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Impact of exposure factor selection on deterministic consumer exposure assessment
Regulatory Toxicology and Pharmacology 94 (2018) 240–244
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Regulatory Toxicology and Pharmacology journal homepage: www.elsevier.com/locate/yrtph
Impact of exposure factor selection on deterministic consumer exposure assessment
T
Hyunkyung Bana, Ji Young Parkb, Daeyeop Leec, Kiyoung Leea,b,∗ a
Department of Environmental Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, South Korea Institute of Health and Environment, Seoul National University, Seoul, South Korea c Risk Assessment Division, National Institute of Environmental Research, Incheon, South Korea b
A R T I C L E I N F O
A B S T R A C T
Keywords: Consumer product Exposure assessment Deterministic Exposure factor
Deterministic exposure assessment has uncertainty about the selection of input parameters on the resulting estimates. The purpose of this study was to compare inhalation exposures estimated by a specific percentile of each of the three exposure factors in deterministic assessment with population exposure. Exposure to nine household care products, namely a deodorizer, six cleaning products, and two disinfectants were investigated. The population exposures were individually calculated for three exposure factors (frequency of use, amount of use, and duration of use) from an existing database of 3333 participants representing the national population. Deterministic exposure assessment was conducted according to various percentiles of exposure factors. 99th percentiles of population exposure in all nine consumer products were 1.3–2.4 times greater than the 95th percentiles. Inhalation exposures based on the 75th percentiles of each of the three exposure factors in deterministic assessment were much lower than the 95th percentiles of the population exposure. Deterministic exposure estimates using 85th to 99th percentiles of each of the three exposure factors were closer to the 95th percentiles of the population exposure. We concluded that exposure factors in deterministic assessment should be greater than the 75th percentile to more precisely estimate exposure of at-risk groups.
1. Introduction
patterns of distribution of exposure factors through the small samples. Even though the pattern of distribution on actual population determined by sample, which percentile is chosen as an input value may affect the uncertainty of the results. Deterministic methods can overestimate exposure levels because they use extreme values for the parameters (Fryer et al., 2006). The multiplication of several high percentiles together may result in the unrealistic estimates. In addition, the uncertainty of a result represented by a single value is not quantitatively considered in exposure estimation (Ferrier et al., 2002). There are some guidelines of selection exposure factors to estimate exposure to CPs by deterministic methods. The Consumer Exposure and Uptake Model (ConsExpo) of the Dutch National Institute for Public Health and The Environment (RIVM) is an example of a CP exposure model (van Veen, 1995). ConsExpo 4.0 recommended that the specific percentile value of each exposure factor's distribution could be used to estimate exposure levels by deterministic approaches (Delmaar and Schuur, 2016; Höglund et al., 2012). To avoid unrealistically high estimates and to maintain conservative exposure estimates, they selected a 75th percentile as a representative value for each parameter (Delmaar and Schuur, 2016). In ConsExpo fact sheets, the 75th percentile of
Exposure to chemicals in consumer products (CPs) is often estimated by indirect assessment using exposure scenarios. Model-based exposure assessments of chemicals in CPs have been conducted using probabilistic and deterministic methods. Probabilistic approaches consider probability distributions in input variables and predict the distribution of exposure in a target population (Cullen and Frey, 1999). Deterministic methods use point estimates of input parameters to provide a single worst-case value (IPCS, 2005). Deterministic methods are often used to screen CPs for hazardous exposures. Despite the allure of the apparent simplicity of deterministic methods, outcome may be strongly dependent on the selection of the percentile of the distribution of exposure factors. Although exposure factors can be obtained through surveys, behavioral observation, or activity models (Parmar et al., 1997), information on exposure factors is often limited. In order to estimate exposure with exposure factors, knowledge of the type of distribution (ie, normal, log-normal, other) is important, especially when estimating higher ranges of exposures (Hakkinen et al., 1991). However, it is difficult to find the exact
∗
Corresponding author. Graduate School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 88026, South Korea. E-mail address: [email protected] (K. Lee).
https://doi.org/10.1016/j.yrtph.2018.02.007 Received 7 November 2017; Received in revised form 8 February 2018; Accepted 9 February 2018 Available online 12 February 2018 0273-2300/ © 2018 Elsevier Inc. All rights reserved.
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exposure factors (frequency, amount, spray duration, and exposure duration) have been chosen as the default values for CPs such as cosmetics, cleaning products, and disinfectant products (Bremmer et al., 2006; Prud'Homme de Lodder et al., 2006a; Prud'Homme de Lodder et al., 2006b). One study conducted deterministic exposure assessment for CPs using ConsExpo's defaults (Gosens et al., 2014). The European Center for Ecotoxicology and Toxicology of Chemicals developed the Targeted Risk Assessment (TRA) tool for first tier assessments of consumer exposure (Ecetoc, 2009). Many default exposure factors in TRA were obtained from the RIVM fact sheets. When specific information was not available, values were derived using expert judgment. The United States Environmental Protection Agency (US EPA) developed the Consumer Exposure Model (CEM) for cleaning products (EPA, 2017). CEM provided the exposure factors by three classes of high, medium and low. Although a certain percentile was proposed in the selection the exposure factor in deterministic assessment the precise degree of uncertainty remained unknown. Errors associated with the selection of a specific percentile for an exposure factor need to be determined. The purpose of this study was to compare inhalation exposures estimated by a specific percentile of each of the three exposure factors in deterministic assessment with population exposure. Population exposure was calculated by exposure factors of 3333 participants representing the national population.
Table 2 Input parameters for inhalation exposure assessment. Parameter
VR (/h)
V (m3)
2.5b 2a 0.5c 2a 2a 2a 2a 0.5c 0.5c
24.5b 9.3a 33.3c 9.3a 9.3a 9.3a 9.3a 33.3c 33.3c
Product Dishwashing detergent Household bleach Fabric deodorizer Bathroom cleaner (Bottle) Bathroom cleaner (Trigger-type) Toilet rim cleaner Mold stain remover Glass cleaner Floor cleaner
Abbreviations: VR, ventilation rate; V, volume of space. a Ventilation rate and volume of the bathroom from KNIER (2015). b Ventilation rate and volume of the kitchen from KNIER. c Ventilation rate and volume of the living room from KNIER.
