Environ. Sei. Technol. l W 2 , 26, 1807-1815
Atkinson, R. J. Phys. Chem. Ref. Data 1989, Monograph 1, 1-246. Atkinson, R. J. Phys. Chem. Ref. Data 1991,20,459-507. Atkinson, R.; Carter, W. P. L. Chem. Rev. 1984,84,437-470. Atkinson, R. Atmos. Environ. 1990,24A, 1-41. Atkinson, R. Znt. J. Chem. Kinet. 1987, 19, 799-828. Atkinson, R. Environ. Toxicol. Chem. 1988, 7, 435-442. Goodman, M. A.; Aschmann, S. M.; Atkinson, R.; Winer, A. M. Arch. Environ. Contam. Toxicol. 1988,17,281-288. Goodman, M. A.; Aschmann, S. M.; Atkinson, R.; Winer, A. M. Environ. Sci Technol. 1988, 22, 578-583. Winer, A. M.; Atkinson, R. In Long Range Transport of Pesticides; Kurtz, D. A., Ed.; Lewis Publishers: Chelsea, MI, 1990; Chapter 9. Atkinson, R. Sei. Total Environ. 1991, 104, 17-33. Worthing, C. R. The Pesticide Manual, 8th ed.;The British Crop Protection Council: Croyden, UK, 1987; pp 201,341. Atkinson, R.; Aschmann, S. M. Znt. J. Chem. Kinet. 1988, 20,513-539. Atkinson, R.; Aschmann, S. M.; Arey, J.; Zielinska, B.; Schuetzle, D. Atmos. Environ. 1989, 23, 2679-2690. Arey, J.; Atkinson, R.; Aschmann, S. M.; Schuetzle, D. Polycyclic Aromat. Compd. 1990, 1, 33-50.
Atkinson, R.; Plum, C. N.; Carter, W. P. L.; Winer, A. M.; Pitts, J. N., Jr. J. Phys. Chem. 1984, 88, 1210-1215. Atkinson, R.; Aschmann, S. M.; Pitts, J. N., Jr. J. Phys. Chem. 1988,92, 3454-3457. Japar, S. M.; Wu, C. H.; Niki, H. J.Phys. Chem. 1976,80, 2057-2062. Arey, J.; Atkinson, R.; Aschmann, S. M. J. Geophys. Res. 1990, 95, 18539-18546. Taylor, W. Do;Allston, T. D.; Moscato, M. J.; Fazekas, G. B.; Kozlowski, R.; Takacs, G. A. Znt. J . Chem. Kinet. 1980, 12, 231-240. Scanlon, J. T.; Willis, D. E. J. Chromatogr. Sei. 1985,23, 333-340. Atkinson, R.; Aschmann, S. M.; Carter, W. P. L.; Winer, A. M.; Pitts, J. N., Jr. J. Phys. Chem. 1982,86,4563-4569. Prinn, R.; Cunnold, D.; Rasmussen, R.; Simmonds, P.; Alyea, F.; Crawford,A.; Fraser, P.; Rosen, R. Science 1987, 238, 945-950. (33) Logan, J. A. J. Geophys. Res. 1985,90, 10463-10482. Received for review April 13,1992. Accepted May 28,1992. This research was supported by ZCI Americas, Znc. through Project Y6-23009-CH (Project Officer Dr. C. K . Tseng).
