Environ. Sci. Techno/. 1995, 29, 2059-2064
Estimated Annual Loads of Selected Organic Contaminants to Chesapeake Bay via a Major Tributary J. TERRY GODFREY,+ G R E G O R Y D. FOSTER,* A N D KATRICE A. LIPPA Department of Chemistry, George Mason University, Fairfm, Virginia 22030-4444
The aim of the Chesapeake Bay Fall Line Toxics Monitoring Program has been to determine the annual loads for selected organonitrogen, organophosphorus, and organochlorine pesticides, polychlorinated biphenyls, and polynuclear aromatic hydrocarbons to Chesapeake Bay derived from above the fall line of the Susquehanna River, the bay’s largest tributary. Load results are presented in this report for 15 samples collected from March 1992 through February 1993. Annual load estimates were obtained using 14 methods, grouped according to whether they are of averaging, ratio, or regression type to ensure convergence to an approximate mean load value for each contaminant. The results indicated a high degree of uniformity across the methods with most yielding relative deviations 1.5 or less when normalized to the mean load derived from all 14 load estimation methods.
Introduction The Chesapeake Bay Fall Line Toxics Monitoring Program (1) has among its goals the determination of the concentrations and annualloads of selectedorganiccontaminants in fluvial transport in the major tributaries of Chesapeake Bay. A subset of a prioritized group of pesticides and other organic contaminants known as the Cheseapeake Bay Toxics of Concern VOC) was selectedfor investigatingloads from March 1992to February 1993;the TOC list was created according to a rating system based on toxicity, use, and detection frequency of toxic substances in the bay ecosystem following specific requirements included in the Chesapeake Bay Toxics Reduction Strategy (2). One of the primary objectives of the Chesapeake Bay Fall Line Toxics Monitoring Program from March 1992 to February 1993 was to determine the magnitude of organic contaminant loads contributed from above the fall line of the Susquehanna River, which alone accounts for ca. 50% of the total freshwater inflow to the bay during base-flow and stormflow hydrologic conditions. * Author to whom correspondence should be addressed. + Present address: Environmental Sensing, Inc., P.O. Box 7362, McLean, VA 22106.
0013-936W95/0929-2059$09,00/0
Q 1995 American Chemical Society
The literaturecontainsa number of references pertaining to computational methods for estimating fluvial sediment and nutrient loads using constituent concentration and river discharge measurements (3-121. Preston et al. (13) have provided an excellent summary of some of these methods with applications that include phosphorus, zinc, lead, and Aroclor 1242. Currently, there are three approaches used to estimate fluvial loads: (a) averaging estimators, (b) ratio estimators, and (c) regression-based estimators. For averaging estimators, annual assessments are made by using averages of concentration or discharge invarious combinations to achieve load estimates. In ratio estimation, flow is treated as an independent variable,and loading is a dependent variable. Regression methods generally employ log-log regression because flow and concentration are assumed to be described by a bivariate log-normal distribution. Since biases exist for log-log estimation,efforts have been devoted to improvingthe basic rating curve (RC)approach of Ferguson (4) and the quasimaximum likelihood estimator (QMLE). Cohn et al. (10, 11) proposed a minimum variance unbiased estimator ( W E ) , which has an improved bias correction factor. Finally, a smearing estimator (SM)has been employed (12) which has a bias correction factor that is the average of the exponentiated log regression residuals. Loads were estimated for organonitrogen and organophosphorus (ONIOP)pesticides (includingsimazine, prometon, atrazine,alachlor, metolachlor, cyanazine, and hexazinone) measured in filteredsurfacewater (i.e.,the dissolved phase),for organochlorines [includingoxychlordane,y-chlordane, a-chlordane,dieldrin, and polychlorinated biphenyls (PCBs)]in both dissolved and particulate phases (i.e., the filtered particles),and for polycyclic aromatic hydrocarbons [ (PAHs) including naphthalene and fluoranthenel in both dissolved and particulate phases. For the group of organic contaminants investigated in this study and the concentrations encountered, deviations about mean values across methods for calculatingloads are deemed acceptable if the agreement is within a factor of 5 (1). Typically, 15 surface water samples are collected annually by the Chesapeake Bay Fall Line Toxics Monitoring Program. D o h et al. (3) routinelyuses 25 samples, and Richards and Holloway (14) illustratethat for samples of the size used in this study bias error on the order of 30-35% maximum can typically arise. Thus, while precise loads cannot be estimated with the sample number reported here, quite reasonable load estimates are achievable for the above group of contaminants. The purpose of this report is to (a) furnish estimates of loads for the selected organic contaminants above the fall line of the Susquehanna River from March 1992to February 1993 determined by the Chesapeake Bay Fall Line Toxics Monitoring Program and (b) define the level of agreement that existed between available load estimation methods as applied to the fall line loads calculated in this study. The ultimate intention of the Chesapeake Bay Fall Line Toxics Monitoring Program is to compare organic contaminant input sources in the bay, such as through fluvial transport, atmospheric deposition, and direct urban runoff, to within an order of magnitude of each other as more loadings data become available for other Chesapeake Bay tributaries.