2.1. Exposure estimation This study considered daily exposure to 9 CPs via inhalation. Exposure estimate was daily exposure when the product was used. Daily inhalation exposure to each product was estimated using Equation (1) based on a model suggested by the National Institute of Environmental Research (KNIER) in Korea (KNIER, 2015):
2. Methods
DEi =
This study utilized use patterns of 9 CPs collected from 3333 participants. In a previous study, exposure factor data for 9 CPs such as a deodorizer (fabric deodorizer), cleaning products (dishwashing detergent, bathroom cleaner (bottle and trigger type), toilet rim cleaner, glass cleaner, and floor cleaner), and disinfectants (household bleach and mold stain remover) were collected (KNIER, 2012). Detailed information pertaining to data collection has been reported elsewhere (Park et al., 2015). Briefly, the nationwide survey was conducted in 15 metropolitan areas and provinces in Korea. The surveyed population included those age 15 years and older. The three exposure factors (frequency of use, amount of use, and duration of use) for the 9 CPs were obtained through face-to-face interviews. The characteristics and percentage of usage of the 9 CPs are presented in Table 1. Exposure factors were obtained from only users of each products (a range of participants: 442-2741 depending on CPs). The percentage of people using dishwashing detergent was the largest (82%) among the 3333 participants, and the percentage of people using floor cleaner was the lowest (13.3%). The survey population is referred to as ‘parent population’ in the following text.
Ap × Wf × exp(− VR × t ) × IR × abs × D × n V ×BW
(1)
where DEi is daily exposure via inhalation (mg/kg/day), Ap is the amount of product used (mg), Wf is the fraction of a specific chemical in the product (unitless), VR is the ventilation rate (h−1), t is the duration of consumer product use (h), IR is the inhalation rate (m3/h), abs is the absorption rate (unitless), D is the exposure duration (operation period) (h/event), n is the frequency of product use (event/day), V is the volume of space in use (m3), and BW is body weight (kg). The input parameters based on the exposure scenarios for each product are given in Table 2. The value of Wf was assumed to be 0.01 (1%). abs was assumed to be 1. By using an assumed weight fraction of 1%, the resulting value can be scaled by the actual weight fraction, when obtained. The IR was assumed to be 0.6 m3/h, which was the mean inhalation rate of the Korean adult population taken from the default database of the Korean exposure factors handbook (Jang et al., 2007). D was assumed to be 82 min, which was the duration of housework from the Korean exposure factors handbook. BW was assumed to be 64.2 kg, which was the mean weight of a Korean adult, as reported in the default database of the KNIER exposure assessment tool (KNIER, 2015). Ap, t, and n were based on survey data. 2.2. Data analysis
Table 1 Characteristics and percentage of usage for the 9 consumer products. Producta
Container type
The percentage of users (%)
The number of users
Dishwashing detergent Household bleach Fabric deodorizer Bathroom cleaner (Bottle) Bathroom cleaner (Trigger-type) Toilet rim cleaner Mold stain remover Glass cleaner Floor cleaner
Pump Bottle Trigger Bottle
82.2 56.4 36.1 20.8
2741 1881 1204 693
Trigger
18.3
609
Bottle Trigger Trigger Bottle
17.7 16.0 14.4 13.3
590 532 480 442
To obtain the exposure distribution of the parent population, the individual exposure of each product user was estimated using exposure factors for the 9 CPs. The exposure of one subject was calculated by three exposure factors (frequency of use, usage amount, and duration of use) from the subject. Based on a deterministic approach, the exposures were estimated using the same percentiles of the three exposure factors. The exposures were calculated with the 50th, 75th, 85th, 95th, and 99th percentiles of each exposure factor, while other factors were based on values previously defined in 2.1. Exposure estimation and Table 2. The point estimates of the exposure values were compared to the exposure distribution of the parent population. R version 3.3.2 (64-bit), an open-source statistical software programming language, was used for calculating median and range of the three exposure factors for the 9 CPs and the differences in exposures between samples and the parent population.
a The formulation of all products was liquid. Reorganized from a previous report (KNIER, 2015).
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Table 3 Descriptive statistics of exposure factors in the parent population. Exposure factors
Frequency of use (event/day)
Amount of use (g/event)
Duration of use (min/event)
Products
No. of users
Median
Range (Min, Max)
Median
Range (Min, Max)
Median
Range (Min, Max)
Dishwashing detergent Household bleach Fabric deodorizer Bathroom cleaner (Bottle)a Bathroom cleaner (Trigger-type)∗ Toilet rim cleaner Mold stain remover Glass cleaner Floor cleaner
2741 1881 1204 692
2.00 0.14 0.29 0.14
(0.03,10.00) (0.01,2.00) (0.01,4.00) (0.03,4.00)
5.80 59.5 3.36 53.20
(1.45,43.50) (11.9,1785.0) (0.84,16.80) (5.32,532.00)
10.00 10.00 1.00 10.00
(0.33,35.00) (0.05,60.00) (0.02,7.00) (0.17,30.00)
604
0.29
(0.03,1.00)
6.19
(1.03,30.96)
10.00
(0.17,30.00)
590 532 480 442
0.23 0.14 0.07 0.14
(0.03,1.00) (0.01,2.00) (0.01,1.00) (0.02,1.43)
70.25 9.60 5.15 53.20
(10.64,532.00) (1.92,76.80) (1.03,30.90) (10.64,532.00)
6.22 5.00 5.00 10.00
(0.08,10.83) (0.17,30.00) (0.05,15.00) (0.17,60.00)
The results are for users only. a One with the bottled bathroom cleaner and five users using the trigger-type bathroom cleaner reported the duration of use were zero. Therefore, this data is not included in this table.
