Reaction mass of zinc oxide and zinc peroxide

Endpoint summary

Additional information

No terrestrial toxicity data are available for the target substance Zinc peroxide. However, extensive information is available on (ionic) Zinc, which is the main determinant of ecotoxicity. A justification for read-across is attached to IUCLID section 13.

 

All cells apart from anaerobic bacteria produce hydrogen peroxide in their metabolism. Furthermore, Hydrogen peroxide is formed abiotically in the environment. To prevent oxidative damage, cells have developed the ability to decompose some amount of excess Hydrogen peroxide (EU RAR, 2003).

In the transformation/dissolution test conducted with the target source Zinc peroxide, it was demonstrated, that only low levels of hydrogen peroxide are released.

 

In the view of the high degradation capacity for hydrogen peroxide in most organisms, it is unlikely that the low levels of hydrogen peroxide released from ZnO2 is distributed in the organisms, and therefore the endogenous steady state levels of hydrogen peroxide are unlikely to be affected.Supporting aquatic toxicity data on hydrogen peroxide further demonstrated higher effects values than the Zinc compounds. Therefore, it is concluded, thatmainly Zn2+ determines ecotoxicity.

 

Setting the PNECadd soil

1. Sources of ecotoxicological data

In the EU risk assessment on zinc, an extensive analysis was made of the available terrestrial toxicity data, available at that time (ECB 2008). The data were carefully scrutinised for quality and relevancy by the Rapporteur and member states. In the present exercise, all data that were considered useful for deriving the PNEC soil in the risk assessment, are used. In addition, an update of the terrestrial toxicity data that became available after the closure of the EU RA, has been made. Based on this update, the PNEC derivation for Zn in soils has been revised. This PNEC derivation is now based on the data and bioavailability models presented in the Risk Assessment Report (RAR) for Zn under the existing substances regulation, the comments of the SCHER on this RAR and new reliable data not yet included in the RAR.

All toxicity data judged reliable and relevant in the RAR for Zn are included (171 NOEC or EC10 values). In 2010, an additional literature search was performed covering the scientific literature since 2000 for new reliable toxicity data for Zn on terrestrial organisms (plants, invertebrates and micro-organisms). This new data search resulted in 43 new NOEC or EC10 values.

 

2. Selection of ecotoxicological data

The toxicity data on invertebrates and plants are from single-species tests that study common ecotoxicological parameters such as survival, growth and/or reproduction. The toxicity data on micro-organisms are from tests in which microbe-mediated soil processes, such as C- and N- mineralisation were studied. These microbial toxicity tests are multiple species tests because these microbe-mediated processes reflect the action of many species in soil microbial communities.

 

Relevance

Biological relevance

The toxicity data on terrestrial organisms are from ecotoxicity tests that study relevant ecotoxicological parameters such as survival, growth, reproduction, litter breakdown, abundance. Relevant endpoints for soil micro-organisms focused on functional parameters (such as respiration, nitrification, mineralisation) and microbial growth, but also enzymatic processes are considered relevant.

 

Relevance of the test media

Only data from observations in natural and artificial (OECD) soil media have been used in this report, tests performed in substrates that were judged as not representative for soils (e. g. nutrient solution, agar, pure quartz sand and farmyard manure) were not included in this effects assessment.

The data used in the effect assessment should be based on organisms and exposure conditions relevant for Europe. Excluding all data derived in non-EU soils would, however, considerably reduce the amount of data to be used. Therefore, also data based on soils collected outside Europe have been used when the soil properties were within the representative range for Europe.

 

Test duration

What comprises “chronic exposure” is a function of the life cycle of the test organisms. A priori fixed exposure durations are therefore not relevant. The duration should be related to the typical life cycle and should ideally encompass the entire life cycle or, for longer-lived species the most sensitive life stage. Retained exposure durations should also be related to recommendations from standard ecotoxicity (e. g. ISO, OECD, ASTM) protocols.

