Environ. Sci. Technol. 1987, 2 1 , 289-295
Determination of Molecular Weight Distributions of Fulvic and Humic Acids Using Flow Field-Flow Fractionation Ronald Beckett"
Water Studies Centre, Chisholm Institute of Technology, Caulfield East, Victoria, Austrialia 3145 Zhang Jue and J. Calvin Giddings
Chemistry Department, University of Utah, Salt Lake City, Utah 841 12 Flow field-flow fractionation (flow FFF) has been used to determine molecular weight distributions of extracted fulvic and humic acids from a variety of freshwater and terrestrial environments. Both number and weight molecular weight averages have been estimated. Some pronounced trends have emerged. Thus, fulvic acids are smaller than humic acids, and the order of increasing size and polydispersity of humates from different sources was aquatic < soil < peat bog < lignite coal. In addition, two highly colored natural waters were run by direct injection without the need for any sample pretreatment. Their molecular weight distributions were closest to that of the soil humate. The new technique of flow FFF is capable of yielding detailed molecular weight information and appears to have considerable potential for investigating these important yet complex compounds. Introduction Humic substances are the colored organic compounds that can often be extracted by bases from soils and sediments or separated from natural waters with resin adsorption. They are generally operationally divided into a number of classes on the basis of their water solubility. Thus, humin is water-insoluble, humic acid is only soluble above pH 2, and fulvic acid is soluble under all pH conditions (1). Humic substances occur widely, being present in almost all terrestrial and aquatic systems. Several theories exist regarding the formation of soil and aquatic humic substances (1). They are usually thought to be produced by a combination of degradation and Condensation reactions of plant breakdown products, although the exact mechanism for this is still to be established with certainty. What is apparent is that humic substances play a crucial role in many biogeochemical processes and in the determination of the properties and behavior of many different materials in natural systems. Early studies of humic substances were dominated by soil scientists because of their importance in determining soil condition and fertility. In more recent years environmental scientists have involved themselves with the nature of these complex materials because of their effect on the speciation, transport, and fate of pollutants in natural aquatic systems ( 2 ) . More particularly, humic substances have been implicated with the health risk associated with the formation of chlorinated hydrocarbons in municipal drinking and wastewater treatment plants due to the reaction of chlorine with natural organics (3). The strong binding capacity of humic and fulvic acids for trace metals is well-known (4), and this has a marked effect on the speciation and toxicity of heavy metals to biota (5). Humic substances are also capable of solubilizing nonpolar organic substances, which would normally be considered water insoluble (6). Coatings of organic substances adsorbed to the surface of suspended solids, sediments, and soils are believed to
control the surface properties of these particles. Not only do these coatings determine the surface charge (7,8) and colloid stability (9-11), but they exert a major influence on the adsorption equilibrium of trace metals (12) and organics (13) between the solid and solution phases. The detailed chemical structure of humic substances has not been evaluated to date. This may be because they consist of a very heterogeneous mixture. However, the application of modern techniques, particularly carbon-13 nuclear magnetic rehonance spectroscopy, is advancing our knowledge of the dominant type of structures present. Thus, it has now been demonstrated that natural aquatic and marine humic substances are less aromatic than previously believed. For example, Malcolm (14) estimates that in stream fulvic and humic acids only 20-30% of the carbons are aromatic. Similarly, Harvey et al. (15) proposed that marine humic substances are predominantly unsaturated. The situation appears to be somewhat different in soil and sediment humic materials (16). The humic skeleton is highly substituted with oxygencontaining functional groups, predominantly carboxyl, phenolic, and methoxyl moieties (14). Humic substances also usually contain 1-2 % nitrogen. These functional groups play a dominant role in determining the properties of humic substances. They are responsible for the water solubility, strong metal complexing ability (In, and intermolecular association (18) of humic substances. The molecular weights of humic substances reported in the literature vary from 500 to 200 000. Many different techniques have been used to measure molecular weight, each based on a different principle, and this may be one reason for the wide variation in the observed molecular weight. In addition, all techniques have their limitations. For example, colligative properties yield a number-average molecular weight with no indication of polydispersity, whereas low-angle X-ray scattering (18) measures the molecular radius of gyration but gives only partial information of the molecular size range. Gel permeation chromatography (19) and ultrafiltration (20) can give molecular weight distributions, but both suffer serious problems due to charge-repulsion effects and solute adsorption. Reviews of the more recent literature (21,22)conclude that at least for aquatic humic substances the molecular weights are somewhat lower than believed in the past. Fulvic acids have a molecular weight of about 800 with humic acids being a little larger, 1500-3000. One reason for the very large molecular weights sometimes reported is the possibility that humic substances may aggregate even to the extent of forming micelle-like structures (18, 23). It has been suggested that these humic aggregates can solubilize nonpolar substances (e.g., pesticides, PCB, PAH) and hence influence the transport and fate of these pollutants. In this paper we explore the use of flow field-flow fractionation (flow FFF) as a new method of determining
0013-936X/87/0921-0289~01,~~lQ 0 1987 American Chemical Society
Envlron. Sci. Technol., Vol. 21, No. 3, 1987 289
FIELD (Cross Flow)
FLOW PROFILE
channel walls)
CHANNEL FLOW VECTORS Zone A
Zone B
Flgure 1. Schematic representation of the flow FFF channel showlng the porous frits that allow the crossflow to pass, thus generating the field that forces molecules to migrate to the accumulatlon wall. Detailed Is the cross-section of the channel showlng the parabolic longitudlnal flow profile and mlgratlng sample clouds.