Dry Deposition of Atmospheric Particles: Application of Current Models to Ambient Data Thomas M. Holsen" and Kenneth E. Noll
Pritzker Department of Environmental Engineering, Illinois Institute of Technology, Chicago, Illinois 60616 The dry deposition mass flux for atmospheric particles was calculated as a function of particle size (mass-flux size distribution). The mass-flux size distribution increased rapidly with particle size with the majority of the calculated flux due to particles larger than 1pm (>go%). The evaluation of dry deposition was based on (1)atmospheric mass-ize distributions between 0.01 and 100 pm diameter obtained from field measuremenh with a cascade impactor and a Noll rotary impactor (NRI) and data in the literature obtained with a wide-range aerosol classifier (WRAC) system, (2) field dry deposition sampling data using a surrogate swface, and (3) dry deposition calculations based on various models that estimate dry deposition velocity as a function of particle size. Results of model calculations using the atmospheric particle size distribution data were compared to measured flux data to show that realistic estimates can be made for the total dry deposition flux. Calculations using models that account for particle size distribution show that results are extremely sensitive to the mass of large particles and that large particles control dry deposition flux due to their high deposition velocities. Current dry deposition modeling techniques that use average particle concentrations and average deposition velocities underestimate the contribution of coarse particles to dry deposition and therefore underestimate dry deposition. Introduction Even though an accurate determination of the dry deposition of contaminslnts is critical in understanding their movement in the environment, there is still no generally acceptable technology for sampling and analyzing dry deposition flux (1-7). The quantification of dry deposition flux is difficult because of large spatial and temporal variations. The use of a surrogate surface to collect dry deposition is a technique that allows a comparison to be 0013-936X192/0926-1807$03.00/0
made of measured and modeled data because it can be used to directly assess deposited material. Surrogate surfaces can be used (1)over extended periods of time and at different locations to provide qualitative information on temporal and spatial variations in dry deposition of a species, (2) to estimate lower limits of aerosol dry deposition to rougher, natural surfaces if they are smooth horizontal collectors that do not appreciably disturb airflow, and (3) as research instruments for investigating the influence of surface geometry, atmospheric properties, and characteristics of the depositing species on dry deposition (3). Dry deposition modeling studies typically use micrometeorological techniques such as eddy correlation, eddy accumulation, and gradients; however, these techniques cannot be reliably applied to larger particles influenced by sedimentation (3). Recent work in our laboratory and by others has shown that pollutants associated with these larger particles can be responsible for an appreciable fraction of the total dry deposition flux. For example Davidson and Friedlander (8) found that in Los Angeles the total mass dry deposition of P b is dominated by sedimentation of the small fraction of large airborne Pbcontaining particles. PCBs in dry deposition near urban areas of Lakes Huron and Michigan have also been found to be associated with large particles (9, 10). Davidson et al. (3) demonstrated that dry deposition of supermicron sulfur-containing particles may be responsible for an appreciable fraction of the total sulfur dry deposition in spite of the relatively large fraction of submicron sulfate in the atmosphere. Studies over the western Mediterranean and North Sea have shown that large mineral aerosol particles dominate the dry deposition flux (11, 12). These large atmospheric particles make up one of the three size modes of atmospheric particles, each of which is usually considered to be log-normally distributed. The
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Table I. Chicago Sample Information % of time
a
av wind speed,
sample no.
samp1ing date, 1991
% of time
exposed"
wind was from land
1-mass 2-maSS 2-lead 2-calcium 3-mass 4-mass 5-mass 6-mass 7-mass 8-mass 9-mass
6/21-6127 7 18-71 16
75 85
41 25
4.7 3.6
7/23-1129 7 / 30-81 6 8/ 9-81 15 8/16-8124 8125-8/29 8131-918 9/17-9129
88
81 94 72 100
53 46 38 46 92 59 69
4.0 4.2 3.2 4.0 3.5 4.0 4.2
87
69
4
5
fine-particle concn (2.5 rm), rg/m3 21.5 17.4 0.015 0.73 16.5 37.9 20.1 24.9 39.9 28.4 25.5
Samples were not exposed during times of rain or threat of rain.