Typically, chronic test duration for the higher plants are within the range of 4 (e. g. the barley root elongation test based on ISO 11269-1 (1995)) and 21 days (e. g. the tomato shoot yield test based on ISO 11269-2 (1995)). OECD n° 208 (plant seedling emergence and growth test, 1984) recommended a test duration of at least 14 days after emergence of the seedlings. For soil invertebrates, assessing the chronic effects of substances on sub-lethal endpoints such as reproduction on oligochaetes has a typical exposure duration of 3 to 6 weeks for the standard organismEnchytraeus albidus(OECD, 2000; ISO 16387). For another standard speciesFolsomia candidasurvival and reproduction is typically assessed after 28 days of exposure (ISO 11267, 1999). Reported test duration using soil micro-organisms vary largely but standard tests last 28 days for carbon transformation (OECD n° 216) and for nitrogen transformation (OECD n° 217).

 

Reliability

Type of test

Both standard test organisms and non-standard species can be used in the framework of a risk assessment. In general, toxicity data generated from standardized tests, as prescribed by organizations such as OECD and USEPA will need less scrutiny than non-standardized test data, which will require a more thorough check on their compliance with reliability criteria before being used. GLP and non-GLP tests can be used provided that the latter fulfil the stipulated requirements.

 

Concentration-effect relationships

Because effect concentrations are statistically derived values, information concerning the statistics should be used as a criterion for data selection. If no methodology is reported or if values are ‘visually’ derived, the data were considered unreliable. Effect levels derived from toxicity tests using only 1 test concentration always results in unbounded and therefore unreliable data. Therefore, only the results from toxicity tests using 1 control and at least 2 Zn concentrations were retained.

Tests that do not comply with the above-mentioned stipulations are rated as not reliable and are not recommended for use in the risk assessment exercise.

 

3. Derivation of EC10/NOEC values

According to the REACH Guidance on information requirements and chemical safety assessment (Chapter R.10.2.2.1), there is a preference to use EC10 values as calculated from the concentration-effect relationship, for derivation of the Predicted No Effect Concentration (PNEC). In some cases no reliable EC10 can be derived because e. g. no significant dose-response curve can be fitted or the EC10 is outside the concentration range tested. When in these cases a bounded NOEC value can be derived, this NOEC value will be used instead of the EC10 for PNEC derivation. No unbounded NOEC (i.e. no effect at highest dose tested) or LOEC (i. e. significant effect at lowest dose tested) values or EC10 values extrapolated outside the concentration range tested are used for derivation of the PNEC.

 

 4. Toxicity data

In accordance to the EU RA, all toxicity data are expressed as added Zn concentration in soil, based on either the nominal dose added or on measured, background corrected soil Zn concentrations.

For plants, in total 45 individual high quality NOEC or EC10 values are selected for the PNEC derivation, representing 18 different species. NOEC or EC10 values vary from 32 mg Zn/kg dw forTrifolium pratenseandVicia sativa(Van der Hoeven and Henzen, 1994) to 5855 mg Zn/kg dw forTriticum aestivum(Warne et al., 2008a).

Information on soil properties allowing bioavailability correction for plants (eCEC and pH) is only available for 31 NOEC or EC10 values, representing 9 different plant species and including the same minimum and maximum values as the total dataset for plants.

In total 61 individual high quality NOEC or EC10 values for reproduction of terrestrial invertebrates are selected for the PNEC derivation. Twenty-four NOEC/EC10 values are available for toxicity of Zn to reproduction of terrestrial arthropods, representing 2 different species and ranging between 14.6 and 1000 mg Zn/kg dw (both forFolsomia candida; Lock and Janssen, 2001c and Lock et al., 2003). The other 37 NOEC or EC10 values cover 6 different worm species and vary from 35.7 mg Zn/kg forEnchytraeus albidus(Lock and Janssen, 2001c) to 1634 mg Zn/kg dw forLumbricus terrestris(Spurgeon et al., 2000).

