/L. The best fitting distribution using the Andersen-Darling goodness-of-fit statistics using the @Risk software was the Weibull distribution (on log-transformed toxicity data), resulting in an added HC5-50 value of 4.6 mg B/L, with confidence limits ranging between 1.9 and 6.2 mg B/L. The larger difference between the 5thand the 95th% confidence level for the Logistic distribution (factor of 3.3) compared to the Normal distribution (factor of 2.6) reflects the higher statistical uncertainties around the 5th percentile estimate using the Logistic distribution. It must be emphasized that both software, i.e. ETx and @Risk use a different approach to account for the sampling uncertainty.

The probability distribution of the boron dataset used for the calculations of the 5th percentile values has been checked with the Anderson-Darling (A/D) and Kolmogorov-Smirnov (K/S) goodness-of-fit tests. The A/D goodness-of-fit test highlights differences between the tail of the distribution(lower tail is the region of interest) and the input data, while the K/S test focuses on differences in the middle of the distribution and is not very sensitive to discrepancies of fit in the tail of the distribution. Based on this analysis, a better fit of the log-transformed data was achieved with the logistic distribution function, while a better fit was obtained with the Normal distribution using the K/S goodness-of-fit statistics. However in all cases the curve fitting functions (best fitting Logistic and Normal) fit reasonably well to the chronic toxicity SSD data and none of the fit functions can be rejected at the 5% significance level.

It is concluded that the Normal distribution using ETx (RIVM) will be used for PNEC derivation.

  • Comparison between field and mesocosm studies and the 5th percentile and mesocosm/field studies to evaluate the laboratory to field extrapolation.

No reliable experimental data are available. However, several field studies have been reported in freshwater locations experiencing high boron concentrations either seasonally or within a relatively small geographic range. These field studies focused on trout because early laboratory reports (Birge and Black 1993) suggested this species was the most sensitive and would likely be affected by concentrations of 1 mg B/L or greater. However, Loewengart (2001) pointed out that wild trout populations flourished in a California (USA) stream with a boron concentration of 13 mg B/L. Meyer et al. (1998) found that trout in the Firehole River (Wyoming, USA) delay spawning to avoid thermal stress, but do so by using stream areas where boron is highest, ranging from 0.4 to 1.2 mg B/L. Guhl (1992) also reported the success of trout in German surface waters and hatcheries ranging from 0.8 to 1.2 mg B/L in Schilling Lake (Bavaria, DE).

Rainbow trout were introduced into a high-boron system in the Puna region of Argentina (Barros 2007). This river system had a gradient of boron concentrations (along with other salts) and Barros found that trout utilized the system with boron concentrations of 2.4 to 14.3 mg B/L. This included construction of redds with successful reproduction. Boron concentrations changed seasonally as the proportion of snow-melt and geothermal groundwater varied.

Although these data are not from designed experiments, they do indicate that ecosystems with high (and fluctuating) boron concentrations can be found in various parts of the world. The successful establishment of a trout population, abruptly introduced into a high boron environment (Argentina) suggests that aquatic organisms, even those considered sensitive, can tolerate such conditions.

  • Comparison between species mean NOEC values and the HC5-50.

It is inherent in the use of the SSD HC5-50 approach that some species mean NOEC or EC10values will be below the 5thpercentile of the SSD. If a key species (either in an ecological or economic sense) is below the 5thpercentile, then concern may remain that the proposed PNEC value is not sufficiently protective (i.e. a larger AF should be considered). Similarly, if all the sensitive taxa are from one trophic group, a larger AF might be considered. The analysis shows that the geometric mean value below the HC5-50 of 5.7 mg B/L. The most sensitive species, i.e. the higher plant S. polyrhizza, has a geometric mean value of 6.0 mg B/L.

It is concluded that no special concerns arise from comparison of the HC5-50 and the taxa with the lowest species means.

Essentiality/deficiency of boron in aquatic environments

The potential essential effects of boron have been studied on a variety of freshwater fish, amphibians, invertebrates and plants. At lower concentrations, boron has been found to be beneficial to some freshwater organisms. For instance, the addition of 0.4 mg/L of boron to ponds used for raising carp increased production by 7.6% (Avetisyan, 1983). Furthermore, Fort et al. (1999) found that boron was nutritionally essential for reproduction and development in frogs (Xenopus laevis). Rowe et al. (1998) found that the shape of the dose-response curve in rainbow trout (Oncorhynchus mykiss) and zebrafish follows the U-shaped adverse response of an essential nutrient. This shape reflects effects of exposure to boron concentrations below the level to meet physiological requirements and toxic effects due to exposure to high concentrations of boron that exceed the threshold for safety. For rainbow trout embryos, long-term exposures below 0.097 mg B/L impaired embryonic growth. In addition, zebrafish (Danio rerio) exposed to boron concentrations below 0.0022 mg B/L experienced zygote death (Rowe et al., 1998).

