ICP Forests Manual 2020 QAQC Labs
Source: environment.data.gov.uk (2026-01-05)

NFI+ Soil Condition & Health. This dataset contains comprehensive soil chemical and physical indicators collected as part of the NFI+ (National Forest Inventory Plus) programme and the BioSoil forest soil and biodiversity monitoring network. The dataset covers forest soil sampling sites across Great Britain and provides baseline and monitoring data on soil condition and health in forest ecosystems.

Chemical indicators measured include: soil pH (measured in water and CaCl2), organic carbon content (%), total nitrogen (%), available phosphorus (mg/kg), exchangeable cations (Ca, Mg, K, Na in cmol/kg), cation exchange capacity (cmol/kg), base saturation (%), and heavy metal concentrations where applicable.

Physical indicators measured include: soil texture (% sand, silt, clay), bulk density (g/cm³), porosity (%), aggregate stability, moisture content at sampling, and soil depth profiles.

Samples were collected at multiple depth intervals (0-10cm, 10-20cm, 20-40cm) using standardised protocols aligned with ICP Forests and BioSoil methodologies.  Each sampling location includes associated metadata on forest type, soil type, elevation and forest origin (plantation or natural).  The data include chemical and physical soil properties.

Quality assurance procedures included duplicate sampling, laboratory reference standards, and inter-laboratory comparisons.

Limitations:  Data coverage is limited to forest soils only. Some historical samples may have incomplete heavy metal analysis. Seasonal variation should be considered when interpreting results.

Data are georeferenced using a delocalised grid. The bounding coordinates for each grid square are included in the dataset. Further information about the delocalisation process is provided in the dataset evidence pack.

The 'NFI+ Soil Condition & Health' is a project funded by the UK Government through Defra’s Natural Capital and Ecosystem Assessment programme (NCEA), England sites, and the Department for Energy, Security and Net Zero (DESNZ), Scotland sites.  Users should ensure they have read the supporting documentation before viewing or making use of the data. This includes the NCEA Analysis Ready Data Limitations Statement and dataset specific limitations/caveats outlined in the supporting documentation, please refer to the 'Metadata' tab on the 'Soil Condition Data 2022-24' dataset  Attribution statement: © Forestry Commission copyright and/or database right 2025. Includes data from Ordnance Survey, British Geological Survey, and the Met Office. All rights reserved.

United Nations Economic Commission for Europe (UNECE)
Convention on Long-range Transboundary Air Pollution (CLRTAP)
International Co-operative Programme on Assessment and
Monitoring of Air Pollution Effects on Forests (ICP Forests)
MANUAL
on
methods and criteria for harmonized sampling, assessment,
monitoring and analysis of the effects of air pollution on forests
Part XVI
Quality Assurance and Control
in Laboratories
Version 2020-1

Prepared by:
ICP Forests Working Group on QA /QC in Laboratories
(Alfred Fürst, Anna Kowalska, Giorgio Brunialti, Nicholas Clarke, Nathalie Cools, Bruno De
Vos, John Derome, Kirsti Derome, Marco Ferretti, Tamara Jakovljevič, Nils König, Aldo
Marchetto, Rosario Mosello, Philip O’Dea, Gabriele Tartari, Erwin Ulrich)

Fürst A, Kowalska A, Brunialti G, Clarke N, Cools N, De Vos B, Derome J, Derome K,
Ferretti M, Jakovljevič T, König N, Marchetto A, Mosello R, O’Dea P, Tartari GA, Ulrich E,
2020: Part XVI: Quality Assurance and Control in Laboratories. Version 2020-1. In: UNECE,
ICP Forests Programme Co-ordinating Centre (ed.): Manual on methods and criteria for
harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on
forests. Thünen Institute of Forest Ecosystems, Eberswalde, Germany, 46 p. + Annex
[http://www.icp-forests.org/manual.htm]
ISBN: 978-3-86576-162-0

All rights reserved. Reproduction and dissemination of material in this information product for
educational or other non-commercial purposes are authorized without any prior written
permission from the copyright holders provided the source is fully acknowledged.
Reproduction of material in this information product for resale or other commercial purposes
is prohibited without written permission of the copyright holder.
Application for such permission should be addressed to:
Programme Co-ordinating Centre of ICP Forests
Thünen Institute of Forest Ecosystems
Alfred-Möller-Str. 1, Haus 41/42
16225 Eberswalde
Germany
Email: xxx@xxx.xx
Eberswalde, 2020

Quality Assurance and Control in Laboratories
Part XVI
Version 2020-1
CONTENTS

1
INTRODUCTION ............................................................................................................................ 5
2
SCOPE AND APPLICATION ......................................................................................................... 5
3
QUALITY ASSURANCE AND CONTROL TOOLS ....................................................................... 6
3.1
REFERENCE MATERIALS ....................................................................................................................... 6
3.1.1
Reference material for water analysis (deposition and soil solution) ............................................... 6
3.1.2
Reference material for foliar and litterfall analysis ........................................................................... 7
3.1.3
Reference material for soil analysis ................................................................................................. 7
3.2
CONTROL CHARTS ................................................................................................................................. 9
3.2.1
Mean Charts (X-Charts) ................................................................................................................... 9
3.2.2
Range Charts (R-Charts) ............................................................................................................... 10
3.2.3
Blank Charts .................................................................................................................................. 10
3.3
DETECTION AND QUANTIFICATION LIMITS ....................................................................................... 11
3.4
CHECKING ANALYTICAL DATA ............................................................................................................ 13
3.4.1
Checking results of water samples ................................................................................................ 13
3.4.2
Checking results of organic and mineral soil samples ................................................................... 24
3.4.3
Checking results of foliage and litterfall samples ........................................................................... 29
3.4.4
Analyses in duplicate ..................................................................................................................... 31
3.4.5
Avoidance of contamination ........................................................................................................... 31
3.5
INTER-LABORATORY QUALITY ASSURANCE .................................................................................... 33
3.5.1
Inter-laboratory comparison tests (ring tests) ................................................................................ 34
3.5.2
Exchange of knowledge and expertise amongst laboratories ........................................................ 38
3.6
QUALITY INDICATORS .......................................................................................................................... 39
3.6.1
Percentage of the results of a ring test within tolerable limits ........................................................ 40
3.6.2
Percentage of the results of a ring test with a precision <10% ...................................................... 40
3.7
QUALITY REPORTS ............................................................................................................................... 40
4
REFERENCES ............................................................................................................................. 41
ANNEX I ................................................................................................................................................ 43
I.1 EXCEL WORKSHEET FOR ION BALANCE (WITH AND WITHOUT DOC CORRECTION),
CONDUCTIVITY, N BALANCE AND NA/CL RATIO CHECKS. ........................................................................ 43
I.2 EXCEL WORKSHEET FOR CONTROL CHARTS ...................................................................................... 43
I.3 LIST OF COMMERCIALLY AVAILABLE REFERENCE MATERIALS ......................................................... 44
I.4 DEFINITIONS AND TERMINOLOGY .......................................................................................................... 45
ANNEX II
MINOR CHANGES AFTER 2020 .................................................................................... 48

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1 Introduction
Chemical analyses are an essential part of monitoring activities related to observations and
measurements of nutrients and pollutants fluxes in forest ecosystems. The value of data
collected under the framework of the ICP Forests programme depends to a great extent on
the quality of the analytical work carried out in supporting laboratories across Europe. To
gain full comparability of the spatial and temporal variability in the data, every effort must be
taken to ensure accuracy of the analytical measurements.
To that end, considerable efforts have been made over the last number of years to improve
the quality of laboratory analyses in the various monitoring programmes within the framework
of the ICP Forests programme. The Soil & Soil Solution, Deposition and Foliage & Litterfall
expert panels and the Working Group on QA/QC in Laboratories have carried out a number
of ring tests and held extensive discussions on harmonising analytical methodology,
including the most appropriate quality control (QC) and quality assurance (QA) to be
employed by participating laboratories. All ringtest results are stored in the PCC database
and can be linked to the monitoring data. Presented quality assurance and control tools
remain in line with the overall QA approach within the ICP Forests, as outlined in the Part III
of the Manual.
2 Scope and application
This paper presents all the QC methods that have been devised for the relevant fields of
analytical chemistry. The aim is to provide those laboratories carrying out analyses of
deposition, soil solution, soil, foliage and litterfall within the ICP Forests programme with a
complete overview of the QC options that can be applied in their laboratories.
The QA programme in each laboratory should be based on both: internal and external quality
control. Among the range of internal control methods that can be employed, the use of
reference materials is highly recommended (Chapter 3.1) as an extremely valuable tool to
ensure accuracy of analytical results. Although the use of Certified Reference Materials can
be limited by expense and availability in large quantities alternative Reference Materials,
such as National or Local Reference Materials can be used on a routine basis to confirm the
analytical methods accuracy and precision over time. The variation and the quality of the
analytical results can be controlled with the use of control charts. The different types of
control charts available, their construction and application are described in Chapter 3.2. The
determination of the detection- and quantification limits is described in Chapter 3.3.
Analytical data must be validated prior to data submission to confirm the correctness of
analyses and reduce the risk of errors. A wide range of data consistency checks are detailed
depending on the type of matrix analysed (Chapter 3.4, 3.4.1-3.4.3), based on the
relationship between the chemical components and/or chemical and physical properties of
the samples.
Participation by laboratories in external inter-calibration exercises plays an integral role in the
laboratory’s QA programme (Chapter 3.5). Otherwise known as ‘ring tests’, they are carried
out on a periodic basis and assess the performance of the participating laboratories as well
as help to ensure the comparability of the data produced by different laboratories over time.
Laboratories encountering difficulties with analytical methodology and/or associated QA/QC
programmes are encouraged to take full advantage of the range of proposals contained
within this document, which include the exchange of analytical expertise, and experiences
between laboratories.

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Evaluation of the quality performance of the participating laboratories within ICP Forests will
be conducted through the use of quality indicators outlined in Chapter 3.6. Information
gathered on the laboratories quality assurance processes/activities will be linked via quality
reports with the submission of data to ICP Forests each year (see Chapter 3.7).
3 Quality assurance and control tools
3.1 Reference materials
Within the ICP Forests programme the use of control charts for each variable and matrix is
mandatory. In order to produce these control charts, a reference material is necessary.
There are two types of reference material:
1 Reference Materials (RM): a material, sufficiently homogeneous and stable with respect to
one or more specified properties, which has been established to be used in a
measurement process: for the calibration of a measurement system, the assessment of a
measurement procedure, for assigning values to materials, and quality control (ISO Guide
30, 2015)
2 Certified Reference Materials (CRM): reference material, characterized by a metrologically
valid procedure for one or more specified properties, accompanied by a certificate that
provides the value of the specified property, its associated uncertainty, and a statement of
metrological traceability. (ISO Guide 30, 2015). The CRM can be of national or
international origin. A list of commercially available CRMs is given in Annex I.4.

Reference materials are available in a range of types and prices. CRMs are expensive and
should be used only when really needed: e.g. calibration, method validation, measurement
verification, evaluating measurement uncertainty, and for training purposes. In many cases,
however, the concentrations are not within the ranges encountered in daily practice. National
Reference Materials are, in many cases, easier to acquire and are often not as expensive as
CRMs. They are usually issued by national laboratories, and are extremely useful for
ensuring laboratory quality within a country.
Furthermore, laboratories must use matrix-matched control samples of demonstrated stability
to demonstrate internal consistency over time, e.g. through control charts. The analyte
concentrations of these samples do not need to be accurately known or traceable. However,
traceability would be a bonus. Here, again, CRMs or ring test samples can be used.
The Local Reference Materials (LRMs) are prepared by the laboratory itself for routine use
and can be easily and cheaply prepared in large quantities. They can often also be prepared
within the concentration ranges for the more important parameters. These LRMs are
extremely important for QA/QC activities, mainly for use in control charts (see Chapter 3.2),
if there is a need to maintain a constant (stable) quality over a long-time scale.
The following reference materials can be used in each field of interest:
3.1.1 Reference material for water analysis (deposition and soil solution)
One common approach is to use natural samples that are preserved with stabilising agents
(e.g. low chloroform concentrations), after first ensuring that their use does not cause
interferences in the analytical methods or has an adverse effect on other activities performed
in the laboratory. The use of natural samples makes it possible to have concentrations close
to those normally measured. It is advisable to use two standards for each type of analysis,

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one of medium-low and one of medium-high concentrations, in relation to the range normally
analysed. The stability of LRMs should be tested; their stability for individual ion species may
vary.
One very cheap method for preparing an LRM is to buy mineral water that has chemical
characteristics close to the range normally measured. Before you can use an LRM, however,
you first have to validate your method with a CRM. You should run your LRM together with
the CRM or a ring test sample so as to determine the conventional true value.
For deposition samples, mineral water derived from volcanic bedrock has very similar
concentrations. For soil solution samples, a specific type of mineral water has to be selected
in accordance with the prevailing soil types in the monitoring network. The advantage of
using mineral water is that they are relatively stable over time as long as the bottles of the
same batch are stored in a dark place. However, mineral water does not contain dissolved
organic carbon (DOC) in a form similar to that occurring in either deposition or soil solution
samples.
3.1.2 Reference material for foliar and litterfall analysis
The matrix properties and the analyte concentrations of the reference material should be
similar to those of the samples from the regional/national network. As there is only a limited
number of certified forest-tree foliage reference material available worldwide, agricultural
plant material with similar matrix and analyte concentrations, e.g. flour, hay, cabbage, olive
leaves, apple leaves, can be used as an acceptable substitute. However, check the analyte
concentrations before placing the order. (A list of commercially available CRM`s is given in
Annex 5.3).
“Old” foliage ring test samples are also stable enough and extensively analysed for use as
reference material in method validation.
One good cheap method for producing a high quality LRM is to prepare foliage material for
use as a ring test sample. In the ring tests, the Forest Foliar Co-ordinating Centre (FFCC)
always utilises dried, powdered foliage samples from one type of tree (leaves/needles) or a
homogenized litterfall sample (one ltterfall fraction). Therefore, the laboratory should initially
separate the needles (one needle set)/leaves from the branches and dry, mill and pre-
homogenise the material (dry weight min. 4-5 kg) prior to dispatch to the FFCC
(http://www.ffcc.at/)
The FFCC homogenizes the sample again, divides it up and uses it in one of the subsequent
ring tests. The advantage for the laboratory is in having a large amount of reference material
with a similar element concentration as their normal samples and known accuracy of the
mean concentration.
3.1.3 Reference material for soil analysis
Soil samples of former l ring tests are stable enough and extensively analysed for use as
reference material in method validation. The FSCC (Forest Soil Co-ordination Centre) and
the Croatian Forest Research Institute CFRI) always utilises dried, milled/sieved samples
from different soil type ( different ranges in CaCO3 content and OC content) for the ring test
program (see Annex 1.3: list of commercially available reference materials).
3.1.3.1 Preparation of local reference material for soils
Due to the type of soil samples and the nature of the two-step analysis, LRM samples are
needed for both the solid phase (to control the quality of digestion) and the liquid phase (to
control the quality of the chemical analyses).