For all 61 reliable toxicity thresholds, the information on soil properties allowing bioavailability correction for plants (eCEC) is available.

For microbial assays, in total 108 individual high quality NOEC/EC10’s are selected for the PNEC derivation. These values represent 4 nitrogen transformation processes, 5 carbon transformation processes and 8 enzymatic processes and range from 17 mg Zn/kg dw for respiration (Chang and Broadbent, 1981 and Lighthart et al., 1983) to 2623 mg Zn/kg dw for phosphatase (Doelman and Haanstra, 1989).

 

Information on the background Zn concentration, allowing correction for differences in bioavailability among soils, is only available for 76 NOEC or EC10 values, representing 13 microbial processes (4 for N cycle, 5 for C cycle and 4 enzymatic processes). The total range in NOEC/EC10 values for the dataset with results for background Zn concentration is the same as for the total dataset for micro-organisms.

 

 5. Calculation of the HC5-50

The available ecotoxicity database for the effect of Zn to soil organisms is large. Therefore, the use of the statistical extrapolation method is –as specified by the Guidance document on information requirements and chemical safety assessment Chapter R.10.3.1.3– preferred for PNEC derivation rather than the use of an assessment factor on the lowest NOEC. The PNEC will be based on the 50% confidence value of the 5th percentile value (HC5-50) and an additional assessment factor taking into account the uncertainty on the HC5-50 (thus PNEC = HC5-50/AF).

 

 5.1. Generic, non-normalised HC5-50

 

The non-normalised terrestrial HC5-50 was derived based on either all individual reliable NOEC/EC10 values or the species mean NOEC/EC10 values for the most sensitive endpoint. Two different approaches were used:

1)   taking into account all data and;

2)   only taking into account the data with information on soil properties allowing correction for differences in bioavailability among soils.

 

It must be stressed that, considering the important influence of soil properties on bioavailability and toxicity of zinc in soils, these approaches are less ecologically relevant for HC5-50 derivation compared to the HC5-50 values taking into account correction for bioavailability (both ageing and effect of variation in soil properties). The cumulative frequency distribution (SSD) of the non-normalised species mean NOEC values for Zn is presented inFigures 9 to 12(see attachment).

Using statistical extrapolation and the log-normal distribution results in a HC5-50 of 35.6 mg Zn/kg based on all individual NOEC/EC10 data and a HC5-50 of 33.7 mg Zn/kg when using all individual NOEC/EC10 data with information on soil properties allowing correction for bioavailability among soils (see table below). Based on the Anderson-Darling goodness-of-fit statistics, the log-normal distribution is accepted for all distributions based on both all individual reliable observations and the species/process mean values.

 

Generic HC5 and HC5-50 (with 5% and 95% confidence interval) values for toxicity of Zn to the terrestrial environment based on a log-normal distribution of non-normalised NOEC/EC10added values.

Scenario	All individual NOEC/EC10 values			Species/process mean values
	N	HC5 (mg Zn/kg)	HC5-50 (mg Zn/kg)	N	HC5 (mg/kg)	HC5-50 (mg Zn/kg)
All data	214	35.6	35.6 (29.1-42.5)	43	51.9	51.3 (34.7-69.4)
Data allowing bioavailability correction	168	33.8	33.7 (26.8-41.2)	30	36.5	35.8 (20.4-54.0)

Using a species/process mean approach for non-normalised data yields higher HC5-50 values compared to SSD based on all individual values: 51.3 and 35.8 mg Zn/kg for the total dataset or the data with information on soil properties allowing correction for bioavailability among soils, respectively. Averaging (geomean) all the results available for one species/process avoids over-representation of commonly tested species or processes, e. g.Eisenia fetida(29 data) or nitrification (20 data). This species/process mean approach is preferred for data corrected for the differences in soil properties when the intra-species variation can be considered as the main source of variation among data for a given species/process. However, this is not the case for the generic approach (non-normalised data) where variation between toxicity data for a certain species or process is also caused by differences in bioavailability among soils.

