NIOSH Manual of Analytical Methods (NMAM) 5th Edition

BACKUP DATA REPORT

NIOSH Method No. 3900

Title: Volatile Organic Compounds, C1 to C10, Canister Method 

Analyte: ethanol, 2-propanol, acetone, 2,3-butanedione, 2,3-pentanedione, 

2,3-hexanedione, dichloromethane, trichloromethane, hexane, benzene, 

toluene, ethylbenzene, o-xylene, m- xylene, p-xylene, methyl methacrylate, 

α-pinene, and d-limonene 

Author/developer: Ryan LeBouf, NIOSH 

Date: 8/30/2018 

Disclaimer: Mention of any company or product does not constitute endorsement by the National Institute for Occupational 

Safety and Health, Centers for Disease Control and Prevention. 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.
BACKUP DATA REPORT

NMAM Method No 3900

Volatile Organic Compounds, C1 toC10, 

Canister Method

Substance

C1-C10 volatile organic compounds

Chemicals Used for Evaluation

Specific chemicals tested were ethanol, 2-propanol, acetone, 2,3-butanedione, 2,3-pentanedione, 2,3- 

hexanedione, dichloromethane, trichloromethane, hexane, benzene, toluene, ethylbenzene, o-xylene, m,pxylene, methyl methacrylate, ɑ-pinene, d-limonene. Custom gas standards (Linde, Braddock, PA) were diluted 

with ultra-high purity nitrogen. Distilled/deionized water (18 MOhm, low VOC content) was produced using a 

Milli-Q Advantage A10 system (Millipore, Burlington, MA). The water should have a low enough residual VOC 

content to not interfere with analysis.

Exposure Limits

Table 1. Exposure Limits (ppm) [1, 2]

Substance OSHA 

TWA

OSHA

Peak

NIOSH 

TWA

NIOSH 

STEL

mg/m3 

per ppm

ethanol 1000 1000 1.89

2-propanol 400 400 500 2.46

acetone 1000 250 2.38

2,3-butanedione 0.005 0.025 3.52

2,3-pentanedione 0.0093 0.031 4.09

2,3-hexanedione 4.67

dichloromethane 25 125 NA (ca) 3.47

trichloromethane 50 2 (60 min) 4.88

hexane 500 50 3.53

benzene 1 5 0.1 (ca) 1 (ca) 3.19

toluene 200 500 100 150 3.77

ethylbenzene 100 100 125 4.34

o-xylene 100 100 150 4.34

m-xylene 100 100 150 4.34

p-xylene 100 100 150 4.34

methyl methacrylate 100 100 4.09

α-pinene 5.57

d-limonene 5.57

Note: ca = carcinogen; NA = not applicable
Synopsis

Traditional occupational exposure monitoring for volatile organic compounds uses sorbent tubes to collect 

analytes from an air stream. Sorbent-based techniques require the user to have some prior knowledge of the 

target analytes and approximate concentrations to effectively sample exposures. Whole-air sampling into 

evacuated canisters requires no prior knowledge of air concentrations as breakthrough is not an issue, can 

handle part per billion (ppb) and part per million (ppm) analyte concentrations, and the technique is amenable 

to a wide range of compounds using a single technique. Canisters coupled with restricted flow controllers makes 

sampling easier for the field industrial hygienist because the flow controllers require no onsite pre- or postcalibration. NIOSH developed the canister method for occupational exposure monitoring of seventeen volatile 

organic compounds. The method was adapted from EPA Method TO-15 [3] and has been designated as NMAM 

Method 3900.

The focus of the canister method development was to validate an evacuated canister-based (passive) sampling 

approach followed by preconcentration of samples (for ppb-level VOCs) and loop injection (for ppm-level VOCs) 

into a gas chromatograph/mass spectrometer (GC/MS). Spiking experiments, LODs/LOQs, and 30-day storage 

stability were assessed [4]. An additional 58-day storage stability experiment is presented here along with an 

interlaboratory study to assess method performance by other laboratories and a chamber study to assess the 

canister method coupled with restricted flow controllers for time-integrated sampling during varying 

environmental conditions.

Sampling

The canister must be evacuated to 0.0066 kPa prior to sampling using a system equipped with a rough pump 

and a high vacuum pump. Sampling may be performed with a fitting for instantaneous samples or a flow 

controller (e.g., diaphragm or capillary) into a large canister (e.g., 6 L) for area sampling or a small canister (e.g., 

400 or 450 mL) for personal sampling. Selection of the appropriate flow controller and flow rate will depend 

upon the size of canister being used and sampling period that is necessary to characterize exposures. For 

diaphragm flow controllers, follow the manufacturers procedures for calibration to an appropriate flow rate 

(e.g., 10 mL/min for a 6 L canister for an 8-hour work shift) prior to each day of sampling; the final pressure in 

the canister should be less than 84.1 kPa. For capillary flow controllers, the flow is governed by the length and 

internal diameter of the capillary. Use a flow controller that will fill the can volume to 30.4 kPa (30% full) during 

your sampling period. Do not exceed a final sample pressure of 50.6 kPa (50% full) as the flow rate will become 

increasingly non-linear. Use a 2-μm, sintered, stainless steel particle filter to protect the canisters when 

sampling in dusty atmospheres.

Pressure Dilutions

Calibration and sample canisters generally require pressure dilutions with ultra-high purity (UHP) nitrogen or air. 

A receiving canister is partially filled to between 5 and 7 psia to reduce the vacuum in the canister prior to 

sample transfer which allows for a less aggressive introduction of the sample and also places the pressure 

differential measurement in the optimal range of the pressure transducer. A portion of the sample, in terms of a 

pressure differential, from the calibration cylinder or sample (e.g., 0.3 to 8 psia) is added to the receiving 

canister at a slow rate (e.g., 0.03 to 0.3 psia/sec depending on the desired pressure differential). Then UHP 

nitrogen or air is added to the receiving canister at a slow rate until the final pressure is reached. The ratio of the 

final pressure to the sample pressure differential is equal to the dilution factor (e.g., 25 psia final/2.5 psia sample 

pressure differential = 10x dilution). Final pressure should not exceed 30 psia to keep the pressure of the sample 

flow path (including sample transfer lines, fittings, traps, and loop if used) to similar pressures as those used to 

calibrate the system, otherwise the volume transferred to the preconcentration system may be different than
requested and require pressure correction factors. Final pressures of 25 psia are recommended for routine 

analysis. While pressure dilutions of up to 100x may be achievable, they are not as reproducible as multiple, 

lower dilutions (e.g., two 10x dilutions for a 100x final dilution). Allow the canisters to equilibrate for a minimum 

of 6 hours after pressurization to allow time for mixing.

Sample Preparation

Pressurize canister samples with UHP nitrogen or air to 152 kPa to obtain a sufficient excess pressure for a 250 

mL nominal injection volume or to flush a 1 mL loop with approximately 100 mL of samples. Final sample 

pressure after analysis should be greater than 50.6 kPa (50% empty) to limit potential for sample dilution due to 

system leakage. An alternative to using the loop for quantitation could be running a loop or low volume injection 

of the canister to investigate the analyte concentration range followed by pressure dilutions to place the diluted 

concentration into the range of the ppb-level calibration curve.

Sample Analysis

Sample analysis requires a whole-air preconcentration system to manage water content and reduce the transfer 

volume to the GC. Some water content is necessary to mimic humidity seen in field samples.

MS Scan and selected ion monitoring (SIM) mode have been previously shown to provide roughly equivalent 

detection limits for aromatics, alkanes, and terpenes when using a thermal desorption/GC/MS system [5]. Using 

MS scan mode allows unknown chemicals to be qualitatively identified and concentrations estimated based on 

the closest internal standard and an assumed response factor. Limits of detection could be improved by using 

SIM mode.

Spiking Experiments

Method bias, precision, and accuracy for 14 volatile organic compounds (ethanol, acetone, 2-propanol, 

dichloromethane, hexane, trichloromethane, benzene, methyl methacrylate, toluene, ethylbenzene, m,p-xylene, 

o-xylene, α-pinene, and d-limonene) were determined at ppb-level and ppm-level concentrations [4]. Backup 

data tables are presented for ppb-levels (Table A1, Appendix A) and ppm-levels (Table A2, Appendix A). Three 

additional analytes (2,3-butanedione, 2,3-pentanedione, and 2,3-hexanedione) were added to the target list 

after this study and method accuracy has been assessed during chamber studies (see section below). The upper 

and lower 95% confidence limit of the average bias was calculated (i.e., ±1.96*standard error). To be acceptable 

either both bias limits must have an absolute value less than 10%, or an absolute value of 10% must fall between 

the limits. For a method to meet the NIOSH “25% accuracy” criterion, the 95% confidence statistic for accuracy 

estimated with the hyperbolic approximation formula must be below 25%. The stability metric is a measure of 

bias comparing average concentration on day 30 or greater to average concentration on day zero; the absolute 

value of the stability metric must be less than 10% to be acceptable. While method performance varied by 

analyte, the validation results demonstrated that this method is a viable air sampling and analytical 

methodology for measuring a wide range of air concentrations of select volatile organic compounds in mixed 

exposure environments.

For the preconcentration method used to measure ppb-level concentrations, all accuracy criteria were within 

acceptable limits for 16 of the 17 target analytes. 2-Propanol failed the 95% accuracy criterion at ppb-level 

concentrations and accuracy validation results are considered inconclusive, perhaps because the water 

management step in the preconcentration may have affected the concentrations of this analyte due to its polar 

nature. Ethanol, however, passed the accuracy criterion despite its similar polarity. Bias was acceptable for all 17 

analytes. The average bias values for 2-propanol and methyl methacrylate were greater than 10% but still within 

acceptable parameters when 95% confidence limits were calculated; further evaluation to confirm or refute this
excessive bias result is warranted, particularly because bias values may have been influenced by error 

introduced during the preparation of standards (e.g., liquid injection into glass bulb or concentration dilution).

Because trichloromethane failed Bartlett’s test of homogeneity (i.e., precision varied across concentrations) for 

ppb-level, the accuracy value for this analyte was not calculated based on a value for precision that was pooled 

across all concentrations investigated. Rather, the accuracy value was calculated based on the worst-case 

concentration-specific precision and bias and a modified accuracy calculation that accounts for the reduced 

degrees of freedom. Further investigation of the precision for trichloromethane using the ppb-level method is 

warranted. In the meantime, the method can be used to quantify this analyte, but the analyst must be cognizant 

of the concentration-dependence of measurement variability.

For the loop method used to measure ppm-level concentrations, all accuracy and bias criterion were within 

acceptable limits for each analyte. Bartlett’s test demonstrated that measurement precision was nonhomogeneous over concentrations for both dichloromethane and methyl methacrylate. The accuracy values for 

these two analytes were calculated based on the worst-case concentration-specific precision and bias and a 

modified accuracy calculation that accounts for the reduced degrees of freedom.

LOD/LOQ Assessment

The initial assessment of LODs/LOQs for the original 14 compounds was completed in 2012 (Table 2) [4]. For the 

preconcentration method used to quantify ppb levels of the target analytes, LODs/LOQs for each analyte were 

determined using five low-level calibration spikes ranging from 0.03 to 1.1 ppb (depending on the analyte) 

prepared in 6 L canisters and analyzed with a nominal injection volume of 500 mL (Table A3, Appendix A) [4].

For the loop method used to quantify ppm levels, LODs and LOQs were determined with five calibration spikes 

ranging from 0.1 to 2 ppm (depending on the analyte) (Table A4, Appendix A) [4]. The LOD was calculated as 3 

times the standard error of the regression divided by the slope of the regression, while the LOQ was calculated 

as 3.33 times the LOD [6].

LODs/LOQs have continued to be assessed over time as the condition of the instruments changed due to age 

and instruments were upgraded or replaced. Current LODs/LOQs for ppb-level as listed in NMAM Method 3900 

appear in Table 3. The values for ppb-level have been updated with an assessment conducted in July 2017 (Table 

A5, Appendix A) using seven separately-prepared, low-level concentrations including 0.1, 0.3, 0.5, 1.0, 2.0, 3.0, 

and 6.0 ppb; each of these levels were produced in 6 L canisters and injected once at 250 mL. The nominal 

injection volume for ppb-level analysis has been reduced from 500 mL to 250 mL for all canisters since the 

original LOD/LOQ assessment in 2012. A nominal injection volume of 250 mL has been found to be amenable 

for both 450 mL canisters and 6 L canisters with minimal effect onsensitivity.

The LODs for ppm-level were not updated from those produced in the initial LOD assessment since loop 

injection is fairly uncommon now. The current recommended strategy for higher concentration samples is 

pressure dilutions into the ppb-level calibration range or loop injection with a dilution factor correction for 

volume injected onto the preconcentrator compared to a nominal 250 mL injection volume.
Table 2a&b. Limits of detection (LODs) and limits of quantitation (LOQs) for target analytes, ppb and ppm 

levels

Table 2a ppb level

Analyte Intercept slope RMSE LOD (ppb) LOQ (ppb)

ethanol 0.073 0.911 0.088 0.3 0.97

2-propanol 0.001 1.093 0.109 0.1 0.23

acetone 0.148 1.037 0.115 0.3 1.1

dichloromethane 0.068 1.113 0.113 0.3 1.0

trichloromethane -0.024 0.989 0.063 0.2 0.64

hexane -0.025 0.958 0.067 0.2 0.70

benzene -0.018 0.950 0.061 0.2 0.64

toluene -0.015 0.959 0.056 0.2 0.58

ethylbenzene -0.012 0.989 0.064 0.2 0.65

m,p-xylene -0.003 0.958 0.065 0.2 0.68

o-xylene -0.006 0.968 0.071 0.2 0.73

methyl methacrylate -0.015 1.002 0.079 0.3 0.79

α-pinene -0.022 0.996 0.073 0.2 0.73

d-limonene -0.011 0.984 0.099 0.3 1.0

Note: RMSE=root mean square error

Table 2b ppm level

Analyte Intercept slope RMSE LOD (ppm) LOQ (ppm)

ethanol -0.019 1.008 0.062 0.2 0.60

2-propanol -0.029 1.028 0.068 0.2 0.63

acetone 0.002 0.938 0.078 0.3 0.76

dichloromethane -0.002 0.923 0.099 0.4 1.1

trichloromethane -0.035 1.043 0.093 0.3 0.89

hexane -0.087 1.181 0.072 0.2 0.61

benzene -0.075 1.163 0.064 0.2 0.55

toluene -0.121 1.303 0.101 0.3 0.77

ethylbenzene -0.121 1.299 0.108 0.3 0.83

m,p-xylene -0.120 1.288 0.110 0.3 0.85

o-xylene -0.055 1.155 0.031 0.1 0.27

methyl methacrylate -0.126 1.341 0.089 0.2 0.66

α-pinene -0.154 1.397 0.116 0.3 0.83

d-limonene -0.210 1.526 0.156 0.4 1.0

Note: RMSE=root mean square error
Table 3. LODs for ppb-levels

Analyte Intercept Slope RMSE

LOD

(ppb)

LOQ

(ppb)

ethanol 0.082 0.426 0.060 0.4 1.3

2-propanol -0.023 0.799 0.121 0.5 1.7

acetone 0.019 0.916 0.111 0.4 1.3

2,3-butanedione* -0.137 0.984 0.085 0.3 1.0

2,3-pentanedione* -0.095 0.988 0.056 0.2 0.67

2,3-hexanedione* -0.141 0.935 0.134 0.4 1.3

dichloromethane -0.076 0.953 0.047 0.2 0.67

trichloromethane -0.087 0.914 0.028 0.1 0.33

hexane -0.092 0.936 0.049 0.2 0.67

benzene -0.086 0.946 0.058 0.2 0.67

toluene -0.091 0.945 0.107 0.3 1.00

ethylbenzene -0.096 0.958 0.102 0.3 1.00

m,p-xylene -0.114 0.975 0.119 0.4 1.3

o-xylene -0.119 0.972 0.091 0.3 1.00

methyl methacrylate -0.128 0.931 0.149 0.5 1.7

α-pinene -0.110 0.920 0.160 0.5 1.7

d-limonene -0.123 0.862 0.241 0.8 2.7

Note: RMSE=root mean square error

*Selected ion monitoring mode used to improve detection limits
Storage Stability Testing

Sample storage stability at room temperature was assessed for 14 analytes at 5 ppb and 0.6 ppm over a period 

of 30 days [4]. For the 5 ppb concentration, a total of twelve 6 L canisters were stored and analyzed repeatedly 

on days zero, 7, 14, 21, and 30. For the 0.6 ppm concentration, 30 canisters were generated and analyzed only 

once according to the following schedule: 12 canisters on day zero, six canisters on day 7, and three canisters 

each on days 14, 21, and 30. The stability metric is bias comparing average concentration on day 30 to average 

concentration on day zero; the absolute value of the stability metric must be less than 0.10 to be acceptable per 

NIOSH guidance [6]. If the analyte is not stable for 30 days, the method is still acceptable as long as a shorter 

stability time is confirmed.