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•
Solid phase:
Take several large (10 to 50 kg) samples from one site (organic and mineral, preferably by
genetic horizon). Dry all the sampled material and homogenise the samples several times to
ensure a uniform homogeneous sample. Split or riffle each sample into several parts and
store in a cool, dry place. It may be worthwhile preparing several sets of the individual soil
types and concentration ranges occurring in the country (e.g. one for clay soils in the coastal
area with high sea salt concentrations, and one for sandy soil from an inland site).
•
Liquid phase:
After digestion of larger amounts of the solid phase LRM, store the solution (liquid phase) in
a cool, dark place.
In general, no control with high analyte concentrations is necessary because the errors are
normally
associated
with
lower
concentrations.
Solutions
with
excessively
high
concentrations often have to be diluted in order to fit within the ranges for which the
analysers have been calibrated.
The amount of LRM has to be large enough to be used for an extended period of time
(preferably up to one year). The amount needed annually will depend on the type of
analytical equipment and methods used by the laboratory. The sample should be stored in
such a way that no or minimal changes occur over time.
3.1.3.2 Calibration of local reference material for soils
After the preparation of the LRM, a test run has to be performed with correctly calibrated
equipment. A number of replicates (e.g. 5 for the solid and 30 for the liquid phase) have to be
analysed for all relevant parameters, and at least one (but preferably more) national or
international reference samples. The absolute accuracy is determined for each parameter on
the latter samples. The standard deviation (SD) calculated from the results of analysis of the
LRM should be as small as possible. The results of the first test run should be treated
according to the ISO standard 7870-2 (2013, Control charts - Shewhart control charts). The
mean value of the parameters for the LRM is of less importance, but it should be within the
same range as the values of the samples that will be subsequently analysed.
Each parameter now has its own SD, which allows evaluation of the parameters and the
relevance of the analysis by the method in question. If the SD is significantly larger than the
expected values, then the relevance of analysing the parameter by the selected method is
low. Other methods/equipment may have to be used to analyse the parameter within an
acceptable range.
This procedure should be repeated whenever equipment is changed, important components
are replaced, or when temporal trends appear in the results. The absolute values obtained
from the national and international reference material are extremely importance in the former
case.
3.1.3.3 Use of local reference material for soils
After successful calibration, a systematic re-analysis of the LRM (liquid phase) is included in
every batch or series of samples. Depending on the number of samples to be analysed and
the methods and equipment used, this could be in the range of one LRM per 10 to 30 real
samples to be analysed. For the solid phase (digestion and analysis) this could be reduced
to one LRM per 30 to 50 samples to be analysed.
The results of the repeated analysis of the LRM permit evaluation of the stability of the
method/equipment over time. Therefore, it is strongly recommended to plot on a graph the
LRM result of every analysis over time (see ISO 7870-2, 2013 and Chapter 3.2).

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3.2 Control charts
Within the ICP Forests programme the use of control charts for each parameter and matrix is
mandatory.
Control charts form an important practical aspect of internal QC in the laboratory. Using
reference materials (see Chapter 3.1) the quality of the method can be checked immediately,
while control charts are a useful tool for checking the quality and the variation in quality over
a longer time scale. The laboratory runs control samples together with the real samples in an
analytical batch and, immediately after the run is completed, the control values are plotted on
a control chart. There are various types of control chart available (for details see the ISO
7870-2, 2013). The most commonly used control charts are the mean charts and range
charts for laboratory control standards, and the blank charts for background or reagent
blank results.
In addition, the control charts can be used for calibration, method validation and comparison,
estimation of measurement uncertainty and limit of detection, checking the drift of equipment,
comparison or qualification of laboratory personnel, and evaluation of proficiency tests.
For more information about the use of control charts see Nordtest report TR 569, 2012.
3.2.1 Mean Charts (X-Charts)
The main aim of the means chart is to check the repeatability of the measurements in every
batch of analyses. It is constructed from the average and standard deviations of a standard,
determined from a solution of one or more analyte(s), or a natural sample, that is sufficiently
stabilised to keep the concentrations constant over time for at least 2-4 months. In the case
of deposition samples, the choice of preservative (e.g. inorganic acids or chloroform) is
determined by the analyte of interest and the conditions under which the analyses are carried
out. It is advisable to use more than one control chart, at different concentration levels for
each analyte.
The means chart is prepared on the basis of the first 20 to 25 measurements used to
calculate the mean concentration (Xm) and the standard deviation (s). These variables are
used to evaluate the upper and lower warning levels (UWL, LWL) and the upper and lower
control levels (UCL, LCL). It is a common practice to use ± 2s and ± 3s limits for the warning
limit (WL) and control limit (CL), respectively (Figure 1). For variables with a non-normal
distribution, transformation to a normal distribution may be necessary.
Assuming that s is correctly estimated, 95% of the measurements should fall within the range
of Xm±2s (WL) and 99% in the range of Xm±3s (CL). In long-term routine analyses, on the
other hand, UWL and LWL may be chosen by the analyst on the basis of experience with
previous control charts or according to specific goals that are to be reached in the analyses.
The means chart can also incorporate a target or nominal value of the analyte in the case of
reference material with the reported concentration. The target control limits may also be
used, and the laboratory results then be compared with these values.
If measurement uncertainty is determined for an analyte as a part of method validation, this
value can be added to a means chart. Measurement uncertainty limits in the chart should lie
between the warning and control limits (2s and 3s), in most case nearer the warning limit.
The results of a control sample should not exceed the measurement uncertainty limits and, in
the case of a synthetic control sample, they should remain between these limits. A target or
nominal value can also be used with the measurement uncertainty limits. Because
measurement uncertainty is proportional to the concentration of the analyte, different
measurement uncertainty limits should be used for different control charts of the same
analyte. With this type of x-chart it is possible to check that the set measurement uncertainty
is achievable in the course of time.

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Every batch of analyses should include one or more measurements of the standard for the
control chart. This measurement is plotted on the control chart: if a measurement exceeds
the CL, the analysis must be repeated immediately. If the repeat is within the CL, then the
analysis can be continued; if it exceeds the CL, the analysis should be stopped and the
problem rectified. In regard to the WL: if two out of three successive points exceed the WL,
then an additional standard should be analysed. If the concentration is less than the WL, the
analysis can be continued; if it exceeds the WL, then the analysis should be stopped and the
problem rectified.

Figure 1: Example of a control chart using mean concentrations. Mean concentration, LWL, UWL
lower, upper warning limit; LCL, UCL lower, upper control limit, calculated on the basis of experience
with previous control charts (R.S.D. = 3 %)
3.2.2 Range Charts (R-Charts)
The difference between two (or more) determinations on the same sample can also be
described on a graph. This R chart is used for checking the repeatability of the analysis,
usually of duplicate determinations. As the range is normally proportional to the sample
concentration, it will therefore be more appropriate to use a control chart where the control
value is the relative range r %.
3.2.3 Blank Charts
A blank is defined as a solution of the purest available water that contains all the reagents
used for the analysis, but not the analyte. The solution should be subjected to all the steps of
the analysis (filtration, digestion, addition of reagents) up until the final measurement. The
blank signal then indicates the sum of the analyte released in the different phases of the
process, and a check must be made in order to exclude the possibility of occasional
contamination. An example of a blank chart is shown in Figure 2. The chart makes it possible
to compare the blank values obtained in different batches of analyses at different times; an
abnormally high blank value indicates the presence of contaminants at some stage of the
process. The upper limit of acceptance is chosen by the analyst, either based on a previous
set of analyses (e.g. two times the mean values of the blank absorbance) or on the
dispersion of values around the mean.

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Figure 2: Example of a blank chart
The standard deviation (sb) of the blanks makes it possible to determine the detection limit
(LOD) and the quantification limit (LOQ) of the analytical method. The LOD in most
instrumental methods is based on the relationship between the gross analyte signal St, the
field blank Sb, and the variability in the field blank (sb). The limit of detection and
quantification may be defined by the extent to which the gross signal exceeds Sb:
LOD = St - Sb  Kd sb
LOQ = St - Sb  Kq sb
Recommended values for Kd and Kq are 3 and 10, respectively (Analytical Methods
Committee, 1987, Currie, L.A. 1999a).
3.3 Detection and quantification limits
Detection and quantification capabilities are fundamental performance characteristics of any
chemical measurement process (Currie, 1999b). For each matrix (soil, water, foliage) and
each analytical method, the limit of detection (LOD) and quantification (LOQ) should be
determined by each laboratory.
The limit of detection (LOD) is the smallest measure, xL, that can be detected with
reasonable certainty for a given analytical procedure.
The value of xL is given by the equation:
xL = xbi + Ksbi
where xbi is the mean of n blank measurements, sbi is the standard deviation of n blank
measurements, and K is a numerical factor chosen according to the confidence level desired
(IUPAC, 1997). For LOD, this K factor is commonly set at 3 (see also Kd in Chapter 3.2.3).
The LOD is the concentration at which we can decide whether an element is present or not.
It is the point where we can just distinguish a signal from the background (Thomsen et al.,
2003).

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It is recommended that the number of blank measurements (n) is higher than 30, preferably
determined under within-lab reproducibility conditions (e.g. different operators, different runs
on different days).
The limit of quantification (LOQ), also referred to as the quantitation limit, is generally agreed
to begin at a concentration equal to 10 standard deviations of the blank (Kq = 10). Therefore,
LOQ is 3.3 times LOD. Quantitatively, the relative standard deviation (RSD) of repeated
measures is 10% at the LOQ, and 33% at the LOD (Thomsen et al., 2003). This is in fact a
statistical simplification of the uncertainty problem near the lower measurements limits, as
explained by Currie (1999a), but in practice it is a useful approximation.
Table 1: IUPAC recommendations for uncertainty associated with limits of detection and
quantification (after Thomsen et al., 2003).

Absolute SD
Relative SD
Limit of detection
LOD
3 σ
33%
Limit of quantification
LOQ
10 σ
10%
Similar results can also be obtained using the standard deviation of the signal of the lowest
calibration standard, instead of the standard deviation of the signal of blank samples. This
method should be used in particular when the signal of blank samples is very low, as in the
case of ion chromatography. A further possibility is the use of the Hubaux-Vox method for
calculating LOD from the error associated to the calibration regression.
A distinction should be made between instrument detection/quantification limits and method
(or matrix) detection limits. Generally, instrument detection limits (IDLs) are based on a clean
matrix. Method/matrix detection limits (MDL) consider real-life matrices such as soil, organic
matter and rainwater. Spectroscopists commonly accept that the MDL can be anywhere from
about two to five times greater than the IDL.
Therefore, labs should clearly mention whether the reported limits are instrument or matrix
detection limits. In the case of environmental research, MDLs are more meaningful than
IDLs.
Measurement precision and concentration (or content) are often clearly related, as shown in
Figure 3. Generally, as the concentration or content of the analyte decreases, the precision
for determination, as expressed in the relative standard deviation increases. When
empirically precision data are gathered for each concentration or content level, a graph may
be constructed as in Figure 3. Each data point represents the RSD of 8 to 20 replicate
measurements per level.
When a curve is fitted with a suitable equation (e.g. y = a x -b), the limits of detection and
quantification may be estimated from this equation by determining the limits at the RSD
values of 30% and 10%, respectively. These limits are illustrated in Figure 3, whereby total N
can be reliably determined in this example at concentrations above the LOQ, whereas
determination becomes highly uncertain between the LOD and LOQ. The curve can be
constructed using the standard deviation obtained from control charts at different
concentration. When the matrix effect is negligible, the standard deviation of the signal of the
calibration standards can also be used.
An example of application of the LOD and LOQ estimation method for the determination of
carbon by the Walkley-Black method in forest soils can be found in De Vos et al. (2007).
This empirical method is time-consuming and laborious. However, it immediately shows the
matrix detection and quantification limits for real-life samples under specific laboratory
conditions.

