Environ. Sci. Technol. 2005, 39, 371-376
Methodology for Predicting OEL from Rodent LD50 Values for Metals and Metallic Compounds H I D E T A K A Y A N A G I D A , * ,† AKIHIRO YAMASAKI,‡ AND YUKIO YANAGISAWA† Department of Environmental System, Institute of Environmental Studies, Graduate School of Frontier Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, Japan, 113-8656, and National Institute of Advanced Industrial Science and Technology (AIST), 16-1 Onogawa, Tsukuba, Japan, 305-8569
The relationship between the occupational exposure limits (OEL) and the lethal dose 50 (LD50) values of rats or mice for metals and metallic compounds was statistically analyzed by a stepwise multivariate regression method. The OEL values were predicted from LD50 values and metallic compensation coefficients (MCC), which were developed as the regression coefficients of dummy variables that represented the metallic element contained in the substance of interest. The value of the MCC indicated the extent of the adverse health effects of the metal in the substance. Smaller values of the MCC were assigned to metals that would have the more severe adverse health effects, such as carcinogenesis, while larger values were given to the less toxic metals. The Health Index (HI) based on the OEL values was proposed as a convenient measure of the toxicity of industrial products. The prediction method could be applied to toxicity risk assessments by using the HI when a designer of consumer products wants to use substances for which OEL values have not been determined. Two case studies were conducted to estimate the potential toxicity of materials used in solders and in rechargeable batteries.
Introduction How do engineers select materials when designing consumer products? The selection criteria of raw materials for consumer products used to be based upon functions and costs. Design engineers, however, should require careful attention to the risks to the environment and to human health of these materials because consumer products are composed of a variety of substances, some of which are harmful (1), and consumers may be at risk of exposure to such toxic substances during use and following disposal. In particular, some home electrical appliances may contain highly toxic substances such as lead, mercury, and cadmium. The use of such toxic substances should be minimized or eliminated by replacing them with less harmful materials to reduce the exposure risks. The Restriction of Hazardous Substances (RoHS) directive (2, 3), which will be implemented in 2006 in Europe, * Corresponding author phone: +81-3-5841-7335; fax: +81-35841-8583; e-mail:
[email protected]. † The University of Tokyo. ‡ National Institute of Advanced Industrial Science and Technology. 10.1021/es0490323 CCC: $30.25 Published on Web 11/24/2004
2005 American Chemical Society
bans any sales of new electrical and electronic appliances containing mercury, lead, cadmium, hexavalent chromium, polybrominated biphenyls, and polybrominated diphenyl ethers. Manufacturers of home electrical appliances, therefore, should develop and use appropriate substitutes for these toxic substances in their manufacturing processes. For instance, lead-free solders for home electric appliances have been developed as safer alternatives to the conventional lead-tin (Pb/Sn) solders (4-6). To estimate the risks posed by consumer products, a convenient measure is necessary that represents the potential toxicity of the substances used. Horvath et al. proposed a toxic emission index, CMU-ET (Carnegie Mellon University Equivalent Toxicity) (7). The CMU-ET was proposed as a numerical index that characterizes the toxicity of the amount of substances and can be applied to toxics release inventory (TRI). The CMU-ET is defined as the summation of the amounts of substances multiplied by a toxic weighting factor for each substance. The toxic weighting factor is defined by the ratio of the TLV (threshold limit values means TLV-TWA defined by ACGIH) of a reference substance to that of the target substance. The TLVReference is arbitrarily set at 1 mg/m3 for 8 h in the CMU-ET. With a slight modification of the CMU-ET, we proposed the HI as a measure to estimate the potential toxicity of consumer products. The HI was defined as the summation of the amounts of substances weighted by the modified toxic weighting factors. The factors were modified by setting the reference value of OEL, OELReference, at 10 mg/m3, the largest value of the OEL among the heavy metals and metallic compounds, assigned to boron oxide, ferrocene, and ferbam. The OEL stands for occupational exposure limits, and in the present study we use the OEL instead of TLV, which is a registered trademark of the ACGIH. With this reference value, the modified toxic weighting factors of any other substances become larger than 1. Therefore, the relative toxicity of the substances can be easily characterized. The HI is useful as a screening tool at the product design stage to select proper materials in terms of toxicity. Although the OEL values have been determined for a large number of substances, it would be almost impossible to cover all substances that could possibly be used in the whole range of commercial products. Estimation or prediction of the OEL values would often be necessary for the evaluation of the total HI of a given material. Such predictions or estimations would have to be based on alternative toxicity data that could cover a wider range of substances. For this purpose, we selected LD50 values for rats and mice (8-10) and sought their correlation with the OEL values. A statistical procedure was employed to explicate the correlation between the OEL and the LD50 values for substances for which both values are available. On the basis of the analysis, a regression equation was obtained for predicting the unknown OEL value from the known value of the LD50. To improve the correlation, correction parameters (metal compensation coefficients; MCC) were introduced as the regression coefficient of a dummy variable denoting the metals contained in the substances. The prediction method was then applied to case studies on toxicity risk assessments of solders and rechargeable batteries by the use of the HI.
