Endocrine Disruptors and Chemicals in Consumer Products
Endocrine Disruptors and Chemicals in Consumer Products
We selected 66 organic chemicals for inclusion in the study based on evidence of endocrine disruption or asthma exacerbation, expected presence in consumer products, and compatibility with analytical methods developed in our household exposure studies (Rudel et al. 2003, 2010). We tested 85 samples representing 213 products in two rounds of chemical analysis. The chemical groups, their typical uses, and the evidence of endocrine disruption or asthma exacerbation are listed in Supplemental Material, Table S1 (http://dx.doi.org/10.1289/ehp.1104052).
We first identified the types of products likely to contain the compounds of interest. Product types included personal care products (e.g., lotion, hair products, toothpaste), cleaners (e.g., laundry detergent, all-purpose cleaner), and other household goods. We then identified several "conventional" products and one "alternative" for each product type. Exclusion criteria for alternative products are listed in Table 1. A product was classified as alternative if the label did not include the terms listed in Table 1. Many of the products that met our criteria for alternative products were marked as "green." We also identified as alternative products six items often used in recipes for homemade cleaners, such as bleach and vinegar. Products that did not meet the "alternative" criteria were classified as conventional. In selecting conventional products, we tried to choose products that are widely used in order to better represent typical exposures. Because we lacked comprehensive information from which to select products, we identified leading companies for the product sector (e.g., hair care) based on market share and selected candidate products from several leading companies. When possible, we also included a generic store-brand product. Final product selections were made informally on the basis of availability and shelf space.
We purchased most alternative products at a nationwide store specializing in natural products, so products met the store's selection criteria, which favored non–petroleum-based—and especially plant-based—ingredients. Most of the conventional products were purchased at major grocery and pharmacy chain stores primarily in fall 2007. We added products for a second round of chemical analysis approximately 1 year later. Names of the products that were tested and their manufacturers are available from Silent Spring Institute (2012).
We analyzed 42 analytical samples composited from 170 conventional products and 43 samples of individual alternative products.
To cost-effectively evaluate typical exposures from conventional products, we composited 170 conventional products into a single sample for each product type (42 analytical samples). We combined equal masses of 1–7 products within a product type and analyzed the mixture as a single sample. The advantage of compositing is that samples may provide more generalizable exposure information. However, composited samples are more limited in that they a) will not reveal an unusually high concentration in a single product if that product is mixed with others having lower concentrations; b) will not reveal a concentration just above the limit of detection (LOD) in a single product if that product is mixed with others having concentrations < LOD; and c) may show a higher detection frequency for chemicals well within the detectable range.
We sought to identify specific products that were free of the chemicals of concern (alternative products), so the products could be used in an intervention study. Thus, we analyzed just 1 alternative product per product type (43 analytical samples, 1 for each of 43 individual products). Therefore, reported detection frequencies and concentrations for conventional and alternative product types are not directly comparable. To provide some information about variability in products within a category, we tested individual samples of 5 alternative sunscreens and calculated an average for the product type "alternative sunscreen."
We analyzed samples in two rounds: 50 compounds in the first round and those same 50 compounds plus 16 other compounds in the second round. Products were composited as described above, and surrogate recovery standards were added. Samples were then extracted with dichloromethane:methanol, passed through a weak anion exchange cartridge, spiked with internal standard, and analyzed by gas chromatography/mass spectrometry in the full scan mode. A separate aliquot was derivitized and analyzed for phenolic compounds.
For each compound, the method reporting limit (MRL) was defined as the maximum analytical LOD and the 90th percentile of the blank concentrations within each analytical round. The reporting limit was 1 μg/g for chemicals in products, but it was reported as > 1 μg/g if there were detectable concentrations in the blank samples (1 chemical in analytical round 1 and 12 chemicals in analytical round 2).