Boron is a constituent of many culture media and dilution waters used in aquatic toxicity tests. For example, the algal growth media used in the OECD 201 test typically contains 0.185 mg/L of boric acid (0.03 mg B/L). Boron is also present in the M4 and M7 media used in OECD 202 for Daphnia. M4 contains ca. 2.9 mg/L boric acid or 0.51 mg B/L, while M7 contains 0.71 mg/L boric acid, or 0.12 mg B/L.

In order to prevent adverse health effects to organisms caused by a deficiency of essential elements, recommended threshold levels for boron (PNEC values) should not fall below the level required by the organism to remain healthy.

Overall conclusion on chronic PNEC-freshwater

In conclusion on the subject of the choice of the assessment factor and considering all arguments above for the derivation of the HC5-50 it is felt that the most appropriate AF would be 2.

The assessment factor 2 takes into account:

  • The extensive database of chronic boron effects to freshwater organisms covering a representative range of plant, invertebrate and vertebrate species and representing 55 high quality NOECs among 17 different species.

  • The extensive database of long term boron effects data to freshwater organisms fulfils the requirements related to the taxonomic groups (families) mentioned in the ECHA Guidance (R.10.3.1.3). Indeed, high quality long term NOEC values are available for 1 unicellular green algal species (Selenastrum capricornutum), 1 higher plant (Spirodella polyrhiza), 1 mollusc (Lampsilis silliquoidea), 1 rotifer (Brachionus calyciflorus), 2 crustacean species (Daphnia magna, Hyalella azteca), 1 insect (Chironomus riparius), 4 amphibian species (Ambystoma maculatum,Xenopus laevis, Rana pipiens, Bufo fowleri) and 6 fish species (Pimephales promelas, Carassius auratus, Oncorhynchus mykiss, Micropterus salmoides, Brachydanio rerio, Ictalurus punctatus).

  • The absence of a field or mesocosm study to evaluate the laboratory to field extrapolation. However, field studies have been reported which make use of naturally occurring waters with high boron concentrations. In several cases these fluctuate during the year, so they do provide a means to demonstrate that levels of boron of about 1 mg B/L and higher do not adversely affect local species, including the sensitive species rainbow trout.

  • The demonstration of a suitable probability distribution (i.e. the normal distribution on the log-transformed toxicity data) of the chronic boron dataset for the calculation of 5thpercentile values.

  • The conclusion that no key species or group of species is consistently below the HC5-50.

Therefore, the proposed freshwater PNECadd, freshwater is based on the best fitting HC5-50 value using an AF of 2, i.e. 2.9 mg B/L.This value, based on added boron concentrations, is therefore taken forward to the risk characterization.

 7.6.1.2 PNEC derivation for intermittent releases

According to REACH Guidance R.16.2.3 if intermittent release is identified, only short-term effects are considered for the aquatic ecosystem and no-effect levels are derived from short-term toxicity data only. Instead of using an assessment factor, it was opted to consider all available short term toxicity data for freshwater organisms in order to derive the short term PNEC for intermittent release. The statistical extrapolation approach using all short term toxicity data presented in corresponding table were used to construct a ‘short term’ species sensitivity distribution and to derive a HC5-50 value for short term toxicity.

The database of short term boron effects to freshwater organisms represents 46 high quality studies among 20 different species. Acute study endpoints (L(E)C50) are available for 2 unicellular green algal species (Chlorella pyrenoidosa, Selenstrum capricornutum), 3 crustacean species (Ceriodaphnia dubia, Daphnia magna, Hyalella azteca), 2 insect species (Chironomus decorus, Allocaphnia vivipara), 4 mollusc species (Lampsilis siliquoidea, Leguma recta, Sphaerium simile, Megalonaisa nervosa), and 7 fish species (Pimephales promelas, Oncorhynchus kisutch, Oncorhynchus tschawytscha, Catastomas latipinnis, Xyrauchen texanus, Ptychocheilus lucius, Gila elegans).

Using the above listed acute toxicity data, a species sensitivity distribution (SSD) and HC5-50 for short term toxicity have been calculated using best fitting approaches. The best fitting curve was the normal distribution function on the log-transformed short term toxicity data. The ETX software (RIVM), using the normal distribution on the log-transformed short term freshwater toxicity data, resulted in a short term HC5-50 value of 27.3 mg B/L (confidence limits between 12.0 and 47.8 mg B/L). The short term HC5-50 value from the log-normal statistical approach is used as the basis for the PNEC calculation. It is further proposed to apply a similar assessment factor as used in the derivation of the long term PNEC (AF = 2).

Hence the proposed short term freshwater PNECadd, freshwater is 13.7 mg B/L.This value, based on added boron concentrations, is therefore taken forward to the risk characterization of intermittent releases.

