DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Institute for Occupational Safety and Health

NIOSH Manual of Analytical Methods (NMAM), 5th Edition

Application of Biological Monitoring Methods for Chemical 

Exposures in Occupational Health 

by D. Gayle DeBord, Ph.D., Dale Shoemaker, Ph.D., Clayton B’Hymer, Ph.D., and John Snawder, Ph.D., 

DABT, NIOSH (retired)

This chapter is a revision and expansion of Chapter F: Application of Biological Monitoring Methods 

published in the 4th edition of the NIOSH Manual of Analytical Methods (NMAM) [NIOSH 1998]. 

Additional information has been incorporated and terminology has been updated to reflect the most 

current practice. A lengthy section on biomonitoring analytical method validation has been added. 

1 Introduction BI-2

2 Applications of biomonitoring BI-2

3 Categories of biomarkers BI-3

4 Drivers for biomonitoring in occupational health studies BI-6

5 General considerations for biomonitoring research studies BI-10

6 Practical considerations for biomonitoring research studies BI-13

7 Biomonitoring analytical method validation BI-23

8 Conclusions BI-34

9 References BI-36
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Application of Biological Monitoring Methods for Chemical Exposures in Occupational Health

1 Introduction

Exposure assessment is a critical part of occupational health studies and risk mitigation 

activities. Biomonitoring can be an essential tool to determine exposures and/or health effects 

resulting from those exposures. According to a National Research Council (NRC) report 

[NRC 2006], biomonitoring can provide an efficient means to measure exposures and, when 

used with other information derived from toxicological, epidemiologic or modeling studies, 

can estimate the absorption into the body (dose), the influence of an exposure to an 

individual, and the potential health risks. Large population studies that incorporate 

biomonitoring efforts can identify chemicals found in the environment (including the 

occupational environment) and monitor the trends and distribution of exposures in the 

general population [CDC 2021, 2022].

Environmental measurements involving various media (such as air, water, soil, food, or 

surfaces) can detect the presence of hazards and sometimes predict individual exposure; for 

example, air monitoring predicts effects when the lung is the target organ. However, to 

evaluate biological, physical, or chemical agents that have been absorbed into the body, the 

magnitude of the absorbed dose and its contribution to total body burden, the measurement 

of biomarkers (biomonitoring) is required. Exposures for which biomarkers can be useful 

include the full range of occupational hazards from noise to stress to chemicals. While this 

document generally focuses on chemical exposures, considerations pertaining to purpose 

(objectives), study design criteria, quality assurance, ethics, and safety have general 

application. The guidance provided in this document is particularly relevant to urine and 

blood biomonitoring and is generally relevant to other biomonitoring matrices and assays in 

which response or susceptibility factors such as genetic markers or gene variants are 

measured. This document generally focuses on the utility of biomonitoring as a research tool. 

Only a few examples that are cited in this chapter are relevant to occupational health practice.

2 Applications of biomonitoring

Biomonitoring has many benefits. Adapted from Decker et al. [2013], biomonitoring benefits 

can include the following:

• Measure actual body burden

• Augment other exposure monitoring tools

• Capture all exposure routes, including dermal

• Detect unexpected exposures or routes of exposure

• Evaluate the effectiveness of control measures, including personal protective

equipment (PPE)

• Provide biomarkers of potential health risks

• Use to reconstruct exposures following acute or accidental events if appropriate

biomarkers are available
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Application of Biological Monitoring Methods for Chemical Exposures in Occupational Health

• Enhance individual or group risk assessments

• Provide valuable information for risk communication

Biomonitoring can enhance exposure assessment and provide information about health 

outcomes, depending upon the selected biomarker(s).

3 Categories of biomarkers

The National Academy of Sciences has defined biomarkers as measurable indicators in a 

biological system or organism. Examples include the presence of a chemical or its metabolite 

within biological specimens, measured alterations in structure or function, or identifiable 

genetic variations (Figure 1) [NRC 1987]. Biomarkers can be classified into three categories: 

exposure, effect or response, and susceptibility (Table 1) [NRC 1987]. While these categories 

can overlap, generally biomarkers of exposure provide a measure of body burden of a 

chemical or its metabolite. Susceptibility biomarkers estimate the impact that individual 

factors can have on exposure, uptake metabolism, and/or repair.

Figure 1 Continuum from exposure to disease. [Adapted from NRC 1987 and 

Schulte and Perera 1993]
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Application of Biological Monitoring Methods for Chemical Exposures in Occupational Health

Table 1. The three types of biomarkers

Type of Biomarker Characteristics Example

Exposure Measurement that reflects 

absorption of a chemical 

into the body

Urine or blood 

concentration of chemical 

or metabolite

Effect Biochemical, 

physiological, or other 

alteration that can be 

measured, which may or 

may not indicate a health 

impairment [WHO 2001]

DNA mutation or 

cytogenetic change

Susceptibility Inherent or acquired 

sensitivities or resistance 

in response to specific 

exposures

Genetic polymorphisms in 

metabolic 

activation/deactivation 

enzymes

[Adapted from DeBord et al. 2015]

a. Biomarkers of exposure

Occupational exposure occurs mainly by inhalation or through the skin [NIOSH 1998]

and less often by ingestion. The route of exposure may affect the internal dose and the

biologically effective dose, which is the dose that interacts with the target organ or

tissue. Biomarker measurements aggregate exposure across all pathways and all agents.

While this aggregation is often advantageous, biomarker measurements cannot be

used by themselves to determine the route(s) of exposure and in certain cases, the

specific exposure agent. Examples of biomarkers of exposure include blood lead

[Sexton et al. 2004], urinary cadmium [Menke et al. 2009], and serum dioxin [Manh et

al. 2014]. Biomarkers of exposure indicate that an individual or population has been

exposed and absorbed an agent into the body. Exposure biomarkers may or may not

reflect future disease or health risk. Exposure concentration, exposure duration, route

of exposure, pharmacokinetics of the chemical as it affects the distribution,

metabolism and elimination, timing of sample collection, physiological variations in

hydration status, and urinary flow can influence biomarker levels [Aylward et al. 2012;

Aylward et al. 2014]. Individual characteristics such as gene variants, exertion, and coexposures can also modify biomarker levels through the modification of absorption or

metabolism.
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b. Biomarkers of effect

Biomarkers of effect (also known as biomarkers of response) measure effects or

responses in the body to an exposure. These changes may be early precursors of

disease, specific clinical changes or markers for preclinical effects, or not be related to

any specific health outcome. Biomarkers of effect may result in a measurable change in

a pathway, macromolecule (DNA, RNA, protein), or other biomolecules. These

changes may be reversible or permanent. An example of a biomarker of effect is the

decreased levels of the enzyme acetylcholinesterase (AChE) in red blood cells (RBCs)

of persons exposed to organophosphate (OP) pesticides [Richards et al. 1978].

Biomonitoring results of blood AChE levels can be used to determine (or confirm)

exposure and to assess potential health risks and treatment needs [Richards et al.

1978]. A continuum exists across the biomarker spectrum. At low levels, a biomarker

may indicate an exposure or an effect. However, as the exposure continues or

increases, the biomarker level also increases, leading to a response or action level that

needs intervention before health is permanently impacted. While biomarkers of

exposure tend to be specific for a particular chemical or agent, biomarkers of effect are

less specific and may show the effect of exposure to multiple chemicals or agents. For

example, measuring blood AChE levels provides evidence of exposure to OP

pesticides, but does not identify the specific pesticide responsible for the decrease in

AChE. Similarly, carboxyhemoglobin levels increase after exposure to both carbon

monoxide and methylene chloride, which is metabolized to carbon monoxide

[Andersen et al. 1991].

c. Biomarkers of susceptibility

Biomarkers of susceptibility indicate whether an individual may be at increased or

decreased risk for developing a disease after an exposure has occurred. Susceptibility

biomarkers may also identify individuals whose body burden may be increased or

decreased relative to other individuals because of differences in absorption,

metabolism, or other biological processes. Biomarkers of susceptibility could include

other exposures from the environment, current health status, or genetic traits, such as

the activity of metabolizing enzymes. For example, the activity of CYP2E1, a

cytochrome P450 metabolic enzyme, can modify benzene toxicity [McHale et al. 2012],

and exposure to ethanol reduces methyl ethyl ketone metabolism [Liira et al. 1990].
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Application of Biological Monitoring Methods for Chemical Exposures in Occupational Health

4 Drivers for biomonitoring in occupational health 

studies

Several factors drive biomarker use in occupational safety and health. These drivers include 

risk management (including compliance), research and public health surveillance, evaluation 

of intervention effectiveness, and risk-assessment processes.

a. Risk management (including compliance)

The National Institute for Occupational Safety and Health (NIOSH) and other

organizations recommend the use of medical surveillance as a component of a

comprehensive risk management program to protect the health of workers [NIOSH

2012]. Frameworks for the use of biomarkers as medical surveillance tools, particularly

when other sources of medical data are not readily available, have been published

[Schulte 2005]; Medical surveillance can help identify sentinel adverse health effects

among workers, suggesting failures in controlling exposures. Thus, help to identify the

need for where improvements in workplace controls, such as engineering, or

administrative controls or personal protective equipment are needed. Also, individual

workers may benefit from the detection of disease in early stages when it may be more

treatable with better clinical outcomes. Several examples of biomarkers that are used

for medical surveillance purposes, including for compliance with established

standards, are described below.

