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Supplementary material to the publication ”How to use human
biomonitoring in chemical risk assessment: Methodological
aspects, recommendations, and lessons learned from HBM4EU”
International Journal of Hygiene and Environmental Health, HBM4EU Special issue
Authors: Tiina Santonen, Selma Mahiout, Paula Alvito, Petra Apel, Jos Bessems, Wieneke
Bil, Teresa Borges, Stephan Bose-O'Reilly, Jurgen Buekers, Ana Isabel Cañas Portilla,
Argelia Castaño Calvo, Mercedes de Alba González, Noelia Domínguez-Morueco, Marta
Esteban López, Ingrid Falnoga, Antje Gerofke, María del Carmen González Caballero, Milena
Horvat, Pasi Huuskonen, Normunds Kadikis, Marike Kolossa-Gehring, Rosa Lange,
Henriqueta Louro, Carla Martins, Matthieu Meslin, Lars Niemann, Susana Pedraza Díaz,
Veronika Plichta, Simo P. Porras, Christophe Rousselle, Bernice Scholten, Maria João Silva,
Zdenka Šlejkovec, Janja Snoj Tratnik, Agnes Šömen Joksić, Jose V. Tarazona, Maria Uhl, An
Van Nieuwenhuyse, Susana Viegas, Anne Marie Vinggaard, Marjolijn Woutersen and Greet
Schoeters
Index
Supplementary table 1. HBM and current RA guidance used by authorities in the EU
(reviewed in (Louro et al., 2019)) .................................................................................................. 2
Supplementary table 2. Examples of health-based guidance or limit values for the internal
exposure available (non-exhaustive) ............................................................................................ 4
Supplementary table 3. Common challenges related to evaluation of representativeness and
applicability of occupational biomonitoring data for risk assessment ......................................... 7
Sources of pre-analytical and analytical uncertainties ................................................................ 9
Supplementary table 4. Issues to be considered in pre-analytical and analytical phase
(see text for details) ................................................................................................................... 9
Pre-analytical phase .................................................................................................................. 9
Analytical phase ....................................................................................................................... 11
Supplementary table 5. Unit conversion examples for human biomonitoring data ................. 12
Supplementary table 6. List of recognized advantages and limitations on the use of effect
biomarkers for risk assessment ................................................................................................... 13
List of abbreviations used in the paper ....................................................................................... 14
References .................................................................................................................................... 19
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Supplementary table 1. HBM and current RA guidance used by authorities in the EU (reviewed in
(Louro et al., 2019))
Regulatory
body,
legislation
Name of the guidance document Notes Reference
ECHA, Biocides Guidance on the Biocidal Products
Regulation, Volume III, Human
Health - Assessment & Evaluation
(Parts B+C)
, ECHA 2017
Short descriptions on the use
of HBM is included. Would
require further, more detailed
guidance.
(Ghazi, 2015)
ECHA, REACH Chapter R.8 of the IR & CSA
guidance: Characterisation of dose
[concentration]-response for human
health, particularly chapters
R.8.1.2.7 Units and Appendix R.8-5
Derivation of DNELs using
biomonitoring data
Although this guidance
provides examples on the
derivation of biomarker-
DNELs, they have not been
often derived and there may
be uncertainties e.g., related
to the robustness of the
biomonitoring data; whether
the available data is robust
enough for the setting of
biomarker DNEL.
(ECHA, 2012)
ECHA,
REACH/OSH
Appendix to Chapter R.8 of the IR &
CSA guidance: Guidance for
preparing a scientific report for
health-based exposure limits at the
workplace.
Short descriptions on the
setting of the biological limit
and guidance values for
occupational exposure. Would
require further, more detailed
guidance.
(ECHA, 2019)
ECHA, REACH Chapter R.14 of the IR & CSA
guidance: Occupational exposure
assessment, particularly chapter
R.14.6.4.4 Biological monitoring
data
Small amount of information
on the use of biomonitoring to
support occupational exposure
assessment. Would require
further, more detailed
guidance.
(ECHA, 2016a)
ECHA, REACH Chapter R.15 of the IR & CSA
guidance: Consumer exposure
assessment
Biomonitoring is mentioned
only once at the end of
chapter R.15.5.4.
Measurements. In addition,
Appendix R.15.2 lists valuable
sources on exposure data.