Methods Data Sources. Collected values of OELs and LD50 values in “The Dictionary of Substances and their Effects (DOSE)” (8) were mainly used as the database for the analysis. The DOSE contains an OEL database of six countries, namely, the United VOL. 39, NO. 1, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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Kingdom, France, Germany, Sweden, the United States, and Japan. Since the OEL values have been independently determined and regulated in each country, some substances were assigned different OEL values by different countries. The smallest OEL value for these countries was used to represent the maximum adverse health effect by a given substance. The LD50 values for rats (LD50rat) and those for mice (LD50mouse) for oral ingestion were used for the statistical analysis together with the OEL values. A total of 121 metallic substances, providing both LD50rat and OEL, were selected as the target substances for the statistical analysis. Similarly 65 metallic substances with LD50mouse and OEL were subject to the regression analyses. These substances used for the rat and mice analyses are tabulated in Appendix 1 and 2, respectively, in the Supporting Information. Health Index. The CMU-ET is defined by n
CMU-ET )
∑(X × w ) i
i
(1)
i)1
Here Xi denotes the mass of the ith substance of the toxic release inventory, and wi indicates its toxic weighting factor. The toxic weighting factor (wi) is given by
wi )
TLVReference TLVi
(2)
where TLVReference denotes the reference value of the TLV set at 1 mg/m3 over 8 h (7). The values of the weighting factor (wi) become larger for the more toxic substances. The CMUET is a convenient and useful index to represent the total toxicity of mixed substances. We adopted the above concept of the CMU-ET to indicate the potential toxicity of commercial products with a slight modification. We propose the HI as a measure of toxicity for a given consumer product with n components: n
HI )
∑(m × w˜ ) i
i
(3)
i)1
where mi is the mass of the ith component in the product. w ˜ i is the modified toxic weighting factor defined as the ratio of the OELReference to the OEL of each component and given by
w ˜i )
OELReference 10 ) OELi OELi
(4)
The OELReference was set at 10 mg/m3. Since the maximum OEL value for heavy metals and metallic compounds assigned to boron oxide, ferrocene, and ferbam (8) is 10 mg/m3, the modified toxic weighting factor of any other substance becomes larger than 1. The modified toxic weighting factors values can be easily envisaged and compared intuitively. Statistical Analysis. A multivariate regression analysis (11) was applied to the LD50rat or LD50mouse values (of which the logarithms were set as independent variables) and the OELs (of which the logarithms were set as dependent variables). In addition, dummy variables denoting the metal element were introduced; the dummy variable of a given metal is set as 1 when that metal is contained, and 0 when it is not contained in the substance. The linear regression coefficients between log(OEL) and log(LD50) (of mouse or rat) were calculated by multivariate analysis with a stepwise linear regression model. The SPSS statistical package for Windows (Version 9.0, SPSS Inc.) was used for the analysis (12). The following analytical conditions were applied; for the stepwise method, a variable was included as an explanatory variable when the probability of F-distribution was less than 0.05 and 372
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FIGURE 1. Result of the regression analysis between log(OEL) and log(LD50rat) without dummy variables. Key: (O) zirconium and its compounds; (0) cadmium and its compounds; (b) metals and metallic compounds. excluded when the probability was larger than 0.1. No collinearity among independent variables was confirmed by a VIF (variance inflation factor) less than 10 (13). The probability of the alternative hypothesis of no correlation was set with p < 0.05 for acceptance as statistically significant.