We included extensive quality assurance/quality control (QA/QC) samples in our analyses. Chemical detection in blanks was infrequent, and elevated MRLs were ≤ 5 μg/g except for cyclosiloxane decamethylcyclopentasiloxane (D5; the only compound detected in > 75% of blanks). Results were blank corrected by subtracting the median blank value from the reported value. Precision was assessed with 13 duplicate samples (relative percent difference was generally < 50%); accuracy was assessed by determining spike recovery for all target compounds in six different matrices (median recoveries across products were generally within 50–150%) and by calculating recoveries of surrogates in all samples (median percent recoveries were within the 50–150% acceptance range for all surrogates in both analytical rounds). For additional details regarding chemical analysis and QA/QC measures, see Supplemental Material, pp. S-9–S-10 (http://dx.doi.org/10.1289/ehp.1104052).
Our analysis of this large data set is visual and exploratory. We graphed product type against compounds detected using a "heat map" approach for conventional and alternative products (Figures 1 and 2, respectively). Only values > MRL or > 1 μg/g are presented. We graphed results for sunscreens in a similar format [see Supplemental Material, Figure S1 (http://dx.doi.org/10.1289/ehp.1104052); results are presented for a composited sample of conventional sunscreens, the calculated composite obtained by averaging results for five alternative sunscreens, and individual results for the five alternative sunscreens).
(Enlarge Image)
Figure 1.
Concentrations of target compounds (left) in conventional consumer products (bottom) by product type. Compounds are grouped by chemical class, with natural and synthetic fragrances distinguished by a dashed horizontal line within the figure. Numbers in parentheses after product type indicate number of products in the composite. Numbers at the top of the figure indicate the number of chemicals detected in each product type; numbers on the right indicate the number of products containing each compound. The first 27 product types (left of the solid vertical line) and the last product type (sunscreen) are also shown in Figure 2, but the remaining product types differ.
To identify chemicals that tend to co-occur because they are used together in a product, we estimated correlations for chemicals simultaneously detected within a product type (e.g., laundry detergent, lipstick). We calculated Kendall's tau adjusted for censored data and with p-values obtained from 10,000 bootstrap replications (Newton and Rudel 2007). The magnitude of Kendall's tau coefficients tends to be smaller than those of the more familiar Spearman's correlation coefficients. We limited this analysis to chemicals detected in more than three analytical samples, and we conducted analyses separately for conventional and alternative products.
Methods
We selected 66 organic chemicals for inclusion in the study based on evidence of endocrine disruption or asthma exacerbation, expected presence in consumer products, and compatibility with analytical methods developed in our household exposure studies (Rudel et al. 2003, 2010). We tested 85 samples representing 213 products in two rounds of chemical analysis. The chemical groups, their typical uses, and the evidence of endocrine disruption or asthma exacerbation are listed in Supplemental Material, Table S1 (http://dx.doi.org/10.1289/ehp.1104052).
Product Selection
We first identified the types of products likely to contain the compounds of interest. Product types included personal care products (e.g., lotion, hair products, toothpaste), cleaners (e.g., laundry detergent, all-purpose cleaner), and other household goods. We then identified several "conventional" products and one "alternative" for each product type. Exclusion criteria for alternative products are listed in Table 1. A product was classified as alternative if the label did not include the terms listed in Table 1. Many of the products that met our criteria for alternative products were marked as "green." We also identified as alternative products six items often used in recipes for homemade cleaners, such as bleach and vinegar. Products that did not meet the "alternative" criteria were classified as conventional. In selecting conventional products, we tried to choose products that are widely used in order to better represent typical exposures. Because we lacked comprehensive information from which to select products, we identified leading companies for the product sector (e.g., hair care) based on market share and selected candidate products from several leading companies. When possible, we also included a generic store-brand product. Final product selections were made informally on the basis of availability and shelf space.
We purchased most alternative products at a nationwide store specializing in natural products, so products met the store's selection criteria, which favored non–petroleum-based—and especially plant-based—ingredients. Most of the conventional products were purchased at major grocery and pharmacy chain stores primarily in fall 2007. We added products for a second round of chemical analysis approximately 1 year later. Names of the products that were tested and their manufacturers are available from Silent Spring Institute (2012).
Sampling Design and Compositing
We analyzed 42 analytical samples composited from 170 conventional products and 43 samples of individual alternative products.