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Figure 3: Relationship between measurement precision (RSD) and N concentration in a test
mineral soil sample
3.4 Checking analytical data
These checks should help the laboratory to detect possible wrong results. So it can be
needed to repeat the analysis once more and correct or confirm the result.
3.4.1 Checking results of water samples
The analytes present in deposition and soil water samples and in soil extracts are mainly in
ionic form. This enables the use of at least two checks on the consistency of the analysis
results obtained for individual samples: i.e. the calculation of the ion balance, and
comparison of the measured conductivity with the conductivity calculated from the sum of
each ion. A third consistency test, which is only valid for deposition samples, employs the
ratio between the Na+ and Cl- concentrations, which should normally be relatively close to the
value in seawater. A fourth check, aimed at identifying analytical errors, is based on the
relationship between the different forms of nitrogen analysed. Other statistical procedures
that employ the relationship between the equivalent sum of ions (cations, anions) and
conductivity can be applied to the datasets. These are based on the relative similarity of the
ratio between certain ions in deposition samples, due to their common origin (e.g. Na+ and
Cl- from sea spray, SO42- and NO3- from combustion processes, Ca++ and alkalinity from soil
dust). However, these methods require a relatively large set of data for the same type of
precipitation before they can be applied to the results of single analyses in order to identify
outlier values.
Examples of the application of these checks on sets of data from different sites in Europe
have been reported by Mosello et al. (2005, 2008).
0.0
0.5
1.0
1.5
2.0
Total N content (g/kg)
0
20
40
60
Relative standard deviation (RSD)
RSD = 4.8316 * N (-0.5835)
LOQ = 0.28
LOD = 0.044

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Most of the calculations needed to use the validation check, starting from concentration
values, can be simplified by using a worksheet file similar to the one given on the
ICP Forests web site (http://icp-forests.net/group/qualityinlaboratories/page/deposition-and-
soil-solution-ringtest-and-qa-qc-information; see also Chapter 5.1).
3.4.1.1 Ion balance
3.4.1.1.1 Ion balance without DOC
Each laboratory performs checks on the chemical analyses by calculating the ion balance
(for bulk open field and wet only deposition) and comparing the measured and calculated
conductivity (for bulk open field [BOF] and wet only deposition, throughfall [THF] and
stemflow [STF]) values in order to validate the results. However, these checks are not always
applicable to soil water samples due to the presence of aluminium and other metals as
various ionic species, especially at a pH < 5 (see Chapter 3.4.1.1.3).
If the threshold values of these checks are exceeded, then the analyses must be repeated. If
the result is confirmed but the threshold values are still exceeded, then the results must be
accepted.
The ion balance is based on the equivalent concentration of anions vs. the concentration of
cations (Σ Cat vs. Σ An):
Σ Cat = [Ca++] + [Mg++] + [Na+] + [K+] + [NH4+] + [H+]
Σ An = [HCO-3] + [SO--4] + [NO-3] + [Cl-] + [Org-]
The contribution of fluoride to the ionic balance is normally insignificant.
The limit of acceptable errors varies according to the total ionic concentration and the type of
solution. The percentage difference (PD) is defined as:
PD = 100 * (Σ Cat –Σ An)/(0.5*(Σ Cat + Σ An))
The limits adopted in the ICP Forests/EU Forest Focus programmes are given in Table 2.
Table 2: Acceptance threshold values in data validation based on ion balance and conductivity
(see definition of CD [the percentage difference between measured and calculated conductivity] in
Chapter 3.3.1.2)
Conductivity (25 °C)
PD
CD
<10 µS cm-1
±20%
±30%
<20 µS cm-1
±20%
±20%
>20 µS cm-1
±10%
±10%
The conversion factors required to transform the units used in the ICP Forests Manual Part
XIV (Deposition) into µeq L-1 are given in Table 3.

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Table 3: The conversion factors used in converting the concentrations used in the ICP Forests
Deposition Monitoring Programme to µeq L-1, and the values of equivalent ionic conductivity at
infinite dilution

Unit
(ICPF standard)
Conversion
factor to
µeq L-1
Equivalent
conductance at
20°C
Equivalent
conductance at
25°C

kS cm2 eq-1
kS cm2 eq-1
pH
Unit
10(6-pH)
0.3151
0.3500
Ammonium
mg N L-1
71.39
0.0670
0.0735
Calcium
mg L-1
49.9
0.0543
0.0595
Magnesium
mg L-1
82.24
0.0486
0.0531
Sodium
mg L-1
43.48
0.0459
0.0501
Potassium
mg L-1
25.28
0.0670
0.0735
Alkalinity
µeq L-1
1
0.0394
0.0445
Sulphate
mg S L-1
62.37
0.0712
0.0800
Nitrate
mg N L-1
71.39
0.0636
0.0714
Chloride
mg L-1
28.2
0.0680
0.0764
Bicarbonate is calculated from total alkalinity (Gran’s alkalinity) in relation to pH, assuming
that total alkalinity is determined only by inorganic carbon species, protons and hydroxide:
TAlk = -[H+] + [OH-] + [HCO3-] + [CO32-]
This definition is not completely correct in the case of high organic carbon concentrations
(DOC > 5 mg C L-1), and in the presence of metals (Al, Fe, Mn etc.) that may contribute to
alkalinity or to the cation concentrations (see Chapters 3.4.1.1.2 and 3.4.1.1.3). This sets
limits on the use of the ion balance check in validating the analyses for certain types of
solution, as summarised in Table 4.
Table 4: Applicability of the validation tests for different types of solution

Ion balance
Ion balance
Conductivity
Na/Cl ratio
N test

DOC
corrected

Bulk
open
field
Y
Y
Y
Y
Y
Wet only
Y
Y
Y
Y
Y
Throughfall
N(3)
Y
Y
Y
Y
Stemflow
N(3)
Y
Y
Y
Y
Soil water
N(3)
N(4)
Y(2)
N
Y
Surface water
Y(1)
Y
Y
N
Y
(1) If DOC <5 mg C L-1 and negligible metal concentrations
(2) If metal concentrations are negligible.
(3) see chapter 3.4.1.1.2
(4) see chapter 3.4.1.1.3
Examples of comparisons between Σ Cat and Σ An are given in Figure 4 for different types of
solution. The departure from zero of the ion balance for different types of deposition sample
is shown, illustrating the failure of the check in the case of Throughfall (THR) and Stemflow
(STF) samples.

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Figure 4: Departure from zeron of the percentage difference between Σ Cat and Σ An (PD)
(above), and (below) of the percentage difference between measured and calculated
conductivity (CD) for different types of deposition sample
0
50
100
150
200
250
300
Bulk open field
Throughfall
Stemflow
<-25
-25/-20 -20/-15 -15/-10 -10/-5
-5/0
0/5
5/10
10/15 15/20
>25
% difference between cation and anion concentrations
Number of data
0
100
200
300
400
Bulk open field
Throughfall
Stemflow
-25
-20
-15
-10
-5
0
5
10
15
20
25
% difference between measured and calculated conductivity
Number of data

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3.4.1.1.2 Ion balance with DOC
Figure 4 clearly illustrates the failure of the ion balance check in the case of THR and STF
samples. This is also the case for soil water samples (not shown in figure) in which, in
addition to high DOC concentrations, elevated concentrations of metals may also be present
(see Chapter 3.4.1.1.3).
The ion balance test can be used to evaluate the ionic contribution of DOC (all solutions are
filtered through 0.45 um membrane filters before analysis) (Mosello et al., 2008). This study
was carried out as part of the activities of the WG on QA/QC in laboratories regularly
performing the chemical analysis of deposition and soil water samples within the framework
of the ICP Forests and the EU/Forest Focus Programmes. About 6000 chemical analyses of
bulk open field, throughfall and stemflow samples, which contained complete sets of all ion
concentrations, alkalinity, conductivity and DOC, carried out in 8 different laboratories, were
used to calculate empirical relationships between DOC and the difference between the sum
of cations and the sum of anions. The aim was to determine the formal charge per mg of
organic C. The samples covered a wide range of geographical and climatic conditions, as
well as variables such as the proximity of the sea (chloride concentration) and the type of
vegetation for THR and STF.
Regression coefficients were obtained for the data sets from each laboratory, as well as for
all the data combined, as follows:
Σ Cat – Σ An = δ1 DOC + δ0
where the units are µeq L-1 for the sum of ions and δ0, mg C L-1 for DOC, and µeq (mg C)-1
for δ1. The regressions were not significant for BOF, because of the relatively high error
associated with the low DOC concentrations. In contrast, the regressions were statistically
highly significant for THR and STF in all the 8 laboratories.
In the next step, the charge contribution of DOC was determined as:
[Org-] = β1*DOC + β0
where [Org-] (µeq L-1) is the ionic contribution of DOC. The value of PD was calculated again
using the Σ An value including [Org-], and evaluated using the threshold values given in
Table 2.
An example of the regression coefficients, β1 and β0, as well as the appropriate statistical
parameters, is given in Table 5. The coefficients were further tested using an independent
set of data from each laboratory. Comparison of the differences between the individual
laboratories and the overall regression coefficients showed that the coefficients were
generally applicable for deposition samples, and also suitable for estimating the contribution
of organic acids in the ion balance test. This means a considerable improvement in the
applicability of the ion balance as a validation criterion for samples with high DOC
concentrations. The improvement in the ion balance check in an example data set is shown
in Figure 5. This evaluation can also be found in the annexed Excel file (downloadable from:
http://icp-forests.net/group/qualityinlaboratories/page/deposition-and-soil-solution-ringtest-
and-qa-qc-information), which contains examples of analysis validation.

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Table 5: Statistical parameters of the regression equations for determining the DOC
contribution to the ion balance. THR = throughfall, STF = stemflow, N = number of samples, σ =
standard deviation.

Units
Broadleaves
Conifers
THR
STF
THR

N
-
1454
597
1657
pH range
U
4.0 - 7.9
3.8 - 8.1
4.1 – 7.0
pH mean± σ
U
5.8±0.6
5.6±0.6
5.3±0.5
DOC range
Mg C L-1
0-37
1-39
0-40
DOC mean± σ
Mg C L-1
8±6
11±7
10±7
∑ Cat range
µeq L-1
37-2736
30-5287
13-2601
∑ Cat mean± σ
µeq L-1
418±321
593±539
316±278
∑ An range
µeq L-1
29-2606
22-5303
10-2584
∑ An mean± σ
µeq L-1
377±304
545±523
279±265
∑ Cat - ∑ An
range
µeq L-1
258
263
225
∑ Cat - ∑ An
mean± σ
µeq L-1
41±59
48±58
37±41
Slope β1
µeq (mg C)-1
6.8±0,16
5.04±0.25
4.17±0.11
Intercept β0
µeq L-1
-12.32±1,63
-6.67±3.29
-5.01±1.32
P-value

<0.0001
<0.0001
<0,0001
R2

0.56
0.4
0.47

Figure 5: Departure from zero of the percentage difference between Σ Cat and Σ An (PD, see
text) without and with DOC correction
0
20
40
60
80
100
Throughfall, no correction
Throughfall, DOC corrected
-25
-20
-15
-10
-5
0
5
10
15
20
25
% difference between cation and anion concentrations
Number of data

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3.4.1.1.3 Ion balance with DOC and metals
The ion balance for soil water samples is more complicated owing to the presence of metals
(e.g. Al, Fe, Mn), their species (e.g. Al3+, Al(OH)2+,Al(OH)2+, Fe3+, Fe(OH)2+, Fe(OH)2+), their
oxidation state (e.g. Fe3+/Fe2+; iron complexed with organic matter can occur in both oxidised
and reduced forms and the reduced forms can exist under oxidising conditions when
complexed with organic matter; see e.g. Clarke and Danielsson, 1995) and metal complexes
with DOC (e.g. DOC-Fe, DOC-Al, DOC-Mn) in the solution.
The calculation of bicarbonate from total alkalinity (see Chapter 3.4.1.1.1) is not completely
correct because it is influenced by the different species of DOC in the solution.
Therefore calculation of the formal charge per mg of organic C from the difference between
the sum of cations and the sum of anions, as described in Chapter 3.4.1.1.2 for throughfall
samples, also has to take into account the metals, their species and their complexes with
DOC:
Σ Cat + Σ Met (all inorg. species) + Σ Met (from DOC complexes)
= Σ An + Σ Org- (from DOC complexes)
where:
Σ Met = Al3+ + Al(OH)2+ +Al(OH)2 + + Fe3+ + Fe(OH)2+ + Fe(OH)2+ + Mn2+ + Mn(OH) + (and other
inorg. species)
Σ Met (from DOC complexes) = Al-DOC + Fe-DOC + Mn-Doc
Σ Org- (from DOC complexes) = DOC-Fe + DOC-Al + DOC-Mn
Normally only the total concentrations of the metals and the total concentration of DOC are
measured in soil solution samples. Therefore calculation of the formal charge per mg of
organic C using the following formula overestimates the formal charge of DOC when the
highest possible charge for the metals (Al3+, Fe3+ ,Mn2+) is used and there is no correction for
bicarbonate:
Σ cat + Σ mettotal – Σ an = δ1 DOC total
In a study conducted by the WG on QA/QC in Laboratories, about 6200 chemical analyses
on soil solution samples (complete sets of all ion and total metal concentrations, alkalinity,
conductivity and DOC, carried out in the laboratories of 6 countries, were used to calculate
empirical relationships between DOC and the difference between the sum of cations and
metals and the sum of anions. The aim was to determine the formal charge per mg of
organic C. The samples cover a wide range of geographical and climatic conditions. The
results are shown in Figure 6:

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Figure 6: Calculation of the formal charge of DOC in 6140 soil solution samples from 5
countries (Germany, Finland, France, Norway and the United Kingdom)
When the calculated charge factor for DOC was included in the ion balances of these soil
solution samples, 64 % of the samples had equal ion balances (within +/- 10 %) while only 30
% of the samples had equal ion balances without using the DOC correction.
The results are different in the individual countries and at different pH values. Therefore the
charge factor value obtained here can only be used as a first step in the procedure. It would
be better to calculate the charge factor for specific countries or for similar types of plot. The
chemical composition of DOC varies with depth down the soil profile (e.g. it is more polar at
greater depth, Clarke et al., 2007), so the charge factor is also likely to vary with depth.
3.4.1.2 Conductivity check
Conductivity is a measure of the ability of an aqueous solution to carry an electric current.
This property depends on the type and concentration of the individual ions and on the
temperature at which conductivity is measured. It is defined as:
K = G * (L/A)
where G = is the conductance (unit: ohm-1 or siemens; ohm-1 is sometime written as mho),
defined as the reciprocal of resistance, A (cm2) is the electrode surface area, and L (cm) is
the distance between the two electrodes. The units of K are ohm-1 cm-1. In the International
System of Units (SI), conductivity is expressed as millisiemens per meter (mS m-1); this unit
is also used by the IUPAC and accepted as the Nordic standard. The unit µS cm-1, where 1
mS m-1 = 10 µS cm-1 = 10 µmho cm-1, is also widely used in practice. The unit adopted in the
ICP Forests programme is µS cm-1, and the reference temperature 25 °C.
Conductivity depends on the type and concentration (activity) of the ions in solution; the
capacity of a single ion to transport an electric current is given, in standard conditions and in
ideal conditions of infinite dilution, by the equivalent ionic conductance (i; unit: S cm2
equivalent-1).
Careful, precise conductivity measurement is an additional way of checking the results of
chemical analyses. It is based on comparison between measured conductivity (CM) and the
conductivity calculated (CE) from the individual ion concentrations (ci), multiplied by the
respective equivalent ionic conductance (i)

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CE =  i ci
The ions used in the conductivity calculations are the same as those used in calculating the
ion balance; the values of i for the different ions at temperatures of 20 and 25°C are given in
Table 3. As the concentrations are expressed in µeq L-1, i is given as kS cm2 eq-1 in order to
obtain the conductivity in µS cm-1. The percentage difference, CD, is given by the ratio:
CD = 100 * (CE-CM)/CM
At low ionic strength (below 100 µeq L-1) in deposition samples, the discrepancy between
measured and calculated conductivity should be no more than 2% (Miles & Yost 1982).
At an ionic strength higher than 100 µeq L-1 (approximately at conductivity higher than 100
µS cm-1) it is necessary to use activity instead of concentration. This can be done by first
calculating the ionic strength (Is, meq L-1) from the individual ion concentrations as follows:
Is = 0.5  ci zi2 / wi
where:
ci = concentration of the i-th ion in mg L-1;
zi = absolute value of the charge for the i-th ion;
wi = gram molecular weight of the i-th ion.
In the range 100-500 µeq L-1 the Davies correction of the activity of each ion can be used, as
proposed e.g. by Stumm and Morgan (1981) and A.P.H.A., A.W.W.A., W.E.F. (2005):
y
Is
Is
Is
=
−
+
−




10
0 5 1
0 3
.
.

Finally, the corrected conductivity is calculated as:
CEcorr=y2 CE= y2  i ci
Immediate comparison of the measured and calculated conductivity makes it possible to
identify single outlier values (see example in the annexed Excel file).
Figure 4 shows the departure from zero of the CD values for different types of deposition
sample. The pattern is different from that for the ion balance: the CD values do not show any
great asymmetry for BOF, THR, or STF. The reason for this is that dissolved organic matter
(DOM), which causes an imbalance between the cation and anion concentrations, does not
contribute significantly to conductivity.
In conclusion, a plot of measured and calculated conductivity is useful in the routine checking
of a set of analyses. Departure of the results from linearity suggests the presence of
analytical or some other kind of error.
3.4.1.3 Na/Cl ratio check
In many parts of Europe sea salt is a major contributor of sodium and chloride ions in
deposition and, as a result, the ratio between the two ions is similar to that of sea salt. This is
true even in parts of Europe situated far from the sea, as has been shown from a statistical
study conducted on more than 6000 samples covering the area from Scandinavia to South
Europe (Mosello et al., 2005). In the validation file
(downloadable from: http://icp-forests.net/group/qualityinlaboratories/page/deposition-and-
soil-solution-ringtest-and-qa-qc-information), samples with a ratio outside the range given

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below are marked as possible failures, and checks and/or re-analyses should be carried out.
The ratio is calculated by expressing the concentrations on a molar (or equivalent) basis.
0.5 < (Na/Cl) < 1.5
If the Na/Cl ratio results systematically fall outside this range, this may be due to poor
analytical quality in the measurement of low concentrations of sodium and chloride.
In some localised areas, where there are other sources of Cl and/or Na (e.g. from
anthropogenic activities), the Na/Cl ratio might be different from that of sea salt. Where this is
suspected, it is recommended to carry out a study to confirm whether this is true or not.
3.4.1.4 N balance check
The test is based on the fact that total dissolved nitrogen (DTN) concentration must be higher
than the sum of nitrate (N-NO3), ammonium (N-NH4) and nitrite (N-NO2) concentrations.
Although the measurement of nitrite is not mandatory in the ICP Forests programme, the
following relationship must be verified, within the limits of analytical errors and whatever unit
is used:
[N-NO3] + [N-NH4] < [DTN]
If the relationship does not hold true, then the determination of one of the forms of nitrogen
must be erroneous. However, if DON is very low, DTN may be approximately equal to NO3-N
+ NH4-N. In this case, normal (random) analytical errors may result in a slightly negative
value of ([DTN] – ([NO3-N] + [NH4-N])), without there being any major problem with the
analyses.
3.4.1.5 Comparison between measured conductivity and ion concentrations
Samples with similar ionic ratios and different ionic concentrations should show linear
correlation between conductivity and the sum of cations and anions. The linearity is valid if
the H+ concentration is low (i.e. pH higher than 5.0). However, because of the high specific
electric conductance of H+ (0.35 kS cm2 eq-1 at 25 °C, Table 3), compared to that of the other
ions (range 0.044-0.080 kS cm2 eq-1), small variations in H+ concentrations produce relatively
strong variations in conductivity. In these cases, a conductivity value corrected for the
contribution of hydrogen ion can be used:
CH+ correct = CM - λ H+ [H+] = CM – 0.35 * 10-pH
where the conductivities are expressed as µS cm-1 at 25°C and [H+] as µeq L-1 (Figure 7).
Results diverging from linearity should be carefully checked in order to see whether there
have been any mistakes in the analyses or in the data processing, or whether the values of
some important ions are missing from the calculation.

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Figure 7: Examples of the relationships between conductivity and Σ Cat or Σ An, above without
the correction for H+ contribution to conductivity, and below with the correction

0
100
200
300
400
500
600
0
5
10
15
20
25
30
Bulk open field
Througfall
Xmeasured (µS cm-1 at 25 °C)
Σ ions (µeq L-1)
0
100
200
300
400
500
600
0
5
10
15
20
25
30
Xmeasured - XH+ (µS cm-1 at 25 °C)
Bulk open field
Througfall
Σ ions - H+ (µeq L-1)

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3.4.1.6 Phosphorus concentration as a contamination check
If bird droppings contaminate the precipitation/throughfall/stemflow sample, this will
considerably alter the chemical composition of the sample. The concentrations of PO43-, K+,
NH4+ and H+, for instance, will be affected. A phosphate concentration of 0.25 mg l-1 has
been suggested as the threshold value for open field sample contamination by bird droppings
(Erisman et al., 2003). High phosphorus concentration can also be due low rain fall, dust,
pollen release, leaf burst or other natural events. In case of relevant bird dropping, it is
expected that other parameter would be affected, such as K, ammonium, total N, pH or
alkalinity. Contamination by bird droppings is not always easily visible, so it may sometimes
be detected only after the chemical analyses have been performed.
3.4.2 Checking results of organic and mineral soil samples
An important step in laboratory QA/QC is to check whether the result of an analysis is within
the expected range and that the general relationships between soil variables are valid.
Therefore two quality check procedures are recommended: plausible range checks and
cross-checks.
3.4.2.1 Plausible range checks for organic and mineral soil samples
For each variable, there is a 95 % probability that the analytical result will fall within the
plausible min-max range given in Table 6. Values outside this range may occur, but they
need to be validated (e.g. checking of equipment and method, dilution factor, reported unit,
sample characteristics, signs of contamination). Re-analysis may be necessary when no
obvious deviations are found in order to ensure that the results are correct.
Specific plausible ranges have been developed for organic material (forest floor, peat) and
mineral soil samples. The number of significant decimal places for each variable is in
accordance with the reporting format given in the ICP Forests Manual Part X (Sampling and
Analysis of Soil).
Generally, the lower limit of the min-max range depends on the limit of quantification (LOQ)
which is, in turn, determined by the instrument, method and dilution factor used. Instead of
merely mentioning ‘LOQ’, we have listed the average LOQ values reported by the soil
laboratories that participated in the 4th FSCC Ring test (Cools et al., 2006) as this was found
to be more informative. Laboratories with lower LOQ values than the average will be able to
quantify lower concentrations reliably. However, each laboratory should always report
concentrations lower than its LOQ as “-1” and reporting the LOQ concentration to the
required number of decimal places in the data quality report.
The maximum value of the plausible range is determined by the maxima (mainly 97.5
percentile values) in the European forest soil condition database. Information on the methods
and data evaluation can be found in the Second European Forest Soil Condition Report (De
Vos and Cools, 2011). As it encompasses all the European soil types, this range is relatively
broad.

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Table 6. Revised plausible ranges for organic and mineral forest soil samples at the European
level. Percentiles are based on Regression on Order Statistics (ROS) taking into account left-
censored data (< LOQ). The number of decimal places indicates the required precision for reporting.
Values in bold are modifications compared to of Part XVI of the ICP Forests Manual (König et al.
2010). The symbol ↑ or ↓ indicate an increase or a decrease.

Parameter
Unit
Organic sample*
Mineral soil sample**
LOQ
Min
Max
LOQ
Min
Max
Moisture content
%wt
0.1
<0.1
10
0.1
<0.1
10
Particle size: clay
%wt
-
-
-
0.5
0.5
51.3↓
Particle size: silt
%wt
-
-
-
0.5
1.5↑
73.0↓
Particle size: sand
%wt
-
-
-
0.5
4.0↑
100
Bulk density1
kg m-3
-
50
800
-
380↑
1800
pH-CaCl2
-
-
2.6↑
6.9↓
-
2.9↑
7.7↓
pH-H2O
-
-
3.3↑
6.9↓
-
3.7↑
8.4↓
CaCO3
g kg-1
3
0↓
360↓
3
0↓
660↓
Organic carbon
g kg-1
1.2
93.0↓
590.0↑
1.2
0.4↓
150.0↓
Total N
g kg-1
0.5
4.3↑
32.0↑
0.1
0.05↓
10.0↓
Free H+
cmol+ kg-1
0.1
0.10↑
17.50↑
0.1
<0.10↓
3
Exchangeable acidity
cmol+ kg-1
0.1
0.05↓
22.50↑
0.1
0.05↓
11.80↑
Exchangeable K
cmol+ kg-1
0.03
0.01↓
5.30↑
0.03
0.006↓
1.30↓
Exchangeable Ca
cmol+ kg-1
0.03
0.17↓
146.00↑
0.03
0.005↓
47.00↑
Exchangeable Mg
cmol+ kg-1
0.03
0.19
21.50↑
0.03
0.002↓
7.15↑
Exchangeable Na
cmol+ kg-1
0.03
0.005↓
2.35↑
0.03
0.002↓
0.45↓
Exchangeable Al
cmol+ kg-1
0.02
0.002↓
16.60↑
0.02
0.018↓
9.00↑
Exchangeable Fe
cmol+ kg-1
0.02
0.0001↓
5.20↑
0.02
0.0004↓
0.70↓
Exchangeable Mn
cmol+ kg-1
0.02
0.0005↓
5.70↓
0.02
0.001↓
0.70↓
Extractable P
mg kg-1
35
80.0↑
2100.0↓
35
35.0↓
1320.0↓
Extractable K
mg kg-1
80
4.0↓
5900.0↑
80
100.0↑
9250.0↓
Extractable Ca
mg kg-1
50
210.0↑
50000.0↓
50
20↓
140000.0↓
Extractable Mg
mg kg-1
35
120.0↑
7600.0↓
35
75.0↑
30500.0↓
Extractable S
mg kg-1
20
460.0↑
6750.0↓
20
10.0↓
1100.0↓
Extractable Na
mg kg-1
20
4.0↓
540.0↓
20
12.0↓
650.0↓
Extractable Al
mg kg-1
10
140.0↑
26000.0↓
10
1100.0↑
55250.0↑
Extractable Fe
mg kg-1
10
140.0↑
42500.0↓
10
880.0↑
62500.0↓
Extractable Mn
mg kg-1
2
0.8↓
3600.0↓
2
8.0↑
1950.0↓
Extractable Cu
mg kg-1
1
0.2↓
75.0↓
1
0.3↓
55.0↓
Extractable Pb
mg kg-1
2.5
0.03↓
245.0↓
2.5
1.0↓
110.0↓
Extractable Ni
mg kg-1
1
0.06↓
45.0↓
1
0.5↓
80.0↓
Extractable Cr
mg kg-1
1
0.1↓
95.0↓
1
1.0↓
80.0↓
Extractable Zn
mg kg-1
2
0.8↓
300.0↓
2
2.5↑
165.0↓
Extractable Cd
mg kg-1
0.5
<0.01↓
2.2↓
0.5
<0.01↓
2.5↓
Extractable Hg
mg kg-1
0.03
<0.01↓
1.65↓
0.03
0.02↓
2.25↑
Total K
mg kg-1
80
50.0↓
25000.0↑
80
2000↑
50000
Total Ca
mg kg-1
50
50.0↑
65000.0↑
50
350.0↑
200000.0↓
Total Mg
mg kg-1
35
100.0↑
42000.0↑
35
180.0↑
42000.0↓
Total Na
mg kg-1
20
<20.0
15000.0↑
20
400.0↑
20000.0↑
Total Al
mg kg-1
10
100.0↑
50000
10
4000.0↑
100000
Total Fe
mg kg-1
10
100.0↑
25000.0↓
10
700.0↑
65000.0↓
Total Mn
mg kg-1
2
20.0↑
3500.0↓
2
15.0↑
2000.0↓
Reactive Al
mg kg-1
50
90↑
12500↑
50
170↑
10300↑
Reactive Fe
mg kg-1
50
170↑
40000↑
50
100↑
13000↑
* Organic samples comprise those of OL, OF, OH, OFH, Hf, Hs, Hfs, H05, H51, H01, H12, H24 and H48 layers
** Mineral samples comprise those of M01, M12, M24 and M48 layers
1 Bulk density was added to the plausible range list