Results and Discussion Relationship between OEL and LD50rat without Dummy Variables. Without using dummy variables in the regression model, the adjusted coefficient of determination of the regression analysis between log(OEL) and log(LD50rat) was 0.23 (p < 0.05, n ) 121). We could observe that some metals and metallic compounds formed distinct groups on the plot of log(OEL) versus log(LD50rat), for example, cadmium and its compounds occurring below the regression line and zirconium above the line (Figure 1). Therefore, we expected a better estimation of the OEL could be obtained by applying the multivariate regression using dummy variables to include the degree of toxicity of each metal. Stepwise Multivariate Regression Analysis between OEL and LD50rat with Dummy Variables. From the stepwise multivariate regression analysis between log(OEL) and log(LD50rat), dummy variables of Cd, Be, Co, Hg, Pt, As, Fe, Ag, Mo, Zr, Mn, Al, Ba, Cu, Zn, Ta, and Rh were chosen as explaining variables, and Cr, Ni, Pb, Sb, Se, Sn, Te, Tl, and V were excluded. The adjusted coefficient of determination increased from 0.23 without using dummy variables to 0.86 with the inclusion of 17 dummy variables. Based on the above statistical analysis, the following regression equation (eq 5) was obtained for OEL and LD50rat: 8
predicted-OELrat ) 0.0485 × LD50rat0.13 × (MCC1) × (MCC2) × ... × (MCCn) (5) We can predict the OEL from eq 5, which contained a metallic compensation coefficient (MCCr) for specific metals and metallic compounds with particularly high or low toxicities. In Table 1, we show the values of the MCCr derived from 10th power of the regression coefficients for the dummy variables. It is necessary to adjust the OEL with MCCr because errors of the same metal and metallic compounds are approximately on the same line parallel to the regression line. The lowest MCCr, 1.83 × 10-2, is assigned to cadmium, and the highest, 52.8, is assigned to molybdenum. The
TABLE 1. Metallic Compensation Coefficients (MCCr) for LD50rat and OEL metal and metallic compd
MCCr
Cd Be Pt Ag As Co Hg
1.83E-02 2.22E-02 2.75E-02 1.20E-01 2.03E-01 2.03E-01 2.12E-01
metal and metallic compd
MCCr
Cu Ba Mn Al Rh Ta Zn Zr Fe Mo
3.08E+00 4.44E+00 5.41E+00 6.63E+00 7.64E+00 8.91E+00 9.16E+00 1.20E+01 1.91E+01 5.28E+01
TABLE 2. Results of Multivariate Regression Analysis for OEL and LD50rat unstandardized coeff
(constant) log(LD50) Cd Be Co Hg Pt As Fe Ag Mo Zr Mn Al Ba Cu Zn Ta Rh
statistic of collinearity
regression coeff
SE
p
tolerance
variance inflation factor
-1.314 0.138 -1.737 -1.654 -0.692 -0.673 -1.560 -0.693 1.281 -0.922 1.723 1.079 0.733 0.821 0.647 0.489 0.962 0.950 0.883
0.110 0.046 0.152 0.205 0.146 0.097 0.209 0.127 0.162 0.249 0.347 0.214 0.166 0.217 0.182 0.138 0.348 0.357 0.350
0.000 0.003 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.001 0.007 0.009 0.013
0.573 0.889 0.950 0.828 0.752 0.921 0.873 0.782 0.962 0.982 0.874 0.888 0.854 0.915 0.830 0.978 0.927 0.964
1.744 1.125 1.053 1.207 1.330 1.085 1.145 1.278 1.040 1.018 1.144 1.126 1.171 1.092 1.205 1.022 1.078 1.038
For compounds containing two kinds of metals such as copper acetoarsenite, the OEL value is predicted by the following equation: 0.138 OEL ) 0.0485 × LD50rat × MCCCu × MCCAs ) 0.138 0.0485 × LD50rat × 3.08 × 0.203 (8)
regression equation was significant at a level of 0.05 as shown in Table 2. No collinearity among the independent variables was confirmed because the VIF was less than 10. Examples of How to Use MCCr in the Prediction of OELs. We show some typical examples of the OEL prediction. The simplest case is to predict the OEL values for compounds with MCCr value of 1, such as nickel chloride. OEL is predicted from the following equation: 0.138 OEL ) 0.0485 × LD50rat
FIGURE 2. Regression line between log(OEL) and log(LD50rat) with dummy variables given by eq 6. Key: (O) metals with MCCr > 1; (0) metals with MCCr < 1; (2) metals with MCCr ) 1.