Would require further, more
detailed guidance.
(ECHA, 2016b)
EFSA,
Pesticides
Review of the state of the art of
human biomonitoring for chemical
substances and its application to
human exposure assessment for
food safety
Review of HBM for chemical
substances and its application
to human exposure
assessment for food safety.
(Choi et al.,
2015)
Human biomonitoring data collection
from occupational exposure to
pesticides
Includes recommendations on
the implementation of HBM as
part of the occupational health
surveillance for pesticides in
Europe.
(Bevan et al.,
2017)
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SCCS,
Cosmetics
SCCS Notes of Guidance for the
testing of cosmetic ingredients and
their safety evaluation - 11th
revision
Human biomonitoring is
included in section 3-3.5.6.
However, HBM data is
considered mainly as
supporting/ complementary
information.
(SCCS, 2021)
SCOEL Methodology for derivation of
occupational exposure limits of
chemical agents - The General
Decision-Making Framework of the
Scientific Committee on
Occupational Exposure Limits
Short descriptions on the
setting of the biological limit
and guidance values for
occupational exposure. Would
require further, more detailed
guidance.
(SCOEL, 2018)
UN FAO Guidelines on the application of risk
assessment for feed
Short descriptions on the use
of HBM is included. Would
require further, more detailed
guidance.
(FAO, 2013)
WHO Biomarkers and risk assessment:
concepts and principles (EHC 155)
WHO guidance on exposure
assessment and the use of
HBM in RA. Many ECHA and
EFSA principles on the use of
HBM are based on these WHO
documents.
(WHO and IPCS,
1993)
Human exposure assessment (EHC
214)
(MacIntosh et
al., 2000)
Biomarkers in risk assessment:
validity and validation (EHC 222)
(WHO and IPCS,
2001)
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Supplementary table 2. Examples of health-based guidance or limit values for the internal exposure available (non-exhaustive)1
HBM value
Responsible body
Population
groups
covered
Description of the values and how they are derived Reference
AOEL and AEL values
EFSA and ECHA
operators,
workers,
bystanders,
residents
Active ingredients in pesticides and biocides values are established by EFSA (AOEL) or ECHA
(AEL), after proposals made by and consultation with member state experts. They can be set
also as internal health-based guidance values to cover all exposure routes. Point of departures
are mostly obtained from short- or mid-term toxicological studies but NOAELs or BMDLs
corrected for oral absorption if below or equal to 80%.
(EFSA, 2012)
BAT, BLW and EKA values
German Committee for
the determination of
occupational exposure
limits (MAK-Commission)
workers MAK Commission derives three types of biomonitoring values based on epidemiological or
toxicological data. Fully health-based values, considered to be protective from the adverse
health effects in occupational exposure, are called Biological Tolerance values (Biologische
Arbeitsstoff-Toleranzwerte, BAT values). Biological Guidance Values (Biologische Leit-Werte,
BLW) can be derived from non-carcinogenic effects of carcinogenic substances and for
substances without sufficient data. EKA levels are exposure equivalents for carcinogenic agents
(DFG, 2019)
BE values
Summit Toxicology (USA),
Health Canada
general
population
BEs (biomonitoring equivalents), published as peer-reviewed journal publications, correspond
to external limit values set by authorities, e.g., TDIs or RfDs. The publications include a
description of how the BE values were derived and therefore, they can be easily modified, if
necessary, when external limit values change.
(Angerer et al., 2011; Hays
and Aylward, 2009; Hays et
al., 2008; Health Canada,
2016)
BEI values
American Conference of
Governmental Industrial
Hygienists (ACGIH)
workers BEIs are derived for the occupational population. They represent the levels of chemicals which
are observed in samples of healthy workers exposed at the TLV level and do not cause adverse
health effects. There are also BEIs for chemicals causing non-systemic effects. BEIs are
developed by a committee consensus after an evaluation process of peer-reviewed published
data.