largest (coarse) particle mode (>2.5 pm) is a result of mechanical abrasion and wind erosion and is presently thought to be removed primarily by dry deposition. The accumulation (fine) mode (-0.1-2.5 pm) is the result of coagulation processes and primary aerosol emission and is removed by precipitation scavenging and dry particle deposition. The smallest particles (30 pm is due to the rapid drop in PCB content for these particles. Flux distributions and cumulative flux distributions for calcium, lead, nitrate, sulfate, and PCBs were developed from the particle size distributions shown above. The cumulative flux distributions clearly show that for all these compounds the majority of the calculated flux (>75%) was due to particles with diameters of >10 pm (Figure 7). (These data are plotted at the midpoint of each interval.) Sulfate had the greatest percentage contribution from the fine-particle mode and calcium the least. Comparison between Measured and Calculated Flux Distributions. The flux distributions for the Chicago continental flow (sample 7) using both the Slinn and Slinn model (16) and the No11 and Fang model (26) are shown in Figure 8. In addition, data obtained from counting particles collected on a deposition plate exposed in Chicago are shown (33). This figure demonstrates that the calculated flux distribution agrees fairly well with actual data. It is also clear from this figure that the model 1812
Environ. Sci. Technol., Vol. 26, No. 9, 1992
100
10
Pcrticle diameter, p m
? a r t i c l e d i a m e t e r , ,urn
Flgure 7. Cumulative calcium, lead, PCB, nitrate, and sulfate flux data Summed in three particle size ranges: 0.1-1, 1-10, and 10-80 pm and plotted as cumulative percents (at the midpoint of each interval). 100
r
-
7
-
l
I ' m ,
e measured data Ch c o g 0 (Watkins, (33)) Chicago
1991 S-SW
wind
0
i
S i i n ~a n d Siinn
- Nall and
E
U*
fang
assumed to be 40 c m / s s c
\ i
J
U
\ L
0.01
0.0001
1
i
t
31
d -
i ' ' I
1 10 P a r t i c l e d i a m e t e r , prn
100
Figure 8. Comparison of the flux distribution for the Chicago continental flow (sample 7) calculated using both the Slinn and Slinn model ( 16)and the Noli and Fang model (26)to data obtained from counting partlcles collected on a deposklon plate exposed in Chicago [Watkins (3311.
of Slinn and Slinn (16) underestimates the contribution of coarse particle to dry deposition and that the addition of the No11 and Fang (26) model for the coarse particles better fits the measured data. Comparison between Measured and Calculated Fluxes. A comparison of the mass, lead, and calcium flux measured with a smooth surrogate surface and flux calculated with a variety of models is shown in Figure 9. These calculated fluxes were computed with the following models: (a) one-step method (see above) using the mass median radius (MMRf)and af of the fine-particle fraction (Table 111) with an deposition velocity estimated with the model of Slinn and Slinn (16); (b) one-step method using the MMRc and ac of the coarse-particle fraction with an deposition velocity estimated with the model of Slinn and Slinn (16); (c) one-step method using the MMRc and uc of the coarse-particle fraction with an deposition velocity estimated with the model of No11 and Fang (26); (d) nine-step model (using the midpoint cutoff diameter of
e
X
3 -
-
\ L
0
3
r
Q.
p e r f e c t prediction
3
v
w
9 step, N&F 9 step, S&S
0.1
'1
0
P
: a
0.001
7
1 I
1
2
3
I
I
'.' I
1
6 7 8 9 2-CoZ-Pb Chicago S a m p l e No. (Table 1 )
4
b
- 1 step MMR, S&S d v - 1 MMR~S&S e
c
A
a
I
0
- 1 step MMRc N & F
5
- 9 step S&S c - 9 step N&F
T
-
f g
6
S&S productc 8&s
h
0 - productc
product,
N&F
Figure 9, Comparison of the flux measured with a smooth surrogate surface and flux calculated with a vartety of dry deposition models using different particle slze classifications. Those labeled S&S and N&F used depositbn velocitles calculated wlth the models of Sllnn and Slinn ( 76) and Noil and Fang (26),respectively. The subscripts c and f refer to the fine-particle and coarse-particle size modes, respectively. The one-step, nine-step, and product models are described in the text.
