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Measures as amended, taking into account amendments up to National Environment Protection (Assessment of Site Contamination) Amendment Measure 2013 (No. 1)
Administered by: Agriculture, Water and the Environment
Registered 03 Jun 2013
Start Date 16 May 2013

Commonwealth Coat of Arms

National Environment Protection (Assessment of Site Contamination) Measure 1999

as amended

made under section 14(1) of the

National Environment Protection Council Act 1994 (Cwlth), the National Environment Protection Council (New South Wales) Act 1995 (NSW), the National Environment Protection Council (Victoria) Act 1995 (Vic), the National Environment Protection Council (Queensland) Act 1994 (Qld), the National Environment Protection Council (Western Australia) Act 1996 (WA), the National Environment Protection Council (South Australia) Act 1995 (SA), the National Environment Protection Council (Tasmania) Act 1995 (Tas), the National Environment Protection Council Act 1994 (ACT) and the National Environment Protection Council (Northern Territory) Act 1994 (NT)

Compilation start date:                     16 May 2013

Includes amendments up to:            National Environment Protection (Assessment of Site Contamination) Amendment Measure 2013 (No. 1)

This compilation has been split into 22 volumes

Volume 1:       sections 1-6, Schedules A and B

Volume 2:       Schedule B1

Volume 3:       Schedule B2

Volume 4:       Schedule B3

Volume 5:       Schedule B4

Volume 6:       Schedule B5a

Volume 7:       Schedule B5b

Volume 8:       Schedule B5c

Volume 9:       Schedule B6

Volume 10:     Schedule B7 - Appendix 1

Volume 11:     Schedule B7 - Appendix 2

Volume 12:     Schedule B7 - Appendix 3

Volume 13:     Schedule B7 - Appendix 4

Volume 14:     Schedule B7 - Appendix 5

Volume 15:     Schedule B7 - Appendix 6

Volume 16:     Schedule B7 - Appendix B

Volume 17:     Schedule B7 - Appendix C

Volume 18:     Schedule B7 - Appendix D

Volume 19:     Schedule B7

Volume 20:     Schedule B8

Volume 21:     Schedule B9

Volume 22:     Endnotes

 

Each volume has its own contents

 

 

 

About this compilation

The compiled instrument

This is a compilation of the National Environment Protection (Assessment of Site Contamination) Measure 1999 as amended and in force on 16 May 2013. It includes any amendment affecting the compiled instrument to that date.

This compilation was prepared on 22 May 2013.

The notes at the end of this compilation (the endnotes) include information about amending Acts and instruments and the amendment history of each amended provision.

Uncommenced provisions and amendments

If a provision of the compiled instrument is affected by an uncommenced amendment, the text of the uncommenced amendment is set out in the endnotes.

Application, saving and transitional provisions for amendments

If the operation of an amendment is affected by an application, saving or transitional provision, the provision is identified in the endnotes.

Modifications

If a provision of the compiled instrument is affected by a textual modification that is in force, the text of the modifying provision is set out in the endnotes.

Provisions ceasing to have effect

If a provision of the compiled instrument has expired or otherwise ceased to have effect in accordance with a provision of the instrument, details of the provision are set out in the endnotes.

 

 

  

  

  


 

National Environment Protection (Assessment of Site Contamination) Measure 1999 National Environment Protection (Assessment of Site Contamination) Measure 1999 National Environment Protection (Assessment of Site Contamination) Measure 1999  National Environment Protection (Assessment of Site Contamination) Measure 1999 National Environment Protection (Assessment of Site Contamination) Measure 1999 National Environment Protection (Assessment of

Schedule B5b

 

 

GUIDELINE ON

Methodology to Derive
Ecological Investigation Levels
in Contaminated Soils

Explanatory note
The following guideline provides general guidance in relation to investigation levels for soil, soil vapour and groundwater in the assessment of site contamination.

This Schedule forms part of the National Environment Protection (Assessment of Site Contamination) Measure 1999 and should be read in conjunction with that document, which includes a policy framework and assessment of site contamination flowchart.

The original Schedule B5 to the National Environment Protection (Assessment of Site Contamination) Measure 1999 has been repealed and replaced by this document, together with Schedule B5a and Schedule B5c.

The National Environment Protection Council (NEPC) acknowledges the contribution of the Commonwealth Scientific and Industrial Research Organisation (CSIRO), the NSW Environment Protection Authority and the NSW Environmental Trust to the development of this Measure.


                                                                                       PageContents
Methodology to derive ecological
investigation levels in contaminated soils

 

1                             Introduction                                                    1

2                             EIL derivation methodology                          2

2.1             Overview of the EIL derivation methodology                          2

2.2             Levels of protection                                                                    2

2.2.1                Levels of protection for specific land uses                      3

2.2.1.1                 National parks and areas with high ecological value         4

2.2.1.2                 Urban residential and public open space                                5

2.2.1.3                 Commercial and industrial land                                              5

2.2.1.4                 Agricultural land                                                                          5

2.3             Determining the most important exposure pathways              5

2.3.1                Exposure pathway assessment for organic
contaminants                                                                   
9

2.3.1.1                 Half-life                                                                                          9

2.3.1.2                 Henry’s law constant                                                                   9

2.3.1.3                 Octanol-water partition                                                            10

2.3.1.4                 Overview of the main exposure pathways for
organic contaminants                                                              
11

2.3.2                Exposure pathway assessment for inorganic
contaminants                                                                 
11

2.3.2.1                 Biomagnification                                                                       11

2.3.2.2                 Henry’s law constant                                                                 12

2.3.2.3                 Overview of main exposure pathways for inorganic
contaminants                                                                              
12

2.4             Derivation of EIL values                                                          12

2.4.1                Collation and screening of data                                     13

2.4.1.1                 Toxicity data collation                                                             13

2.4.1.2                 Quantitative structure-activity relationships                    14

2.4.1.3                 Quantitative activity-activity relationships                       15

2.4.1.4                 Equilibrium partitioning method                                          15

2.4.1.5                 Screening and selection of toxicity data                               16

2.4.2                Standardisation of the toxicity data                              18

2.4.2.1                 Measures of toxicity                                                                  18

2.4.2.2                 Conversion from total to added concentrations                 20

2.4.2.3                 Duration of exposure                                                                20

2.4.2.4                 The use of toxicity data for endemic or overseas
species                                                                                          
20

2.4.3                Incorporation of an ageing and leaching factor           21

2.4.4                Comparison of available toxicity data to the minimum
data requirements                                                         
21

2.4.5                Calculation of the added contaminant limit using a
species sensitivity distribution approach                      
24

2.4.6                Normalisation of toxicity data to an Australian
reference soil                                                                 
25

2.4.7                Calculation of the added contaminant level using
an assessment factor approach                                    
27

2.4.8                Accounting for secondary poisoning and
biomagnification                                                           
28

2.4.9                Calculation of the ambient background
concentrations                                                               
29

2.4.9.1                 Inorganic contaminants                                                           29

2.4.9.2                 Organic contaminants                                                              30

2.4.10             Calculation of the EIL                                                   30

2.4.11             The reliability of the EIL                                               31

2.4.12             Evaluation of the appropriateness of the
derived EILs                                                                 
31

2.4.13             Strengths and limitations of EIL derivation
methodology                                                                  
31

2.4.13.1               Strengths                                                                                       32

2.4.13.2               Limitations                                                                                  32

3                             Technical notes on methods used in the
EIL derivation methodology                       
33

3.1             Methods to account for the effect of soil characteristics
on toxicity and bioavailability                                                 
33

3.1.1                Chemical estimates of bioavailability                            34

3.1.2                Normalisation relationships                                           35

3.1.3                Normalisation of toxicity data to a reference soil        37

3.2             Methods to calculate soil quality guidelines                            38

3.2.1                Species sensitivity distribution methods                        38

3.2.2                How do SSD methods work?                                         39

3.2.2.1                 Criticisms                                                                                     41

3.2.2.2                 Strengths and limitations                                                         42

3.2.3                Assessment factor methods                                            42

3.2.3.1                 Criticisms                                                                                     44

3.2.3.2                 Strengths and weaknesses                                                         44

3.2.4                Geometric mean methodology of the US EPA              45

3.2.4.1                 Strengths and limitations                                                         45

3.2.5                Methods for calculating EILs                                        46

3.2.6                Secondary poisoning and biomagnification                   46

3.2.7                Methods for accounting for secondary poisoning         46

3.2.8                Using biomagnification algorithms                                46

3.2.9                Using a default biomagnification factor                        47

3.2.10             Increasing the percentage of species to be protected   48

3.3             Determining ambient background concentrations                48

3.3.1                Inorganics                                                                      48

3.3.2                Background concentration models                                48

3.3.3                Organics                                                                         49

4                             Bibliography                                                 51

5                             Appendices                                                    64

5.1             Appendix A: Review and comparison of frameworks
for deriving soil quality guidelines in other countries           
64

5.1.1                A1: USA                                                                         64

5.1.2                A2: The Netherlands                                                      65

5.1.3                A3:  Canada                                                                   65

5.1.4                A4:  EU and UK                                                             66

5.1.5                A5: Germany                                                                 67

5.1.6                A6: New Zealand                                                           68

5.2             Appendix B: method for deriving EILs that protect
aquatic ecosystems                                                                   
68

5.2.1                Determining the leaching potential of inorganic
contaminants                                                                 
68

5.2.2                Determining the leaching potential of organic
contaminants                                                                 
69

5.2.3                Calculation of EILs that protect aquatic ecosystems   69

5.2.3.1                 Inorganic contaminants                                                           69

5.2.3.2                 Organic contaminants                                                              70

5.3             Appendix C: Methods for determining the
bioavailability of contaminants and how this could
be incorporated into the ERA framework                            
70

6                             Glossary                                                         72

7                             Shortened forms                                            75

 


 


1                  Introduction

This guideline presents the methodology for deriving terrestrial ecological investigation levels (EILs) for three groups of land uses: (1) areas of ecological significance (2) urban residential/public open space, and (3) commercial/industrial. The methodology was developed to protect soil processes, soil biota (flora and fauna) and terrestrial invertebrates and vertebrates and is presented in this Schedule. Also addressed is the strength and limitations of the EIL derivation methodology. Technical notes on the methods used in the methodology are also provided. In developing the EIL derivation methodology, the approaches used by other countries were investigated and a summary of these is presented in Appendix A.