3. Results and discussion
kg/day for dishwashing detergent, household bleach, fabric deodorizer, bottled bathroom cleaner, trigger-type bathroom cleaner, toilet rim cleaner, mold stain remover, glass cleaner, and floor cleaner, respectively. Inhalation exposure of all users was calculated by exposure factors of the larger population. Since exposure factors of the Korean nationwide database were used, the exposure distribution could represent nationwide exposure distribution among users. The exposure limit is often based on the 95th percentile of risk or exposure for protection of susceptible populations (Delmaar and Schuur, 2016; Höglund et al., 2012). Due to skewed exposure distribution, the 99th percentile of exposure was 1.4–2.4 times greater than the 95th percentile. Therefore, selection of the 95th percentile and 99th percentile of exposures for establishment of exposure limits could significantly change the outcome.
3.1. Exposure factors of CPs in the parent population The exposure factors of the 9 CPs were obtained from 3333 participants. The number of users, medians, and ranges of the exposure factors in the parent population are shown in Table 3. The most frequently used product was dishwashing detergent (median = 2/day). The median frequency use of glass cleaner was the lowest (approximately 2/month). The median amount used per application was highest for bottled bathroom cleaner (70.25 g/event) followed by toilet rim cleaner and floor cleaner (both 53.20 g/event), and lowest for fabric deodorizer (3.36 g/event). Trigger-type bathroom cleaner, bottled bathroom cleaner, household bleach, dishwashing detergent, and floor cleaner had the largest median durations per event of 10 min, while fabric deodorizer had the shortest median duration of 1 min. 3.2. Inhalation exposures of CPs in the parent population
3.3. Inhalation exposures of CPs by the deterministic method with each percentile of the exposure factors in the parent population
The inhalation exposure for 9 CPs of the parent population is shown in Table 4. The exposure estimate was the daily exposure for the product. It should be noted that ingredient weight was assumed to be 1% for this calculation. The bottled bathroom cleaner had the highest mean exposure of 0.26 ± 0.73 mg/kg/day, followed by toilet rim cleaner of 0.17 ± 0.17 mg/kg/day. The exposure to trigger-type bathroom cleaner was approximately 11% of the bottled bathroom cleaner. The glass cleaner had the lowest exposure of 0.0025 ± 0.0045 mg/kg/day. The 95th percentiles of inhalation exposures for the parent population were 0.12, 0.060, 0.19, 0.90, 0.11, 0.53, 0.048, 0.0076, and 0.19 mg/
Table 5 demonstrates the inhalation exposures of 9 CPs using specific percentiles of the exposure factors through the deterministic method. The exposure estimates were calculated by various percentiles of the exposure factors using the deterministic method. The inhalation exposures by 75th percentile of exposure factors were 0.060, 0.017, 0.11, 0.25, 0.031, 0.30, 0.027, 0.0052, and 0.11 mg/kg/day for dishwashing detergent, household bleach, fabric deodorizer, bottled bathroom cleaner, trigger-type bathroom cleaner, toilet rim cleaner, mold stain remover, glass cleaner, and floor cleaner, respectively. The inhalation exposures based on the 75th percentiles of the three
Table 4 Inhalation exposure of the parent population to consumer products. Products
Dishwashing detergent Household bleach Fabric deodorizer Bathroom cleaner (Bottle) Bathroom cleaner (Trigger-type) Toilet rim cleaner Mold stain remover Glass cleaner Floor cleaner
No. of users
Inhalation exposure of the parent population (mg/kg/day) Mean ± S.D.
50th
75th
85th
95th
99th
2741 1881 1204 692
0.049 ± 0.042 0.018 ± 0.022 0.054 ± 0.054 0.26 ± 0.73
0.040 0.0099 0.029 0.11
0.060 0.022 0.058 0.27
0.080 0.033 0.092 0.38
0.12 0.060 0.19 0.90
0.19 0.10 0.26 1.9
604
0.029 ± 0.036
0.016
0.034
0.051
0.11
0.17
590 532 480 442
0.17 ± 0.17 0.018 ± 0.023 0.0025 ± 0.0045 0.060 ± 0.069
0.098 0.012 0.0013 0.029
0.20 0.025 0.0026 0.080
0.30 0.032 0.0040 0.11
0.53 0.048 0.0076 0.19
0.82 0.091 0.018 0.38
The results are for users only. With assumption of 1% ingredient weight. S.D., Standard deviation.
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parent population exposures in a deterministic approach, the quantile of exposure factors should be the 99th, 95th, 85th, 95th, 90th, 85th and 90th, 85th, 88th, and 90th percentiles for dishwashing detergent, household bleach, fabric deodorizer, bottled bathroom cleaner, triggertype bathroom cleaner, toilet rim cleaner, mold stain remover, glass cleaner, and floor cleaner, respectively. When inhalation exposures to CPs were calculated using the 75th percentiles of each of the three exposure factors, the exposures were less than 95 percentile of the population exposure. Using ConsExpo 4.0, selection of 75th percentile of exposure factors was based on concerns relating to overestimation. When three exposure factors are included in an exposure algorithm, selection of the 75th percentile of the exposure factor can theoretically lead to approximately a 99th percentile of exposure (Delmaar and Schuur, 2016). However, error associated with the selection of a specific percentile for exposure factor has not been previously determined. Based on our findings, it is difficult to protect the high exposure group by determining the exposure of the worst case by using the 75th percentiles of each of the three exposure factors. Exposure factors using the deterministic method should exceed the 75th percentile. The strength of this study is the comparison of the deterministic exposures with the population exposures using real world personal exposure factor data. Inhalation exposures to CPs by the 75th percentiles of each of the three exposure factors were compared with the exposure distribution of the parent population. In a previous study, exposures by deterministic methods were compared with exposures by probabilistic approaches. The deterministic exposure assessment with defaults from ConsExpo 4.0 (mostly 75th percentiles) produced higher values than the probabilistic approach with exposure information from
Table 5 The inhalation exposures of consumer products by the deterministic method with each percentile of the exposure factors. Products
Dishwashing detergent Household bleach Fabric deodorizer Bathroom cleaner (Bottle) Bathroom cleaner (Trigger-type) Toilet rim cleaner Mold stain remover Glass cleaner Floor cleaner
Inhalation exposure by deterministic method with each percentile of the exposure factors (mg/kg/day) 50th
75th
85th
95th
99th
0.040
0.060
0.073
0.079
0.11
0.0084 0.030 0.075
0.017 0.11 0.25
0.036 0.16 0.48
0.064 0.40 1.1
0.16 0.56 1.6
0.017
0.031
0.10
0.10
0.16
0.088 0.016
0.30 0.027
0.45 0.046
1.1 0.062
1.6 0.19
0.0013 0.027
0.0052 0.11
0.0052 0.15
0.031 0.36
0.10 0.82
The results are for users only. With assumption of 1% ingredient weight.