 

Generic species/process mean values.

All data	Species/microbial process	Mean NOEC/EC10	Data allowing bioavailability correction	Species/microbial process	Mean NOEC/EC10
		mg Zn/kg			mg Zn/kg
1	Vicia sativa	32	1	Vicia sativa	32
2	Trifolium pratense	45	2	Hordeum vulgare	33
3	Denitrification	62	3	Denitrification	39
4	Glutamic acid mineralization	64	4	Trifolium pratense	45
5	Nitrate reductase	67	5	Glutamic acid mineralization	55
6	Urease	72	6	Urease	73
7	Respiration	89	7	Zea mais	83
8	Hordeum vulgare	89	8	Respiration	83
9	Enchytraeus albidus	94	9	Enchytraeus albidus	94
10	Vigna mungo L.	100	10	Nitrification	120
11	Nitrification	120	11	Sinella curviseta	180
12	Dehydrogenase	128	12	Dehydrogenase	195
13	Sorghum bicolor	141	13	Allium cepa	200
14	Zea mais	171	14	Avena sativa	200
15	Sinella curviseta	180	15	Trigonella poenum graceum	200
16	N-mineralization	185	16	Glucose mineralization	204
17	Triticum vulgare	200	17	N-mineralization	211
18	Spinacea oleracea	200	18	Maize residue mineralization	241
19	Avena sativa	200	19	Folsomia candida	246
20	Allium cepa	200	20	Eisenia fetida	284
21	Trigonella poenum graceum	200	21	Brassica rapa	300
22	Amidase	200	22	Acetate mineralization	303
23	Glucose mineralization	204	23	Eisenia andrei	320
24	Maize residue mineralization	241	24	Aporrectodea caliginosa	342
25	Folsomia candida	246	25	Arylsulphatase	406
26	Eisenia fetida	284	26	Lumbricus terrestris	520
27	Medicago sativa	300	27	Triticum aestivum	584
28	Beta vulgaris	300	28	Phosphatase	826
29	Brassica rapa	300	29	Ammonification	1000
30	Acetate mineralization	303	30	Lumbricus rubellus	1634
31	Eisenia andrei	320
32	Aporrectodea caliginosa	342
33	Arylsulphatase	378
34	Lactuca sativa	400
35	Pisum sativum	400
36	Lycopersicon esculentum	400
37	Phosphatase	444
38	Lumbricus terrestris	520
39	Triticum aestivum	584
40	Phytase	590
41	Ammonification	1000
42	Lumbricus rubellus	1634
43	Pyrophosphatase	1640

 

There is no clear distinction among the three trophic levels (plants, invertebrates and micro-organisms) in their sensitivity to Zn in soil. Both individual and species/process mean toxicity data for the various plant and invertebrates species and microbial processes strongly overlap (Figures 9 to 12). Therefore, all data are pooled together into one species sensitivity distribution for the derivation of the PNEC value.

 

For 46 NOEC or EC10 values, no information is available on soil properties allowing correction for differences in bioavailability among soils (CEC and pH for plants and background Zn concentration for microbial processes). The toxicity data with information on these soil properties available cover 30 species/processes compared to a total of 43 for the total dataset. Toxicity data for 9 plant species and 4 microbial processes (all enzymatic processes) do not have information on the soil properties required. The reduced dataset however still covers the same extreme values (both minimum and maximum) as the total dataset. The HC5-50 values as calculated from a log-normal distribution are consistently lower for the reduced dataset compared to the total dataset (both based on all individual values as based on species/process mean values). It can therefore be concluded that the dataset with information on soil properties is both representative for the total dataset and conservative for the assessment of toxicity of Zn to terrestrial organisms.

 

Figure9(see attachment). The species sensitivity distribution based on all individual EC10 and NOEC values selected for PNEC derivation.