(ACGIH, 2022)
1 Up-to-Date list of available biomonitoring guidance values for the general population can be found from the ISES i-HBM Working Group: Human Biomonitoring Health-
Based Guidance Value (HB2GV) Dashboard, available at: https://www.intlexposurescience.org/i-hbm/
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BLV values, EU
Previously the Scientific
Committee on
Occupational Exposure
Limits (SCOEL), set up by
the European
Commission. Currently
(since 2019) ECHA Risk
assessment committee
(RAC)
workers BLVs are health-based values meant to be applied at workplaces. BLVs typically correspond to
an occupational exposure limit value (OEL) for a given substance. The values can be found in the
respective SCOEL/RAC OEL recommendations. Until now, only the BLV for Lead (Pb) has been
included in the EU Chemical Agents Directive (98/24/EC), the other values are only
recommendations from SCOEL/RAC.
SCOEL document library:
https://ec.europa.eu/social
/main.jsp?catId=148&langId
=en&intPageId=684
RAC OEL recommendations:
https://echa.europa.eu/en/
oels-activity-list
BLV values
ANSES, France
workers ANSES BLVs are national values derived for the occupational population. ANSES BLVs are
derived for relevant chemicals if routes other than inhalation contribute largely to absorption
and for cumulative pollutants, to take into account interindividual factors. (ANSES 2021). BLVs
can be derived for substances with or without a threshold effect.
https://www.anses.fr/en/co
ntent/biological-limit-
values-chemicals-used-
workplace
DNELbiomarker
ECHA/RAC, REACH
registrants
general
population and
workers
If applicable data are available, DNELbiomarker values can be derived by REACH registrants,
although not many have been set so far.  Some DNELbiomarkers have been derived by ECHA/RAC as
part of the restriction process, for example a for n-methylpyrrolidone (NMP)
(ECHA, 2012)
HBM-I and II values
German HBM
Commission at German
Environment Agency
(Umweltbundesamt –
UBA)
general
population
HBM-I and II values are derived toxicologically and/or epidemiologically.
The HBM-I value represents the concentration of a substance in human biological material at
and below which, according to the knowledge and judgement of the HBM Commission, there is
no risk for adverse health effects and, consequently, no need for action.
The HBM-II value represents the concentration of a substance in human biological material at
and above which, according to the knowledge and judgement of the HBM Commission, there is
an increased risk for adverse health effects. Consequently, there is an immediate need for
exposure reduction measures and the provision of biomedical advice.
(Apel et al., 2017)
HBM-GVs
HBM4EU
general
population and
workers
HBM-GVs have been derived within the HBM4EU consortium for the general and occupational
populations, depending on the substance. A jointly agreed strategy was followed, and
depending on data availability, the values are based on epidemiological data, toxicity reference
values, or PODs from animal studies. Each value derivation was subjected to a consultation
process before adoption.
The HBM-GV for the general population is defined as the concentration of a substance or its
specific metabolite(s) in human biological material at and below which, according to current
(Apel et al., 2020)
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knowledge, there is no risk of health impairment anticipated. Thus, this value is equivalent to
the HBM-I value of the German Human Biomonitoring Commission.
The HBM-GV for occupationally exposed adults is defined as the concentration of a substance or
its relevant metabolite(s) in human biological material aiming to protect workers exposed to the
respective substance regularly (each workday), and over the course of a working life from the
adverse effects related to medium- and long-term exposure.
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Supplementary table 3. Common challenges related to evaluation of representativeness and
applicability of occupational biomonitoring data for risk assessment
Challenge Response
Missing data for
contextual information
on operational
conditions and risk
management measures
(RMMs) implemented
This is a frequent issue in occupational studies. The relevance of this issue is
dependent on the amount of missing data for operational conditions and RMMs
(no data or only a few datapoints). When available, the dataset may provide
valuable data to identify which RMMs are more effective. Even in case of
limited contextual data HBM dataset can be considered at least as
supplementary data to support modelling or external exposure data. This
applies especially to large datasets.
What should be
considered for defining
the sampling strategy?
Previous knowledge of toxicokinetic and workplace/tasks characteristics is
fundamental for defining the sampling strategy. The more extensive this
knowledge is, the better the sampling can be defined and less assumptions will
be needed for risk assessment. Altogether these data will allow defining an
adequate sampling strategy, considering:
- Pre- and post-shift sample collection – useful to attribute exposure to
occupational context and differentiate between tasks and exposure
scenarios (e.g., food and tobacco consumption, air pollution)
- Half-life of chemicals and/or metabolites – as mentioned above, it is
very important to consider this aspect, since it will determine what is
the best timeframe for sample collection (e.g., immediately after the
shift, next morning and/or end of the working week). In the case of
long half-life chemicals (e.g., Pb), the sampling time might not be
relevant.
