Environ. Sci. Technol. 2004, 38, 3567-3573
Estrogen Content of Dairy and Swine Wastes D . R A J R A M A N , * ,† E L I Z A B E T H L . W I L L I A M S , †,‡ ALICE C. LAYTON,§ ROBERT T. BURNS,† JAMES P. EASTER,§ A D A M S . D A U G H E R T Y , †,| M I C H A E L D . M U L L E N , †,⊥ A N D GARY S. SAYLER§ Biosystems Engineering & Environmental Science Department, The University of Tennessee, 2506 E. J. Chapman Drive, Knoxville, Tennessee 37996-4531, and Center for Environmental Biotechnology, The University of Tennessee, 676 Dabney Hall, Knoxville, Tennessee 377996-1605
Naturally occurring estrogens in animal wastes may cause negative environmental impacts, yet their abundance in animal waste treatment and storage structures is poorly documented. To better quantify estrogen concentrations in animal wastes, multiple waste samples were collected from treatment and storage structures at dairy and swine facilities and analyzed for concentrations of 17βestradiol (E2), estrone (E1), and 17R-estradiol by gas chromatography-mass spectroscopy and by enzyme linked immunosorbent assay (E2 only). Mass ratios of each estrogen to the macronutrients nitrogen, phosphorus, and potassium were also determined. Because manure application rates are typically macronutrient-based, estrogen to macronutrient ratios are proportional to areal mass application rates of estrogen to fields. Swine farrowing waste (from farrowing sows and piglets) had the highest ratios of E2 to macronutrients. Mean ratios in swine farrowing waste were roughly twice those in swine finishing waste (from growing male and nonpregnant female animals) and more than four times higher than those in dairy waste (from lactating cows in various stages of their reproductive cycles); these differences were statistically significant (R ) 0.05). Estrone followed a similar trend. In contrast, ratios of 17R-estradiol to macronutrients were highest in dairy operations. These results can be used to better predict estrogen loading rates on fields receiving swine and dairy wastes.
Introduction Steroidal estrogens such as 17R-estradiol, 17β-estradiol (E2), and estrone (E1) are recognized as potential pollutants of * Corresponding author phone: (865)974-7722; fax: (865)974-4514; e-mail:
[email protected]. † Biosystems Engineering & Environmental Science Department. ‡ Present address: Tennessee Technological University, Cookeville, TN 38505. § Center for Environmental Biotechnology. | Present address: United States Department of Agriculture Natural Resources Conservation Service, Manchester Field Service Center, 1008 East End Rd., Manchester, TN 37355. ⊥ Present address: Department of Agronomy, N122 Agricultural Science Center-North, The University of Kentucky, Lexington, KY 40546-0091. 10.1021/es0353208 CCC: $27.50 Published on Web 05/13/2004
2004 American Chemical Society
surface waters (1-4) because of their impacts on a wide variety of aquatic organisms. Human waste is one source of these compounds, with approximately three-fourths of the human waste in the United States being treated and discharged by publicly owned treatment works (POTWs) (5). Animal agriculture may also be a major emitter of these compounds in the United States (6, 7). Reported fecal and urinary excretion rates for dairy cattle and sows range from 0.1 to 160 mg d-1 (1000 kg live animal weight)-1 (6). When compared to per capita estrogen emissions from POTWs, bovine estrogen emissions in the United States might be an order of magnitude greater than that of humans. Despite evidence that manurial estrogens may be emitted to the environment in large quantities and that surface water and groundwater may be impacted by these compounds (8, 9), the abundance of these compounds in animal waste treatment and storage structures is poorly documented, making it difficult to determine which watersheds are at greatest risk from such pollution. In addition, determining and reporting estrogen abundances in animal waste treatment and storage structures is complicated by the open nature of most structures, which allows a variety of physical, chemical, and biological transformations to occur in them (e.g., partial anaerobic digestion, dilution with rainwater, thermophilic aerobic degradation); these transformations can alter estrogen concentrations significantly (4, 7). Previous work has shown that estrogen losses in runoff from fields receiving animal manure correlate with the mass of estrogen applied per unit area (10-13). Generally, estrogen concentrations in manure are reported on a dry weight basis. Although reporting estrogen concentrations on a dry weight basis cancels dilution and concentration effects, dry weight estrogen concentration does not necessarily predict estrogen pollution potentials of an animal waste. This is because animal wastes are typically land-applied at rates sufficient to meet crop nutrient requirementsshistorically nitrogen (N) but increasingly phosphorus (P) (14). When waste is applied to meet crop N requirements, the greater the estrogen to N ratio, the greater the estrogen application rate (mass/ area), and hence the greater the likelihood of causing environmental pollution. Thus, estrogen to macronutrient ratio may be a predictor of pollution potential of a waste. The major objective of our work was to quantify E1, E2, and 17R-estradiol concentrations in swine and dairy waste treatment and storage structures typical of the southeastern United States. The structures assayed represent the vast majority of dairy facilities in the eastern United States and swine facilities in the southeastern and western United States (15). To facilitate comparisons among farms and farm types, we computed ratios of estrogen to the macronutrients N, P, and potassium (K).