exposure factors were much lower than the 95th percentile of exposure estimates of the parent population for all CPs (Fig. 1). The inhalation exposures by 75th percentile of exposure factors were 50%, 28%, 58%, 28%, 28%, 57%, 56%, 68%, and 58% of the 95th percentiles of the parent population exposure for dishwashing detergent, household bleach, fabric deodorizer, bottled bathroom cleaner, trigger-type bathroom cleaner, toilet rim cleaner, mold stain remover, glass cleaner, and floor cleaner, respectively. To estimate the 95th percentile of
Fig. 1. Comparison between inhalation exposure with the 75th percentiles of each of the three exposure factors and the 95th percentiles of parent population exposure.
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a small survey of 28 children (Gosens et al., 2014). However, the study compared deterministic and probabilistic exposures using different exposure factor data sources. Moreover, exposures by deterministic approaches were compared with those using a probabilistic approach without considering individual exposure distributions. This study applied exposure factors of a large population. Such a large sample size is not available in most studies. It may be possible to have larger variation with smaller sample sizes. Therefore, error associated with the selection of a specific percentile for exposure factors in small sample sizes should be investigated. Another limitation was the number and type of CPs. Error of a specific percentile for exposure factors could be related to the distribution of exposure factors. More diverse CPs should be investigated for selection of suitable percentiles for deterministic exposure assessment.
Reports/2016/december/ConsExpo_Web_Consumer_exposure_models_Model_ documentation (accessed August 2017). European Centre for Ecotoxicology and Toxicology of Chemicals (Ecetoc), 2009. TR 107 : Addendum to ECETOC Targeted Risk Assessment Technical Report No. 93. European Centre for Ecotoxicology and Toxicology of Chemicals. http://www.ecetoc.org/ publication/tr-107-addendum-to-ecetoc-targeted-risk-assessment-technical-reportno-93/ (accessed August 2017). Environmental Protection Agency (EPA), 2017. Consumer Exposure Model (CEM) Version 2.0 User's Guide. U.S. Environmental Progection Agency, Washington, DC. https:// www.epa.gov/tsca-screening-tools/consumer-exposure-model-cem-version-20-usersguide (accessed August 2017). Ferrier, H., Nieuwenhuijsen, M., Boobis, A., Elliott, P., 2002. Current knowledge and recent developments in consumer exposure assessment of pesticides: a UK perspective. Food Addit. Contam. 19, 837–852. Fryer, M., Collins, C.D., Ferrier, H., Colvile, R.N., Nieuwenhuijsen, M.J., 2006. Human exposure modelling for chemical risk assessment: a review of current approaches and research and policy implications. Environ. Sci. Pol. 9, 261–274. Gosens, I., Delmaar, C.J., ter Burg, W., de Heer, C., Schuur, A.G., 2014. Aggregate exposure approaches for parabens in personal care products: a case assessment for children between 0 and 3 years old. J. Expo. Sci. Environ. Epidemiol. 24, 208–214. Hakkinen, P., Kelling, C., Callender, J., 1991. Exposure assessment of consumer products: human body weights and total body surface areas to use, and sources of data for specific products. Vet. Hum. Toxicol. 33, 61–65. Höglund, L., Räisänen, J., Hämäläinen, A.-M., Warholm, M., van der Hagen, M., Suleiman, A., Kristjánsson, V., Nielsen, E., Kopp, T.I., 2012. Existing Default Values and Recommendations for Exposure Assessment: a Nordic Exposure Group Project 2011. Nordic Council of Ministers. http://urn.kb.se/resolve?urn= urn:nbn:se:norden:org:diva-2230 (accessed March 2017). International Programme on Chemical Safety (IPCS), 2005. Principles of Characterizing and Applying Human Exposure Models. World Health Organization, Geneva. http:// www.who.int/iris/handle/10665/43370 (accessed August 2017). Jang, J., Jo, S., Kim, S., Kim, S., Cheong, H., 2007. Korean Exposure Factors Handbook. Ministry of Environment, Seoul, pp. 3–224. http://library.me.go.kr/search/ DetailView.ax?sid=1&cid=175874 (accessed March 2017). Korea National Institute of Environmental Research (KNIER), 2012. Final Report of Development of Methodology for Exposure Factors of Consumer Products. . http:// library.me.go.kr/search/DetailView.ax?sid=1&cid=5582553 (accessed March 2017). KNIER, 2015. Methods for Risk Assessment. http://www.nier.go.kr/NIER/cop/bbs/ selectNoLoginBoardList.do?bbsId=BBSMSTR_000000000031&nttId=0& bbsTyCode=BBST06&bbsAttrbCode=BBSA03&authFlag=Y&pageIndex=1& menuNo=15001&searchCnd=0&searchWrd=%EC%9C%84%ED%95%B4% EC%84%B1 (accessed March 2017). Park, J.Y., Lee, K., Hwang, Y., Kim, J.H., 2015. Determining the exposure factors of personal and home care products for exposure assessment. Food Chem. Toxicol. 77, 105–110. Parmar, B., Miller, P.F., Burt, R., 1997. Stepwise approaches for estimating the intakes of chemicals in food. Regul. Toxicol. Pharmacol. 26, 44–51. Prud'Homme de Lodder, L., Bremmer, H., Pelgrom, S., Park, M., Van Engelen, J., 2006a. Disinfectant Products Fact Sheet. To Assess the Risks for the Consumer. http://www. rivm.nl/en/Documents_and_publications/Scientific/Reports/2006/augustus/ Disinfectant_Products_Fact_Sheet_To_assess_the_risks_for_the_consumer (accessed August 2017). Prud'Homme de Lodder, L., Bremmer, H., van Engelen, J., 2006b. Cleaning Products Fact Sheet. To Assess the Risks for the Consumer. http://www.rivm.nl/en/Documents_ and_publications/Scientific/Reports/2006/augustus/Cleaning_Products_Fact_Sheet_ To_assess_the_risks_for_the_consumer (accessed August 2017). van Veen, M.P., 1995. Consexpo: a Program to Estimate Consumer Product Exposure and Uptake. http://www.rivm.nl/en/Documents_and_publications/Scientific/Reports/ 1995/maart/CONSEXPO_a_program_to_estimate_Consumer_Product_Exposure_and_ Uptake (accessed August 2017).