- Workplace/task characteristics and representativity – it is important to
know in detail the tasks performed and the workplace conditions to
identify what will be the potential sources of exposure and
moments/tasks that will probably imply exposure (e.g., which tasks
implicate the use/exposure to the substance being evaluated). In
addition, especially in case of short half-life chemicals it needs to be
ensured that the day the sample has been taken represents a typical
workday.
- Exposure peaks – in line with the previous points, these peaks should
be identified and considered when designing the sample collection,
gathering data from workplace and substance’ toxicokinetic. These
might be particular tasks that imply higher exposure due for instance
to the amount of the substance used and/or the conditions for the
development of those tasks (e.g., lack of local exhaust ventilation,
confined spaces)
Background exposure
(from sources other than
occupational context)
Availability of the information on background exposure in the country/area.
This might represent information from general population studies or from a
specific occupationally non-exposed cohort. If there is a possibility that there
might be e.g., country differences in the background exposure of the general
population, the background data should preferably represent the given area.
Research studies typically include a specific control group. This may include
workers performing administrative tasks in a company or workers from a
different company/sector. If controls are recruited from the same company, it
needs to be checked that indirect exposure does not occur in the
company/companies enrolled in the study. Pre-shift samples (in Monday
morning after weekend) might also provide useful information on the
background exposure levels but only in case of short half-life chemicals without
any accumulation in the body. Even some substances with short half-life from
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blood may show extended excretion in the urine e.g., due to slow dermal or
lung absorption.
Similar Exposure Group
considerations
This allows to identify group of workers (with similar exposure profiles, meaning
similarity and frequency of the tasks performed, the materials and processes
with which they work, and the similarity of the way they perform the tasks and
RMMs available) with higher exposure and risk and where additional RMMs
need to be implemented first.
Added value of HBM
data when compared
with industrial hygiene
monitoring data (e.g.,
air, dermal, surfaces).
What HBM data can add
and how does it
complement the
industrial hygiene data?
HBM provides results for aggregated exposures, i.e., considering all possible
routes (inhalation, ingestion, dermal absorption). This represents an enormous
added value compared to other methods because the overall exposure is
determined.
However, it should be referred that using only HBM data makes it more difficult
to identify the exposure route contributing more for the overall exposure and
that should be targeted for the additional RMMs. Here, the collection of
contextual data supports the interpretation of HBM and industrial hygiene data
(e.g., air monitoring, dermal wipe samples) the identification of the relevant
exposure route(s) and the effectiveness of the RMMs in place.
Results below LOD/LOQ The analytical methods selected for samples’ analysis should be fit for purpose,
i.e., with performance parameters that are adequate for analysis of the
predicted levels of exposure, matrices, and substances. Especially in case of
CRM substances it needs to be ensured that LOQs are low enough (preferably
LOQs being ≤10% of the biological guidance or limit values)
Selection bias This is common challenge in case of all occupational exposure data and
therefore not specific to HBM data. Most poorly performing workplaces might
not be willing to participate monitoring campaigns and the better performing
companies are over-represented in the dataset. If the aim is to gather
representative data from the given industry field attention should be paid on
the recruitment of the participants to minimize the impact of the selection bias.
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Sources of pre-analytical and analytical uncertainties
Supplementary table 4. Issues to be considered in pre-analytical and analytical phase (see text for
details)
Pre-analytical phase Specific notes
Sample contamination and use of non-
contaminated materials
Especially important when parent compound is analysed.
In occupational context, especially samples for metal
analyses need careful control of contamination.
Materials used should be pre-tested for impurities.
Sample type and sample collection time First morning void, morning, post-shift (in occupational
settings) spot sample or 24-h urine sample.
Especially important for short-lived chemicals in
occupational settings.
Sample stability during transportation and
storage
Especially important for chemicals with limited lifespan in
biological matrixes, for example volatile substances.
Urinary dilution Very dilute urine samples should be applied with caution.
Creatinine adjustment or specific gravity adjustment
typically applied to control this.
Analytical phase Specific notes
Sample analyses should be carried out by a
laboratory which passed high quality QA/QC or
by an accredited laboratory.
Accreditation guarantee that appropriate quality
assurance procedures are applied.
State-of-art analytical instrumentation and
methods should be used whenever possible.