All 14 analytes at ppb-level were stable (i.e., less than 10% change) over a period of 30 days (Table A6, Appendix 

A). All 14 analytes at ppm-levels were stable for 30 days, except α-pinene and d-limonene, which remained 

stable for 21 days (Table A7, Appendix A). The shorter acceptable storage time for α-pinene and d-limonene may 

be due to losses from chemical reactions with other components of canister contents, particularly oxidizing 

species such as ozone or hydroxyl and nitrate radicals naturally found in indoor air [7, 8].

Additional Storage Stability Testing

Sample storage stability at room temperature was assessed for 17 analytes (14 original analytes plus 2,3- 

butanedione, 2,3-pentanedione, and 2,3-hexanedione) at 10 ppb over a period of 58 days (Table A8, Appendix 

A). A total of twelve 6 L canisters were individually prepared, stored, and analyzed repeatedly on days zero, 9, 

14, 22, 29, and 58. Storage stability of the 17 analytes was confirmed for 58 days at approximately 10 ppb for 

most analytes with the exception of ethanol, 2-propanol, and acetone (stable for 30 days according to the first 

storage stability test) (Table 4).

Table 4. Additional storage stability for 58 days

Analyte

58 day 

Stability, 

Bias (%)

ethanol -0.5 (30 days)

2-propanol 7.8 (30 days)

acetone 3.1 (30 days)

2,3-butanedione 4.1

2,3-pentanedione 2.7

2,3-hexanedione -2.9

dichloromethane 0.22

trichloromethane 3.1

hexane 3.6

benzene 3.6

toluene 1.6

ethylbenzene -0.6

o-xylene -3.5

m,p-xylene -8.1

methyl methacrylate 1.5

α-pinene -3.3

d-limonene -1.9
Dynamically-generated Samples: Chamber Studies

Canister method was challenged using alpha-diketones in chamber studies under varying environmental 

conditions to assess sampling and analytical accuracy following ASTM D6246 [9]. The following conditions were 

assessed: temperature, humidity, wind speed, and concentration. Additional VOCs (ethanol, acetone, 2- 

propanol, dichloromethane, trichloromethane, methyl methacrylate, hexane, benzene, toluene, o-xylene, m,pxylene, α-pinene, d-limonene) were present during low and high humidity trials to assess the effect of humidity 

on method performance for these analytes. Bias pulse tests were performed to assess the bias associated with a 

known drop in flow rate (~13% over the sampling period) for capillary flow controllers. Flow rate bias (bias 

pulse) was assessed using the difference in measurements for peak exposures occurring at the beginning and 

end of the sampling period. Tests were also performed to assess inter-day and inter-sampler variation. A 

dynamic volatile organic compound generation and sampling system was used to produce known concentrations 

of challenge agents in a glass sampling chamber with 18 sampling ports. The system was placed in a large walkin environmental control chamber to regulate temperature. Capillary flow controllers were constructed using 

Swagelok connections and deactivated fused-silica tubing to collect air samples at 15 mL/min for 15 minutes 

into a 450 mL canister. Canisters were pressurized with ultra-high purity nitrogen and analyzed using an 

autosampler/preconcentrator system attached to a gas chromatograph/mass spectrometer. Results were 

corrected for pressure dilution and compared to a theoretical concentration calculated from flow dilution of the 

certified gas standard used in the generation system. Accuracy was calculated for each target analyte for all 

conditions combined. Variance estimates for each factor influencing accuracy were used to apportion the 

relative influence of each test condition on the overall performance of the method. Upper confidence limits on 

accuracy (A95) were below 0.25 for all analytes: 0.085 for 2,3-butanedione, 0.142 for 2,3-pentanedione, and

0.194 for 2,3-hexanedione. Overall precision was largest for 2,3-hexanedione at 0.061 with 27.4% of the total 

variance due to inter-day variability. The peak exposure condition accounted for less than 6% of the variability 

regardless of analyte, meaning the known drop in flow rate from the capillary flow controller did not adversely 

affect air sampling. Canister method is a reliable, robust sampling and analytical method that may be used for a 

variety of analytes (e.g., C1 to C10) under varying environmental conditions.

Chamber Study Methods

Chamber Study protocol

The VOC generation system was constructed in a large walk-in environmental chamber (Nor-Lake Scientific, 

Hudson, WI) to control temperature close to the test condition. The test chamber was a glass cylinder with nine 

ports for sampling (replica of OSHA test chamber). VOC concentration homogeneity was assessed by sampling 

silica gel sorbent tubes (SKC cat no. 226-183) at each port. Tubes were analyzed using a modified OSHA 1016 

method (mass spectrometry instead of flame ionization detection) [10]. Variability between ports was 4.1% for 

2,3-butanedione, 4.4% for 2,3-pentanedione, and 3.9% for 2,3-hexanedione. The temperature, humidity, and 

flow rate of the dilution air to test chamber was controlled by a Miller-Nelson unit (Model HCS-501, Assay 

Technology, Livermore, CA). The VOC concentration was generated by flow dilution of a certified calibration gas 

cylinder at approximately 2 ppm (±5% analytical accuracy) (Linde Spectra Environmental Gases, Alpha, NJ) using 

a mass flow controller (Aalborg, Orangeburg, NY). Blank samples collected prior to conducting the studies 

ensured system cleanliness. No alpha-diketones were detected above the MDL (0.01 μg/sample which is 0.95 

ppb 2,3-butanedione for a 15 minute air sample collected at 200 mL/min). Trials were conducted at a constant 

concentration. For the bias pulse experiments, the sampler was attached to the test chamber for 1 out of 15 

minutes at the beginning or end of the sampling period. For the rest of the trial, the sampler was placed in a 

tedlar bag filled with UHP nitrogen gas.

NIOSH draft canister method was used to analyze canister samples. Briefly, canisters were pressurized to 1.5 

times atmospheric pressure with UHP nitrogen prior to analysis on an Entech 7200/7032
preconcentrator/autosampler (Simi Valley, CA) attached to an Agilent 6890/5975 gas chromatograph/mass 

spectrometer. Canister injection volume was 25, 50, or 250 mL adjusted to produce a mass loading on the 

column within the calibration curve. Calibration curve dynamic range was from 0.2 to 40 ppb. Canisters were 

allowed to equilibrate for at least three hours prior to analysis. Each analyte response was referenced to an 

internal standard response and compared to a calibration curve. Internal standards were bromochloromethane, 

1,4-difluorobenzene, chlorobenzene-d5 at 25 ppb with a 50 mL loading on the preconcentrator module prior to 

sample loading. A pressure dilution factor was applied to the measure concentration for final reporting. Bias was 

calculated as the difference between the final result and the flow dilution theoretical value (assuming no error in 

the theoretical value). Bartley calculation was employed for point estimate accuracy and confidence limits on 

accuracy [11]. Variance estimates for each trial as well as aggregate bias and overall inter-sampler variation 

were used to apportion the relative percentage effect (squared error over total squared error) of each of the 

conditions on method performance. Bias pulse was calculated as the difference between the beginning and end 

pulse measurements divided by the sum of the measurements[9].

Chamber Study Design

The effect of environmental conditions on method performance in terms of bias and precision were assessed 

using different trials based on ASTM D6246 [9]. The experimental trials included humidity, wind speed, 

concentration, inter-day, beginning pulse, and end pulse (Table 5). High wind speed was difficult to achieve with 

the VOC generation system, so a low wind speed (0.009 m/s) was substituted. The rest of the trials were 

conducted between 0.033 and 0.042 m/s depending on VOC generation needs. Additional VOCs (ethanol, 

acetone, 2-propanol, dichloromethane, trichloromethane, methyl methacrylate, hexane, benzene, toluene, oxylene, m,p-xylene, α-pinene, d-limonene) were present during low and high humidity trials at the same nominal 

concentration as the alpha-diketones (400 ppb). Trial sample sizes ranged from 5 to 12 due to constraints on the 

analytical system at the time of analysis (i.e., other projects like NIOSH Health Hazard Evaluation samples took 

precedence) and lost samples or outlier results. Lost samples were due to low liquid nitrogen pressure during 

analysis causing a failed injection. For the high humidity trial, 12 samples were collected but one was lost; one 

was visually identified as an outlier (bias -0.33 for 2,3-butanedione, -0.64 for 2,3-pentanedione, and -0.81 for

2,3-hexanedione) and removed. For the high temperature trial, 6 samples were collected; one was visually 

identified as an outlier (bias -0.18 for 2,3-butanedione, -0.39 for 2,3-pentanedione, and -0.79 for 2,3- 

hexanedione) and removed. For the low humidity trial, 6 samples were collected; one was visually identified as 

an outlier (bias -0.01 for 2,3-butanedione, -0.22 for 2,3-pentanedione, and -0.37 for 2,3-hexanedione) and 

removed. For the end pulse trial, 9 samples were collected; one sample was lost.
Table 5. Summary of Environmental Conditions

Trial Name N

Mean 

Temperature 

(°C)

Mean 

Relative 

Humidity 

(%RH)

Target 

Concentration 

(ppb)

Theoretical 

Concentrations

of 2,3- 

Butanedione 

(ppb)

Theoretical 

Concentrations

of 2,3- 

Pentanedione 

(ppb)

Theoretical 

Concentrations 

of 2,3- 

Hexanedione 

(ppb)

high humidity 10 24.9 71.0 400 381 377 367

normal 

humidity 9 25.9 49.5 70 73.7 74.8 69.8

low humidity 5 24.9 19.5 400 381 377 368

low wind 

speed 12 21.8 26.1 20 21.5 20.8 20.4

low 

concentration 6 25.8 40.0 20 22.2 21.4 21.0

high 

temperature 5 36.0 13.0 20 22.2 21.4 21.0

Inter-day #1 12 25.5 42.5 100 98.6 95.3 93.4

Inter-day #2 12 25.5 42.8 100 98.6 95.3 93.4

beginning 

pulse 9 25.6 42.0 450 465 450 441

end pulse 8 25.9 42.5 400 409 396 388
Chamber Study Results/Discussion

2,3-Butanedione upper confidence limit on accuracy (A95) was 0.085 with a mean bias of 0.022 and an overall 

precision of 0.032 (Table 6). Humidity had a negligible effect on the method performance (0.5 to 2.6% effect). 

The greatest effect on method performance was due to inter-day variability (33.2% effect), followed by high 

temperature (32.1% effect). Bias pulse had a negligible effect on method performance (5.1% effect).

Table 6. Bias, Precision, and Accuracy Estimates for 2,3-Butanedione

Trial Name N Mean Bias Precision %effect

high humidity 10 0.070 0.010 0.7

normal humidity 9 -0.052 0.008 0.5

low humidity 8 0.027 0.020 2.6

low wind speed 12 0.053 0.034 7.6

low concentration 6 0.050 0.069 32.1

high temperature 5 0.010 0.070 33.2

inter-day 24 -0.007 0.031 6.3

bias pulse 17 0.047 5.1

bias 3.2

inter-sampler 8.8

Overall 0.022 0.032

A 0.075

A95 0.085
2,3-Pentanedione A95 was 0.142 with a mean bias of 0.048 and an overall precision of 0.048 (Table 7). Humidity 

had a negligible effect on method performance (0.2 to 1.3% effect). High temperature had the greatest effect on 

method performance presumably due to variable test results (41.3% effect). Low concentration had a 21.4% 

effect on method performance. The effect of bias pulse was minimal (5.4% effect).

Table 7. Bias, Precision, and Accuracy Estimates for 2,3-Pentanedione

Trial Name N Mean Bias Precision %effect

high humidity 10 0.012 0.011 0.5

normal humidity 9 0.034 0.019 1.3

low humidity 8 -0.019 0.006 0.2

low wind speed 12 0.054 0.039 5.6

low concentration 6 0.114 0.077 21.4

high temperature 5 0.082 0.107 41.3

inter-day 24 0.061 0.047 8.0

bias pulse 17 0.066 5.4

bias 8.5

inter-sampler 7.9

Overall 0.048 0.048

A 0.127

A95 0.142
2,3-Hexanedione A95 was 0.194 with a mean bias of -0.060 and an overall precision of 0.061 (Table 8). Humidity 

had a variable effect on method performance (0.3 to 7.9% effect). Inter-day variability and low windspeed had 

the greatest effect on method performance presumably due to a few test results in this trial that were 

inconsistent with the rest of the observations. Bias pulse had little effect on method performance (5.9% effect).

Table 8. Bias, Precision, and Accuracy Estimates for 2,3-Hexanedione

Trial Name N Mean Bias Precision %effect

high humidity 10 -0.010 0.015 0.9

normal humidity 9 -0.013 0.043 7.9

low humidity 8 -0.041 0.008 0.3

low wind speed 12 -0.087 0.076 24.2

low concentration 6 -0.106 0.027 3.1

high temperature 5 -0.147 0.040 6.7

inter-day 24 -0.018 0.081 27.4

bias pulse 17 0.065 5.9

bias 15.1

inter-sampler 8.3

Overall -0.060 0.061

A 0.168

A95 0.194

Additional VOCs were present during low and high humidity trials at a target concentration of 400 ppb. For the 

high humidity trial (n=10), A95 for the additional VOCs were below 0.25 except for ethanol and methyl 

methacrylate (Table 9). The lower confidence limit for ethanol was 0.39 meaning the method did not pass the 

accuracy criterion for this analyte during this specific trial. Polar, early-eluting compounds like ethanol are 

notoriously challenging to quantify in the presence of humidity for two reasons. During the water management 

step of some preconcentrators, water is removed on a cold trap and some polar analytes are also lost.

Preconcentrator operating conditions need to be optimized to remove water while minimizing the removal of 

polar compounds. In the case of ethanol at high humidity, the mean bias was low at -0.347 but the precision was 

good at 0.04 indicating a consistent loss of ethanol (and presumably water) during the water management step. 

Reducing the cold trap temperature to reduce the amount of water removed may be an effective strategy to 

reduce the loss of polar compounds during the water management steps. Another strategy would be to separate 

the analyte of interest from the water peak by adjusting chromatographic and/or preconcentration conditions. It 

is recommended to work with the manufacturer on optimizing the operating conditions of the preconcentrator 

for your analytes. The lower confidence limit for methyl methacrylate was 0.15 meaning the method was 

inconclusive for this analyte during this specific trial. For the low humidity trial (n=5), A95 for the additional VOCs 

were all below 0.25 meaning the method passed the 95% accuracy criterion (Table 9).
Table 9. Additional VOC Accuracy Results for High (n=10) and Low (n=5) Humidity Trials

High Humidity Low Humidity

Analyte Mean Bias Precision A95 Mean Bias Precision A95

2,3-Butanedione 0.070 0.010 0.099 0.027 0.020 0.110

2,3-Hexanedione -0.010 0.015 0.052 -0.041 0.008 0.075

2,3-Pentanedione 0.012 0.011 0.045 -0.019 0.006 0.046

Acetone 0.063 0.020 0.122 0.023 0.015 0.088

α-Pinene 0.045 0.008 0.069 0.007 0.032 0.134

Benzene 0.024 0.016 0.071 0.040 0.013 0.096

Trichloromethane 0.136 0.025 0.209 0.085 0.026 0.196

d-Limonene 0.020 0.012 0.055 -0.014 0.040 0.172

Ethanol -0.347 0.040 0.465 -0.096 0.035 0.241

Ethylbenzene 0.034 0.011 0.065 0.024 0.008 0.058

2-Propanol 0.083 0.021 0.143 0.075 0.025 0.181

m,p-Xylene 0.028 0.012 0.064 0.003 0.005 0.023

Methyl Methacrylate 0.051 0.093 0.331 -0.002 0.013 0.052

Dichloromethane 0.059 0.015 0.104 0.032 0.013 0.087

Hexane 0.054 0.046 0.187 0.020 0.019 0.102

o-Xylene 0.054 0.011 0.084 0.012 0.007 0.041

Toluene 0.009 0.010 0.037 -0.003 0.010 0.043

Chamber Study Conclusions

Upper confidence limits on accuracy (A95) were below 0.25 for all analytes: 0.085 for 2,3-butanedione, 0.142 for 

2,3-pentanedione, and 0.194 for 2,3-hexanedione. Absolute value of bias was less than 0.10 for each analyte.