(6)
Since the value of LD50rat of nickel chloride can be referred as 105 mg/kg from Appendix 1 (Supporting Information), the OEL value of nickel chloride is predicted as 0.092 mg/m3. The predicted OEL value of 0.092 mg/m3 is reasonably consistent with the actual values of 0.1 mg/m3 referred from Appendix 1 (Supporting Information). For oxine copper with a MCCr value of copper, 3.08 (Table 1), the OEL value can be predicted by a following equation:
OEL ) 0.0485 × LD50rat0.138 × MCCCu ) 0.0485 × 0.138 LD50rat × 3.08 (7)
The predicted value of OEL for oxine copper is 0.48 mg/m3 and approximately one-half of the actual value of 1 mg/m3. These predicted and actual values are in the same order of magnitude.
where MCCCu () 3.08) and MCCAs () 0.203) are MCCr for copper and arsenic, respectively. The predicted OEL for acetoarsenite is 0.047 mg/m3, the same order of magnitude as the actual value of 0.01 mg/m3. Discussion of Metal Compensation Coefficients (MCCr). The metal compensation coefficients (MCCr) were introduced as regression coefficients for dummy variables representing metals contained in the substances under consideration. In this section, the values of MCCr are examined in terms of the adverse health effects caused by metals and metallic compounds. The metals treated in this study can be divided into three groups according to the values of the MCCr: (a) MCCr ) 1, (b) MCCr < 1, and (c) MCCr >1. The regression line given by eq 6 is shown in Figure 2, where circles indicate metals and metallic compounds of MCCr > 1 and squares represent those of MCCr < 1. Circles for MCCr > 1 are distributed above the regression line, while most of squares for MCCr < 1 are located below the line. Triangles show MCCr ) 1 and distribute around the regression line of eq 6. For the metals in the MCCr ) 1 group a, the OEL can be directly predicted from the LD50rat value. On the other hand, to predict the value of the OEL from the LD50rat value, correction with the MCCr is essential for metals in groups b or c. The metals belonging to group b, with MCCr < 1, appear to have the more severe adverse health effects, such as carcinogenesis or asthma, as shown in Table 3. Whereas most of the metals in group c, with MCCr > 1, have no severe health effects as shown in Table 3, and some are essential elements for human. MCCr could be interpreted as a weighting factor to magnify the severity of the adverse health effects of the metals on human health. Stepwise Multivariate Regression Analysis between OEL and LD50mouse with Dummy Variables. Results similar to those determined for the rat data were obtained for the regression equation and compensation coefficients for the LD50mouse data. The stepwise multivariate regression analysis resulted in the adoption of dummy variables for Cd, Fe, Be, As, Hg, Os, Cr, Zr, Mo, and Ag, while Al, Ba, Co, Cu, Mn, Se, Sn, Te, Tl, V, and Zn were excluded as explaining variables. The adjusted VOL. 39, NO. 1, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 3. Relationship between MCC and Occupational Casesa occupational casesb no.
metal
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 20 21 22 23 24 25 26 27 28 29
Os Cd Be Pt Ag As Co Hg Cr Ni Cr Sn Te Sb Pb Tl Se V Cu Ba Mn Al Rh Ta Zn Zr Fe Mo
E
C
A
b
b b
GID
CNS
F
HD
P
MF
SA
PA
b
b b b
b b
b
I b
b* b**
b b
b b
b
b b b b
b b
b b b b
b b b b
b b b
b b b b
b
b b
b
b b
b
b
b b b b b b
b b
b
b b
b b
b
b
b b b b b
b
b b
b
MCCr
b***
1.85E-02 2.22E-02 2.75E-02 1.20E-01 2.03E-01 2.03E-01 2.12E-01 1.00E+00 1.00E+00 1.00E+00 1.00E+00 1.00E+00 1.00E+00 1.00E+00 1.00E+00 1.00E+00 3.15E+00 4.51E+00 3.52E+00 6.89E+00 7.85E+00 9.35E+00 1.75E+01 1.24E+01 1.98E+01 5.28E+01
MCCm 8.70E-03 1.10E-02 1.30E-02 8.20E-02 3.00E-02 1.50E-01 1.10E-01
1.09E+01 6.53E+00 1.75E+01
a Data sources: refs 14-18. b Key; E, episode; C, carcinogen; A, asthma; GID, gastrointestinal distress; F, fertility; HD, heart disease; P, pulmonary; MF, metal fume fever; SA, skin allergy; PA, platinosis (*), argyria (**), and siderosis (***); I, Irritation.