To cost-effectively evaluate typical exposures from conventional products, we composited 170 conventional products into a single sample for each product type (42 analytical samples). We combined equal masses of 1–7 products within a product type and analyzed the mixture as a single sample. The advantage of compositing is that samples may provide more generalizable exposure information. However, composited samples are more limited in that they a) will not reveal an unusually high concentration in a single product if that product is mixed with others having lower concentrations; b) will not reveal a concentration just above the limit of detection (LOD) in a single product if that product is mixed with others having concentrations < LOD; and c) may show a higher detection frequency for chemicals well within the detectable range.
We sought to identify specific products that were free of the chemicals of concern (alternative products), so the products could be used in an intervention study. Thus, we analyzed just 1 alternative product per product type (43 analytical samples, 1 for each of 43 individual products). Therefore, reported detection frequencies and concentrations for conventional and alternative product types are not directly comparable. To provide some information about variability in products within a category, we tested individual samples of 5 alternative sunscreens and calculated an average for the product type "alternative sunscreen."
Chemical Analysis
We analyzed samples in two rounds: 50 compounds in the first round and those same 50 compounds plus 16 other compounds in the second round. Products were composited as described above, and surrogate recovery standards were added. Samples were then extracted with dichloromethane:methanol, passed through a weak anion exchange cartridge, spiked with internal standard, and analyzed by gas chromatography/mass spectrometry in the full scan mode. A separate aliquot was derivitized and analyzed for phenolic compounds.
For each compound, the method reporting limit (MRL) was defined as the maximum analytical LOD and the 90th percentile of the blank concentrations within each analytical round. The reporting limit was 1 μg/g for chemicals in products, but it was reported as > 1 μg/g if there were detectable concentrations in the blank samples (1 chemical in analytical round 1 and 12 chemicals in analytical round 2).
We included extensive quality assurance/quality control (QA/QC) samples in our analyses. Chemical detection in blanks was infrequent, and elevated MRLs were ≤ 5 μg/g except for cyclosiloxane decamethylcyclopentasiloxane (D5; the only compound detected in > 75% of blanks). Results were blank corrected by subtracting the median blank value from the reported value. Precision was assessed with 13 duplicate samples (relative percent difference was generally < 50%); accuracy was assessed by determining spike recovery for all target compounds in six different matrices (median recoveries across products were generally within 50–150%) and by calculating recoveries of surrogates in all samples (median percent recoveries were within the 50–150% acceptance range for all surrogates in both analytical rounds). For additional details regarding chemical analysis and QA/QC measures, see Supplemental Material, pp. S-9–S-10 (http://dx.doi.org/10.1289/ehp.1104052).
Data Analysis
Our analysis of this large data set is visual and exploratory. We graphed product type against compounds detected using a "heat map" approach for conventional and alternative products (Figures 1 and 2, respectively). Only values > MRL or > 1 μg/g are presented. We graphed results for sunscreens in a similar format [see Supplemental Material, Figure S1 (http://dx.doi.org/10.1289/ehp.1104052); results are presented for a composited sample of conventional sunscreens, the calculated composite obtained by averaging results for five alternative sunscreens, and individual results for the five alternative sunscreens).
(Enlarge Image)
Figure 1.
Concentrations of target compounds (left) in conventional consumer products (bottom) by product type. Compounds are grouped by chemical class, with natural and synthetic fragrances distinguished by a dashed horizontal line within the figure. Numbers in parentheses after product type indicate number of products in the composite. Numbers at the top of the figure indicate the number of chemicals detected in each product type; numbers on the right indicate the number of products containing each compound. The first 27 product types (left of the solid vertical line) and the last product type (sunscreen) are also shown in Figure 2, but the remaining product types differ.
To identify chemicals that tend to co-occur because they are used together in a product, we estimated correlations for chemicals simultaneously detected within a product type (e.g., laundry detergent, lipstick). We calculated Kendall's tau adjusted for censored data and with p-values obtained from 10,000 bootstrap replications (Newton and Rudel 2007). The magnitude of Kendall's tau coefficients tends to be smaller than those of the more familiar Spearman's correlation coefficients. We limited this analysis to chemicals detected in more than three analytical samples, and we conducted analyses separately for conventional and alternative products.
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