Known flow rate drop over the sampling period did not affect overall accuracy as seen in the bias pulse trial 

which accounted for less than 6% of the variance regardless of analyte. Additional VOCs (ethanol, acetone, 2- 

propanol, methylene chloride, chloroform, methyl methacrylate, hexane, benzene, toluene, xylenes, alphapinene, d-limonene) were present during low and high humidity trials. Humidity did not have an effect on 

method performance for most analytes. Canister method is a reliable, robust sampling and analytical method 

that may be used for a variety of airborne analytes (e.g., C1 to C10) under varying environmental conditions.
Interlaboratory Study (ILS)

Authors: Ryan F. LeBouf, H. Amy Feng, and Stanley A. Shulman

An evacuated canister method for sampling and analysis of select volatile organic compounds was developed at 

the NIOSH Respiratory Health Division Organic Laboratory in Morgantown, WV. An inter-laboratory study was 

conducted to assess repeatability and reproducibility of the test method prior to incorporation of the method in 

the NIOSH Manual of Analytical Methods. Nine laboratories completed the study on the test mix containing 17 

analytes: ethanol, 2-propanol (isopropyl alcohol), acetone, dichloromethane (methylene chloride), 

trichloromethane (chloroform), hexane, 2,3-butanedione, 2,3-pentanedione, 2,3-hexanedione, benzene, 

toluene, ethylbenzene, o-xylene, m,p-xylene, methyl methacrylate, α-pinene, and d-limonene. Blind spiked test 

samples were shipped to each laboratory. Spikes consisted of two concentration ranges, three nominal levels, 

and three replicates per combination (n=18 samples per laboratory, except for the reference laboratory who 

analyzed between three and six replicates). For quality control, two samples containing UHP nitrogen were 

included as laboratory blanks for each round. Spikes were produced in batches to accommodate each round 

which included one to three laboratories at the same time depending on laboratory availability and feasibility in 

sample preparation. Reference canisters were produced with each batch and analyzed in-house to ensure spike 

generation was acceptable prior to shipment. Precision estimates for repeatability ranged from 0.04 to 0.55 at 

ppb concentrations and from 0.10 to 0.47 for ppm concentrations over all analytes and nominal levels. Precision 

estimates for reproducibility ranged from 0.10 to 0.62 at ppb concentrations and from 0.19 to 0.58 at ppm 

concentrations, depending on analyte and nominal levels. Plots of h and k statistics which measure the

between- and within-laboratory consistency indicated inconsistencies with reported results from laboratories 5, 

6, and 7. All results were retained due to the low number of participating laboratories. Training on pressure 

dilution techniques and preconcentration systems as well as proficiency testing should be periodically 

conducted to ensure operating laboratories maintain optimal canister method performance.

ILS Methods

ILS Participating Laboratories

The canister method was based on a validation study by a single laboratory [4]. Laboratories were recruited 

from American Industrial Hygiene Association (AIHA)-accredited or National Environmental Laboratory 

Accreditation Program (NELAP)-accredited laboratories which had the experience and equipment necessary to 

analyze canister samples. Ten laboratories were engaged with one lost due to attrition. Laboratories were 

scheduled together to receive the test samples when possible to reduce the number of reference samples 

needed for quality assurance. Laboratories were numbered 0 to 8 for anonymity at aggregation, where 

laboratory 0 was the reference laboratory. Individual laboratory results were given to each participating 

laboratory upon completion. A protocol, a draft NMAM method, and a results template were provided by email 

to each participating laboratory along with a certified gas standard to construct a preliminary calibration curve 

at least two weeks prior to receiving the test samples.

Laboratories conducted the study between September 2013 and August 2014 with one laboratory (laboratory 6) 

repeating the study in October 2015 due to poor performance with biases ranging from -1.0 to 2.75, attributable 

to instrument variability and exceeding the recommended sample storage time (30 days). After the ILS study, 

sample storage stability was demonstrated to be 58 days for most analytes.

For the most part, contract laboratories were equipped to analyze canisters using EPA Method TO-15 which is a 

sensitive method for analyzing ambient air toxics. Analysis of TO-15 canister samples requires the use of a 

preconcentrator with a large injection volume ranging from 250 to 500 mL. The reference laboratory injected 

250 mL for ppb-level samples. Most participating laboratories did not have a loop available for ppm-level 

analysis necessitating pressure or volume dilutions to decrease instrument mass loading into their ppb-level
quantitation range. The reference laboratory, therefore, used two techniques to assess ppm-level 

concentrations: nitrogen pressure dilution and loop injection. Since most laboratories performed pressure 

dilutions (except laboratory 3 which used a 1 mL loop injection), these data are included for ppm-level reference 

laboratory data (laboratory 0).

ILS Study Design

The test method was challenged at two concentration ranges (ppb and ppm) with three nominal levels (low, 

medium, high) per concentration range. The nominal levels were 5, 10, and 15 ppb and 0.8, 1.3 and 1.7 ppm. 

Three replicate canisters at each concentration were tested including a set analyzed in-house to assess spike 

generation. Each canister contained a mixture of 17 analytes. Two laboratory blanks with ultra-high purity 

nitrogen were shipped with the samples. The first round of testing involved two laboratories and served as a 

pilot study to identify any issues with the study protocol or method instructions. No issues were revealed during 

the pilot study.

ILS Spike Preparation

PPB-range samples were prepared from a gas standard (Linde, Braddock, PA) using flow dilution with nitrogen or 

using pressure dilutions with nitrogen. Linde is an ISO 17034-accredited reference material provider. PPM-range 

samples were prepared using pressure dilutions with nitrogen. Most samples were prepared in 450 mL canisters, 

except for laboratory three who requested 6 L canisters for ppb-range spikes to enable multiple injections. Two 

canisters for each laboratory were pressurized with nitrogen for laboratory blanks. Each canister was spiked with 

all 17 analytes. Example canister data are shown for laboratory 6 at 0.8 ppm in Table 10. Multiple spike batches 

were required due to canister equipment inventory and logistics in preparation: five spike batches for ppb-level 

and eight spike batches for ppm-level. Except for the reference laboratory, each laboratory analyzed canisters 

from just one spike-batch. For ppm-level each lab 1 to 8 analyzed canisters from a different spike-batch. For 

ppm-level spike batch 6 and 7, the generation procedure was changed from a manifold system that 

simultaneously produced up to nine canisters at a given concentration to a master cylinder preparation of a 

given concentration followed by pressure transfers. For ppb-level, laboratories 1 and 2 analyzed canisters from 

the same batch, and laboratories 3, 4 and 5 analyzed canisters from the same batch. Laboratories 6, 7, and 8 

each had their own spike-batches. For the statistical analysis each laboratory except the reference laboratory 

received three canisters at each of the three ppb levels and three canisters at each of the three ppm levels. The 

reference laboratory received more for some batches.
Table 10. Example canister data for laboratory 6 at 0.8 ppm nominal concentration

Analyte Theoretical 

Value (ppb)

Can 526 

Reported 

Value

Can 883 

Reported 

Value (ppb)

Can 974 

Reported 

Value (ppb)

2,3-Butanedione 0.8036 0.25 0.56 1.0

2,3-Hexanedione 0.7756 0.30 0.04 1.3

2,3-Pentanedione 0.7956 0.25 0.59 1.0

Acetone 0.8676 0.24 0.53 1.0

Benzene 0.8396 0.27 0.32 1.1

Trichloromethane 0.7796 0.28 0.63 1.2

d-Limonene 0.7716 0.21 0.21 1.1

Ethanol 0.7916 0.18 0.45 0.88

Ethylbenzene 0.8396 0.28 0.24 1.2

2-Propanol 0.8116 0.21 0.55 0.93

Methyl Methacrylate 0.8116 0.24 0.48 0.93

Dichloromethane 0.8116 0.26 0.52 1.1

Toluene 0.8436 0.28 0.28 1.1

α-pinene 0.7876 0.24 0.26 1.1

m,p-Xylene 0.8636 0.26 0.11 1.1

Hexane 0.8476 0.28 0.55 1.1

o-Xylene 0.8396 0.27 0.24 1.2

ILS Data Analysis

SAS version 9.3 (SAS Institute Inc., Cary, NC) was used to manage data and perform statistical analyses. JMP 

version 12 (SAS Institute Inc.) was used for graphics. The decision was made to include reference laboratory data 

from just one spike-batch, because there was no reason to give more weight to the reference laboratory than to 

other laboratories. The reference laboratory results from the first spike batches of ppm and ppb were used. In 

addition, whereas the ppm data had three replicates per batch for the reference lab, the ppb reference 

laboratory data for the first spike batch had six replicates. For consistency and in order to maintain equal 

numbers of analyses for each lab, as is done in ASTM E691 [12], only three of the six replicates for the first spike 

batch of reference lab data were used to obtain the variance estimates. As a check, after the above analyses 

were completed, the model was refitted with all six ppb replicates from the first spike batch for the reference 

lab. With just one exception, acetone at 10 ppb, estimated relative standard deviations never differed by more 

than 0.032 from those given in Table 12. (For acetone at 10 ppb, the difference of total RSD is within the 0.032 

bound but the two other components differ by more.) This size difference is considered negligible. Total 

observations (excluding blanks) used for the results in Tables 13 and 14 equaled 2754 (9 laboratories × 17 

analytes × 2 ranges × 3 levels × 3 replicates).

The 2754 observations were divided into 102 sets of 27 values, formed from the nine laboratories with three 

replicates at each of the six concentration levels for each analyte used in the study. For each of these 102 sets, 

the within-laboratory (repeatability), between-laboratory, and total (reproducibility) standard deviations were 

computed for the statistical model using the method provided by ASTM E691. Furthermore, since theoretical 

values varied slightly by batch, each standard deviation was divided by the average of the batch theoretical 

values for the given analyte and concentration level combination to obtain the relative standard deviation (RSD). 

E691 uses the analysis of variance method (ANOVA) to estimate the required standard deviations using eqs (6),
(7), (8), and (10) in ASTM E691. Example calculations of the RSDs are given in Table 11 for benzene at the 0.8 

ppm level. The ASTM statistical models do not deal with samples from canisters. However, some discussion of 

the canister data is presented below.
Table 11. Example calculations for relative standard deviations and outlier tests for benzene at 0.8 ppm level

Batch Lab Theoretical 

Value (ppm) Can

Reported 

Value 

(ppm)

Note

Within 

lab 

variance

Note Lab means

1 0 0.868 895 0.763 0.014 hi mean 0.896

1 0 0.868 546 0.932

1 0 0.868 893 0.993

1 1 0.868 901 0.83 0 0.839

1 1 0.868 902 0.84

1 1 0.868 946 0.847

2 2 0.848 519 0.727 0.002 0.715

2 2 0.848 523 0.666

2 2 0.848 532 0.753

3 3 0.8623 964 0.83 0 0.812

3 3 0.8623 517 0.81

3 3 0.8623 535 0.795

4 4 0.8601 955 0.898 0 2nd largest mean 0.891

4 4 0.8601 534 0.896

4 4 0.8601 968 0.88

5 5 0.8597 953 0.859 0.001 0.888

5 5 0.8597 965 0.876

5 5 0.8597 531 0.928

8 6 0.8396 526 0.27 largest 

variance 0.217 low mean 0.563

8 6 0.8396 883 0.32

8 6 0.8396 974 1.1

6 7 0.8043 995 0.85 0.001 0.877

6 7 0.8043 534 0.88

6 7 0.8043 533 0.9

7 8 0.8044 906 0.658 0 2nd lowest mean 0.655

7 8 0.8044 947 0.662

7 8 0.8044 979 0.645

theoretical 

average

0.846044 Avg within 

lab variance 0.026 variance of means 0.0146

Between lab variance 0.0058

Stdev 0.077

Within lab 

std dev 0.16 Between lab

std dev

0.077

Total std dev 0.18

within lab

RSD 0.19 between lab

RSD

0.09

total RSD 0.211 mean lab mean 0.793

Test Statistics 1% values

Grubbs1,hi = 0.855 (hi mean-mean average)/stdev < 2.387

Grubbs1, low= 1.901 Abs((low mean-mean average)/stdev) < 2.387

Grubbs2, hi= 0.776 6/8*var(without 2 highest mns)/var of means > 0.0851

Grubbs2, low=

Cochran

0.219

0.92

6/8*var(without 2 lowest mns)/var of means

largest variance/variance sum; significant for lab 6

>

>

0.0851

0.573
ASTM E691 was also used to develop h (between-laboratory) and k (within-laboratory) consistency statistics 

prior to outlier removal. Critical values are 2.23 and -2.23 for h-statistic and 2.09 for k-statistic (from Table 5 of 

ASTM E691). Data were screened for possible outliers by investigating data points in h,k-plots. Outlier tests were 

performed for each analyte, concentration level combination for which the total RSD was greater than 0.6,

using a two-step removal rule, adapted from ISO 5725 [13]. In step one, the highest and lowest laboratory 

means were used in separate Grubbs tests for comparison to the two-tailed critical value (2.387, for 9 

laboratories, 1% level from Table 5 of ISO 5725, see Table 11 for example calculation). If the critical value was 

exceeded in either test, then all data from the responsible laboratory were removed (n=51 removed) for the 

particular analyte, concentration level combination. In step two, if no outliers were removed in step one, an 

additional Grubb’s test was done by computing the statistic based on the two largest or two lowest laboratory 

means. If these statistics are less than the 1% critical value (0.0851, for 9 laboratories, from Table 5 of ISO 5725, 

see Table 11 for example calculation) then all of the data from responsible laboratories were removed (n=24 

removed). A total of 2.7% (75/2754) were removed, leaving 2679 data points. After removal, only one analyte, 

2,3-Hexanedione at 5 ppb, had total RSD greater than 0.6 (Table 13). Measurements that were removed by this 

removal rule were not used in subsequent computations of the RSDs, which are shown in Tables 13 and 14.

Laboratories with poor performance were contacted regarding these data to investigate potential causes for 

outlying data.

In the process described above, the aim was not to systematically remove all outliers. The procedure was only 

applied to analytes with very large total RSDs, those greater than 0.6. The largest RSD was about 1.9. The 75 

values mentioned above were taken from 21 different analyte, nominal combinations. For the 18 (of the 21) 

different ppb-range combinations, the ratio of between-laboratory to within-laboratory RSD was at least 1.55, 

while for the remaining three ppm-range combinations, two of them had between-laboratory RSDs less than 

within-laboratory RSD. The bulk of the data suggested that the main reason for the large total RSDs was large 

between-laboratory variability, for which outliers are identified by the two Grubbs tests. These identified 

outliers were always outliers larger than the other data. (Recall that the tests are two-sided.) In addition, all 

except six of the outliers were from two labs which each made some changes to the method instructions 

(discussed below). After removal, all total RSDs were less than 0.51, with one exception with an RSD greater 

than 0.6, 2,3-Hexanedione at 5 ppb. Neither Grubbs outlier test gave a significant result. The decision was made 

not to remove the data. It happens that the Cochran test (from Table 4 of ISO 5725) did identify one outlying 

laboratory, but removal of that laboratory changed the RSDs by very little.

The above procedure differs somewhat from that in ISO 5725 which suggests that the Cochran test for outliers 

in within-laboratory variability be used first. It happens that of the 21 different analyte, nominal combinations, 

18 had significant results by Cochran’s test. For 15 of these 18, the laboratories with the largest variances were 

also responsible for the large Grubbs statistics (which included the two ppm analytes for which between RSD 

was less than within RSD). Thus, by both Grubbs and Cochran tests there were three laboratories for which a 

different laboratory was identified by the Cochran test than by the Grubbs tests, in which case the within RSD is 

larger than it would be if the laboratory had been removed. In summary, by the two-step removal rule used 

here, most of the extremely large outliers were removed by simple application of the rule.

ILS Results/Discussion

ILS Blank Canisters

Laboratories 5 – 7 reported analyte concentrations in UHP nitrogen blank canisters. The highest blank level 

reported by laboratory 7 was 19 ppb for m,p-xylene. The same UHP nitrogen tested by the reference laboratory 

during the same period showed m,p-xylene below the detection limit (<0.28 ppb). Most laboratories reported 

analyte concentrations in the blanks less than detectable or less than one ppb. No blank corrections were used 

on reported spike canister results.
l ls

Variation of Spike Batches and Estimation of Bias, Based on Reference Laboratory Canisters

It is possible that the estimates in Tables 13 and 14 are larger than they would be if all samples at each 

concentration level had been collected in a single batch for ppb and a single batch for ppm. Recall that the 

reference laboratory made measurements in every spike batch, but only the reference laboratory results from 

the first batch were included in the data used for Tables 13 and 14. If only that laboratory’s data are used and if 

that laboratory is not overly variable, we would expect that the between batch RSD, would be much smaller 

than those from the inter-laboratory study results. When a laboratory is used only in one batch and a batch 

includes only one laboratory, there is no way to know whether the data indicate something about the laboratory 

or about the batch. This issue makes the data analysis more complicated, as discussed below. Only the 

reference laboratory data avoid thisproblem. 

The statistical models of interest for measurements (x) of an analyte at a concentration level are given below. 