Experimental Methods Types of Storage Facilities. Modern animal production facilities use a range of systems to handle the significant quantities of manure produced. A brief description of systems relevant to this study is provided here: Manure storage pits and holding ponds are designed to store only the volume of manure and wastewater produced over a given time period, typically 6 months. Both are completely emptied at the end of the design storage interval, and the contents are land-applied. Pits contain high-solids, high-strength manure slurries that are anaerobic at all depths. Anaerobic lagoons are designed to provide a volume for additional water to dilute the waste so that partial anaerobic VOL. 38, NO. 13, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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digestion of the slurry is achieved prior to land application. This may provide some odor reduction as compared to pit storage systems. Lagoons are partially emptied on an annual or semiannual basis, and the slurry is land-applied. Lagoons typically have depths of 4-7 m and may have low dissolved oxygen present in the topmost 1 m due to surface aeration via wind and wave action. However, lagoons can be expected to be anaerobic at deeper levels. Swine hoop finishing systems, a type of dry waste management, utilize bedding material to collect and store animal waste. The manure/bedding mixture is collected in a dry state and land-applied on a 6-month cycle. Dairy dry stacks, another type of dry waste management system, are essentially piles of manure and bedding scraped from floors. Dry waste systems are typically aerobic near their outer edges but become anaerobic on their interiors. Depending on their moisture content and on local climate, these systems may undergo partial composting. Facilities Sampled. Manure stored at 8 dairy and 11 swine facilities was sampled twice during a 1-yr period; one sampling event occurred during winter, and one occurred during summer. Because modern animal production facilities import and export animals nearly continuously, the average growth stage of animals does not vary much with time, and mean excretion rates are expected to be fairly stable. Differences between summer and winter sampling would be expected to be primarily due to environmental effects on the stored manure rather than on changes in livestock excretion. Three wet dairy systems (holding ponds) and five dairy dry stacks with varying moisture content were sampled. Five wet swine farrowing systems (four pits and one lagoon), three wet swine finishing systems (lagoons), and three dry swine finishing systems (hoop structures) were sampled. To determine the spatial heterogeneity of estrogen distribution in each system, multiple samples were collected during each farm visit. Safety Precautions: Remotely Piloted Vehicle. The collection of samples from the middle of animal waste holding ponds and lagoons presented a drowning hazard and a pathogen-exposure hazard to research personnel. The safe collection of such samples was facilitated by the design and construction of a remotely piloted vehicle (pontoon boat) that permitted collection of 1-L samples from lagoons and holding ponds, without requiring research personnel to embark in small boats. Pontoons were fiberglass-covered expanded polystyrene, decking was plywood, and propulsion and steering was by off-the-shelf components employed in radio-controlled scale-model boats. A second motor operated a winch to lower or raise the sampling container, while a servomechanism was used to trigger the sampler. Sample Locations. Samples were collected from nonagitated holding ponds and lagoons. If the farm used a twostage lagoon system, then samples were collected from the primary lagoon only. In each liquid storage system, a total of eight samples were collected. Four 1-L samples were collected along a depth profile near the center of the liquid storage area, using the remotely piloted vehicle. The top and bottom samples were taken 0.3 m from the surface and from the bottom; the others were taken at one-third and twothirds of the total depth. Four additional samples were collected from surface locations: one sample from each corner of the area, if accessible. When the influent pipe was readily accessible, one sample was collected from the area immediately adjacent to the pipe instead of from the closest corner to the pipe. The surface samples were collected with a 500-mL polyethylene dipper (14-242-5, Fisherbrand, Pittsburgh, PA). Samples were also collected from nonagitated pits under slatted floors in swine rearing facilities. When possible, samples were collected close to the pit outlet. Four 1-L samples were collected along a depth profile 3568