 

7.6.1.3 PNEC derivation for marine and estuarine water

In general there exist very limited data on acute and long term toxicity of boron to marine organisms. Three fish species (Limanda limanda, Menidia beryllina and Cyprinodon vareigatus), two crusteacean species (Americamysis bahia and Litopenaeus vannamei) are represented, one echinoderm species (Anthocidaris crassipina), and 19 algal species are represented. However, most of these toxicity data are based on nominal value and cannot therefore be considered for PNEC derivation.

Marine waters contain about 5 mg B/L, (see Section 4, General discussion of environmental fate and pathways), so it may be expected that marine organisms are more tolerant of boron than freshwater organisms. However the lack of a suitable database prevents direct evaluation of this expectation.

The available studies suggest that boron toxicity may vary with salinity. Li et al (2007) reported acute toxicity to the white tiger shrimp Litopeneaus vannei, to be 25 mg B/L at 3‰ and 80 mg B/L at 20‰. Thompson et al. (1976) found underyearling coho salmon more susceptible to boron toxicity (12-d LC5012.2 mg B/L) in seawater (28‰) than in freshwater (12-d LC50113 mg B/L). However, under natural conditions, coho underyearlings remain in freshwaters, so the salinity itself may have contributed to physiological stress. Similarly, Litopeneaus are reported to show optimal growth at about 20‰. Pillard et al. (2002) measured borate ion toxicity to the mysid shrimp, Americamysis bahia and reported acute values of 310, 290 and 380mg B4O7-2/L for salinities of 10‰, 20‰, and 31‰, respectively. They concluded however, that the differences in salinity had no distinct impact on the tolerance to borate. More recent study reports from Hicks (2011) have also shown similar NOECs for a 28d test. The most sensitive endpoint was reproduction and gave a NOEC of 16.6 and 18.6 mg B/L for a salinity of respectively 8 and 20‰.

To address the concern about differential toxicity of boron as a function of salinity, additional studies are needed. These would preferably use organisms that occur in areas of varying salinity, to avoid the confounding factor of salinity itself being a stress on many organisms.

Because of the very limited data for marine species, it is proposed that the marine PNEC is the same value as proposed for freshwater organisms. This represents a conservative proposal, given the naturally higher boron concentrations in the marine environment.

Based on the PNECadd,freshwater of 5.7 mg B/L derived with an SSD approach and an assessment factor of 2, it can be assumed that the PNECadd, freshwater also protects the marine environment (open sea).Therefore, the PNECadd,marine water of 2.9 mg B/Lis taken forward to the risk characterization.

 

 

7.6.1.4 PNECadded derivation for sediments

According to chapter R.10 of the Guidance on IR and CSA the PNEC for sediment (PNECfreshwater, sediment) should be preferably derived from whole sediment toxicity data for freshwater benthic organisms (sediment-dwelling organisms). In the absence of whole sediment toxicity data for benthic organisms, the PNEC for sediment may provisionally be calculated using the equilibrium partitioning (EP) method.

 

The high water solubility of boric acid and corresponding low sorption to sediment means that a sediment-only exposure is not possible. Standard protocols involve spiking the test substance to the sediment at the initiation of the study. The overlying water is not spiked. However, boric acid will readily dissolve from the sediments during the course of a sediment study, so the test organisms will actually experience both water-borne and sediment-borne exposures.

 

For boron two whole sediment chronic toxicity tests with the midge Chironomus riparius have been performed (Hooftman, 2000 and Gerke, 2011b). The Hooftman study (2000) had a static test design using 6 test concentrations (18-320 mg B/kg dry wt.) spiked in a formulated sediment. Only nominal values were reported by Hooftman. The test resulted in a NOEC value of 180 mg/kg dry wt.

 

In the test design of Gerke (2011b), a formulated sediment was spiked with 6 different B concentrations between 6.5 and 200 mg B/kg (as nominal concentrations). After spiking, dilution water (i.e. overlying water) was added to the prepared formulated sediment where after midge larvae (C. riparius) were added after 2 days of equilibration for 28 days toxicity testing. The test yielded a measured unbounded NOEC of 37.8 mg/kg dry wt (as mean total measured concentration). 

 

Mass balance calculations of B content in the different phases (please refer to Figure C in the attached document "Additional information on PNEC derivation") of the ABC Laboratories test revealed that there is a significant gradual decrease of B concentrations in the solid phase, while an increase of B concentrations in the overlying water is observed during the 30-day testing period. Indeed, at test initiation (after a 2 day equilibration period) 31% of the B load was present in the overlying water, while 38% of the B load was found in the solid phase. However, at test termination (after 30 days), ± 80% of the B load was present in the water phase, while ± 20% remained in the solid phase.