These models, for both ppb and ppm (102 data sets with 27 measurements in each set) refer to nine 

laboratories and three samples per laboratory 

M1: xls = μ + al + bls,

where μ is the true mean of the analyte, al is the difference from the overall mean of laboratory l, and bls is the 

difference from the lab l mean of the sth sample of lab l. al and bls are normally distributed with means 0, and 

variances σ2 and σ2 , respectively.

It is convenient to express the variances relative to the mean. We do not know the overall mean, but we think 

that the average of the batch theoretical values, M, for the analyte at the concentration level is a good guess. We 

rewrite model M1 as:

M2: xls /M = μ/M + al / M + bls /M.

From model M2, we refer to σl /M and σls /M, respectively, as the between laboratory and within laboratory 

relative standard deviations (RSD).

In Table 11, the within, between, and total laboratory standard deviations were computed and then converted 

to RSDs by dividing these estimates by the average of the spike batch theoretical means, 0.846. This approach 

corresponds to model M1. In model M2 estimation, the reported values (ppm) are divided by 0.846, and the 

same calculations are carried out, as is done for model M1. However, the estimates for within, between, and 

total laboratory standard deviations are actually the RSDs because we have already divided by 0.846.

There is another source of variation here, that between batches:

M3: xls /M = μ/M + al / M + cb /M + bls /M,

where σb /M is the RSD for between batch variability component, cb , which is normally distributed with mean 0.

The concern is that between lab RSDs from the inter-laboratory analysis may be increased due to the batch 

variability, which is due to batch generation changes over time. The statistical model used in E691 (M1 or 

equivalently M2) gives just two RSD estimates - between and within laboratory. For the ppm data only batch 1 

has two laboratories (laboratory 1 and the reference laboratory.) The other batches each have measurements 

from just one laboratory and each laboratory is in just one batch. For this case the estimated between 

laboratory RSD from the E691 model will include all between laboratory variability and almost all between batch 

variability, because there is no way to separate these two sources because of the design. The ppb data are more 

complicated because batches 1 and 2 each include three laboratories; batches 3, 4, and 5 each have just one 

laboratory. (Batch 3 originally included laboratories 6 and 7 data, but those data were removed and laboratory 6
was evaluated again in batch 5.) We must assess here how much the batch RSDs increases the between 

laboratory estimate from models M1 and M2.

The statistical model including the variable “batch” (M3) has been fitted, but estimates are not reliable. This 

model will not work for ppm because almost every laboratory is in its own batch, and between batch and 

between laboratory variability cannot be separated. For ppb, there is some replication of laboratory in batch, 

but, there are also three laboratories which are each used in one batch. The resulting estimates from this larger 

model for both batch and laboratory are difficult to interpret.

In fact, for our purposes, we do not require precise estimates of between batch RSDs. Also, there is reason to 

think that these RSDs are small. For every batch of analyte and concentration level for both ppb and ppm, a 

theoretical concentration was specified. For ppb, the 51 RSDs of the theoretical concentrations (relative to their 

average) for the 51 analyte, concentration levels never exceeded 0.1. The corresponding limit for ppm was 0.05. 

These estimates indicate small between batch variability, but we also want a more data-based estimate for the 

batches. The E691 model can give sensible estimates if we can show that the batch RSDs do not enlarge the 

between laboratory variability by much.

For both ppm and ppb the issue is whether we can use the estimates of model M2 when the data come from 

model M3. For ppm, as stated above, we know that the ppm between laboratory estimates will be inflated by 

the variability associated with the between batch RSD. For ppb the design is more complicated. Computer 

simulations using the E691 model (M2) indicate that, on average, the between batch relative variances (squares 

of RSDs) do not increase the between laboratory relative variance estimates (squares of the RSDs) more than 

the true value of the between batch variance. A summary of the simulations is in Table 12. Columns 2, 3, 4, and 

5 give the specified values needed for the simulations.
Table 12: Computer simulations of between batch influence on between laboratory relative variance 

estimates

Column 1 Column 2 Column 3 Column 4 Column 5 Column 6 Column 7 Column 8

RSD, within 

Laboratories

RSD,

between 

batches

RSD

between 

laboratories

Square root 

of sum of 

RSD2(s) from 

columns 3

and 4

Estimated 

between 

laboratory 

RSD

Estimated 

within 

laboratory 

RSD

True between 

laboratory 

RSD, from 

formula

Simulation 1 0.1 0.1 0.5 0.510 0.507 0.11 0.508

Simulation 2 0.2 0.2 0.4 0.447 0.438 0.2 0.440

Simulation 3 0.2 0.2 0.3 0.361 0.354 0.2 0.351

Simulation 4 0.15 0.15 0.15 0.212 0.205 0.15 0.203

Simulation 5 0.1 0.075 0.075 0.15 0.101 0.1 0.102

Note: For column 2-5 RSDs, specified parameter settings for the 2500 normally distributed data samples. For column 6 and 7, average 

RSD Estimates from simulations from Model M2 (square root of simulations mean value for either between lab or within lab variance )

Following the structure of most of the ppb data, each simulation sample had 27 simulated values, three values 

for each of nine laboratories with specified between laboratory RSD. In addition, there were five batch values, 

which follow the structure of the ppb data, using the specified RSD for batch, and are consistent with model M3. 

There were 27 residual error values using the RSD specified by the simulation. Note that the RSD corresponding 

to the sum of the true between batch and between laboratory relative variances (Column 5) always exceeds the 

RSD corresponding to the estimated laboratory relative variance (Column 6). It was determined mathematically 

that the statistical expected value of the between laboratory mean square for laboratories equals the square 

root of the sum of the RSD2 for laboratories + 5/6 RSD2 for batches (Column 8) (See Appendix A for a derivation 

of this result). Columns 6 and 8 agree very well, which supports the accuracy of the formula used in Column 8.

Columns 2 and 7 agree very well, showing that the estimate of average within laboratory RSD is close to the true 

value. Thus, the overestimate in the between laboratory RSD does not exceed that associated with the between 

batch RSD. Thus, for both ppm and ppb the overestimate in laboratory RSD will not exceed that associated with 

the between batch RSD. (Because of removal of outliers, some analytes had fewer than nine laboratories. For 

those situations, too, computations indicate that the overestimate in laboratory RSD will not exceed that 

associated with the between batch RSD.)

In order to get a data-based estimate of batch variability, the reference laboratory analyses from every batch 

were used to calculate RSD estimates for the between batch variability and within batch variability for each 

analyte, concentration level combination for ppb and ppm. The model used for these variances is analogous to 

model M2, except that in place of al / M and bls /M, we use ab / M and bbs /M, which, respectively, denote 

between batch and within batch components. The variabilities associated with these data would include both 

the batch variability over time and that of the reference laboratory variability. In the analyses discussed here the 

data include ppb data for six canisters from batches 1, 2 and 5 and three canisters from batches 3 and 4. Two
measurements at 5 ppb for each of three analytes were removed because of Grubbs tests. PPM results are 

based on three canisters for each of seven batches, except that at the 0.8 ppm level, two canisters were 

removed based on Grubbs tests.

We are interested in determining how much the between laboratory RSD is inflated by the between batch 

variability. Recall that the RSD values are relative to the average of the batch theoretical values for the given 

analyte and concentration level. Thus, all RSDs for each analyte, concentration level combination are divided by 

this average theoretical value. Each RSD can be converted to a standard deviation by multiplying it by the 

theoretical average. This is correct for both the model M2 for the laboratory data and the model discussed in 

the previous paragraph for the reference laboratory data. In accordance with the results shown above for ppm 

and ppb, a modified result for the laboratory RSD_ILS was calculated as:

lab RSD_ILS (mod) = [(lab RSD_ ILS)2 – (batch RSD_Reflab)2

)]0.5, 

In terms of the Table 11 data, lab RSD_ILS squared value is (0.077/0.846)2 = 0.092 = 0.0081, where 0.846 is the 

average theoretical value for the analyte at the given concentration, and 0.077 is the estimated standard 

deviation from the ILS. From the reference laboratory analysis, the batch RSD Reflab value is 0.024 / 0.846 and 

(0.024/0.846)2 = 0.0282 = 0.00078. Thus, lab RSD_ILS (mod) = sq. rt((0.077/0.846)2 - (0.024/0.846)2

) = sq. rt. (

0.0081- 0.00078) = 0.086. The lab RSD_ ILS value was 0.09. Thus the modified value differs from the estimated 

RSD by 0.09-0.086 = 0.004, a small difference. This supports the idea that the between batch variability is small 

compared to the between laboratory variability. The estimate 0.086 represents the estimate of the between 

laboratory RSD, after removing the variability associated with the between batch variability. We know from the 

statistical analysis results above that, on average, the estimated between laboratory RSD2 from the ILS study will 

not exceed the true between laboratory RSD2 by more than the between batch RSD2 (here 0.00078); by 

removing this excess we can determine the effect of between batch variability on the between laboratory RSD 

estimates.

The aim of this work is not to replace the RSDs of the ILS by the modified values. We wish to see if the modified 

(“mod”) estimates support the reasonableness of the unmodified estimates. We will assess this by determining 

the size of the difference between the original estimates and the modified estimates. Note that there are 

models for which the RSDs from the reference laboratory data exceeded those from the ILS study, which led to 

negative differences of [(lab RSD_ ILS)2 – (batch RSD_Reflab)2

)], for which a square root could not be taken to 

calculate lab RSD_ILS (mod). There were twelve negative differences for [(lab RSD_ ILS)2 – (batch RSD_Reflab)2

)]. 

For all twelve differences, lab RSD_ ILS values were less than 0.1, and six were less than 0.01. This kind of result 

is not unexpected, where the variances are based on models with small sample size and when lab RSD_ ILS 

values are small. Set DIF=lab RSD_ILS -lab RSD_ILS (mod) when lab RSD_ILS (mod) can be calculated. Ideally DIF 

values are small, which indicates that when the variability associated with between batch variation is removed, 

the between laboratory estimates change little. There were four instances among the 102 models where DIF 

exceeded 0.05. The largest difference was 0.11. This result, together with the fact that the cases for which the 

reference laboratory between batch RSD estimate exceeded the ILS between laboratory RSD estimate occurred 

when the between laboratory ILS was less than 0.1, suggests that there seems little indication that between 

batch variability had large effect for many of the laboratory RSD estimates. Also we recognize that the between 

batch estimates from the reference laboratory data are overestimates, since the reference laboratory data 

include that lab’s variability, in addition to batch variability over time. Recall that the reference laboratory 

results from the first spike batches of ppm and ppb were used for the results in Tables 13 and 14. The above 

analysis indicates that estimated between laboratory RSDs from the inter-laboratory study would not vary much 

if another reference laboratory spike-batch were used, since the reference laboratory variability was usually 

much smaller than the between laboratory variability.
Bias across spike batches was also estimated using the reference laboratory data to ensure consistency in spike 

batch generation over time. Bias was fairly consistent, falling within ±10% of the theoretical value for most 

analytes and nominal levels for both ppb-range (Table B1, Appendix B) and ppm-range (Table B2, Appendix B). 

Representative box plots of bias are presented for ethanol (Figure 1 left) and for 2,3-butanedione (Figure 1 

right). Excursions outside the ±10% criterion were observed for ethanol, which is notoriously difficult to analyze 

due to its low molecular weight and polarity, in spike 3 and 4 at 5, 10, and 15 ppb (Figure 1 top left). Additional 

excursions were observed for ppm-level range for ethanol (Figure 1 bottom left). Ethanol for spike batch 6 and 7 

showed a positive bias compared to spike batches 1 to 5 due to the change in generation procedure. For 2,3- 

Butanedione, bias across spike batches was more stable with many boxes falling within the ±10% of the 

theoretical value.

Estimates of total RSD presented here are not significantly affected by these shifts in bias among different spike 

batch generations. Shifts in bias across batches are associated with between-batch variability, and as was stated 

above, the between-batch variability results in only small increases in the reproducibility RSD estimates for most 

analytes.
Figure 1. Representative box plots of reference laboratory bias across spike batches and nominal level for 

ethanol (left) and 2,3-butanedione (right). n=3 per box. Solid line represents +/- 10% bias criterion.

ILS Canister Precision

ILS Precision Estimates

Recall that no effort was made to remove every potential outlier. Only for analyte, nominal combinations with 

total RSD greater than 0.6 were the results of outlier tests used to remove data, and, for ppm, there were just 

three analyte, nominal combinations in this range, from each of which the outlying laboratory’s data were 

removed. In this discussion we consider canister variability, whereas in previous outlier discussion we 

considered only laboratory variability. For instance, for 0.8 ppm in Table 11, only three of seventeen estimates 

of between-laboratory RSDs exceed within-laboratory RSDs. Laboratory 6 gave a significant result for the 

Cochran test for outlying variance (from ISO 5725, Table 4) for 12 of the 17 models used to produce the variance 

estimates for 0.8 ppm data in Table 11. If one canister (974) from laboratory 6 is removed, the results change so 

that 13 between-laboratory RSDs exceed within-laboratory RSDs (see Table 10 for the laboratory 6 
concentrations by canister and Table 11 for the Cochran test calculation). However, the reproducibility RSDs 

change by no more than 0.025. Note that canister 974 data were not omitted from the calculations shown in 

Table 11. The discussion above is included in order to show the sensitiveness of the results, not to remove any 

more data. We also note that there is no way to know whether between-batch variability is introduced during 

the generation process or by the participating laboratory during analysis.

The ppb-level results in Table 13 show mostly higher between- than within-laboratory RSDs (35/51 or 68.6%), 

and although the ppm do not, this may be partly due to the presence of some outliers. Also, the ratio of 

between to within laboratory RSD is quite high for ppb, on average about two.

When analysis of variance is computed separately for each level’s reproducibility values as function of analyte 

and concentration level, neither ppb nor ppm gives results significant at the 5% level.

There is useful information in the outlier analysis. Of the 75 values removed as outliers and not used in the 

estimates in Tables 13 and 14, all but six came from laboratories 5 and 7. Laboratory 7 produced 57 values, of 

which 42 were at 5 ppb, 12 were at 15 ppb, and three were at 0.8 ppm. Laboratory 5 produced 12, of which six 

were at ppb levels (three at 5 ppb and three at 15 ppb), and three at ppm levels. There were no outliers at the 

10 ppb level or at the 1.7 ppm level. Thus, almost all outliers were for ppb levels. All outliers were for large 

magnitude measurements, that is, values larger than the mean. Thus, there is no surprise that the ppb estimates 

tended to have large between-laboratory RSDS than within-laboratory RSDs. In addition, ppb levels have more 

total variation than ppm. There are 10 reproducibility RSDs for ppb greater than or equal to 0.5 compared to 

three for ppm. Furthermore, there are 32 reproducibility RSDs for ppb greater than or equal to 0.3 compared to 

11 for ppm. On the other hand, whereas the smallest reproducibility RSD for ppm is approximately 0.19, there 

are 14 ppb reproducibility RSDs less than or equal to 0.19. Thus, the ppm data have less spread in reproducibility 

than the ppb. Likewise, there are just two ppm within-laboratory RSDs less than or equal to 0.14 but 27 for ppb. 

There appear to be four ppm values less than or equal to 0.14 in Table 14, but that is just due to rounding. There 

are four ppb within-laboratory RSDs 0.3 or larger, compared to three for ppm. Thus, except for the high withinlaboratory RSDs, ppb RSDs have a wider spread than ppm.

The reasons for these differences between ppb and ppm are not clear. It does seem that the low levels used for 

ppb are associated with large positive bias for some laboratories, although that does not occur at 10 ppb, where 

results look more like ppm level results than like ppb level results. Analytes are more alike at the ppm level than 

at the ppb level, in the sense that there are many ppb RSDs less than and others more than most ppm RSDs. This 

may mean that at high levels (ppm), the instructions for the method are followed better by all labs than at lower 

levels (ppb) or, perhaps more likely, method precision becomes poorer as analyte concentration decreases from 

ppm to ppb levels.

Also, RSDs for ppb may be influenced by some of the potential outlier laboratories, whose practices are 

discussed below (laboratories 5, 6, and 7).

In summary, for ppb, estimates for repeatability ranged from 0.04 to 0.55 and estimates for reproducibility 

ranged from 0.10 to 0.62. For ppm, estimates for repeatability ranged from 0.10 to 0.47 and for reproducibility 

from 0.19 to 0.58.

For comparison, ASTM D6196 provides some insight about precision estimates from thermal desorption tubes, a 

complementary sampling and detection technique that provides sensitive estimates of airborne VOC 

concentrations [14]. They reported mean ISO repeatability estimates between 0.072 and 0.216 for hydrocarbons 

C3 to C11 from Coker et al. [15] which are compared to canister method ILS results of within-laboratory RSDs 

from 0.04 to 0.55 with a median of 0.14 for ppb and from 0.1 to 0.47 with a median of 0.19 for ppm. They 

reported ISO reproducibility estimates between 0.259 to 0.432 compared to canister method ILS results of total
RSDs from 0.1 to 0.62 with a median of 0.37 for ppb and from 0.19 to 0.58 to with a median of 0.23 for ppm. 