the molecular weight distribution of humic substances. We report the molecular weight distributions of a number of fulvic and humic acids extracted from different terrestrial and freshwater environments, namely, rivers, soil, peat bog, and lignite coal. It is shown that in favorable cases molecular weight distributions of aquatic organic substances can be determined by direct injection of the filtered water. Theory Field-flow fractionation (FFF) is a set of chromatography-like analytical separation techniques carried out in unpacked ribbon-shaped channels. The theory of FFF is well developed and has recently been summarized by Giddings (24). In a flow FFF channel the walls are made from permeable membranes and frits so that a crossflow of carrier solution (flow rate V,) can be maintained at a right angle to the normal flow down the channel (Figure 1). This crossflow forces the sample molecules or particles against the accumulation wall where they form a cloud whose mean thickness 1 depends on the crossflow linear velocity U and the diffusion coefficient D of the sample species. The retention parameter is related to these terms by (25)
where w is the channel thickness, V" is the channel void volume, and V, is the volumetric crossflow rate. With the initiation of channel flow, a parabolic flow velocity profile develops across the thin dimension of the channel as depicted in Figure 1. Thus, thicker sample clouds are caught up in the faster moving flow lines and are swept down the channel faster than the more compact clouds of smaller sample species. Separation according to 290
Environ. Scl. Technol., Vol. 21, No. 3, 1987
cloud thickness 1 is thus achieved as the various sample clouds migrate down the channel at different velocities under the influence of the channel flow. As shown by eq 1,D is the only parameter dependent on the molecular weight of sample species controlling 1 and A. Thus, the migration rates and the separation process are governed by component D values. It can be shown that the retention ratio is related to X by (26)
where V, is the sample retention volume. By use of these simple equations the diffusion coefficient of any component of the fractionated sample can be calculated from the measured retention volume of that component and the other experimental parameters. Experimental Section The channel was formed by clamping a 0.0508 cm thick Teflon sheet, with the channel shape cut out, between two stainless steel frits (inlaid into stainless steel blocks). The lower frit, which would become the accumulation wall, was covered with a polysulfone membrane (Millipore PTGC) with a nominal molecular weight cut-off of 10000 for globular proteins. The channel dimensions were as follows: length (inlet to outlet) 40.4 cm, breadth 2.0 cm, thickness 0.0508 cm, angle at end 35O, giving a geometric void volume (V") of 3.88 cm3. The channel and field flow rates were maintained at approximately 4 mL/min for the humic substances but sometimes less for the PSS standards with either peristaltic pumps (Gilson Minipuls 2), a Kontron L c pump 414,or in-house built 500 mL capacity single-piston pumps. A valve system was installed so that either or both flow
0
~
Table I. Sources oP Humic Substances Used in This Work name Suwannee fulvate
type river
10
15
20
25
30
35
1
1
1
1
1
1
(
obtained from location and references IHSS"
-Suwannee Humate
40
-
Suwannee River, Georgia (28, 29)
IHSS R. Malcolmb Mendicino, North California (28, 29) R. Malcolm soil Mattole humate J. HedgesC Destin, Florida (30) sand Florida humate Steele Lake, J. Hedges Washington humate peat Washington (30) bog Leonardite humate lignite R. Malcolm Biscoyne Mine, South Dakota coal Aldrich Co. Aldrich humate Ottway Ranges, river WSCd Redwater Creek Victoria, Australia water Western Victoria, wsc lake Inkpot water Australia Suwannee humate Mattole fulvate
5
river soil
a International Humic Substances Society. US.Geological Survey, Denver, Co. School of Oceanography, University of Washington, Seattle, WA. d Water Studies Centre, Melbourne, Australia.
