Environ. Sci. Technol. W9Q, 2 4 , 926-927
ordinary kriging known as indicator kriging (7) could be used to better handle problems with loss of precipitation from the samples. The deposition estimates made by Voldner and Alvo based on direct interpolation of deposition values provide useful information. Their deposition estimates based on direct interpolation of concentrations are useful only as an example of the bias possible when unequal support sizes are ignored. Registry No. Sulfur, 7704-34-9; nitrogen, 7727-37-9.
Literature Cited (1) Voldner, E. C.; Alvo, M. On the estimation of sulfur and
(2) (3) (4) (5)
(6) (7)
nitrogen wet deposition to the Great Lakes. Enuiron. Sci. Technol. 1989,23, 1223-1232. Bilonick, R. A. Risk-qualified maps of hydrogen ion concentration for the New York State area for 1966-1978. Atmos. Environ. 1983, 17, 2513-2524. Bilonick, R. A. The space-time distribution of sulfate deposition in the Northeastern United States. Atmos. Environ. 1985, 19, 1829-1845. Journel, A. G.; Huijbregts, Ch. J. Mining Geostatistics; Academic Press: New York, 1978. Guertin, K.; Villeneuve, J.; Deschenes, S. The choice of working variables in the geostatistical estimation of the spatial distribution of ion concentration from acid precipitation. Atmos. Enuiron. 1988, 22, 2787-2801. Granat, L. Sulfate in precipitation as observed by the European Atmospheric Chemistry Network. Atmos. Environ. 1978, 12, 413-424. Bilonick, R. A. Monthly hydrogen ion deposition maps for the northeastern U.S. from July 1982 to September 1984. Atmos. Environ. 1988, 22, 1909-1924.
R. A. Bllonick Consolidation Coal Company Consol Plaza Pittsburgh, Pennsylvania 1524 1
SIR: Two possible approaches for estimating sulfur and nitrogen deposition to the Great Lakes and basins are discussed. The first proposes to estimate deposition by interpolating depositions at the sites whereas the second proposes to interpolate concentration and precipitation at the sites separately and then multiply the interpolated values on a grid by grid basis. In situations where there are a large and well-spaced number of sites with adequate deposition data, the first approach is entirely suitable and prevents the difficulties cited above. Bilonick fails to recognize that the measured deposition at a site may not represent the actual deposition. Frequently, the site precipitation is underestimated due to undercatch of the collector, missing events, or evaporative losses. Measured deposition in the first two cases provides an underestimate of the annual deposition, even though the precipitation weighted mean concentration may approach the actual annual value. A simple scaling with the annual precipitation amount as recorded by a colocated rain gauge, and done for the major networks, reduces the error [Olsen et al. (I)]. Since the GLAD and GLP networks were not equipped with rain gauges, such a correction was not feasible. A simple interpolation of site deposition would propagate the errors spatially. If the low precipitation value, however, is caused by evaporative losses and no chemical degradation occurs in the sample, the measured concentration is too high, while the measured deposition is essentially correct. In this case, interpolation of site deposition is valid. Adjustment of site precipitation on the other hand, would lead to overestimation. The precipitation values for the GLAD and GLP net926
Environ. Sci. Technol., Vol. 24, No. 6, 1990
works, even for sites with 100% data capture, appear low compared to recordings by neighboring rain gauges. The reason for this discrepancy is not clear. Hence, the two approaches for estimating deposition were used. The method of estimating concentration and precipitation separately was further motivated by the observation that spatial variability in concentration for sulfur and nitrogen is generally less than spatial variability in precipitation. To illustrate the problem, suppose that site precipitation in the example provided is underestimated by 30% due to undercatch or missing events, but that concentration is correct. location concentration, mg/L precipitation, L deposition, mg
I 1
X2
x3
1.0 3.5 3.5
? ? ?