The Occupational Safety and Health Administration (OSHA) has three regulations

that require biomonitoring in certain situations (Table 2). For benzene, 29 CFR

1910.1028 [OSHA 1980] requires urinary monitoring of phenol if an employee has

been exposed to benzene in an unplanned release. The employee provides a urine

specimen at the end of the work shift and another one 72 hours later. If the result of

the 72-hour test is below 75 milligram (mg) phenol/liter (L) urine, then no further

testing is required. If the 72-hour test result is greater than 75 mg phenol/L urine, then

additional medical surveillance is instituted at monthly intervals for 3 months.

Table 2. Chemicals requiring biomonitoring by OSHA

Chemical Biomarker(s)

Benzene Phenol in urine

Cadmium Cadmium in urine; beta-2-

microglobulin in urine; 

cadmium in blood

Lead Lead in blood; zinc protoporphyrin

[OSHA 1978, 1980, 1981]
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OSHA requires several biomonitoring tests for employees exposed to cadmium at or 

above the action level for 30 or more days per year (or in a 12-month consecutive 

period) [OSHA 1981]. The action level is an airborne cadmium concentration of 2.5 

micrograms per cubic meter (μg/m3) calculated as an 8-hour time-weighted average. 

Biomonitoring tests include cadmium in urine (CdU), standardized to grams of 

creatinine (g/Cr); beta-2 microglobulin in urine (β2-M), standardized to grams of 

creatinine (g/Cr), with pH specified; and cadmium in blood (CdB), standardized to 

liters of whole blood (lwb). β2-M is a marker of potential and actual kidney damage. 

Additional actions are required depending upon the results and may include further 

monitoring, worksite assessment, or even medical removal of the employee [OSHA 

1981]. 

The third chemical for which OSHA requires biomonitoring is lead. OSHA 29 CFR 

1910.1025 requires biomonitoring of all general industry employees who are or may be 

exposed to lead in workplace air at or above 50 μg/m3 averaged over an 8-hour 

workday. Monitoring of blood lead and zinc protoporphyrin levels is required every 6 

months. The requirement changes to every 2 months if the blood lead level is at or 

above 40 μg/100 g of whole blood or to monthly if the blood lead levels are high 

enough that an employee is medically removed from work [OSHA 1978].

NIOSH has no recommended exposure limits (RELs) based on biomonitoring. Other 

organizations have developed biomonitoring-based exposure limits. The American 

Conference of Governmental Industrial Hygienists (ACGIH) publishes a list of 

biological exposure indices (BEIs®

) for approximately 30 chemical agents [ACGIH 

2022]. The German Deutsche Forschungsgemeinschaft (DFG), Health and Safety 

Executive (HSE) in the UK, France’s Agency for Food, Environmental and 

Occupational Health and Safety (ANSES), and the Swiss Accident Insurance Fund 

(Suva) also routinely publish lists of biological exposure limits [ANSES 2016; DFG 

2015; HSE 2020; Suva 2019].

b. Research and public health surveillance

A major aim of biomarker research is to develop and validate biomarkers that reflect

specific exposures or are quantitatively linked to adverse outcomes in humans to

enable their use in risk prediction. Surveillance and research efforts can aid in

identifying hazards or monitoring exposure trends over time. Biomonitoring can

provide information on what has been absorbed or taken up into the body and, when

used in conjunction with environmental exposure monitoring, may indicate whether

environmental monitoring alone is sufficient if the purpose of biomonitoring is only to

detect exposure and not to give a measure of exposure intensity. If a substance has a
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sufficiently long half-life in the body, biomonitoring can be used to estimate 

cumulative dose after repeated exposures and can help characterize the contribution 

from multiple exposure routes (e.g., inhalation and dermal). Sampling of 

environmental media focuses on a single route, while biomonitoring assesses exposure 

across all routes.

Biomarker levels are affected by a number of different factors. These include the 

specimen matrix, such as blood or urine; the timing of the specimen collection; and the 

pharmacokinetics of absorption, distribution, metabolism, and excretion (ADME) 

[Aylward et al. 2014]. Individual differences in physiological parameters, such as age, 

weight, and liver function, may affect the distribution of a chemical, and genetic

polymorphisms of metabolic enzymes can alter the concentration and pattern of 

metabolites. Differences in physical activity may affect the amount of chemical

absorbed. Kidney function can affect the clearance or excretion of a chemical or 

metabolite.

Biomonitoring may have special utility in assessing dermal exposure because sampling 

methods for skin may not be readily available for most chemicals, and few, if any, 

reference standards are available [Ashley et al. 2011]. However, because biomarkers 

integrate exposure across all routes, it may not be possible to determine the amount of 

a measured biomarker attributable to skin absorption alone [Decker et al. 2013], unless 

inhalation exposure is precluded, for example, by use of an air-supplied respirator. 

Biomonitoring may also be useful in assessing the biological effects from the breach of 

or improper use of personal protective equipment (PPE) and may allow for comparing 

exposures/doses associated with different work practices and tasks.

A few surveillance programs include biomonitoring. One example is the Adult Blood 

Lead Epidemiology and Surveillance (ABLES) program established by NIOSH in 1987 

to monitor occupational lead exposure [NIOSH 2021]. National blood lead level data 

are published in the Centers for Disease Control and Prevention’s (CDC) Morbidity 

and Mortality Weekly Report (MMWR) and elsewhere. CDC also has a National 

Biomonitoring Program within the National Center for Environmental Health. This 

program periodically measures the amount of certain chemicals and metabolites in 

blood and urine samples collected from a representative sample of the U.S. population. 

CDC has issued several reports and a series of updated tables with the results of this 

biomonitoring program. An additional goal of this program is to provide U.S. 

population-based reference ranges for the monitored chemicals [CDC 2021, 2022].

NIOSH uses biomonitoring in its occupational health research. In a study to assess 

worker exposures to JP-8 jet fuel at U.S. Air Force bases, three different biomarkers of 
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Application of Biological Monitoring Methods for Chemical Exposures in Occupational Health

exposure, S-benzylmercapturic acid (BMA), S-phenylmercapturic acid (PMA), and (2-

methoxyethoxy)acetic acid (MEAA), were measured in urine to determine which 

biomarker most accurately assessed exposure [B’Hymer et al. 2012]. Statistical analysis 

of the measured analytes showed that the urinary concentration of MEAA, whether 

adjusted for creatinine or not, was the most accurate or appropriate biomarker for JP-8 

exposure. Correction of biomarker measurements for urine dilution is discussed in 

more detail later in this chapter.

c. Evaluation of intervention effectiveness

Measuring biomarker levels can be an effective means of evaluating interventions.

Evaluating some interventions by measuring air levels may provide an indication of

how well the control is working. However, for other interventions, such as those used

to control dermal exposure, the effectiveness of controls may not be readily apparent

without carrying out biomonitoring.

A study of paving workers exposed to polycyclic aromatic compounds (PACs) while

working with hot-mix asphalt (HMA) used biomonitoring to evaluate the effectiveness

of various interventions [McClean et al. 2012]. Air and hand-wash samples were

analyzed for the parent compounds corresponding to the urinary PAC metabolites.

Interventions assessed included dermal protection, a powered air-purifying respirator

(PAPR), biodiesel substitution (100% biodiesel provided to replace the diesel oil

normally used by workers to clean tools and equipment), and reduced temperature of

the HMA. Biodiesel substitution, dermal protection, and PAPR use were all associated

with decreased urinary PAC concentrations compared with baseline concentrations.

Higher HMA temperatures were positively associated with urinary PAC

concentrations. Biodiesel substitution and lower HMA temperatures were associated

with decreased PAC levels in air and on skin [Cavallari et al. 2012a,b].

d. Biomarker research for improving assessment of risk

Establishing a dose of concern and a mode of action are two primary components of

risk assessment [DeBord et al. 2015]. Environmental exposures can directly or

indirectly cause alterations in several biological pathways that can be measured. For

example, development of gene expression profiles helps identify genes, pathways, or

networks that are specific to the toxic endpoint of interest [Brown and Botstein 1999].

Toxicogenomics biomarker data have also been used to demonstrate benchmark dose

estimates [Thomas et al. 2011].

Determining internal dose is important in risk assessment and provides highly relevant

information that is more closely associated with disease response than external

exposure estimates [Aylward and Hays 2011]. The capability of biomarkers to generate
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information that can be used for internal dose estimation and response markers is 

important in their use in risk assessment.

Few studies have incorporated biomarkers of susceptibility into risk assessments. The 

advantages of susceptibility data have been discussed [Demchuk et al. 2007; 

Lohmueller et al. 2003; Schulte et al. 2015; Scinicariello et al. 2010]. These advantages 

include identification of the most genetically susceptible groups and opportunities for 

targeting preventative or therapeutic actions to high-risk populations.