Sensitivity and specificity of the method used
for the given substance and sample matrix
should be reported.
The limit of quantification (LOQ) should be low enough to
quantify the substance in adequate percentage of the
samples.
Pre-analytical phase
When using HBM data for the risk assessment purposes, it is important to note most errors in
analytical testing occur before any analysis has been performed (see e.g., (Bonini et al., 2002; Plebani,
2006). In order to minimize errors in pre-analytical part it is important to pay special attention to
sampling materials, sample collection, sample processing after collection, sample stability during
transportation to the analytical laboratory, and sample processing once received in the laboratory
(WHO, 1996) (Supplementary table 4). In HBM4EU, errors and variability caused by sampling phase
has been minimised by creating detailed SOPs for the sampling (Esteban López and Castaño, 2018).
In case of occupational trace metal analysis, sample contamination is one of the main sources of error.
Contamination may be derived from the sample collection vessels and materials, from the clothes and
skin of the subject or the sample collector, from the air, and from the chemicals added to the sample
(e.g., preservatives, anti-coagulants). Thus, only special vessels and tubes suitable for the given
sampling purpose should be used, and they should be of an appropriate purity. Contamination of
hands and clothes is an issue especially in occupational biomonitoring and may often explain the
outliers (extremely high levels in some samples) in the dataset. Sample contamination is usually not
an issue when metabolite is being analysed.
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Concentrations of some chemicals in body fluids are often not constant but may vary with time.
Differences in sample collection time may explain some variability between datasets. Especially in
occupational biomonitoring exposure, sample collection time is very critical. Chemicals with
elimination half-life of some hours (e.g., many organic solvents, phthalates, bisphenols) are rapidly
cleared from the body. Thus, sample collection should be carried out right after the exposure ends (in
occupational exposure assessment the sampling time is post-shift). Sample collection time is not as
critical for chemicals with long half-life (e.g., cadmium, lead, PCBs, PFOA, PFAS).
Stability of HBM samples might be affected during the storage and transportation. Some organic
chemicals have limited lifespan in biological matrixes (WHO, 1996). Accordingly, sample
transportation should be carried out according to the instructions given by the analytical laboratory.
Samples should be stored in appropriate vessels/tubes resistant to variations in temperature and
humidity, and to physical impacts (e.g., dropping on the floor). One should verify that the sample is
properly packed to stay stable during the sample transportation (including mailing). Long shipment
times should be avoided especially when the sample stability is of concern. If analysis is not carried
out immediately after sample is received in the laboratory, appropriate storage conditions should be
arranged in the laboratory.
Urinary dilution is one source of variation (see below). Accordingly, passing of urine should preferably
be avoided, for example, within three hours before collecting the void. Also, excessive consumption
of beverages should be avoided before the sampling.
Urine concentration can vary widely due to changes in fluid intakes and fluid losses through
sweating. Although collection of 24-h urinary volume is the most appropriate sample, due to
practical reasons spot urine samples are typically collected. When chemical concentration is
dependent on urine production levels, it is often reported either relative to urinary creatinine
concentration to correct for variable dilutions in spot samples (creatinine adjustment/correction) or
normalized to a certain average urine specific gravity (relative density). The creatinine adjustment
method is more often used although it is known that the production of creatinine reflects muscle
mass, which in part leads, e.g., to gender and age differences in creatinine concentrations.
Creatinine adjustment is an appropriate method when the analyte and creatinine are excreted in the
similar rate. Another method – although less frequently used – is normalization to a specific gravity
(SG). Recent study even suggests applying urinary dilution correction based on SG (Sauve et al.,
2015). SG method has also an advantage that the unit stays the same as of the original, non-
normalized result. However, since there is no agreement in the scientific community whether
correction for creatinine or specific gravity should be applied for certain chemicals, there might be
three types of HBM data available in the literature: non-corrected, creatinine adjusted and SG
normalized data.
It is generally accepted that very dilute or very concentrated urine samples are usually not suitable for
HBM. Some organisations have proposed ‘acceptable creatinine and specific gravity ranges’ for HBM.
According to the World Health Organisation (WHO) urine specimens should have a creatinine
concentration of >0.3 g/l (3 mmol/l) and <3.0 g/l (26.5 mmol/l) or a specific gravity of >1.010 and
<1.030. Specimens falling outside of these ranges should be discarded, as it is unlikely that any
correction will give accurate results (WHO, 1996). ACGIH has adopted the same ranges (ACGIH, 2022).