 

This methodology should be considered together with National water quality management strategy – Australian and New Zealand guidelines for fresh and marine water (ANZECC& ARMCANZ 2000) where there are risks of impact to the aquatic ecosystem.

2                  EIL derivation methodology

2.1              Overview of the EIL derivation methodology

The methodology was developed being cognisant of both the methods used in other jurisdictions and of the existing methods used in Australia to derive water and sediment quality guidelines (ANZECC & ARMCANZ 2000; Simpson et al. 2005; Simpson & Batley 2007). The methodology is flexible and can deal with a variety of different land uses, risk pathways and toxicity data. It could be used to derive not just EILs but also other soil quality guidelines (SQGs) that have different purposes and/or different land uses. Examples of other SQGs include negligible risk target values, clean-up guidelines (goals that a site remediation must meet), intervention values (guidelines that, if exceeded, require immediate action in the form of remediation), and agricultural guidelines (guidelines to protect the long-term sustainability of agricultural land). The same basic methodology could also be used to derive guidelines for contaminants in products that are added to soil such as soil amendments, biosolids, fertilisers and re-use of wastes or by-products. In fact, guidelines for cadmium, copper and zinc for Australian biosolids applied to agricultural land have been developed using a very similar method (Warne et al. 2007, Heemsbergen et al. 2009). While the methodology can be used to derive other SQGs, this guideline will henceforth only focus on EILs.

 

An overview of the EIL derivation methodology is given in Figure 1. It consists of three main steps:

1.      choosing the level of protection desired for the site

2.      assessing exposure pathways

3.      collating appropriate data for the selected exposure pathways and deriving EILs.

 


                       

Figure 1. Overview of the methodology for the derivation of EILs.

2.2              Levels of protection

Selecting the level of protection to be provided to a site or soil is one of the most important steps in the EIL derivation methodology.

 

The level of protection provided will depend on:

1.      The species and ecological functions that should be protected—every land use has specific functions and species that should be protected in order to ensure the land can continue to be used for that purpose. These functions and species include plants, soil microbial processes, soil and terrestrial invertebrates and vertebrates. For example, it would not be expected that all terrestrial species would be protected in an urban residential setting but it would be in national parks and areas of high ecological value.

2.      The exposure pathways that are relevant for the land use—for terrestrial ecosystems in general, there are multiple potential exposure pathways. However, not all exposure pathways will be relevant for any particular land use. For example, exposure pathways that involve biomagnification are unlikely to be relevant to small industrial sites, as their surface area is limited.

3.      The extent to which the species and ecological functions will be protected—using the preferred method for deriving EILs (that is, species sensitivity distribution (SSD) methods), it is possible to protect a hypothetical percentage of species/ecological functions (e.g. 99% or 95%) by an EIL. The extent of protection (that is, the percentage of species protected) can be changed depending on land use. For example, relatively low protection could be provided for commercial/industrial areas, and high protection for national parks and other high ecological value lands.

The land use-based approach has been adopted by several countries (for example, Germany and Canada). The Canadian soil quality guidelines (CCME 2006, Appendix A3) include four land-use types—agricultural, residential/parkland, commercial and industrial. Each land use has a list of relevant ecological receptors of concern to be included in the derivation of the Canadian SQGs. Furthermore, at industrial and commercial sites, a low level of adverse effects would be expected to occur in less than half of the species in the terrestrial community, as the CCME set the species protection level at 50%. Therefore, each land use type has its own SQG (CCME 2006).

 

The Australian and New Zealand water quality guidelines (WQGs) (ANZECC and ARMCANZ 2000) include a similar approach, which provides different levels of protection (that is, percentage of species) to aquatic ecosystems depending on how pristine the ecosystem is (that is, their current conservation status).

For pristine and thereby high conservation value ecosystems, slightly to moderately disturbed, and highly disturbed ecosystems, the default levels of protection in Australian aquatic ecosystems are 99% (PC99), 95% (PC95) and 90% (PC90) or 80% (PC80) of species, respectively (ANZECC & ARMCANZ 2000).

 

The EIL derivation methodology was used to derive a series of SQGs for eight contaminants using three different sets of toxicity data and thus providing three different levels of protection (Schedule B5c). For practicable application, the National Environment Protection (Assessment of Site Contamination) Measure (the NEPM) has adopted a combination of lowest observed effect concentration (LOEC) and 30% effect concentration data (EC30) for derivation of the EILs[1]. For further information about this toxicity data refer to the Glossary and relevant Section.

2.2.1        Levels of protection for specific land uses

For all land uses (urban residential, public open space, commercial, industrial, agricultural, national parks/areas with high ecological value), with the exception of agriculture (see paragraph below on agricultural land), the following ecological receptors are relevant:

·         biota supporting ecological processes, including microorganisms and soil invertebrates

·         native flora and fauna

·         introduced flora and fauna

·         wildlife, i.e. secondary poisoning in birds and small rodents.

 

Henceforth, the above list of protected organisms will be referred to as ‘species and soil microbial processes’.

 

The level of protection provided varies depending on the land use and whether the contaminant in question biomagnifies. Different levels of protection are aimed at protecting certain percentages of species and soil microbial processes[2]. The percentages of species to be protected will apply to the land uses irrespective of the purpose of the SQG. If a protection level is set at 80%, then theoretically 20% of the species and soil processes are at risk of experiencing adverse effects.

 

The toxic effects that this 20% of species/soil processes may experience will vary depending on the type of toxicity data that was used to derive the SQG. For example, for SQGs derived using NOEC (no observed effect concentration) or EC10 data, the potentially affected 20% of species/soil processes would experience toxic effects that were not significantly different to the controls or up to a 10% effect respectively. For SQGs based on EC50 data, the potentially affected 20% of species/processes could experience a 50% effect.

 

Biomagnification and the corresponding levels of protection should be enacted only when:

·         the contaminant meets the criteria for biomagnification

·         the surface area of the contaminated land exceeds a certain minimum surface area. The minimum surface area for urban residential/public open space is 250 m2 and the minimum surface area for commercial, industrial and agricultural land is 1,000 m2.

 

A summary of the percentages of species and soil microbial processes to be protected in soil with different land uses is given in Table 1 below.

Table 1. Percentage of species and soil processes to be protected for different land uses

Land use

Standard % protection

Biomagnificationa % protection

Urban residential

80

85b

Public open space

80

85b

Commercial

60

65c

Industrial

60

65c

Agricultural

95d and 80e

98c,d and 85c,e

Areas of ecological significance

99

99

a if a contaminant meets the criteria for biomagnification, b if surface area exceeds 250 m2, c if surface area exceeds 1,000 m2, d agricultural crops, e for soil processes and terrestrial fauna.

The level of protection for some of the land uses are the same. Therefore, some of the land uses have been combined. Thus, in essence, there are only four different land uses: 1) national park/area with high ecological value, 2) urban residential/public open space, 3) commercial/industrial, and 4) agricultural. The NEPM focuses on the first three groups.

2.2.1.1       National parks and areas with high ecological value

National parks and areas with high ecological value are near-pristine ecosystems and should remain in that condition. As far as possible, it should be ensured that these ecosystems are not affected by soil contamination. Therefore, the appropriate level of protection is 99% of species. As this is the maximum percentage of protection possible (due to the statistical method used to calculate SQG), 99% is also the species protection setting for contaminants that biomagnify.

2.2.1.2       Urban residential and public open space

Henceforth, this grouping of land uses will be referred to as ‘urban residential’. Urban residential lands are not pristine, rather, they are extensively modified, but they still retain many important functions and species. Stakeholders would expect these to be maintained. For example, it would be reasonable to expect that such land uses should sustain plant growth of both introduced (ornamental) and native species. To ensure viable growth of plant species, not only should plant toxicity data be considered but also soil health (for example, nutrient cycling and microbial functions). Nutrient cycling in soil ecosystems is essential for plant growth and therefore both microorganisms and soil invertebrates should be protected. Microorganisms are responsible for many processes regarding nutrient cycling—decomposition of organic matter, and N and P cycling processes (Marschner & Rengel 2007). Soil invertebrates have a number of important functions, including interacting with microorganisms regarding nutrient cycling, and modifying soil structure. In addition, many birds and small terrestrial animals feed on plants and soil invertebrates in urban areas. Therefore, secondary poisoning for some contaminants should be assessed to ensure adequate protection is provided to organisms high in urban food chains.