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For some parameters, national plausible ranges will be narrower due to the restricted set of
soil and humus types and their local characteristics. It would be worthwhile developing
regional plausible ranges specifically for soil samples originating from the region.
If the values obtained in the analyses are outside the plausible range, the values should be
marked with a flag for further investigation by the head of the laboratory and/or the
responsible scientist. The head of the laboratory should be able to make comments in their
report on possible reasons for the deviating value(s).
3.4.2.2 Cross-checks between soil variables
Because different parameters are determined on the same soil sample and many soil
variables are auto-correlated, cross-checking is a valuable tool for detecting erroneous
analytical results. Obviously, soils high with a high organic matter content should have high
carbon and (organically bound) nitrogen concentrations. Calcareous soils should have
elevated pH values, high exchangeable and total Ca concentrations, but low exchangeable
acidity.
Simple cross-checks have been developed for easy verification and detection of erroneous
results.
3.4.2.2.1 pH check
The soil reaction of organic and mineral soil material is measured potentiometrically in a
suspension of a 1:5 soil:liquid (v/v) mixture of water (pHH2O) or 0.01 mol/l calcium chloride
(pHCaCl2). The actual pH (pHH2O) and potential pH (pHCaCl2) are generally well correlated.
Outliers may be detected using simple linear regression.
Theoretically, without taking measurement uncertainty into account, the difference between
both pH measurements should be less than 1 pH-unit. In practice, the difference between
both pH measurements is generally less than 1.2 pH-unit, with pHCaCl2 always less or equal
to pHH2O.
Check algorithm: 0 < [pHH2O - pHCaCl2] ≤ 1.2
Note that for peat soils, the difference between both pH measurements may be higher, up to
1.5 pH-units.
3.4.2.2.2 Carbon check
According to the ICP Forests Manual Part X (Soil sampling and analysis), the recommended
method for C determination is dry combustion using a total analyser (ISO 10694, 1995). In
general, total organic carbon is obtained by subtracting inorganic carbon (TIC) from total
carbon (TC), both of which are determined by the same analyser.
Inorganic carbon can be estimated from the carbonate measurement (ISO 10693, 1995)
using a calcimeter (Scheibler unit).
Check algorithm: [CCaCO3+TOC] ≤ TC with CCaCO3 = CaCO3 x 0.12
and
Check algorithm: CCaCO3 ≈ TIC
The latter check cannot be performed if the carbonate concentration is below the LOQ (3 g
kg-1 carbonate or 0.36 g kg-1 TIC).

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3.4.2.2.3 pH-Carbonate check
Routinely determining carbonate in soil samples with low pH values is a waste of time and
resources. Carrying out a fast, cheap pH measurement can be used to decide whether
carbonates are present and carbonate analysis is necessary.
For an organic sample (> 200 g kg-1 TOC):
Check algorithm: if pHCaCl2 < 6.0 then CaCO3 < 3 g kg-1 (= below LOQ)
For a mineral soil sample:
Check algorithm: if pHH2O < 5 then CaCO3 < 3 g kg-1 (= below LOQ)
or: if pHCaCl2 < 5.5 then CaCO3 < 3 g kg-1 (= below LOQ)
Conversely, if pHCaCl2 > 6, quantifiable amounts of carbonate are most likely present in the
sample.

3.4.2.2.4 C/N ratio check
Most of the nitrogen in a forest soil sample is organically bound. Carbon and nitrogen are
linked through the C/N ratio of organic matter, which varies within a specific range.
For an organic sample (> 200 g kg-1 TOC):
Check algorithm: 5 < C/N ratio < 100
For a mineral soil sample:
Check algorithm: 3 < C/N ratio < 75

3.4.2.2.5 C/P ratio check
Similarly to C/N, the C/P ratio varies within expected ranges for organic and mineral soil
samples.
For an organic sample (> 200 g kg-1 TOC):
Check algorithm: 100 < C/P ratio < 2500
Note that for peat soils, the C/P ratio may be greater than 2500. In the 5th FSCC soil ring test,
the C/P ratio of the peat sample was ca. 4500.
For a mineral soil sample:
Check algorithm: 8 < C/P ratio < 750

3.4.2.2.6 C/S ratio check
The C/S ratio varies within specific ranges for organic samples only.
For an organic sample (> 200 g kg-1 TOC):
Check algorithm: 20 < C/S ratio < 1000

3.4.2.2.7 Extracted/total element check
In both organic and mineral soil samples the concentration of the aqua regia extractable
elements K, Ca, Mg , Na, Al, Fe and Mn (semi-total extraction) should be less than their total
concentrations after complete dissolution (total analysis).

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Therefore:
Check algorithm: Extracted element by aqua regia ≤ Total element
for the elements K, Ca, Mg ,Na, Al, Fe and Mn.

3.4.2.2.8 Reactive Fe and Al check
Acid oxalate extractable Fe and Al indicate the active ( "amorphous") Fe and Al compounds
in soils. Their concentration should be less than the total Fe and Al concentration.
Check algorithm:
Reactive Fe ≤ Total Fe
Reactive Al ≤ Total Al
For mineral soil samples, reactive Fe is usually less than 25 % of the total Fe, and reactive Al
less than 10 % of the total Al.

3.4.2.2.9 Exchangeable element/total element check
The elements bound to the cation exchange complex in the soil are also readily extracted
using Aqua regia. Therefore, the concentration of exchangeable cations should always be
lower than their Aqua regia extractable concentration.
A conversion factor is needed to convert from cmol(+) kg-1 to mg kg-1.
Check algorithm:
(Kexch x 391) ≤ Extracted K
Check algorithm:
(Caexch x 200) ≤ Extracted Ca
Check algorithm:
(Mgexchx 122) ≤ Extracted Mg
Check algorithm:
(Naexch x 230) ≤ Extracted Na
Check algorithm:
(Alexchx 89) ≤ Extracted Al
Check algorithm:
(Feexchx 186) ≤ Extracted Fe
Check algorithm:
(Mnexchx 274) ≤ Extracted Mn
In general, the ratio between an exchangeable element and the same extracted element is
higher in organic matrices than in mineral soil.

xx-xxxx-xxxx Free H+ and Exchangeable acidity check
Two checks can be applied to Free H+ and Exchangeable acidity (EA).
Check algorithm: Free H+ < EA
Check algorithm: EA ≈ Alexch+ Feexch+ Mnexch+ Free H+
For mineral soil samples, Free H+ is usually < 60 % of the Exchangeable acidity.

xx-xxxx-xxxx Particle size fraction sum check
According to the ICP Forests Manual Part X (Sampling and analysis of soil), laboratories
have to report the proportion of sand, silt and clay fractions in mineral soil samples. However,
different methods are used for determining each fraction. After shaking with a dispersing
agent, sand (63 μm-2 mm) is separated from clay and silt with a 63 μm sieve (wet sieving).

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The clay (< 2 μm) and silt (2-63 μm) fractions are determined using the standard pipette
method (sedimentation).
When correctly applying the Soil manual procedure (SA03) (ICP Forests Manual Part X (S
ampling and analysis of soil), which is based on ISO 11277 (2009) and includes the
correction for the dispersing agent, the sum of the three fractions should be 100 %. The
mass of the three fractions should equal the weight of the fine earth (0- 2mm fraction), minus
the weight of carbonate and organic matter which have been removed.
Check algorithm: Σ [ clay (%), silt (%), sand (%) ] = 100 %
Ensure that the clay, silt and sand fractions are reported in the right format as mistakes occur
regularly, even in ring tests.
3.4.3 Checking results of foliage and litterfall samples
In comparison to the quality checks for the analytical results on soil, deposition and soil
solution samples, devising robust procedures for checking foliage and litterfall analytical data
is relatively difficult. In unpolluted areas, the concentration range of analytes in foliage is
usually small compared with that in other matrices and so most of the results are plausible.
Correlations between elements in foliage could be one possible tool for checking analytical
results, but this is only suitable in cases where the sample plots are located very close to
each other and have similar soil characteristics and the same tree species. As a result, this is
probably not a useful procedure for checking the results in a European-wide survey.
3.4.3.1 Plausible range check for foliage
In order to provide the laboratories carrying out foliage analyses with support on QA/QC
issues, a preliminary list of plausible ranges for the element concentrations in foliage was
agreed on at the 4th Expert Panel Meeting in Vienna 1997. However, these limits were very
broad (see: http://bfw.ac.at/600/pdf/ Minutes_4.pdf).
In order to improve the list and put it on a more sound statistical basis, the Forest Foliar
Coordinating Centre removed 5% of the lowest and 5% of the highest results from the
European Level I database. 90% of all the submitted Level I results fell within these limits. As
the manual covers a large number of different tree species, it was necessary, in order to
obtain sufficient data for meaningful statistical analysis, to group them into the main tree
genera (Stefan et al., 1997). The new limits were adopted at the Expert Panel Foliage and
Litterfall meeting in Madrid/Spain (2007).
The Joint Research Centre was asked to carry out a statistical evaluation on the submitted
Level II results in order to obtain statistical information about the concentration range for
different tree species. The 5% and the 95% percentile limits for each tree species were
calculated. 90% of the submitted results fell within these limits (see Table 7). Results falling
outside these limits should be checked and, if necessary, be re-analyzed. For the optional
parameters As, Co, Cr, Hg and Ni there are at the moment no enough values in the database
to calculate these plausible range check limits.
The report of the Level I foliage survey (Stefan et al., 1997) clearly shows that element
concentrations in foliage vary considerably in different parts of Europe. There is a thus a
need to calculate these limits for each country/laboratory using their own results. This would
result in narrower limits that would provide a more reliable tool for detecting non-plausible
results.

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Table 7: Plausible range of element concentrations in the foliage of different tree species
calculated from the Level II data sets (indicative values in italic)

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3.4.3.2 Plausible range check for litterfall
Developing tolerable limits for litterfall is a more difficult task than that for foliage. Following
collection, litterfall is sorted into different fractions – at a minimum two, foliar and non-foliar
litter. Most countries sort the litterfall into three fractions – foliage, wood and fruit cones &
seeds. Litterfall can then be analyzed either as a pooled sample or per fraction.
The plausible range of the results of the chemical analysis of litter must be much bigger than
for foliage. An important fraction in the litter is the foliar fraction, and for this fraction plausible
ranges for selected tree species, based on the expert experience, are given in Table 8.
Plausible ranges for non-foliar fraction in litterfall are given in the Manual part XIII
(Ukonmaanaho at al., 2020).
Table 8: Plausible range of element concentrations in the foliar-litter of different tree species
(indicative values in italic)
3.4.4 Analyses in duplicate
Performing duplicate analyses represents a very worthwhile quality check. The samples or
digestion solutions/extracts are measured twice independently for the individual parameters,
the results are compared, and their repeatability determined.
As this is a very time-consuming and expensive procedure when the number of samples is
large, it may be sufficient to analyse only part of the samples in duplicate. If this is adopted,
the samples should be randomly selected and analysed again at the end of the batch. Thus,
one can check repeatability on the one hand and make sure that samples weren't mistakenly
exchanged during the course of the analysis. If a mistake was determined, all samples in the
batch will need to be re-analysed in duplicate.
3.4.5 Avoidance of contamination
The contamination of samples can occur at any stage from the field to the final analysis
result, including sampling in the in the field during, transportation to the laboratory, and the
pre-treatment and analysis of the samples in the laboratory.
Tree Species
(Foliar litter)
Limit
C
S
N
P
K
Ca
Mg
Zn
Mn
Fe
Cu
B

g/100g
mg/g
mg/g
mg/g
mg/g
mg/g
mg/g
μg/g
μg/g
μg/g
μg/g
μg/g
Betula pendula
Low
29.0

7.30
0.20
0.30
5.,00
1.00
105.00
600
45.0
6

high
33.0

21.00
1.20
1.40
12.50
2.00
170.00
3000
300.0
19
38
Castanea sativa
Low
39.0

9.00
0.20
0.20
4.50
1.40
35.00
700

5

high
42.0

13.00
0.70
0.55
10.50
2.00
45.00
2500
90.0
13
100
Fagus sylvatica
Low
46.0
1
9.00
0.50
2.00
4.00
0.80
25.00
650
70.0
4
2

high
51.0
2.2
19.00
1.90
8.00
17.00
2.00
35.00
1600
140.0
7
40
Fraxinus excelsior
low
47.0