5 General considerations for biomonitoring research 

studies

a. Factors that influence biomonitoring

Absorption can occur after dermal contact, inhalation, ingestion, or from a

combination of these routes. The extent of absorption from an exposure and the rate of

absorption depend on the properties of the chemical, such as solubility in lipids and

water, and the route of exposure. Once absorbed, a chemical is distributed and

partitions into various tissues because of tissue differences for such factors as perfusion,

lipophilicity, pH, and permeability. Highly water-soluble chemicals may be distributed

throughout the total body water, while more lipophilic substances may concentrate in

body fat or other lipid rich tissues, such as the brain.

The loss of a chemical compound from the body can loosely be defined as elimination,

which depends on metabolism and excretion. Chemical compounds or their

metabolites may be eliminated by numerous routes, including fecal excretion, urinary

excretion, exhalation, perspiration, and lactation. A chemical compound can be

excreted from the body without metabolism, in which case the parent compounds may

be detectable in the urine, breath, or fecal material. In other cases, the chemical may be

metabolized through oxidation, reduction, hydrolysis, or a combination of these

processes, often followed by conjugation with an endogenous substrate. Conjugation of

a chemical or metabolite is often a pathway for excretion as it frequently makes a

compound more water soluble so that it is more easily eliminated via the kidney.

Conjugation reactions include glucuronidation, amino acid conjugation, acetylation,

sulfate conjugation, and methylation [NIOSH 1998].

Metabolism, excretion, and the rates thereof, can be affected by age, diet, general health

status, race, gender, and other factors. In general, metabolic products will be more

water soluble than the parent chemical compounds. Where metabolism yields more

than one product, the relative amounts of each and the parent-metabolite ratios are
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affected by an individual's general health status, diet, genetic makeup, degree of 

hydration, time after exposure, and other factors. The kidney is the major organ of 

excretion for some chemicals and is the primary route for water-soluble substances. 

Substances enter the urine by either glomerular filtration, tubular secretion, or 

sometimes both mechanisms [NIOSH 1998]. Excretion through the feces is an 

important route for many lipid-soluble compounds, although urinary levels of these 

materials are also present.

Selection of an appropriate biomarker for an exposure requires sufficient knowledge of 

the distribution, metabolism, and excretion of the toxicant to select the proper 

compound to be determined, biological medium to be sampled, and time for obtaining 

a specimen. Often, most of the available toxicological and pharmacological information 

is from experimental animals and, thus, may not be directly applicable to humans.

While addressing biomonitoring in emergency response, Decker et al. [2013] provided 

a list of considerations for conducting biomonitoring. The first consideration was the 

appropriateness of conducting biomonitoring, which includes whether biomonitoring 

could add information that is actionable and interpretable. The second consideration 

was the feasibility of conducting biomonitoring with respect to logistics and available 

methodology. 

b. Monitoring goals

Air monitoring (or workplace environmental monitoring) and biomonitoring have

complementary goals and are frequently applied simultaneously in industrial hygiene

investigations [NIOSH 1998]. Compared with biomonitoring, air monitoring offers

advantages in certain situations. If the agent has acute toxic effects on the respiratory

tract or eyes, air monitoring is the logical tool for measuring the exposure [Hathaway

and Proctor 2004]. For some chemicals, direct-reading instruments can be used to

detect peak air concentrations of potentially dangerous chemicals, especially in acute,

rapidly evolving situations. Biological monitoring offers a better estimate of exposure in

situations where routes of exposure other than inhalation are significant.

Toxicokinetics, which is the rate at which a chemical enters the body and the fate of the

chemical once in the body, has a key role in the timing of sample collection. The

elimination half-life, or the time it takes to eliminate half of the chemical from the body,

determines how long a chemical or metabolite remains in the system. For example, lead

in blood has a half-life of about a month while many organic solvents have a half-life of

less than an hour. The amount of a chemical that accumulates in the body is dependent

on its elimination half-life, as well as any on-going exposure and the intensity of that

exposure. Levels of rapidly disappearing biomarkers (short half-lives) primarily reflect
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exposures during the previous several hours. On the other hand, biomarkers that 

disappear over the course of several weeks may reflect one, several, or numerous 

exposure incidents occurring anytime during a period of several weeks prior to the 

measurement. Some toxicants accumulate in one or several parts of the body and are in 

dynamic equilibrium with the sites of toxicity. In the case of polychlorinated biphenyls 

(PCBs), which accumulate in fatty tissue, the blood level of PCBs reflects the amount 

stored in the body [Brown and Lawton 1984; Zong et al. 2015]. When the site of critical 

action for a toxicant is known, the concentration of the biomarker at that site can be 

used as a measure of the biologically effective dose.

c. Biological matrices

The most common matrices used for biomonitoring are exhaled air, blood, and urine

[Landi and Caporaso 1997; NIOSH 1998]. Other matrices such as hair, nails, saliva, and

feces also have been used.

The matrix to be sampled is a critical decision and will depend upon a number of

elements including the timing of the sample and the chemical that is being measured.

Monitoring for persistent chemicals in blood enables exposed populations to be

compared with national reference ranges obtained by efforts such as CDC’s

Biomonitoring Program and the elimination of the chemical compound to be followed

over a long period of time [NRC 2006]. Most biomarkers present in the body can be

found in the blood for some period of time after exposure [Needham et al. 2005]. A

chemical in the blood is in dynamic equilibrium with various parts of the body: the site

of entry, the tissues in which the chemical is stored, and the organs in which it is

metabolized or from which it is excreted. Thus, the concentration of a biomarker in the

blood may differ between regions of the circulatory system. This would be the case

during pulmonary uptake or elimination of a solvent, which would cause differences in

concentration between capillary blood (mainly arterial blood) and venous blood. Some

considerations of monitoring chemicals in the blood are listed below:

• The gross composition of blood is relatively constant between individuals and

except for certain highly lipophilic compounds, it is not necessary to correct for

volume or hydration differences such as for urine.

• Obtaining specimens is straightforward and with proper care can be

accomplished with relatively little risk of contamination. However, an important

consideration is that obtaining blood specimens requires an invasive procedure

and should be performed only by trained personnel [Taylor et al. 2004].

• A hydrophobic chemical will persist in the body, which results in a longer halflife and may allow for accurate measurement of the chemical years later [Calafat

et al. 2015; NRC 2006]. The downside is that it may be difficult to determine the

timing of exposure. Hydrophilic chemicals may be rapidly cleared from blood so
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sample collection may need to occur within minutes to hours of exposures [NRC 

2006]. 

The toxicokinetics of the chemical is also a primary concern [Calafat et al. 2015]. The 

concentration of the biomarker in urine is usually correlated to its mean plasma level 

during the period the urine dwells in the bladder [Rosenberg et al. 1989]. In some 

instances, the urine concentration is affected by the amount of the biomarker stored in 

the kidneys, as with metals such as cadmium and chromium. The accuracy of the 

exposure estimate, using urine monitoring, depends upon the sampling strategy. The 

most influential factors are time of collection relative to the time of exposure, urine 

output, and specific biomarker characteristics. Measurements from 24-hour specimens 

are often more representative than from spot samples and are usually a better estimate 

of exposure. If the exposure is relatively constant or the half-life is long, then the 

concentration in a spot sample may reasonably predict the 24-hour concentration; 

however, if variability is high or the half-life is short (a few hours), then a spot sample 

may not be very predictive. However, collection, stabilization, and transportation of 24-

hour specimens in the field are difficult and often not feasible.

6 Practical considerations for biomonitoring 

research studies

a. Study design

Attention needs to be paid to scientific rigor when using biomarkers in occupational

health studies. When developing biomarker methods, in most cases no gold standards

exist for comparison or evaluation of results [NRC 2006]. Biomonitoring studies may

be able to determine the extent of exposure, internal dose and, depending upon the

biomarker, may be able to demonstrate risk from exposure. Because the levels of most

biomarkers are usually measured in microgram or lower quantities, concentrations may

be subject to large variability because of influences such as diet, lifestyle habits, and

other environmental exposures. While environmental monitoring provides information

on what is in the area, biomonitoring is person-specific, which may generate concern

among participants. Health-related concerns may arise from measurements that are

outside of the normal or reference range should a normal or reference range be known.

Some individuals may have concern over any exposure especially if the results are not

easily interpretable in terms of their current or future health.

b. Population selection

The selection of the population for biomonitoring is typically straightforward in

occupational settings. Generally, an important concept to consider is the inclusion of
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both male and female workers of all ages at the worksite. However, if possible, the 

population should be representative as a whole with respect to such parameters as age, 

sex, and risk classes [NRC 2006]. Confounders such as other exposures that might 

impact the level of the biomarker of interest should be assessed. Even when there is 

scientific rigor in selecting the population, a large degree of uncertainty will still exist 

because of random variations. The smaller the group, the greater the uncertainty because 

of inter-individual differences and laboratory variations [NRC 2006].

Statistical design is an important aspect of any study but is particularly important in 

biomonitoring studies to ensure that any effects seen are true and not the result of 

confounding variables [Gosho et al. 2012; NRC 2006]. One of the limitations of some 

population studies is the lack of statistical power associated with many biomarker 

measurements because of small population sizes or small changes in biomarker levels 

[Gosho et al. 2012; Hunter 1997]. A description of the basic characteristics of each 

biomarker variable is needed. These characteristics include number of observations, 

mean, standard deviation, and confidence limits. All of these should help reveal which 

data are below the limits of detection, missing, miscoded, or outliers.

c. Selection of biomonitoring methods

The occupational health professional and the analytical laboratory scientist should

decide on appropriate methods so that the test results are interpretable and relevant to

the exposure situation. Some methods already exist. For example, 50 BEIs currently

cover exposure to several hundred compounds (because of non-specificity) [ACGIH

2022]. Each of these BEIs has documentation that includes many of the issues (e.g.,

sampling time, interferences) discussed below for the development of a new biomarker.