While some of these values are higher than those reported in ASTM D6196, more laboratories were used in this 

study (nine vs. four).
Table 13. Between- and within-laboratory and total RSD estimates for ppb-level analytes.

Note that “Within RSD” is the same as the repeatability RSD, and “Total RSD” is the same as the 

reproducibility RSD.

5 ppb 10 ppb 15 ppb

Analyte

Between 

RSD

Within 

RSD

Total 

RSD

Between 

RSD

Within 

RSD

Total 

RSD

Between 

RSD

Within 

RSD

Total 

RSD

2,3-Butanedione 0.30 0.10 0.32 0.16 0.23 0.28 0.53 0.18 0.56

2,3-Hexanedione 0.58 0.23 0.62 0.32 0.29 0.43 0.42 0.31 0.52

2,3-Pentanedione 0.53 0.15 0.55 0.28 0.25 0.37 0.49 0.29 0.57

Acetone 0.07 0.10 0.12 0.07 0.55 0.56 0.39 0.18 0.43

Benzene 0.13 0.09 0.16 0.00 0.14 0.14 0.37 0.09 0.38

Trichloromethane 0.14 0.12 0.18 0.06 0.14 0.15 0.45 0.10 0.46

d-Limonene 0.48 0.15 0.51 0.40 0.17 0.43 0.15 0.11 0.19

Ethanol 0.30 0.17 0.35 0.35 0.36 0.50 0.09 0.10 0.13

Ethylbenzene 0.31 0.11 0.33 0.18 0.21 0.28 0.50 0.11 0.51

2-Propanol 0.28 0.32 0.43 0.34 0.14 0.37 0.13 0.08 0.15

Methyl

methacrylate 0.43 0.16 0.46 0.14 0.19 0.23 0.40 0.18 0.44

Dichloromethane 0.05 0.10 0.11 0.00 0.16 0.16 0.32 0.11 0.34

Toluene 0.16 0.11 0.19 0.05 0.14 0.15 0.41 0.11 0.42

α-Pinene 0.24 0.12 0.26 0.19 0.25 0.31 0.38 0.09 0.39

m,p-Xylene 0.38 0.11 0.39 0.49 0.21 0.53 0.38 0.11 0.40

Hexane 0.08 0.10 0.13 0.09 0.04 0.10 0.38 0.11 0.40

o-Xylene 0.08 0.12 0.15 0.26 0.24 0.36 0.57 0.09 0.58

Note: Outliers removed prior to generation of RSD estimates.
Table 14. Between- and within- laboratory and total RSD estimates for ppm-level analytes.

Note that “Within RSD” is the same as the repeatability RSD, and “Total RSD” is the same as the 

reproducibility RSD.

0.8 ppm 1.3 ppm 1.7 ppm

Analyte

Between 

RSD

Within 

RSD

Total 

RSD

Between 

RSD

Within 

RSD

Total 

RSD

Between 

RSD

Within 

RSD

Total 

RSD

2,3-Butanedione 0.14 0.18 0.23 0.05 0.19 0.20 0.12 0.15 0.19

2,3-Hexanedione 0.00 0.32 0.32 0.21 0.26 0.33 0.22 0.24 0.32

2,3-Pentanedione 0.29 0.25 0.38 0.15 0.22 0.26 0.18 0.16 0.24

Acetone 0.10 0.17 0.20 0.34 0.47 0.58 0.14 0.15 0.20

Benzene 0.09 0.19 0.21 0.13 0.18 0.22 0.17 0.15 0.22

Trichloromethane 0.04 0.21 0.21 0.15 0.19 0.25 0.14 0.20 0.25

d-Limonene 0.32 0.27 0.41 0.13 0.23 0.26 0.21 0.17 0.27

Ethanol 0.32 0.39 0.51 0.21 0.17 0.27 0.26 0.10 0.28

Ethylbenzene 0.12 0.23 0.26 0.00 0.22 0.22 0.19 0.14 0.23

2-Propanol 0.16 0.29 0.33 0.13 0.17 0.22 0.12 0.15 0.19

Methyl

methacrylate 0.15 0.18 0.23 0.07 0.19 0.20 0.13 0.14 0.19

Dichloromethane 0.04 0.19 0.19 0.15 0.17 0.23 0.12 0.18 0.21

Toluene 0.17 0.20 0.26 0.09 0.19 0.21 0.18 0.14 0.23

α-Pinene 0.14 0.22 0.26 0.00 0.23 0.23 0.19 0.16 0.24

m,p-Xylene 0.46 0.24 0.52 0.23 0.23 0.33 0.25 0.18 0.30

Hexane 0.07 0.18 0.19 0.14 0.17 0.22 0.15 0.16 0.22

o-Xylene 0.12 0.24 0.27 0.00 0.23 0.23 0.19 0.16 0.24

Note: Outliers removed prior to generation of RSD estimates.
h- and k-Statistics

k-Statistic for ppb-level within-laboratory variability by analyte shows a majority of the laboratories and analytes

were less than the 2.09 criterion (413/459 or 90.0%) (Figure 2, 3, and 4). Most of these excursions above the

criterion were laboratory 7 at 5 ppb (Figure 2), laboratory 5 at 10 ppb (Figure 3), and laboratories 8 and 6 at 15

ppb (Figure 4).

Figure 2. Within-laboratory statistic (k-plot) for 5 ppb nominal level. Dashed line = 2.09 criterion.
Figure 3. Within-laboratory statistic (k-plot) for 10 ppb nominal level. Dashed line = 2.09 criterion.

Figure 4. Within-laboratory statistic (k-plot) for 15 ppb nominal level. Dashed line = 2.09 criterion.
k-Statistic for ppm-level within-laboratory variability by analyte shows most laboratories and analytes were less

than the 2.09 criterion (428/459 or 93.2%) (Figures 5, 6, and 7). Most of these excursions above the criterion

were laboratory 6 at 0.8 ppm (Figure 5), laboratory 5 at 1.3 ppm (Figure 6), and laboratory 6 at 1.7 ppm (Figure

7).

Figure 5. Within-laboratory statistic (k-plot) for 0.8 ppm nominal level. Dashed line = 2.09 criterion.
Figure 6. Within-laboratory statistic (k-plot) for 1.3 ppm nominal level. Dashed line = 2.09 criterion.

Figure 7. Within-laboratory statistic (k-plot) for 1.7 ppm nominal level. Dashed line = 2.09 criterion.
h-Statistic for ppb-level between-laboratory variability by analyte shows most laboratories and analytes

(430/459 combinations or 93.7%) were within the ±2.23 criterion (Figures 8, 9, and 10). Most of the excursions

outside the criterion were due to laboratory 7 (25/29 excursions) at 5 ppb (Figure 8), at 10 ppb (Figure 9), and at

15 ppb (Figure 10). The rest were due to laboratory 5 at 10 ppb (Figure 9). A pattern of mostly positive h-values

for laboratory 5 and laboratory 7 is opposed to the mostly negative h-values for other laboratories, although

some laboratory/analyte combinations do not follow this trend (e.g., laboratory 6 positive h-values for some

analytes at 5 ppb but negative at 10 ppb and at 15 ppb).

Figure 8. Between-laboratory statistic (h-plot) for 5 ppb nominal level. Dashed line = 2.23 to -2.23 criteria.
Figure 9. Between-laboratory statistic (h-plot) for 10 ppb nominal level. Dashed line = 2.23 to -2.23 criteria.

Figure 10. Between-laboratory statistic (h-plot) for 15 ppb nominal level. Dashed line = 2.23 to -2.23 criteria.
h-Statistic for ppm-level between-laboratory variability by analyte shows most laboratories and analytes

(450/459 combinations or 98.0%) were within the ±2.23 (Figures 11, 12, and 13). The excursions were limited to

laboratory 5 and laboratory 7. A pattern of positive and negative h-values by laboratory with the number of

positive values approximately equal to the number of negative laboratories is observed at the ppm-level. This is

a normal pattern to inter-laboratory studies and did not require investigation.

Figure 11. Between-laboratory statistic (h-plot) for 0.8 ppm nominal level. Dashed line = 2.23 to -2.23 criteria.
Figure 12. Between-laboratory statistic (h-plot) for 1.3 ppm nominal level. Dashed line = 2.23 to -2.23 criteria.
Figure 13. Between-laboratory statistic (h-plot) for 1.7 ppm nominal level. Dashed line = 2.23 to -2.23 criteria.

Laboratories 5, 6, and 7 were investigated based on the h- and k-plots and outlier results (These laboratories 

contributed to all but six of the 75 deleted values.) For dilution of ppm-level samples, laboratory 5 performed 

syringe transfers to a tedlar bag. Ideally, a canister-to-canister pressure dilution would take place although in the 

original draft NMAM provided to the test laboratories syringe transfers from a glass bulb to a canister were 

discussed for calibration standard preparation. Laboratory 5 performed well on a number of 

analyte/concentration combinations but some individual canisters and combinations had elevated bias and 

precision indicative of poor instrument performance (e.g., preconcentrator may have not made an incorrect 

injection volume or trapping temperatures may not have been stable). Laboratory 6 took 42 days to analyze 

samples with a calibration to sample analysis duration of 28 days. This time may be too long for the analytical 

system to maintain stability of the calibration curve. For dilution of ppm-level samples, laboratory 6 performed 

10 mL gas transfers using a syringe into a 1-L canister and pressurized to ambient. Laboratory 6 agreed to 

conduct the inter-laboratory study again with a fresh set of spiked samples. Laboratory 6 repeat data was 

marginally better than before and used in the current analysis. Laboratory 7 calibrated at ambient pressure but 

the spike samples were pressurized. This can cause issues with delivery volumes in the preconcentrator system 

as internal tubing effective volume is greater under pressure. For dilution of ppm-level samples, laboratory 7 

performed pressure dilutions using a homemade pressure transducer system. Laboratory 7 also calibrated up to 

100 ppb instead of the recommended 20 ppb which can increase variability on the low end of the calibration 

curve. Since the original draft NMAM method was disseminated to the laboratories, we have found that using a 

500 mL injection of our 20 ppb standard can effectively extended the calibration range to 40 ppb, depending on 

the analyte, without compromising variability. Laboratory 7 declined to repeat the analysis. Reported results
from Laboratory 7 were quite different from theoretical values indicating an incorrect calibration standard 

preparation at the laboratory or poorly prepared spikes, although reference canisters for this trial did not 

indicate poor preparation. Large variability in Laboratory 7 data may have been due to preconcentrator system 

instability and/or a higher than normal dynamic range of 5 to 100 ppb. In particular, 5 ppb results were much 

higher than other labs, and most of the analytes were identified as outliers at 5 ppb for laboratory 7.

ILS Conclusions

Precision estimates for repeatability ranged from 0.04 to 0.55 at ppb concentrations and from 0.10 to 0.47 for 

ppm concentrations. Precision estimates for reproducibility ranged from 0.10 to 0.62 at ppb concentrations and 

from 0.19 to 0.58 at ppm concentrations, depending on analyte and nominal. Plots of h- and k-statistics 

indicated inconsistencies with reported results from laboratories 5, 6, and 7. These laboratories were 

investigated to determine the cause of the inconsistent results. Issues with storage time and adherence to the 

method protocol were encountered but results were retained due to the low number of laboratories 

participating. Precision varied by laboratory with more than half of the laboratories performing well indicating a 

fairly robust method across laboratories. Training on pressure dilution techniques and preconcentration systems 

as well as proficiency testing should be periodically conducted to ensure operating laboratories maintain 

optimal canister method performance.
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Appendix A: Backup Data for Spiking Experiments, LOD, and Stability
Table A1: PPB-level spiking experiment data

Analyte

Nominal 

Target 

Concentration 

(ppb)

Sample 

1

Sample 

2

Sample 

3

Sample 

4

Sample 

5

Sample 

6

Sample 

7

Sample 

8

Sample 

9

Mean 

Bias CV

ethanol 1 -0.109 -0.157 -0.148 -0.120 -0.184 -0.125 -0.043 -0.105 -0.124 0.042

ethanol 4 0.039 -0.094 -0.092 -0.010 -0.070 -0.039 0.036 -0.086 -0.040 0.056

ethanol 10 -0.221 -0.176 -0.056 -0.058 -0.054 -0.142 -0.153 -0.126 -0.045 -0.115 0.064

2-propanol 1 -0.192 -0.293 -0.194 -0.277 -0.235 -0.132 -0.129 -0.190 -0.205 0.060

2-propanol 4 0.014 -0.192 -0.066 -0.025 -0.169 -0.030 0.067 -0.040 -0.055 0.087

2-propanol 10 -0.234 -0.176 -0.035 -0.054 -0.033 -0.110 -0.141 -0.102 -0.089 -0.108 0.067

acetone 1 -0.038 -0.089 0.000 -0.053 -0.122 -0.019 0.202 -0.027 -0.018 0.097

acetone 4 0.075 -0.079 0.035 0.065 -0.059 0.048 0.081 -0.024 0.018 0.063

acetone 10 -0.138 -0.127 -0.050 -0.070 -0.050 -0.106 -0.138 -0.107 -0.070 -0.095 0.036

dichloromethane 1 0.043 0.065 0.016 0.056 0.052 0.016 0.012 0.032 0.036 0.020

dichloromethane 4 0.094 0.080 0.144 0.122 0.082 0.125 0.131 0.094 0.109 0.024

dichloromethane 10 -0.040 -0.066 -0.066 -0.072 -0.050 -0.053 -0.084 -0.059 -0.067 -0.062 0.013

trichloromethane 1 -0.027 0.004 -0.039 -0.020 0.004 -0.047 -0.060 -0.035 -0.028 0.023

trichloromethane 4 0.070 0.037 0.110 0.101 0.049 0.112 0.088 0.054 0.078 0.029

trichloromethane 10 -0.044 -0.062 -0.069 -0.061 -0.046 -0.065 -0.073 -0.058 -0.061 -0.060 0.010

hexane 1 -0.093 -0.055 -0.101 -0.089 -0.047 -0.112 -0.117 -0.085 -0.087 0.025

hexane 4 0.135 0.115 0.178 0.168 0.110 0.176 0.155 0.122 0.145 0.028

hexane 10 -0.042 -0.063 -0.066 -0.059 -0.064 -0.076 -0.072 -0.068 -0.059 -0.063 0.010

benzene 1 -0.129 -0.103 -0.150 -0.142 -0.091 -0.157 -0.155 -0.150 -0.134 0.025

benzene 4 0.082 0.062 0.125 0.114 0.059 0.118 0.101 0.067 0.091 0.027

benzene 10 -0.024 -0.044 -0.051 -0.035 -0.047 -0.057 -0.051 -0.055 -0.042 -0.045 0.010

toluene 1 -0.182 -0.173 -0.207 -0.213 -0.178 -0.219 -0.219 -0.229 -0.203 0.021

toluene 4 0.089 0.078 0.140 0.139 0.077 0.125 0.120 0.102 0.109 0.026

toluene 10 -0.005 -0.037 -0.035 -0.006 -0.016 -0.035 -0.029 -0.032 -0.029 -0.025 0.013

ethylbenzene 1 -0.202 -0.173 -0.188 -0.178 -0.195 -0.191 -0.181 -0.219 -0.191 0.015

ethylbenzene 4 0.075 0.087 0.101 0.118 0.081 0.081 0.121 0.128 0.099 0.021

ethylbenzene 10 -0.020 -0.061 -0.048 -0.009 -0.042 -0.055 -0.032 -0.047 -0.054 -0.041 0.017
Table A1 (continued): PPB-level spiking experiment data

Analyte

Nominal 

Target 

Concentration 

(ppb)

Sample 

1

Sample 

2

Sample 

3

Sample 

4

Sample 

5

Sample 

6

Sample 

7

Sample 

8

Sample 

9

Mean 

Bias CV

m,p-xylene 1 -0.230 -0.196 -0.209 -0.193 -0.224 -0.220 -0.210 -0.244 -0.216 0.017

m,p-xylene 4 0.041 0.056 0.064 0.091 0.047 0.046 0.092 0.104 0.068 0.024

m,p-xylene 10 -0.031 -0.080 -0.062 -0.014 -0.054 -0.070 -0.043 -0.060 -0.070 -0.054 0.021

o-xylene 1 -0.242 -0.208 -0.217 -0.201 -0.224 -0.228 -0.214 -0.256 -0.224 0.018

o-xylene 4 0.051 0.075 0.077 0.101 0.062 0.060 0.109 0.136 0.084 0.029

o-xylene 10 -0.015 -0.073 -0.052 0.004 -0.040 -0.059 -0.028 -0.047 -0.061 -0.041 0.024

methyl 

methacrylate 1 -0.256 -0.295 -0.234 -0.314 -0.349 -0.207 -0.230 -0.230 -0.265 0.049

methyl 

methacrylate 4 0.072 -0.108 0.023 0.070 -0.092 0.035 0.082 0.013 0.012 0.073

methyl 

methacrylate 10 -0.102 -0.096 -0.012 -0.011 0.005 -0.064 -0.080 -0.064 -0.052 -0.053 0.039