12.00
0.75
0.40
20.00
2.00
15.00
110
120.0
7

high
47.0

18.00
1.50
1.40
25.00
3.50
20.00
200
200.0
9
50
Quercus frainetto
Low

1.1
8.00
1.10
4.50
14.00
1.20

(Q. conferta)
high

1.1
11.70
1.30
5.20
18.30
1.40

Quercus petraea
Low
46.0

8.00
0.30
2.00
7.00
1.30
14.00
700
50.0
5

high
51.0

12.00
0.60
4.00
10.00
2.00
25.00
1700
200.0
8
35
Quercus robur
Low
46.0
0.85
10.00
0.82
4.00
5.00
1.00
15.00
1000
90.0
6
7
(Q. pedunculata)
high
51.0
1.7
19.00
2.00
8,00
13.00
2.00
25.00
1200
150.0
7
35
Abies cephalonica
Low

8.00

2.70
11.00
1.00

high

13.00

8.30
24.00
1.50

Picea abies
Low

1
6.50
0.60
1.00
2.50
0.70

(P. excelsa)
high
52.0
1.5
12.60
1.20
4.20
16.00
2.20

Picea sitchensis
Low
44.0
1
6.00
0.60
1.50
4.00
0.60
15.00
250
40.0
2

high
53.0
1.1
13.00
1.10
3.00
11.00
1.00
35.00
1400
120.0
4
35
Pinus sylvestris
Low
49.0
0.62
5.00
0.40
1.00
2.00
0.50
20.00
180
35.0
2

high
53.0
0.62
10.00
0.80
3.00
11.00
0.80
45.00
800
150.0
5
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3.4.5.1 Water analyses
As outlined deposition samples can become contaminated during the sampling period, e.g.
as a result of bird droppings, and the laboratory should be informed about signs of such
contamination (see Chapter 3.4.1.6). The transfer of deposition and soil water samples in the
field from the sampling devices to the bottles used for transportation to the laboratory is
another stage where contamination of the samples can occur. The best way to avoid this
problem is to transport the collection devices (bottles, bags etc.) directly to the laboratory, if
possible. The most important point during this step, as well as throughout the whole sample
preparation procedure in the laboratory, is to avoid skin contact with the samples by using
disposable gloves (non talc), and the use of clean equipment (e.g. glass- and plastic ware).
Special care must be taken when filtering the samples, and at least separate plastic tubing (if
used) and/or filtering devices for different types of sample (bulk, throughfall, stem flow, soil
solution) should be used. Rinsing the filter capsule or funnel between the samples with the
next sample, as well as with purified water, is highly recommended. If this is not possible,
then an adequate amount of the next sample should be discarded after filtering before taking
a sample for analysis. Control samples (ultra pure water) should be used after every 20 to 30
samples depending on the type of filtering system. It is always recommendable to start
working with cleaner samples (e.g. bulk first with low analyte concentrations) and continue
with the other types of sample in sequential order. Attention should also be paid to the
different characteristics of the individual sample plots and their specific concentrations.
It is important that filters used are appropriate to the analyses to be carried out, e.g. paper
filters can affect ammonium and DOC determinations through contamination and the release
of paper fibres that of course contain C. In some cases, the opposite may occur: sample loss
through adsorption on filters. For the filtration of samples on which DOC is to be determined,
glass fibre filters are recommended.
The filters and the amount of ultra pure water needed to rinse off possible contaminants
should be tested and checked by using blank charts. The filters should be handled with clean
forceps.
One highly recommendable procedure is to use a separate set of bottles for preparing the
standard solutions for every single type of analysis. If the pH or conductivity value for a
sample is exceptionally high, then it is recommendable to inform the persons carrying out the
other analyses (which are usually performed later) about the “atypical” sample.
3.4.5.2 Organic and mineral soil analyses
Samples of organic and mineral soil material need several preparatory steps prior to
analysis. Contamination can occur in each of these steps.
Cleanliness of equipment, glass- and plastic-ware, is a prerequisite for avoiding
contamination and conforming with good laboratory practice.
Milling and/or sieving is the first step in the pre-treatment of organic and mineral soil
samples.
The milling equipment is one possible source of contamination. Metals, especially, may be
released through abrasion of the inner compartments or sieves. In the laboratory responsible
for preparing the FSCC ring test samples, the use of a hammer-mill system with a titanium
rotor and a stainless steel sieve was tested for milling organic samples. Milling resulted in
elevated Ni and Cr concentrations of up to 3.6 and 2.2 mg kg-1, respectively, whereas for
manual pulverization the increase was below 0.6 mg kg-1 for both metals. Although no
systematic contamination was observed, the degree of contamination appeared to be a
function of the hardness of the sample material (wood, bark) and the age of the sieve. The
use of titanium rotors and sieves is therefore recommended, as well as periodical
replacement of the sieves.

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According to the ICP Forests Manual Part X (Sampling and analysis of soil), mineral soil
samples should not be milled, but sieved with a 2 mm sieve. These sieves should be clean,
with no traces of rust (i.e. oxidation on their metallic parts). Attention should be paid to
ensure that no residues from tools (crusher, pestle, brush, cleaning equipment) end up in the
samples as a result of thorough cleaning by brushing or wiping. This also holds true for other
equipment (sample divider, mixer, splitter, riffler). When pre-treating silty or clayey soil
samples, appropriate ventilation methods (i.e. air extraction equipment) should be used to
avoid contamination of other samples or equipment via the air as well as for the health and
safety of the laboratory technician.
If a separate container is used to weigh and transfer sub-samples to extraction vessels, then
it should be carefully brushed clean between samples to avoid cross-contamination. All
glass- and plastic-ware should be cleaned by rinsing with a dilute acid solution or appropriate
cleaning agent. Rinsing twice with distilled or de-ionized water and drying before re-use is a
common practice.
Ions adsorbed on the inner surfaces of extraction flasks or sample bottles coming into
contact with extracts may be a source of contamination for subsequent analyses using the
same containers.
Finally, some types of filter paper used for filtration may contain contaminants. Many
laboratories encounter problems with Na+ or other cations. Careful analysis of blanks and the
filter material may indicate problematic elements that enhance the background noise.
3.4.5.3 Foliar and litterfall analyses
There are many possible contamination sources in foliage and litterfall analyses. A short
overview is given in Table 9.
Table 9: Possible contamination sources in foliage and litterfall analyses for some elements
Element
Possible contamination source
N
NH3 from the laboratory air (only if the Kjeldahl method is used),
reagents
S
Water (distilled or deionised), reagents
P
Dishwasher (detergent), water (distilled or deionised), reagents
Ca
Soil contamination from sampling, water (distilled or deionised), glassware,
reagents
Mg
Soil contamination during sampling, water (distilled or deionised), glassware,
reagents
K
Dishwasher (detergent), water (distilled or deionised), glassware, reagents
Zn
Soil contamination during sampling, Dishwasher (detergent), water (distilled or
deionised), glassware, dust, reagents
Mn
Reagents
Fe
Soil contamination during sampling, water (distilled or deionised), glassware,
dust, reagents
Cu
Water (distilled or deionised), glassware, reagents
Pb
Soil contamination during sampling, glassware, dust, reagents
Cd
Soil contamination during sampling, glassware, dust, reagents
B
Water (distilled or deionised), glassware, reagents
Co, Cr, Ni
Instruments made of stainless steel used in sampling, pre-treatment etc., soil
contamination during sampling
Hg
Soil contamination during sampling, glassware, dust, reagents
3.5 Inter-laboratory quality assurance
In addition to the quality assurance carried out within each laboratory, there are also quality
checks and procedures that can be used between different laboratories. These include ring

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tests, as well as the exchange of expertise and analytical methods employed between
laboratories. In the case of international programmes, especially, the use of identical
analytical methods and regular ring tests are of particular importance in ensuring
comparability and joint evaluation of the data.
3.5.1 Inter-laboratory comparison tests (ring tests)
A series of inter-laboratory comparison tests is an excellent tool for improving the quality of
the data produced by the participating laboratories over time. There are the twin benefits of
improved expertise in using harmonised analytical methods as well as the use of the
remaining ring test sample material as reference material for subsequent analyses.
In instances where the analytical data generated in environmental monitoring or long-term
ecological research programmes are of poor quality, then this may prevent the detection of
trends, resulting in delays of up to three decades before they can be identified (Sulkava et
al., 2007). To address this issue, tolerable limits for the deviation of the individual test result
from the comparison mean value were selected for each variable measured. Results falling
outside the tolerable limits indicate problems in the analytical procedure, or more general
quality problems in the laboratory. The tolerable limits were set in order to act as a driving
force to reduce measurement uncertainty and increase the comparability of results among
the participating laboratories. As a result, the tolerable limits have, in some cases, been
adjusted downwards in order to maintain their role as a driver for quality improvement as an
increasing number of the laboratories meet this quality requirement.
Within the ICP Forests programme the participation and if needed a re-qualification in
different ring tests is mandatory for all laboratories which are analysing samples from the ICP
Forests programme.
In order to participate in the ring tests, each laboratory has to register on-line. Information
about the registration procedure will be sent to all responsible persons for the ring tests in the
laboratory, the NFC and the ring test will be announced on the ICP Forests webpage.
These ring tests are carried out between the involved laboratories at regular intervals in order
to ensure comparability of analytical data. This involves the dispatch several samples to the
participating laboratories. Prior to the dispatch of the ring test samples to the participating
laboratories, the samples must be checked for homogeneity, and should be analysed using
previously agreed analytical methods. The current list with the agreed analytical method
codes is online available in the explanatory items from the LQA files of the different surveys
(see: https://icp-forests.org/documentation/).
The participating laboratories should carry out the ring tests as a part of their normal
laboratory analysis runs so that the functioning of their normal routine activities can be
checked.
For the standard evaluation the median value is used as target value of the ringtest. For the
extended evaluation the outlier free mean value of all labs results is used as target value. For
this extended evaluation there are a number of computer programs on the market that
comply with standards such as DIN 38402/42 (2005-09), and these can be used for
evaluating the analysis data. Custom-made programmes can also be used. The deviation
from the target value and the coefficient of variation must be reported for each parameter
and for each sample. The used evaluation procedures can be found for each survey on the
ICP Forests QA/QC webpage.
In order to evaluate the results of ring tests and of the participating laboratories, tolerable
limits from the target value, expressed as a percentage for each parameter and method,
have to be determined. As a rule, the permitted deviations for double-stepped analytical
methods (e.g. digestion/extraction and subsequent determination of the element
concentration in the solution) are significantly larger than for direct element determination. A

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special procedure was foreseen for result below the limit of quantification. A maximum
acceptable limit of quantification (LOQ) was set to avoid the use of not sensitive enough
analytical methods. If a LOQ above this limit is reported the laboratory will fail for this
parameter/sample combination.
All laboratories participating within ICP Forests monitoring programme will get/can download
a qualification report after taking part in a ring test. In this report, information about the
analysed and not analysed parameters and the passing of the qualification criteria for each
parameter will be listed.
The qualification criterion is that 50 % or more of the results of all ring test samples for a
particular parameter must be within the appropriate tolerable limit.
The WG on QC/QA in Laboratories and the various expert panels of the ICP Forests
programme have proposed tolerable limits for all samples and parameters. They are
described in detail in the following chapters.
Laboratories without qualification for all parameters have to requalify by reanalysis of the ring
test samples and can receive help through the assistance program for laboratories organized
by the Working Group QA/QC in Laboratories. The laboratories have to report the new
results to the organizers of the ring test together with the original reports of the analytical
instruments and information about weight factors, dilution factors, control charts etc. and
information about the reasons for the bad results during the ring test and the measures
against similar errors in future. The ring test organizers will then decide about the report from
the laboratory. If the results are within the tolerable limits and the submitted additional
information is consistent the laboratory will receive a requalification report.
When a lab did not qualify and did not make efforts to requalify, ICP Forests PCC will send a
letter to the National Focal Centre and inform them about the consequence that their data
possibly cannot be used for evaluations on an European level.
The results of the ring tests are integrated in the database of the PCC (see 3.7). This means
that the bad ring test results will be marked as disqualified and this information can be used
as a selection criterion for the monitoring data used in evaluations.
3.5.1.1 Water (deposition and soil solution) ring tests
Water ring test samples, have been stabilized (i.e. by means of filtration through a 0.45 μm
membrane filter and addition of appropriate acid where required). When mailed to the
laboratories, the samples have to be packed in non-breakable flasks, and water samples
should be kept cool during transportation. It is necessary to set a time period for completion
of analysis and reporting of results. This avoids chemical/biological changes in the samples
which, in turn, would lead to differences in the results.
Contact address, time schedule, evaluation procedure and older ring test reports from the
Atmospheric deposition and soil solution Working Ringtest programme are available under
http://icp-forests.net/group/qualityinlaboratories/page/deposition-and-soil-solution-ringtest-
and-qa-qc-information.
The tolerable limits and maximum acceptable LOQ for water ringtests are given in Table 10.
In case of very low concentrations (more than 33% of the laboratories reports a LOQ) this
parameter/sample combination will be manually excluded from the evaluation.

