Rocks, Soils, and Water Quality. Relationships and Implications for Effects of Acid Precipitation on Surface Water in the Northeastern United States Edward Kaplan,*t Henry C. Thode, Jr., and Alicia Protas Biomedical and Environmental Assessment Division, National Center for the Analysis of Energy Systems, Department of Energy and Environment, Brookhaven National Laboratory, Upton, New York 11973
Distribution of rocks and soils in Northeast counties were investigated for the degree to which they influence pH and alkalinity in surface waters. Using 283 counties, path analysis resulted in two models of equivalent explanatory power. Each model indicated the importance of both rocks and soils as determinants of pH and alkalinity in surface waters, and as important factors in the sensitivity of natural waters to acidification from acid precipitation. Previous studies have emphasized the importance of bedrock geology, at the expense of knowledge about soils, in an understanding of waters sensitive to the effects of acid precipitation. Our regional analysis found that rocks were contributors to the buffering capacity of surface water; however, the presence of a large percentage of alfisol soils better indicates locations of waters with higher levels of alkalinity, and thus of greater resistance to effects of acid rain.
Introduction Numerous authors have documented changes in Scandinavian water quality due to air pollution (1-3). Limited data have been used to suggest similar mechanisms for the northeastern U.S. (4-6). A geochemical hypothesis has been advanced (7) stating that “because of slower geochemical weathering rates in areas of metamorphic and igneous bedrock geology. . . lakes in these areas will be dilute and will have low alkalinities [and will thus be more sensitive to acidification by acid precipitation] .” The hypothesis emphasizes the importance of bedrock geology for regional analysis with local variations in water quality expected also from soil type and vegetation (8, 9). This paper examines the distribution of rocks and soils in the New England and Middle Atlantic regions of the U S . and describes interrelations between both rocks and soils with the alkalinity and pH of surface waters in these regions. A data base describing the distribution of rocks, soils, and land use was compiled for U.S. counties east of the 100th meridian (10) using data provided by D. Olsen of Oak Ridge National Laboratory. This information was merged with water-quality, socioeconomic,and energy data for 317 counties constituting the New England and Middle Atlantic states (11). t New address:Division of Regional Studies, Brookhaven National Laboratory. 0013-936X/81/0915-0539$01.25/0
@ 1981 American Chemical Society
Bedrock Data Bedrocks were classified as one of five types: intrusive igneous, metamorphic, consolidated sedimentary, unconsolidated and weakly consolidated sedimentary, and extrusive igneous. Intrusive igneous rock results when magma cools and solidifies below the surface of the earth. This type of rock reaches the surface when erosion removes the overlying rock. Metamorphic rocks are those that have recrystallized as a result of changes in their physical environment. They are usually observed in mountain ranges and areas of deep erosion. Sedimentary rocks are formed by deposits of solid material that accumulate upon the earth’s surface under the influence of various media (i.e., air, water, ice, gravity). The primary difference between consolidated and unconsolidated sedimentary rocks is permeability, which is influenced by particle size and the degree of cementation between particles. There was no extrusive igneous rock in the area of concern. Parametric correlation coefficients between the various rock types were significant at the 0.05 level, the only positive correlation existing between igneous and metamorphic rocks (see Table IA). Soil Data Soil data consisted of the percentages of nine different classes represented in each county. However, for the 331 counties considered in this analysis, only four soil classes were found: alfisols, inceptisols, spodosols, and ultisols. Table I1 illustrates general properties of these soils. All parametric correlations between soil classes were negative and significant a t the 0.001 level (see Table IB). Rock-Soil Relationships Important relationships bearing upon the GallowayCowling hypothesis are those between rocks and soils. There are several high positive correlations: igneous and metamorphic rocks with spodosols, consolidated rocks with both alfisols and inceptisols, and unconsolidated rocks with ultisols. Cluster analysis, a multivariate classification technique used to-explore similarities and dissimilarities among groups of variables, was applied to the rock and soil data. Counties were grouped into clusters based upon a Euclidean distance measure in which the percentages of each rock or soil type in each county were considered as basis vectors. Figure 1 shows five groups of counties based upon the combined distributions of four rock types; Figure 2 shows the five clusters based upon the four soil classes. Volume 15,Number 5,May 1981 539
Table 1. Statistically Significant Parametric Correlation Coefficients
% intrusive igneous % metamorphic % consolidated sedimentary % unconsolidated sedimentary % alfisol % inceptisol % spodosol
YO ultisol a
% intrusive
YO meta-
igneous
morphlc
A. rocks YOconsolldated sedlmentary
6. SOllS
YOunconsolidated
Yo
%
sedimentary
alflsol
Inceptlsoi
% spodosol
% uit1soIs
0.28 -0.43
-0.72
-0.11
-0.16
-0.23
-0.30
0.46
-0.25
-0.27
-0.35
0.46
-0.18
-0.41
a
-0.36
-0.42
-0.26
-0.30
0.64 -0.15
0.73
a
-0.50
-0.69 -0.33
0.61
-0.23
-
Not statistically significant.