VOL. 29. NO. 8, 1995 / ENVIRONMENTAL SCIENCE & TECHNOLOGY m ZOSS
FIGURE 1. This map of the Susquehanna River and Chesapeake Bay region illustrates the fall line araa at Conowingo Dam.
Knowledge of source mass balances in Chesapeake Bay will enable the most effective water quality management
strategies to be devised that protect living resources from organic chemical pollution.
Experimental Section Organic contaminant concentrations were acquired from the Chesapeake Bay Fall Line Toldcs Monitoring Database (1). Discharge data for the Susquehanna River fall line were obtained from the United States Geological Survey via a permanent gaging station at Conowingo Dam, Maryland (15). Methods of sampling and analysis have been described in detail elsewhere (1,16,17). Briefly, each surface water sample was obtained from the Susquehanna River at Conowingo, MD (Figure 11, using an equal-discharge increment method. A typical sample consisted of 40 L of surface water that was filtered to separate solids less than 0.7pm in diameter with Whatman GFlF filters (Whatman, Inc., Clifton, NJ), and 10-15-L subsamples of the filtrate were concentrated25 000-fold using liquid-solid extraction with C- 18 bonded-phase silica or graphitized carbon black sorbents; the particulates isolated on GF/F filters were Soxhlet extracted;and all solvent extractswere concentrated and analyzed using gas chromatography (GC) or gas chromatography/massspectrometry (GC/MS). The organic contaminants were quantified in the sample extracts using a Hewlett-Packard (HP; Palo Alto, CAI 5890 Series 11 CG equipped with a fused-silica capillary column (HP-l,30m x 0.25 mm) and an electron capture detector (organochlo2060
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rine pesticides and PCBs, determined as the sum of 112 congeners) and by G U M S (ON/OP pesticides and PAHs) using an HP 5890A GC coupled to a Finnigan INCOS 50 (Finnigan MAT, San Jose, CA) mass spectrometer also equipped with a fused-silica capillary column (HP-5,30m x 0.25 mm). The data employed in this analysis included censored values, which were adjusted to the detection limit of the analyticalmethod when no measured values were obtained (IS).In each case, however, the number of detections was treated as a factor in assessing the quality of nondetects: nondetects below 33%were not included in the evalualtion. In this study, it was not possible to obtain a large number of river fall line concentration data points owing to the low detection limits employed and the diversity of organic contaminants in question. Typically, 15 surface water samples were collected and processed during the 1-year period. Fourteen approaches to estimating loads were used, including six averaging, four ratio, and four regression estimators,which are illustrated in Tables 1-3, respectively. Nomenclature for the equations appearing in Tables 1-3 is provided in Appendix A. Load computations were performed using MATLAB (The Math Works, Inc., Natick, MA). The mean and relative deviation, calculated as (individual method load - mean load) + mean load, for the 14 load estimators were used to determine the Susquehanna River fall line loads and the degree of load agreement between the methods, respectively.