α-pinene 1 -0.186 -0.139 -0.168 -0.141 -0.160 -0.188 -0.161 -0.199 -0.168 0.022

α-pinene 4 0.018 0.081 0.057 0.081 0.052 0.070 0.103 0.145 0.076 0.038

α-pinene 10 -0.013 -0.093 -0.065 -0.006 -0.054 -0.052 -0.028 -0.058 -0.076 -0.049 0.029

d-limonene 1 -0.246 -0.183 -0.150 -0.156 -0.191 -0.150 -0.143 -0.169 -0.173 0.034

d-limonene 4 -0.022 0.104 -0.059 0.021 0.030 0.066 0.116 0.312 0.071 0.114

d-limonene 10 0.011 -0.121 -0.053 0.048 -0.044 -0.063 0.040 -0.042 -0.094 -0.035 0.058
Table A2: PPM-level spiking experiment data

Analyte

Nominal 

Target 

Concentration 

(ppm)

Sample 

1

Sample 

2

Sample 

3

Sample 

4

Sample 

5

Sample 

6

Sample 

7

Sample 

8

Sample 

9

Mean 

Bias CV

ethanol 0.8 0.179 0.143 0.112 0.079 0.112 0.100 0.085 0.138 0.129 0.120 0.031

ethanol 1 0.075 0.140 0.206 0.107 0.176 0.151 0.152 0.152 0.164 0.147 0.038

ethanol 2 -0.002 -0.012 0.020 0.037 0.046 0.014 0.023 0.006 0.017 0.019

2-propanol 0.8 0.143 0.158 0.140 0.130 0.140 0.115 0.064 0.141 0.143 0.130 0.027

2-propanol 1 0.098 0.105 0.146 0.064 0.156 0.132 0.166 0.150 0.162 0.131 0.035

2-propanol 2 -0.039 -0.010 0.022 0.017 0.026 0.017 -0.020 0.030 0.005 0.025

acetone 0.8 0.171 0.136 0.179 0.122 0.130 0.131 0.127 0.167 0.146 0.145 0.021

acetone 1 0.081 0.120 0.144 0.080 0.132 0.132 0.100 0.108 0.120 0.113 0.023

acetone 2 -0.030 -0.007 0.008 0.003 0.018 0.008 0.005 0.010 0.002 0.015

dichloromethane 0.8 -0.003 -0.014 -0.005 -0.015 -0.018 -0.043 -0.021 -0.004 -0.015 -0.015 0.012

dichloromethane 1 -0.082 -0.101 -0.042 -0.017 -0.021 -0.021 0.048 -0.011 0.042 -0.023 0.049

dichloromethane 2 -0.144 -0.085 -0.075 -0.081 -0.014 -0.019 -0.038 -0.010 -0.058 0.046

trichloromethane 0.8 0.161 0.138 0.133 0.136 0.146 0.108 0.117 0.145 0.124 0.134 0.016

trichloromethane 1 0.057 0.039 0.047 0.031 0.035 0.035 0.044 0.019 0.039 0.038 0.011

trichloromethane 2 -0.029 -0.011 0.004 0.004 0.008 -0.001 0.001 0.001 -0.003 0.012

hexane 0.8 0.136 0.125 0.119 0.123 0.132 0.107 0.104 0.133 0.123 0.123 0.011

hexane 1 0.078 0.060 0.085 0.060 0.064 0.072 0.073 0.057 0.085 0.070 0.011

hexane 2 -0.046 -0.017 -0.007 0.009 0.002 -0.002 -0.004 -0.004 -0.009 0.017

benzene 0.8 0.089 0.104 0.099 0.076 0.086 0.061 0.071 0.100 0.076 0.085 0.015

benzene 1 0.102 0.092 0.092 0.067 0.079 0.079 0.081 0.072 0.092 0.084 0.011

benzene 2 0.013 0.014 0.030 0.035 0.022 0.019 0.026 0.010 0.021 0.009

toluene 0.8 0.063 0.064 0.048 0.051 0.036 0.023 0.032 0.085 0.038 0.049 0.019

toluene 1 0.072 0.070 0.078 0.053 0.065 0.048 0.058 0.050 0.070 0.063 0.011

toluene 2 0.056 0.046 0.056 0.078 0.054 0.044 0.063 0.035 0.054 0.013

ethylbenzene 0.8 0.092 0.093 0.064 0.079 0.064 0.077 0.073 0.114 0.055 0.079 0.018

ethylbenzene 1 0.042 0.032 0.049 0.016 0.044 -0.005 0.038 0.013 0.032 0.029 0.018

ethylbenzene 2 0.026 0.011 0.015 0.049 0.041 0.049 0.051 0.034 0.034 0.016
Table A2 (continued): PPM-level spiking experiment data

Analyte

Nominal 

Target 

Concentration 

(ppm)

Sample 

1

Sample 

2

Sample 

3

Sample 

4

Sample 

5

Sample 

6

Sample 

7

Sample 

8 Sample 9

Mean 

Bias CV

m,p-xylene 0.8 0.090 0.104 0.074 0.090 0.061 0.074 0.071 0.125 0.053 0.082 0.022

m,p-xylene 1 0.027 0.001 0.026 0.001 0.021 -0.021 0.014 -0.002 0.009 0.008 0.015

m,p-xylene 2 0.013 -0.002 0.007 0.030 0.034 0.052 0.049 0.027 0.026 0.019

o-xylene 0.8 0.090 0.092 0.061 0.090 0.061 0.074 0.071 0.125 0.065 0.081 0.021

o-xylene 1 0.043 0.034 0.059 0.026 0.062 0.004 0.039 0.031 0.043 0.038 0.017

o-xylene 2 0.041 0.015 0.019 0.058 0.051 0.058 0.066 0.038 0.043 0.019

methyl 

methacrylate 0.8 0.089 0.066 0.049 0.077 0.062 0.037 0.009 0.074 0.040 0.056 0.025

methyl 

methacrylate 1 0.081 0.087 0.071 0.046 0.075 0.066 0.076 0.068 0.087 0.073 0.013

methyl 

methacrylate 2 0.034 0.031 0.051 0.051 0.044 0.040 0.053 0.037 0.043 0.009

α-pinene 0.8 0.106 0.121 0.078 0.106 0.078 0.115 0.100 0.153 0.081 0.104 0.024

α-pinene 1 0.060 0.050 0.084 0.017 0.071 0.005 0.056 0.048 0.050 0.049 0.024

α-pinene 2 0.055 0.018 0.027 0.055 0.054 0.066 0.069 0.047 0.049 0.018

d-limonene 0.8 0.186 0.188 0.132 0.199 0.119 0.220 0.179 0.256 0.186 0.185 0.041

d-limonene 1 0.131 0.172 0.197 0.089 0.224 0.067 0.152 0.185 0.155 0.152 0.050

d-limonene 2 0.099 -0.006 0.004 0.060 0.058 0.094 0.079 0.063 0.056 0.039
Table A3: PPB-level limit of detection data, 2012

Analyte

Nominal 

Target 

Concentration 

(ppb)

Measured 

Concentration 

(ppb)

Theoretical 

Concentration 

(ppb) Bias

ethanol 0.03 0.115 0.033 2.495

ethanol 0.07 0.140 0.066 1.137

ethanol 0.5 0.595 0.519 0.146

ethanol 0.7 0.570 0.689 -0.172

ethanol 1 1.075 1.033 0.041

2-propanol 0.03 0.060 0.033 0.807

2-propanol 0.07 0.085 0.066 0.286

2-propanol 0.5 0.615 0.524 0.174

2-propanol 0.7 0.600 0.695 -0.137

2-propanol 1 1.225 1.042 0.175

acetone 0.03 0.210 0.034 5.269

acetone 0.07 0.235 0.067 2.523

acetone 0.5 0.720 0.528 0.364

acetone 0.7 0.705 0.701 0.006

acetone 1 1.335 1.051 0.271

dichloromethane 0.03 0.140 0.032 3.321

dichloromethane 0.07 0.165 0.064 1.562

dichloromethane 0.5 0.620 0.510 0.216

dichloromethane 0.7 0.665 0.677 -0.017

dichloromethane 1 1.305 1.015 0.286

trichloromethane 0.03 0.020 0.033 -0.388

trichloromethane 0.07 0.055 0.065 -0.155

trichloromethane 0.5 0.500 0.516 -0.030

trichloromethane 0.7 0.560 0.684 -0.181

trichloromethane 1 1.045 1.026 0.019

hexane 0.03 0.020 0.033 -0.396

hexane 0.07 0.050 0.066 -0.241

hexane 0.5 0.495 0.522 -0.051

hexane 0.7 0.540 0.692 -0.220

hexane 1 1.025 1.038 -0.012

benzene 0.03 0.030 0.033 -0.080

benzene 0.07 0.050 0.065 -0.230

benzene 0.5 0.485 0.514 -0.057

benzene 0.7 0.540 0.682 -0.208

benzene 1 1.005 1.023 -0.018
Table A3 (continued): PPB-level limit of detection data, 2012

Analyte

Nominal 

Target 

Concentration 

(ppb)

Measured 

Concentration 

(ppb)

Theoretical 

Concentration 

(ppb) Bias

toluene 0.03 0.030 0.033 -0.094

toluene 0.07 0.055 0.066 -0.164

toluene 0.5 0.500 0.521 -0.040

toluene 0.7 0.565 0.691 -0.182

toluene 1 1.025 1.037 -0.011

ethylbenzene 0.03 0.035 0.033 0.064

ethylbenzene 0.07 0.060 0.066 -0.084

ethylbenzene 0.5 0.525 0.519 0.012

ethylbenzene 0.7 0.575 0.688 -0.165

ethylbenzene 1 1.060 1.033 0.027

m,p-xylene 0.03 0.050 0.033 0.534

m,p-xylene 0.07 0.060 0.065 -0.076

m,p-xylene 0.5 0.510 0.514 -0.008

m,p-xylene 0.7 0.555 0.682 -0.186

m,p-xylene 1 1.030 1.023 0.007

o-xylene 0.03 0.045 0.033 0.380

o-xylene 0.07 0.060 0.065 -0.076

o-xylene 0.5 0.520 0.514 0.011

o-xylene 0.7 0.550 0.682 -0.194

o-xylene 1 1.040 1.023 0.017

methyl methacrylate 0.03 0.035 0.033 0.061

methyl methacrylate 0.07 0.060 0.066 -0.087

methyl methacrylate 0.5 0.535 0.520 0.028

methyl methacrylate 0.7 0.560 0.690 -0.189

methyl methacrylate 1 1.085 1.035 0.048
Table A3 (continued): PPB-level limit of detection data, 2012

Analyte

Nominal 

Target 

Concentration 

(ppb)

Measured 

Concentration 

(ppb)

Theoretical 

Concentration 

(ppb) Bias

α-pinene 0.03 0.030 0.033 -0.088

α-pinene 0.07 0.050 0.065 -0.235

α-pinene 0.5 0.515 0.518 -0.006

α-pinene 0.7 0.555 0.687 -0.192

α-pinene 1 1.065 1.031 0.033

d-limonene 0.03 0.060 0.033 0.835

d-limonene 0.07 0.050 0.065 -0.232

d-limonene 0.5 0.525 0.516 0.018

d-limonene 0.7 0.520 0.684 -0.240

d-limonene 1 1.080 1.026 0.052
Table A4: PPM-level limit of detection data

Analyte

Nominal Target 

Concentration 

(ppb)

Measured 

Concentration 

(ppb)

Theoretical 

Concentration 

(ppb) Bias

ethanol 2 2.010 1.945 0.034

ethanol 1.3 1.220 1.305 -0.065

ethanol 0.8 0.810 0.878 -0.077

ethanol 0.4 0.445 0.441 0.008

ethanol 0.2 0.210 0.221 -0.049

ethanol 0.1 0.130 0.111 0.172

2-propanol 2 2.065 1.962 0.052

2-propanol 1.3 1.240 1.317 -0.058

2-propanol 0.8 0.820 0.886 -0.074

2-propanol 0.4 0.450 0.445 0.010

2-propanol 0.2 0.215 0.223 -0.035

2-propanol 0.1 0.120 0.112 0.072

acetone 2 1.945 1.978 -0.017

acetone 1.3 1.140 1.327 -0.141

acetone 0.8 0.785 0.893 -0.121

acetone 0.4 0.450 0.449 0.002

acetone 0.2 0.230 0.225 0.024

acetone 0.1 0.140 0.113 0.240

dichloromethane 2 1.875 1.911 -0.019

dichloromethane 1.3 1.040 1.282 -0.189

dichloromethane 0.8 0.740 0.863 -0.142

dichloromethane 0.4 0.400 0.434 -0.078

dichloromethane 0.2 0.230 0.217 0.060

dichloromethane 0.1 0.150 0.109 0.376

trichloromethane 2 2.085 1.932 0.079

trichloromethane 1.3 1.195 1.297 -0.078

trichloromethane 0.8 0.800 0.872 -0.083

trichloromethane 0.4 0.455 0.439 0.038

trichloromethane 0.2 0.215 0.219 -0.018

trichloromethane 0.1 0.120 0.110 0.091

hexane 2 2.300 1.955 0.177

hexane 1.3 1.390 1.311 0.060

hexane 0.8 0.875 0.882 -0.008

hexane 0.4 0.440 0.444 -0.008

hexane 0.2 0.190 0.222 -0.144

hexane 0.1 0.100 0.111 -0.099
Table A4 (continued): PPM-level limit of detection data

Analyte

Nominal 

Target 

Concentration 

(ppb)

Measured 

Concentration 

(ppb)

Theoretical 

Concentration 

(ppb) Bias

benzene 2 2.235 1.926 0.160

benzene 1.3 1.365 1.293 0.056

benzene 0.8 0.865 0.870 -0.005

benzene 0.4 0.440 0.437 0.007

benzene 0.2 0.190 0.219 -0.132

benzene 0.1 0.100 0.109 -0.083

toluene 2 2.530 1.948 0.299

toluene 1.3 1.470 1.308 0.124

toluene 0.8 0.930 0.878 0.059

toluene 0.4 0.450 0.441 0.020

toluene 0.2 0.190 0.222 -0.144

toluene 0.1 0.100 0.111 -0.099

ethylbenzene 2 2.520 1.941 0.298

ethylbenzene 1.3 1.450 1.303 0.113

ethylbenzene 0.8 0.910 0.874 0.041

ethylbenzene 0.4 0.450 0.439 0.025

ethylbenzene 0.2 0.190 0.221 -0.140

ethylbenzene 0.1 0.100 0.110 -0.091

m,p-xylene 2 2.480 1.923 0.290

m,p-xylene 1.3 1.420 1.291 0.100

m,p-xylene 0.8 0.890 0.866 0.028

m,p-xylene 0.4 0.440 0.435 0.011

m,p-xylene 0.2 0.190 0.219 -0.132

m,p-xylene 0.1 0.100 0.110 -0.091

o-xylene 1.3 1.460 1.291 0.131

o-xylene 0.8 0.910 0.866 0.051

o-xylene 0.4 0.440 0.435 0.011

o-xylene 0.2 0.185 0.219 -0.155

o-xylene 0.1 0.100 0.110 -0.091

methyl methacrylate 2 2.580 1.946 0.326

methyl methacrylate 1.3 1.540 1.306 0.179

methyl methacrylate 0.8 0.950 0.877 0.083

methyl methacrylate 0.4 0.460 0.440 0.045

methyl methacrylate 0.2 0.195 0.221 -0.118

methyl methacrylate 0.1 0.090 0.111 -0.189
Table A4 (continued): PPM-level limit of detection data

Analyte

Nominal Target 

Concentration 

(ppb)

Measured 

Concentration 

(ppb)

Theoretical 

Concentration 

(ppb) Bias

α-pinene 2 2.680 1.938 0.383

α-pinene 1.3 1.550 1.301 0.191

α-pinene 0.8 0.940 0.873 0.077

α-pinene 0.4 0.450 0.438 0.027

α-pinene 0.2 0.185 0.220 -0.159

α-pinene 0.1 0.090 0.110 -0.182

d-limonene 2 2.880 1.930 0.492

d-limonene 1.3 1.690 1.295 0.305

d-limonene 0.8 0.890 0.869 0.024

d-limonene 0.4 0.440 0.437 0.007

d-limonene 0.2 0.170 0.220 -0.227

d-limonene 0.1 0.090 0.110 -0.182
Table A5: PPB-level limit of detection data, 2017