 

As urban residential lands are modified ecosystems, it would not be warranted or realistic to protect 95% of species and functions. Yet a reasonably high degree of protection is required in order to maintain the desired receptors and ecological functions. It has therefore been decided to protect 80% of species and soil microbial processes appropriate to this land use. For contaminants with a potential for biomagnification, the percentage of species protected should be raised by 5% to 85%.

2.2.1.3       Commercial and industrial land

Henceforth, these two land uses will be referred to as commercial/industrial land use. Ecosystems in commercial/industrial lands can be highly artificial. However, soils should still support the basic soil processes and should be able to recover if land use changes. Therefore, 60% of species will be protected for non-biomagnifying contaminants present in commercial/industrial land and 65% for contaminants that show biomagnification potential.

2.2.1.4       Agricultural land

The protection of crop species is vital to maintaining the sustainability of agricultural land and therefore 95% of the crop and grass species will be protected for this land use. Other plant species will not be used in the derivation of agricultural SQGs and therefore it will not be known what level of protection is provided by the SQG to native flora. Soil processes and soil invertebrates are highly important to ensure nutrient cycling to sustain crop species. However, tillage and the use of pesticides/herbicides make it unrealistic to protect 95% of soil processes and soil invertebrates and therefore only 80% of these will be protected. If a contaminant shows biomagnification potential, the percentage of species protected should be raised to 98% for crop species and 85% for soil processes and soil invertebrates. The lower of these two derived SQG values has been adopted as the agricultural SQG, and is included for information purposes only.

2.3              Determining the most important exposure pathways

It is important to determine the relevant exposure pathways for the combination of specific contaminants at a specific land use. For the sake of simplicity, many of the exposure pathways have been grouped into three pathways:

1.      Direct toxicity — this is where the exposure to the organism occurs directly from either soil, soil pore water or air in soil pores. This includes pathways 1, 2 and 4 in Box 1 below.

2.      Biomagnification — this includes all exposure pathways where the source of the contaminant is food (organisms lower in the food chain). This includes pathways 3, 10, 11 and 12 in Box 1.

3.      Metabolites — Metabolites are the breakdown products of the parent contaminant and require their own exposure pathway assessment.

The importance of the various exposure pathways can be determined by categorising the physicochemical properties of the toxicant and those of the receiving soil that control the environmental fate of chemicals. An overview of compartments within soil and the physicochemical properties that determine the fate of contaminants is given in Box 2 below. Several of the physicochemical properties shown are soil-dependent, for example, soil pH, cation exchange capacity, organic matter, clay content and dissolved organic carbon.

 

However, others are physicochemical properties of the contaminant itself, for example, partitioning between octanol and water (Kow), its soil to water partition coefficient (Kd), Henry’s law constant (H). These physicochemical properties can be used to determine the most important exposure pathways for contaminants. Organic and inorganic contaminants have different physicochemical properties that control their environmental fate and therefore different schemes for assessing exposure routes have been developed.

 

The EIL derivation methodology aims to protect soil and terrestrial species and soil processes. Potential off-site migration and its potential impacts are not included in the methodology. A recommended method for deriving EILs and/or other SQGs that also protects aquatic ecosystems is presented as an Appendix. Another issue that was considered for incorporation into the EIL derivation methodology was the bioavailability of the contaminants before addition to soil; for example, soluble contaminants versus those bound in insoluble forms. While this is a central issue in the management of contamination, it is not currently possible to incorporate this into the derivation of EILs and/or SQGs and the derivation assumes contaminants are 100% bioavailable. Some information on potential methods for assessing bioavailability and how it could be incorporated into a more detailed site-specific risk assessment is provided as an Appendix.

 




Box 1. Overview of potential exposure pathways in terrestrial ecosystems

Exposure pathways

1.      Soil – organism (via ingestion, organisms include herbivores and soil dwellers)

2.      Soil – soil organism (passive absorption)

3.      Soil – soil organisms – soil predators

4.      Soil – plants

5.      Soil – surface water – aquatic organisms

6.      Soil – groundwater – stygofauna

7.      Soil – groundwater – surface water – aquatic organisms

8.      Soil – groundwater – sediment – mieofauna

9.      Soil – air – terrestrial species

10.  Soil – plant – herbivores – carnivores

11.  Soil – soil organisms  and/or soil predators – terrestrial predators

12.  Soil – groundwater – surface water – aquatic organisms – aquatic predators

The exposure pathways can be grouped together:

·         The direct toxicity pathways are 1, 2 and 4 and should be addressed for all contaminants.

·         Leaching pathways include pathways 6, 7 and 8 and are relevant for site-specific ecological risk assessment. It will not be considered for general EIL derivation.

·         Secondary poisoning includes pathways 3, 10, 11 and 12 and should be addressed for contaminants having biomagnification potential in the food web.

·         A site-specific pathway for sloping land is pathway 5 and this should be assessed for contamination situated on slopes where down-slope migration of the contamination is possible. It will not be considered for general EIL derivation.

·         Pathway 9 requires harmonisation of air quality guidelines with the soil quality guidelines but will not be used in the current process. Inhalation is more a human health issue and therefore the health investigation levels (HILs) using human toxicology assessment of inhalation is a much more accurate measurement of potential risk.

Box 2. Soil compartments, routes of environmental exposure and the key physicochemical properties that govern the distribution of a contaminant

`

 

 

Properties controlling the environmental fate and exposure routes of chemicals:

(1)     soil porosity, water holding capacity (WHC), soil-water partition coefficient (Kd), precipitation

(2)     octanol-water partition coefficient (Kow), soil pH, pMn+ (free ion), ionic activity, electrical conductivity, and dissolved organic carbon (DOC)

(3)     soil pH, cation exchange capacity (CEC), Kd, organic matter (OM), clay, DOC

(4)     diet, metabolism, octanol-water partition coefficient (Kow)

(5)     ingestion rate (diet), metabolism, absorption through skin, soil pH, CEC, Kd, OM, clay, DOC, Kow

(6)     sublimation constant (Ks)

(7)     amount soil ingested, Kd, metabolism

(8)     boiling point, Kow, Henry’s gas law constant (KH)

(9)     boiling point, Kow, surface area, turbulence, wind speed

(10)  erosion, plant coverage, WHC, % moisture

(11)  sublimation constant (dust to air), Kd (air to dust), density of dust

(12)  lung type, Kd, Kow, breathing rate x volume

(13)  wind speed, vicinity of water body.

 

2.3.1        Exposure pathway assessment for organic contaminants

The environmental fate of organic contaminants is largely controlled by three physicochemical properties:

1.      half-life (t1/2)

2.      Henry’s law constant (H)

3.      octanol-water partition coefficient (Kow) which, in general, determines a contaminant’s potential to cause secondary poisoning.

2.3.1.1       Half-life

The half-life (t½) of a contaminant is a measure of persistence of the contaminant in the environment. It represents the time taken for 50% of the contaminant to be lost from the environment. The loss may occur through biodegradation (microbially mediated degradation) or abiotic pathways (hydrolysis, oxidation, reduction, etc.). The more persistent a contaminant in the environment (that is, larger t½), the longer is the potential exposure time of species to the contaminant and the more deleterious the effects that could occur[3].

 

In order to classify contaminants in terms of their half-lives, the most relevant comparison is their persistence (based on half-life) to the generation time of soil organisms. Soil organisms do vary greatly, with some microbes having generation times of hours, while earthworms have a generation time of approximately one year. A generic generation time of three months for soil organisms (microorganisms were not considered) was selected and the resulting categories of biodegradation rates can be found in Table 2 below.

 

Half-lives of contaminants depend on the soil physicochemical properties and therefore preference should be given on half-life values based on Australian soils. However, if this information is not available for Australian soils, then appropriate overseas studies can be used.

Table 2. Biodegradation rates, half-lives and the classification to be used in assessing the importance of the various exposure pathways for organic contaminants.

94% of contaminant degraded in (months)

t1/2 (days)

t1/2 Classification

<3

<22.5

Fast (F)

3-6

22.5-45

Moderately fast (M)

>6

>45

Slow (S)

 

2.3.1.2       Henry’s law constant

Henry’s law constant (H) is a measure of the volatility of the contaminant. The higher the volatility (or value of H) the more of the contaminant will volatilise and be found in the soil air and in the atmosphere. H is a temperature-dependent constant.

 

Together with the t1/2 of the contaminant, H is used to assess the transfer and persistence of the contaminant in the soil, as vapour transport for many contaminants may constitute an important pathway of loss and exposure to organisms.

 

Several researchers have used different cut-off values of H to class contaminants into volatile and non-volatile categories but, in most cases, for aquatic environments. Jury et al. (1983, 1984) categorised the behaviour of trace organic contaminants in soils using H (among other properties) and this is useful to assess the importance of the various exposure pathways for organic contaminants (see Table 3 below). Jury et al. (1983) used the Henry’s law constant in dimensionless form as the ratio of concentration in the gas phase to concentration in the liquid phase, both in units of molar concentration, that is, H = (molar concentration in air)/(molar concentration in water)[4]. This is the most relevant form for estimation of the mass distribution of a chemical.