Table II. General Properties and Uses of Soil Classes a relative age
inceptisols
alfisols
characteristics
uses
relatively new
often formed from volcanic ash found in steep lands and depressions extensive leaching poorly drained, fine sand resistant parent material low organic matter
woodland cultivated crops (only if artificial drainage is provided)
intermediate
significant weatherability (carbonate leaching) with replacement of exchangeable cations (Ca, Mg, K, Na) accumulation of Fe, AI pH less than 5 not uncommon low base status high moisture content
forest pasture hayland cultivated crops wildlife preserves
old
high clay and moisture content high cation and exchangeability
cultivated crops hayland pasture range forest timber production
plant nutrients medium to high base status ultisols
a
oldest
extensive clay content extensive leaching leads to severe removal of bases tendency toward large slopes and low fertility tendency toward water saturation
Reference 12.
The spatial distributions of both rock types and soil classes, as well as corresponding correlation coefficients (Table I), are important and require some explanation. Specifically, why are the indicated correlations between igneous and metamorphic rocks and alfisols negative, whereas those with spodosols positive? A possible answer is that the former soils are older than the latter, and are possibly present because of glaciation, where the presence of spodosbls are more indicative of weathering processes (12). This would lend further importance of soils to the Galloway-Cowling hypothesis, which has heretofore primarily been based upon weathering of bedrock in a give-n study area. Water-Quality Data The conceptual basis of the Galloway-Cowling hypothesis lies in the degree to which a body of water is sensitive to acidification due to acid precipitation. The primary deter540
Environmental Science & Technology
minant of this sensitivity is the buffering capacity of water measured in terms of its alkalinity. In a study of the interrelationships between energy and socioeconomic activities and water quality, Kaplan and Thode ( 1 1 ) discuss the attributes of an information file in which data were obtained from the EPA STORET information system for 65 water-quality variables during the period 1955-1977. Data were obtained from lakes and streams, statistically filtered, and aggregated on a county basis. The methodology used to create the information file is described in ref 11. (Note that our data include streams; Arnold et al. (13) have recently emphasized the susceptibility of streams to effects of acid precipitation.) The Appendix (see paragraph at end of text regarding supplementary material) shows the number of observations of both alkalinity and pH in the 283 counties constituting this study. This data base was used to extract information for pH and total alkalinity for 283 counties of the New England and Middle Atlantic states for which data were available.
Figure 3. Geographic distribution of water-quality clusters.
expected in each cell under the null hypothesis of independence lie., no relation) hetween row and column variables. Chi-square for Tahle 111 was found to be 205, significant heyond the 0.001 level for 12 degrees of freedom. We find twice as many counties as expected with alkalinity 5 50 mg/L and pH 5 6 5 , whereas we observe far fewer counties than expected when alkalinity 5 SO mg/L hut pH > 7.5. (N.b. all alkalinity concentrations expressed as mg/L of CaCOA.) An opposite situation is apparent for cases where alkalinity > 200 mg/L. That is, while we observe the expected low number of counties with pH 5 6.5, we find almost 3 times as many counties with pH > 7.5. We know that equilibrium considerations lead to positive correlations between alkalinity and pH. However, Table 111 indicates that counties with weak buffer systems appear to be more sensitive than expected (under the null hypothesis) to those events which would lower values of pH. Figure 3 shows the distribution of water-quality variables based upon this contingency table li.e., Table 111). (Unshaded counties are those with missing or incomplete information.)