 

Figure10(see attachment). The species sensitivity distribution based on all individual EC10 and NOEC values with information on soil properties allowing correction for bioavailability among soils.

 

 

Figure11(see attachment).The species sensitivity distribution based on all species/process mean values.

 

Figure12(see attachment). The species sensitivity distribution based on species/process mean values for data with information on soil properties allowing correction for bioavailability among soils.

 

 

4.2. Normalised HC5-50 for soils: implementation of bioavailability

 

In the zinc risk assessment (ECB 2008), a detailed analysis of bioavailability of zinc to soil organisms was made. As a result, an approach for making a bioavailability correction on the ecotoxicity values and PNEC was developed and applied. The same approach is applied in the present analysis.

 

The general frame work for implementation of bioavailability into derivation of PNEC values is presented inFigure 13and uses the following steps:

1.    Select the reliable NOEC/EC10 data with information on soil properties allowing correction for bioavailability among soils.

2.    Derive the NOEC/EC10_added values by subtracting the Zn background concentration of the tested control soils from the total NOEC/EC10 values (measured NOEC/EC10) or use the NOEC/EC10_addedvalues from nominal NOEC/EC10 values.

3.    Apply an “ageing” factor of 3[1] to all NOEC/EC10_addedvalues. This is a conservative value, applied to all soil types (cfr EU risk assessment, ECB 2008).

In the added risk approach, application of a generic lab-to-field correction factor (i.e. a constant factor independent of soil properties) can also be done after steps 4, 5 or 6 instead of after step 2. This will not affect the final result of the HC5-50. It is here proposed after step 2, in accordance to the approach followed in the EU Risk assessment. This approach is also consistent with the total risk approach as used for other metals (e.g. Cu and Ni).

4.    Normalise each (lab-to-field corrected) NOEC/EC10_added value towards a reference soil using the appropriate slope.

 

 

NOEC/EC10ref= NOEC/EC10 x [abioticfactor-ref/abioticfactor]^slope

 

 

 

The available knowledge on the soil-type dependent bioavailability resulted in the following equations that are proposed for correcting bioavailability in soil (EU risk assessment, ECB 2008):

 

·        LogEC50=1.4+1.14*logCEC (F. candida)

·        LogEC50=1.9+0.79*logCEC (E. fetida)

·        LogEC50=1.1+0.87*logCEC +0.12*pH (wheat)

·        LogEC50=1.2+0.76*logZnBG (nitrification)

·        LogEC50=1.7+0.76*logZnBG (respiration)

 

Where

·        EC50 is the 50% effect concentration (mg Zn/kg)

·        CEC is the effective cation exchange capacity (cmol_c/kg), i.e. the CEC measured at the pH of the soil

·        ZnBG is the zinc background concentration in soil (mg Zn/kg). Note that background concentrations refer to ambient concentrations, not natural background. The soils used in the experiments were sampled from agricultural areas (not all), i.e. the tests and the assessments are made on soils that may already contain zinc from diffuse sources.

 

The slope derived for the springtailFolsomia candidawill be used for all terrestrial arthropods, while the slope derived forEisenia fetidawill be applied for all toxicity data for soft-bodied terrestrial invertebrates. All plant data will be normalised with the slopes derived for wheat and all NOEC or EC10 data for microbial processes will be normalised with the same slope derived for both nitrification and respiration.

The CEC at soil pH is usually not reported but can be predicted from %clay, %OM and soil pH based on an existing multivariate model that is calibrated on natural soils (Helling et al., 1964):

CEC=(30.4+4.4*pH)*%clay/100 + (-35+30*pH)*%OM/100

 

For OECD soils, it is assumed that the clay has no CEC contribution since, unlike prevailing clay types in Europe, kaolin clay has no permanent charge and its variable charge is inferior to that of the organic matter which is at least 5% in these studies. This assumption yields CEC=14.4 cmol_c/kg at pH 6.

5.    Where multiple data are available for the same species/process and endpoint, calculate the species/process mean value for the most sensitive endpoint for each species or process.

