Envbun. Sd. rocim
issa. 27, 2685-z6vi
Modeling the Decrease in Dissolved Copper in Puget Sound during the Early 1980st Anthony J. Paulron,' Edward D. Cokelet, Richard A. Feely, Robert J. Stewart,* and Herbert C. Curl, Jr.s
NOAAlPaciHc Marine Environmantai Laboratwy. 7600 Sand Point Way Northeast, Seattle. Washington 98115 30'
The oceanic,riverine,geochemical, atmospheric,maritime, industrial, and municipal sources of dissolved Cu were applied to a twdayer, segmented-boxmodel incorporating the major features of the circulation in Puget Sound. In seven out of eight regions during 1986, the results of this independent model were comparable to the observed dissolved Cu concentrations. The discrepancy between the results from observations and the model in the most landward region was used to infer the strength of a previously unidentified source. The model also correctly predicted a decrease in the dissolved Cu concentration observed in a region off Seattle between 1981 and 1986 88 a consequence of decreased loadings realized from municipal and industrial pollution abatement programs.
15'
480
Introduction With ever-increasingpopulations in coastal regions, the management of estuarine water quality requires accurate models for predicting contaminant concentrations. Numerical models of estuarine circulation have been developed over the past 20 years (I)to provide the foundation for this predictive capability. By adding source loadings of conventional pollutants to flow fields generated by numerical circulation models, the distributions of pollutants observed in estuariesare beginning to he simulated (2, 3). Concurrent with the development of numerical circulationandwater quality models,mass balancesmodels of toxic contaminants have been developed for many estuaries (4-6). However,onlyrecently have the flow fields of numerical circulation models been coupled with source loadings of toxic contaminants to simulatefield conditions (7,8).In this paper, we combine the inputs of Cu with a numerical model of the circulation of Puget Sound and compare the modeled results to the observed concentrations. The complex geometry of Puget Sound, with ita two side channels (Hood Canal and Saratoga Passage) and numerous sills, presents an especially difficult modeling challenge (Figure 1). The wide distribution of natural and anthropogenic sources of toxic trace metals prevents using a model based on a single tracer, such as salinity. Beside the oceanic source, trace metals from natural sources are delivered by 12 river systems draining watersheds throughout the region. In contrast, most of the anthropogenic sources of trace metals in Puget Sound originatefrom the Tacoma-Seattle-Everett metropolitan area. In our first attempt at modeling a toxic contaminant Address correspondence to this author at his preamt address: Spo!ianeReaearchCenter,U.S.BureauofMines.315E. Montgomery Ave., Spokane. WA 99207-2291. t Contribution No. 1342 from NOAA/Pacific Marine Environmental Laboratory. t Present addresa: The Digital Analogica CO.. P.O. Box 84702, Seattle. WA 98124. I Present address: NOS/Hazardoua Materials Reaponse Branch, 7600 Sand Point Way NE, Seattle, WA 98115.
45'
30'
15'
15'
123"
45'
30'
15'
Flour0 1. W t Sound system: station A. govemmem locks: station E, Lake Washington Ship Camk statkm C. Shilshole marina.
in such a complex system, we chose to model dissolved Cu because it is essentially conservative in Puget Sound (9). Therefore, we were able to ignore most geochemical reactions with particles, which in turn allowed us to omit the difficult task of modeling suspended sediment transport. Geochemical reactions associated with the release of Cu from settling particles were modeled on large spatial scales as reactions independent of dissolved Cu concentrations. The resulting model accurately predicted observed Cu concentrations both spatially and temporally, given the uncertainties of observed dissolved Cu concentrations in Puget Sound.
Methods The Mode1.Theadvectivereachesbetweenshallowsills in Puget Sound (Figure 1)were modeled by a steady state,
Thi¶ arWe not aublsot Io U.S. Cqvbhl. FW!almd 1993 by th8 Anwk-sn Chmkal
Saw
Envton. Sd. Tsd.lml.. Vol. 27, No. 13, 1993 2085
(12). The concentration of the conservative constituent within each layer (Cj) is calculated:
R9
/
Figure 2. Schematic of water-quality model from Cokelet et ai. (74. Thick arrows represent two-layered reach flows, and thin arrows represent refluxingand effluxing. Not all arrows for mlxing zone 2 are shown.