 

The dimensionless form of H based on concentrations (on a molar concentration basis) is the most commonly used of the dimensionless values (Staudinger & Roberts, 1996). The US EPA has published a calculator where Henry’s law constant, H, can be estimated in different unit forms and at different temperatures. This can be accessed at www.epa.gov/athens/learn2model/part-two/onsite/esthenry.htm.

Table 3. Henry’s law constant (H dimensionless) values to be used in assessing the importance of the various exposure pathways for organic contaminants

Henry’s constant value

(cm3 air/cm3 solution)

Classification

 >2.5 x 10 -3

Highly volatile (H)

2.5 x 10-7-2.5 x 10 -5

Moderately volatile (M)

<2.5 x 10 -7

Not volatile (L)

 

2.3.1.3       Octanol-water partition

The octanol-water partition (Kow) is the ratio of the concentration of a contaminant that is dissolved in n-octanol to that dissolved in water at equilibrium and at a specified temperature. It is used as a surrogate to estimate the potential for contaminants to accumulate in tissue, both plant and animal (Connell 1989, Posthumus & Slooff 2001). The Kow values can often be so large that the values are usually expressed as the logarithm to base 10 (that is, log Kow). Contaminants with high log Kow values are more likely to accumulate in plants and soil invertebrates than contaminants with low Kow values (Connell 1989, Posthumus & Slooff 2001). If further magnification of these contaminants occurs in the food chain, the predators might experience toxicity while its prey does not. This effect is known as secondary poisoning.

 

Contaminants with log Kow values below 4 are not considered to biomagnify, while highly fat soluble, lipophilic contaminants with log Kow values equal to or greater than 4 are most likely to biomagnify. For most contaminants, it is expected that metabolism, excretion and degradation rates exceed the bioaccumulation rates at concentrations equivalent to the trigger values for protecting aquatic ecosystems (ANZECC & ARMCANZ 2000). Hence, only for contaminants with log Kow values equal to or greater than 4 should secondary poisoning be considered. This approach is also consistent with the starting point to consider biomagnification used in the Australian and New Zealand WQGs (ANZECC & ARMCANZ 2000).

 

For the purpose of this methodology, the log Kow values of contaminants are divided into two classes. These are:

·         low, log Kow <4: the contaminant has a low potential to biomagnify

·         high, log Kow ≥ 4: the contaminant has a high potential to biomagnify.

2.3.1.4       Overview of the main exposure pathways for organic contaminants

Table 4 below presents the various combinations of the three physicochemical properties of organic contaminants described above and the resulting two exposure routes that are considered the most important for deriving EILs and/or SQGs.

 

Slowly degrading contaminants (that is, t1/2 = slow, Table 2) with high log Kow values and low H will have biomagnification as the most important exposure pathway followed by direct toxicity. If, however, these slowly degrading, high log Kow contaminants have a high H, then direct toxicity will be the most important exposure pathway, followed by biomagnification.

 

For rapidly degrading contaminants (that is, t1/2 = fast), the metabolites of the contaminant might have a larger impact on the environment than the parent contaminant. Therefore, it is necessary to assess the toxicity of the parent contaminant and to separately assess the toxicity and exposure pathways of the metabolites, as these can be markedly different from the parent contaminant. It would be preferable for metabolites to have their own EIL and/or SQG values. However, in practice, the number of EILs and/or SQGs for metabolites will be very limited due to a lack of knowledge of their toxicity and environmental fate.

Table 4. The properties (half-life t½; logarithm of the octanol-water partition coefficient log KOW; Henry’s gas law constant H) used to assess the importance of the various exposure pathways for organic contaminants and the corresponding two most important routes

t½a

 

Log Kowb

 

H b

 

Exposure routes to be considered

Primary

Secondary

S

H

L-M

Biomagnification

Direct toxicity

S

H

H

Direct toxicity

Biomagnification

S

L

L-M

Direct toxicity

Metabolites

S

L

H

Direct toxicity

Metabolites

M or F

H

L-M

Direct toxicity

Metabolites

M or F

H

H

Direct toxicity

Metabolites

M or F

L

L-M

Direct toxicity

Metabolites

M or F

L

H

Direct toxicity

Metabolites

a. S = slow, M = moderately fast, F = fast. b. H = high, M = medium, L = low

2.3.2        Exposure pathway assessment for inorganic contaminants

2.3.2.1       Biomagnification

There is no straightforward physicochemical property of inorganics that will predict their biomagnification potential, unlike organic contaminants. In the past, the bioconcentration, bioaccumulation and biomagnification factors (BCF, BAF and BMF respectively) have been used for this purpose, but this is not appropriate (Luoma & Rainbow 2008). Unless there is clear evidence that an inorganic element does not biomagnify, it should be considered to biomagnify and therefore secondary poisoning should be considered when deriving the EIL and/or SQG for that contaminant. A preliminary list of inorganic elements that do and do not biomagnify is given in Table 5 below.

Table 5. A preliminary list of inorganics known to biomagnify or known to not biomagnify based on information in the literature.

Biomagnification status

Inorganic contaminants

Known to biomagnify

Cd, Hg (especially methyl forms), Se

Known to not biomagnify

As, Cu, Fe, Mg,  Pb, Zn

 

Only three biomagnification classes for inorganics should be used: known biomagnifiers, known non-biomagnifiers, and unknown biomagnifiers (which are then treated as biomagnifiers pending further investigation).

2.3.2.2       Henry’s law constant

Henry’s law constant (H) is a measure of the volatility of the element, as described previously. Inorganic elements and contaminants in general have very low volatility. Therefore, exposure pathways involving volatility should only be considered for mercury. These have not been included in the method used to determine the important exposure routes for inorganics.

2.3.2.3       Overview of main exposure pathways for inorganic contaminants

Table 6 below presents the two exposure routes for inorganic contaminants that are considered the most important for deriving EILs and/or SQGs, depending on whether the contaminant biomagnifies or not.

 

For unknown and known biomagnifying inorganics, secondary poisoning should be addressed. For all inorganic contaminants, direct toxicity to relevant species and soil processes should be addressed.

 

Table 6. The property used to conduct the inorganic contaminant exposure pathway assessment with the corresponding two most important exposure routes

Biomagnifies

 

Exposure routes to be considered

Primary

Secondary

Yes

Biomagnification

Direct toxicity

No

Direct toxicity

-

Unknown

Biomagnification

Direct toxicity

2.4              Derivation of EIL values

A schematic of the methodology to derive EILs for contaminants is given in Figure 2 below. The main steps in the methodology are:

1.      collation and screening of the data

2.      standardisation of the toxicity data

3.      incorporation of an ageing/leaching factor for aged contaminants

4.      calculation of the added contaminant limit (ACL) by either the SSD or assessment factor (AF) approach, depending on the toxicity data

5.      normalisation of the toxicity data to an Australian reference soil. This is only done if the SSD approach is used to calculate the ACL

6.      accounting for secondary poisoning for those contaminants that are considered to biomagnify in the food web

7.      calculation of the ambient background concentration (ABC) of the contaminant in the soil (if appropriate)

8.      calculation of the EIL or SQG by summing the ACL and ABC values

EIL = ABC + ACL                                                                             (equation 1)

 

The separation of naturally occurring concentrations of a contaminant and the added contaminant in deriving EILs and/or SQG is based on the ‘added risk approach’ (Struijs et al. 1997; Crommentuijn et al. 1997). This approach assumes that the availability of the ABC of a contaminant is zero or sufficiently close that it makes no practical difference. But, more importantly, it assumes that the background ‘has resulted in the biodiversity of ecosystems or serves to fulfil the needs for micronutrients for the organisms in the environment’ (Traas 2001). Therefore, the approach views only the effect of added contaminants to the environment as adverse. This approach is mostly relevant for ecological risk assessment (ERA) but less relevant for human risk assessment.

 

Evidence supporting the assumptions of the added risk approach has been provided by Posthuma (1997) and Crommentuijn et al. (2000b) and by work showing that the availability of metal salts decreases over time through aging processes (Posthuma 1997; Song et al. 2006). However, for microbial communities the background might be important regarding the development of tolerance to the metals (Díaz-Raviña & Bååth 1996; Bååth et al. 1998; Rutgers et al. 1998; McLaughlin & Smolders 2001; Rusk et al. 2004; Fait et al. 2006; Broos et al. 2007). Some of these studies found positive relationships between metal background concentration and effect concentrations, which could indicate that microbial communities in soils with relatively high background metals have evolved to be more tolerant to additional metal. Although these studies have shown that background concentration might not be completely inactive, adaptation of microbial communities does not lead to an underestimation of the ACL; rather, it is more likely to cause overprotection for microorganisms.

2.4.1        Collation and screening of data

2.4.1.1       Toxicity data collation

The first step in the methodology of deriving an EIL and/or SQG is to conduct a literature review and/or to search databases, such as the US EPA ECOTOX database (US EPA 2004), Australasian ecotoxicology database[5] (Warne et al. 1998; Warne & Westbury, 1999; Markich et al. 2002; Langdon et al. 2009) or the ECETOC database (ECETOC 1993), for available toxicity data for the contaminant in question. Unlike the situation in the derivation of HILs, it is not appropriate to have a hierarchy of data sources to be used in deriving EILs and/or SQGs. For most metals and well-known organic contaminants, toxicity data in addition to that found in the above databases will be available in the literature. Therefore, one should not rely solely on these databases.