However, if no method currently exists, a new one can be developed and validated. The

goal of the biomonitoring should be consistent with the goal of the industrial hygiene

investigation. Is the goal to measure exposure or a health effect related to the exposure or

both? The method needs to be evaluated at least for the required sensitivity, specificity,

and biological relevance [NRC 2006]. Toxicokinetics and feasibility should also be

considered. Interferences from diet, drugs, alcohol, disease states, or other chemicals or

agents should be considered when interpreting the data. The method should also have a

sufficiently low limit of detection to differentiate exposed from unexposed workers, and

any effects because of the sample matrix need to be assessed. In general, blood, serum,

and urine specimens require different sample preparations and may require separate

methodologies to eliminate matrix effects. Because of sample instability, some methods

may not be practical or feasible. The method should include guidelines for interpreting

the data. To minimize the risk of harm or discomfort to workers, when two

biomonitoring methods will provide the same information, the less invasive method
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should be used. For example, methods using urine or exhaled breath are preferred over 

those using blood if similar information can be obtained. 

d. Sampling strategy

Attention to specimen handling and collection is essential for obtaining quality data

[NIOSH 1998]. Consider these factors when collecting samples: the timing for the

specimen collection, a baseline for the biomarker, possibilities for contamination, the

need for preservatives, the stability of the biomarker, and sampling logistics. The

analytical laboratory should be consulted for standard operating procedures for

specimen collection and storage [NRC 2006]. Analytical methods should provide specific

directions on the collection, storage, and transport of specimens to the laboratory.

Adherence to these directions is of the utmost importance to ensure sample integrity.

The method should include instructions for the timing of specimen collection, that is,

whether specimens should be obtained during the work shift, at the end of the shift, or at

some other time during the work week, although investigators may choose a different

timing than recommended in the method depending on the purpose of the

biomonitoring. The longer the half-life of the chemical, the less critical is the timing of

the collection for exposure biomarkers [Lauwerys and Hoet 1993]. Some toxicants will

be normally present or have a background level in the body; others will accumulate in

the body over time, such as over a work week, based on factors like exposure and

biological half-lives. Therefore, it may be necessary to take a baseline measurement of

the biomarker concentration prior to the start of the work week or workday to

determine if the biomarker concentration increased over the time frame of investigation

[Lauwerys and Hoet 1993]. Recent trends in changing work schedules and work shifts

may have implications for biomonitoring and should be considered when developing a

sampling strategy. Care should be taken not to contaminate the specimen with either

chemicals or microbes, although the former is less a problem when metabolites are

measured. The proper preservative (for urine or blood specimens) or anticoagulant for

blood should be used, if appropriate. Stability of the biomarker is assured through

proper storage and shipment of the specimen to the laboratory and proper storage by the

laboratory.

e. Correction of urinalysis data for dilution

The interpretation of biomarkers in individual urine samples can be affected by urine

dilution (or concentration if a worker is dehydrated). The hydration status of the worker

can cause substantial variation in analyte concentration. This variation can be attributed

to fluid intake, temperature of the work environment, and the individual’s physical

workload. In addition, the excretion mechanism of a specific biomarker can be altered if

the urine is either very concentrated or very dilute [Rosenburg et al. 1989]. The ranges of

acceptability for urine dilution are not well documented. However, the common practice
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to compensate for urine dilution is to adjust the measured concentration to a normalized 

value [Boeniger et al. 1993; Carrieri et al. 2001]. The most common normalization 

methods are based on creatinine levels, specific gravity, and urine output. These 

methods have been described in detail [NIOSH 1998]. Urine osmolality has also been 

suggested as a normalization method [Yeh et al. 2015].

Creatinine adjustment is the most frequently used method to normalize for urine 

dilution. Creatinine is excreted by glomerular filtration at a relatively constant rate of 

1.0–1.6 g/day. Urinary creatinine concentration can be determined by spectrometric or 

kinetic methods based on the Jaffe alkaline picrate reaction, enzymatic methods, and

other methods based on mass spectrometry and liquid chromatography [Spencer 1986]. 

The adjusted value is expressed as the quantity of biomarker per unit quantity of 

creatinine. Adjustment for creatinine concentration while correcting for dilution 

introduces additional variation, which must be considered when the data are evaluated 

[Sauve et al. 2015]. Among the factors affecting the rate of creatinine excretion are the 

muscularity of the individual, physical activity, urine flow, time of day, diet, pregnancy, 

age, and disease [Barr et al. 2005; Boeniger et al. 1993]. Adjustment to the creatinine 

level is not appropriate for some compounds. For example, methanol is excreted from 

the kidney primarily by tubular secretion, a mechanism independent of creatinine’s 

excretion by glomerular filtration. The mechanism of excretion of a biomarker can be 

altered if the urine is very concentrated or very dilute. Measurements of samples having 

creatinine concentration outside the range of 0.5 to 3 g/L are unreliable [Rosenberg et al. 

1989]. Sauve et al. [2015] reported significant bias when using creatinine to normalize 

urinary biomarker levels.

Specific gravity (sp.g.) is used to normalize an analyte’s concentration in urine by 

multiplying the measured concentration of the biomarker by the ratio of [(sp.g.ref1)/(sp.g.spec –1)], where sp.g.spec is the specific gravity of the urine specimen and 

sp.g.ref is the average reference specific gravity of human urine [Cone et al. 2009; 

Goldberger et al. 1995; Levine and Fahy 1945; WADA 2016]. The reference value of 

1.020 is often used, especially in the United States, but a value of 1.015 was used in a 

study of Bangladeshi females [Miller et al. 2004]. 

The choice of which of these two normalization methods to use, if any, should be 

influenced by the chemical of interest in a study. Often there is little difference, but there 

are exceptions. In a study of the biomarker 1,6-hexamethylene diamine [Gaines et al. 

2010], it was determined that sp.g. adjustment was superior to creatinine adjustment. 

The mechanism of biomarker excretion can be altered if the urine is very concentrated 

or very dilute. Measurements of samples having sp.g. outside the range 1.010–1.030 have 

been reported as being unreliable [Rosenberg et al. 1989]. 
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Urine osmolality is a measure of the number of dissolved particles per unit of water in 

the urine [Yeh et al. 2015]. The effect of sociodemographic factors such as age, gender, 

race, body mass index, chronic kidney disease status, daily total protein intake, plain 

water intake, and blood osmolality is not as great for urine osmolality as it is for 

creatinine.

Finally, urinary output has been used to normalize biomarker levels. The measured 

concentration of the biomarker is multiplied by the ratio R/0.05, where R is the urine 

output for the sample in liters per hour (L/h). The urine output for the sample is 

computed from the volume (liters) of the sample and the time (hours) elapsed since the 

last voiding. The adjustment is to a mean output of 0.05 L/h, which is an average for 

adults [Rosenberg et al. 1989]. This method also has possible limitations and 

uncertainties from individual variation and hydration [NIOSH 1998].

f. Quality assurance

An effective quality assurance program is needed to ensure good quality data. In 1992,

the Health Care Finance Administration and the Public Health Service published

regulations implementing the Clinical Laboratory Improvement Amendments (CLIA) of

1988 to ensure that the analysis of human specimens was done accurately and under

good quality control procedures [42 CFR 493 1992]. Any analysis of human specimens

that can be used by a health care practitioner to assess the health of the individual or

used in the diagnosis, prevention, or treatment of disease or impairment falls under the

CLIA requirements. The CLIA Quality Assurance Program includes these key

components [NIOSH 1998]:

• Strict management of specimen collection, handling, storage, and transportation,

thus ensuring sample integrity

• Thorough verification of a method by the laboratory before use on field

specimens

• High level of analytical quality control

• Participation in proficiency testing programs, if available

• Documented instrument evaluation and maintenance programs

• Investigation of communication failures and complaints

• Documentation of performance and corrective measures

Standard operating procedures should be developed for collecting, shipping, and 

processing of biological specimens [NRC 2006]. In addition, the stability of the 

biomarker and its concentration range in the population should be determined. Field 

blanks, spikes, and duplicates should all be part of the quality assurance program. A field 
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blank is a collection container that can be empty or filled with uncontaminated water 

and analyzed alongside specimens to determine if the collection container is a source of 

contamination. Spikes, whether done in the field or in the analytical laboratory, are 

usually performed by adding a known amount of chemical or its metabolite into the 

collection container. Duplicates are created by splitting a specimen into two collection 

containers. Tracking of specimens in the field after collection, during shipping, and after 

receipt at the laboratory is necessary to ensure sample integrity. The data needed include 

date and time of collection, specimen number and description of the specimen (type of 

specimen, volume, etc.), and shipping information (receipts, tracking numbers, and 

inventory) [NRC 2006]. If shipping samples, Department of Transportation (DOT) and 

International Air Transport Association (IATA) shipping regulations apply: 

IATA/ICAO Dangerous Goods by Air, including DOT 49 CFR Part 172.704–172.804 

and IATA/ICAO Shipping Infectious Substances and Diagnostic Specimens [GPO 2021; 

IATA 2022].

g. Laboratory and field safety

When dealing with human specimens, a biosafety program is essential [NIOSH 1998].