two-layered, segmented-box model (Figure 2). The transport-weighted average salinities for each layer were calculated using multi-year salinity observations and current velocity data (10). Mean transports through the advective reaches were deduced from the transportweighted average salinities, river runoff, and the conservation of salt water and freshwater (11). To complete the model of circulation in Puget Sound, mixing in the sill zones had to be estimated. When seawater from a seawardflowing layer of a reach enters the mixing zone above a sill, it can either reflux back into the landward-flowing layer of the same reach or efflux into the seaward-flowing layer of the adjacent reach (Figure 2). The extent of refluxing in a mixing zone is very important because refluxing retains pollutants within the system landward of the mixing zone and leads to higher concentrations. Where a river enters a mixing zone, the mathematical solution that addresses the fraction of water refluxing and effluxing becomes underdetermined, and assumptions are required. In this model, it is assumed that all of the Duwamish River flows seaward. This solution to mixing requires that about 50 % of Colvos Passage outflow refluxes into East Passage, thus some of Tacoma's pollutants are retained in East Passage. The solution for mixing zone 2 (MZ2) has 31% of the upper layer of the Point Jefferson reach refluxing back into the lower layer, thus trapping some of Seattle's pollutants within the main channel of Puget Sound. Given the estimates of transports within advective reaches and mixing in sill regions, a steady-state, twolayered, segmented-box model of water quality was developed (12). The fluxes (mass/s) of a conservative constituent flowing through the reaches (8can be calculated using matrix algebra:
f = A-'p (1) where p is the vector containing the steady-state pollutant loadings of a constituent (mass/s) discharged into the two layers of the eight reaches and from each river system, and A-' is the inverse of the matrix A that contains the mathematical description of mixing in each mixing zone 2688
Envlron. Scl. Technol., Vol. 27, No. 13, 1993
Ci = f i / Q i (2) where Qi is the water transport (volume/s) within the ith layer. Application of Model to Dissolved Cu. Application of the model to a conservative toxic contaminant requires the calculation of dissolved pollutant loadings ( p ) from oceanic, riverine, atmospheric, maritime, industrial, and municipal sources and from geochemical processes using all data presently available for the Puget Sound system. The calculations of dissolved Cu fluxes are described briefly below while a more detailed description of these calculations can be found in the supplementary material associated with this article (Appendix A). The observed dissolved Cu concentration in the lower layer of the New Dungeness reach (reach 2) in 1986 (13) was used as a boundary condition to estimate the oceanic source. Riverine sources were calculated from river discharges (10) and dissolved Cu concentrations of freshwater entering Puget Sound (6,13-15), assuming conservative behavior of Cu (16,17). Geochemical sourcesof dissolved Cu include (1) the release from marshes calculated from the normalized flux from the Skagit Bay marsh (see Appendix A) and the area of Puget Sound emergent growth vegetation, (2) the release from interstitial subtidal porewater calculated from the normalized porewater flux rate 10.18 pg cm-2 yr-l (911 and the subtidal surface area (18),and (3) the transfer of biologicalparticulate material between the upper layers of reaches and the lower layers calculated from the amount of releasable Cu per gram of settling organiccarbon (19)times the flux of settling organic carbon (20,21). The flux of dissolved Cu from atmospheric sources was calculated to be the total atmospheric Cu flux [Cu concentrations in the air mass 16-3000 ng m3 (22-26)l times a settling velocity of 1cm s-l(26)1 multiplied by the fraction of atmospheric Cu dissolvable in seawater [40%, (26)l. Maritime sources include (1)anthropogenic sources from shipbuilding facilities in Elliott Bay (17)10.02 g/s in 1986and 0.36 g/s in 1981 (27)1, (2) shipbuilding and s h i p repairing facilities in the Lake Washington Ship Canal, calculated from the difference in the average dissolved Cu concentrations between station A (28) and station B (15) (Figure l),and (3) the production of dissolved Cu within the marinas [(29,30), see Appendix AI. Industrial inputs to Puget Sound from six oil refineries, six pulp mills, and other point and nonpoint industrial sources were collected from National Pollutant Discharge Elimination System (NPDES) monthly monitoring reports, priority pollutant scans associated with renewal of NPDES permits, class I1 compliance inspections, and directly from the dischargers. The industrial fluxes of dissolved Cu decreased from 0.3196 g/s in 1981 to 0.2271 g/s in 1986 as a result of qualitycontrol criteria imposed on one of the feed stocks used in the Kraft pulp process and because pollution control at a copper smelter were improved in late 1981 and the smelter was closed in early 1986. Loadings from the 52 largest municipal sewagetreatment plants discharging into Puget Sound (31) were calculated either by examining NPDES permit files or by obtaining data from treatment plant managers. In lieu of data on the fraction of total Cu present in the dissolved form in the effluent for a given treatment plant, Cu partitioning data in the effluent from one Seattle primary treatment plant and one Seattle secondary treatment plant (6) were used to calculate dissolved Cu fluxes to Puget Sound.