 

For many organic contaminants there will be no toxicity data available. If there is no toxicity data available, models can be used to predict toxicity. These models include quantitative structure-activity relationships (QSARs) and quantitative activity-activity relationships (QAARs). The Australian and New Zealand WQGs (ANZECC and ARMCANZ 2000) used QSARs to derive trigger values (TVs) for narcotic organic contaminants (for example, ethanol for marine waters) when there was insufficient data. If QSARs or QAARs are not available, the equilibrium partitioning method (Van Gestel 1992; ECB 2003) can be used if toxicity data is available for aquatic species.

 

Figure 2. Schematic of the methodology for deriving ecological investigation levels (EILs) for Australian soils.

2.4.1.2       Quantitative structure-activity relationships

QSARs are empirical relationships between the toxicity of contaminants to a particular test organism and one or more physicochemical properties of the contaminant. QSARs are derived for contaminants with either the same mechanism of action or similar contaminant structures. The most widely used physicochemical property is log Kow. An example of a typical QSAR is presented below:

log EC 50  = -0.72 log Kow + 3.37                                 (equation 2)

 

where log EC50 (μmol/L) is the concentration at which 50% growth inhibition of lettuce (Lactuca sativa) was observed (Hulzebos et al. 1991).

 

The toxicity of contaminants with the same mechanism of action or chemical structure as those in the QSAR can be predicted based on their physicochemical properties. The prediction is made by substituting the value of the contaminant into the QSAR. If equation 2 was being used, the log Kow of a contaminant would be substituted into the equation.

 

QSARs have been developed for terrestrial plants (Hulzebos et al. 1991) and invertebrates (Van Gestel et al. 1991); however, they are not as widely available as for aquatic species (Posthumus & Slooff 2001). Only QSARs derived using terrestrial species should be used to derive EILs and other SQGs.

2.4.1.3       Quantitative activity-activity relationships

The simplest forms of QAARs are empirical relationships that model the toxicity of contaminants with the same mechanism of action to one species using toxicity data of another species. These are termed binary relationships. An example (Westbury et al. 2004) is provided below:

log EC50 (C. d.) = 0.848 log LC50 (P. r.) + 0.047          (equation 3)

 

where log EC50 (C. d.) is the log of the concentration that causes a 50% immobilisation of the cladoceran Ceriodaphnia dubia, and log LC50 (P. r.) is the log of the concentration that kills 50% of the fish Poecilia reticulata.

 

More complex QAARs have been developed that relate the toxicity of contaminants simultaneously to multiple species (Raimondo et al. 2007; Morton et al. 2008). Both the simple and more complex QAARs allow toxicity data for one or more species to be used to estimate the toxicity to another species. Thus they can fill some of the data gaps that often occur in deriving EILs or their equivalents.

2.4.1.4       Equilibrium partitioning method

The equilibrium partitioning method (EqP) is used to predict the toxicity of a contaminant in soils based on aquatic toxicity data. The EqP is based on the assumption that the main route of exposure for soil organisms is the soil pore water concentration (Van Gestel 1992; ECB 2003). Therefore the EqP is not suitable for:

·         contaminants with log Kow values >4 (as they partition to soil rather than soil pore water)

·         contaminants with a specific mode of action (e.g. endocrine disruptors)

·         species that are exposed primarily through food

·         aquatic species that have no direct terrestrial equivalent (e.g. fish)

·         species where the main exposure pathway in terrestrial systems is dissimilar to that in water.

 

Therefore, the EqP method should only be used to assess the toxicity of the following taxonomic groups, as they meet the above criteria: annelida, bacteria, fungi, hexapoda (larvae only), nematoda, protozoa and tardigrades.

 

The EqP estimate of a NOEC for a contaminant in soil (NOECsoil) is calculated from the NOEC of aquatic species as indicated below:

                                                         (equation 4)

where RHOsoil is the bulk density of the saturated soil and Kd is the soil-water partitioning coefficient (L/kg) (ECB 2003).

While there has been work done overseas to assess the validity of the EqP method (Van Beelen et al. 2003), there has been no such work undertaken in Australia. This is not a preferred method as Australian soils are relatively old, have low concentrations of nutrients, low organic carbon contents and different clay mineralogy (Taylor 1983), and are thus quite different from European and North American soils.

2.4.1.5       Screening and selection of toxicity data

The next step in the methodology is to determine the suitability of the available toxicity data. Toxicity data is considered acceptable when the:

·         difference between tested concentrations was not greater than five-fold

·         exposure duration was greater than or equal to 24 hours

·         toxicity end point measured was growth, seedling emergence, lethality, immobilisation, reproduction, population growth or the equivalent

·         measured toxic effect was a given percentage effect concentration (e.g. LC10, EC50) or were NOEC, LOEC or MATC (see the Glossary) values.

 

Biomarker end points, like enzyme production, lysosomal damage and avoidance responses, are considered to be less ecologically relevant and therefore they should not be used for the derivation of EILs unless data is limited and the predictive methods discussed in the previous section are not suitable. Biomarker tests are very sensitive and are therefore considered as early warning tests. However, if such data is used to derive EILs, this should be clearly stated. Biomarker data can be highly relevant for site-specific ecological risk assessment.

 

Once the unsuitable toxicity data has been removed, the next step is to assess the quality of the remaining data. Such screening methods are used in the methodologies of most countries to derive environmental quality guidelines (EQGs); for example, in Denmark, the Netherlands and the USA. However, in most cases, how the data was screened is not described. A screening method was used for the Australian and New Zealand WQGs (Warne et al. 1998; Warne 2001). This method assessed whether appropriate experimental designs, chemical analyses and statistics were used to obtain the toxicity data.

 

The method was based on the method used within the US EPA AQUIRE database, which was later renamed the US EPA ECOTOX database (US EPA 1994, 2004) but was improved by Warne et al. (1998).

 

These methods were subsequently reviewed and further improved by Hobbs et al. (2005). The Hobbs et al. (2005) data quality assessment procedures were modified so they were suitable for terrestrial ecotoxicity data (see Table 7) for use in this guideline.

 

Table 7. Scheme to assess the quality of terrestrial ecotoxicology data. This has been modified from the aquatic scheme of Hobbs et al. (2005).

Question

Marks awarded

1

Was the duration of the exposure stated (e.g. 48 or 96 hours)?

10 or 0

2

Was the biological end point (e.g. immobilisation or population growth) stated and defined (10 marks)? Award 5 marks if only the biological end point is stated.

10, 5 or 0

3

Was the biological effect stated (e.g. LC or NOEC)?

5 or 0

4

Was the biological effect quantified (e.g. 50% effect, 25% effect)? The effect for NOEC and LOEC data must be quantified.

5 or 0

5

Were appropriate controls (e.g. a no-toxicant control and/or solvent control) used?

5 or 0

6

Was each control and contaminant concentration at least duplicated?

5 or 0

7

Were test acceptability criteria stated (e.g. mortality in controls must not exceed a certain percentage) (5 marks)?

or

Were test acceptability criteria inferred (e.g. test method used (US EPA, OECD, ASTM, etc.)) (award 2 marks). Note: Invalid data must not be included in the database.

5, 2 or 0

8

Were the characteristics of the test organism (e.g. length, mass, age) stated?

5 or 0

9

Was the type of test media used stated?

5 or 0

10

Were the contaminant concentrations measured?

4 or 0

11

Were parallel reference toxicant toxicity tests conducted?

4 or 0

12

Was there a concentration–response relationship either observable or stated?

4 or 0

13

Was an appropriate statistical method or model used to determine the toxicity?

4 or 0

14

For NOEC/LOEC data, was the significance level 0.05 or less?

or

For LC/EC/BEC data, was an estimate of variability provided?

4 or 0

 

15

Were the following parameters measured and stated? (3 marks if measured and stated, 1 if just measured)

pH

OM or OC content

clay content

CEC

 

 

3, 1 or 0

3, 1 or 0

3, 1 or 0

3, 1 or 0

16

 

Was the temperature measured and stated?

3 or 0

17

Was the grade or purity of the test contaminant stated?

3 or 0

18

Were other cations and/or major soil elements measured?

or

Were known interacting elements on bioavailability measured (e.g. Mo for Cu and Cl for Cd)?

3 or 0

19

For spiked soils with metal salts: were the soils leached after spiking?

3 or 0

20

Were the incubation conditions and duration stated?

3, 1 or 0

 

Total score

Total possible score for the various types of data and contaminants: 102

 

 

Quality score (%) (Total score /102 * 100)

 

 

Quality class (H ≥80%, A 51%–79%, U ≤ 50%)a

 

a H = high quality, A = acceptable quality and U = unacceptable quality.

 

Each experimentally derived toxicity datum should have its quality assessed by the data quality assessment scheme (Table 7), which asks 20 questions, with marks awarded depending on the answer to the questions. The quality score for each datum is determined by expressing the total score obtained as a percentage of the maximum possible score. The toxicity data is then classified into three classes depending on the quality score. Data with a quality score ≤50%, between 51% and 79% and ≥80% were classed as unacceptable (U), acceptable (A), and high (H) quality respectively. Only acceptable and high quality data should be used to derive EILs.