each AAPSS and NRI stage) with deposition velocities estimated with the model of Slinn and Slinn (16); (e) nine-step model with deposition velocities estimated with the model of Slinn and Slinn (16) with the No11 and Fang (26) modification; ( f ) product model computed by multiplying the measured fine-particle concentration by the MMDf deposition velocity calculated with the Slinn and Slinn model (16); (g) product model computed by multiplying the measured coarse-particle concentration by the MMDc deposition velocity calculated with the Slinn and Slinn model (16); and (h) product model computed by multiplying the measured coarse-particle concentration by the MMDc deposition velocity calculated with the Noll and Fang model (26). The last three calculations using the indirect product models are similar to indirect method of calculating flux from ambient air quality data currently used by many investigators to represent dry deposition flux (34, 35). It is obvious from the results shown in Figure 9 that models which use only the fine-particle concentration and a MMD or MMR deposition velocity to calculate the flux rate (a and f) severely underestimate the measured flux (from 25 to 3000 times). This finding supports the work shown above which found that fine particles are responsible for only a small fraction of the dry deposition flux. The one-step model using coarse-particle values and deposition velocities calculated with the model of Slinn and Slinn (16) (model b) in general overpredicts the measured flux; however, for some of the samples it significantly underpredicts the measured flux. This same model using deposition velocities calculated with the model of Noll and Fang (26) (model c) in general greatly overpredicts the measured flux. Both models b and c however underestimate the P b and Ca flux. The nine-step models (d and e) are the most stable and give the best results, model d which uses deposition velocities calculated from the Slinn and Slinn (16) model slightly underpredicts the measured flux (3-10 times), and model e which uses deposition velocities calculated from the No11 and Fang (26) model
0
:: 0.000: 1
I
I
2
I
I
I
I
I
I
3
4
5
6
7
8
9
Chicago S a m p l e No. (Table I)
Figure 10. Comparison of the upward flux measured with a smooth surrogate surface and upward flux calculated with the nine-step model. Those labeled S&S and N&F used depositlon velocities calculated wkh the models of Slinn and Slinn (76) and Noll and Fang (26), respectively.
slightly overpredicts the measured flux (2-3 times). The product models that use the coarse-particleconcentrations and MMDc deposition velocities also agree fairly well with measured results, model g which uses deposition velocities calculated from the Slinn and Slinn (16) model slightly underpredicts the measured flux (3-10 times), and model h which uses deposition velocities calculated from the Noll and Fang (26) model slightly overpredicts the measured flux (2-5 times). Both models g and h however underestimate the P b flux that was present to a large extent in the fine-particle mode. Upward Flux. The observation that coarse particles can be transported great distances implies that there are atmospheric processes capable of imparting an upward movement to some coarse particles that can counteract their large settling velocities. In order to evaluate these upward transport velocities, particles were also collected on the bottom of the deposition plate (Table 111). The downward facing plate will only collect particles whose upward particle motion is large enough to overcome gravitational sedimentation. The ratio of upward to downward flux was fairly constant and averaged 0.30 (Table 111). The upward flux was calculated by multiplying atmospheric concentrations by deposition velocities from the model of Slinn and Slinn (16) with Stokes settling velocity subtracted to simulate upward particle motion due to atmospheric turbulent diffusion and the No11 and Fang (26) model for particle deposition velocities to a downward facing plate in conjunction with the Slinn and Slinn model (16). Figure 10 shows a comparison of the measured and model-calculated flux to the downward facing plate and shows that the Slinn and Slinn model (16) significantly underpredicts the upward flux (at least 1000 times); however, the No11 and Fang model (26) overpredicts the upward flux by approximately 3-4 times. These data demonstrate (1) that atmospheric coarse particles can have a vertical velocity in either the positive (downward) or negative (upward) direction, (2) that the upward velocity is significant and can be estimated using current models, and (3) that some coarse particles can be expected to remain airborne for much longer time periods than would be expected based only on their gravitational settling velocities. Environ. Sci. Technol., Vol. 26, No. 9, 1992