The distribution of water quality in the surface waters of counties was examined by using a 4 X 5 contingency table based upon the value of alkalinity (4 categories) vs. the values of pH I S categories). This is shown in Table 111, where numbers before parentheses are used to represent the number of counties obserued to possess the attributes indicated for each cell. Numbers in parentheses indicate the number of counties
Rocks, Soils,and Water Qualit) Similarities between Figures 1-3 are striking and are better understood by using cross tabulations similar to those in the previous section. Table IV indicates the existence of a set of statistically significant relationships between rock and soil clusters. While there are only 283 counties with complete water data, all 331 counties in the region under consideration have complete rock and soil data. All counties were used in
Table 111. Crosstabulation of counties by Concentration of Alkalinity (in mglL as CaC03) and pH a alk.llnny
0-50 50+-100 loo+-200 >200
pH
5%
W9) o(5)
o(4) O(1) 19
s+-e.5
.sf-7.0
7.0+-7.5
>7.5
row lc4.I.
14(7) O(7) O(3) O(1) 14
56(29) 3(15) W2) o(4) 59
44(46) 40(24) lO(19)
5(47) 28(24) 46(19) 18(6) 97
138 71 56 18 283
O(6)
column totals 94 chi-square = 205 (12df). P < 0.0001 * Numbers beforeparenlheser represent number of cwnties obsewsdto pcssess attributes of celt numbers in parenteses indicate number 01 counties expected under null hypothesis 01 independence between row and column variables.
Volume 15,Number 5 . May 1981 541
Table IV. Counties with high percentages of spodosols (cluster S1) also appear to have high percentages of metamorphic rock (cluster R l ) . Counties consisting mainly of alfisols (and less so of inceptisols; cluster S2) also consist primarily of consolidated sedimentary rock (cluster R2). These results are consistent with Table I and emphasize the sharp distinctions between county clusters of rocks and of soils. The figures provide qualitative comparisons of the distributions of the rock and soil clusters, and water-quality groups based on the contingency table. (These groups are referred to as water clusters in the discussion below.) Quantitative information is given in Tables V and VI, showing the manner in which rock and soil clusters individually relate to water clusters. Chi-square for rock-water relationships is half that for soil-water groups. Those counties with low values of alkalinity and pH are not those with higher percentages of igneous rock (cluster R5) but rather those with consolidated rock (cluster R2). Indeed, chi-square of Table Vis as large as it is primarily because of large discrepancies between the observed and expected number of counties in only three cells: R2-W2 (where there are fewer than expected counties with low alkalinity, mid pH, and consolidated sedimentary rock) and cells Rl-W2 and R2-W5 (where we find more counties than expected). Thus consolidated sedimentary and metamorphic rock relate most strongly with water quality on the large regional scale with which we are presently concerned. Stronger soil-water relationships are seen in Table VI. Here there are at least six cells with large discrepancies between expected and observed numbers of counties. Where the occurrences of spodosols are high (cluster Sl),there are more than the expected number of counties with weaker buffering capacity and lower pH, and fewer than expected counties with high alkalinity and pH values. The reverse is true for those counties consisting primarily of alfisols: there are fewer
counties than expected with lower alkalinity and pH values, and more when these variables have higher values. The impact of ultisols (with the low base status) is also manifest on water quality: as the percentages of these soils increase (i.e., S1-S3 vs. S4 and S5),we find more counties than expected with lower alkalinity and pH (Wl), and fewer counties than expected with high alkalinity and pH (W6). A relatively stronger soil-water relation can be noticed by comparing Kruskal’s asymmetric X for Tables V and VI. X is a proportional-reduction-in-error measure, that is, the percent reduction in the expected numbers of errors that would have been made in classifying the counties into water groups if the independent variable (rock or soil) had not been used (14). The reduction in error from knowing which rock cluster the counties belong to is 15%, while knowing the counties’ soil clusters reduces the error by 29%. While x2 and X show the association between two variables, certain factors may affect their magnitude. This in turn may affect the direct comparison of two tables. One important factor affecting these statistics is marginal variation, both within and between tables. To minimize the effect of marginal variation in the independent variables, we standardized each row to 100%and computed the x2 and X statistics. Again, the soil-water relation is stronger than the rock-water relation based on both x2 (375 for soil vs. 344 for rock) and X (28.3% for soil vs. 9.6% for rock). Path analysis was used to investigate potential causal relationships between rock, soil, and water-quality data. The technique was developed in the early 1920’s by geneticist Sewall Wright to examine observed interrelated variables that are assumed to be completely determined by exogenous variables. Path diagrams depict the hypothesized causal relations among variables. Causality is shown by a single-headed arrow
Table IV. Crosstabulation of Counties in Terms of Rock and Soil Clusters sal1 clusters a
s1 rock clusters
R1, >50% metamorphic R2, 100% consolidated sedimentary R3,>50% consolidated sedimentary and metamorphic R4 unconsolidated sedimentary and metamorphic R5 igneous, metamorphic, and consolidated sedimentary
column totals chi-square = 405 (16df), P < 0.001
90% spodosol
51(13) 3(48) 6(9) 9(5) 8(2) 77
s2 8 0 % alllsol, 20% lnceptlsol
53 80% Inceptlsol, 20% alllsol
54 50% ultlsol; rernalnder Is alllsol or Inceptlsol
1(16)
4(7) 7(24) 27(4) 1(3) O(1) 39
1(19) 97(68) 5413) 7(7) o(3) 110
88(55)
O(10) O(6) O(2) 89
55 00% ultlsol
row totals
1(3) lO(10) O(2) 5(1) O(0) 16
58 205 38 22 8 331
a Numbers before parentheses represent number of counties observedto possess attributes of cell; numbers in parentheses indicate number of counties expected under null hypothesis of independence between row and column variables.