3.0 0.7 2.1
The deposition at x 2 is estimated to be 2.8 mg by Bilonick, a 30% underestimate compared to the real value of 4.0 mg. On the other hand, separate interpolation of the concentration and precipitation yields the estimates 2.0 mg/L and 2.1 L, respectively. If deposition is obtained by multiplying the interpolated values of concentration and precipitation, the estimate is 4.2 mg, which is close to the real value of 4.0 mg. The question of how best to take advantage of the data available from the denser precipitation networks also remains open. The suggestion made by Bilonick to develop a regression equation relating deposition to precipitation is fraught with danger since it does not take into account any spatial considerations. It would indeed be welcome if the problem were that simple since it would render unnecessary the development of any physically based comprehensive deposition models. The suggestion to use cokriging “would require the estimation and modeling of semivariograms for deposition and precipitation as well as the covariogram between deposition and Precipitation”, as indicated by Bilonick. It would be difficult to estimate the latter accurately with the existing site data. Registry No. Sulfur, 7704-34-9; nitrogen, 7727-37-9.
Literature Cited (1) Olsen, A. R.; Bigelow, D. S.; Chan, W. H.; Clark, T. L.; Lusis,
M. A.; Misra, P. K.; Vet, R. J.; Voldner, E. C. Unified Wet Deposition Data Summaries for North America: Data Summary Procedures and Results for 1984. Atmos. Environ., in press.
E. Voldner Atmospheric Environment Service Environment Canada Downsview, Ontario, Canada K1N 6N5
M. Alvo” Department of Mathematics University of Ottawa Ottawa, Ontario, Canada K I N 6N5
Comment on “Characterization of Environmental Tobacco Smoke” SIR: In reporting their analyses of components of environmental tobacco smoke (ETS), Lofroth et al. ( I ) characterized some of the substances as “genotoxic” and “hazardous”. The implications of human toxicity to ETS components were not supported by the authors and are of questionable appropriateness. Neither ETS nor com-
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@ 1990 American Chemical Society
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ponents designated as carcinogens have been adequately shown to cause pulmonary cancer in animals via inhalation (2). Others have commented on “an absence of metaplastic changes in the bronchi of nonsmokers” (3)that would be expected if ETS or its components were causally associated with lung cancer. Lofroth et al. also cited the National Research Council and others as reporting “excess lung cancer deaths in individuals exposed to ETS. Those conclusions were based on studies that have been widely criticized because they associated risk not with ETS but only with marriage of nonsmokers to smokers, they were subject to significant misclassification errors, and they failed to consider relevant confounding factors (4-10). Speculation about the properties of ETS and its components was not supported by the authors’ findings.
Literature Cited (1) Lofroth, G.; Burton, R. M.; Forehand, L.; Hammond, S. K.;
Sella, R. L.; Sweidinger, R. B.; Lewtas, J. Characterization of environmental tobacco smoke. Environ. Sei. Technol. 1989,23,610-614. (2) Aviado, D. M. Suspected pulmonary carcinogens in environmental tobacco smoke. In Indoor and Ambient Air Quality; Perry, R., Kirk, P. W., Eds.; Selper, Ltd.: London, 1988; pp 141-146. (3) Wynder, E. In Roundtable Discussion. Preu. Med. 1984, 13, 730-746. (4) Ahlborn, W.; Uherla, K. Passive smoking and lung cancer: Reanalyses of Hirayama’s data. In Indoor and Ambient Air Quality; Perry, R., Kirk, P. W., Eds.; Selper, Ltd.: London, 1988; pp 169-178. (5) Balter, N. J.; Schwartz, S. L.; Kilpatrick, S. J.; Witorsch, P. Causal relationship between environmental tobacco smoke and lung cancer in non-smokers: A critical review of the literature. Proc.--APCA Annu. Meet. 1986,8680.9. (6) Kilpatrick, S. J.; Viren, J. Age as a modifying factor in the association between lung cancer in non-smoking women and their husbands smoking status. In Indoor and Ambient Air Quality; Perry, R., Kirk, P. W., Eds.; Selper, Ltd.: London, 1988; pp 195-202. (7) Lee, P. N. An alternative explanation for the increased risk of lung cancer in non-smokers married to smokers. In Indoor and Ambient Air Quality;Perry, R., Kirk, P. W., Eds.; Selper Ltd.: London, 1988; pp 149-158. (8) Lee, P. N. Lung cancer and passive smoking: Association an artefact due to misclassification of smoking habits? Toxicol. Lett. 1987, 35,,,157-162. (9) Letzel, H.; Bliimner, E.; Uberla, K. Meta-analysis on passive smoking and lung cancer effects of study selection and misclassification of exposure. In Indoor and Ambient Air quality; Perry, R., Kirk, P. W., Eds.; Selper Ltd.: London, 1988; pp 293-302. (10) Uberla, K. Lung cancer from passive smoking: Hypothesis or convincing evidence? Znt. Arch. Occup. Environ. Health 1987,59,421-437.