streams could be operating at any time. Needle valves on both outlet streams were used to balance the channel and crossflows. An injection valve (Valco) delivered 40 FL of sample to the channel. A fixed-wavelength (254 nm) UV detector (Altex) or Spectroflow (SF770, GM770, Schoeffel Instrument Corp.) variable-wavelength detector and chart recorder (Omniscribe, Houston Instruments) were used to record the fractogram. The dead volume from the end of the channel to the detector was 70 pL. Samples were loaded into the injector sample loop and allowed to flow a short distance down the channel. The channel flow was stopped while the crossflow was maintained, allowing about 1 . 5 P of solvent to pass. This procedure allows the sample to relax to its steady-state condition (27). The channel flow was then resumed and the fractogram recorded. The carrier solvent contained 0.05 M tris(hydroxymethy1)aminomethane (TRISMA, Aldrich), 0.0268 M HN03, and 0.00308 M NaN3, and the pH was 7.9. The fulvic and humic acid samples, in the form of their solid sodium salts, were obtained from various sources as outlined in Table I and references 28-30. These were dissolved in the TRISMA buffer to give solutions of about 0.25 mg/mL. Two natural water samples were obtained from Australia and injected untreated. Redwater Creek is in the Ottway Ranges, Victoria, with total dissolved organic matter (DOM) of about 60 mg/L, and Inkpot is a small lake in western Victoria with DOM N 40 mg/L. Poly(styrenesu1fonate) molecular weight standards (Polysciences, Inc.) were made up in water (1 mg/mL). Bovine serum albumin, ovalbumin, transfer RNA, and RNase were also run as test samples. Results and Discussion
Fractograms of Fulvic and Humic Acids, Some typical fractograms of fulvic and humic acids are shown in Figure 2. The abscissa in these graphs gives the retention volume, which is approximately inversely proportional to the diffusion coefficient which, in turn, is a nonlinear function of molecular weight. Thus, although not linearly related, an increase in the retention volume signifies an increase in the molecular weight. The ordinate is proportional to absorbance of 254-nm UV radiation. These samples would be expected to have a relatively large number of chromophores active at 254 nm, and thus the detector sensitivity will be high, giving rise tq a low de-
I
100 -
l
I
I
I
,
( 1
1
R
,"[V 0
5 10
0 0
I
10 20
15 30
20 40
25
30
50
60
35 70
40 80
........ Florida Humate
- Washington Humate
-
........Aldrich Humate
0
10
0
5
1
.
1
20 10
30 15
40 20
50 25
60 30
70
I
I
t
I
I
I
1
35
40
80
35 40
Redwater Creek
0
0
5
10
15 20 25 30 Elution Volume (mL)
Figure 2. Flow FFF fractograms of sodium fuivate and humate samples and two natural waters. Both longitudlnaland cross-channel flow rates were about 4 mL/min. The ordinate is proportional to the UV absorbance at 254 nm. (a) Suwannee stream fuhrate and humate; (b) Mattole soil fulvate and humate: (c) Florida cemented sand humate and Washington peat bog humate; (d) Aidrich sodium humate and Leonardite lignk coal humate; (e) Redwater Creek water and Inkpot Pond water.
tection limit (about 0.02 mg of DOM/mL for many samples). The Suwannee humate sample was run at a series of wavelengths between 240 and 650 nm to test if different Environ. Sci. Technoi., Vol. 21, No. 3, 1987 291
Table 11. Flow FFF Data for Reference Humic Substances
sample Suwannee stream fulvate Suwannee stream humate Mattole soil fulvate Mattole soil humate
MW" by flow by other D,, X lo', Hhatr, FFF methods cm2 s-lb cmc 860 1490 1010
1750
750 1500 1200 -2000
3.23 3.76
4.2 5.6 4.9
3.01
6.1
4.10
"W = molecular weight. For FFF the values are for the peak maximum. Other methods were vapor pressure osmometry and low-angle X-ray scattering performed at USGS Denver (R. Malcolm, personal communication). D,, = diffusion coefficient at peak maximum. Hinatr = instrumental plate height of FFF peaks which includes the DolydisDersitv of the samde.