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Coarse particles are made up of two distinctly different types of particles: particles with large deposition velocities that ignore air eddies and simply deposit on the ground under the influence of gravity and particles of intermediate size that follow the air eddy to a certain extent but continue moving along straight lines when the air changes direction. These intermediate size particles can have large atmospheric residence times and be transported long distances. These particles have roughly equal probability of traveling upward or downward on air eddies. Those travelling downward near the ground deposit more quickly than small particles, while those traveling upward will tend to continue their motion slightly longer. This upward motion can far exceed gravity and can carry these particles into regions of larger eddies and faster winds, promoting their ability to remain suspended in the atmosphere (26). The difference in the upward and downward particle fluxes has been shown to influence both the shape and location of the peak in the coarse-particle size distributions shown in Figure 1 and is important to understanding the longrange transport of atmospheric particles and their global behavior. Summary and Conclusions The comparison of measured and calculated fluxes made in this paper supporh previous results (3,8-12)that coarse particles and compounds associated with them are responsible for the majority of dry deposition. Deposition models that account for the coarse-particle mode agree fairly well with particle flux measured with a smooth surrogate surface. The calculation procedure that works best divides the fine- and coarse-particledistributions into a number of intervals and assigns an appropriate deposition velocity to each interval. The calculated fluxes for each interval are then summed to calculate the total flux. Currently used product models that account for only the fine-particle phase (like model f above) or the fine-particle phase and a portion of the coarse-particle phase (like PMlO measurements) do not provide physically meaningful results because deposition velocities vary with particle size and particle size distributions are highly variable. Incomplete information about complete size distribution of atmospheric particles has a much larger influence on the prediction of flux than the use of different models to predict deposition velocities. As was shown above, the models which only used fine-particle mass underestimate the measured flux by up to 3000 times. The difficulties in measuring complete size distributions [especially coarse particles over 10 pm diameter (see review by Garland and Nicholson (36)]suggest that it may be easier to directly measure flux than to indirectly compute flux which requires measurement of complete size distributions. Currently used dry deposition modeling techniques that use average-particle concentrations and deposition velocities greatly underestimate the contribution of coarse particles to dry deposition, and most estimates of dry deposition inputs into mass balance models are probably underestimated. The fate of these large particles in the environment is not well understood, which has implications in both local-scale pollutant transport and large-scale global modeling. With the increasing evidence that large particles, and pollutants associated with them, are responsible for a significant fraction of the measured dry deposition, these dry deposition models need to be reexamined. 1814
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Acknowledgments Special thanks are expressed to Wen-Jhy Lee, GuorCheng Fang, and Jui-Min Lin for Chicago sample collection and William J. Franek, for supervising cascade impactor sample collection. Registry No. Ca, 7440-70-2; Pb, 7439-92-1.