~~
Table I. Fluxes of Dissolved Cu (g/s) from Rivers and into Reaches in 1986 Assuming the Flocculation and Subsequent Release Wastewater Scenario
diffusion marinas and total by oceanic riverine from biological atmospheric shipbuilding layer source source marshes sediments transport deposition and ship repair industrial municipal location 4.4600 4.46 New Dungeness Lower 0.0073 0.0005 0.0009 0.0041 0.0018 New Dungeness Upper 0.1265 0.0576 0.0060 0.0620 Point Jefferson Lower 0.0927 0.1485 0.0227 0.0092 0.0289 -0.0060 0.0010 Point Jefferson Upper 0.0411 0.0801 0.0030 0.0145 0.0216 East Passage 0.0057 0.0003 0.0022 0.0029 0.0003 Colvos Passage 0.0072 0.0049 0.0022 Gordon Point Lower 0.0038 0.0002 -0.0022 0.0028 0.0030 Gordon Point Upper 0.0100 0.0041 0.0059 Devil’s Head Lower 0.0076 0.0001 0.0047 -0,0041 0.0052 0.0017 Devil’s Head Upper 0.0074 0.0030 0.0044 Tala Point Lower 0.0010 0.00012 -0.0030 0.0009 0.0019 Tala Point Upper 0.0127 0.0052 0.0075 Hazel Point Lower 0.0074 0.0016 0.0012 -0.0052 0.0016 0.0082 Hazel Point Upper 0.0163 0.0055 0.0108 Saratoga Passage Lower 0.0214 0.0017 0.0022 -0.0055 0.0094 0.0136 Saratoga Passage Upper 0.0229 0.0229 R1 0.0757 0.6260 0.0437 0.1047 0.0121 0.0865 0.2310 0.0723 R2 0.0340 0.3114 0.1695 0.0360 0.0037 0.0008 0.0650 0.0023 R3 0.4840 0.0163 0.0852 0.2791 0.0280 0.0016 0.0730 0.0008 R4 0.0573 0.0017 0.0019 0.0300 0.0236 R5 0.0090 0.0589 0.0034 0.0052 0.0059 0.0230 0.0124 R6 0.0150 0.0010 0.0048 0.0042 0.0050 R7 0.0372 0.0005 0.0023 0.0184 0.0160 R8 0.4195 0.0005 0.0036 0.0042 0.0048 0.2560 0.1505 R9 0.5922 6.9540 0.2729 0.2271 0.1558 -0.0000 0.2081 4.46 0.7006 0.3374 total by type
It has been suggested that biogeochemical processes may also affect the fate of Cu in sewage effluent (32). In laboratory studies, 40% of the dissolved Cu in primary effluent initially flocculated, after which the Cu in the resultant particulate matter was gradually released back into solution (19). This biogeochemical process (flocculation with subsequent remobilization after settling) was simulated in the model by subtracting 40% of the dissolved Cu in the effluent from the loading of the upper layer of the Point Jefferson reach and by adding the exact loading to the lower layer. This modification does not change the overall loading of dissolved Cu. Sources of dissolved Cu in 1986are summarized in Table I, while uncertainties are listed in Table B1 (Appendix B). Between 1982and 1983, the City of Seattle’s Water Department initiated a waterhardening program. The fluxes of metals entering the Municipality of Metropolitan Seattle (METRO) treatment plants decreased in the early 1980s as a result of the water hardening and because pretreatment standardswere more strictly enforced. The flux of dissolved Cu from municipal sources decreased from 0.79 f 0.16 g/s in 1981 to 0.59 f 0.10 g/s in 1986 (Appendix B: Tables B2 and B3). Remobilization of metals directly from wastewater particles without flocculation is also thought to increase dissolved metal loadings from municipal sources (32).This wastewater scenario is simulated by adding loadings from every pulp mill and municipal treatment plant equal to 67% of their particulate Cu loadings (19). The release of Cu from wastewater particles increased the total dissolved Cu flux by 0.4357 g/s in 1981 and by 0.207 g/s in 1986 relative to conservative mixing (Appendix B: Tables B2 and B3). The only discretion we exercised in adjusting the dissolved Cu loadings was to change the flux and discharge location of dissolved Cu on the basis of the assumed geochemistry of wastewater. The pollutant loadings from individual columns or groupings of columns in Table I were applied to the model to estimate the effects of each source on dissolved Cu
concentrations. The summation of the oceanic and riverine sources was applied to the model as a p vector; and the resultant concentrations reflect baseline conditions that were consistent with the salinity distribution. Marsh sources, diffusion of Cu from subtidal sediments, and the vertical transport of biological detritus were summed and applied to the model to reflect the effects of natural geochemicalprocesses. Atmospheric, maritime, industrial, and municipal sources were each applied to the model to estimate the effects that these anthropogenic sources had on the Cu distribution in Puget Sound. Since the model is additive, the above concentrations from all the different sources were added to produce a modeled prediction of dissolved Cu concentration in each layer. This prediction was exactly the same as the prediction obtained by applying the total p vector in the last column of Table I. Observed Dissolved Cu Concentrations. Dissolved Cu concentrations in acidified Puget Sound seawater were measured between 1981and 1986by employingthe Chelex100 preconcentration method and ultratrace clean-room techniques (33). The slow flow rate yielded extraction efficienciesthat were greater than 90 % for spiked samples (0.61 pg/L) and standard reference material (CASS-1and NASS-11, and that did not change between 1981and 1986. The blank-corrected results for 1986are given in Paulson et al. (13)while earlier results are given in Paulson et al. (34). The detection limit decreased from 0.015 rg/L in 1981(based on variations in field filtering blanks) to 0.010 rg/L in 1986 (based on instrumental detection limits). The sampling variability decreased from 0.03 pg/L (1 a) in 1980 to 0.01 rg/L in 1986. In order to directly compare the observed results with those produced by the model, the observed concentrations must be averaged over the depth of the layer with each individual observation weighted by the transport of water at that depth. Mathematically, the transport-weighted average concentration for a layer (Cut,)can be expressed Environ. Sci. Technol., Vol.