Table V. Crosstabulation of Counties In Terms of Rock and Water Clusters
rock clusters
R1, >50% metamorphic R2, 100% consolidated sedimentary R3,>50 % consolidated sedimentary and metamorphic R4,unconsolidated sedimentary and metamorphic R5, igneous, metamorphic and consolidated sedimentary
column totals chi-square = 95 (20 df), P < 0.001, = 0.15
alkallnlty a PH
water cluster types b w3 w4 5 50 50-100 7-8 7-8
lO(7) 25(33) 6(4) 7(3) l(1) 49
lO(11) 52(48) 2(6) 7(5) O(2) 71
row totals
42 190 25
19 7 283
Alkalinity in mg/L as CaC03 Numbers before parentheses represent number of counties observed to possess attributes of cell: numbers in parentheses indicate number of counties expected under null hypothesis of independence between row and column variables.
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Environmental Science & Technology
Table VI. Crosstabulation of Counties in Terms of Soil and Water Clusters
soli clusters
S1,90% spodosol S2,80% alfisol, 20% inceptisol 53,80% inceptisol, 20% alfisol S4,50 % ultisol, remainder alfisol or inceptisol S5,90% ultisol
column totals chi-square = 198 (20df), P < 0.001, = 0.29
alkalinity PH
*
w1
w2
1 50
150
1 6
6-7
2(4) O(5) 4(2) lO(7) 3(1) 19
33(14) 2(20) 1 3 ~ 21(25) 1(4) 70
water cluster groups b w3 w4 1 50 50-100 7-8 7-8
w5 100-200 7-8
W6 >200 7.5-8
row totals
ll(10) 3(14) 1 1(6) 22(17) 2(3) 49
1(11) 43(16) O(6) 9(20) 3(3) 56
O(3) 17(5) O(2) l(6) O(1) 18
55 80 32 100 16 283
8(14) 15(20) 4(8) 37(25) 7(4) 71
a Alkalinity in mg/L as CaC03. Numbers before parentheses represent number of counties observedto possess attributes of cell: numbers in parentheses indicate number of counties expected under null hypothesis of independence between row and column variables.