Literature Cited Sehmel, G. A. Atmos. Enuiron. 1984, 14, 983-1011. Davis, C. S.; Wright, R. G. J. Geophys. Res. 1985, 90, 2091-2095. Davidson, C. I.; Lindberg, S. E.; Schmidt, J. A.; Cartwright, L. G.; Landis, L. R. J. Geophys. Res. 1985,90,2123-2130. Droppo, J. G., Jr. J. Geophys. Res. 1985, 90, 2111-2118. Wesley, M. L.; Cook, D. R.; Hart, R. L.; Speer, R. E. J. Geophys. Res. 1985,90, 2131-2143. Nicholson, K. W. Atmos. Environ. 1988, 22, 2653-2666. Dolske, D. A,; Gatz, D. F. J. Geophys. Res. 1985, 90, 2076-2084. Davidson, C. I.; Friedlander, S. K. J. Geophys. Res. 1978, 83, 2343-2352. Murphy, T. J. In Toxic Contaminants in the Great Lakes; Nriagu, J. O., Milagros, S. S., Eds.; John Wiley and Sons: New York, 1984; Chapter 3. Holsen, T. M.; Noll, K. E.; Liu, S. P.; Lee, W. J. Environ. Sci. Technol. 1991,25, 1075-1081. Bergametti, Dulac, F.; Buat-Menard, P.; Ezat, U.; Melki, S.; G. Tellus 1989,41B, 362-378. Injuk, J.; Otten, P.; Rojas, C.; Wouters, L.; Van Grieken, R. Atmospheric Deposition of Heavy Metals into the North Sea; Department of Chemistry, University of Antwerp: Wilnjk, Belgium, 1990; Final Report Project NOMIVE*2 Task No. DGW-920. McDonald, R. L.; Unni, C. K.; Duca, R. A. J. Geophys. Res. 1982,87, 1246-1250. Arimoto, R.; Duce, B. J.; Ray, B. J.; Unni, C. K. J. Geophys. Res. 1985, 90, 2391-2408. Arimoto, R.; Duce, B. J. J . Geophys. Res. 1986, 91, 2787-2792. Slinn, S. A.; Slinn, W. G. N. Atmos. Environ. 1980, 14, 1013-1016. Noll, K. E.; Fang, K. Y. P.; Watkins, L. A. Atmos. Environ. 1988,22, 1461-1468. McCready, D. I. Aerosol Sci. Technol. 1986, 5, 301-312. Cahill, T. Aerosol Measurement; University Presses of Florida: Gainesville, 1979. Noll, K. E.; Pontius, A.; Frey, R.; Gould, M. Atmos. Environ. 1985,19, 1931-1943. Noll, K. E.; Fang, K. Y. P. Presented a t the 79th Annual Meetine of the Air Pollution Control Association. Minneapolis, h N , June 22-27, 1986. Noll, K. E.; Yuen, P. F.; Fang, K. Y. P. Atmos. Environ. 1990,24A, 903-908. Sehmel, G. A. Aerosol Sci. 1973, 4 , 125-138. Davidson, C. I.; Friedlander, S. K. J. Geophys. Res. 1978, 83, 2343-2352. Williams, R. M. Atmos. Enuiron. 1982, 16, 1933-1938. Noll, K. E.; Fang, K. Y. P. Atmos. Environ. 1989, 23, 585-594. Rodes, C. E.; Holland, D. M.; Purdue, L. J.; Rehme, K. A. J. Air Pollut. Control. Assoc. 1985, 35, 345-354. Lundgren, D. A,; Paulw, H. J. J. Air Pollut. Control. Assoc. 1975,25,1227-1231. Lewis, C. W. Atmos. Environ. 1981, 15, 2639-2646. Noll, K. E.; Fang, K. Y. P.; Khalili, E. Aerosol Sci. Technol. 1990,12,28-38. Savoie, D. L.; Prospero, J. M. Geophys. Res. Lett. 1982,9, 1207-1220. Wolff, G. T. Atmos. Environ. 1984, 18, 977-981. Watkins, L. A. Masters Thesis, Illinois Institute of Technology, 1986. Strachan, W. M.; Eisenreich, S. J. Mass Balancing of Toxic Chemicals in the Great Lakes: The Role of AtmosDheric Deposition; International Joint Comrniss;on Wo;kshop
Environ. Sci. Technol. 1992, 26, 1815-1821
Report, Scarborough, Ontario,1986; IJCW Scarborough, 1989. (35) Kelly, T. J.; Czuczwa, J. M.; Sticksel, P. R.; Sverdrup,G. M.; Koval, P. J.; Hodanbosi, R. F. J. Great Lakes Res. 1991, 17 (4), 504-516.
(36) Garland, J. A.;Nicholson, K. W. J . Aerosol Sci. 1991,22 (4), 479-499.
Receiued for review March 2,1992. Reuised manuscript received May 21, 1992. Accepted May 28, 1992.
Historical Inputs of Polychlorinated Biphenyls and Other Organochlorines to a Dated Lacustrine Sediment Core in Rural England Gordon Sanders, * Kevin C. Jones, and John Hamllton-Taylor
Institute of Environmental and Biological Sciences, Lancaster University, Lancaster LA1 4YQ, United Kingdom Helmut Dorr
Instltute of Earth Sciences, University of Heidelberg, Im Neuenheimer Feid 366, D-6900 Heidelberg, Germany Lacustrine sediment cores were obtained from Esthwaite Water, a seasonally anoxic rural English lake. Samples were sectioned, radioisotopically dated, and analyzed for 22 individual polychlorinated biphenyl (PCB) congeners and 1,1,l-trichloro-2,2-bis(p-chlorophenyl)ethane (DDT) and its metabolites. The most abundant congeners in the and 180. PCB sediments were 28,44,66,110,138,149,153, fluxes to the sediment increased slowly from the late 1920s/early 1930s until the late 1940s, escalating sharply thereafter. Maximum PCB fluxes (3.26 ng cm-2 year-l) occurred to sediments dated from the late 1950s/early 1960s. During the following decade inputs of PCBs decreased rapidly, concurrent with restrictions on production and use. Present input levels are ca. 2.17 ng cm-2 year-', with lower chlorinated congeners making the major contribution. Inputs of DDT and its analogues peaked in the mid-1950s (19.2 ng CDDT cm-2 year-'), with DDD the major metabolite. The possible influence of postdepositional factors and coring artifacts on the concentration depth profiles and the apparent presence of PCBs in pre-1930 sediments are discussed.