27, No. 13, 1993 2887
Dis. Cu ( u g l l ) 0.1 I-C~
20
40
a) 0.5,
C
DATA with errors
0.4
- Fit to DATA - - VELOCITY
Seaward Flowing Layers
E ij ij
T,
Transport 1.1b wee;zverage 4
60
0 d
0 0 0
80
0
/ /
5
I I
100
I I
I
120
Hood Canal
I I
b) 0 5 -
I
140
I I \ \
Landward Flowlng Layers
04-
160
i
\
180
\ I
0
15
15
Velocity (cm/sec) Flgure 3. Examples of the vertlcal averaging of 1966 observed Cu concentratlonswelghted on the basis of water transport. The observed wlth the resuitlng average concentration at a depth with errors (-0-) spline fit (- -). Usingthe spline flt of Cu and currents (- -), the transportwelghted average concentrations are calculated with their errors
-
(-ll-). as: c [ C u l u w dz CU, =
i?w
dz
(3)
where [CUI,u,and w are the dissolved Cu concentrations, velocity normal to the layers, and width across the layer as a function of depth, respectively; zt and Zb are the upper and lower depths of the layer (IO),respectively. This operation is illustrated for 1986 from the two-layer Point Jefferson reach (Figure 3). The cubic spline fit reflects the variation in the dissolved Cu concentration collected at a given depth from two stations within the reach during a variety of tidal conditions over a 1-week period. The interface between the two layers lies where the current crosses from positive (seaward flowing) in the surface layer to negative (landward flowing) at depth.
Results Predictions Resulting from Estimated Loadings. One feature of this model is its ability to estimate the concentrations that the various sources contribute to the overall dissolved Cu concentration. The modeled results for the 1986 dissolved Cu loading conditions were established incrementally by applying the loadings from individual or groupings of columns (Table I). When oceanic and riverine sourceswere applied to the model, the modeled predictions of dissolved Cu concentrations in the main channel of the Puget Sound system (Figure 4a) increased in the seaward-flowing layers (upper layer for two-layer reaches) from 0.17 pg/L in the Point Jefferson reach (salinity = 29.2%) to 0.18 pg/L in the Devils Head reach (salinity = 28.9%). The oceanic-riverine modeled con2688
Envlron. Scl. Technol., Vol. 27, No. 13, 1993
Flgure 4. Comparlson of observed dissolved Cu concentrations in Puget Sound In 1966 wlth modeled results as a consequenceof various sources.
centration for Saratoga Passage was higher (0.215 pg/L) because of its greater portion of freshwater (salinity = 25.2%0). In the main channel and in Hood Canal, geochemical processes (marsh sources, diffusion from subtidal sediments, and biological transport) increased concentrations between 0.02 and 0.04 pg/L in both landward- and seaward-flowinglayers. Geochemical processes increased upper-layer Saratoga Passage concentrations by 0.07 pg/L because of its proximity to the two largest marshes (Skagit Bay and the Snohomish River). Atmospheric deposition increased concentrations between 0.009 and 0.017 pg/L throughout Puget Sound. Shipbuilding and ship-repairing facilities in Elliott Bay and along the Lake Washington Ship Canal, and pleasure boats in marinas (maritime activities) increased concentrations by 0.028 pg/L in the seaward-flowing (upper) layer of the Point Jefferson reach. Transport of this water to the landward-flowing bottom layers of the main basin, Hood Canal, and Saratoga Passage via the Admiralty Inlet mixing zone (MZ2) and the many marinas around Puget Sound increased concentrations between 0.013 and 0.017 pg/L in other layers of Puget Sound. Industrial sources increased concentrations between 0.010 and 0.019 pg/L, with the higher values found in the layers seaward of Seattle and Tacoma. In the upper layer of the Point Jefferson reach and in all inland reaches, municipal sources increased concentrations between 0.05 and 0.06 pg/L. Mixing in the Admiralty Inlet mixing one (MZ2) resulted in concentration increases of about 0.03 pg/L for the side channels, which receive little municipal wastewater. The observed transport-weighted dissolved Cu concentrations (0.16-0.47 g/L) were on the low end of the concentration
Table 11. Comparison of Cu Concentrations (in fig/L) Observed in Puget Sound During 1986 and 1981 with Modeled Results for Three Wastewater Scenariosp
layer
conserv.
floc.
remob.