 

The mean overlying water concentration in Gerke (2011b), i.e. 20.9 mg/L (based on geometric mean concentration over the testing period), are at the level of the total NOEC value (i.e. 20.4 mg/L) derived in a similar 28d water-only test with C. riparius (ABC Laboratories, 2011a). Please refer to the following table. In a similar way the boron concentration (i.e. geometric mean t0-t28= 42 mg/L) measured in the water column at the LOEC level in the Hooftman (2000) study equals the water only LOEC value obtained in the same water only test.

 

Exposure system

NOEC (mg B/L)

LOEC (mg B/L)

Reference

Water-only

20,4

43,3

Gerke, 2011a

Water/sediment

≥20,9

>20,9

Gerke, 2011b

 

NR

43,1

Hooftman, 2000

NR: not reported

 

It is clear that the chronic toxicity results on survival/emergence for C. ripariuscould be completely explained by the boron concentrations measured in the water column. Therefore, the additional contribution of boron adsorbed to sediment to the observed toxicity is expected to be negligible. This observation comes as no surprise since the estimated Kd values for boron are very low (mean value of 1.94 and 3.0 L/kg for respectively freshwater and marine sediment; 3.5 L/kg for suspended solids), indicating that boron has no tendency to adsorb to sediments.

 

The weight of evidence provided by the lack of partitioning (Kd estimates) and the results of the water only/whole sediment toxicity tests indicate that it is unlikely that boron will exert toxic effects via the sediment compartment and that the derivation of a PNEC sediment is not warranted for boron and could waived based on exposure considerations.

   

7.6.1.5 PNEC derivation for soil

The available ecotoxicity database for the effect of boron on soil organisms is large and covering series of trophic levels and species. Therefore, the use of the statistical extrapolation method is preferred for PNEC derivation rather than the use of an assessment factor on the lowest NOEC, as specified by the Guidance document on information requirements and chemical safety assessment Chapter R.10.3.1.3. The PNEC is based on the 50% confidence value of the 5th percentile value of the effect NOEC/EC10data (HC5-50) and an additional assessment factor taking into account the uncertainty on the HC5-50 (thus PNEC = HC5-50/AF). The advantage of this statistical extrapolation method is that it uses the whole sensitivity distribution of species in an ecosystem to derive a PNEC instead of taking only the lowest long-term NOEC.

 

Selected toxicity data

The bounded Klimisch 1 and 2 toxicity data that were considered relevant for PNEC derivation were used to calculate the geometric species mean value per endpoint. The most sensitive endpoint was selected for the assessment factor or statistical extrapolation (SSD) approach.

 

As discussed above, data are not corrected for ageing or soil properties. Because of the absence of a significant ageing effect, toxicity data for various equilibration periods (ageing) are treated as replicates and a geomean value is calculated for this soil-endpoint combination. The toxicity data derived in different soils are also taken together considering the very limited effect of soil properties on boron toxicity in soil and the PNEC derivation is based on one species/soil mean value for each endpoint. Because of the large difference in bioavailability between boron naturally present in soils and added soluble boron, the PNEC is based on added boron concentrations.

 

In total, 15 different taxonomic groups were covered. EC10/NOEC values are available for different monocotyledon and dicotyledon plants belonging to 8 different families. Chronic NOEC values are available for 6 different invertebrate taxonomic groups, including soft-bodied and hard-bodied invertebrate species. Reliable chronic EC10 values are included for two microbial processes affecting the nitrogen cycle. An overview of the calculated geometric species mean value (based on added boron concentrations) used for the PNEC calculation is presented in the following table. The lowest species mean NOEC was observed for Zea mays (7.2 mg B/kg) and the nematode Caenorhabditis elegans was the least sensitive organism (species mean value of 86.7 mg B/kg).

Group

Scientific name

Most sensitive endpoint

Geometric mean NOEC/EC10value

(mg B/kg dw)

Plants

Triticum aestivum (wheat)

Root yield

16.7

Plants

Zea mays (Corn)

Shoot yield

7.2

Plants

Avena sativa (oat)

Shoot biomass

11.0

Plants

Medicago sativa (Alfalfa)

Shoot yield

11.4

Plants

Hordeum vulgare (Barley)

Root elongation

13.2

Plants

Brassica napus (Canola)

Shoot biomass

13.9

Invertebrates

Folsomia candida (springtail)

Juvenile production

15.4

Plants

Calamagrostis canadensis (Bluejoint marsh reed)

Seedling emergence

16.6

Invertebrates

Enchytraeus luxuriosus (worm)

Reproduction

17.0

Plants

Trifolium pratense (Red clover)

Seedling emergence

17.1

Plants

Lycoperiscon esculentum (Tomato)

Seedling emergence

20.6

Invertebrates

Enchytraeus crypticus (worm)

Reproduction

22.6

Invertebrates

Hypoaspis aculeifer (mite)

Reproduction

22.7

Plants

Daucus carota (Carrot)

Seedling emergence

24.8

Plants

Beckmannia syzigachne (American sloughgrass)

Seedling emergence

24.8

Plants

Phleum pratense (Timothy)