4. Conclusions Inhalation exposure by deterministic methods could have errors due to selection of percentile of exposure factors. Inhalation exposures by the 75th percentiles of each of the three exposure factors were much lower than the 95th percentiles of the parent population exposures. For 9 CPs, exposures using a range between the 85th to 99th percentiles of each of the three exposure factors were closer to the 95th percentiles of the exposures of the parent population. Based on our findings, the deterministic method should use exposure factor of greater than 75th percentile in our data set. Further examination of other data set for suitable exposure factors should be conducted for generalization. Acknowledgements This study is supported by the Korea Ministry of Environment as “The Environmental Health Action Program” (#2015001940002). Transparency document Transparency document related to this article can be found online at http://dx.doi.org/10.1016/j.yrtph.2018.02.007 References Bremmer, H., Prud'Homme de Lodder, L., Van Engelen, J., 2006. Cosmetics Fact Sheet. To Assess the Risks for the Consumer. Updated version for ConsExpo 4. http://www. rivm.nl/en/Documents_and_publications/Scientific/Reports/2006/augustus/ Cosmetics_Fact_Sheet_To_assess_the_risks_for_the_consumer_Updated_version_for_ ConsExpo_4 (accessed March 2017). Cullen, A.C., Frey, H.C., 1999. Probabilistic Techniques in Exposure Assessment: a Handbook for Dealing with Variability and Uncertainty in Models and Inputs. Springer Science and Business Media, New York. Delmaar, J., Schuur, A.G., 2016. ConsExpo Web: Consumer Exposure Models-model Documentation. http://www.rivm.nl/en/Documents_and_publications/Scientific/
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Impact of exposure factor selection on deterministic consumer exposure assessment
T
Hyunkyung Bana, Ji Young Parkb, Daeyeop Leec, Kiyoung Leea,b,∗ a
Department of Environmental Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, South Korea Institute of Health and Environment, Seoul National University, Seoul, South Korea c Risk Assessment Division, National Institute of Environmental Research, Incheon, South Korea b
A R T I C L E I N F O
A B S T R A C T
Keywords: Consumer product Exposure assessment Deterministic Exposure factor
Deterministic exposure assessment has uncertainty about the selection of input parameters on the resulting estimates. The purpose of this study was to compare inhalation exposures estimated by a specific percentile of each of the three exposure factors in deterministic assessment with population exposure. Exposure to nine household care products, namely a deodorizer, six cleaning products, and two disinfectants were investigated. The population exposures were individually calculated for three exposure factors (frequency of use, amount of use, and duration of use) from an existing database of 3333 participants representing the national population. Deterministic exposure assessment was conducted according to various percentiles of exposure factors. 99th percentiles of population exposure in all nine consumer products were 1.3–2.4 times greater than the 95th percentiles. Inhalation exposures based on the 75th percentiles of each of the three exposure factors in deterministic assessment were much lower than the 95th percentiles of the population exposure. Deterministic exposure estimates using 85th to 99th percentiles of each of the three exposure factors were closer to the 95th percentiles of the population exposure. We concluded that exposure factors in deterministic assessment should be greater than the 75th percentile to more precisely estimate exposure of at-risk groups.
1. Introduction
patterns of distribution of exposure factors through the small samples. Even though the pattern of distribution on actual population determined by sample, which percentile is chosen as an input value may affect the uncertainty of the results. Deterministic methods can overestimate exposure levels because they use extreme values for the parameters (Fryer et al., 2006). The multiplication of several high percentiles together may result in the unrealistic estimates. In addition, the uncertainty of a result represented by a single value is not quantitatively considered in exposure estimation (Ferrier et al., 2002). There are some guidelines of selection exposure factors to estimate exposure to CPs by deterministic methods. The Consumer Exposure and Uptake Model (ConsExpo) of the Dutch National Institute for Public Health and The Environment (RIVM) is an example of a CP exposure model (van Veen, 1995). ConsExpo 4.0 recommended that the specific percentile value of each exposure factor's distribution could be used to estimate exposure levels by deterministic approaches (Delmaar and Schuur, 2016; Höglund et al., 2012). To avoid unrealistically high estimates and to maintain conservative exposure estimates, they selected a 75th percentile as a representative value for each parameter (Delmaar and Schuur, 2016). In ConsExpo fact sheets, the 75th percentile of
Exposure to chemicals in consumer products (CPs) is often estimated by indirect assessment using exposure scenarios. Model-based exposure assessments of chemicals in CPs have been conducted using probabilistic and deterministic methods. Probabilistic approaches consider probability distributions in input variables and predict the distribution of exposure in a target population (Cullen and Frey, 1999). Deterministic methods use point estimates of input parameters to provide a single worst-case value (IPCS, 2005). Deterministic methods are often used to screen CPs for hazardous exposures. Despite the allure of the apparent simplicity of deterministic methods, outcome may be strongly dependent on the selection of the percentile of the distribution of exposure factors. Although exposure factors can be obtained through surveys, behavioral observation, or activity models (Parmar et al., 1997), information on exposure factors is often limited. In order to estimate exposure with exposure factors, knowledge of the type of distribution (ie, normal, log-normal, other) is important, especially when estimating higher ranges of exposures (Hakkinen et al., 1991). However, it is difficult to find the exact
∗
Corresponding author. Graduate School of Public Health, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 88026, South Korea. E-mail address: [email protected] (K. Lee).
https://doi.org/10.1016/j.yrtph.2018.02.007 Received 7 November 2017; Received in revised form 8 February 2018; Accepted 9 February 2018 Available online 12 February 2018 0273-2300/ © 2018 Elsevier Inc. All rights reserved.