1986 observ.
floc. + 0.2g/s SPS sourceb
conserv.
1981 floc. remob. 0.17 0.25 0.32 0.44 0.35 0.37 0.39 0.40 0.40 0.41 0.28 0.29 0.29 0.29 0.34 0.41
observ.
0.17 0.26 0.35 0.48 0.38 0.41 0.42 0.43 0.43 0.44 0.30 0.31 0.31 0.31 0.36 0.43
0.16 0.17 0.16 0.16 New Dungeness Lower 0.16 0.16 0.22 0.25 0.22 0.21 New Dungeness Upper 0.21 0.21 0.31 0.27 0.30 0.27 0.27 0.25 0.26 Point Jefferson Lower 0.35 0.44 0.41 0.35 0.34 0.33 0.33 Point Jefferson Upper 0.30 0.33 0.30 0.29 0.28 0.28 East Passage 0.34 0.32 0.36 0.32 0.34 0.30 0.30 Colvos Passage 0.34 0.38 0.33 0.32 0.31 0.31 Gordon Point Lower 0.36 0.38 0.34 0.35 0.32 0.32 Gordon Point Upper 0.36 0.38 0.34 0.40 0.32 0.32 Devil’s Head Lower 0.41 0.40 0.35 0.47 0.33 0.33 Devil’s Head Upper 0.25 0.25 0.25 0.28 Tala Point Lower 0.24 0.24 0.25 0.29 0.25 0.26 0.24 0.24 Tala Point Upper 0.25 0.28 0.25 0.29 Hazel Point Lower 0.24 0.24 0.25 0.29 0.25 0.25 0.25 0.28 Hazel Point Upper 0.28 0.34 0.29 0.30 0.27 0.27 Saratoga Passage Lower 0.36 0.36 0.36 0.41 0.35 0.35 Saratoga Passage Upper Wastewater scenarios: conserv. = conservative mixing; floc. = flocculation of dissolved Cu in wastewater and subsequent remobilization; remob. = remobilization of Cu from wastewater particles. South Puget Sound source (R6)of 0.2 g/s inferred from inverse modeling effort added to the loadings shown in Table I.
range for the open waters of United States estuaries 10.23.2 pg/L (35)l and were at least a factor of 5 less than the acute water quality standard for dissolved Cu (36). The small differences in the modeled results under the three wastewater scenarios for the 1986 loading conditions (columns 1-3 of Table I1 and Table B4) indicate that wastewater chemistry did not play a major role in determining Cu distributions in Puget Sound. It should be noted that the excellent agreement between observed and modeled Cu results was not generated by adjusting the model, which was constructed independently on the basis of observed river runoff and salinity data. Model Calibration. Only the discrepancy between the observed and modeled results in the Devils Head reach (South Puget Sound) was significant enough to warrant further attention. In the first instance in which Cu data were allowed to influence the construction of the model, Cu was used as the third tracer to provide closure to the solution of mixing in mixing zone 5 (R5). Given the observed dissolved Cu concentrations (Table 11) and the Culoadings for all four adjacent layers (Table I) and mixing zone 5, a deterministic solution in which 26 % of the Devils Head seaward-flowing (surface) layer is refluxed into the landward-flowing (lower) layer was obtained. Modifying the model to reflect the deterministic solution to mixing in R5 was not adequate to raise predicted dissolved Cu concentrations in the Devils Head reach to the observed values. Therefore, we applied inverse modeling techniques to quantify the unidentified sources of dissolved Cu. Lacking the resources to develop a rigorous inverse model, we applied the more expedient trial and error method. A loading representing the unidentified source was added to the known loadings for mixing zone 6 (R6) and was adjusted until the sum of the differences between the observed and modeled Cu transport values in the seaward-flowing layers in the Devils Head and Gordon Point reaches was minimized. The trial and error efforts produced a result of 0.20 g/s, and its effects are shown in column 5 of Table 11. It should be noted that a 0.20 g/s input is greater than the Cu input from the largest sewage treatment plant discharging into Puget Sound. Given the predominately rural land use patterns in the region, the unidentified source is probably a nonpoint one, such as leaking septic systems, or natural
geochemical fluxes from the large areas of nonemergent growth or intertidal zones in the shallow arms west of the reach. The additional 0.20 g/s increased the modeled Cu concentration in the upper layer of the Devil’s Head reach from 0.35 to 0.41 mg/L, which nearly agrees with the observations. The additional South Puget Sound source increased the Gordon Point prediction by only 0.02 to a value of 0.36 pg/L (which is within the error of the observed result) because of the larger water transport in this reach. Because the unidentified source was only inferred and its temporal variability is not known, the source loading inferred from the 1986 data was not applied to the 1981 period. Validation of the Model. Our 6-year sampling program in Puget Sound detected a decrease in dissolved Cu in the Point Jefferson reach between 1981 and 1986, a time of significant reductions in Cu loadings from municipal, maritime, smelting, and paper-pulping sources. The availability of Cu loading data from the sampling program and from the round of NPDES renewals in 1981 allowed us to attempt to verify the model using an independent data set. Decreases in concentrations between 1981 and 1986 as predicted from the model were compared with the observed decreases. The result was a predicted decrease (0.09 f 0.07 pg/L) only slightly larger than the observed decrease (0.07 f 0.04 pg/L). The agreement between modeled and observed concentrations as a result of decreased loading of dissolved Cu provides the validation that will allow environmental managers to apply this model with confidence. The larger loadings of Cu, especially particulate Cu, in 1981 from municipal and pulp mill treatment plants provide a better opportunity to examine how effluent wastewater geochemistry affects receiving-water Cu concentrations (Table I1and Table B4). By discharging some of the Cu to the lower layer without increasing the total dissolvedCu loading, the modeled simulation suggeststhat flocculation of wastewater followed by subsequent release can increase dissolved Cu concentrations by 0.01-0.02 pg/L relative to the conservative mixing scenario landward of the Point Jefferson reach. Direct remobilization of Cu from wastewater particles resulted in modeled dissolved Cu concentrations in the Point Jefferson and Colvos Passage reaches that were 0.07 pg/L greater than observed Environ. Sci. Technol., Vol. 27, No. 13. 1993
2889
concentrations (second to the last column of Table I1 vs last column). This suggeststhat direct remobilizationfrom wastewater particles is probably not occurring to a great extent in Puget Sound.