Seedling emergence

26.0

Plants

Agropyion dasystachyum (Northern wheatgrass)

Root length

28.0

Plants

Agropyion riparium (Streambank wheatgrass)

Root yield

28.0

Plants

Agropyion smithii (Western wheatgrass)

Shoot length

28.0

Plants

Brasica oleracea (cabbage)

Root length

28.0

Plants

Brassica rapa (Turnip)

Root yield

28.0

Plants

Bromus marginatus (Mountain bromegrass)

Shoot yield

28.0

Plants

Cucumis sativa (Cucumber)

Root length

28.0

Plants

Glycine max (Soybean)

Root length

28.0

Plants

Koeleria macrantha (june grass)

Root yield

28.0

Plants

Linum usitatissimum (Flax)

Root yield

28.0

Plants

Lolium perenne (Perennial ryegrass)

Root length

28.0

Plants

Latuca sativa (Lettuce)

Seedling emergence

28.9

Plants

Festuca rubra (Red fescue)

Seedling emergence

30.1

Invertebrates

Onychiurus folsomi (springtail)

Juvenile production

31.0

Invertebrates

Eisenia andrei (earthworm)

Growth (juvenile dry weight)

36.1

Microbial

Substrate induced nitrification

Substrate induced nitrification

41.3

Plants

Raphanus sativus (Radish)

Shoot yield

42.0

Invertebrates

Pecilus cupreus (carabid beetle)

feeding rate

47.5

Microbial

Nitrogen transformation

N transformation

48.1

Plants

Schizachyrium scoparius (Little bluestem)

Seedling emergence

48.9

Plants

Allium cepa (Spanish onion)

Shoot length

56.0

Invertebrates

Eisenia fetida (earthworm)

Reproduction

70.1

Invertebrates

Caenorhabditis elegans (Nematode)

Reproduction

86.7

 

Number of families

Individual species

Taxonomic group

(family level for plants)

Higher plants

Dicotyledonous plants

1

Lactuca sativa

Asteraceae

2

Raphanus sativus, Brassica napus, Brassica oleracea, Brassica rapa

Brassicaceae

3

Cucumis sativa

Cucurbitaceae

4

Glycine max, Trifolium pratense, Medicago sativa

Fabaceae

5

Lycopersicon esculentum

Solanaceae

6

Daucus carota

Apiaceae

Monocotyledonous plants

7

Allium cepa

Alliaceae

8

Agropyion riparium, Agropyion dasystachyum, Agropyion smithii, Avena sativa, Beckmannia syzigachne, Bromus marginatus, Calamagrostis canadensis, Fetusca rubra, Hordeum vulgare, Lolium perenne, Triticum pratense, Zea mays, Linum usitatissimum, Schizachyrium scoparius, Koeleria macrantha, Phleum pratense

Poaceae

Invertebrates

Arthropoda

9

Folsomia candida, Onychirius folsomii

Collembola, Isotomidae

10

Hypoaspis aculeifer

Arachnida, Acari, Laelapidae (predatory mite)

11

Pecilus cupreus

Insecta, Coleoptera, Carabidae (carabid beetle)

Other invertebrates

12

Eisenia andrei,Eisenia fetida

Lumbricidae

13

Enchytraeus crypticus,Enchytraeus luxuriosus

Enchytraeidae

14

Caenorhabditis elegans

Nematoda

Micro-organisms

15

Nitrogen transformation, Substrate induced nitrification functions

 

 

 

Statistical extrapolation approach

To estimate the HC5-50 value from the terrestrial toxicity data, the statistical extrapolation method as described by Aldenberg & Jaworska (2000) was used for calculating the median fifth percentile (HC5-50) of the best fitting distribution curve.

 

The log-logistic distribution was selected as the best fitting distribution based on the Anderson-Darling test (please refer to Figure D in the attached document "Additional information on PNEC derivation"). The HC5at the 50th% confidence limit (together with 5thand 95thconfidence limits) derived from the log-logistic distribution, is 11.3 (8.8 – 13.5) mg B/kg (based on added boron concentrations).

 

The HC5-50 value based on the log-normal distribution (11.4 mg B/kg dw) is higher compared to the HC5-50 value derived by the log-logistic distribution and the confidence is wider (8.9 – 13.8 mg B/kg). The Anderson-Darling statistics show that the log-normal distribution results in a weaker fit of the tails of the distribution.