*
molecular weight fractions produced variations in the W-visible spectrum. All fractograms had the same shape; however, the sensitivity decreased substantially at higher wavelengths. The nominal molecular weight cut-off for the lower wall membrane of 10000 obviously has little relevance for these samples as fractograms were successfully recorded. The apparent lack of permeability of the membrane for these relatively low molecular weight humic substances is no doubt due to a combination of two factors. First, they are not globular but probably have a somewhat expanded conformation due to the repulsion of charged functional groups. Second, charge repulsion between the molecules and the membrane (or species adsorbed on the membrane) could prevent the humate ions from entering the pores. There are some obvious and repeatable differences between the fractograms, as can be seen from the data summarized in Table 11. These differences are in excellent accord with expected trends. Thus, the largest diffusion coefficients recorded for the peak maxima (DmJ were found for the fulvic acids, which were expected to have the lowest molecular weights. They were also expected to be the least polydisperse (R. Malcolm, personal communication), which is indicated by the fact that they have the The quantity lowest instrumental plate heights (Hinstr). Hinstr is the experimental plate height measured from the peak width at half-height minus the calculated contribution due to nonequilibrium effects during elution (31)and thus gives some indication of the relative sample polyfor the monodisperse poly(styrenedispersity. The Hinstr sulfonate) molecular weight standards used were close to zero, indicating that factors other than sample polydispersity, such as relaxation and sample injection, were not contributing significantly to Hinstr. Fractograms were also obtained for two natural waters without any sample pretreatment. These samples contain unusually high dissolved organic matter concentrations (40 and 60 mg/L), but since the detector sensitivity was 0.04 AUFS for these runs, it should be possible to record fractograms of samples with considerably lower organic content. Molecular Weight Calibration. Flow FFF can be used to determine the molecular diffusion coefficient from eq 1 and 2. However, the relationship between this quantity and the sample molecular weight (M) is not simple and depends on the molecular conformation. In this work we have used a series of poly(styrenesu1fonate)(PSS) molecular weight standards ranging from 4000 to 100000 to construct the calibration line shown in Figure 3. Each PSS sample was run at several different field and channel flow rates in the range 1-4 mL/min. There was a trend for the observed diffusion coefficient at the peak maximum (DmJ to decrease slightly as the field was in292
Envlron. Scl. Technol., Vol. 21, No. 3, 1987
Humic Substances
v
Xld 8 -5.5
$
-6.0
O
m
A
-6. 52.5
3.0
3.5
4.5
4.0
Log Mo 1ecu 1 o r
5.0
5.5
We1 ght
Figure 3. Molecular weight Calibration line constructed using the measured diffusion coefficients of poly(styrenesu1fonate)molecular weight standards. The line is the least-squares regression with only these f i e points. Also plotted on the graph are polnts for the reference Suwannee stream and Mattole soil fulvate and humate samples. Data used for these polnts were the diffusion coefficient measured at the peak maximum of the flow FFF fractograms and the molecular weights estlmated by Independent methods (see Table 11).
creased. This variation was about 5-15% in the range tested and is not predicted by current FFF theory. It may be due to sample interactions, either intermolecular or involving the lower membrane, both of which would be more pronounced at smaller X values. This also could be the cause of the small concentration dependence of the calculated diffusion coefficient, which was overcome by running dilute samples (1 mg/mL) where this effect is small. The variation of the peak position with field strength was handled by plotting D , against X and reading off the diffusion coefficient (Do.o8)corresponding to a X of 0.08. This value of X was chosen as it was about the mean for the reference humic samples at the cross flow that they were run (4 mL/min). The calibration line shown in Figure 3 is a plot of log Do,osagainst log M . This was a straight line (R = 0.998), confirming the simple power law relationship found for many macromolecules (32): DO.08
= a/Mb
(3)
In this case a = 1.42 X and b = 0.422. The Suwannee and Mattole humic samples have had their molecular weights estimated by other methods (R. Malcolm, USGS, Denver, personal communication). In order to check the validity of the calibration curve, the points correponding to the diffusion coefficient estimated by flow FFF (at a crossflow rate of 4 mL/min) and the molecular weight obtained independently for these humic samples are also plotted in Figure 3. These points appear to fall satisfactorily on the calibration curve. A number of protein samples were run; however, the calibration line generated did not pass through the humate reference points. This was taken to imply that because of the expected difference in molecular shape between the globular proteins and the humate polyions, this calibration was not applicable for the class of compound being studied in this work. Molecular Weight Distributions