0.40 0.35
Discussion Coupling loading data for a conservative contaminant to existing numerical models of estuarine circulation can provide researchers and water-quality managers with valuable insights into physical and biogeochemical processes. The model allows us to quantify the effects of each source on dissolved Cu concentrations, which cannot be accomplished with the usual widely spaced and infrequent observations. Application of the model used in this study contributed to a better understanding of Cu loadings since it provided us with the framework to evaluate new data as they became available. Differences between earlier versions of the modeled and observed results were significant, so possible additional sources (marshes, marinas, and Lake Washington Ship Canal, for instance) were investigated. As NPDES permits were being reissued in the mid-l980s, the data required for these permits were incorporated into the loadings. Had the model been coupled to a real-time monitoring program in 1986, the location of the additional inferred source of Cu in South Puget Sound could have been narrowed and possibly identified. If one assumes that the dissolved Cu in the river water above our sampling stations is controlled by natural processes rather than by the anthropogenic inputs from small communities, the net contributions of oceanic, riverine, and geochemical sources can be considered to be the natural background level. For the case of the Point Jefferson reach, the background dissolved Cu concentration was estimated to be between 0.20 and 0.22 pg/L (Figure 4). The observed concentration in 1981 (0.41 pg/L) indicated that anthropogenic inputs had resulted in the doubling of dissolved Cu concentrations in this region. The model suggested that the pollution abatement programs undertaken in the early 1980s have reduced dissolved Cu concentrations from 100% ' above background in 1981 to about 65% above background in 1986. The model also demonstrated the effect that the large anthropogenic sources of Cu discharged into the main basin had on concentrations in less populated regions of Puget Sound. For a terminal reach landward of a mixing zone (i,e,, South Puget Sound), it should be noted that the concentration of seawater entering the reach is defined by advective transport from the next seaward reach (East Passage) together with inputs from the mixing zone (Commencement Bay, Tacoma). The anthropogenic inputs directly entering South Puget Sound increase dissolved Cu concentrations above the concentration level defined by main basin inputs. To a lesser extent, physical mixing in the Admiralty Inlet mixing zone (MZ2) also transports the large amounts of anthropogenic Cu discharged into the main basin to the more rural side channels (Saratoga Passage and Hood Canal) resulting in increases of 25 % and 40 % above natural background concentrations, respectively. The validation of the model provided by the temporal data also provides the means to evaluate the consequences of individual pollution abatement programs. The single act of applying quality-control criteria to feed stocks of Kraft mills (i.e., a decreased loading of 0.34g/s) decreased dissolved Cu concentrations by about 0.025 pg/L through2890
Envlron. Scl. Technol., Vol. 27, No. 13, 1993
32
I
3
O3O 0.25
0.20 0.15 0.10
0.05 0.0 __
ObSeNed Modeled 1981
ObSeNed Modeled 1986
Figure 5. Modeled and observed results in the Point Jefferson reach for 1981 and 1986,
out the main basin during the 1980s. The net effect of the pollution abatement program instituted by Seattle shipyards and the Tacoma smelter (-0.42 g/s) and their eventual closure (-0.09 g/s) resulted in an estimated decrease of 0.062 pg/L in the main basin. The stricter enforcement of pretreatment standards and the beginning of the water-hardening program significantly reduced Cu loading from METRO'S treatment plants. However, these decreases in dissolved Cu loadings were partially offset by increases in Cu loadings from other municipalities, mainly as a result of increased flows. On the whole, the decreased municipal contributions lowered dissolved Cu concentrations by an estimated 0.02 pg/L in the main basin (Figure 5). The validation of this contaminant loading-circulationcoupled model provides water-quality managers with the tool needed to predict changes in water quality as a result of proposed pollution abatement programs, such as conversion of municipal sewage plants to secondary treatment. The effect of one proposed pollution abatement program regarding receiving-waterconcentrations can also be compared to that of another program on the basis of effort and