 

Derivation of Assessment Factor (Uncertainty analysis)

Because the available ecotoxicity database for the PNEC derivation is large, the use of the statistical extrapolation method is preferred for PNEC derivation rather than the use of an assessment factor on the lowest NOEC, as specified by the Guidance document on information requirements and chemical safety assessment Chapter R.10.3.1.3.This ECHA Guidance document recommends that the PNEC be derived using an assessment factor combined with the 50 % confidence value of the 5th percentile value of the SSD (thus PNEC = HC5-50/AF),with an AF between 1 and 5, to be judged on a case-by-case basis. Based on the available data, the following points have to be considered when determining the size of the assessment factor:

 

1. The overall quality of the database and the end-points covered, e.g., if all the data are generated from “true” chronic studies;

 

The B-database covered ecologically relevant endpoints. The selected endpoints were all relevant for potential effects at the population level: yield based on roots, shoots or whole plant biomass, root elongation, shoot length, seedling emergence for the terrestrial plants; mortality, weight, juvenile production, hatching, reproduction and feeding rate for the invertebrates; substrate induced nitrification and N-transformation for microbial processes.

The EC10/NOEC data were extracted from tests performed in natural and artificial soils, covering the following range of soil characteristics shown in the following table (pH value, organic carbon, clay content and background boron concentration). The toxicity data for plants covered the representative range in soil properties encountered in European soils. The data for invertebrates were derived in soils with median soil properties and toxicity to micro-organisms was mainly tested in more sensitive soils (low organic carbon and clay content).

 

Parameter

 

Plants

Invertebrates

Microbial tests

pH

Toxicity studies

4.4-8.4

5.1-6.5

5.2-7.6

 

Europe

 

4.3-7.4

 

Org C (%)

Toxicity studies

0.1-30.7

0.9-7.4

0.9-2.1

 

Europe

 

1.0-5.8

 

Clay (%)

Toxicity studies

2-59

15-30

2-13

 

Europe

 

6-37

 

Background boron (mg/kg)

Toxicity studies

1-32

1-9.9

2.3-11.0

 

Europe

 

No data

 

 

Data are either from tests focusing on sensitive life stages (e.g. root elongation, reproduction) or from “chronic exposure” (e.g. growth, reproduction). The exposure time for most plant studies varied between 4 and 24 days. Two longer-term studies with Medicago sativa and Zea mays are also included (up to 90 and 70 days, respectively).Exposure times between 4 and 63 days were reported for the tests with invertebrates. The test duration for the bioassays on microbial nitrogen processes varied between 28 and 102 days.

 

Conclusion: True chronic data are available for multiple endpoints for plant species, invertebrate species and microbial processes, and for a range of soil types. The overall quality of the database can therefore be considered optimal.

 

2. The diversity and representativeness of the taxonomic groups covered by the database;

 

From the extracted data, it is clear that the boron database largely fulfils the requirement of 10-15 different EC10/NOEC values (preferably more than 15) from chronic/long term studies for different species covering at least 8 different taxonomic groups from 3 trophic levels. The database contains a total of 235 individual NOEC or EC10values, resulting in 39 different “species mean” values. An overview of all corresponding families covered by the terrestrial database is given in corresponding table .The database includes 28 geometric mean values EC10/NOEC values for the plants, 9 geometric mean values for the invertebrates and 2 chronic EC10value for a microbial process:

 

·        In total, 192 individual NOEC or EC10values are available for 28 different plant species belonging to 7 different families, including agricultural species and covering both monocotyledon and dicotyledon plants.

·        36 chronic NOEC values are available for 9 different invertebrate species covering hard and soft bodied organisms with different exposure routes and feeding strategies belonging to 6 large taxonomic groups: Collembola, Arachnida, Insecta, Lumbricidae, Nematoda and Enchytraeidae.

·        9 individual EC10values for 2 microbial processes affecting the nitrogen cycle are included in the database. No data are available for microbial processes affecting the carbon cycle. However, based on the experience for other inorganic substances (Cu, Ni, Zn, etc.) microbial processes affecting the nitrogen cycle are generally more sensitive compared to carbon respiration assays.

 

The term “taxonomic group” set out in the Guidance Document leaves room for interpretation. Taxonomic group is indeed a broad definition, pointing to any level of classification or taxonomy (“a group or category, at any level, in a system for classifying plants or animals”) and the Guidance Document R.10 does not explicitly define the level of classification to be used. The requirements for different taxonomic groups are well defined for aquatic organisms, but not for terrestrial organisms. Moreover, the Guidance Document also states that deviations from these recommendations can be made, on a case-by-case basis, through consideration of sensitive endpoints, sensitive species, mode of toxic action and/or knowledge from structure-activity considerations.

 

Borates are known for their fungicidal, bacteriocidal, and insecticidal properties. Thus, the inclusion of toxicity tests on species of these groups is of paramount importance for an SSD approach, as species of the afore mentioned taxa could be the most sensitive. Based on the information available, there is no direct evidence or indication that some sensitive organisms are lacking in the database:

 

Plants seem to be most sensitive and are well covered in the database (28 species for 7 different families)

·        The only available toxicity data for fungi are based on culture solutions (Bowen and Gauch, 1966) and NOEC values derived for 5 different fungi range between 5 and 1200 mg B/L), while the typical solution-based NOEC values for plants are in the range of 1 to 10 mg B/L. Based on this information, there is no indication that fungi are more sensitive to boron when compared to plants.