(i.e,, path), and interaction (Le., correlation) between two variables is shown by a curving path with arrows at each end. Each path has associated with it a coefficient pLjwhich can be interpreted as a regression coefficient in that it is the amount of change in the dependent variable caused by a unit change in the independent variable (with all other independent variables held constant). The coefficient on the interaction arrows in simply the correlation coefficient pil. A path diagram was first constructed by using all four rock types and soil classes. Not all paths were found to be statistically significant ( P < 0.01). Attempts were made to form as parsimonious a diagram as possible to avoid a situation where too many variables were included in a causal structure when a smaller subset would, in fact, explain an equivalent amount of variance. This is shown in Figure 4, where the values in parentheses are the variances explained by the path diagram. This figure elaborates upon the previous discussion relating bedrocks to soil class. The model has large explanatory power for the spodosol class ( R 2 = 0.77) but offers less causality for alfisols (R2 = 0.19). Path analysis was next used to ascertain causality of rocks and soils on water quality. However, instead of relating rocks and soils to each water-quality variable, factor analysis was first used to provide a single functional form for a simple path diagram. One water-quality factor (WQF) was found which accounted for 82% of the variance in the water data. Using standardized variables, that is
where X is the mean and ux the standard deviation, we determined the resulting factor as The causality between rocks and the WQF is shown in Figure 5 . At the 0.01 level of significance, only consolidated
sedimentary rock contributes to WQF, explaining 12% of the variance in the factor. The causal structure of Figure 4 was used to ascertain the influence of rocks and soils on the WQF. Figure 6 shows that the only direct path to WQF comes from alfisols; no direct paths are evident from rocks to WQF ( P < 0.01). The explanatory power (as measured by the R 2 )using soils is 4 times greater than when using only rocks. These path diagrams indicate that only in counties which tend to have a higher percentage of alfisols will the WQF be expected to increase. That is, the buffering capacity of surface water will be expected to be greater in those counties. An alternative causal structure of equivalent explanatory power is shown in Figure 7 ,which can be considered complementary to Figure 6. Again we find no direct paths from rocks to the WQF. The presence of spodosols, ultisols, and inceptisols will tend to indicate surface waters with lower alkalinity and hence of relatively greater susceptibility to acidification from acid precipitation. Relationship to E f f e c t s of Acid Precipitation Several authors have indicated that acidity of rainwater appears to have increased recently in the Middle and North Atlantic regions of the U.S. (15).The effects of such a phe-
IIgneousl IMetomorphicl Consolidated Sedimen tory
0.35
Figure 5. Relationship of rock types to water quality ( n = 283,P < 0.01).
Alfisols (0.19)
Unconsolidated
0.63
Sedimentor y
Figure 4. Relationship of rocks to soils for 283 northeast counties ( P < 0.01).
Figure 6. Relationship of rocks and soils to water quality ( n = 283,P
< 0.01).
Volume 15,Number 5, May 1981 543
Igneous
I Alfisols (0.19)I I
(0.48)
each group. Water-quality clusters were formed on the basis of pH and alkalinity. (3) Visual inspection of maps describing the regional distribution of these clusters indicated strong qualitative similarities between soil-water clusters, less so between rockwater clusters. (4) Crosstabulations and path analyses confirmed the primary influence of soils on water quality. ( 5 ) Geochemical hypotheses advanced heretofore stress the importance of bedrock geology in determining regional sensitivities of surface waters to effects of acid precipitation, This study indicates a serious shortcoming of such an approach insofar as the hypothesis neglects the importance of soils and especially their predominance over rocks in determining regional descriptions of water quality.
+yL.....i,,,,yy Sedimentary
Figure 7. Alternative structure of relationship of rocks and soils to water quality ( n = 283,P < 0.01).
nomenon have been discussed in relation to soils ( 5 ) ,as well as to aquatic and terrestrial ecosystems (4,6-8, 16). A geochemical hypothesis ( 7 , 9 ) has been advanced in an attempt both to assess impacts and to identify those surface waters most sensitive to increased acid precipitation. The hypothesis emphasizes the role of bedrock geology in determining the existence of surface waters most sensitive to impacts from acid precipitation. While most authors agree that the effect of soils on this resistance to acidification may be large, particularly a t individual sites, they underemphasize the importance of soils on water chemistry in regional analyses. Instinctively one expects soils to have an important, if not overriding, influence on the buffering capacity of surface waters. Except when falling directly into a water body, rain must