Pathogens, such as hepatitis B and human immunodeficiency virus (HIV), may be

present in blood, saliva, semen, and other body fluids. Pathogens can be transmitted by

an accidental nick with a sharp object; exposure through open cuts, skin abrasions,

including dermatitis or acne; and indirectly through contact with a contaminated

environmental surface. There are four major ways to reduce the potential for exposure to

biological pathogens [CDC 2020]:

• Primary barriers (safety equipment): Examples include biosafety cabinets,

enclosed containers and self-sheathing needles.

• Personal protective equipment (PPE): Examples include gloves, coats, safety

glasses, and face shields.

• Secondary barriers (facility design and construction): Examples include

ventilation strategies, decontamination systems, facility design, and vaccine

usage.

• Facility practices and procedures: Examples include training and biosafety

programs.

OSHA requires employers to implement an exposure control plan when employees may 

be exposed to bloodborne pathogens [OSHA, no date; OSHA, 1992]. Standard 

precautions should be practiced with every biological specimen collected or received in 

the laboratory. It is not possible to know if a particular specimen contains pathogens; 

therefore, each sample should be treated as if contaminated with pathogens.
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Field personnel can be at risk for exposure to biological pathogens as well. They should 

follow standard precautions, such as using gloves during specimen collection and 

processing for shipment. Field staff should be vaccinated for hepatitis B if they will be 

collecting blood. In addition, field staff should know the procedures in their 

organization’s bloodborne pathogen policy in the event they are exposed to blood. 

h. Ethics

Ethical considerations are paramount when conducting biomonitoring. An Institutional

Review Board (IRB) should review research studies before collecting specimens [NRC

2006]. Among other considerations, the sampling method should be appropriate for the

requirements of the investigation [NIOSH 1998], and the procedures should cause

minimal harm to participants. The benefits of using invasive methods must outweigh the

risks. Informed consent from the participant is required and the results should be kept

confidential to the extent allowed by law [5 CFR 552a 1974]. Should an employer initiate

a biomonitoring program, these criteria should be considered [Adapted from Schulte

and DeBord 2000]:

• Specification of goals of the program

• Selection of validated biomarkers

• Establishment of clinical utility, if applicable

• Acceptance by the population being studied (informed consent received)

• Establishment of link to exposure or disease

• Protection of privacy and confidentiality

• Notification of results to participants

• Process for addressing results and outliers

i. Interpretation of results

The measured concentration of a given biomarker needs to be appropriately interpreted.

The first consideration is whether the biomarker is above background levels. If above,

this would indicate that an exposure was occurring or had occurred. The second

consideration is whether the measured level represents a risk. In industrial hygiene

practice, increased biomarker concentrations can help identify the source of the

exposure and determine the effectiveness of controls implemented to mitigate the

exposure.

Two main approaches can be used to interpret biomonitoring results: descriptive and

risk-based [NRC 2006]. The descriptive approach uses a statistical review of the data to

establish ranges and comparisons of the individual results to subgroups within the

cohort or to a control or reference group. Characterization of the half-life of the

chemical agent to determine how long the exposure may have been going on, or whether
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the exposure is recent, may also be relevant [Aylward et al. 2012]. Risk-based approaches 

are more intensive [NRC 2006]. A risk-based approach could be used when exposureresponse relationships have been demonstrated so that a measured exposure is known to 

cause an effect. One example would be decreased AChE levels after exposure to OP 

pesticides. 

Additional approaches to risk-based interpretation include the use of forward or reverse 

dosimetry to interpret the measured biomarker concentrations. Reverse dosimetry can 

be used in conjunction with knowledge about the timing of exposure and 

pharmacokinetic information to estimate an external exposure resulting in the measured 

biomarker concentration. This estimated exposure can be compared to exposure 

guidance values such as OELs, reference doses, or other risk-based guidance values 

[Clewell et al. 2008]. Forward dosimetry estimates the concentration of a biomarker 

consistent with exposure at an exposure guidance value. This concentration can be used 

as a screening value to evaluate measured biomarker concentrations and to interpret the 

exposure as below, near, or above the risk-based exposure guidance value. The forward 

dosimetry approach has been used extensively in both occupational and environmental 

biomonitoring contexts [Angerer et al. 2011; Lauwerys and Hoet 1993; Zidek et al. 2017].

j. Reference levels

For proper interpretation of biomarker levels, biomarker measurements should be

compared to biological action levels, if available. In the absence of published biological

action levels, biomarker measurement levels indicating occupational exposure have been

inferred by comparison with the normal background levels of the biomarker. Biological

action levels vary in their derivation, some from correlations with exposure, others with

health effects. These action levels should be used only when one has a full understanding

of their derivation.

Sources of biological action levels:

• Biological Exposure Indices (BEI®) adopted by the American Conference of

Governmental Industrial Hygienists (ACGIH) [ACGIH 2022]

• Biological Tolerance Values for Working Materials (BAT) published by the

Deutsche Forschungsgemeinschaft's (DFG) Commission for the Investigation of

Health Hazards of Chemical Compounds in the Work Area [DFG 2015]

• Swiss Accident Insurance Fund (Suva) [Suva 2019]

• Summary of Recommendations in Industrial Chemical Exposure. Guidelines for

Biological Monitoring [Lauwerys and Hoet 1993]

• OSHA standards [OSHA 1978, 1980, 1981]
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• Japan Society for Occupational Health. Recommendation of occupational

exposure limits (2016–2017) [JSOH 2016]

• Health and Safety Executive, UK. Biological monitoring guidance values [HSE

2020]

• European Scientific Committee on Occupational Exposure Limits (SCOEL)

[2014]

• ANSES. Biological limit values for chemicals used in the workplace. [ANSES

2019]

Population-based sources for reference values:

• German Human Biomonitoring Commission (HBM) values

[Angerer et al. 2011; Apel et al. 2017; Schulz et al. 2011]

• Centers for Disease Control and Prevention. National Report on Human

Exposure to Environmental Chemicals [CDC 2021]

• Biomonitoring Equivalents [Aylward et al. 2013; Exley et al. 2015; St-Amand et

al. 2014; Zidek et al. 2017]

A limited but growing number of chemicals have values that can be used to interpret the 

measured biomarker concentrations in the context of health risk or external exposure 

guidance values. For those that do not, other approaches for interpretation can be used. 

In occupational health studies, when biomarker data are available for exposed and 

unexposed populations that are otherwise similar, the upper limit of the range for the 

nonexposed population may serve as a reference level [NIOSH 1998]. For those 

biomarkers for which no measurable background level in nonoccupationally exposed 

humans has been demonstrated, the reference level is effectively the detection limit of 

the analytical method. In any case, levels of the biomarker above the reference level 

suggest an occupational exposure but give no information on the potential health effect. 

The extent to which biomarkers can be used is dependent upon a number of critical 

factors: (1) adequate investment in validation, (2) obtaining international agreement on 

exposure guidelines, (3) exploring the utility of biomarkers in regulation, (4) applying 

biomarkers to critical occupational safety and health questions, (5) developing the 

exposome, (6) utilizing biomarkers to address emerging occupational health issues, and 

(7) continuing to address the ethical and social justice issues related to biomarkers.

[Schulte and Hauser 2012].

k. Variability

Biological monitoring data are subject to several sources of variability [Aylward et al.

2014; Droz 1989; NIOSH 1998] that need to be considered when interpreting results.

Sources of variability [Aylward et al. 2014; NIOSH 1998] include the following:
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• Rates at which an agent is taken up by the body, metabolized, and excreted.

These rates vary from person to person and are affected by the person's age, sex,

and physical workload.

• Route of exposure. For example, absorption through the lungs is typically much

faster than absorption through the skin. Thus, the appearance and elimination of

a biomarker will be slower if the agent enters through the skin. If the biomarker

is rapidly excreted, the optimum timing for collecting biological samples will be

different for the two routes of entry.

• Fluctuations in environmental exposure. Such fluctuations will be tracked by the

levels of rapidly eliminated biomarkers, i.e., those reflecting exposure in the

previous several hours.

• Personal protective equipment worn, and a person's work practices.

• Existence of a biomarker in both a free and a conjugated form, the relative

proportions of which can vary substantially from person to person. For example,

aniline is present in urine as both the free amine and as acetanilide, its acetyl

derivative [Greenberg and Lester 1947]. Some persons are genetically

predisposed to excrete primarily free aniline, while others, primarily, acetanilide.

• Concurrent exposure to several chemicals that compete for the same

biotransformation pathways in the body. This may lead to altered metabolism

and excretion, which could change the relationship between exposure or health

effect and the level of the biomarker [Ogata et al. 1993]. In one example, the

variability of metabolites of several solvents was evaluated [Valcke and Haddad

2015]. Modelling evidence indicates the impact of multiple exposures depends

upon chemical concentrations, biochemical properties, subpopulations, and

internal dose metrics considered.