 

Only toxicity data expressed as either added or total soil concentrations should be used to derive EILs. There is considerable evidence both from overseas (Smolders et al. 2003; Smolders et al. 2004; Oorts et al. 2006; Zhao et al. 2006) and within Australia (Broos et al. 2007; Warne et al. 2008b) that chemical extract concentrations; for example, calcium chloride, ammonium nitrate and soil solution extracts, are not necessarily better measures of bioavailability than total concentrations for inorganic contaminants where contamination occurred in soluble forms. Furthermore, there is also considerably more toxicity data expressed as total metal concentration, and there is regulatory acceptance and understanding of this concentration measure.

2.4.2        Standardisation of the toxicity data

By this point in the methodology, the available toxicity data has been collated or models used to derive estimates and the data has been assessed for its appropriateness and quality. The obtained data requires standardisation in terms of four factors:

1.      measures of toxicity

2.      the toxicity expressed in terms of added concentrations

3.      duration of exposure

4.      use of toxicity data for endemic or overseas species.

Please note that this is not the normalisation step that accounts for the effect that soil characteristics have on toxicity values.

2.4.2.1       Measures of toxicity

There are many different measures of toxicity. The most frequently used toxicity measures to derive EQGs are NOECs and EC/LC50-type data. However, not all studies report these particular measures of toxicity; for example, the toxicity may be reported as an EC25 or an LC40. Therefore, in order to maximise the data available to derive EILs, it may be necessary to estimate the reported toxic effect.

 

A number of studies (Moore & Caux 1997; US EPA 1991; Hoekstra &Van Ewijk 1993) have shown that NOECs, while not statistically different from the control, typically correspond to a 10-30% effect, with 75% of NOECs corresponding to less than a 20% effect (Moore & Caux 1997). LOEC values would of necessity cause higher percentage effects and have a median of 30% (Moore & Caux 1997). For the purposes of this methodology, toxicity data that caused less than a 20% effect; for example, EC0 to ≤EC19, are considered equivalent to NOEC data and for brevity are referred to as NOEC and EC10 data. Toxicity data that cause a 20-40% effect are considered equivalent to LOEC data and are referred to throughout this guideline as LOEC and EC30 data. Toxicity data that cause >40-60% effect are considered equivalent to EC50 data and are referred to as EC50 data.

 

Due to the general paucity of terrestrial ecotoxicology data, if toxicity data is not expressed as a single value but instead is given as ranges, then the lowest value of the range should be used in order to provide a conservative estimate of the toxicity. In certain studies, the lowest toxicant concentration had already caused significant toxic effects and therefore toxicity data are given as a < or ≤ value. If possible, the percentage effect that the reported concentration caused should be determined and, using the ranges stated in the previous paragraph, be considered equivalent to NOEC, LOEC or EC50 data, and they should be converted accordingly. Toxicity with an effect greater than 60% should not be used to derive EILs. If, in studies, the highest tested concentration did not cause an effect or a statistically significant effect on the test species (that is, an unbounded NOEC), then the toxicity data should be given a > value and treated as an EC10. This is done as it is a conservative approach and will result in more toxicity data available for EIL and/or SQG derivation.

 

As stated earlier, EILs are to be derived using LOEC and EC30 toxicity data. But such data is not always generated in toxicity studies. Therefore, in order to maximise the data available to derive EILs, toxicity data can be converted to LOEC and EC30 data. Two different approaches were applied to the different measures of toxicity data in the Australian and New Zealand WQGs (ANZECC & ARMCANZ 2000). For organics, only chronic NOEC data was considered acceptable to derive high reliability TVs, while only acute EC/LC50 values were suitable for moderate reliability TVs and either NOEC or EC/LC50 data was suitable for low reliability TVs (Warne 2001). In contrast, for metals, chronic NOEC, LOEC, EC/LC50 and maximum acceptable toxicant concentrations (MATC) values could be used provided all non-NOEC values were converted to NOEC values (Warne 2001). This was done using a series of default conversion factors (see Table 8 below). The reason for the different approaches was that for the organic contaminants, generally the chronic data was NOEC values, whereas the vast majority of the chronic metal toxicity data was EC/LC50 values (Warne 2001).

 

 

Table 8. Default conversion factors used to convert different chronic measures of toxicity to chronic NOECs in the Australian and New Zealand WQGs (ANZECC & ARMCANZ 2000). Values are from Warne (2001).

Toxicity dataa

Conversion factor

EC50 to NOEC or EC10

5

LOEC or EC30 to NOEC or EC10

2.5

MATC* to NOEC or EC10

2

a EC50, EC30 and EC10 values are the concentrations that cause a 50%, 30% or 10% effect, NOEC = the no observed effect concentration, LOEC = lowest observed effect concentration, MATC = the maximum acceptable toxicant concentration and is the geometric mean of the NOEC and LOEC.

The more flexible method that was applied to the metals in the Australian and New Zealand WQGs (ANZECC & ARMCANZ 2000) and the conversion factors that were used (see Table 8) were used in the EIL derivation methodology. It should be noted that these conversion factors are based on expert judgement (Warne pers. comm.). Therefore, if sufficient terrestrial data is available to derive terrestrial conversion factors then these should be used. For example, data from the Australian National Biosolids Research Program indicates that the phytotoxicity chronic EC10 to chronic EC50 conversion factor for cations such as Cu and Zn was 3 (unpublished data).

 

Compared to aquatic toxicity studies, there is a limited number of terrestrial toxicity studies. Therefore, maximum use must be made of the available toxicity data and data should be converted from one measure to another (see above).

 

However, if more data become available then it should be used in the following descending order of preference:

1.      30% effect data (e.g. EC30, LC30)

2.      LOEC data

3.      10% or 50% effect data (e.g. EC10, LC50)

4.      NOEC and MATC.

There are a number of well-acknowledged limitations to NOEC and LOEC data (Newman 2008; Fox 2008; Warne & Van Dam 2008). Some scientists (Chapman et al. 1996) have argued that they should not be used to derive EQGs. However, they continue to be used for that purpose because no regulatory authority has recommended an alternative measure of toxicity be used and because a large amount of this type of data is available. For these reasons, the Australian and New Zealand WQGs (ANZECC & ARMCANZ 2000) used NOEC data but suggested that the use of NOEC data ’be phased out‘ as EC10-type data become available. Warne and Van Dam (2008) have gone one step further by calling for  a ban on the generation and use of NOEC and LOEC data in Australia. Since the Australian and New Zealand WQGs were published, more researchers are reporting EC/LC10 to EC/LC20-type toxicity data. The use of point estimate toxicity data is therefore preferred.

2.4.2.2       Conversion from total to added concentrations

The EIL derivation methodology makes a clear distinction between natural background concentration, which is the natural level of contaminants in the soil, and ABC, naturally occurring background and the contaminant levels that have been introduced from diffuse or non-point sources by general anthropogenic activity not attributed to industrial, commercial, or agricultural activities. Therefore, it is preferable that all toxicity data is expressed as an added concentration. If the toxicity data is not expressed in terms of added contaminant then they should be converted to that form, if possible. This can be achieved by subtracting either the ABC, if it is known, or the average concentration in the control soil (that is, the test soil with no addition of the test contaminant) from the total concentrations and then re-calculating the toxicity. If background concentrations are not given then, for some inorganics, the method of Hamon et al. (2004) can be used to estimate ABC in Australian soils or the Dutch background correction equations (Lexmond et al. 1986) can be used to estimate the background concentration. Alternatively, one can set a default background level or assume that the background concentration was zero.

2.4.2.3       Duration of exposure

The Australian and New Zealand WQGs (ANZECC & ARMCANZ 2000) make a clear distinction between chronic and acute toxicity data and convert TVs derived using acute EC/LC-type data to chronic TVs by using, in order of decreasing preference, acute to chronic ratios (ACRs) or a default AF of 10. This approach is very common and widely used in water quality guidelines (ANZECC & ARMCANZ 2000; CCME 1991; US EPA 1991) but is not used in soil guidelines. This is due mostly to the fact that the exposure duration of most terrestrial ecotoxicity tests is three to four weeks. Therefore, conversion factors should only be used for short-term exposure tests. If ACR values are available then they should be used to convert acute terrestrial toxicity data. Only if ACR values are not available should a default AF of 10 be used, which is consistent with the approach adopted by the Australian and New Zealand WQGs (ANZECC & ARMCANZ 2000).

2.4.2.4       The use of toxicity data for endemic or overseas species

In deriving any EQGs, the question always arises as to whether toxicity data for overseas species should be used. By using toxicity data for overseas species, the assumption is made that they have the same sensitivity as endemic species. The validity of this assumption has been questioned and examined in a number of studies using aquatic species (Dyer et al. 1997; Markich & Camilleri 1997; Brix et al. 2001; Hobbs et al. 2004; Hose & Van den Brink 2004; Maltby et al. 2005; Chapman et al. 2006; Kwok et al. 2007). However, the evidence is conflicting, with some studies (Maltby et al. 2005; Hose & Van den Brink 2004) finding no differences while others have found differences (Dyer et al. 1997; Markich & Camilleri 1997; Brix et al. 2001; Hobbs et al. 2004; Chapman et al. 2006; Kwok et al. 2007). Kwok et al. (2007) combined results from SSD analysis with ERA principles to determine that, in order to protect 95% of tropical aquatic species, toxicity data for temperate aquatic species should be divided by a factor of 10. Using a similar methodology, Hobbs (2006) found that if Australasian species were to be protected from 95% of chemicals, then toxicity data for northern hemisphere freshwater and marine/estuarine species would have to be divided by 6.2 and 2.2 respectively. The inconsistency in the published results led Chapman et al. (2006) to conclude that ’toxicity data from one geographic region will not be universally protective of other regions‘.