cost. For instance, the effectiveness in decreasing Puget Sound Cu concentrations by educating workers at a small shipyard on the Lake Washington Ship Canal to control oversprays could be compared to the effects achieved by converting municipal plants to secondary treatment. Conclusions Loadings of dissolved Cu from oceanic, riverine, atmospheric, maritime, industrial, and municipal sources and from geochemical processes were coupled to a model of the circulation of Puget Sound. Through an iterative process, important sources were identified and quantified to the point where modeled concentrations were not significantly different from observed concentrations in seven of the eight regions. An unknown source of dissolved Cu larger than that from the largest sewage treatment plant was inferred on the basis of the discrepancies between the model and the observations in South Puget Sound. Given the decreased loading of dissolved Cu between 1981 and 1986 as a result of a variety of pollution abatement programs, the model accurately predicted the observed decrease of 0.07 pg/L in the central main basin of Puget Sound. Validation of the model provides water-quality managers with a powerful tool to assess and predict the
effects of specific pollution abatement programs. The utility of even the most powerful model is limited by the temporal and spatial density of the field data used for verification. Likewise,its utility is limited by the frequency of source-loading boundary-condition data and the spatial density of the initial-condition data during model simulations. This application suggests that a box model which averages over large spatial regions (segmented boxes) and long time periods (quasi-steady-state) is appropriate given the amount of field and source data available for most estuaries. Within the limitation of data availability, segmented-box models are especially appropriate for other U. S. estuaries that have side channels (e.g., Chesapeake Bay) or multiple junctions (e.g., San Francisco Bay and the Hudson River-Raritan Bay-Long Island Sound system). Supplementary Material Available Appendix A (9 pages of text, 6 tables, references, and 1figure) provides data on the detailed mass loading of Cu in Puget Sound from salt marshes, atmospheric deposition, oil refineries, pulp mills, other industrial sources, and municipal sewage treatment plants. Appendix B (4 tables) contains tables of errors for the values shown in Tables I and I1 and the summation of mass loadingsof Cu in 1981and 1986,along with errors for the different scenarios of wastewarter geochemistry. These 30 pages of supplementary material 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, 24X reduction, negatives) may be obtained from Microforms Office, American Chemical Society, 1155 16th St. N.W., Washington, DC 20036. Full bibliographic citation (journal, title of article, names of authors, inclusive pagination, volume number, and issue number) and prepayment, check or money order for $50.50 for photocopy ($53.50 foreign) or $10.00 for microfiche ($11.00 foreign), are required. Canadian residents should add 7% GST.
Literature Cited (1) Spaulding, M. L. Estuarine and Coastal Modeling; ASCE, Washington, DC, 1990, 536 pp. (2) DiToro, D. M.; Thomas, N. A.; Herdendorf, C. E.; Winfield, R. P.; Connolly,J. P. J . Great Lakes Res. 1987,13,801-825. (3) Stoddard, A. In Estuarine and Coastal Modeling; ASCE, Washington, DC, 1990, pp 361-370. (4) Santachi, P. H.; Nixon, S.; Pilson, M.; Hunt, C. Estuarine Coastal Shelf Sei. 1984, 19, 427-449. (5) Church, T. M. Mar. Chem. 1986,18,393-406. (6) Paulson, A. J.; Feely, R. A.; Curl, H. C., Jr.; Crecelius, E. A.; Romberg, G. P. Environ. Polut. 1989,57, 317-339. (7) Adams, E. E.; Kossik, R. F.; Cosler, D. J.; MacFarlane, J. K.; Gschwend, P. M. In Estuarine and Coastal Modeling, ASCE: Washington, DC, 1990, pp 380-389. (8) Klomp, R. Sci. Total Environ. 1990, 97/98, 103-114. (9) Paulson, A. J.; Feely, R. A,; Curl, H. C., Jr.; Crecelius, E. A.; Geise1man.T. Geochim. Cosmochim.Acta 1988.52.1765, , 1779. Cokelet,E. D.; Stewart, R. J.;Ebbesmeyer, C. C. The annual mean transport in Puget Sound; NOAA Technical Memo ERL-PMEL-92, NOAA: Seattle, WA, 1990, 59 pp. Cokelet, E. D.; Stewart, R. J. J. Geophys. Res. 1985, 90, 7287-7306. Cokelet, E. D.; Stewart, R. J., Ebbesmeyer, C. C. In Puget Sound Research '91 Proceedings; Puget Sound Water Quality Authority: Olympia, WA, 1991; Vol. 1,pp 99-108. Paulson, A. J.; Curl, H. C., Jr.; Feely, R. A.; Krogslund, K. A.; Hanson, S. Trace metal and ancillary data in Puget Sound: August 1986; NOAA Data Report ERL-PMEL-32; NOAA: Seattle, WA, 1991.