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exposure factors (frequency, amount, spray duration, and exposure duration) have been chosen as the default values for CPs such as cosmetics, cleaning products, and disinfectant products (Bremmer et al., 2006; Prud'Homme de Lodder et al., 2006a; Prud'Homme de Lodder et al., 2006b). One study conducted deterministic exposure assessment for CPs using ConsExpo's defaults (Gosens et al., 2014). The European Center for Ecotoxicology and Toxicology of Chemicals developed the Targeted Risk Assessment (TRA) tool for first tier assessments of consumer exposure (Ecetoc, 2009). Many default exposure factors in TRA were obtained from the RIVM fact sheets. When specific information was not available, values were derived using expert judgment. The United States Environmental Protection Agency (US EPA) developed the Consumer Exposure Model (CEM) for cleaning products (EPA, 2017). CEM provided the exposure factors by three classes of high, medium and low. Although a certain percentile was proposed in the selection the exposure factor in deterministic assessment the precise degree of uncertainty remained unknown. Errors associated with the selection of a specific percentile for an exposure factor need to be determined. The purpose of this study was to compare inhalation exposures estimated by a specific percentile of each of the three exposure factors in deterministic assessment with population exposure. Population exposure was calculated by exposure factors of 3333 participants representing the national population.
Table 2 Input parameters for inhalation exposure assessment. Parameter
VR (/h)
V (m3)
2.5b 2a 0.5c 2a 2a 2a 2a 0.5c 0.5c
24.5b 9.3a 33.3c 9.3a 9.3a 9.3a 9.3a 33.3c 33.3c
Product Dishwashing detergent Household bleach Fabric deodorizer Bathroom cleaner (Bottle) Bathroom cleaner (Trigger-type) Toilet rim cleaner Mold stain remover Glass cleaner Floor cleaner
Abbreviations: VR, ventilation rate; V, volume of space. a Ventilation rate and volume of the bathroom from KNIER (2015). b Ventilation rate and volume of the kitchen from KNIER. c Ventilation rate and volume of the living room from KNIER.
2.1. Exposure estimation This study considered daily exposure to 9 CPs via inhalation. Exposure estimate was daily exposure when the product was used. Daily inhalation exposure to each product was estimated using Equation (1) based on a model suggested by the National Institute of Environmental Research (KNIER) in Korea (KNIER, 2015):
2. Methods
DEi =
This study utilized use patterns of 9 CPs collected from 3333 participants. In a previous study, exposure factor data for 9 CPs such as a deodorizer (fabric deodorizer), cleaning products (dishwashing detergent, bathroom cleaner (bottle and trigger type), toilet rim cleaner, glass cleaner, and floor cleaner), and disinfectants (household bleach and mold stain remover) were collected (KNIER, 2012). Detailed information pertaining to data collection has been reported elsewhere (Park et al., 2015). Briefly, the nationwide survey was conducted in 15 metropolitan areas and provinces in Korea. The surveyed population included those age 15 years and older. The three exposure factors (frequency of use, amount of use, and duration of use) for the 9 CPs were obtained through face-to-face interviews. The characteristics and percentage of usage of the 9 CPs are presented in Table 1. Exposure factors were obtained from only users of each products (a range of participants: 442-2741 depending on CPs). The percentage of people using dishwashing detergent was the largest (82%) among the 3333 participants, and the percentage of people using floor cleaner was the lowest (13.3%). The survey population is referred to as ‘parent population’ in the following text.
Ap × Wf × exp(− VR × t ) × IR × abs × D × n V ×BW
(1)
where DEi is daily exposure via inhalation (mg/kg/day), Ap is the amount of product used (mg), Wf is the fraction of a specific chemical in the product (unitless), VR is the ventilation rate (h−1), t is the duration of consumer product use (h), IR is the inhalation rate (m3/h), abs is the absorption rate (unitless), D is the exposure duration (operation period) (h/event), n is the frequency of product use (event/day), V is the volume of space in use (m3), and BW is body weight (kg). The input parameters based on the exposure scenarios for each product are given in Table 2. The value of Wf was assumed to be 0.01 (1%). abs was assumed to be 1. By using an assumed weight fraction of 1%, the resulting value can be scaled by the actual weight fraction, when obtained. The IR was assumed to be 0.6 m3/h, which was the mean inhalation rate of the Korean adult population taken from the default database of the Korean exposure factors handbook (Jang et al., 2007). D was assumed to be 82 min, which was the duration of housework from the Korean exposure factors handbook. BW was assumed to be 64.2 kg, which was the mean weight of a Korean adult, as reported in the default database of the KNIER exposure assessment tool (KNIER, 2015). Ap, t, and n were based on survey data. 2.2. Data analysis
Table 1 Characteristics and percentage of usage for the 9 consumer products. Producta
Container type
The percentage of users (%)
The number of users
Dishwashing detergent Household bleach Fabric deodorizer Bathroom cleaner (Bottle) Bathroom cleaner (Trigger-type) Toilet rim cleaner Mold stain remover Glass cleaner Floor cleaner
Pump Bottle Trigger Bottle
82.2 56.4 36.1 20.8
2741 1881 1204 693
Trigger
18.3
609
Bottle Trigger Trigger Bottle
17.7 16.0 14.4 13.3
590 532 480 442
To obtain the exposure distribution of the parent population, the individual exposure of each product user was estimated using exposure factors for the 9 CPs. The exposure of one subject was calculated by three exposure factors (frequency of use, usage amount, and duration of use) from the subject. Based on a deterministic approach, the exposures were estimated using the same percentiles of the three exposure factors. The exposures were calculated with the 50th, 75th, 85th, 95th, and 99th percentiles of each exposure factor, while other factors were based on values previously defined in 2.1. Exposure estimation and Table 2. The point estimates of the exposure values were compared to the exposure distribution of the parent population. R version 3.3.2 (64-bit), an open-source statistical software programming language, was used for calculating median and range of the three exposure factors for the 9 CPs and the differences in exposures between samples and the parent population.
a The formulation of all products was liquid. Reorganized from a previous report (KNIER, 2015).