 

The other factor that needs to be considered in resolving this issue is that from a statistical point of view EILs and/or SQGs become increasingly reliable as the number of species for which there is toxicity data increases. Therefore, as a pragmatic compromise, toxicity data for both endemic and overseas species should be used to derive EILs. This is consistent with the Australian and New Zealand WQGs (ANZECC & ARMCANZ 2000). However, if there are four or more toxicity data measurements in Australia for a species; that is, they meet the minimum data requirements to derive EILs and SQGs, then this should be used in preference to toxicity data for the same species tested overseas.

2.4.3        Incorporation of an ageing and leaching factor

Typically, soil toxicity tests use soils that have been freshly spiked with the contaminant in question. There are very limited amounts of toxicity data available for soils where the contaminant was added some time prior to testing, let alone field-aged soils contaminated by a variety of sources of contaminants with varying bioavailability. The predominance of laboratory-spiked toxicity data has implications for the derivation of EILs due to ageing and leaching.

 

Ageing or natural immobilisation (attenuation) is the process by which many contaminants (both inorganic and organic), when added to soil, will bind over time to various soil components (Barrow 1986; Hamon et al. 2007; Smolders & Degryse 2007) and this can reduce the concentration of the contaminant that is biologically available (McLaughlin et al. 2000a). Leaching is a process that removes readily soluble soil components such as salinity from soils. Most laboratory-spiked toxicity tests do not leach the soils after the spiking and this has the effect of increasing the ionic strength, decreasing soil pH, increasing aqueous concentrations of dissolved cations (such as Ca, Mg, K, Cd, Cu, Ni, Pb, etc.) and anions (Cl, SO4, NO3, etc.), and ultimately increasing the toxicity (Stevens et al. 2003). A study by Oorts et al. (2006) examined the magnitude of the ageing and leaching effects on the toxicity of Cu and concluded that leaching accounts for the majority of the observed difference in toxicity between freshly spiked and aged soils. A study by Smolders et al. (2009), the findings of which have been incorporated into the Flemish SQGs (VLAREBO 2008), derived ageing/leaching factors (ALFs) for Zn2+ (3), Cu2+ (2), Ni2+ (1-3), Co2+ (1.1-3.5), Pb2+ (4.2), Cd2+ (1) based on toxicity measures in a variety of European field and freshly spiked soils.

 

This is the only study that has generated such ALFs across a wide range of soils and ecotoxicity end points. These ALFs were developed based on a maximum of 18 months ageing and leaching (Smolders et al. 2009). These ALFs should be used in deriving EILs when the contaminants have been present in the soil for at least 2 years. This would be achieved by multiplying the non-aged and non-leached toxicity data by the appropriate ageing/leaching factor, thus decreasing their ’effective‘ toxicity. Thus, EILs for both fresh (contaminants have been in the soil for less than 2 years) and aged (the contaminants have been in the soil for greater than 2 years) contamination can be derived.

Currently, there are very few ALFs available, particularly for Australian soils. There are no ALFs for organic chemicals. When ALFs are not available, it is not possible to derive EILs for aged contamination. In such cases, there are two potential approaches. Firstly, conduct research to derive ALFs for the contaminant of concern or, secondly, conduct direct toxicity assessments (DTA) using soil from the site under investigation. If sufficient toxicity tests are conducted, then site-specific EILs could be derived in much the same manner as deriving site-specific WQGs (ANZECC & ARMCANZ 2000).

2.4.4        Comparison of available toxicity data to the minimum data requirements

There are two potential methods that can be used to derive ACLs:  the AF method — a worst-case scenario approach, and the SSD method — a risk-based approach. Both approaches require a minimum amount of toxicity data to derive EILs. The preferred methodology to calculate EILs is the SSD approach because this is a risk-based approach. However, which method is used to derive EILs depends on the number of species and taxonomic groups for which there are toxicity data (see Table 9 below).

 

Unlike the toxicity data for terrestrial species, toxicity data for soil processes is not based on single species but rather a community of microbial species that perform that soil process. Thus, strictly speaking, it is not suitable for use in SSD methods. However, these processes are important measures of soil ecosystem health and should be protected. The preferred method for deriving EILs is therefore to use the normal single species toxicity data but also soil process toxicity data.

 

SSD methods require a minimum set of toxicity data for the aquatic environment, which is usually specified in terms of a minimum number of species and taxonomic groups for which data is required. However, such an approach is not suitable for soil processes where the desirable data types are the number of soil processes and the number of nutrient groups.

 

A nutrient group is considered to be all toxicity end points measured that relate to a particular nutrient (see Table 11 below). For example, toxicity data for substrate-induced nitrification, potential nitrification rate and denitrification would all belong to the nitrogen nutrient group.

 

As the number of species and taxonomic groups or soil processes and nutrient groups for which toxicity data is available decreases, the confidence that the resulting EIL will provide the desired level of protection also decreases. In an attempt to compensate for this, the percentage of species and/or soil processes to be protected by the EILs increases as the number of species or soil processes and taxonomic groups or nutrient groups for which toxicity data is available decreases (see Table 9 below).

Table 9. Number of species or functional processes and number of taxonomic groups or nutrient groups needed for the SSD and AF approaches and the corresponding level of protection provided for residential land. The same principle of increasing the level of protection as the amount of toxicity data decreases also applies to other soil quality guidelines and for other land uses (i.e. the default level of protection would increase by 5% if there was data for 5 to 8 species or functional processes)

Number of species or functional processes

Number of taxonomic or nutrient groups

Methodology to derive EIL

Percentage of species to be protected

9

3

SSD Burr III

80% a

5-8

3

SSD Burr III

85% a

3-8

<3

AF

Not relevantb

a add 5% to the percentage of the species or soil processes to be protected if the contaminant is a biomagnifier.

b The AF does not determine EILs based on protecting a certain percentage of species.

 

The decision by regulatory agencies about the minimum data requirements is often arbitrary (Pennington 2003) and is based on pragmatic considerations. The US EPA requires at least eight species (US EPA 1999), the Dutch suggests ten species for EQGs (van Vlaardingen & Verbruggen 2007) although some studies have used five species (Van de Plassche et al. 1993; ANZECC & ARMCANZ 2000) and four species (Crommentuijn 2000a), and between five and eight species (OECD 1992, 1994). Since 2000, a number of publications have shown the importance of having larger data sets. For example, Newman et al. (2000) used non-parametric methods to estimate for 30 toxicants that approximately 15 to 55 (with a median of 30) species were needed per toxicant in order produce reliable EQGs. In another example, Wheeler et al. (2002) estimated that a minimum of 10 to 15 species per toxicant are needed. Subsequently, the European Union (EU) has recommended in the technical guidance document on aquatic risk assessment (ECB 2003) that the minimum toxicity data requirement is ten species that belong to eight taxonomic groups. Thus, while it is preferable to use toxicity data sets containing more species and taxonomic groups (or more soil processes and nutrient groups), this must be weighed against the fact that for soil and terrestrial ecosystems there is a general lack of toxicity data. If it were decided to use the same minimum data requirements as the EU, then EILs could be derived for only a limited amount of contaminants using the preferred SSD method. Other contaminants would have to be derived using the second choice AF method, likely to generate highly conservative criteria. It is imperative to acknowledge the situation for terrestrial systems and to set reasonable minimum data requirements for the SSD method, in order that the majority of the EILs are derived by the preferred SSD method.

 

Studies by the Danish EPA (Pedersen et al. 1994) and the OECD (1995) indicated that WQGs derived using data sets containing less than five values were very dependent on the spread of the values, whereas for data sets containing five or more values, this effect was markedly reduced. Therefore, the recommended minimum number of species and/or soil processes required to use the SSD approach is five. The minimum number of taxonomic or nutrient groups for toxicity data required in order to use the SSD method was reduced to three. Between five and eight species and/or soil processes, the SSD approach still has a large variation and uncertainty and therefore the protection level should be increased by 5% of species and/or soil processes in order to be more certain that the desired level of protection is achieved. If toxicity data for more than eight species and/or soil processes is available, the SSD approach is deemed to be sufficiently robust to set the protection limit for the appropriate land use (Table 9 above).

 

In order to determine which method (either the SSD method or the AF method) can be used to derive the EIL, the screened toxicity data should firstly be grouped together on the basis of species or soil processes. Then, using the information presented in Tables 10 and 11 below, the number of taxonomic groups and/or nutrient groups for which toxicity data is available can be determined.

 

If there is sufficient terrestrial toxicity data for a contaminant, toxicity data derived by models like QSARs or QAARs and the equilibrium partitioning approach should not be used. However, if there is insufficient terrestrial toxicity data available to meet the SSD requirements, the modelled data should be used in combination with measured toxicity data. The minimum data requirements to use the SSD and AF methods are the same when using a data set containing both measured and modelled toxicity data as when using only measured toxicity data. However, only low reliability EILs can be generated using modelled toxicity data (Section 2.4.11).