(14) Murray, J. W. In Sources and Fates of Aquatic Pollutants: Hites, R. A, Eisenreich, S. J., Eds.; American Chemical Society: Washington, DC, 1987, pp 153-184. (15) Paulson, A. J.; Curl, H. C., Jr.; Feely, R. A.; Massoth, G. J.; Krogslund, K. A.; Geiselman, T.; Lamb, M. F.; Kelly, K. Trace metal and ancillary data in the watersheds and urban embayments of Puget Sound; NOAA Data Report ERLPMEL-30; NOAA: Seattle, WA, 1991; 72 pp. (16) Paulson, A. J.; Feely, R. A.; Curl, H. C., Jr.; Tennant, D. A. Estuarine Coastal Shelf Sei. 1989, 28, 231-248. (17) Paulson, A. J.; Curl, H. C.; Jr.; Feely, R. A. Mar. Pollut. Bull. 1989,20, 549-555. (18) McLellan,P.M.AnareaandvolumestudyofPugetSound; Technical Report No. 21, Department of Oceanography, University of Washington: Seattle, WA, 1954; 39 pp. (19) Paulson, A. J.; Curl, H. C., Jr.; Cokelet, E. D. Mar. Chem. 1991,33,41-60. (20) Baker, E. T.; Feely, R. A.; Landry, M. R.; Lamb, M. F. Estuarine Coastal Shelf Sei. 1985,21, 859-877. (21) Hedges, J. I.; Clark, W. A.; Cowie, G. L. Limnol. Oceanogr. 1988,33, 1137-1152. (22) Romberg, G. P.; Pavlou, S. P.; Shokes, R. F.; Hom, W.; Crecelius, E. A.; Hamilton, P.; Gunn, J. T.; Muench, R. D.; Vinelli, J. Toxicant Pretreatment Planning Study Technical Report C l : Presence, Distribution and Fate of Toxicants in Puget Sound and Lake Washington; Municipality of Metropolitan Seattle (METRO): Seattle, WA, 1984; 231 pp. (23) Spyridakis,D. E.; Barnes,R. S. Contemporary and historical trace metal loadings to the sediments of four lakes of the Lake Washington drainage; University of Washington: Seattle, WA, 1978. (24) Evaluationofatmosphericdepositionoftoxiccontaminants to Puget Sound; Puget Sound Water Quality Authority: Seattle, WA, 1991, 173 pp. (25) Crecelius, E. A. Atmos. Environ. 1981, 15, 579-582. (26) Hardy, J. T.; Crecelius, E. A. Environ. Sei. Technol. 1981, 15, 1103-1105. (27) Paulson, A. J.;Hubbard, T. P.; Curl, H. C., Jr.; Feely, R. A.; Sample, T. E.; Swartz, R. G. In Proceedings of the Sixth Symposium on Coastal and Ocean Management; ASCE: Washington, DC, 1989; Vol4, pp 3916-3930. (28) Paulson, A. J. NOAA/Pacific Marine Environmental Laboratory, unpublished data, 1990. (29) Crecelius, E. A,; Fortman, T. J.;Kiesser, S. L., Apts, C. W.; Cotter, 0. A. Survey of contaminants in two Puget Sound Marinas; U. S. E P A Seattle, WA, 1989; 30 pp. (30) Goodwin, R. F. Washington State Marine Directory, Washington Sea Grant Office: Seattle, WA, 1991; WSGAS901-12 (on disk). (31) Puget Sound Water Quality Authority, Issue Paper: Industrial and municipal discharges; Seattle, WA, 1986, pp 52-55. (32) Rohatgi, N; Chen, K. H. J . WaterPollut.Control Fed. 1975, 47, 2298-2316. (33) Paulson, A. J. Anal. Chem. 1986,58, 183-187. (34) Paulson, A. J.; Curl, H. C., Jr.; Feely, R. A.; Geiselman, T.; Krogslund, K. A.; Massoth, G. J.; Lamb, M. F.; Kelly, K. Trace metal and ancillary data in the open waters of Puget Sound: 1981-1985; NOAA Data Report ERL-PMEL-31; NOAA: Seattle, WA, 1991; 56 pp. (35) Paulson, A. J.; Feely, R. A. Mar. Pollut. Bull. 1985,16,285291. (36) Washington AdministrativeCode, Chapter 173-201A,adopted Nov 25, 1992, Olympia, WA.
Received for review July 13,1992. Revised manuscript received June 21, 1993. Accepted July 7, 1993.' Abstract published in Advance ACS Abstracts, September 1, 1993.
Envlron. Scl. Technol., Vol. 27, No. 13, 1993 2891