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Table 10: Tolerable Limits and max. accept. LOQ for water ring tests
Parameter
Unit
Conc.
Range
low
Tolerable
Limit low
Conc.
Range
high
Tolerable
Limit high
max.
LOQ
Alkalinity
μeq/l
≤ 100
± 40%
>100
± 25%
10
Ammonium
mg-N/l
≤ 0.25
± 25%
> 0.25
± 15%
0.08
Calcium
mg/l
≤ 0.25
± 20 %
> 0.25
± 15%
0.2
Chloride
mg/l
≤ 1.5
± 25 %
> 1.5
± 15%
0.2
Conductivity (at 25 C)
μS/cm
≤ 10
± 20 %
> 10
± 10%
5
Dissolved Organic Carbon
mg-C/l
≤ 1
± 30 %
> 1
± 20%
1
Magnesium
mg/l
≤ 0.25
± 25 %
> 0.25
± 15%
0.1
Nitrate
mg-N/l
≤ 0.5
± 25 %
> 0.5
± 15%
0.08
pH
pH
> 5
0.2 units
≤ 5
0.1 units
-
Phosphate
mg P/l

± 20 %

± 20%
0.1
Potassium
mg/l
≤ 0.5
± 25 %
> 0.5
± 15%
0.4
Sodium
mg/l
≤ 0.5
± 25 %
> 0.5
± 15%
0.1
Sulphate
mg-S/l
≤ 1
± 20 %
> 1
± 10%
0.1
Total Nitrogen
mg-N/l
≤ 0.5
± 40 %
> 0.5
± 20%
0.5
Aluminium
mg/l
≤ 0.1
± 30 %
> 0.1
± 15%
0.05
Iron
mg/l

± 30 %

± 30 %
0.02
Manganese
mg/l
≤ 0.025
± 15%
> 0.025
± 10%
0.01
Cadmium
µg/l
≤ 1
± 40 %
> 1
± 30%
0.1
Cobalt
µg/l
≤ 1
± 40 %
> 1
± 30%
0.1
Chromium
µg/l
≤ 1
± 40 %
> 1
± 20%
0.5
Copper
µg/l
≤ 2
± 40 %
> 2
± 20 %
1
Nickel
µg/l
≤ 1
± 40 %
> 1
± 20 %
0.5
Lead
µg/l
≤ 1
± 40 %
> 1
± 25 %
0.5
Zinc
µg/l
≤ 30
± 35 %
> 30
± 25 %
10

3.5.1.2 Soil ring tests
Contact address, time schedule, evaluation procedure and older ring test reports from the
Soil
Ring
test
programme
are
available
under
http://icp-
forests.net/group/qualityinlaboratories/page/soil-ringtest-and-qa-qc-information.
The
used
tolerable limits and maximum acceptable LOQ for soil ring tests are given in Table 11. In
case of very low concentrations (more than 33% of the laboratories reports a LOQ) this
parameter/sample combination will be manually excluded from the evaluation.

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Table 11: Tolerable Limits and max. accept. LOQ for soil ring tests
Parameter
Unit
Conc.
Range
low
Tolerable
Limit low
Conc.
Range
high
Tolerable
Limit high
max.
LOQ
Moisture
%
≤1.0
25
>1.0
15
0.2
Particle size clay
%
≤10.0
50
>10.0
35
1
Particle size silt
%
≤20.0
45
>20.0
30
1
Particle size sand
%
≤30.0
45
>30.0
25
1
pHCaCl2
pH

5

5
-
pHH2O
pH

5

5
-
CaCO3
g/kg
≤50
130
>50
40
5
OC
g/kg
≤25
20
>25
15
2
Total N
g/kg
≤1.5
30
>1.5
10
0.2
Exchangeable Acidity
cmol(+)/kg
≤1.00
90
>1.00
35
0.25
Exchangeable Al
cmol(+)/kg
≤0.50
105
>0.50
30
0.1
Exchangeable Ca
cmol(+)/kg
≤1.50
65
>1.50
20
0.1
Exchangeable Fe
cmol(+)/kg
≤0.02
140
>0.02
50
0.1
Exchangeable K
cmol(+)/kg
≤0.10
45
>0.10
30
0.1
Exchangeable Mg
cmol(+)/kg
≤0.25
50
>0.25
20
0.1
Exchangeable Mn
cmol(+)/kg
≤0.03
45
>0.03
25
0.1
Exchangeable Na
cmol(+)/kg

80

80

Free H
cmol(+)/kg

100

100
0.25
Extractable Al
mg/kg or ppm ≤2500
50
>2500
20
50
Extractable Ca
mg/kg or ppm ≤500
70
>500
30
100
Extractable Cd
mg/kg or ppm ≤0.25
100
>0.25
55
1
Extractable Cr
mg/kg or ppm ≤10
40
>10
25
3
Extractable Cu
mg/kg or ppm ≤5
40
>5
15
2
Extractable Fe
mg/kg or ppm ≤2500
40
>2500
15
50
Extractable Hg
mg/kg or ppm
75

75
0.05
Extractable K
mg/kg or ppm ≤500
60
>500
40
100
Extractable Mg
mg/kg or ppm ≤500
60
>500
15
70
Extractable Mn
mg/kg or ppm ≤150
30
>150
15
8
Extractable Na
mg/kg or ppm ≤75.0
65
>75.0
50
100
Extractable Ni
mg/kg or ppm ≤10
40
>10
15
3
Extractable P
mg/kg or ppm ≤150
45
>150
20
50
Extractable Pb
mg/kg or ppm
30

30
5
Extractable S
mg/kg or ppm
35

35
50
Extractable Zn
mg/kg or ppm ≤20
40
>20
20
4
Reactive Al
mg/kg or ppm ≤750
30
>750
15
100
Reactive Fe
mg/kg or ppm ≤1000
30
>1000
15
100
Total Al
mg/kg or ppm ≤20000
35
>20000 10
50
Total Ca
mg/kg or ppm ≤1500
20
>1500
15
100

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Total Fe
mg/kg or ppm ≤7000
20
>7000
10
50
Total K
mg/kg or ppm ≤7500
15
>7500
10
100
Total Mg
mg/kg or ppm ≤1000
60
>1000
10
70
Total Mn
mg/kg or ppm ≤200
25
>200
10
8
Total Na
mg/kg or ppm ≤1500
20
>1500
10
100
3.5.1.3 Plant (foliar, litterfall and ground vegetation biomass and nutrients) ring tests
Contact address, time schedule, evaluation procedure and older ring test reports from the
Needle/Leaf Interlaboratory Comparison Programme are available under http://icp-
forests.net/group/qualityinlaboratories/page/foliage-and-litterfall-ringtest-and-qa-qc-
information. The used tolerable limits and maximum acceptable LOQ for plant ring tests are
given in Table 12. In case of concentrations below the “lowest evaluated result” this
parameter/sample combination will be manually excluded from the evaluation.
Table 12: Tolerable Limits and max. accept. LOQ and evaluation limit for foliage and litterfall
ring tests (Fürst 2019)
Parameter
Unit
Conc.
Range
low
Tolerable
Limit low
Conc.
Range
high
Tolerable
Limit high
max.
LOQ
Lowest
evaluated
result
Arsenic
ng/g
< 50
30%
> 50
20%
50
20
Boron
μg/g
< 5.0
30%
> 5.0
20%
1
-
Cadmium
ng/g

30%

30%
50
20
Calcium
mg/g
< 3.0
15%
> 3.0
10%
0.5
-
Carbon
g/100g

5%

5%
10
-
Chromium
μg/g
< 1
35%
> 1
25%
1
0.5
Cobalt
μg/g
< 0.1
35%
> 0.1
25%
0.1
0.05
Copper
μg/g

20%

20%
1
1
Iron
μg/g
< 20
30%
> 20
20%
5
-
Lead
μg/g
< 0.50
40%
> 0.50
30%
0.50
0.20
Magnesium
mg/g
< 0.50
15%
> 0.50
10%
0.3
-
Manganese
μg/g
< 20
20%
> 20
15%
5
-
Mercury
ng/g
< 50
30%
> 50
20%
20
10
Nickel
μg/g
< 1
30%
> 1
20%
1
0.50
Nitrogen
mg/g
< 5.0
15%
> 5.0
10%
2
-
Phosphorous
mg/g
< 0.50
15%
> 0.50
10%
0.3
-
Potassium
mg/g
< 1.0
15%
> 1.0
10%
0.5
-
Sulphur
mg/g
< 0.50
20%
> 0.50
15%
0.3
-
Zinc
μg/g
< 20
20%
> 20
15%
5
-
3.5.2 Exchange of knowledge and expertise amongst laboratories
The inter-laboratory comparisons conducted within the framework of ICP Forests are aimed
at testing the proficiency of the laboratories, i.e. evaluating the comparability of the results
and, if possible, identifying the main causes of errors. The laboratories must be involved in
discussions on the outcome of the ring tests in order to assess and where necessary
optimise their analytical quality.

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Laboratories with unacceptable results in ring tests will be invited to participate in an
assistance programme organised by the WG on QA/QC in Laboratories. Close cooperation
between these laboratories and laboratories with good laboratory practices is considered to
be an effective way of improving laboratory proficiency.
When determining the scope for assistance, it is necessary to take into account, not only the
results of the ring test but the current state of the implementation of a quality programme in
the laboratory, as well as the analytical methods used in the laboratory in question. Such
information will be ascertained beforehand from a questionnaire that the laboratory will be
obliged to complete. The assistance will consist of a few days’ visit to the laboratory, as well
as a return visit, in order to identify and rectify easily detectable problems in the laboratory
organisation and/or specific analytical processes.
It is essential that the members of the staff actually involved in the analytical work participate
in the assistance programme.
The initial step of the assistance programme will entail drafting a list of analytical problems,
with the emphasis on specific parameters analysed in the ICP Forests monitoring
programme. Following from the inter-laboratory exchange visits, a short report will be
produced detailing the laboratory’s activities, the analytical problems encountered and
suggestions about how best they can be remedied. Through this assistance programme, the
laboratory will be provided with sufficient information to enable them to make improvements
in the quality of their analytical results.
3.5.2.1 Exchange of analytical expertise
All laboratories are strongly invited to share their experience through internal info-sheets,
developed as an easy tool for the exchange of information among laboratories about studies
carried out in the laboratory which otherwise would not be published. The info-sheets are
short Word files containing concise information about method comparison, development and
implementation of new methods, material tests (e.g. on contamination or adsorption
problems), sample pre-treatment, sample storage and technical information. Thus the work
performed in one laboratory can help to avoid duplication in others.
The circulation of information between the WG on QA/QC in Laboratories and the
participating laboratories is ensured through the WG’s own website. This information,
including details about past and ongoing ring tests, Excel files for QA/QC, downloadable
scientific publications, analytical info-sheets, relevant contact addresses and useful links can
be found at http://icp-forests.net/group/qualityinlaboratories.
A forum for sharing analytical experience is the bi-annual meeting of the heads of the
laboratories. All laboratories are invited to give a presentation about their analytical work, the
methodical problems and about their experience with new instrumentation.
3.5.2.2 Exchange of samples
The exchange of a limited number of routine samples between two laboratories is a simple
and easy way to test the quality and comparability of the methods used. About 20 routine
samples should be analysed in each laboratory and the results compared. This ensures that
differences in the analytical methods used and problems encountered can be quickly and
easily be identified, and steps taken to rectify the situation.
3.6 Quality indicators
The development of the quality over time can be followed by using quality indicators.

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While, there are a number of quality indicators that can be used to evaluate the development
of the participating laboratories QC/QA programme within ICP Forests, only 2 indicators were
ultimately selected:
•
Percentage of the results of a ring test within tolerable limits -
•
Percentage of the results of a ring test with a precision <10% (not applicable to water ring
tests)
They can be determined from results of the ring tests.
3.6.1 Percentage of the results of a ring test within tolerable limits
In each ring test, the number of results within the tolerable limits for all mandatory parameter
will be related as a percentage to the total number of possible results. Where results are
missing, they will be counted as outside the tolerable limit. It is expected that the percentage
of results within the tolerable limits should increase as the laboratories analytical expertise
improves over time.
3.6.2 Percentage of the results of a ring test with a precision <10%
Normally, the precision (i.e. the repeatability of a result within a laboratory) should be <10 %
for all parameters. In ring tests, with the exception of water samples, each sample typically
has to be analysed 3 or 4 times. Therefore, where possible, the precision for each parameter
can be calculated. The number of times the precision is <10 % can then be determined as a
percentage of the total number of results where this calculation could be made.
Ideally, the precision <10 % should be between 90 to 100 % for all parameters analysed and
should become constant over time.
3.7 Quality reports
In the ICP Forests monitoring programme it is mandatory to submit a LQA-File with the
monitoring data from deposition, foliage, litterfall, ground vegetation biomass and nutrients,
soil and soil solution. To ensure the linkage to the monitoring data, the quality report must
have the same key information as the data submission report (e.g. plot No., country code,
year, lab code). The quality information parameters which have to be reported are:
• country code
• year
• plot No.
• start and end date (needed for linking to the ring test results)
• lab code
• LOQ for each parameter (if needed) in the same units of the monitored parameter
• method codes (coded like in ring test reports) for each parameter,
• mean and standard deviation (%) for each parameter from control charts (if a laboratory
use more than one control chart for a parameter it has to submit only data from one
control chart in a normal concentration range) in the same units of the monitored
parameter.
The quality report forms are part of the data submission forms (see ICP Forests Manual Part
XVII Data submission forms).
A summary of each ring test is submitted from the ring test provider after the re-qualification
period to PCC. This file contains the following information:

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• survey code (e.g. FO, LF, SS, SO, DE)
• survey year (link to the corresponding LQA-File)
• ring test number
• method codes (should be the same like in the LQA-File)
• lab code (link to the corresponding LQA-File)
• ring test result (percentage of correct results)
• ring test result (percentage of correct results) after re-qualification
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the organic ion charge. J. Limnol 67: 1-15
Nordtest report TR 569. 2012. Internal Quality Control – Handbook for Chemical Laboratories. ISSN
xx-xxxx-xxxx / Edition 4 (Approved 2012-01)
Stefan, K., Fürst, A., Hacker, R., Bartels, U. 1997. Forest Foliar Condition in Europe - Results of large-
scale foliar chemistry surveys, ISBN 3-901347-05-4, EC-UN/ECE -FBVA 1997.
Stumm, W. & J.J. Morgan. 1981. Aquatic chemistry. Wiley & Sons, New York: 780 pp.
Sulkava, M., Luyssaert, S., Rautio, P., Janssens, I. A., Hollmén J. 2007. Modeling the effects of
varying data quality on trend detection in environmental monitoring Elsevier Science Direct,
Ecological Informations 2, 167-176.
Thomsen, V., Schatzlein, D. & Mercuro, D. 2003. Limits of detection in spectroscopy. Spectroscopy,
18, 112–114. van Reeuwijk, L.P and Houba, V.J.G. 1998. Guidelines for Quality Management in
Soil and Plant Laboratories. FAO Soils Bulletin – 74. FAO. Rome.
Ukonmaanaho, L., Pitman R, Bastrup-Birk A, Breda N, Rautio P, 2020: Sampling and Analysis of
Litterfall., Part XIII. In: Manual on methods and criteria for harmonized sampling, assessment,
monitoring and analysis of the effects of air pollution on forests. UNECE ICP Forests
Programme Co-ordinating Centre, Eberswalde, ISBN: 978-3-926301-03-1. [http://www.icp-
forests.org/Manual.htm] , 15 p.