Alan W. Katzensteln Katzenstein Associates 51 Rockwood Drive Larchmont, New York 10538
Comment on “Prediction of Aqueous Solubility of Organic Chemicals Based on Molecular Structure. 2. Application to PNAs, PCBs, PCDDs, etc.” SIR: A recent series of papers published in this journal (1-3) provide a means of estimating aqueous solubility on
the basis of structural information. A model that is based 0013-936X/90/0924-0927$02.50/0
on a combination of group contribution terms and connectivity terms treats a large set of non-hydrogen-bonding organic compounds. Unfortunately, there are some errors and omissions in the data set. These errors and omissions bias the conclusions in favor of the model proposed in those papers. Also certain data have been accepted or rejected on the basis of screening by the very model that they are supposed to verify. Furthermore, because it fails to take into account the effects of crystallinity upon solubility, the model is conceptually weak and should not be considered seriously. A large number of models have been proposed for the estimation of the aqueous solubility of organic compounds. Those models based entirely upon group contribution approaches are doomed to failure because they cannot account for the differences in the solubilities of isomeric groups of compounds. These differences are usually due to the effects of the crystallinity of the solute on its solubility. The role of crystallinity in decreasing solubility is easily quantitated by the van’t Hoff equation. This has been called the crystal-liquid solubility ratio, the ideal solubility of the crystal, and the fugacity ratio by various workers. Its applicability has been amply demonstrated (4-1 1). As a first approximation, the van’t Hoff equation predicts that the room temperature solubility of a rigid molecule will decrease 10-fold for every 100 “C increase in melting point, Thus, for isomeric groups of compounds having similar values for log KO,,connectivity indexes, polarizability, etc., the log of the solubility should be inversely proportional to the melting point. This is clearly evident from the data in Table I. From the above it would be expected that an approach such as that of Nirmalakhandan and Speece, which ignores the effect of crystallinity, would result in a systematic underestimation of the solubilities of high-melting isomers. This fact is obscured in the papers because of six types of error. 1. Incorrect values are reported for some of the highmelting solutes. 2. Certain compounds have been omitted. 3. Some of the data have been “preferentially accepted or rejected ... on the basis of the premise that it is reasonable to use the model to screen the data.” 4. Most of the data are for liquids. 5. Correction factors, such as the constant for dioxins, inadvertently partially compensate for the effects of the crystallinity of the group. 6. Some of the solubilities listed represent the hypothetical supercooled liquid while others do not. In other words, the data have already been corrected for the effects of crystallinity. It is clear from the data in the tables that the model proposed in ref 3 yields gross errors, even for the relatively simple compounds selected. This is because a model for solubility that does not account for crystallinity is simply inappropriate. The papers give the impression that the solubility estimations are based upon only three variables, when in reality there are as many variables as there are functional groups. This is because the polarizability parameter CP is really an accumulation of a large number of group contribution values, Le., CP = a(C1) + b(H) + c(F) + d(1) + f(ketone or aldehyde) + g(dioxin) + h(NH2) + i(NH) + j(N0,) + l(doub1e bond) + m(a1kane or alkene) where the lower case letters are regression-determined coefficients for the corresponding atoms and groups that appear in parentheses. It has already been demonstrated
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