• Concurrent exposure to several chemicals that are metabolized to the same

biomarker. This complicates the interpretation of the biological monitoring data.

For example, trichloroacetic acid is a biomarker for trichloroethylene, 1,1,1-

trichloroethane, and perchloroethylene.

• Consumption of alcoholic beverages [Fiserova-Bergerova et al. 1993]. Ethanol is

metabolized by three pathways used for metabolism of other organic chemicals.

After consuming one drink, the ethanol concentration in the blood is about 1000

times higher than from a typical occupational exposure and may significantly

affect the metabolism of industrial chemicals.

• Medications [Rosenberg 1994], health, and diet.

• Smoking and other lifestyle factors.
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Because of the variability in biomarker concentration, judgments on the exposure or 

health risk of workers frequently cannot be made based on a single sample [NIOSH 

1998]. It may be necessary to collect multiple or repeated samples.

7 Biomonitoring analytical method validation

It is imperative that bioanalytical methods are well characterized, fully validated, and 

documented to a satisfactory standard to yield reliable results. Bioanalytical methods must 

also be validated for use in the field [Hunter et al. 2010; Schulte and DeBord 2000], which is 

not covered in this chapter. This chapter draws heavily from two primary sources: guidelines 

published by the U.S. Food and Drug Administration (FDA) [FDA 2018; Viswanathan et al. 

2007], which have been a biomonitoring standard since their original publication in 2001; and 

a set of guidelines from the European Medicines Agency [EMA 2011]. Several other validation 

guidelines and research papers were also consulted and distilled into this final set of 

recommendations [Bader et al. 2012; Causon 1997; Green 1996; Hartmann et al. 1998; ICH

2005; Irish National Accreditation Board 2019; Linder and Wainer 1998; Mikkelsen and 

Cortón 2004; Peters and Maurer 2015; Theodorsson 2012; UNODC 2009; Wieling et al. 1996]. 

These references demonstrate that while there is general agreement for some aspects of 

bioanalytical method validation, many differences of opinions remain on the terminology 

employed, the number of parameters that should be evaluated, and how best to evaluate many 

of those parameters. The driving force behind most of the literature guidelines has been the 

need to analyze for pharmaceuticals in biological matrices. Therefore, some adaptations of the 

above guidelines have been taken into consideration for use in occupational exposure 

assessment.

Acceptance criteria that are wider than those defined in this guideline section may be 

appropriate in special situations. Acceptance criteria should be defined ahead of time and be 

appropriate for the intended use of the method.

This section is not intended to serve as a decision guide on the following:

• Whether biomonitoring is appropriate. Readers are referred to a decision matrix

proposed by Decker et al. [2013].

• Method development. Method development steps should be completed before

method validation occurs.

• Interpreting biomonitoring data.

• Selecting the appropriate analytical technique and methodology.

While this section addresses laboratory method validation, validation of the biomarker needs 

to be accomplished for the population of interest. This validation process utilizes two steps: 
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clinical validity and clinical utility [NIOSH 2010]. Clinical validity evaluates how well the test 

performs in the field, such as the number of false positives or negatives, while clinical utility 

evaluates how useful the information is.

This guidance generally applies to bioanalytical procedures, such as gas chromatography (GC) 

and high-performance liquid chromatography (LC), regardless of the type of detector utilized 

(e.g., ultraviolet, electron capture, flame ionization, atomic emission, mass spectrometric, 

etc.). These guidelines are applicable for quantitative determination of analytes in biological 

matrices such as blood, serum, plasma, urine, saliva, etc. The recommendations can be 

modified depending on the analytical method used and the matrix involved. Because of some 

unique and inherent characteristics of ligand-binding assays or immunoassays, this set of 

guidelines may not be appropriate for such assays. The FDA guidelines discuss immunoassays 

more comprehensively [FDA 2018]. While the general validation principles can and should be 

applied, the specific guidelines and limits described herein may not be applicable for 

immunoassays or for all bioanalyses cases. For more in-depth discussion of the reasons for 

this and about the adjusted recommendations, consult the guidelines from the FDA and the 

European Medicines Agency [EMA 2011; FDA 2018; Viswanathan et al. 2007].

a. Validation parameters

Method validation should include determining the following parameters: selectivity,

carryover, calibration curve, bias and precision, accuracy, stability, matrix effects,

recovery, robustness, limits of detection and quantitation (LOD and LOQ), and

acceptance criteria for an analytical run, quality control samples, and calibration

standards. Each of these parameters will be defined and discussed in more detail. These

parameters should be reported when validating the method. Immunoassays have

slightly different terminology than chemistry methods with respect to validation

parameters and terminology [Andreasson et al. 2015]. To date, no immunoassays are

represented in the NMAM and this chapter has focused on chemistry methods.

1) Selectivity

Selectivity is the ability of the method to measure unequivocally and to

differentiate the analyte(s) in the presence of other components that may be

expected to be present in the sample. These other components might include

metabolites, parent compound(s), impurities, degradation products, matrix

components, etc. Sometimes the term specificity is used interchangeably with

selectivity, although specificity implies that a method produces a response for a

single analyte. Because that is rarely the case, the term selectivity will be used here.

Multianalyte methods should be able to differentiate analytes of interest from one

another and from the matrix. Likewise, if an internal standard (or multiple internal

standards) is employed, the method must be able to differentiate it as well.
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Selectivity should be investigated in ten (or more) individual sources of the 

appropriate blank matrix. The use of fewer sources is acceptable for rare or difficult 

to obtain matrices. Investigating suspected interferences may be done by spiking 

these compounds at realistic concentrations into blank matrices or matrices spiked 

with low levels of the analyte(s) of interest. Suspected interferences may be difficult 

to obtain or predict. It is common to disregard an interference if it is less than 20% 

of the LOQ for the analyte and less than 5% for any internal standard signal [EMA 

2011]. 

While interferences should ideally be kept to a minimum, it is recognized that 

some biomarkers are produced endogenously while others are present because of

background (nonoccupational) exposures. These types of methods can still be 

invaluable to differentiate between occupationally exposed and nonoccupationally 

exposed individuals and should, therefore, not be dismissed because of measurable 

background levels, especially if those levels are known and consistent.

2) Carryover

Carryover should be addressed and minimized during method development.

“Carryover” is a type of sample contamination. Carryover causes a target analyte

response to be generated in later measurements, or runs, where the sample does

not actually contain the analyte or contains less than indicated by the intensity of

the response. Carryover should be assessed during validation by injecting one or

more blank samples after a high concentration sample or a high calibration

standard. If carryover is unavoidable, specific measures should be provided in the

method to prevent or correct for carryover so that it does not affect accuracy and

precision of the samples. This could include the injection of matrix blanks or

solvent blanks after certain samples. Randomizing samples should be avoided in

this case as it may interfere with assessing carryover. While “there is no standard

acceptable magnitude of carryover for a passing analytical analysis” [Viswanathan

et al. 2007], a good measure would be to use the same guideline as that of the blank,

i.e., carryover in the blank sample following the high concentration standard

should be less than 20% of the LOQ for the analyte and less than 5% for any

internal standard signal [EMA 2011].

3) Calibration curve

The calibration or standard curve is the relationship between the instrument

response and known concentrations of the analyte. Each analyte studied in the

method should have a separate calibration curve. Calibration standards should be

prepared in the same matrix as the intended samples by spiking the blank matrix
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with known concentrations of the analyte. A sufficient number of standards should 

be used to adequately define the relationship between concentration and response. 

This number will be a function of the anticipated range of values and the nature of 

the analyte/response relationship. Use the simplest model that adequately describes 

the concentration-response relationship. A calibration curve should consist of a 

blank sample (a matrix sample processed without analyte and without internal 

standard), a zero sample (a matrix sample processed without analyte and with 

internal standard), and at least six nonzero standards. Usually, the blank and zero 

samples are not used when calculating the calibration curve parameters, but there 

may be methods where use of the zero sample is appropriate.

More calibration standards may be required to adequately describe a higherfunction (nonlinear) model. Selection of weighting and use of complex regression 

equations should be justified. The calibrators may be injected using single or 

replicate samples.

Calibration curve parameters (slope, intercept, and correlation coefficient) should 

be reported, but are themselves not sufficient to determine the validity of the curve. 

The back calculated concentrations of the nonzero calibration standards should be 

within ±15% of the nominal value, except for the LOQ for which it should be 

within ±20%. At least 75% of the calibration standards should meet this criterion. 

Excluding the standards should not change the calibration model used. A 

minimum of three valid calibration curves should be reported during the 

validation study. The range of the calibration curve determines the range of the 

method (more on this in the Accuracy section). 

4) Bias and precision

Bias is a systematic deviation of the method average or measured value from an

accepted value [ASTM 2020a]. Precision is the closeness of agreement of a series of

individual measures of multiple aliquots of a single, homogeneous sample.

Precision is usually expressed as the coefficient of variation (CV) or the relative

standard deviation (RSD), which are different terms for the same thing. Generally,

the same data and runs used to determine accuracy are used to determine

precision. Precision is often divided into three separate, but important parts:

• Short-term precision, sometimes called repeatability or within-run

precision. Repeatability expresses the precision under the same operating

conditions over a short interval, usually a single run or a 24-hour window.