TABLE 1
TABLE 3
Averaging Estimatorsd
Regression Estimatorsd
method no.
method no.
expression
1 (ref 1)
N
L,, = x
11 (ref 9)
"1
365
c j x qu
e1
Ls,(k) = qr
expression
I=",
+
cktk ck+,(2400 -
rk)
12 (ref 9)
2400
L,,,,
= LRces~2'2
13 (ref 12)
2 (ref 3)
14 (ref I O ) 3 (ref 3) a
Refer to Appendix A for nomenclature.
1
i
5 (ref 4)
1
x
Refer to Appendix A for nomenclature. SO
'0
1W
150
TABLE 2 expression
7 (ref 6)
8 (ref 7)
i0
u)o
350
1 400
Enended Julian Day
Ratio Estimatorsa method no.
2.b
L= L=iO+-
[i]Q
FIGURE 2. Discharge for the Susquehanna River in the extended Julian day period March 1,1992-February 28,1993. Sample days are indicated by the small circles. I.2
N(n- 1 ) - N - 1 ( 1 - rq)
1
0.8
6
0
4 0.6 s A 5
0.4
Refer to Appendix A for nomenclature. 0.2
Results The hydrographfor the SusquehannaRiver for March 1992February 1993 is displayed in Figure 2. Samplingwas more frequent during seasonswith relativelyhigh flow, especially in the spring months when most of the major storms occurred. However, not every storm event was sampled, as shown in Figure 1. A storm-flow sample in this study (operationallydefined) was collected whenever discharge in the Susquehanna River exceeded 2500 m3/s at Conowingo, MD. An effort was made to ascertain the effect of the number of samples on the estimated loads achieved from this process. Figure 3 illustrates the ratio of loads estimated separately for 18 and 138 samples, respectively, corresponding to nitratelnitrite data acquired by James et al. (6)
0 1
2
3
1 4
5
6
7
8
9
1 0 1 1 1 2 1 3 1 4
METHODS
FIGURE 3. Ratio for nitratelnitrite loads calculated on the basis of N = 18 sample points [L(lS)l and loads calculated on the basis of N = 138 sample points [L(138)]. These loeds are calculated for the Susquehanna River at the fal\ line batween 1988 and 1994 and represent the 14 methods of Tables 1-3.
for the period 1988-1994 for the Susquehanna River fall line. As Figure 3 illustrates, both the ratio and regression methods yield little change across the increase in sample number. Furthermore, the averaging method typically yields ratios in the range +1.16 to -0.43. These ratios are VOL. 29, NO. 8, 1995 /ENVIRONMENTAL SCIENCE &TECHNOLOGY
2061
r
B
1
0
0
DEVIATION
DEVIATION
I
1
'4
F
E
12
I
11
i
n I 3
c I
-1
,
0
DEVIATION
I
,
1
0
.l
#vu'IK)w
-1
I
0
1
DEVIATION
FIGURE 4. Calculated fractional deviation of loads for the 14 methods of Tables 1-3 about the mean load (given in Table 4): (A) atrazine, (B) metolachlor, (C) t-PCBs, (D) fluoranthene, (E) naphthalene, and fF) a-chlordane.