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in the pit, using a remotely triggered water sampler (JT-1, LaMotte Co., Chestertown, MD). The top and bottom samples were taken 0.3 m from the surface and from the bottom; the others were taken at one-third and two-thirds of the total depth. For each swine hoop structure, two representative locations were identified in the dunging area. At each of these locations, 500-g samples were collected from the top, middle, and bottom of the material by means of a shovel and portable scale. The two samples from each depth were combined to form a composite sample for that depth. Dairy dry-stack systems fell into two categories depending on the management practices of the farm. Systems with welldrained friable solids that allowed air movement through the stack were referred to as solid dry-stack systems, while those containing a high moisture content muck were euphemistically referred to as semisolid. Solid dry stacks were divided into new and old regions, and the age of the manure in the new and old regions was estimated through conversation with the owner/operator of the facility. Three representative sample locations were identified within the old and new areas. At each of these three locations, a 500-g sample from the top, middle, and bottom of the stack was taken using a shovel (sometimes assisted by a front-end loader); then the three samples were combined to form a composite sample for that depth. Sampling locations in semisolid dry stacks were determined as described for dry-stack solid systems above. Samples were collected with a 1-L collection device designed and fabricated in our lab. The device consisted of a rectangular aluminum container with an open top and an upward-opening bottom, attached to the end of a 3-m pole. The upward-opening door allowed semisolid materials to push up through the sampler as the sampler moved down through the stack. As the sampler was raised, the door shut, thereby collecting a sample from the desired depth. If the sampler was unable to reach the bottom of the area, then the deepest depth possible was collected, and the depth was noted. Sample Storage and Transport. Dry and semisolid samples were placed in plastic bags for acidification and transport and were transferred to plastic bottles upon arrival at the laboratory; wet samples were placed in plastic bottles for acidification and transport. Acidification to pH 2 was accomplished by adding approximately 25 mL of 4 N H2SO4 to each sample; pH was checked with colorimetric pH paper. Samples were then placed in a cooler filled with ice and transported back to the lab, which took anywhere from 1 to 36 h. Samples were stored at 4 °C until analysis. Analytical Methods. Samples were extracted in triplicate. In the case of liquid samples, a 10-mL subsample was placed in a 40-mL glass vial along with 10 mL of deionized water, approximately 100 µL of 6 N NaOH, and 10 mL of ethyl ether. In the case of dry samples, a 5-g subsample was placed in a 40-mL glass vial along with 5 mL of deionized water, 3 drops of 6 N NaOH, and 5 mL of ethyl ether. A four-step serial dilution (1:1, 1:3, 1:9, 1:27) was performed for each triplicate sample. Wet and dry samples were shaken for 2 h on a vertical shaker and then centrifuged at 2300g for 10 min. One milliliter of ether was extracted from each sample and blown down with N2 gas. Samples were tested for E2 using an ELISA immunoassay (Assay Design, Inc., Ann Arbor, MI), per the manufacturers instructions. 17R-Estradiol, E1, and E2 levels were also assayed with the GC-MS per the methods reported in ref 7. In addition, samples were analyzed for total solids (TS), volatile solids (VS), ash, total Kjeldahl nitrogen (TKN), total phosphorus (TP), and potassium (K) using Standard Methods (16); chemical oxygen demand (COD) was assayed by a digestion spectrophotometric method (Method 8000, HACH Company, Loveland, CO.)