Analyte

Nominal 

Target 

Concentration 

(ppb)

Measured 

Concentration 

(ppb)

Theoretical 

Concentration 

(ppb) Bias

ethanol 0.1 0.106 0.11 -0.033

ethanol 0.25 0.204 0.26 -0.214

ethanol 0.5 0.247 0.51 -0.516

ethanol 1 0.536 1.03 -0.480

ethanol 2 2.729 2.05 0.331

ethanol 3 1.490 3.09 -0.518

ethanol 6 2.627 6.08 -0.568

2-propanol 0.1 0.095 0.11 -0.135

2-propanol 0.25 0.195 0.27 -0.279

2-propanol 0.5 0.323 0.55 -0.412

2-propanol 1 0.830 1.1 -0.246

2-propanol 2 1.940 2.19 -0.114

2-propanol 3 2.479 3.3 -0.249

2-propanol 6 5.179 6.5 -0.203

acetone 0.1 0.138 0.12 0.150

acetone 0.25 0.234 0.28 -0.165

acetone 0.5 0.464 0.56 -0.171

acetone 1 1.053 1.13 -0.068

acetone 2 2.294 2.25 0.019

acetone 3 3.027 3.39 -0.107

acetone 6 6.125 6.68 -0.083

2,3-butanedione 0.1 0.000 0.11 -1.000

2,3-butanedione 0.25 0.184 0.26 -0.294

2,3-butanedione 0.5 0.380 0.52 -0.269

2,3-butanedione 1 0.924 1.05 -0.120

2,3-butanedione 2 1.837 2.09 -0.121

2,3-butanedione 3 2.836 3.15 -0.100

2,3-butanedione 6 6.047 6.2 -0.025

2,3-pentanedione 0.1 0.000 0.09 -1.000

2,3-pentanedione 0.25 0.124 0.23 -0.461

2,3-pentanedione 0.5 0.354 0.46 -0.230

2,3-pentanedione 1 0.810 0.92 -0.119

2,3-pentanedione 2 1.649 1.83 -0.099

2,3-pentanedione 3 2.735 2.76 -0.009

2,3-pentanedione 6 5.239 5.43 -0.035
Table A5 (continued): PPB-level limit of detection data, 2017

Analyte

Nominal 

Target 

Concentration 

(ppb)

Measured 

Concentration 

(ppb)

Theoretical 

Concentration 

(ppb) Bias

2,3-hexanedione 0.1 0.072 0.09 -0.197

2,3-hexanedione 0.25 0.158 0.22 -0.281

2,3-hexanedione 0.5 0.285 0.45 -0.366

2,3-hexanedione 1 0.629 0.9 -0.301

2,3-hexanedione 2 1.410 1.79 -0.212

2,3-hexanedione 3 2.216 2.7 -0.179

2,3-hexanedione 6 4.961 5.32 -0.067

dichloromethane 0.1 0.008 0.11 -0.932

dichloromethane 0.25 0.212 0.26 -0.183

dichloromethane 0.5 0.371 0.52 -0.288

dichloromethane 1 0.937 1.04 -0.099

dichloromethane 2 1.864 2.07 -0.100

dichloromethane 3 2.963 3.12 -0.050

dichloromethane 6 5.753 6.14 -0.063

trichloromethane 0.1 0.000 0.11 -1.000

trichloromethane 0.25 0.170 0.27 -0.370

trichloromethane 0.5 0.369 0.54 -0.317

trichloromethane 1 0.907 1.08 -0.161

trichloromethane 2 1.917 2.15 -0.108

trichloromethane 3 2.889 3.24 -0.108

trichloromethane 6 5.725 6.38 -0.103

hexane 0.1 0.065 0.11 -0.410

hexane 0.25 0.193 0.27 -0.287

hexane 0.5 0.407 0.54 -0.246

hexane 1 0.908 1.09 -0.167

hexane 2 1.877 2.17 -0.135

hexane 3 2.928 3.27 -0.105

hexane 6 5.980 6.44 -0.071

benzene 0.1 0.070 0.11 -0.363

benzene 0.25 0.209 0.27 -0.227

benzene 0.5 0.425 0.54 -0.213

benzene 1 0.897 1.08 -0.169

benzene 2 1.846 2.15 -0.141

benzene 3 2.992 3.24 -0.077

benzene 6 5.977 6.38 -0.063
Table A5 (continued): PPB-level limit of detection data, 2017

Analyte

Nominal 

Target 

Concentration 

(ppb)

Measured 

Concentration 

(ppb)

Theoretical 

Concentration 

(ppb) Bias

toluene 0.1 0.115 0.11 0.045

toluene 0.25 0.248 0.27 -0.081

toluene 0.5 0.411 0.53 -0.224

toluene 1 0.866 1.07 -0.190

toluene 2 1.780 2.13 -0.165

toluene 3 2.856 3.21 -0.110

toluene 6 5.976 6.32 -0.054

ethylbenzene 0.1 0.099 0.11 -0.096

ethylbenzene 0.25 0.239 0.26 -0.079

ethylbenzene 0.5 0.412 0.52 -0.207

ethylbenzene 1 0.846 1.05 -0.195

ethylbenzene 2 1.768 2.09 -0.154

ethylbenzene 3 2.844 3.15 -0.097

ethylbenzene 6 5.930 6.2 -0.044

m,p-xylene 0.1 0.101 0.11 -0.080

m,p-xylene 0.25 0.249 0.27 -0.076

m,p-xylene 0.5 0.415 0.55 -0.246

m,p-xylene 1 0.897 1.1 -0.185

m,p-xylene 2 1.832 2.19 -0.164

m,p-xylene 3 3.070 3.3 -0.070

m,p-xylene 6 6.311 6.5 -0.029

o-xylene 0.1 0.082 0.11 -0.253

o-xylene 0.25 0.203 0.26 -0.221

o-xylene 0.5 0.369 0.52 -0.290

o-xylene 1 0.852 1.05 -0.188

o-xylene 2 1.766 2.09 -0.155

o-xylene 3 2.938 3.15 -0.067

o-xylene 6 5.967 6.2 -0.038

methyl methacrylate 0.1 0.103 0.1 0.026

methyl methacrylate 0.25 0.204 0.25 -0.183

methyl methacrylate 0.5 0.345 0.51 -0.323

methyl methacrylate 1 0.783 1.02 -0.233

methyl methacrylate 2 1.601 2.03 -0.211

methyl methacrylate 3 2.538 3.06 -0.171

methyl methacrylate 6 5.634 6.03 -0.066
Table A5 (continued): PPB-level limit of detection data, 2017

Analyte

Nominal 

Target 

Concentration 

(ppb)

Measured 

Concentration 

(ppb)

Theoretical 

Concentration 

(ppb) Bias

α-Pinene 0.1 0.143 0.11 0.301

α-Pinene 0.25 0.245 0.26 -0.057

α-Pinene 0.5 0.392 0.52 -0.246

α-Pinene 1 0.767 1.05 -0.269

α-Pinene 2 1.602 2.09 -0.233

α-Pinene 3 2.657 3.15 -0.157

α-Pinene 6 5.742 6.2 -0.074

d-limonene 0.1 0.191 0.11 0.733

d-limonene 0.25 0.263 0.26 0.012

d-limonene 0.5 0.367 0.52 -0.293

d-limonene 1 0.670 1.04 -0.356

d-limonene 2 1.336 2.07 -0.355

d-limonene 3 2.346 3.12 -0.248

d-limonene 6 5.393 6.14 -0.122
Table A6: PPB-level storage stability study, 30 day

Analyte

Mean 

Concentration 

Day 0

(ppb) n= 12

Mean 

Concentration 

Day 30

(ppb) n= 12

CV

Day 0

CV

Day 30

Bias 

Day 30

ethanol 5.44 5.40 12.6 27.3 -0.006

2-propanol 5.52 5.87 11.2 24.7 0.064

acetone 5.83 5.98 10.9 21.1 0.025

dichloromethane 5.91 5.66 3.3 4.1 -0.041

trichloromethane 5.88 5.90 2.7 2.7 0.003

hexane 5.97 6.15 2.1 2.5 0.031

benzene 5.92 6.02 2.2 3.7 0.016

toluene 6.17 6.08 2.9 7.3 -0.014

ethylbenzene 5.75 5.94 4.5 8.1 0.033

m,p-xylene 5.49 5.65 6.8 8.5 0.029

o-xylene 5.56 5.76 7.3 9.1 0.038

methyl methacrylate 6.09 5.70 7.1 13.5 -0.064

α-pinene 5.79 5.24 5.2 16.6 -0.095

d-limonene 5.65 5.93 14.9 13.8 0.051
Table A7: PPM-level storage stability study, 30 day

Analyte

Mean 

Concentration 

Day 0

(ppm) n= 12

Mean 

Concentration 

Day 21

(ppm) n=3

Mean 

Concentration 

Day 30

(ppm) n=3

CV Day 

0

CV

Day 21

CV

Day 30

Bias 

Day 21

Bias 

Day 30

ethanol 0.771 0.797 0.700 4.0 16.7 8.9 0.034 -0.092

2-propanol 0.783 0.770 0.760 3.9 3.4 7.0 -0.016 -0.029

acetone 0.759 0.910 0.747 3.4 33.3 3.9 0.199 -0.016

dichloromethane 0.763 0.677 0.727 8.9 23.9 7.9 -0.113 -0.047

trichloromethane 0.673 0.660 0.660 0.9 2.6 3.0 -0.019 -0.019

hexane 0.713 0.707 0.713 1.2 2.2 1.6 -0.008 0.001

benzene 0.658 0.723 0.707 3.4 4.4 0.8 0.099 0.073

toluene 0.639 0.703 0.680 4.5 5.9 0.0 0.100 0.064

ethylbenzene 0.628 0.663 0.633 2.9 1.7 3.3 0.056 0.008

m,p-xylene 0.609 0.633 0.597 3.2 2.4 5.9 0.040 -0.021

o-xylene 0.625 0.650 0.623 3.2 2.7 3.3 0.040 -0.003

methyl 

methacrylate 0.644 0.693 0.653 3.8 5.8 4.7 0.076 0.014

α-pinene 0.638 0.677 0.527 3.4 1.7 50.5 0.061 -0.174

d-limonene 0.703 0.680 0.507 7.1 5.3 54.1 -0.032 -0.279
Table A8: PPB-level storage stability study, 58 day

Analyte

Mean 

Concentration 

Day 0

(ppb) n= 12

Mean 

Concentration 

Day 58

(ppb) n= 12

CV

Day 0

CV

Day 58

Bias 

Day 58

ethanol 9.17 10.68 2.8 3.3 16.50

2-propanol 9.84 11.45 2.8 14.2 16.10

acetone 10.27 11.41 2.3 6.8 11.17

2,3-butanedione 9.94 10.35 2.0 7.7 4.06

2,3-pentanedione 10.17 10.44 1.5 6.7 2.70

2,3 hexanedione 10.59 10.30 1.8 8.5 -2.88

dichloromethane 9.85 9.87 1.2 2.8 0.22

trichloromethane 9.98 10.30 1.0 3.6 3.13

hexane 10.41 10.78 1.2 4.6 3.55

benzene 9.98 10.34 1.2 3.5 3.58

toluene 10.58 10.75 1.2 2.6 1.57

ethylbenzene 10.51 10.45 1.4 3.4 -0.57

m,p-xylene 10.82 9.94 1.2 22.4 -8.13

o-xylene 10.47 10.11 1.1 5.1 -3.45

methyl methacrylate 10.07 10.22 1.2 3.7 1.46

α-pinene 10.38 10.03 0.7 5.1 -3.34

d-limonene 10.37 10.18 1.3 9.6 -1.87
Appendix B: Spike Batch Bias

Table B1: Reference laboratory PPB-range spike batch bias (mean and one standard deviation) across nominal 

level

Identifier 5 ppb 10 ppb 15 ppb

Analyte Spike Batch N Mean SD N Mean SD N Mean SD

ethanol 1 3 0.070 0.159 3 -0.131 0.025 3 -0.064 0.034

ethanol 2 3 -0.015 0.057 3 -0.049 0.089 3 0.048 0.016

ethanol 3 3 0.331 0.017 3 0.205 0.044 3 0.416 0.460

ethanol 4 3 0.370 0.040 3 0.153 0.016 3 0.187 0.081

ethanol 5 3 -0.179 0.006 3 -0.121 0.083 3 -0.024 0.037

2-propanol 1 3 0.131 0.037 3 0.023 0.024 3 0.063 0.048

2-propanol 2 3 0.078 0.042 3 0.008 0.193 3 0.107 0.053

2-propanol 3 3 0.101 0.058 3 0.027 0.034 3 0.123 0.123

2-propanol 4 3 0.055 0.022 3 0.087 0.037 3 0.050 0.036

2-propanol 5 3 -0.021 0.019 3 -0.055 0.186 3 0.052 0.057

acetone 1 3 0.283 0.058 3 0.057 0.004 3 0.073 0.028

acetone 2 3 0.182 0.024 3 0.075 0.112 3 0.095 0.012

acetone 3 3 0.046 0.016 3 0.013 0.036 3 0.086 0.135

acetone 4 3 0.064 0.010 3 -0.005 0.010 3 0.009 0.029

acetone 5 3 0.006 0.025 3 -0.063 0.090 3 0.019 0.027

2,3-butanedione 1 3 0.086 0.029 3 -0.012 0.002 3 0.047 0.031

2,3-butanedione 2 3 0.045 0.017 3 0.024 0.100 3 0.077 0.004

2,3-butanedione 3 3 0.043 0.022 3 0.004 0.038 3 0.044 0.060

2,3-butanedione 4 3 0.010 0.009 3 -0.020 0.012 3 0.016 0.027

2,3-butanedione 5 3 -0.026 0.021 3 -0.082 0.109 3 0.020 0.020

2,3-pentanedione 1 3 0.097 0.048 3 -0.025 0.006 3 0.064 0.037

2,3-pentanedione 2 3 0.012 0.013 3 -0.058 0.217 3 0.080 0.006

2,3-pentanedione 3 3 0.095 0.006 3 0.003 0.051 3 0.046 0.079

2,3-pentanedione 4 3 -0.019 0.008 3 -0.032 0.015 3 0.007 0.022

2,3-pentanedione 5 3 -0.050 0.019 3 -0.304 0.451 3 -0.015 0.037

2,3-hexanedione 1 3 0.130 0.089 3 0.004 0.008 3 0.093 0.036

2,3-hexanedione 2 3 0.017 0.016 3 -0.107 0.347 3 0.105 0.013

2,3-hexanedione 3 3 0.143 0.036 3 0.000 0.059 3 0.032 0.083

2,3-hexanedione 4 3 -0.016 0.009 3 -0.042 0.015 3 -0.014 0.025

2,3-hexanedione 5 3 -0.041 0.016 3 -0.325 0.516 3 -0.030 0.002
Table B1 (continued): Reference laboratory PPB-range spike batch bias (mean and one standard deviation) 