6.    Build the species sensitivity distribution (SSD) from the species/process geomean NOEC/EC10 values and derive the HC5/ HC5-50

 

Soils with larger CEC (large clay content and OM content) also tend to contain larger zinc concentrations. In the 15 soils collected for the research program on bioavailability of Zn in soils, there was a positive correlation between these two parameters (LogZn_BG=-0.1+0.72*log CEC; p<0.001; R²=0.67).

Taking into account the effect of soil properties, allows to normalise all individual EC10 or NOEC values and to calculate soil-specific HC5-50 values. As an example, this approach is applied for 8 different soil types (Table112). These soils are the same as those referred to in the EU risk assessment. For all soil types, the log-normal distribution results in a good fit according to the Anderson-Darling goodness-of-fit test. The HC5-50 values for these soil scenarios clearly stress the importance of soil properties on the predicted hazard of zinc in soils (Figure 14). The HC5-50 varies almost 10-fold between soils (between 30 mg Zn/kg for the sandy forest soil and 282 mg Zn/kg for the river clay soil). Normalised species/process mean values for the 8 soil scenarios are presented inTable113.

The bioavailability factors (BioF, i.e. ratio of normalised and generic species/process mean HC5 values) based on the SSD including plants, invertebrates and micro-organisms agree well with the minimum BioF factors reported in the RAR for zinc for either plants and invertebrates or microbial processes as based on the mean slopes.

 

Figure13.Flow chart for the implementation of bioavailability factors into PNEC derivation for Zn.

 

HC5 and HC5-50 (with 5% and 95% confidence interval) for the terrestrial environment based on lab-field corrected and normalised NOEC/EC10 values and a log-normal distribution.

Scenario	pH (water)	CEC at soil pH (cmolc/kg)	Soil total Zn (mg/kg)	HC5 All values	HC5 Species mean	HC5-50 Species mean	BioFsoil^$
Generic HC5* for selected data for which abiotic factors exist				101	109	107 (61-162)
Cattle farms, sandy soil (extensive) (1993)	5.83	10.97	28	94	93	91 (54-134)	0.8
Cattle farms, sandy soil (intensive) (1993)	5.94	6.98	32	85	72	71 (41-106)	0.7
Cattle farms, sandy soil (1994)	5.9	7.7	31	88	77	76 (44-112)	0.7
Forest, sandy soil (1994)	3.7	5.03	10.1	40	31	30 (17-47)	0.3
Arable farm, sandy soil (1995)	5.96	16.53	31	110	120	118 (71-173)	1.1
Cattle farm, peaty soil (1995)	5.93	33.04	124	279	264	260 (155-381)	2.4
Arable farms – marine clay soil (1996)	8.09	14.42	68	167	188	185 (113-265)	1.7
Cattle farms – river clay soils (1996)	6.59	28.88	172	316	287	282 (168-415)	2.6

^$: BioF = HC₅/HC₅generic for species mean values

*: including lab-field factor of 3

 

 

Figure 14 (see attachment). The species sensitivity distributions for the 8 soil scenarios as fitted by the log-normal distribution.

 

The soils listed below and figure 14 are examples. Calculations can be done when soil characteristics are documented using the soil bioavailability calculator.

 

Normalised and lab-field corrected species/process mean NOEC/EC10 values for the 8 soil scenarios.