substantially less than +3.00 to -0.33, which are the accepted norm for analytes of this class (1). Figure 4 , panels A-F, illustrates the calculationsofloads for each method as the relative deviation about the mean load, and the relative deviations shown are representative of the full suite of organic contaminants for which loads were estimated. The mean loads for the full suite of organic contaminants are listed in Table 4. In Figure 4A, the deviations for atrazine in filtered surface water are shown, where it should be noted that the maximum deviation encountered for estimation of the load across all methods ranged from -0.40 to +0.40 for 13 detections (Nd) out of a total of 15 (NJ samples (i.e., Nd/Nt = 13/15). In Figure 4B, metolachlor is presented for filtered surface water, and the deviationsrange from -0.24 to +0.55 acrossthe methods with Nd/Nt = 15/15. Shown in Figure 4C for total PCBs (t-PCBs)in filtered surface water samples, the deviations range from -0.55 to t-1.12 with Nd/Nt = 13/15, which is somewhat larger but nonetheless well within the factor of 4 that has come to characterize loads of these types of chemicals. Figure 4D illustrates the deviations for fluo2062
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ranthene in the particulate phase where the deviations ranged from -0.64 to f1.04 with Nd/Nt= 12/15. Figure 4E presents the deviations for naphthalene in filtered surface water, and the deviations ranged from -0.44 to +0.93 with Nd/N, = 13/15. Finally, the deviations for a-chlordane in filtered surface water ranged from -0.78 to +1.46 with Nd/ Nt = 11/15. The meanloads for the organic contaminantsconsidered in this study with the number of detections greater than or equal to 5 out of a possible 15 samples (33%limit) reported by the ChesapeakeBay Fall Line Toxics MonitoringProgram Database (1) are listed in Table 4. Several organic contaminants, including diazinon, malathion, aldrin, permethrin, fenvalerate, 4,4'-DDT, benz[alanthracene, and benzo[alpyrene, were excluded from load estimation because these compounds did not meet the 33% limit of detectionfrequencyin either the dissolvedor the particulate phases or both (referto ref 11,and it was felt that meaningful load esimates could not be provided in these cases. Also indicated in Table 4 are the deviations for each compound in question. The loads are specified in kilograms per year,
As has been pointed out by Preston et al. (131,differences
a single sampling frequency and one flow-stratification pattern constitute a subset of their results. For the cases considered, all their results converged toward the same bias. Typically, this bias is 30-35% of the mean loading when 12 samples are used. Hence, it is reasonable to infer, in our case for 15 samples,bias errors from the true loading of the order of 30-35% for our methods 3 and 9. Regular samplingrules out an important source of error, the river characteristics themselves. Figure 2 illustrates the samplingpattern used in this study it is roughly regular, albeit sparse, and major storms were sampled. Another source of error is the physicochemical nature of each contaminant considered. The model dictates what underlying assumptions are made about flow and concentration relationships, such as a bivariate log-normal relationship implicit in the regression approaches. Hydrologic variability introduces changes in flow that may not be resolved based on sparse sampling. The key here is to introduce an interpolated value for the concentration and use the exact discharge values across the entire year, as was accomplished in this study. Finally, the physicochemical characteristicsdeterminefactors such as in which phase the contaminant is likely to be detected. In this work, the chemical characteristics have been somewhat removed from consideration through the use of phase-specific measurements. The ONlOP pesticides represented the most abundant compound and correspond to those frequently found in use, atrazine being the most common. Within this class virtually all activity is in the dissolved phase. The lowest loading values tended to be represented by the regression estimators, which were still close to the other techniques. This was a very consistent set of loading estimates. The organochlorine pesticides and PCBs loadings in the dissolved phase were substantially less than the ON/OP pesticides, as expected. While these compounds are no longer in active use, they still persist in significant trace amounts in the Chesapeake Bay watershed. Finally, the PAHs yielded nominal scatter and were most commonly found in the particulate phase except for naphthalene, which is less hydrophobic.