• Intermediate precision, sometimes called between-run, between-day, or

inter-assay precision, captures variables within a single laboratory. These
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variables could include different days, analysts, equipment, etc. It is not 

considered necessary to study these effects individually, and the extent to 

which intermediate precision should be established is dictated by the 

intended use of the method.

• Long-term precision, or reproducibility, expresses the precision between

labs (collaborative study, round-robin, inter-laboratory trial) [ASTM 2022].

The extent to which reproducibility should be established is dictated by the

intended use of the method. One must use caution as many authors use the

term reproducibility for within-laboratory studies at the level of

intermediate precision, which can lead to confusion.

Biological monitoring methods should always be evaluated for repeatability and for 

selected areas of intermediate precision. The precision between days is nearly 

always an important parameter. Checking the precision after changing lots or 

manufacturers of solvent, e.g., different chromatography columns or extraction 

cartridges, is also encouraged, while changing analysts or instrumentation is often 

less important and sometimes impractical or impossible. For methods that are 

published in NMAM, the process of an independent laboratory analysis aims to 

measure reproducibility by having at least one independent laboratory analyze a 

series of blind samples. Consensus standards often require testing by multiple 

laboratories (e.g., ASTM) for full validation.

Precision should be measured using a minimum of five samples per level at a 

minimum of three levels. As previously mentioned, these samples and levels will 

usually be the same used to measure accuracy. The within-run precision 

(repeatability, as measured by CV or RSD) should be within 15% for the upper two 

concentration levels and within 20% for the low level. More levels, more replicates, 

or both are acceptable. Intermediate precision (between days, etc.) follows the same 

guidelines, with acceptable precision of no greater than 15% RSD at the upper 

levels and no greater than 20% at the lower level.

5) Accuracy

Accuracy is the closeness of the determined value obtained by the method to the

true or accepted value of the analyte [FDA 2018]. The term trueness is sometimes

used for this value. Accuracy is a function of bias and precision [NIOSH 1995] and

should be assessed on samples spiked with known amounts of the analytes. These

samples should be spiked from a stock solution of the analyte prepared separately

from that used to make up the calibration standards.
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Accuracy should be determined using a minimum of three samples per level at a 

minimum of three levels. The low level should be within 3 times the LLOQ, the 

medium level near the center of the calibration curve, and the high level within 

75% of the ULOQ [NIOSH 1995]. The mean concentration should be within 15% 

of the nominal value for the upper two concentration levels and within 20% of the 

nominal value for the low level [FDA 2018]. More levels, more replicates, or both 

are recommended during method validation.

Accuracy should be estimated between runs by analyzing at least three 

concentration levels from at least three runs, and analyzed on at least two different 

days. The mean concentration should be within 15% of the nominal value for the 

upper two concentration levels and within 20% of the nominal value for the low 

level [FDA 2018].

6) Stability

An evaluation of stability should be conducted to ensure that steps taken during

sample preparation, analysis, and storage, affecting the concentration of the analyte

in the matrix, are evaluated so that parameters can be set around the sampling and

analysis. Stability in a biological fluid is a function of the storage conditions, the

chemical properties of the analyte(s), the matrix, and the container system. The

stability of an analyte in a particular matrix and container system is relevant only

to that condition and should not be extrapolated to other matrices and container

systems. Conditions used in stability experiments should be the same or similar to

those used for actual study samples. Such conditions might include sample matrix,

anticoagulants, preservatives, container materials, storage conditions, and

analytical conditions.

Stability determinations should use a set of samples from a freshly prepared stock

solution of the analyte spiked into the appropriate matrix. Stability should be

evaluated using low (within three times the LLOQ) and high (near the ULOQ)

concentrations with at least three replicates for each level under each stability

condition evaluated. One set of samples is analyzed immediately after preparation

and the other set(s) after the applied storage condition(s). The stability samples are

analyzed against a calibration curve from freshly spiked calibration standards and

the obtained mean concentrations are compared to the nominal concentrations.

The mean concentration at each level should be within 20% of the nominal

concentration.
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Biological monitoring methods should be evaluated for sample stability. For 

NMAM methods, the specific stability test will be indicated. The following stability 

tests are recommended:

• Short-term stability. The stability samples are kept at room temperature (or

sample processing temperature if different) for a minimum of 4 to 24 hours

and then analyzed. The length of time should be equal to or exceed the

expected time that the samples will be maintained at that temperature

during the study or course of the analysis. In the case of chromatographic

analysis, a cooled autosampler may be required to improve sample stability.

Stability should be verified at room temperature and in cooled conditions

such as found with an autosampler. Light sensitivity should also be

evaluated, and it should be determined whether the samples require amber

glassware or sample vials to extend stability. In the case of chromatographic

analysis, which may require long run times, stability should be evaluated to

a minimum of 72 hours (or an appropriate time period if solutions would

normally be left standing for longer periods) should a system failure require

delayed injection of samples stored in an autosampler.

• Long-term stability. The stability samples are stored (most likely in a

freezer but it could be in a refrigerator in some circumstances) for at least

the same duration as planned for the study samples and preferably longer.

Data points may be collected at intermediate times through use of either

replicate aliquots or from re-analysis of the same samples provided

sufficient volume is originally supplied. The samples should be stored

under the same conditions as planned for the field/study samples, so if

different storage temperatures will be used (some samples at –20 °C and

some at –70 °C, for example), then studies at both temperatures should be

performed.

• Freeze and thaw stability. The stability samples are stored and frozen at the

intended storage temperature for 24 hours and then thawed at room or

processing temperature. When the samples are completely thawed, they

should be refrozen under the same conditions for 12 to 24 hours. A

minimum of three freeze-thaw cycles should be performed but more are

required if the field/study samples are expected to go through a higher

number of freeze-thaw cycles. Analysis of the stability samples after each

freeze-thaw cycle is not required nor advised. Analysis at the completion of

three (or more) cycles is sufficient to show stability unless determining the
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maximum number of cycles over which there is analyte(s) stability is 

desired.

• Stock solution and working solutions stability. It is not necessary to study

the stability of every concentration of working solutions. The stability of the

analyte(s) stock solution(s), at least one concentration of the analyte(s)

working solutions, and the internal standard(s) solution(s) should be

studied. The solutions should be evaluated at room temperature for at least

six hours (or an appropriate time period if the solutions would normally be

stored or left standing out for longer periods). If the stock solutions are

refrigerated or frozen during storage or usage, the stability should be

documented by comparison with freshly prepared solutions.

• Post-preparative stability. The stability of the sample after processing

should also be examined. If the samples are stored after processing (either

in a dry extract or in the injection phase), then stability samples should be

treated the same, looking at both the analyte(s) and the internal

standard(s). Stability of the processed sample should also be assessed oninstrument or in the autosampler at those times and temperatures,

considering the anticipated run time for the batch size.

• Reinjection stability. The reinjection stability and reproducibility should be

evaluated to determine if all or parts of an analytical run could be

reanalyzed in case of instrument failure. This parameter only pertains to

particular techniques like chromatographic or spectrometric analysis.

7) Matrix effects

Matrix effects are especially important for methods that employ massspectrometric (MS) detection and should be investigated. The matrix effect in MS

is typically because of the suppression or enhancement of ionization of analytes by

the presence of matrix components in the biological specimens [Smeraglia et al.

2002; Trufelli et al. 2011]. For each analyte and internal standard, the matrix factor

(MF) should be calculated using at least six lots of blank matrices from individual

donors. Pooled matrices should not be used. If the matrix is difficult to obtain, less

than six different lots may be used, but this should be justified, documented, and

matrix effects still investigated.

The MF is calculated by taking the ratio of the peak analyte response (usually peak

area for chromatographic and spectrometric analysis) in the presence of matrix

(measured by analyzing blank matrix spiked with analyte(s) after extraction) to the
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peak response in the absence of matrix, which is a pure solution of the analyte(s). 

An MF of 1 signifies no matrix effect. An MS value of less than 1 suggests 

ionization suppression, and a value of greater than 1 may be due to ionization 

enhancement. For a method utilizing internal standards (IS), the MF of the IS(s) is 

also calculated; then the IS-normalized MF is calculated by dividing the MF of the 

analyte by the MF of the internal standard.

An MF or IS-normalized MF of 1 is not necessary for a reliable bioanalytical assay. 

However, highly variable MF in individual samples would introduce variability and 

would be one of the causes of a lack of reproducibility in the analysis. The MF or 

IS-normalized MF should be examined at two concentration levels: low (within 

three times the LLOQ) and high (near the ULOQ) for each of the six individual 

matrix lots. The variability in matrix factors (as measured by CV or RSD) should 

be less than 15% for both concentration levels. Higher variability may suggest the 

need for an internal standard or the need to change to a different internal standard. 

For example, in MS, it is recommended to use an isotopically-labeled IS that 

adequately mimics the analyte and thereby reduces the variability [Trufelli et al. 

2011]. 

8) Recovery

Recovery is the measured amount of a spiked quantity of the analyte divided by the

theoretical value for that analyte in the sample analyzed; this ratio is a measure of

the recovery for a quality control sample. The determination of recovery of a

bioanalytical method is done by adding a known amount of analyte (or internal

standard) to the matrix followed by analysis using the method. The method

response is compared to the theoretical concentration of pure authentic standard,

expressed in percent. Absolute recoveries can be difficult to obtain for methods

that include a derivatization step, as the derivatives are often not available as

reference substances.