 


 

Table 10. The taxonomic groups for terrestrial species

Taxonomic group

Examples of species in this group

Mollusca

Snails, slugs

Annelida

Enchytraeids, earthworms

Nematoda

Nematodes

Hexapoda

Insects, springtails

Myriapoda

Centipedes, millipedes

Chelicerata

Mites, spiders

Crustaceans

Woodlice

Algae

Algae

Plantae

Plants

Fungi

Fungi

Bacteria

Bacteria

Protozoa

Amoebas, ciliates, flagellates

Tardigrada

Water bears

Chordata

Reptiles, mammals, birds

 

Table 11. The nutrient groups for soil (i.e. microbial and fungal) processes

Nutrient group

Soil process

Examples of end points

C cycle

Aerobic decomposition

Basal respiration, substrate-induced respiration

N cycle

N mineralisation/ammonification

Urease activity, NH4 production

 

Nitrification

NO3 production, substrate-induced respiration

 

Denitrification

Nitrate reductase

 

Nitrogen fixation

Nitrogenase activity

P cycle

P mineralisation

Phosphatase, Py-phosphatase

S cycle

S mineralisation

Aryl-sulfatase

 

 

2.4.5        Calculation of the added contaminant limit using a species sensitivity distribution approach

The SSD approach is a statistical method to calculate a soil concentration that theoretically protects a specified percentage of species and/or soil processes. The SSD method used to derive the Australian and New Zealand WQGs (ANZECC & ARMCANZ 2000) was the Burr Type III method (Shao 2000), which was incorporated into the BurrliOZ program (Campbell et al. 2000) that is available from: www.cmis.csiro.au/Envir/burrlioz/
Download1.htm.

 

If there are screened toxicity data values for a contaminant to at least five species or soil processes for three taxonomic or nutrient groups, then there is sufficient data to calculate an ACL using the Burr Type III SSD method.

 

All SSD methods use a single numerical value to describe each species or soil process for which toxicity data is available. The means by which a single value was obtained for each species or soil process (Van de Plassche et al. 1993) are set out below:

·         if there were only one toxicity datum, that was taken to represent the species or process

·         if there were several toxicity values for the same end point, the geometric mean of the values was calculated and was taken to represent the species or process

·         if there were several toxicity values for different end points (e.g. mortality or reproduction), the end point with the lowest geometric mean was taken to represent the species or process.

 

SSD methods require the toxicity data to have a uni-modal distribution. If the data set is not uni-modal (for example, insecticides are more toxic to insects than mammals), then the toxicity data belonging to the most sensitive distribution should be used for ACL derivation, as recommended by Warne (1998, 2001) when deriving WQGs.

2.4.6        Normalisation of toxicity data to an Australian reference soil

The use of normalisation relationships is an attempt to minimise the effect of soil characteristics on the toxicity data so the resulting toxicity data will more closely reflect the inherent sensitivity of the test species to the contaminant. If toxicity data more closely reflects species sensitivity, then a more accurate calculation of the soil concentration that should protect a certain percentage of species and soil processes can be made. Derivation of soil-specific EILs and the use of normalisation relationships to normalise toxicity data can only be done if there is sufficient data to use the SSD method. Toxicity data should not be normalised if the available toxicity data is only sufficient to meet the minimum data requirements of the AF approach.

 

If the toxicity data for a contaminant has been demonstrated to be affected by soil characteristics, (that is, by statistically significant (p ≤ 0.05) normalisation relationships between toxicity data and soil characteristics), then the toxicity data must be normalised to the Australian reference soil (see Table 12 below).


 

Table 12. Values of soil characteristics for the Australian reference soil to be used to normalise toxicity data

Soil property

Value

pH:     

6

Clay:   

10%

CEC:

10   cmol/kg

Org. Carbon:

1% or equivalent OM

 

Normalisation relationships are currently limited to a few combinations of contaminants, species and countries from which the soils are obtained (Smolders et al. 2004; Li et al. 2003; McLaughlin et al. 2006; Song et al. 2006; Broos et al. 2007; Warne et al. 2008a; 2008b). This is predominantly due to the concept of developing normalisation equations for terrestrial ecotoxicity data being relatively recent and the size of, and cost of conducting, such work.

 

The lack of normalisation equations for a wide variety of species can be overcome by applying the relationships across species within the following groupings of the taxonomic groups:

·         plants, algae

·         annelids, nematode, mollusca, protozoa

·         hexapoda, myriapoda, chelicerata, tardigrada

·         microbial and fungal functional end points.

These groupings are based on the basic body design of the organisms and the likely exposure route of organisms to the contaminant; that is, being exposed by the direct environment or through food. The following four derivation steps are listed in order of descending order of preference:

1.      If normalisation relationships for all four taxonomic groupings are available and each grouping meets the minimum data requirements to use the SSD approach, derive a set of soil-specific ACL values for each grouping and then the lowest ACL for the soil in question is adopted.

2.      If normalisation relationships for all four taxonomic groupings are available but at least one grouping does not meet the minimum data requirements to use the SSD approach, apply the normalisation relationships and combine all the data in one SSD calculation. Then use the normalisation relationships to derive a set of ACLs for each taxonomic grouping and the lowest ACL for the soil in question is adopted.

3.      If normalisation relationships are available for some groupings then apply them to the appropriate data and combine all the data (including the non-normalised toxicity data) in one SSD calculation. Then use the normalisation relationships to derive a set of ACLs for each grouping of organisms that have a normalisation relationship and the lowest ACL for the soil in question is adopted.

4.      If normalisation relationships are not available, then pool all data and derive one generic ACL.

The above steps are used to standardise the derivation of realistic EILs that are protective but at the same time ensure that the EILs do not become too conservative.

 

If the toxicity data shows a significant relationship with specific soil characteristics; for example, soil pH, organic carbon or clay content, cation exchange capacity (CEC), soil-specific ACL values can be calculated using those relationships. The toxicity data is first normalised to the reference Australian soil using the methods described above, and the ACL value derived using the SSD approach is valid for the Australian reference soil. Using the normalisation relationships, ACL values can then be calculated for different soil types. For example, if toxicity data showed a relationship with pH, different ACL values can be calculated for a range of soil pH conditions.

 

The lack of normalisation equations for soils from Australia can be overcome by using normalisation relationships developed with soils from other countries, particularly Europe and America. However, these normalisation relationships should only be used when they are derived from soils similar to Australian soils and/or their validity for Australian soils has been assessed and found suitable. The importance of this was shown by a study of Broos et al. (2007), which assessed the normalisation relationships of Smolders et al. (2004) and Oorts et al. (2006) for microbial nitrification in soils. They re-analysed the overseas data after removing microbial toxicity data for soils with organic compound concentrations greater than those found in Australian soils. This resulted in a change of soil characteristics, explaining the variance in the toxicity data.

 

A second option to overcome the lack of normalisation relationships in the literature is to examine the currently available toxicity data and use regression analyses on the collated data to determine if a significant relationship exists between toxicity and soil characteristics.

 

Normalisation relationships from field studies are preferred over those from laboratory studies. All the normalisation relationships for toxicity, apart from those developed by Broos et al. (2007) and Warne et al. (2008b), model laboratory-based data (Rooney et al. 2006; Smolders et al. 2003; Smolders et al. 2004; Oorts et al. 2006; EU 2006b; Song et al 2006, Warne et al 2008a). Warne et al. (2008b) found that field-based normalisation relationships gave much more accurate estimates of field phytotoxicity than laboratory-based normalisation equations. Therefore, field-based normalisation relationships should be used in preference to laboratory-based normalisation relationships. It is, however, realised that the current lack of field-based normalisation relationships will unavoidably necessitate the use of laboratory-based relationships, despite their limitations.

 

If multiple normalisation relationships are available within a taxonomic group of organisms, then the most geographically appropriate normalisation relationship should be applied to the toxicity data. For example, a European normalisation relationship would be applied to European data and an Australian normalisation relationship would be applied to Australian data. If there are multiple geographically appropriate normalisation relationships for a group of organisms, then the relationship with the lowest slope should be used, as this will give the most conservative normalised toxicity data (EC 2008).

2.4.7        Calculation of the added contaminant level using an assessment factor approach

If the minimum data requirements for the SSD approach cannot be met, the AF approach should be used to derive EILs. The AF is a ‘worst-case scenario’ type of approach. In this approach the lowest toxicity value for a contaminant; that is, the most sensitive data point, is divided by an AF in order to derive an ACL.

 

                                 (equation 5)

 

Equation 5 applies to the derivation of EILs; if other SQGs were to be derived, then different toxicity data would be substituted in the equation. The magnitudes of the AFs depend on the available toxicity data and are given in Table 13 below. If there is toxicity data for less than three species, the AF is 500, due to the lack of information and thereby the high uncertainty in estimating the risk posed by the contaminant in the soil. If there is toxicity data for more than three species the AF decreases, depending on how many taxonomic or nutrient groups are represented (see Tables 10 and 11 above for taxonomic and nutrient groups respectively). If field data or model ecosystems with multiple species tested are available, an assessment has to be made as to how well the study represents the field situation and how protective the toxicity data is. An AF of 10 should be used if the EIL is calculated using mesocosm or microcosm data.

Table 13. Assessment factors to be used to derive ACL using the AF approach (adapted from ANZECC & ARMCANZ 2000).

Toxicity data available for derivation of ACL

Number of species