·        The available data for the toxicity of boron to micro-organisms is limited to nitrogen transformation processes. However, comparison with data for plants and invertebrates shows that these microbial processes are not the most sensitive to boron toxicity. This data gap is not considered significant because microbial processes affecting the N cycle in soil are generally more sensitive to contamination than other microbial process (e.g. respiration, C mineralisation etc.).

·        High quality chronic NOEC or EC10values are available for several arthropod species, including insects and mites. Diet-based toxicity data are also available for a range of other insects, but these studies are considered not relevant for assessment of direct soil toxicity and derivation of a PNECsoil.

 

In conclusion, based on the information available, it can be concluded that the most sensitive species are likely to be present in the database.

 

Conclusion: The database is composed of plant, invertebrate and microbial data with most data for the former. The overall quality of the database is considered close to optimal, but may be limited by a lack of reliable toxicity data for microbial C transformation in soil.

 

3. Statistical uncertainties around the 5thpercentile estimate, e.g., reflected in the goodness-of-fit or the size of confidence interval around the 5thpercentile;

 

Different distributions have been evaluated for different soil types or scenarios. Both statistical (e.g. Kolmogorov-Smirnov, Andersen-Darling tests) and visual (e.g. Q-Q plots) goodness-of-fit techniques were used in order to select the most appropriate distribution function for the compiled long term data set. The final distribution function was selected on the basis of the Anderson-Darling goodness-of-fit test as this test highlights differences between the tail of the distribution (the lower tail is the region of interest) and the input data. The log-normal probability distribution of the boron dataset has been checked with both the Kolmogorov-Smirnov and the Anderson-Darling goodness-of-fit test. Based on the Anderson-Darling goodness-of-fit test, the lognormal distribution was selected as the best fitting distribution (0.05 ≤ p ≤ 0.1).

The HC5 at the 50th % confidence limit (together with 5th and 95th confidence limits) derived from the log-normal distribution is 11.29 (8.8 – 13.5) (based on added boron concentrations).

 

Conclusion: The log-normal distribution has been selected as the best fitting distribution for derivation of the HC5-50.

 

4. Evaluation of NOEC values below the HC5-50

 

A comparison of the HC5-50 value with the NOEC/EC10values shows that only the species mean NOEC/EC10value for Zea mays falls below the HC5-50 derived by the best fitting distribution. The value of 7.2 mg B/kg dw for Zea mays was based on shoot yield in a 70-day growth test.

 

Some individual NOEC or EC10values were also below the HC5-50 value of 11.3 mg B/kg dw: barley root elongation in Zwijnaarde, Wingene, Maizeret and Ramona soils (geomean values 4.5, 7.6, 9.3 and 6.1 mg B/kg dw, respectively), alfalfa shoot yield (NOEC 5-20 mg B/kg dw), Eisenia andreijuvenile growth in artificial soil (5.2 mg B/kg dw) and substrate induced nitrification in Zwijnaarde soil (geomean 6.0 mg B/kg). It has to be noted that many of these soils have an organic carbon content and clay content below the 10thpercentile for European soils (1% organic carbon and 6% clay), indicating low buffering capacity.

 

Conclusion: Only 1 species mean NOEC/EC10value was found below the HC5-50 value (but was within a factor 2).

 

5. Comparisons between field/microcosm studies and the 5thpercentile to evaluate the laboratory to field extrapolation.

 

No reliable field studies for the effect of boron on terrestrial organisms were available. One available field study allowed the derivation of threshold concentrations of boron for plants in soils at the field scale (Gupta and Cutcliffe, 1984). In this study, cabbage (Brassica oleraceavar. capitata L. ‘Houston Evergreen’) and field beans (Phaseolus vulgaris L. ‘Seafarer’) were grown successively for two seasons at four locations after a single application of borate (control, 2.2, 4.4 and 8.8 kg B/ha, corresponding to control, 1.6, 3.1 and 6.3 mg B/kg dw based on the assumption of homogeneous mixing of boron in the upper 10 cm). No significant adverse effects of increasing boron concentration were observed for cabbage yield at any location in either cropping season. Yields of dry bean seeds were significantly affected during the first cropping season, with NOEC values of 1.6 and 3.1 mg added B/kg, depending on the location. At these application rates, sub-optimal growth was still found for cabbage at some locations, pointing to potential boron deficiency. No effects of boron application on dry bean seed yield were observed during the second cropping season. However, these results were not judged to be reliable (Klimisch 3) because no standard guidelines were followed, and no information on actual soil boron concentration during the test period was reported. (Note that no standard guidelines for such studies exist.)

No field data or mesocosm studies were available with data on invertebrates and micro-organisms.

 

Conclusion: No reliable field studies for the effect of boron on terrestrial organisms were available. Some field data for agricultural plant species give conflicting results, with indications for both boron deficiency and boron toxicity at the same soil boron concentration, depending on the plant species.