Cattle farm, sandy soil (extensive)		Cattle farm, sandy soil (intensive)		Cattle farm, sandy soil		Forest, sandy soil		Arable farm, sandy soil		Cattle farm, peaty soil		Arable farm, marine clay soil		Cattle farm, river clay soil
Species/ process	Mean NOEC/EC10	Species/ process	Mean NOEC/EC10	Species/ process	Mean NOEC/EC10	Species/ process	Mean NOEC/EC10	Species/ process	Mean NOEC/EC10	Species/ process	Mean NOEC/EC10	Species/ process	Mean NOEC/EC10	Species/ process	Mean NOEC/EC10
Hordeum vulgare	69	Hordeum vulgare	48	Hordeum vulgare	52	Hordeum vulgare	20	Denitrification	88	Hordeum vulgare	186	Denitrification	160	Hordeum vulgare	199
Denitrification	82	Vicia sativa	75	Vicia sativa	81	Vicia sativa	30	Hordeum vulgare	103	Denitrification	253	Hordeum vulgare	165	Vicia sativa	310
Vicia sativa	108	Denitrification	90	Denitrification	88	Denitrification	38	Urease	157	Vicia sativa	290	Vicia sativa	256	Denitrification	324
Urease	145	Allium cepa	144	Allium cepa	155	Allium cepa	58	Vicia sativa	160	Urease	449	Urease	285	Enchytraeus albidus	514
Allium cepa	207	Trigonella poenum	144	Trigonella poenum	155	Trigonella poenum	58	Nitrification	234	Allium cepa	556	Enchytraeus albidus	297	Urease	576
Trigonella poenum	207	Trifolium pratense	153	Urease	157	Trifolium pratense	62	Glutamic acid mineralization	273	Trigonella poenum	556	Sinella curviseta	370	Allium cepa	594
Nitrification	217	Urease	160	Trifolium pratense	165	Urease	67	Respiration	291	Enchytraeus albidus	572	Nitrification	425	Trigonella poenum	594
Trifolium pratense	220	Sinella curviseta	162	Enchytraeus albidus	181	Brassica rapa	71	Allium cepa	307	Trifolium pratense	590	Allium cepa	491	Trifolium pratense	630
Enchytraeus albidus	239	Enchytraeus albidus	167	Sinella curviseta	181	Zea mais	87	Trigonella poenum	307	Nitrification	672	Trigonella poenum	491	Brassica rapa	726
Glutamic acid mineralization	253	Brassica rapa	176	Brassica rapa	190	Nitrification	100	Trifolium pratense	326	Brassica rapa	680	Glutamic acid mineralization	496	Sinella curviseta	816
Brassica rapa	253	Zea mais	214	Zea mais	230	Sinella curviseta	111	Enchytraeus albidus	331	Glutamic acid mineralization	782	Trifolium pratense	521	Nitrification	861
Respiration	269	Nitrification	240	Nitrification	234	Glutamic acid mineralization	116	Brassica rapa	375	Zea mais	824	Respiration	528	Zea mais	880
Sinella curviseta	271	Glutamic acid mineralization	279	Glutamic acid mineralization	273	Respiration	124	Sinella curviseta	432	Respiration	834	Aporrectodea caliginosa	574	Aporrectodea caliginosa	994
Zea mais	307	Respiration	298	Respiration	291	Enchytraeus albidus	129	Zea mais	455	Sinella curviseta	952	Brassica rapa	600	Glutamic acid mineralization	1003
Maize residue mineralization	441	Aporrectodea caliginosa	324	Aporrectodea caliginosa	350	Avena sativa	194	Maize residue mineralization	477	Aporrectodea caliginosa	1106	Zea mais	728	Respiration	1069
Aporrectodea caliginosa	463	Folsomia candida	418	Folsomia candida	467	Maize residue mineralization	203	Glucose mineralization	502	Maize residue mineralization	1368	Maize residue mineralization	867	Eisenia andrei	1667
Glucose mineralization	464	Avena sativa	478	Maize residue mineralization	477	Glucose mineralization	214	Aporrectodea caliginosa	640	Glucose mineralization	1439	Glucose mineralization	911	Eisenia fetida	1679
Acetate mineralization	656	Maize residue mineralization	489	Glucose mineralization	502	Aporrectodea caliginosa	250	Acetate mineralization	709	Avena sativa	1845	Folsomia candida	956	Maize residue mineralization	1754
Avena sativa	688	Glucose mineralization	514	Avena sativa	515	Folsomia candida	288	Arylsulphatase	866	Eisenia andrei	1854	Eisenia andrei	963	Lumbricus terrestris	1822
Folsomia candida	700	Eisenia andrei	543	Eisenia andrei	587	Acetate mineralization	302	N-mineralization	894	Eisenia fetida	1867	Eisenia fetida	970	Glucose mineralization	1845
Eisenia andrei	776	Eisenia fetida	547	Eisenia fetida	591	Arylsulphatase	370	Avena sativa	1018	Lumbricus terrestris	2027	Lumbricus terrestris	1053	Avena sativa	1970
Eisenia fetida	782	Lumbricus terrestris	593	Lumbricus terrestris	641	N-mineralization	381	Eisenia andrei	1073	Acetate mineralization	2033	Acetate mineralization	1288	Folsomia candida	2109
Arylsulphatase	802	Acetate mineralization	726	Acetate mineralization	709	Eisenia andrei	419	Eisenia fetida	1081	Folsomia candida	2459	Arylsulphatase	1574	Acetate mineralization	2607
N-mineralization	827	Arylsulphatase	888	Arylsulphatase	866	Eisenia fetida	422	Folsomia candida	1117	Arylsulphatase	2485	N-mineralization	1624	Arylsulphatase	3187
Lumbricus terrestris	848	N-mineralization	916	N-mineralization	894	Lumbricus terrestris	458	Lumbricus terrestris	1173	N-mineralization	2563	Avena sativa	1629	N-mineralization	3287
Phosphatase	1191	Phosphatase	1318	Phosphatase	1286	Phosphatase	549	Phosphatase	1286	Phosphatase	3689	Phosphatase	2337	Phosphatase	4730
Dehydrogenase	1193	Dehydrogenase	1321	Dehydrogenase	1289	Dehydrogenase	550	Dehydrogenase	1289	Dehydrogenase	3698	Dehydrogenase	2342	Dehydrogenase	4742
Ammonification	1748	Triticum aestivum	1394	Triticum aestivum	1502	Triticum aestivum	565	Ammonification	1888	Triticum aestivum	5377	Lumbricus rubellus	3308	Lumbricus rubellus	5726
Triticum aestivum	2004	Lumbricus rubellus	1865	Ammonification	1888	Ammonification	805	Triticum aestivum	2968	Ammonification	5416	Ammonification	3431	Triticum aestivum	5740
Lumbricus rubellus	2665	Ammonification	1935	Lumbricus rubellus	2015	Lumbricus rubellus	1440	Lumbricus rubellus	3685	Lumbricus rubellus	6368	Triticum aestivum	4748	Ammonification	6945