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Annex I
I.1 Excel worksheet for ion balance (with and without DOC
correction), conductivity, N balance and Na/Cl ratio checks.
The Excel worksheet permits different quality checks to be performed, as described in the
text (Chapter 3.4.1). It can be downloaded from the ICP Forests website (http://icp-
forests.net/group/qualityinlaboratories/page/deposition-and-soil-solution-ringtest-and-qa-qc-
information ). It can be used as a tool for checking the results and as a file for data storage,
according to the requirements of the operator and the procedure for data handling in the
laboratory. The sheet contains green cells in which new data are to be entered using the
units given at the top of the column. The units are the same as those in the ICP Forests
database, and the correct use of units is essential for all further checking (ion balance,
measured/calculated conductivity check etc.) of the results. Information about the type of
sample (BOF, THR, STF) and the type of forest cover on the plot (BL = broadleaves, CON =
conifers) is required for DOC correction of the ion balance calculation. They are used as
strings for the calculations, and therefore they must be entered correctly.
After entering the data in the green cells, the sheet calculates the ion balance (in
accordance with the method described in Chapter 3.4.1.1.1) and the calculated
conductivity, with and without correction for the ion strength (Chapter 3.4.1.2). The results
of the tests are expressed in the worksheet as OK (test passed) or NO (test not passed) in
the columns highlighted in yellow. The DOC contribution to ion balance is calculated using
the empirical regressions described in Chapter 3.4.1.1.2. Selection of one the three
alternative regression equations is based on the codes depicting the type of sample and the
type of forest cover, as given in Table 5.
The principles and validation criteria for the Na/Cl ratio and N forms balance (i.e. N balance
check) are described in Chapters 3.4.1.3 and 3.4.1.4. The graphs help in interpreting the
results and identifying outliers. There are three graphs in the Excel worksheet: one for the ion
balance, one for the comparison between measured and calculated conductivity, and one for
the Na/Cl ratio. Other graphs can easily be added by the analysts themselves, e.g. for the
comparison between measured conductivity and sum of anions or sum of cations and the
conductivity corrected for the contribution of H+ and the sum of cations, with H+ excluded
(Figure 7).
The Excel worksheet includes a sheet (notes) giving the meaning of the acronyms and a
summary of the adopted validation criteria.
The theoretical and statistical bases applied in developing the validation criteria for
deposition data in the worksheet are based on thousands of full analysis sets provided by
different laboratories, and are representative of different forest types and climatic conditions
in Europe, ranging from northern Finland to southern Italy. The results of this work have been
published in two papers (Mosello et al., 2005, 2008).
I.2 Excel worksheet for control charts
The Excel worksheet that can be used for creating control charts (Chapter 3.2.1), can be
downloaded
from
the
ICP
Forests
website
(http://icp-
forests.net/group/qualityinlaboratories/page/deposition-and-soil-solution-ringtest-and-qa-qc-
information ): click on “Excel file with instruction and example of control chart use”. It also
includes instructions on how to use the worksheet.

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http://icp-forests.net/page/icp-forests-manual
I.3 List of commercially available reference materials
Reference
material
Matrix
Type
Comments
Supplier
ERM-CA408
water
simulated rain
water
low contents

European Commission,
Directorate-General Joint
Research Centre
Directorate F – Health,
Consumers and
Reference Materials
Reference Materials Unit
Retieseweg 111
B-2440 Geel
Belgium
E-Mail: jrc--rm-
distribution@ec.europa.e
u
Webpage:
https://ec.europa.eu/jrc/e
n/reference-materials

ERM-CA616
water
Ground water
high
carbonate
content

see above
ERM-CA615
water
Groundwater
metals

see above
BCR-142R
soil
light sandy soil

see above
BCR-143R

soil
sewage sludge
amended soil
heavy metal
pollution

see above
BCR-146R
soil/organic
material
sewage sludge
of industrial
origin
heavy metal
pollution

see above
BCR-320R
soil
channel
sediment

see above
FSCC RM1
soil
loamy forest soil
moderate
concentrations
, rather acid

ICP Forests Soil
Coordinating Centre
Gaverstraat 4
9500 Geraardsbergen
Belgium
xxx@xxx.xx
Sample A from the
8th soil inter-
laboratory test
(ICP Forests)
soil
terra rossa - red
soil

Croatian Forest
Research Institute
Division for forests
ecology
Dr Tamara Jakovljević
Cvjetno naselje 41
10450 Jastrebarsko
Croatia
Email:tamaraj@sumins.h
r
Webpage:www.sumins.h
r
Sample C from the
8th soil inter-
laboratory test
(ICP Forests)
soil
loam
High carbon
content
see above
Sample E from the
9th soil inter-
soil
forest floor
OC 10-20%
see above

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Reference
material
Matrix
Type
Comments
Supplier
laboratory test
(ICP Forests)
1570a
plant
spinach leaves
Trace
elements
European Commission,
Directorate-General Joint
Research Centre
Directorate F – Health,
Consumers and
Reference Materials
Reference Materials Unit
Retieseweg 111
B-2440 Geel
Belgium
E-Mail: jrc--rm-
distribution@ec.europa.e
u
Webpage:
https://ec.europa.eu/jrc/e
n/reference-materials
1573a
plant
tomato leaves

see above
Sample 3 from the
11th needle/leaf
inter-laboratory
test
(ICP Forests)
plant
maple leaves

Federal Research and
Training Centre for
Forests, Natural Hazards
and Landscape
Mr. Alfred Fürst
Seckendorff-Gudent
Weg 8
A-1131 Vienna
Austria
E-Mail:
xxx@xxx.xx
Web: www.ffcc.at
Order per fax: +43-1-
87838-1250
Sample 4 from the
21st needle/leaf
inter-laboratory
test
(ICP Forests)
plant
beech leaves
(litter fall)
& heavy
metals
see above
Sample 3 from the
16th needle/leaf
inter-laboratory
test
(ICP Forests)
plant
pine needles

see above
Sample 2 from the
19th needle/leaf
inter-laboratory
test
(ICP Forests)
plant
spruce needles
& heavy
metals
see above
I.4 Definitions and terminology
accuracy – the closeness of agreement between a test result and the accepted reference
value.
NOTE: The term accuracy, when applied to a set of test results, involves a combination of
random components and a common systematic error or bias component. (ISO 3534-1:1993)

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action limits – the limits on a control chart, normally set at three standard deviations either
side of the mean.
blank – is defined as the sample without the analyte.
blank determination - an analysis of the sample without the analyte or attribute, or an
analysis without the sample e.g. going through all steps of the procedure with the reagents
only. (van Reeuwijk and Houba, 1998)
blank chart – a type of control chart used to detect the possibility of occasional
contamination with the use of blank.
calibration – operation that, under specified conditions, in a first step, establishes a relation
between the quantity values with measurement uncertainties provided by measurement
standards and corresponding indications with associated measurement uncertainties and, in
a second step, uses this information to establish a relation for obtaining a measurement
result from an indication. (JCGM 2012)
control levels/limits – the limits on a control chart such that, when data points fall outside
them, special causes of variation must be suspected. These are normally three standard
deviations either side of the mean.
control sample – (sequence – control sample) a sample with an extreme content of the
analyte but falling within the working range of the method. It is inserted at random in a batch
to verify the correct order of the samples. (van Reeuwijk and Houba, 1998)
conventional true value – quantity value attributed by agreement to a quantity for a given
purpose. (JCGM 2012)
error (measurement error) – measured quantity value minus a reference quantity value
(JCGM 2012)
or:
the collective noun for a departure of the result from the true value. (van Reeuwijk and
Houba, 1998)
homogeneity - the degree to which items (e.g. tested substance) are similar.
instrument limit of detection (IDL) – the concentration equivalent to a signal, due to the
analyte of interest, which is the smallest signal that can be distinguished from background
noise by a particular instrument. The IDL should always be below the method detection limit,
it is not used for compliance data reporting, but may be used for statistical data analysis and
comparing the attributes of different instruments. (Standard Methods, 18th Edition)
interlaboratory comparison - organization, performance and evaluation of tests on the
same or similar test items by two or more laboratories in accordance with predetermined
conditions. (ISO/IEC 17043:2010)
limit of detection (LOD) - measured quantity value, obtained by a given measurement
procedure, for which the probability of falsely claiming the absence of a component in a
material is β, given a probability α of falsely claiming its presence. (JCGM 2012)
limit of quantification (LOQ) – performance characteristic that marks the ability of a CMP to
adequately “quantify” an analyte (…) The ability to quantify is generally expressed in terms of
the signal or analyte (true) value that will produce estimates having a specified relative
standard deviation (RSD), commonly 10%. (IUPAC:1997)
local reference material (LRM) – a material prepared and standardized in a laboratory,
used especially for daily control of analytical process.
or:
a local reference sample (in-house) for which one or more property values have been
established by the user laboratory, possibly in collaboration with other laboratories. The

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sample should be sufficiently stable and homogeneous for the properties concerned (van
Reeuwijk and Houba, 1998)
matrix - the components of material system, except the analyte. Used to refer to the physical
and chemical nature of the speciment, the substances present, and their concentrations.
(Westgard J.O., 2003)
mean chart (X-chart) – a control chart used to check the repeatability of the measurements.
In this chart the sample means are plotted in order to control the mean value of a variable.
measurement uncertainty – non-negative parameter characterizing the dispersion of the
quantity values being attributed to a measurand, based on the information used. (JCGM
2012)
(method) validation – verification, where the specified requirements are adequate for an
intended use. (JCGM 2012)
nominal value - rounded or approximate value of a characterizing quantity of a measuring
instrument or measuring system that provides guidance for its appropriate use. (JCGM 2012)
outlier - an observation that is numerically distant from the rest of the data.
precision – the closeness of agreement between independent test results obtained under
prescribed conditions. (ISO 3534-1:1993)
random errors - a component of measurement error that in replicate measurements varies
in an unpredictable manner. (JCGM 2012)
range chart (R-chart) – a type of control chart used to check the repeatability of the
analysis, usually of duplicate determinations. In this chart, the sample ranges are plotted in
order to control the variability of a variable.
reference material (RM) – a secondary reference material or substance, one or more of
whose property values are accurately determined by number of laboratories with a stated
method, and which values are accompanied by an uncertainty at a stated level of confidence.
The origin of the material and the data should be traceable (van Reeuwijk and Houba, 1998)
or:
a material, sufficiently homogeneous and stable with reference to specified properties, which
has been established to be fit for its intended use in measurement or in examination of
nominal properties. (JCGM 2012)
repeatability conditions – conditions where independent test results are obtained with the
same method on identical test material in the same laboratory by the same operator using
the same equipment within short intervals of time. (ISO 3534-1:1993)
or:
condition of measurement, out of a set of conditions that includes the same measurement
procedure, same operators, same measuring system, same operating conditions and same
location, and replicate measurements on the same or similar objects over a short period of
time. (JCGM 2012)
replicate result - the result of replicated measurement. (van Reeuwijk and Houba, 1998)
reproducibility conditions – conditions where test results are obtained with the same
method on identical material in different laboratories by different operators using different
equipment. (ISO 3534-1:1993)
or:
condition of measurement, out of a set of conditions that includes different locations,
operators, measuring systems, and replicate measurements on the same or similar objects.
(JCGM 2012)

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systematic error – a component of measurement error that in replicate measurements
remains constant or varies in a predictable manner. (JCGM 2012)
traceability- a property of the result of a measurement or the value of a standard whereby it
can be related to stated references, usually national or international standards through an
unbroken chain of comparison (PAC, 1995, 67, xx-xxxx-xxxx (Nomenclature in evaluation of
analytical
methods
including
detection
and
quantification
capabilities
(IUPAC
Recommendations 1995))
warning limits – the limits on a control chart, normally set at two standard deviations either
side of the mean.
Annex II Minor changes after 2020
Date
Minor change to latest published
version in 2020
Affected sections of this document