TABLE 4. Results of Multivariate Regression Analysis for OEL and LD50mouse unstandardized coeff
(constant) Cd Fe log(LD50m) Be As Hg Os Cr Zr Mo Ag
statistic of collinearity
unstandardized coeff
SE
p
-1.514 -1.970 0.814 0.353 -1.873 -1.517 -0.854 -2.061 -0.957 1.038 1.242 -1.085
0.171 0.177 0.200 0.068 0.290 0.244 0.150 0.403 0.217 0.290 0.403 0.405
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.003 0.010
variance tolerance inflation factor 0.918 0.853 0.761 0.962 0.922 0.829 0.983 0.889 0.967 0.982 0.974
1.090 1.173 1.313 1.040 1.084 1.206 1.018 1.125 1.034 1.019 1.027
coefficient of determination in the first step and the final step were 0.23 and 0.85, respectively (p < 0.05, n ) 65). The regression equation was significant at a level of 0.05 as shown in Table 4. No collinearity among the independent variables was confirmed because the VIF was less than 10. Regression equation for prediction of the OEL for mouse was obtained as follows: 0.353 predicted-OELmouse ) 0.0306 × LD50mouse × (MCC1) × (MCC2) × ... × (MCCn) (9)
where MCCm is the metal compensation coefficients for LD50mouse. Their values are shown in Table 3. Correlation between Predicted Values of OEL from LD50rat and LD50mouse. Figure 3 shows the relationship between the predicted values for the OELs by eq 5 (predicted-OELrat) and those by eq 9 (predicted-OELmouse). Values for both LD50rat 374
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FIGURE 3. Relationship between the predicted values of OELrat and OELmouse by using the regression eqs 5 and 9. and LD50mouse are available for 47 metals and metallic compounds. A good correlation with an adjusted determination coefficient R 2 ) 0.89 was obtained. Consistent values for OELs could, therefore, be predicted from both prediction eqs 5 and 9. Examples of Toxicity Risk Assessment for Metal Solders and Rechargeable Batteries. The prediction equations were applied to two case studies concerning estimation of the toxicity of materials used in solders and in rechargeable batteries. The HI was used as an index for the toxicity of the products. If the OEL value for a metal was unknown, it was predicted from LD50 values using eq 5 or eq 9. Solders containing lead have been widely used in electric appliances for connecting electric components to circuit boards. However, the RoHS directive, which becomes effective in Europe in 2006, will ban the use of solders containing lead, and the conventional lead containing solders must be
FIGURE 4. Comparison of the toxicity of the solders by the Health Index.
FIGURE 5. Comparison of the toxicity of the rechargeable batteries by the Health Index.
TABLE 5. OEL and Modified Toxic Weighting Factors of Metals Used in Soldersa
a
metal
OEL (mg/m3)
modified toxic weighting factor ()10/OEL)
Sn Pb Zn Ag Cu
2 0.05 1.38b 0.01 0.1
5 200 3.94 1000 100
Data source: ref 19.
b
Predicted value by eq 5 from LD50rat of Zn.
replaced by lead-free alternatives. Promising alternative solders that have been developed to date are shown in Figure 4. Since elemental zinc (Zn) does not have a OEL value in any country, the OEL value was predicted using eq 5 with LD50 data published by the U.S. EPA, 3550 mg/kg (19). As a result, the predicted value of OEL for Zn was 1.38 mg/m3, as shown in Table 5. The HI value for a conventional lead solder (63Sn37Pb, composed of 63 wt % of tin and 37 wt % of lead) was the highest of the four solders studied, reflecting the high toxicities of lead. A lead free solder containing silver (96.3Sn3Ag0.7Cu), had the highest HI value of the three lead free solders, mainly because of the high toxicity of the silver it contained. Rechargeable batteries have been widely used in personal portable electronic products for supply of electronic energy. The personal portable electronic products have been increasing recently and rechargeable batteries have become indispensable for them. We assessed the human toxicity of rechargeable batteries such as sulfuric lead acid (20, 21), Ni-Cd (22), and Ni-MH (23). We predicted OELs of the
metals and metallic compounds used in the rechargeable batteries (8, 24). As a result, the Ni-Cd battery showed the highest value for HI of three batteries, shown in Figure 5. This is mainly due to the high toxicity of the cadmium it contained. The Ni-MH battery showed the lowest HI value for the three batteries studied. Thus, the toxicities can be conveniently evaluated, and the results could be interpreted and intuitively compared by use of the HI. We excluded metallic compounds of lanthanides group in the above assessment. Although lanthanides such as cerium, lanthanum, prascodymium, and samarium are frequently used in recent advanced technology, the OEL of these metals is not regulated in the United States, the United Kingdom, France, Germany, Sweden, and Japan (8). Since the lifetime of the products with the advanced technology tends to be short, the health risk induced by these rare metals need to be carefully assessed.
Acknowledgments The authors thank Dr. Jun Yoshinaga at the University of Tokyo, for his invaluable discussions.
Supporting Information Available Two tables of data for metal and metallic compounds. This material is available free of charge via the Internet at http:// pubs.acs.org.
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Received for review June 27, 2004. Revised manuscript received October 4, 2004. Accepted October 7, 2004. ES0490323