Following digitization, the abscissa of the fractogram was rescaled to give molecular weight with the calibration outlined above (except in the case of the Leonardite sample which was run months later and a recalibration of the and b = 0.422). Since the system gave a = 6.00 X transformation from elution volume to molecular weight is nonlinear ( M 0~ v2.37for well-retained molecules), this necessitates modifying the ordinate in order to maintain the correct proportionality between sample mass and area
Table 111. Molecular Weight Estimates (Fractogram Peak Maximum, Number Average, and Weight Average) and Polydispersity Indicators ( M , / M n Ratio and Instrumental Plate Height of the Fractogram Peak) for Some Humic Substances
sample Suwannee stream fulvate Suwannee stream humate Mattole soil fulvate Mattole soil humate Florida sand humate Washington peat humate Leonardite coal humate Aldrich humate Redwater Creek water Inkoot Pond water
.05
0
2000
4000
6000
I
I
I
8000
10000
........ Suwonnee Humate Suwonnee
1150 1580 1390 1940 2250 3020 3730 3070 1760 1480
1910 4390 3900 6140 7960 17800 18700 14500 4900 3830
1.66 2.78 2.81 3.16 3.54 5.89 5.01 4.72 2.78 2.59
-
-
4.2 5.6 4.9 6.1 6.3 8.2 16.4 9.0 7.6 9.6
under the curve. This was achieved by multiplying the ordinate yi by AVi/AMi, where AVi is the difference between the elution volume (minus the extra column dead volume) for consecutive digitized points and AMi is the corresponding difference in molecular weight for these same points. Since at low elution volumes the void peak interferes with the true sample signal and the resolution is poor in this region, points corresponding to molecular weights less than 300 were neglected. Each distribution was normalized so that the total area under the curve between the minimum (300) and maximum molecular weight values was set to 100. Molecular weight distributions obtained in this way are given in Figure 4. It will be noticed that these curves no longer contain the distinct peaks that the fractograms exhibit. Either a very broad peak is present, or the distributions decrease continuously from low to high molecular weight. This transformation illustrates very clearly the difficulty of interpreting the shape of the raw fractograms in a meaningful way, This danger of producing experimental output far removed from the real information being sought applies also to related techniques. Size exclusion chromatography, in particular, is prone to form narrow peaks from broad distributions because its elution volume range is so limited. The fact that very broad molecular weight distributions often containing no peak at all are obtained from these humic substances seems fairly reasonable in terms of their probable origin Thus, the molecular weight distributions are most likely the result of both degradation of higher molecular weight plant material and polymerization, condensation, and aggregation reactions (22). From the molecular weight distributions, both number(M,) and weight- (M,) average molecular weights were computed. These are listed in Table 111. Because of the relatively poor resolution of the digitization (150 points) and the lower molecular weight limit of 300, some error was probably introduced in this procedure. Again, it can be seen that the M , values for the four reference samples agree fairly well with the molecular weights obtained by other methods (see Table 11). Due to the considerable tailing of the distributions to higher molecular weight, the M , values are considerably larger than M,. However, the long tails, which are highly weighted in calculating M,, contribute to uncertainty in M, because the low concentration levels in the tails cannot be reliably measured. There may also be some error introduced for the higher molecular weight components since these would yield lower X values; the molecular weight
-
Fulvote
molecular weight polydispersity peak Hinetr M , MwfMn cm max M , 860 1490 1010 1750 2060 2430 4050 3270 1860 1330
1
I
..........."..........
........ Motto I e -Mattole
Humate
Fulvote
.03
5
.01.01
.I
0
5
LL
00
I 2000 2000
I 4000 4000
6000 6000
8000 8000
10000 10000
0
5000
10000
15000
20000
25000
x
0
5
& -03
I
al
I
I
I
-Stream
LL
_---so1 1 -.-.
Peat Bog
........Lignite
wO
5000
10000
15000
2000
4000
6000
Cool
20000
25000
-Redwoter Creek Water
........I n k p a t Water
0
0
I
I
I
2000
4000
6000
Molecular
.......... 8000
10000
We1g h t
Figure 4. Molecular weight distributions of humic substances estimated from the dlgitlzed flow FFF data. The lower molecular weight limit is about 300. The area under the curves between any two molecular weight llmRs represents the percentage by weight of the sample in that range: /.e., the total area under each curve is normalized to be 100. (a) Suwannee stream fulvate and humate; (b) Mattole soil fulvate and humate: (c) Humates from the Suwannee stream, Mattole soil, Washington peat bog, and Leonardite lignite coal locations; (d) Redwater Creek water and Inkpot Pond water.