 

Overall conclusion on PNECsoil

Because of the large difference in bioavailability between boron naturally present in soils and added soluble B, risks of added soluble boron will be assessed based on added boron. Based on the above uncertainty analysis and the availability of studies on the effects of soil types and ageing on the toxicity of boron to soil organisms, it is proposed to apply an Assessment Factor of 2 on the HC5-50added of 11.3 mg B/kg dw, resulting in a PNECadded for the soil compartment of 5.7 mg B/kg dw.

 

The assessment factor of 2 takes into account:

·        the extensive database of chronic boron effects on terrestrial organisms covering a representative range of plant and invertebrate species, microbial processes and soil conditions for Europe;

·        that there are no indications that there are some sensitive species or taxonomic groups lacking in the database;

·        the good fit of the log-logistic distribution to the dataset;

·        the presence of some NOEC/EC10values below the HC5-50;

·        the lack of good field validation;

·        the small gap between boron deficiency and toxicity for plants; and

·        information on the relatively limited effect of soil properties and ageing reactions on boron toxicity in soil.

7.7 Environmental classification justification

- Acute reference values

An overview of the selected high quality species mean/lowest short-term standard freshwater toxicity data for the 3 different pH classes is provided in the following table.

Test organisms

L(E)C50 (mg/L)

pH: 5.5 – 6.5

pH: 6.5 – 7.5

pH 7.5 – 8.5

Algae

 

 

 

Pseudokirchnerella subcapitata (growth rate)

n

Min

Max

Geometric mean/lowest value

 

/

/

/

/

 

/

/

/

/

 

1

52.4

52.4

52.4

Invertebrates

 

 

 

Ceriodaphnia dubia

n

Min

Max

Geometric mean/lowest value

 

/

/

/

/

 

2

91

102

91

 

6

93

165

119

Fish

 

 

 

Pimepales promelas

n

Min

Max

Geometric mean/lowest value

 

/

/

/

/

 

/

/

/

/

 

1

79.7

79.7

79.7

 

 

- Chronic reference values

An overview of the selected high quality species mean/lowest long-term standard freshwater toxicity data for the 3 different pH classes is provided in the following table.

Test organisms

NOEC/L(E)C10 (mg/L)

pH: 5.5 – 6.5

pH: 6.5 – 7.5

pH 7.5 – 8.5

Algae

 

 

 

Pseudokirchnerella subcapitata (growth rate)

n

Min

Max

Geometric mean/lowest value

Most sensitive endpoint

 

/

/

/

/

/

 

/

/

/

/

/

 

1

17.5

17.5

17.5

growth rate

Invertebrates

 

 

 

Daphnia magna

n

Min

Max

Geometric mean/lowest value

Most sensitive endpoint

 

/

/

/

/

/

 

/

/

/

/

/

 

5

6.6

18.2

14.2

reproduction

Fish

 

 

 

Oncorhynchus mykiss

n

Min

Max

Geometric mean/lowest value

Most sensitive endpoint

 

/

/

/

/

/

 

2

5.1

31.4

5.1

mortality

 

3

9.9

41.5

9.9

mortality

Ictalurus punctatus

n

Min

Max

Geometric mean/lowest value

Most sensitive endpoint

 

/

/

/

/

/

 

1

11.9

11.9

11.9

mortality

 

3

3.5

47.0

3.5

mortality

Carassius auratus

n

Min

Max

Geometric mean/lowest value

Most sensitive endpoint

 

/

/

/

/

/

 

/

/

/

/

/

 

4

20.9

37.8

30.3

mortality

Brachydanio rerio

n

Min

Max

Geometric mean/lowest value

Most sensitive endpoint

 

/

/

/

/

/

 

/

/

/

/

/

 

1

6.9

6.9

6.9

growth (weight)

Micropterus salmoides

n

Min

Max

Geometric mean/lowest value

Most sensitive endpoint

 

/

/

/

/

/

 

/

/

/

/

/

 

1

36.8

36.8

36.8

mortality

Pimepales promelas

n

Min

Max

Geometric mean/lowest value

Most sensitive endpoint

 

/

/

/

/

/

 

/

/

/

/

/

 

1

21.6

21.6

21.6

mortality

 

 

A summary of the selected acute and chronic reference values at the different pH’s is provided in the following table.

pH range

Reference values (mg B/L)

Acute reference value

Chronic reference values

pH 5.5 – 6.5

pH 6.5 – 7.5

pH 7.5 – 8.5

/

91.0

52.4

/

5.1

3.5

 

The following ecotoxicity reference values for the soluble Boron were identified:

- lowest Acute ecotoxicity reference value of 52.4 mg B/L for Pseudokirchneriella subcapitata

- and a long term ERV: 3.5 mg/L for Ictalurus punctatus

Conclusion on classification

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