among groups of estimators are usually due to different model assumptions when applied to the data in question. In this study, the relative deviationswere found to be similar across each of the three groups of estimators. Hence, it is not too surprising that agreement between ratio and regression estimators was found to occur. The issue of loading accuracy and precision lends itself to two factors. First, when the number of sample points increases by a factor of roughly 7, the loading estimate for nitratednitrites changes very little for ratio and regression methods, as demonstrated in Figure 3, and is well within accepted limits for averaging estimators. Secondly, Richards and Holloway (14) concluded that nominal errors in loading estimates of 30-35% typically occur for sample numbers of the size used in this study. Both of these results are acceptable in a community that has come to expect deviations as much as a factor of 4 in reported organic contaminant loadings. The approach employed by Richards and Holloway used two mean loading estimators similar to our methods 3 and 9. They developed 24 strategies based on these two methods, four sampling frequencies, and three flowstratificationpatterns. Our methods 3 and 9 coupled with
The following conclusions concerning the loading results hold for the analysis reported in this study: (1) The reported loads do not change substantiallywith increases in sample number. (2) Based on a survey of the literature, it is reasonable to expect that a systematic error of 30% is the maximum possible in the mean loading estimates for this study with a total of 15 samples collected throughout the year. (3) For averaging estimators the following are true: (a) (Methods 2-5) loading differences are less than a factor of 2. (b) (Methods 2-6) loading differences are less than a factor of 3. (c) (All methods) for ONlOP compounds, differences are less than a factor of 2. (4) For ratio estimators (all methods), differences are generally less than a factor of 2. (5) For regression estimators, differences are generally less than a factor of 2. (6) For all estimators, differences are generallyless than a factor of 3. The order of difficulty in calculating estimates lies first with the simplest methods (the ratio estimators), next
TABLE 4
Mean Loads and Deviations for Selected Organic Contaminants deviation range’ contaminant
M Y b mean load (kgiyr)
max
min
ON/OP Pesticides (Dissolved Only)
simazine prometon atrazine alachlor metolachlor cyanazine hexazinone
9/15 7115 13/15 5115 15/15 8115 8/15
419 137 1306 196 837 464 126
0.41 0.82 0.40 0.78 0.52 0.51 1.07
-0.28 -0.27 -0.34 -0.29 -0.24 -0.37 -0.60
Organochlorine Pesticides and PCBs (Dissolved)
oxychlordane y-chlordane a-chlordane dieldrin t-PCBs
8/15 8/15 11/15 6/15 13/15
35 37 68 22 114
1.39 1.50 1.45 1.23 0.88
-0.65
-0.70 -0.74 -0.64 -0.33
Organochlorine Pesticides and PCBs (Particulate)
oxychlordane y-chlordane a-chlordane dieldrin t-PCBs
6/15 6/15 5/15 7/15 13/15
naphthalene fluoranthene
13/15 6/15
naphthalene fluoranthene
9/15 12/15
10 6 6 17 125
0.94 0.97 0.98 1.25 1.12
-0.41 -0.28 -0.34 -0.64 -0.54
0.93 1.41
-0.44 -0.54
1.21 1.04
-0.80 -0.65
PAHs (Dissolved) 141 35
PAHs (Particulate) 34 308
a Maximum and minimum relative deviations found for the 14 load estimation methods. Number of detects (NJ relative to the total number of samples collected (Nt).
with the ON/OPpesticidesbeing the most abundant. Each deviation measurement was normalized to the mean load in order to provide a relative deviation.
Discussion
Conclusions
VOL. 29, NO. 8, 1995 /ENVIRONMENTAL SCIENCE & TECHNOLOGY
2063
the averaging estimators, and finally the regression estimators. The present study encompassed 15 samples across the year from March 1992 to February 1993. This represents the approximate maximum rate at which samples can be gathered and processed for the organic contaminants examined in this study. In the future, it is likely that a comparable or reduced sampling regime will be implemented for this tributary for the analytes in question for loading estimates.
The authors are grateful to the Metropolitan Washington Council of Governments (through MWCOG Contract 93004) and the Maryland Department of the Environment for providing partial support for this study. Finally, a note of thanks is due Dr. Adil N. Godrej from the Virginia PolytechnicUniversity for his thoughtfulreview of the early manuscript.