Introduction Persistent organochlorine compounds (OCs), including the polychlorinated biphenyls (PCBs) and organochlorine pesticides, are among the most important environmental pollutants. PCBs were first produced commercially in 1929 and used in electrical transformers and capacitors (closed systems) as heat-transfer fluids, their chemical inertness and ability to withstand high temperatures making them ideal for this purpose. They were also incorporated into plasticising agents, cements, paints, and carbonless paper (open systems) (1). Over the years PCBs have entered the environment following direct release from 'open systems' and from industrial effluents, landfills, and the failure of electrical equipment. The pesticide l,l,l-trichloro-2,2bis(p-chloropheny1)ethane (DDT) has been widely used as a pest control agent in tropical regions since the end of World War 11. Indeed, its use in Third World countries may have increased recently, at a time when the development of newer pesticides and government legislation have restricted its use in many industrialized countries in the Northern Hemisphere (2). The PCBs and DDT (including its breakdown products DDD and DDE) enter the atmosphere by volatilization or in association with aerosols and can be transported over long distances before deposition onto land or water surfaces (3). Consequently OCs are now ubiquitous across the globe (2-51, even in remote polar regions (6, 7). They become 0013-936X/92/0926-1815$03.00/0
biomagnified through the food chain and may have adverse effects in aquatic and terrestrial organisms (6). These often subtle ecotoxicological effects have sustained and stimulated research on the environmental importance of PCBs and DDT into the 1990s. Although much effort is being directed toward determining current environmental levels of OCs, it is important to obtain data on long-term changes in their environmental burden. Prior to 1966 there was no information on PCBs in environmental samples, although production and use data have been used to infer that the bulk of PCBs were emitted into the environment prior to that date (8, 9). Historical data on the atmospheric loadings of pollutants can be reconstructed by analysis of dated undisturbed deposits of sediments, peat, and ice cores. Lacustrine sediments are particularly useful for this purpose, provided they are undisturbed and some distance from the lake shore, thereby receiving minimal catchment runoff inputs (8-12). Following aerial deposition to the lake surface, hydrophobic OCs become adsorbed onto organic-rich suspended material in the water column and are ultimately deposited to the underlying sediment. Due to their inert character, the bulk of OCs are likely to remain unchanged following deposition, although diagenesis within the water column is of greater importance for some groups. The sediment record can therefore be used to reconstruct a historical chronology of inputs. Eisenreich et al. (8)have previously demonstrated the validity of studying dated lake sediment cores for historical monitoring of OCs. However, the majority of such studies have been undertaken on lake sediments in North America, while European data are limited (13). In this paper the temporal trends of PCBs and CDDT in the dated sediments of Esthwaite Water, a rural lake in northern England are reported. Esthwaite Water is a small, eutrophic, and seasonally anoxic lake, situated 60 km north of Lancaster (see Figure 1). It receives a small amount of treated domestic sewage into the north basin, but is remote from major residential or industrial conurbations. Materials and Methods Sampling. Five Mackereth minicores (id. 6.5 cm) (14) supplemented by five Jenkin cores (i.d. 6.9 cm) (15)were taken at sample station X (grid reference 54'21' N, 3'00' W-see Figure 1)in the north basin of Esthwaite Water in February 1990. The sampling site was chosen to reflect an average sedimentation rate for the entire water body since it is situated at an intermediate water depth (10-11 m), away from any shoreline and stream influences (16).
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