calibration was standardized for X values of 0.08. In principle, this could be empirically corrected for, but this was not attempted here. Instead, future work will concentrate on finding better frit and membrane materials for constructing the channel in the hope that a more uniform crossflow and perhaps less sample wall interaction will produce results more consistent with FFF theory. Also given in Table I11 are two indicators of the sample polydispersity, the Hinstr values from the fractograms and Envlron. Sci. Technol., Vol. 21, No. 3, 1987
293
the ratio MJM,,. The latter ratio is commonly used in polymer science. However, because of the tailing problem, it does not give a reliable measure of the spread of the molecular weight distribution, particularly in the case of the very polydisperse samples. For example, the Leonardite coal sample appears to be much more polydisperse than the Washington peat bog sample when we look at their fractograms and molecular weight distributions. This is reflected in the Hinstr values but not the M,/M, ratios because of both the smaller M,, value of the Washington peat bog sample and the large weighting given to a small amount of material in the extreme tails of the distributions as discussed above. A number of trends are apparent that may reflect the different origin of the samples. The fulvic acid molecules are smaller and less polydisperse than the humic acid samples (Figure 4a,b), which is consistent with the fact that they are the more water-soluble fraction of humic substances. The molecular weight and polydispersity of the humic acids appear to follow a clear trend depending on their source (Figure 44. Thus, both of these parameters increase in the order water < soil < peat bog < lignite coal. This is most likely a consequence of the mechanism of formation of the different substances. Thus coal, peat, and soil humic matter represent different stages in the transformation of terrestrial plant material, and this trend to higher molecular weight may point to the fact that one of the dominant processes is molecular polymerization and condensation. Alternatively, this trend may represent the order of increasing tendency to form aggregates from smaller molecules. There is some doubt about the mechanism involved in producing aquatic humic substances. The fact that they have a lower molecular weight could reflect the different nature of aquatic plant and animal organic matter or the dominance of different chemical reactions than are occurring in soils. Alternatively, if stream humic substances are essentially soil organic matter leached or eroded into rivers or lakes, then it would also be logical that they would tend to contain a greater proportion of the more soluble lower molecular weight fractions. Freshwater humic substances usually contain about 90% fulvic acid, although the amount is probably less for the colored waters used in this study. However, the molecular weight distributions of the Redwater Creek and Inkpot waters were somewhat larger than either the Suwannee or Mattole fulvate samples (Figure 4D).The Redwater Creek distribution is very similar to the Mattole humate with the Inkpot being slightly lower in molecular weight. This could be an indication that the organic material in these two systems is mainly of terrestrial origin which is leached into the water, rather than being produced in situ by normal aquatic processes. Alternatively, special conditions may operate in the production of such highly colored waters that also lead to the generation of higher molecular weight organic molecules (29). Whether the molecular weight distributions are substantially influenced by the different vegetation found in the Australian environment or by the extraction scheme used with the Suwannee and Mattole samples is unknown at present. Conclusions
In this work it has been shown that flow FFF can be used to deteriniie molecular weight distributions of natural humic substances. These distributions are very broad and often show that there is a steady decrease in the fractional mass of sample as the molecular weight increases with no peak maximum observed. This is new information on the structure of this important class of compounds. 294
Environ. Sci. Technol., Vol. 21, No. 3, 1987