IRSST proposed same upper limit for creatinine and SG as WHO and ACGIH but suggested 0.5 g/l (4.4
mmol/l) for the lower creatinine limit (SG 1.010) (Gagné, 2019). On the other hand, DFG applied the
same lower limit as IRSST but proposed upper limit of 2.5 g/l (22.1 mmol/l) for creatinine and 1.024
for SG (Weihrauch et al., 2012).
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A common recommendation in HBM is to discard the urine sample if it falls outside the ‘acceptable
range’, and flag the result as ‘dilute’, ‘low/high creatinine or SG’, ‘sample integrity failed’, etc. In some
cases, this is an appropriate recommendation especially when it is possible to request a new urine
sample. Unfortunately, especially in population studies it is often not possible to collect a new urine
specimen. Accordingly, various practices have been applied for how to treat dilute and concentrated
urine specimen. Also, in many studies and reports no information is given for urinary dilution. It can
be speculated that few specimens falling outside the ‘acceptable range’ do not necessarily question
the results in case of large study groups (i.e., high number of samples). But in case of very small study
group (low number of samples) even few specimens falling outside the ‘acceptable range’ might cause
uncertainty to the results. Nevertheless, the urinary results outside the ‘acceptable range’ should
always be applied with caution. It is recommended to pay attention how the urinary results are
reported, how the urinary dilution has been taken into consideration, and how the specimen falling
outside the ‘acceptable range’ have been dealt with.
Creatinine adjustment (and specific gravity normalization) can have a substantial effect on the HBM
results in an individual level. In a group level, especially with very large study groups, the effect is less
prominent. Although adjusted/normalized HBM data are recommendable over data without any
dilution corrections, lack of correction does not preclude the use of HBM data for risk assessment
purposes.
Analytical phase
Essential part of the HBM campaign is that all sample analyses are carried out by an accredited
laboratory or a laboratory which successfully passed demanding QA/QC measures such as those of
HBM4EU whenever possible (Esteban Lopez et al., 2021). Accreditation and certification require
quality control, which means that accredited laboratories must use appropriate quality assurance
procedures. This means undertaking of internal quality assurance (e.g., frequent audits, replicate
analysis, use of certified reference materials) and successful performance in external quality control
schemes where available. The level of quality control should be reported in the publications and
reports. New analytical methods or methods currently developed are not necessarily accredited yet
but also here appropriate quality control should be applied when possible, and quality assurance
procedures should be reported.
It is recommended to use the state-of-art analytical instrumentation for the analysis. In the present-
day HBM this means, e.g., LC-MS/MS, GC-MS/MS, GC-HRMS. Despite the analytical instrumentation
used, the laboratory carrying out HBM analysis should always report the sensitivity of the method
used for the given substance and sample matrix, preferably in the form of the limit of quantification
(LOQ2). The LOQ should be low enough to quantify the substance in adequate percentage of the
samples. It should be noted that the LOQ may vary even between analytical series carried out in the
same laboratory. Accordingly, the LOQ must be regularly verified. Note that there are differences in
LOQs between laboratories – sometimes even substantial differences. When analytical method is
adopted from the literature, no literature LOQs should be applied but the LOQ should always be
determined in the laboratory carrying out the actual analysis. There are various methods to define the
LOQ, which means that it is highly recommended to report the given method.
2 LOQ is the lowest possible concentration of the analyte that can be quantified by the method in a reliable way. Reliable
means that a suitable precision and trueness must exist and must also be demonstrated. LOQ is higher than the limit of
detection (LOD). The LOD is the lowest possible concentration of the chemical that can be detected (but not quantified)
with certain degree of confidence. Although LOQ is recommended parameter to report the performance of the analytical
method in quantitative analysis, it is acceptable to report the LOD. In this case it is also advisable to report how the LOD
was derived. If no LOQ/LOD is reported, the data should be used with caution.