Appendix A Nomenclature In this appendix, the equations appearing in Tables 1-3 are discussed. It will be appropriate to describe each method with regard to the symbolsappearingin the method. Symbols not specified within a given method are carried over from other methods within a class. Averaging Methods. Method 1: LBFand LSF represent base-flow and storm-flow loading; N is the number of sample periods; cj is the concentration for the jth period; qu is the discharge for the ith day of thejth period; nj is half the number of days in thejth period; q k is the discharge on the kth day; ck is the concentration on the kth day; and tk is the start time on the kth day. Method 2: q,k is the discharge on t h e m day of the kth quarter; Nk is the number of days in the kth quarter; n k is the number of Samples in the kth quarter; and Cik is the ith concentration in the kth quarter. Method 3: ci is the concentration on the ith day; 9i is the discharge on the ith day; and N is the number days. Method 4: N m is the number of days in the mth month; qim is the discharge on the ith day of the mth month; nm is the number samples in the mth month; and cjk is the concentration on the jth day in the rnth month. Method 5: Nk is the number of days in the kth quarter; q i k is the discharge on the ith day in the kth quarter; n k is the number of samples in the kth quarter; and Cjk is the concentration on the jth day in the kth quarter. Method 6: Nk is the number of days in the kth stratification; nk is the number of sample days in the kth stratification;q j k is the discharge on the ith sample day of the kth stratification;and Cik is the concentration on the ith sample day of the kth stratification. Ratio Methods. Method 7: 1 is the average daily load: q is the average daily discharge; and Q is the total flow. Method 8: r is the average concentration; N is the total number of days; n is the number of days sampled. Methods 9 and 10: R is the estimated average concentration over population (frequentlytaken to be r):s4 is the covariance between load and flow: and sq2 is the variance in flow. Regression Methods. Method 11: LRC is the “rating curve” loading; qn is the daily discharge; Bo and are the 2064
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 29, NO. 8, 1995
regression coefficients from
where c is the concentration and 9 is the discharge. Method 12: LQMLEis the “quasi-maximum likelihood estimator” loading; and sa2 is from
where c(n1 is the concentration on the nth day. Method 13: LSMis the “smearing”load; N is the total number of samples;and e@ is the* log regression residual. Method 14: LMWEis the “minimum variance unbiased estimator”loading; m is the number of degrees of freedom in the regression (2); and gm and Vare from eqs 15 and 6, respectively, of ref 14.
literature Cied (1) Foster, G. D.; Lippa, K. A. In Chesapeake Bay Fall Line Toxics Monitoring Program 1992 Fiml Report; Nemura, A., Dobler, E., Eds.; Chesapeake Bay Program Office: Annapolis, MD, 1994. (2) Chesapeake Bay Program Office. Chesapeake Bay Basinwide Toxics Reduction Strategy; Agreement Commitment Report, Chesapeake Executive Council: Annapolis, MD, 1988. (3) Dolan, D. M.; Yui, A. K.; Geist, R. D. 1. Great LakesRes. 1981, 7, 207-214. (4) Ferguson, R. I. Earth Surf: Processes Landforms, 1987, 12, 95104. (5) Verhoff, F. H.; Yaksich, S. M.; Melfi, D. A.I. Environ. Eng. 1980, 106, 591-608. (6) Cochran, W. G. Sampling Techniques;John Wiley & Sons, New York, 1977. (7) Hartley, H. 0.;Ross, A. Nature 1954, 174, 270-271. (8) Tin, M. 1. Am. Stat. Assoc. 1965, 60, 294-307. (9) Ferguson, R. I. Water Resour. Res. 1986, 22, 74-76. (10) Cohn, T. A.; DeLong, L. L.; Gilroy, E. J.; Hirsch, R. M.; Wells, D. K. Wuter Resour. Res. 1989, 25, 937-942. (11) Cohn, T. A.; Caulder, D. L.; Gilroy, E. J.; Zynjuk,L. D.; Summers, R. M. Water Resour. Res. 1992, 28, 2353-2363. (12) Gilroy, E. J.; Hirsch, R. M.; Cohn, T. A. WuterResour. Res. 1990, 26, 2069-2077. (13) Preston, S . D.; Bierman, V. J., Jr.; Silliman, S. E. Water Resour. Res. 1989,25, 1379-1389. (14) Richards, R. P.; Holloway, J. Wuter Resour. Res. 1987,23, 19391948. (15) James, R. W.; Simmons, R. H.; Strain, B. F. WuterResourcesDatu Maryland and Deleware Water Year; Water-Date Report MDDE-92-1; Towson, MD, 1992; pp 87-88. (16) Shan, T.-H.; Hopple, I. A.; Foster, G. D. Bull. Environ. Contam. Toxicol. 1994, 53, 382-389. (17) Foster, G. D.; Lippa, K. A. I. Food Ag. Chem. Submitted for publication. (18) Slymen, D. J.; de Peyster, A. Environ. Sci. Technol. 1994, 28,898-902.
Received for review December 23, 1994. Revised manuscript received April 13, 1995. Accepted May 16, 1995.@ ES940764C Abstract published in Advance ACS Abstracts, July 1, 1995.