The molecular weight distributions of humic substances from a number of different terrestrial and freshwater environments have been measured. Aquatic humic substances are smaller than those found in soils, and fulvic acids are smaller than humic acids. The exact reason for these trends is not known as there is still considerable controversy concerning the mechanisms of their formation and the subsequent transformations they may undergo. Flow FFF would appear to give the most detailed molecular weight information of any of the techniques that have been applied to humic substances. It could therefore provide a most useful addition to the range of measurements used in studying the changes that are induced in the short term by varying environmental conditions (e.g., pH, ionic strength, and other water-quality parameters) and in the long term by biogeochemical processes. In addition, with the advances being made in ultratrace analytical methods, it may be possible to utilize the fractionating ability of flow FFF to obtain information on the association of pollutants (e.g., trace metals and toxic organics) with different humic fractions. Acknowledgments
Ron Malcolm and John Hedges kindly supplied some of the humic samples. We thank Ron Malcolm for many helpful suggestions and advice. Literature Cited (1) Aiken, G. R.; McKnight, D.M.; Wershaw, R. L.; MacCarthy, P., Eds. Humic Substances in Soil, Sediment and Water; Wiley-Interscience: New York, 1985. (2) Hart, B. T., Ed. The Role of Particulate Matter in the Transport and Fate of Pollutants; Water Studies Centre, Chisholm Institute of Technology: Melbourne, Australia, 1986. (3) Christman, R. F.; Norwood, D. L.; Millington, D. S.; Johnson, J. D.; Stevens, A. A. Enuiron. Sci. Technol. 1983, 17, 625-628. (4) Hart, B. T. Enuiron. Technol. Lett. 1981, 2, 95-110. (5) Florence, T. M.; Lumsden, B. B.; Fardy, J. J. Anal. Chim. Acta 1983, 151, 291-295. (6) Chiou, C. T.; Malcolm, R. L.; Brinton, T. I.; Kile, D. E. Environ. Sci. Technol. 1986, 20, 502-508. (7) Hunter, K. A.; Liss, P. S. Limnol. Oceanogr. 1982, 27, 322-335. (8) Beckett, R. In The Role of Particulate Matter in the Transport and Fate of Pollutants; Hart, B. T., Ed.; Water Studies Centre, Chisholm Institute of Technology: Melbourne, Australia, 1986; pp 113-142. (9) Gibbs, R. J. Environ. Sci. Technol. 1983, 17, 237-243. (10) Tipping, E.; Higgins, D. C. Colloids Surf. 1982, 5, 85-92. (11) Beckett, R.; Nicholsen, G.; Hart, B. T., submitted for publication in Geochim. Cosmochim. Acta. (12) Davis, J. A. Geochim. Cosmochim. Acta 1984,48,679-691. (13) Chiou, C. T.; Porter, P. E.; Schmeddling, D. W. Enuiron. Sci. Technol. 1983, 17, 227-231. (14) Thurman, E. M.; Malcolm, R. L. In Aquatic and Terrestrial Humic Materials; Christman, R. F.; Gjessing, E. T., Eds.; Ann Arbor Science: Ann Arbor, MI, 1983; 1-23. (15) Harvey, G. R.; Boran, D. A.; Chesal, L. A.; Tokar, J. M. Mar. Chem. 1983, 119-132. (16) Wilson, M. A. J. Soil Sci. 1981, 32, 167-186. (17) Reuter, J. H.; Perdue, E. M. Geochim. Cosmochim. Acta 1977,41, 325-334. (18) Wershaw, R. L.; Pinckney, D. J. J . Res. U.S. Geol. Surv. 1973, 1, 701-707. (19) Swift, R.; Posner, A. M. J. Soil Sci. 1971, 22, 236-249. (20) Buffle, J.; Deladsey, J. P.; Haerdi, W. Anal. Chim. Acta 1978,101,339-357. (21) Malcolm, R. L. In Humic Substances in Soil Sediment and Water: Aiken. C. R.: McKnieht. D. M.: Wershaw, R. L.; MacCarthy, Eds.; Wiley-Interscience: 'New York, 1985; Chapter 7, pp 181-209.
Environ. Sci. Technol. 1987, 27, 295-299
(29) Thurman, E. M.; Malcolm, R. L. Environ. Sci. Technol. 1981,15,463-466. (30) Ertel, J. R.; Hedges, J. I. In Aquatic and Terrestrial Humic Materials; Christman, R. F.; Gjessing, E. T., Eds.; Ann Arbor Science: Ann Arbor, MI, 1983; pp 143-163. (31) Giddings, J. C.; Karaiskakis, G.; Caldwell, K. D.; Myers, M. N. J. Colloid Interface Sei. 1983, 92, 66-80. (32) Tanford, C. Physical Chemistry of Macromolecules;Wiley: New York, 1961; Chapter 6.
Thurman, E. M. Organic Geochemistry of Natural Waters; Martinus Nijhoff/Junk The Hague, The Netherlands, 1985; pp 304-312.
Thurman, E. M.; Wershaw, R. L.; Malcolm, K. L.; Pinckney, D. J. Org. Geochem. 1982,4, 27-35. Giddings, J. C. Sep. Sci. Technol. 1984,19, 831-847. Wahlund, K.-G.; Winegarner, H. S.; Caldwell, K. D.; Giddings, J. C. Anal. Chem. 1986,58, 573-578. Giddings, J. C.; Yang, F. J.; Myers, M. N. Anal. Chem. 1976, 48,1126-1132. Yang, F. J.;Myers, M. N.; Giddings, J. C. Anal. Chem. 1977, 49,659-662.