FIGURE 1. Recovery ratios based on spike matrix and spike-blank data over both summer and winter sampling periods. Error bars represent standard deviation. ELISA, enzyme-linked immunosorbent assay, used for 17β-estradiol (E2); GC, gas chromatography-mass spectrometry, used for both E2 and estrone (E1). Mean CV values for spike-blank data were 34% for ELISA E2, 34% for GC E2, and 29% for GC E1. For each structure, a blank, spike-blank (100 ppb E2 and 500 ppb E1 in DI water), and three matrix spikes (100-500 ppb E2, depending on method and sample; 500 ppb E1) were created and assayed. The matrix spike concentration was subtracted from the sample background concentration, and this difference was divided by the water spike concentration to determine the recovery ratio. Statistical analyses were performed using a commercial software package (SPSS for Windows, Release 10.0.1, Statistical Product and Service Solutions, Chicago, IL), and Tamhane’s T2 post-hoc test for mean comparisons for the case of unequal variances was employed as a conservative test to assess statistical significance between facility types.
Results and Discussion Sample Integrity. Previous experiments (7) suggested that solvent extraction of manure slurries followed by ELISA or derivitization and GC-MS analysis was sufficient to quantify the estrogens in manure samples with relatively high concentrations of estrogens (>1 ppb). Such concentrations are typically 100-1000-fold higher than those found in wastewater treatment plant influents or water samples (2, 17). To extend the detection limit to the 0.25-100 ppt range needed for some environmental samples, such as activated sludge, cleanup steps and concentration and more sensitive chromatography instruments (e.g., tandem mass spectrometry, LC/MS) are warranted (17). Three farms did not have matrix spikes run for at least one of the estrogen tests, yielding a total of 35 ELISA E2 matrix spike results and 37 GC-MS E1 and E2 matrix spike results. Mean recovery ratios are presented in Figure 1. The results indicate that the ELISA E2 method was unbiased for the winter data set but that the summer ELISA E2 data may be overestimated by a factor of 1.5. The GC-MS E2 spike recovery ratios are similar to the ELISA ratios. The scatter evident in the data reflects the challenge of measuring estrogens in manure samples and is a reminder that reporting such data with much more than (10% accuracy is unrealistic. Survey Results. (a) Estrogen Concentrations. In this study, two methods were used to quantify E2 due to the fact that contaminants may interfere with estrogen quantification (2, 6, 18). Because the ELISA method is based on im-
munological reactions and colorimetric quantification and the GC-MS method is based on the chromatographic retention and selective ionization, interfering contaminants may be expected to be different for each assay. The ELISAGC ratio was determined for each sample in the data set. Samples with ratios greater than 3-fold (either way) were removed from the data set; if the majority of samples from a single farm sampling event were outside the acceptable limit, the entire farm’s data were excluded. Six farm sampling events, comprising 32 samples, were excluded. Of these, three were swine hoop structures, two were dairy dry-stack systems, and one was a swine farrowing pit; all had high solids contents relative to slurry waste storage systems. The predominance of dry-handling systems in those rejected suggests that matrix interference problems may have caused the discrepancies. Of the remaining 194 samples, 2% (four samples) exhibited 3-fold or greater discrepancy between ELISA and GC-MS results, perhaps reflecting assay errors, so they were also removed. The 17β-estradiol values from ELISA and GC methods were correlated for the remaining 190 samples with r ) 0.82. The ELISA method had a lower detection threshold than did the GC-MS E2 method; therefore, the E2 data reported are based upon the ELISA results. Estrone and 17Restradiol results are from the GC-MS method. Wet basis estrogen concentrations ranged from below detection levels to 105 ng L-1 and varied with animal type and with storage structure (Figure 2A, Table 1). The large error bars in Figure 2A primarily reflect the broad range of concentrations observed within each grouping and not large measurement errors within the samples (Table 1). The coefficient of variation within triplicate E2 ELISA assays was 6%, and that of the E1 GC-MS assay was 30%. On a wet basis, highest mean E2 concentrations were observed in swine finishing hoop structures, while high mean E1 concentrations were observed in swine finishing hoop structures, in swine farrowing pit slurries, and in dairy dry-stack semisolids. 