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Table 3 Descriptive statistics of exposure factors in the parent population. Exposure factors
Frequency of use (event/day)
Amount of use (g/event)
Duration of use (min/event)
Products
No. of users
Median
Range (Min, Max)
Median
Range (Min, Max)
Median
Range (Min, Max)
Dishwashing detergent Household bleach Fabric deodorizer Bathroom cleaner (Bottle)a Bathroom cleaner (Trigger-type)∗ Toilet rim cleaner Mold stain remover Glass cleaner Floor cleaner
2741 1881 1204 692
2.00 0.14 0.29 0.14
(0.03,10.00) (0.01,2.00) (0.01,4.00) (0.03,4.00)
5.80 59.5 3.36 53.20
(1.45,43.50) (11.9,1785.0) (0.84,16.80) (5.32,532.00)
10.00 10.00 1.00 10.00
(0.33,35.00) (0.05,60.00) (0.02,7.00) (0.17,30.00)
604
0.29
(0.03,1.00)
6.19
(1.03,30.96)
10.00
(0.17,30.00)
590 532 480 442
0.23 0.14 0.07 0.14
(0.03,1.00) (0.01,2.00) (0.01,1.00) (0.02,1.43)
70.25 9.60 5.15 53.20
(10.64,532.00) (1.92,76.80) (1.03,30.90) (10.64,532.00)
6.22 5.00 5.00 10.00
(0.08,10.83) (0.17,30.00) (0.05,15.00) (0.17,60.00)
The results are for users only. a One with the bottled bathroom cleaner and five users using the trigger-type bathroom cleaner reported the duration of use were zero. Therefore, this data is not included in this table.
3. Results and discussion
kg/day for dishwashing detergent, household bleach, fabric deodorizer, bottled bathroom cleaner, trigger-type bathroom cleaner, toilet rim cleaner, mold stain remover, glass cleaner, and floor cleaner, respectively. Inhalation exposure of all users was calculated by exposure factors of the larger population. Since exposure factors of the Korean nationwide database were used, the exposure distribution could represent nationwide exposure distribution among users. The exposure limit is often based on the 95th percentile of risk or exposure for protection of susceptible populations (Delmaar and Schuur, 2016; Höglund et al., 2012). Due to skewed exposure distribution, the 99th percentile of exposure was 1.4–2.4 times greater than the 95th percentile. Therefore, selection of the 95th percentile and 99th percentile of exposures for establishment of exposure limits could significantly change the outcome.
3.1. Exposure factors of CPs in the parent population The exposure factors of the 9 CPs were obtained from 3333 participants. The number of users, medians, and ranges of the exposure factors in the parent population are shown in Table 3. The most frequently used product was dishwashing detergent (median = 2/day). The median frequency use of glass cleaner was the lowest (approximately 2/month). The median amount used per application was highest for bottled bathroom cleaner (70.25 g/event) followed by toilet rim cleaner and floor cleaner (both 53.20 g/event), and lowest for fabric deodorizer (3.36 g/event). Trigger-type bathroom cleaner, bottled bathroom cleaner, household bleach, dishwashing detergent, and floor cleaner had the largest median durations per event of 10 min, while fabric deodorizer had the shortest median duration of 1 min. 3.2. Inhalation exposures of CPs in the parent population
3.3. Inhalation exposures of CPs by the deterministic method with each percentile of the exposure factors in the parent population
The inhalation exposure for 9 CPs of the parent population is shown in Table 4. The exposure estimate was the daily exposure for the product. It should be noted that ingredient weight was assumed to be 1% for this calculation. The bottled bathroom cleaner had the highest mean exposure of 0.26 ± 0.73 mg/kg/day, followed by toilet rim cleaner of 0.17 ± 0.17 mg/kg/day. The exposure to trigger-type bathroom cleaner was approximately 11% of the bottled bathroom cleaner. The glass cleaner had the lowest exposure of 0.0025 ± 0.0045 mg/kg/day. The 95th percentiles of inhalation exposures for the parent population were 0.12, 0.060, 0.19, 0.90, 0.11, 0.53, 0.048, 0.0076, and 0.19 mg/
Table 5 demonstrates the inhalation exposures of 9 CPs using specific percentiles of the exposure factors through the deterministic method. The exposure estimates were calculated by various percentiles of the exposure factors using the deterministic method. The inhalation exposures by 75th percentile of exposure factors were 0.060, 0.017, 0.11, 0.25, 0.031, 0.30, 0.027, 0.0052, and 0.11 mg/kg/day for dishwashing detergent, household bleach, fabric deodorizer, bottled bathroom cleaner, trigger-type bathroom cleaner, toilet rim cleaner, mold stain remover, glass cleaner, and floor cleaner, respectively. The inhalation exposures based on the 75th percentiles of the three
Table 4 Inhalation exposure of the parent population to consumer products. Products
Dishwashing detergent Household bleach Fabric deodorizer Bathroom cleaner (Bottle) Bathroom cleaner (Trigger-type) Toilet rim cleaner Mold stain remover Glass cleaner Floor cleaner
No. of users
Inhalation exposure of the parent population (mg/kg/day) Mean ± S.D.