Malcolm,R. L.; Thurman, E.M.; Aiken, G. R. Trace Subst. Environ. Health 1977, 11, 307-314.
Received for review July 8,1986. Accepted November 10,1986. Financial support was obtained from Department of Energy Contract DE-AC02-79EV10244.
Composition of Snowmelt and Runoff in Northern Michigan Steven H. Cadle" and Jean M. Dasch Environmental Science Department, General Motors Research Laboratories, Warren, Michigan 48090
Robert Vande Kopple University of Michigan Biological Station, Pellston, Michigan 49769
Snowmelt and runoff were studied during the 1982-1983 and the 1983-1984 winters at the University of Michigan Biological Station, which is located near the northern tip of Michigan's lower peninsula. The first 50% of the snowpack acidity was released in meltwater and rainwater equal to 25% of the original snowpack water content. Interaction between the meltwater and the litter layer produced large changes in the concentrations of most species. Runoff to two streams had high Sod2and very low NO3- concentrations. It is concluded that most of the NO3- is either biologically utilized or retained in the watershed, even during the early snowmelt period at this site.
Introduction The acidification of aquatic ecosystems by acid deposition occurs in two forms: long-term acidification and short-term, episodic acidification. Long-term acidification is believed to be due primarily to sulfuric acid deposition (1). Nitric acid is less important because it can be utilized in the environment by processes that produce alkalinity (2). Episodic events can be caused by runoff from the spring snowmelt. Nitric acid may be a significant contributor to those events for three reasons. First, nitric acid concentrations in wet deposition show little seasonal variation, while sulfuric acid concentrations are highest in the summer and lowest in the winter (3,4). Therefore, nitric acid can be the dominant acid in snow. Second, the ionic constituents of snow are released in the early meltwater (5). Thus, the nitric acid concentration in the water entering the ecosystem can be significantly higher than normal. Third, the biological processes that utilize nitrate may be inactive during the melt period. Therefore, the nitric acid may be present in the runoff (2). Overall, this process is of concern because it occurs at a sensitive time in the life cycle of aquatic species (1). Several studies have investigated the importance of nitric acid during the snowmelt period in eastern North America. Both Stottlemyer (6) and Hemond and Eshleman (7) found almost complete NO3- retention in Lake Superior basin watersheds and Bickford Reservoir, MA, watersheds, respectively. Jeffries and Snyder (8) and Jeffries and Semkin (9)found small sustained increases 0013-936X/87/0921-0295$01.50/0
in NO< in some streams in Ontario. In contrast, Likens et al. (IO),Galloway et al. (II), and Driscoll and Schafran (12) have reported large NO3- pulses at Hubbard Brook and various Adirondack Lakes. All of these studies reported that significant amounts of SO-: are exported to streams and lakes during the snowmelt period. Further investigations of episodic events are needed in order to characterize the regional differences in the importance of NO3-. In this study the relative importance of NO3- to the acidity of snowmelt and runoff at a site in northern Michigan has been determined. Comprehensive studies of wet deposition and dry deposition to the snowpack at as has an this site have been reported elsewhere (4,13), earlier study of snowmelt and runoff (14,15).
Experimental Section Site. The study was conducted during the 1982-1983 and 1983-1984 winters at the University of Michigan Biological Station, which is located near the tip of Michigan's lower peninsula. Snow-core samples were collected in a 0.2-ha open field surrounded by deciduous forest. Lysimeter samples were collected at a location under a deciduous canopy 30 m from the edge of the open field. The soil at this location is a spodosol (Rubicon Series). Streamwater samples were collected from Beavertail and Van Creeks. Beavertail Creek is a small cold-water creek with a 534-ha watershed. Stream length from the headwaters to the sampling site was 4.4 km. Total vertical rise in the watershed is 25 m. Most of the watershed is forested with a coniferous canopy adjacent to the stream and a deciduous canopy of sugar maple and aspen in the uplands. Van Creek is a cold-water creek that drains a 2643-ha watershed. Stream length from the headwaters to the sampling site was 13.3 km. Total vertical rise in the watershed is 58 m. Most of this watershed is also forested. There are tag alder and coniferous swamp areas along the stream course while the uplands are almost exclusively mixed aspen and sugar maple stands. In addition, seven pools were sampled. These included five beach pools around Douglas Lake and two woodland pools. The continuous canopy over the woodland pools is mostly red maple.
0 1987 American Chemical Society
Environ. Sci. Technol., Vol. 21, No. 3, 1987
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