across nominal level

Identifier 5 ppb 10 ppb 15 ppb

Analyte Spike Batch N Mean SD N Mean SD N Mean SD

dichloromethane 1 3 0.107 0.003 3 -0.004 0.006 3 0.017 0.021

dichloromethane 2 3 0.089 0.023 3 0.057 0.005 3 0.049 0.006

dichloromethane 3 3 -0.014 0.024 3 -0.021 0.023 3 -0.009 0.039

dichloromethane 4 3 -0.009 0.009 3 -0.031 0.012 3 -0.022 0.021

dichloromethane 5 3 -0.019 0.012 3 0.005 0.006 3 0.008 0.006

trichloromethane 1 3 0.127 0.025 3 0.074 0.005 3 0.083 0.025

trichloromethane 2 3 0.122 0.027 3 0.091 0.006 3 0.068 0.004

trichloromethane 3 3 -0.018 0.025 3 -0.023 0.020 3 -0.016 0.025

trichloromethane 4 3 -0.017 0.006 3 -0.030 0.014 3 -0.024 0.020

trichloromethane 5 3 -0.017 0.015 3 -0.010 0.010 3 -0.005 0.002

hexane 1 3 0.037 0.029 3 0.004 0.004 3 0.016 0.022

hexane 2 3 0.054 0.024 3 0.071 0.006 3 0.063 0.007

hexane 3 3 -0.042 0.016 3 -0.016 0.011 3 0.005 0.025

hexane 4 3 -0.041 0.006 3 -0.026 0.012 3 0.000 0.018

hexane 5 3 -0.040 0.016 3 0.005 0.006 3 0.016 0.005

methyl methacrylate 1 3 0.122 0.074 3 -0.009 0.001 3 0.048 0.033

methyl methacrylate 2 3 -0.025 0.014 3 -0.059 0.180 3 0.063 0.003

methyl methacrylate 3 3 -0.041 0.015 3 -0.010 0.044 3 0.037 0.071

methyl methacrylate 4 3 -0.052 0.004 3 -0.031 0.017 3 0.008 0.021

methyl methacrylate 5 3 -0.056 0.012 3 -0.122 0.102 3 -0.053 0.004

benzene 1 3 0.052 0.022 3 0.005 0.004 3 0.018 0.024

benzene 2 3 0.040 0.022 3 0.048 0.010 3 0.047 0.005

benzene 3 3 -0.010 0.022 3 -0.015 0.017 3 -0.006 0.025

benzene 4 3 -0.015 0.004 3 -0.022 0.013 3 -0.014 0.019

benzene 5 3 -0.021 0.006 3 -0.029 0.013 3 -0.006 0.014

toluene 1 3 0.079 0.032 3 0.011 0.002 3 0.035 0.026

toluene 2 3 0.024 0.021 3 0.049 0.020 3 0.072 0.004

toluene 3 3 -0.020 0.026 3 -0.005 0.043 3 0.002 0.040

toluene 4 3 -0.031 0.009 3 -0.026 0.015 3 -0.010 0.020

toluene 5 3 -0.045 0.008 3 -0.034 0.009 3 -0.035 0.004

ethylbenzene 1 3 0.088 0.035 3 0.013 0.006 3 0.033 0.030

ethylbenzene 2 3 0.045 0.022 3 0.062 0.035 3 0.085 0.002

ethylbenzene 3 3 -0.003 0.019 3 -0.009 0.022 3 0.011 0.027

ethylbenzene 4 3 -0.030 0.004 3 -0.018 0.014 3 0.007 0.022

ethylbenzene 5 3 -0.069 0.005 3 -0.039 0.006 3 -0.031 0.003
Table B1 (continued): Reference laboratory PPB-range spike batch bias (mean and one standard deviation) 

across nominal level

Identifier 5 ppb 10 ppb 15 ppb

Analyte

Spike 

Batch N Mean SD N Mean SD N Mean SD

m,p-xylene 1 3 0.081 0.031 3 0.007 0.006 3 0.029 0.031

m,p-xylene 2 3 0.048 0.021 3 0.056 0.041 3 0.084 0.002

m,p-xylene 3 3 0.020 0.016 3 0.002 0.025 3 0.017 0.039

m,p-xylene 4 3 -0.012 0.005 3 -0.013 0.014 3 0.002 0.023

m,p-xylene 5 3 -0.053 0.008 3 -0.035 0.011 3 -0.034 0.006

o-xylene 1 3 0.075 0.025 3 0.015 0.006 3 0.029 0.029

o-xylene 2 3 0.049 0.018 3 0.073 0.030 3 0.087 0.003

o-xylene 3 3 -0.018 0.014 3 -0.039 0.012 3 -0.010 0.048

o-xylene 4 3 -0.049 0.002 3 -0.045 0.015 3 -0.029 0.026

o-xylene 5 3 -0.060 0.005 3 -0.041 0.016 3 -0.030 0.004

α-pinene 1 3 0.033 0.030 3 0.008 0.006 3 0.010 0.024

α-pinene 2 3 0.049 0.026 3 0.080 0.004 3 0.080 0.004

α-pinene 3 3 -0.029 0.030 3 -0.053 0.007 3 -0.014 0.052

α-pinene 4 3 -0.072 0.007 3 -0.050 0.015 3 -0.013 0.057

α-pinene 5 3 -0.070 0.007 3 -0.097 0.117 3 -0.027 0.007

d-limonene 1 3 0.031 0.022 3 0.016 0.005 3 0.034 0.027

d-limonene 2 3 -0.007 0.023 3 0.057 0.032 3 0.092 0.006

d-limonene 3 3 -0.089 0.039 3 -0.081 0.047 3 0.004 0.086

d-limonene 4 3 -0.154 0.015 3 -0.092 0.027 3 -0.040 0.033

d-limonene 5 3 -0.077 0.006 3 -0.065 0.022 3 -0.039 0.007
Table B2: Reference laboratory PPM-range spike batch bias (mean and one standard deviation) across 

nominal level

Identifier 0.8 ppm 1.3 ppm 1.7 ppm

Analyte

Spike 

Batch N Mean SD N Mean SD N Mean SD

ethanol 1 3 -0.465 0.157 3 -0.204 0.125 3 -0.172 0.021

ethanol 2 3 -0.235 0.092 3 -0.238 0.021 2 -0.206 0.116

ethanol 3 3 -0.285 0.027 3 -0.187 0.061 3 -0.175 0.051

ethanol 4 3 -0.312 0.093 3 -0.166 0.029 3 -0.065 0.062

ethanol 5 3 -0.286 0.164 3 -0.060 0.023 3 -0.058 0.037

ethanol 6 2 0.140 0.088 3 0.266 0.068 3 0.210 0.063

ethanol 7 2 0.252 0.122 3 0.214 0.050 3 0.281 0.128

2-propanol 1 3 -0.259 0.211 3 -0.006 0.110 3 -0.044 0.085

2-propanol 2 3 -0.054 0.006 3 -0.047 0.044 2 0.022 0.084

2-propanol 3 3 -0.136 0.003 3 -0.066 0.055 3 -0.138 0.055

2-propanol 4 3 -0.149 0.085 3 -0.105 0.017 3 -0.032 0.064

2-propanol 5 3 -0.108 0.056 3 -0.047 0.027 3 -0.029 0.017

2-propanol 6 2 0.065 0.142 3 0.081 0.123 3 -0.017 0.046

2-propanol 7 2 0.039 0.029 3 0.082 0.048 3 0.038 0.091

acetone 1 3 -0.109 0.188 3 0.112 0.082 3 0.076 0.039

acetone 2 3 0.084 0.034 3 0.062 0.052 2 0.127 0.078

acetone 3 3 -0.003 0.037 3 -0.002 0.004 3 -0.030 0.039

acetone 4 3 -0.049 0.057 3 -0.011 0.019 3 0.008 0.028

acetone 5 3 0.001 0.097 3 0.038 0.023 3 0.031 0.029

acetone 6 2 0.017 0.018 3 0.046 0.044 3 0.038 0.047

acetone 7 2 0.069 0.075 3 0.030 0.004 3 0.057 0.063

2,3-butanedione 1 3 -0.075 0.190 3 0.134 0.073 3 0.110 0.039

2,3-butanedione 2 3 0.086 0.018 3 0.085 0.051 2 0.175 0.060

2,3-butanedione 3 3 0.051 0.008 3 0.050 0.031 3 0.020 0.032

2,3-butanedione 4 3 -0.009 0.036 3 0.030 0.015 3 0.048 0.034

2,3-butanedione 5 3 0.059 0.041 3 0.080 0.026 3 0.067 0.037

2,3-butanedione 6 2 0.034 0.044 3 0.081 0.030 3 0.079 0.055

2,3-butanedione 7 2 0.057 0.070 3 0.066 0.009 3 0.096 0.056

2,3-pentanedione 1 3 -0.171 0.220 3 0.088 0.095 3 0.060 0.049

2,3-pentanedione 2 3 -0.007 0.020 3 0.022 0.025 2 0.133 0.073

2,3-pentanedione 3 3 0.003 0.008 3 0.055 0.063 3 0.004 0.027

2,3-pentanedione 4 3 -0.054 0.040 3 0.024 0.038 3 0.047 0.033

2,3-pentanedione 5 3 0.055 0.015 3 0.086 0.024 3 0.069 0.048

2,3-pentanedione 6 2 -0.001 0.091 3 0.039 0.033 3 0.030 0.052

2,3-pentanedione 7 2 -0.010 0.056 3 0.027 0.035 3 0.048 0.062
Table B2 (continued): Reference laboratory PPM-range spike batch bias (mean and one standard deviation) 

across nominal level

Identifier 0.8 ppm 1.3 ppm 1.7 ppm

Analyte

Spike 

Batch N Mean SD N Mean SD N Mean SD

2,3-hexanedione 1 3 -0.264 0.243 3 -0.013 0.089 3 -0.053 0.062

2,3-hexanedione 2 3 -0.089 0.012 3 -0.076 0.013 2 0.047 0.035

2,3-hexanedione 3 3 -0.071 0.014 3 0.004 0.058 3 -0.051 0.035

2,3-hexanedione 4 3 -0.116 0.024 3 -0.030 0.057 3 0.004 0.029

2,3-hexanedione 5 3 0.015 0.021 3 0.058 0.039 3 0.035 0.032

2,3-hexanedione 6 2 0.039 0.229 3 -0.001 0.019 3 -0.033 0.049

2,3-hexanedione 7 2 -0.027 0.081 3 -0.011 0.047 3 -0.011 0.062

dichloromethane 1 3 0.057 0.130 3 0.141 0.049 3 0.122 0.020

dichloromethane 2 3 0.149 0.024 3 0.098 0.063 2 0.179 0.045

dichloromethane 3 3 0.066 0.026 3 0.036 0.024 3 0.018 0.026

dichloromethane 4 3 0.028 0.030 3 0.023 0.020 3 0.026 0.034

dichloromethane 5 3 0.084 0.029 3 0.066 0.040 3 0.043 0.011

dichloromethane 6 2 0.063 0.004 3 0.078 0.015 3 0.103 0.067

dichloromethane 7 2 0.081 0.069 3 0.068 0.002 3 0.092 0.032

trichloromethane 1 3 0.166 0.143 3 0.245 0.026 3 0.223 0.017

trichloromethane 2 3 0.252 0.032 3 0.190 0.060 2 0.238 0.018

trichloromethane 3 3 0.095 0.016 3 0.036 0.019 3 0.014 0.033

trichloromethane 4 3 0.057 0.024 3 0.008 0.015 3 0.010 0.024

trichloromethane 5 3 0.100 0.041 3 0.041 0.019 3 0.037 0.035

trichloromethane 6 2 0.053 0.014 3 0.082 0.017 3 0.100 0.070

trichloromethane 7 2 0.075 0.078 3 0.067 0.005 3 0.090 0.033

hexane 1 3 0.083 0.143 3 0.178 0.019 3 0.170 0.015

hexane 2 3 0.169 0.031 3 0.134 0.059 2 0.176 0.001

hexane 3 3 0.041 0.022 3 0.029 0.019 3 0.017 0.034

hexane 4 3 0.031 0.027 3 0.002 0.022 3 0.013 0.021

hexane 5 3 0.069 0.022 3 0.039 0.019 3 0.028 0.017

hexane 6 2 0.051 0.009 3 0.103 0.019 3 0.135 0.069

hexane 7 2 0.081 0.083 3 0.092 0.005 3 0.122 0.034

methyl methacrylate 1 3 -0.119 0.192 3 0.088 0.078 3 0.059 0.042

methyl methacrylate 2 3 0.029 0.014 3 0.023 0.031 2 0.125 0.059

methyl methacrylate 3 3 -0.030 0.007 3 0.038 0.060 3 -0.006 0.030

methyl methacrylate 4 3 -0.057 0.032 3 0.003 0.039 3 0.032 0.027

methyl methacrylate 5 3 0.041 0.025 3 0.066 0.026 3 0.058 0.020

methyl methacrylate 6 2 0.033 0.099 3 0.040 0.026 3 0.050 0.061

methyl methacrylate 7 2 -0.005 0.058 3 0.024 0.029 3 0.049 0.062
Table B2 (continued): Reference laboratory PPM-range spike batch bias (mean and one standard deviation) 

across nominal level

Identifier 0.8 ppm 1.3 ppm 1.7 ppm

Analyte

Spike 

Batch N Mean SD N Mean SD N Mean SD

benzene 1 3 0.032 0.137 3 0.129 0.031 3 0.117 0.017

benzene 2 3 0.119 0.027 3 0.082 0.056 2 0.143 0.022

benzene 3 3 0.060 0.034 3 0.028 0.019 3 0.012 0.030

benzene 4 3 0.038 0.029 3 0.003 0.009 3 0.014 0.024

benzene 5 3 0.071 0.019 3 0.039 0.020 3 0.029 0.013

benzene 6 2 0.057 0.001 3 0.079 0.019 3 0.097 0.068

benzene 7 2 0.077 0.074 3 0.070 0.006 3 0.089 0.037

toluene 1 3 0.006 0.142 3 0.108 0.052 3 0.084 0.026

toluene 2 3 0.092 0.021 3 0.051 0.048 2 0.124 0.043

toluene 3 3 0.053 0.025 3 0.023 0.027 3 -0.006 0.022

toluene 4 3 0.027 0.032 3 -0.006 0.011 3 -0.001 0.024

toluene 5 3 0.077 0.013 3 0.042 0.022 3 0.028 0.023

toluene 6 2 0.039 0.017 3 0.070 0.019 3 0.088 0.075

toluene 7 2 0.072 0.096 3 0.045 0.008 3 0.077 0.040

ethylbenzene 1 3 -0.058 0.137 3 0.034 0.062 3 0.007 0.031

ethylbenzene 2 3 0.036 0.019 3 -0.018 0.030 2 0.043 0.054

ethylbenzene 3 3 -0.049 0.011 3 -0.050 0.035 3 -0.082 0.013

ethylbenzene 4 3 -0.057 0.043 3 -0.080 0.015 3 -0.076 0.019

ethylbenzene 5 3 0.010 0.019 3 -0.022 0.029 3 -0.039 0.029

ethylbenzene 6 2 0.025 0.022 3 0.053 0.021 3 0.065 0.071

ethylbenzene 7 2 0.016 0.056 3 0.034 0.008 3 0.074 0.055

m,p-xylene 1 3 -0.093 0.135 3 0.006 0.067 3 -0.027 0.037

m,p-xylene 2 3 0.010 0.020 3 -0.048 0.031 2 0.018 0.055

m,p-xylene 3 3 -0.060 0.013 3 -0.064 0.034 3 -0.101 0.010

m,p-xylene 4 3 -0.068 0.041 3 -0.093 0.022 3 -0.091 0.020

m,p-xylene 5 3 0.003 0.018 3 -0.031 0.033 3 -0.055 0.033

m,p-xylene 6 2 0.047 0.054 3 0.038 0.020 3 0.044 0.066

m,p-xylene 7 2 0.012 0.051 3 0.021 0.012 3 0.056 0.040

o-xylene 1 3 -0.099 0.137 3 -0.008 0.067 3 -0.043 0.040

o-xylene 2 3 0.004 0.018 3 -0.062 0.029 2 -0.001 0.050

o-xylene 3 3 -0.086 0.013 3 -0.091 0.035 3 -0.129 0.009

o-xylene 4 3 -0.089 0.041 3 -0.120 0.015 3 -0.123 0.019

o-xylene 5 3 -0.019 0.019 3 -0.060 0.027 3 -0.083 0.038

o-xylene 6 2 -0.013 0.012 3 -0.004 0.012 3 0.003 0.065

o-xylene 7 2 -0.039 0.042 3 -0.026 0.017 3 0.027 0.061
Table B2 (continued): Reference laboratory PPM-range spike batch bias (mean and one standard deviation) 

across nominal level

Identifier 0.8 ppm 1.3 ppm 1.7 ppm

Analyte

Spike 

Batch N Mean SD N Mean SD N Mean SD

α-pinene 1 3 -0.100 0.138 3 -0.036 0.054 3 -0.056 0.038

α-pinene 2 3 -0.020 0.012 3 -0.084 0.026 2 -0.060 0.028

α-pinene 3 3 -0.114 0.010 3 -0.129 0.043 3 -0.151 0.015

α-pinene 4 3 -0.104 0.044 3 -0.154 0.002 3 -0.156 0.015

α-pinene 5 3 -0.058 0.024 3 -0.109 0.012 3 -0.110 0.041

α-pinene 6 2 -0.030 0.044 3 0.016 0.027 3 0.039 0.071

α-pinene 7 2 -0.040 0.056 3 -0.024 0.009 3 0.031 0.061

d-limonene 1 3 -0.337 0.135 3 -0.216 0.090 3 -0.249 0.060

d-limonene 2 3 -0.215 0.014 3 -0.266 0.017 2 -0.205 0.059

d-limonene 3 3 -0.292 0.039 3 -0.280 0.062 3 -0.307 0.013

d-limonene 4 3 -0.270 0.043 3 -0.303 0.028 3 -0.302 0.011

d-limonene 5 3 -0.219 0.012 3 -0.239 0.029 3 -0.254 0.043

d-limonene 6 2 -0.085 0.061 3 -0.027 0.119 3 -0.104 0.041

d-limonene 7 2 -0.195 0.030 3 -0.125 0.068 3 -0.139 0.107