either flow across or percolate through soils before entering streams or lakes. While areas exist where outcroppings bring rocks in direct contact with rain, it is apparent that in an overwhelming number of cases rainfall must interact with soils. The importance of soils in determining chemistry of surface waters has been demonstrated in previous sections. As shown in Figures 6 and 7 , no direct paths were found from rocks to the WQF; all causality attributed to rocks is mediated through the presence of soils. The relationship of the results reported here to that of the aforementioned literature is interesting. Specifically, the geochemical hypothesis implies that areas underlain with igneous rock will tend to contain surface waters of low buffering capacity. While this may be true at specific sites, on the regional basis used herein (i.es, New England and Middle Atlantic states), we have seen that only a small number of counties are composed primarily of igneous rock. An overwhelmingly large number of counties were found to be composed primarily of metamorphic and/or consolidated sedimentary rock. Furthermore, the strongest relationships were found between the distribution of water quality (Le., alkalinity and pH) and soils, not between water quality and bedrocks. Figures 6 and 7 imply that areas with a low WQF, and thus presumably more sensitive to impacts of acid precipitation, will be found in counties with large percentages of inceptisols, spodosols, and ultisols. The effects of igneous and metamorphic rocks in these areas are seen in that 77% of the variance in the spodosol data can be accounted for by the (increasing) presence of both mentioned rock types. S u m m a r y and Conclusions
(1) Data on the distribution of rocks and soils were available for 331 counties in the New England and Middle Atlantic States. Data on water quality (alkalinity and pH) were available for 283 of these counties. (2) Cluster analyses were performed on county level rock and soil data to discern potential interrelationships within
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L i t e r a t u r e Cited (1) Henriksen, A,; Wright, R. F. “Concentrations of Heavy Metals
in Small Norwegian Lakes”; SNSF Project Report, SNSF Project, 1432 AaS-NHL, Norway, 1976. (2) Dickson, W. Neth. Int. Verein. Limnol. 1978,20,851-6. (3) Wright, R. F.; Henriksen A. “Sulfur: Acidification of Freshwater,” International Svmvosium. Emissions and the Environment. wv. 277-301, May 1579. (4) Hornbeck, J. “Sulfate Patterns in Precipitation, Streamflow and Soil Solution, the Their Response to Forest Management”; Forestry Sciences Laboratory: Durham, NH, 1974. (5) Cronan, C. S.; Schofield, C. L. Science 1979,204,304-6. (6) Schofield, C. L. “The Ecological Significance of Air Pollution Induced Changes in Water Quality of Dilute Lake Districts in the Northeast”; Transactions of the Northeast Fish and Wildlife Conference, 1973. (7) Galloway, J. N.; Cowling, E. B. J . Air Pollut. Control Assoc. 1978, 28,3. 18) ~, Likens. G. E.: Wright. R. F.: Gallowav. “ , J. N.: Butler. T. J. Sei. Am. 1979,241,42-51. (9) . . Hendrev. G.: Gallowav. J. N.: Norton, S. A.:.Schofield, C. L. “Geological and Hydrochemical’Sensitivity of the Eastern U.S. to Acid Precipitation”; BNL Report, draft, Oct 5 1979. (10) Protas, A. Progress Report, Phase’I:Rocks, Soils, and Land Use Data; BNL Report, in press. (11) Kaplan, E.; Thode, H. C. Jr. “New Techniques for Analyzing Relationships between Energy and Water Quality”; BNL 51068, Sept 1979. (12) Buol, S. W.; Hole, F. D.; McCracken, R. J. “Soil Genesis and Classification;” The Iowa State University Press: Ames, IA, 1973. (13) Arnold, D. E.; Light, R. W.; Dymond, V. J. “Probable Effects of Acid Precipitation on Pennsylvania Waters”; Pennsylvania Cooperative Fishery Research Unit, University Park, PA (report available from EPA/Corvallis Environmental Research Laboratory, Corvallis, OR, EPA-600/3-80-012). (14) Reynolds, H. T. “Analysis of Nominal Data”; Sage University Paper series on Quantitative Applications in the Social Sciences, 08-007; Sage Publications: Beverly Hills, CA, 1977. (15) Hornbeck, J. W.; Likens, G. E.; Eaton, J. S. Water, Air, Soil Pollut. 1977, 7,355-65. (16) Schofield, C. L. “Lake Acidification in the Adirondack Mountains of New York: Causes and Consequences”; Proceedings of the First International Symposium on Acid Precipitation and the Forest Ecosystem; Dockinget, L. S., Ed.; U.S. Department of Agriculture, Forest Service General Technical Report NE-23, 1976.
__
Received for review April 16,1980. Accepted November 20,1980. Supplementary Material Available: Appendix showing alkalinity and pH data (6 pages) will appear following these pages in the microfilm edition of this volume of the journal. Photocopies of the supplementary material from this paper or microfiche (105 X 148 mm, X reduction, negatives) may be obtained from Business Operations, Books and Journals Division, American Chemical Society, 1155 16th St., N.W., Washington, D.C. 20036. Full bibliographic citation (journal, title of article, author) and prepayment, check or money order for $6.00 for photocopy ($7.50 foreign) or $4.00 for microfiche ($5.00 foreign), are required.