The recovery of an analyte should be optimized to ensure efficient and

reproducible extraction. While it is often desirable to obtain recovery as close to

100% as possible to maximize the accuracy and sensitivity of a method, recovery

need not be 100% as long as the recovery is consistent and reproducible enough to

obtain acceptable precision and bias [FDA 2018; UNODC 2009]. Extraction

recovery is generally an issue investigated during the analytical method

development. The recovery needs to be consistent, precise, and reproducible, while

the absolute value for recovery is less important, and is not required to meet any

certain threshold and should be fit for purpose. Recovery values can be calculated

from the same data and runs used in the accuracy and precision parts of the
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validation. This involves analysis of five replicates at three concentration levels. 

The recovery should be reproducible (better than 20% as measured by CV or RSD) 

for each of the concentration levels.

9) Robustness/ruggedness

Robustness or ruggedness is a measure of the susceptibility of a method to small

changes that might occur during routine analysis. Validation of a method need not

necessarily include ruggedness testing, but it can be very helpful during the method

development phase. Problems that may occur during validation are often detected

in initial ruggedness testing. Warnings can be included in the methodology about

parameters that must be tightly controlled to achieve the desired levels of precision

and accuracy.

While not every parameter of every type of method can be listed here, it is

important for the analyst to consider every step of the method and strive to include

ruggedness testing in each step. Some typical examples of variations include pH (of

samples, mobile phases, extraction solvents), mobile phase composition, and

columns (GC, LC, and extraction columns/cartridges with different lots or

suppliers). Other sources can include variations in temperature (room, oven,

column, processing, thawing, autosampler, etc.), flow rate (both in extraction and

analysis), and various volumes and times used during the entire bioanalytical

method.

10) Limit of detection and limit of quantification

The limit of detection (LOD), also referred to as the method detection limit

(MDL), is the lowest concentration of an analyte that the bioanalytical procedure

can reliably differentiate from background noise [FDA 2018]. The LOD should not

be used for quantitative measurement but may be useful for semi-quantitative or

qualitative determinations [Tiwari and Tiwari 2010]. Several literature methods for

determining the LOD can be utilized [Bader et al. 2012; NIOSH 1995]. In brief, one

method involves the analysis of five or more low-level standards (prepared in the

appropriate matrix) that range from less than the expected LOD to no greater than

10 times the expected LOD. These standard responses are plotted and the

regression equation and standard error (sy) are calculated. The LOD is then 3

sy/slope. Alternatively, analyze at least 10 standards near the anticipated LOD and

calculate the standard deviation [ASTM 2020b]. The LOD (MDL) is reported as 3

times the standard deviation of the blank signal (correcting for background). The

lower limit of quantitation (LLOQ) is concomitantly taken as 10 times the standard

deviation of the blanks.
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The LLOQ is the lowest concentration of analyte in a sample that can be quantified 

reliably with acceptable accuracy and precision. Calibration standards should 

include the lower and upper limits of quantitation (L/U LOQ) as values should not 

be extrapolated beyond the range of the calibration curve. The accuracy and 

precision criteria at each end of the curve have been found to be acceptable within 

the defined criteria. In addition, the analyte signal of the LLOQ should be at least 

10 times sy/slope (or 3.33×LOD).

The LOD, LLOQ, and ULOQ should be reported in the validation report. These 

should also be reported when samples are analyzed. When reporting 

biomonitoring results, report results below the LOD as “not detected (ND),” and 

report results between the LOD and LLOQ numerically and enclose in parentheses 

to denote the greater imprecision of these results. It is also common to use one less 

significant figure when reporting results in this area.

11) Acceptance criteria for an analytical run

An analytical run consists of the following components:

• A matrix blank (a processed matrix sample without analyte and without

internal standard)

• A zero sample (a processed matrix sample with internal standard)

• A minimum of six nonzero calibration levels

• A minimum of three levels of quality control (QC) samples in duplicate or

a minimum of 5% of the total number of unknown samples, whichever is

higher. Add QC samples in multiples of two when additional samples are

needed

• Field/study/unknown samples

All samples should be processed as a single batch. Calibration standards and QC 

samples should be spiked independently using separately prepared stock solutions. 

A single batch is comprised of samples handled at the same time, by the same 

analyst, under homogeneous conditions. 

The standard curve samples, blanks, QCs, and unknowns can be arranged as 

considered appropriate within the run and to support detection of drift or 

carryover over the course of the run. Acceptance criteria should be pre-established 

in the protocol, in the study plan, or in a standard operating procedure. In cases 

where a whole run consists of more than one batch, acceptance criteria should be 

applied to the whole run and to the individual batches.
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12) Accuracy acceptance criteria for QC samples [EMA 2011; FDA 2018]

The accuracy values of the QC samples should be within ±15% for the upper

concentration (50%–100% ULOQ) and within ±20% for the lower concentration

(100%–200% LLOQ). At least 67% of the QC samples should be within these limits

for a run to be accepted. Furthermore, at least 50% of the QC samples at each

concentration should comply with this criterion.

In the case where the criteria are not fulfilled, the analytical run should be rejected.

The samples can be re-extracted and analyzed, corrective action can be taken on

the instrumentation, or other cause(s) of the failure investigated, analyzed, and

corrected. In the case of the simultaneous determination of several analytes, there

will be a calibration curve for each analyte in the method. If an analytical run is

acceptable for one analyte but is rejected for another analyte, the data for the

accepted analyte can be used.

13) Accuracy acceptance criteria for calibration standards [EMA 2011; FDA 2018]

The back-calculated concentrations of the calibration standards should be within

±15% of the nominal value, except for the LLOQ, which should be within ±20%. A

minimum of 75% of the calibration standards should be within these limits for the

analytical run to qualify. If one of the calibration standards does not meet these

criteria, the standard should be discarded, provided the calibration model does not

change, and the calibration curve without this standard should be re-evaluated and

regression analysis performed.

If the discarded calibration standard is the LLOQ or the ULOQ, then the next

calibration standard becomes the LLOQ or ULOQ, and the calibration range is

narrower for that run. The revised calibration range should cover all

concentrations of QC samples.

8 Conclusions

This chapter describes biomonitoring as a valuable tool for conducting research and public 

health surveillance, evaluating intervention effectiveness, and improving risk assessments. A 

major aim of biomarker research is to develop and validate biomarkers that reflect specific 

exposures or are quantitatively linked to adverse outcomes in humans to enable their use in 

risk prediction. The NRC identified several strategies to use biomarkers of effect to extrapolate 

dose and to evaluate dose response [NRC 2007]. Biomonitoring can be used not only to 

identify exposures but also to evaluate exposure trends over time. While several international 

agencies have developed biological exposure levels for some chemicals, OSHA has only 

mandated three tests for occupational compliance.
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Some general considerations for biomonitoring research have been noted, such as the goals of 

biomonitoring for the study or program being initiated. The biological matrices of interest, 

whether blood, urine, exhaled breath, or some other specimen, will have an integral role in the 

selection of the biomarker most pertinent for the study. If the goal is to understand whether 

exposure occurred or not, it may be possible to measure the chemical or its metabolite in 

exhaled breath or urine. If the goal is to determine a health risk, then measurement of a 

biomarker of effect may be more appropriate.

Appropriate study design and statistical analyses in occupational research studies will aid in 

ensuring that studies of biomarkers have scientific rigor and will minimize uncertainty. 

Written standard operating procedures and a quality assurance program will help improve 

data quality and provide interpretable results. Adherence to field and laboratory safety 

procedures are imperative to protect staff from exposure to bloodborne pathogens. Ethical 

considerations should be at the forefront of any biomonitoring investigation.

Biomarkers need to be validated in the laboratory and for the population for which they will 

be used. The steps for laboratory validation of the analytical methods are covered in this 

chapter. Equally important is the population validation of biomonitoring methods, which is 

not in the scope of this chapter [Schulte and DeBord 2000]. Population validation determines 

the utility of the method in various population groups. It entails understanding interpersonal 

variability according to demographic and behavioral characteristics, determining the 

underlying prevalence of the marker, and identifying the optimal handling and logistical 

considerations [Schulte and DeBord 2000; Schulte and Perera 1997].

The potential of biomonitoring in occupational health is tempered by limitations in study 

design, interpretation of results, communication of results, and ethical issues. The ability of 

advances in existing and emerging technologies to develop new biomarker methods exceeds 

our practical ability to evaluate and validate all of them. The real challenges for occupational 

health professionals are to decide which measurement methods may be of value to understand 

what information biomarker measurements are providing, and, finally, to determine 

appropriate actions based on that information.

Disclaimer 

Mention of any company or product does not constitute endorsement by the National 

Institute for Occupational Safety and Health (NIOSH), Centers for Disease Control and 

Prevention (CDC). In addition, citations to websites external to NIOSH do not constitute 

NIOSH endorsement of the sponsoring organizations or their programs or products. 

Furthermore, NIOSH is not responsible for the content of these websites. All web addresses 

referenced in this document were accessible as of the publication date.
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