6. PNECadd soil

Based on an extensive uncertainty analysis, and in particular the availability of normalisation models a large toxicity database covering a representative range in plant and invertebrate species, microbial processes and soil conditions, and an extensive field validation, it can be concluded that the available database and models allow for the derivation of an HC5-50 that is protective for the terrestrial environment by statistical extrapolation.

 

3 types of PNECadd soil can be considered:

- The generic PNEC_add based on the entire ecotoxicity database is 35.6 mg Zn/kg.

-The generic PNEC_add can (in accordance with the EU risk assessment) be multiplied with a default “lab-to-field” correction factor of 3 for taking into account differences of zinc bioavailability after ageing (generic PNEC_addincluding ageing= 107mg/kg dw.)

-If information on soil type and soil conditions is available, a soil-specific PNEC_added can be calculated, by applying a further correction for bioavailability. A tool is available for this. For illustration, some examples were developed in the present analysis resulting in PNEC_added values for soil types representative for the EU conditions between approx. 30 and 300 mg Zn/kg.

 

 

[1]The factor 3 is the generic default and is applied for the generic soil in the usual case that ageing has occurred for one year or longer. A ratio of 2 should only be used in cases wherea rapid increase in zinc soil concentration could occur, e.g. due to the melting of snow,when ageing has occurred for less than one year (EU RA, ECB 2008).