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Supplementary table 5. Unit conversion examples for human biomonitoring data
From To Conversion Example conversion for
Bisphenol A**
µg/l µmol/l ÷ MW ÷ 228.3
µmol/l µg/l × MW × 228.3
µg/l nmol/l × 1000 ÷ MW × 1000 ÷ 228.3
nmol/l µg/l × MW ÷ 1000 × 228.3 ÷ 1000
µg/l µg/g creatinine* × 1 / a × 0.74
µg/l µmol/mol creatinine* ÷ MW × 1 / b ÷ 228.3 × 83
µg/g creatinine µmol/mol creatinine ÷ MW × 113.1 ÷ 228.3 × 113.1
µmol/mol creatinine µg/l* × MW ÷ 1 / b × 228.3 ÷ 83
MW = Molecular Weight (g/mol); a = mean urinary creatinine concentration in g/l; b = mean urinary creatinine concentration
in mol/l; MW of creatinine = 113.1 g/mol.
* = Rough approximations. Note that these approximations can assist with converting published data but should not be used
for direct numerical conversion in reporting data. In the latter case the creatinine concentration must be measured in the
laboratory.
** Example: Bisphenol A, MW 228.3 g/mol, 1 litre of urine contains approximately 1.36 g of creatinine (equivalent to 12.02
mmol/l creatinine) (Cocker et al., 2011). Note that also other mean/median creatinine values have been proposed – typically
they vary between 1.0 and 1.6 g/l (ACGIH, 2022; Bader et al., 2013; Weihrauch et al., 2012).
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Supplementary table 6. List of recognized advantages and limitations on the use of effect
biomarkers for risk assessment
Advantages Limitations and uncertainties
Identify early biological effects before disease
development
Integrating multiple sources and routes of exposure
Some can be determined in specimens collected using
non-invasive methods (urine, exfoliated buccal cells,
saliva)
Contributing to mechanistic insights leading to AOPs
establishment
Reflecting the combined effects of Mixtures exposure
Identify susceptible individuals in a population
Contribute to identify subgroups at a higher risk, e.g., in
occupational settings
Together with exposure biomarkers, contribute to link
exposure to health outcome
Lack of specificity in relation to a specific substance
Some allow a direct interpretation in terms of risk at an
individual level, but others provide information that can
only be interpreted at the population level
For many, reference or guidance levels are not available
Low number of studies using effect biomarkers
measurement limits comparability
Often affected by lifestyle factors, e.g., smoking, diet
and high (natural) background variation that have to be
taken into account
Privacy issues, particularly sensitive if studying
susceptible individuals
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List of abbreviations used in the paper
1-OHPyr 1-hydroxy-pyrene
2,4/2,6-TDI toluene diisocyanate
3-PBA 3-phenoxybenzoic acid
4-FPBA 4-fluoro-3-phenoxybenzoic acid
AAMA acrylamide mercapturic acid
AChE acetylcholinesterase
ADI acceptable daily intake
AOP adverse outcome pathway
AR attributable risk
As(III) arsenic ion (oxidation state +3)
As(V) arsenic ion (oxidation state +5)
ATSDR Agency for Toxic Substances and Disease Registry
BaA benzo(a)anthracene
BaP benzo(a)pyrene
BbF benzo(b)fluoranthene
BBzP benzyl butyl phthalate
BCEP bis(2-chloroethyl) phosphate
BCIPP bis(1-chloro-2-propyl) phosphate
BDCIPP bis(1,3-dichloro-2-propyl) phosphate
BE biomonitoring equivalent
BHR bronchial hypersensitiveness
BLL blood lead levels
BLV biological limit value
BMDL Benchmark Dose (Lower Confidence Limit)
BP-3 benzo-phenone-3
BPA bisphenol A
BPS bisphenol S
Cd cadmium
CHMS Canadian Health Measure Survey
CHR chrysene
CI confidence interval
ClF3CA 3-(2-chloro-3,3,3-trifluoroprop-1-enyl)-2,2-dimethylcyclopropanecarboxylic acid
(often also abbreviated CFMP)
15/21
Cr(III) trivalent chromium
Cr(VI) hexavalent chromium
DALY disability-adjusted life year
DBCA 3-(2,2-dibromovinyl)-2,2-dimethylcyclopropanecarboxylic acid
DCCA 3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropanecarboxylic acid