50th
75th
85th
95th
99th
2741 1881 1204 692
0.049 ± 0.042 0.018 ± 0.022 0.054 ± 0.054 0.26 ± 0.73
0.040 0.0099 0.029 0.11
0.060 0.022 0.058 0.27
0.080 0.033 0.092 0.38
0.12 0.060 0.19 0.90
0.19 0.10 0.26 1.9
604
0.029 ± 0.036
0.016
0.034
0.051
0.11
0.17
590 532 480 442
0.17 ± 0.17 0.018 ± 0.023 0.0025 ± 0.0045 0.060 ± 0.069
0.098 0.012 0.0013 0.029
0.20 0.025 0.0026 0.080
0.30 0.032 0.0040 0.11
0.53 0.048 0.0076 0.19
0.82 0.091 0.018 0.38
The results are for users only. With assumption of 1% ingredient weight. S.D., Standard deviation.
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parent population exposures in a deterministic approach, the quantile of exposure factors should be the 99th, 95th, 85th, 95th, 90th, 85th and 90th, 85th, 88th, and 90th percentiles for dishwashing detergent, household bleach, fabric deodorizer, bottled bathroom cleaner, triggertype bathroom cleaner, toilet rim cleaner, mold stain remover, glass cleaner, and floor cleaner, respectively. When inhalation exposures to CPs were calculated using the 75th percentiles of each of the three exposure factors, the exposures were less than 95 percentile of the population exposure. Using ConsExpo 4.0, selection of 75th percentile of exposure factors was based on concerns relating to overestimation. When three exposure factors are included in an exposure algorithm, selection of the 75th percentile of the exposure factor can theoretically lead to approximately a 99th percentile of exposure (Delmaar and Schuur, 2016). However, error associated with the selection of a specific percentile for exposure factor has not been previously determined. Based on our findings, it is difficult to protect the high exposure group by determining the exposure of the worst case by using the 75th percentiles of each of the three exposure factors. Exposure factors using the deterministic method should exceed the 75th percentile. The strength of this study is the comparison of the deterministic exposures with the population exposures using real world personal exposure factor data. Inhalation exposures to CPs by the 75th percentiles of each of the three exposure factors were compared with the exposure distribution of the parent population. In a previous study, exposures by deterministic methods were compared with exposures by probabilistic approaches. The deterministic exposure assessment with defaults from ConsExpo 4.0 (mostly 75th percentiles) produced higher values than the probabilistic approach with exposure information from
Table 5 The inhalation exposures of consumer products by the deterministic method with each percentile of the exposure factors. Products
Dishwashing detergent Household bleach Fabric deodorizer Bathroom cleaner (Bottle) Bathroom cleaner (Trigger-type) Toilet rim cleaner Mold stain remover Glass cleaner Floor cleaner
Inhalation exposure by deterministic method with each percentile of the exposure factors (mg/kg/day) 50th
75th
85th
95th
99th
0.040
0.060
0.073
0.079
0.11
0.0084 0.030 0.075
0.017 0.11 0.25
0.036 0.16 0.48
0.064 0.40 1.1
0.16 0.56 1.6
0.017
0.031
0.10
0.10
0.16
0.088 0.016
0.30 0.027
0.45 0.046
1.1 0.062
1.6 0.19
0.0013 0.027
0.0052 0.11
0.0052 0.15
0.031 0.36
0.10 0.82
The results are for users only. With assumption of 1% ingredient weight.
exposure factors were much lower than the 95th percentile of exposure estimates of the parent population for all CPs (Fig. 1). The inhalation exposures by 75th percentile of exposure factors were 50%, 28%, 58%, 28%, 28%, 57%, 56%, 68%, and 58% of the 95th percentiles of the parent population exposure for dishwashing detergent, household bleach, fabric deodorizer, bottled bathroom cleaner, trigger-type bathroom cleaner, toilet rim cleaner, mold stain remover, glass cleaner, and floor cleaner, respectively. To estimate the 95th percentile of
Fig. 1. Comparison between inhalation exposure with the 75th percentiles of each of the three exposure factors and the 95th percentiles of parent population exposure.
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a small survey of 28 children (Gosens et al., 2014). However, the study compared deterministic and probabilistic exposures using different exposure factor data sources. Moreover, exposures by deterministic approaches were compared with those using a probabilistic approach without considering individual exposure distributions. This study applied exposure factors of a large population. Such a large sample size is not available in most studies. It may be possible to have larger variation with smaller sample sizes. Therefore, error associated with the selection of a specific percentile for exposure factors in small sample sizes should be investigated. Another limitation was the number and type of CPs. Error of a specific percentile for exposure factors could be related to the distribution of exposure factors. More diverse CPs should be investigated for selection of suitable percentiles for deterministic exposure assessment.
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4. Conclusions Inhalation exposure by deterministic methods could have errors due to selection of percentile of exposure factors. Inhalation exposures by the 75th percentiles of each of the three exposure factors were much lower than the 95th percentiles of the parent population exposures. For 9 CPs, exposures using a range between the 85th to 99th percentiles of each of the three exposure factors were closer to the 95th percentiles of the exposures of the parent population. Based on our findings, the deterministic method should use exposure factor of greater than 75th percentile in our data set. Further examination of other data set for suitable exposure factors should be conducted for generalization. Acknowledgements This study is supported by the Korea Ministry of Environment as “The Environmental Health Action Program” (#2015001940002). Transparency document Transparency document related to this article can be found online at http://dx.doi.org/10.1016/j.yrtph.2018.02.007 References Bremmer, H., Prud'Homme de Lodder, L., Van Engelen, J., 2006. Cosmetics Fact Sheet. To Assess the Risks for the Consumer. Updated version for ConsExpo 4. http://www. rivm.nl/en/Documents_and_publications/Scientific/Reports/2006/augustus/ Cosmetics_Fact_Sheet_To_assess_the_risks_for_the_consumer_Updated_version_for_ ConsExpo_4 (accessed March 2017). Cullen, A.C., Frey, H.C., 1999. Probabilistic Techniques in Exposure Assessment: a Handbook for Dealing with Variability and Uncertainty in Models and Inputs. Springer Science and Business Media, New York. Delmaar, J., Schuur, A.G., 2016. ConsExpo Web: Consumer Exposure Models-model Documentation. http://www.rivm.nl/en/Documents_and_publications/Scientific/
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