DECOS Dutch Expert Committee on Occupational Safety
DEHP bis(2-ethylhexyl) phthalate
DiBP diisobutyl phthalate
DiNP diisononyl phthalate
DMA dimethylarsinic acid
DMF n,n-dimethyl-formamide
DnBP di-n-butyl phthalate
DON deoxynivalenol
EBC exhaled breath condensate
EBoD environmental burden of disease
EC European commission
ECHA European Chemicals Agency
ECHA-RAC risk assessment committee at ECHA
EDI estimated daily intake
EFSA European Food Safety Authority
ELCR excess lifetime cancer risk
ESB German Environmental Specimen Bank
FAO Food and Agriculture Organization of the United Nations
FIOH Finnish Institute of Occupational Health
FLEHS Flemish Environment and Health Study
FSIQ full-scale IQ points
Fue fractional urinary excretion
GDPR General Data Protection Regulation
GenPop general population
GerES German Environmental Survey of Children and Adolescents
GM geometric mean
HB2GV Human Biomonitoring Health-based Guidance Value Dashboard
HBGV health-based guidance value
HBM human Biomonitoring
16/21
HBM4EU European Human Biomonitoring Initiative
HBM-GV human biomonitoring guidance value derived within the HBM4EU project
HBM-PoD  human biomonitoring points of departure
HDA hexamethylene diamine, HDI metabolite
HDI hexamethylene diisocyanate
Hg mercury
HI hazard index (approach)
HQ hazard Quotient
HR hazard ratio
iAs inorganic arsenic
ICD International statistical classification of deseases and related health problems (by
WHO)
ICI/EQUAS HBM4EU interlaboratory comparison and external quality assurance
JMPR Joint FAO/WHO Meeting on Pesticide Residues
LOAEL lowest observed adverse effect level
LOD limit of detection
LOQ limit of quantification
MDA 4,4ʹ-methylenedianiline, MDI metabolite
MDI 4,4’-methylene diphenyl diisocyanate
MeHg methylmercury
MMA monomethylarsonic acid
MoE margin of exposure (approach)
MRA mixture risk assessment
NCO isocyanate (chemical group)
NEP 1-ethylpyrrolidin-2-one
NHANES National Health and Nutrition Examination Survey
NMP 1-methyl-pyrrolidin-2-one
NOAEL no-observed-adverse-effect level
OEL occupational exposure limit value
OPFR organophosphorus flame retardants
OSH occupational safety and health
o-toluidine ortho-toluidine
P50 median
P75 75th percentile
17/21
P90 90th percentile
P95 95th percentile
PAH4 consists of: BaA, BbF, BaP, CHR
PAHs polycyclic aromatic hydrocarbons
Pb lead
PBPK physiologically based pharmacokinetic (modelling)
PDI probable daily intake
PFAS per- and polyfluoroalkyl substance
PFBS perfluorobutanesulfonic acid
PFDA perfluorodecanoic acid
PFDoDA perfluorododecanoic acid (often also abbreviated PFDoA)
PFHpS perfluoroheptanesulfonic acid
PFHxA perfluorohexanoic acid
PFHxS perfluorohexanesulfonic acid
PFNA perfluorononanoic acid
PFOA perfluorooctanoic acid
PFOS perfluorooctanesulfonic acid
PFUnDA perfluoroundecanoic acid (often also abbreviated PFUnA)
PoD point of departure
PPP plant protection products
RA risk assessment
RAC Risk assessment committee at ECHA
RBC red blood cell
RBP retinol-binding protein
RCR risk characterisation ratio
REACH Registration, Evaluation, Authorisation and Restriction of Chemicals, Regulation (EC)
No 1907/2006
RMMs risk management measures
RPF relative potency factor
RQ risk quotient
RR relative risk
SCCS Scientific Committee on Consumer Safety
SCOEL Scientific Committee on Occupational Exposure Limits
SES socioeconomic status
18/21
SG specific gravity
TCEP tris (2-chloroethyl) phosphate
TCIPP tris (chloropropyl) phosphate
TCPy 3,5,6-trichloro-2-pyridinol
TDA toluene diamine, metabolite of toluene diisocyanate
TDCIPP tris (1,3-dichloro-2-propyl) phosphate
TDI tolerable daily intake
t-TDI temporary tolerable daily intake
trans-CDCA trans-chrysanthemum dicarboxylic acid
TWI tolerable weekly intake
U- urinary
U-Cr urinary total chromium
US EPA United States Environmental Protection Agency
WHO World Health Organisation
β2M beta-2-microglobulin
19/21
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