17Restradiol was primarily detected in dairy samples, with highest concentrations observed in dairy dry-stack semisolids. In contrast, when evaluated on a dry basis, highest E2 and E1 concentrations were observed in swine farrowing pits (Figure 2B), reflecting the relatively low total solids content (ca. 1-3%). The high variability observed in the swine farrowing VOL. 38, NO. 13, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 2. Mean wet basis (A) and dry basis (B) 17β-estradiol (E2), estrone (E1), and 17r-estradiol (17r) concentrations by facility type over both summer and winter sampling events, representing all samples collected at each facility type; 190 samples total. SFIL, swine finishing lagoon (n ) 47); SFIH, swine finishing hoop structure (n ) 6); SFAL, swine farrowing lagoon (n ) 16); SFAP, swine farrowing pit (n ) 28); DDM, dairy dry-stack semisolid (n ) 30); DDS, dairy dry-stack solid (n ) 15); DHP, dairy holding pond (n ) 48). pits reflects the variability in estrogen concentrations between farms and sampling dates. In general, the differences between wet and dry basis results illustrated in Figure 2, panels A and B, reflect the differences in moisture content among the various wastes tested. As previously stated, manure application rates are based upon manure nutrient content (typically N or P), not upon dry or wet mass application rates. Thus, neither the wet nor dry basis concentrations are good predictors of estrogen pollution potential. Since estrogen to macronutrient ratios can serve as such a predictor, the remainder of the data is presented on the basis of estrogen to macronutrient ratios. (b) Impact of Operation Type. Ratios of E2 to macronutrients vary significantly between waste management system types (Figure 3). The contention that dry basis estrogen concentrations are not good predictors of estrogen pollution potential is borne out by comparing the E2 dry basis results to the E2:macronutrient ratios presented in Table 1. An example of this difference occurs when comparing swine farrowing waste stored in pits (SFAP) versus lagoons (SFAL). 3570
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On a dry basis, mean swine farrowing pit slurry E2 concentrations were 3× greater than were lagoon E2 concentrations. However, when land applying these wastes to meet an N-fertilizer requirement, the pit slurry would result in an E2 areal application rate only half that of the lagoon contents. If these wastes were land-applied to meet a P-fertilizer requirement, the two wastes would yield nearly identical E2 areal application rates. These differences arise from longrecognized differences in nutrient conservation between waste handling systems (19). Figure 3 also illustrates the large differences in E2 to macronutrient ratio between different types of animal wastes. For example, swine farrowing wastes had E2:N ratios 3-9× greater than observed in dairy wastes. This result suggests that land application of waste to meet crop N requirements may pose a greater threat in watersheds with high densities of swine farrowing units than in those with similar densities of dairy facilities. (c) Waste Handling System Effects. Ratios of E2:N and E2:P were not significantly different among DDM, DDS, and
TABLE 1. Mean Abundances of 17β-Estradiol and Estrone at Facilities Sampled Reflecting both Summer and Winter Sampling Events at All Locations at Each Facilitya 17β-estradiol to macronutrient ratio (ppt/ppm)
estrone to macronutrient ratio (ppt/ppm)
farm id
17β-estradiol (ppb)
E2:N
E2:P
E2:K
estrone (ppb)
E1:N
E1:P
E1:K
SFIL-1 SFIL-2 SFIL-3
2.4 ( (2.1) 1.8 ( (1.6) 3.3 ( (1.1)
8.4 ( (3.3) 2.6 ( (0.1) 7.3 ( (2.8)
31 ( (14) 6.7 ( (2.3) 55 ( (19)
6.9 ( (3.1) 5.3 ( (2.1) 5.6 ( (1.7)
11 ( (8.6) 5.9 ( (6.8) 14 ( (4.6)
41 ( (12) 7.5 ( (1.9) 28 ( (5.5)
140 ( (37) 18 ( (5.1) 220 ( (53)
30 ( (3.6) 16 ( (10) 23 ( (7.3)
SFIH-1 SFIH-2
32 ( (2.8) 49 ( (6.4)
4.2 ( (0.9) 8.1 ( (0.6)
7.4 ( (1.1) 12 ( (0.7)
9.1 ( (3.0) 9.2 ( (3.0)
30 ( (8.0) 78 ( (19)
3.8 ( (1.1) 13 ( (3.5)
6.9 ( (1.9) 19 ( (6.8)
8.6 ( (3.8) 15 ( (5.2)
SFAL
3.9 ( (2.0)
27 ( (8.0)
52 ( (19)
21 ( (10)
5.9 ( (4.5)
46 ( (16)
89 ( (46)
33 ( (12)
SFAP-1 SFAP-2 SFAP-3 SFAP-4
22 ( (16) 14 ( (11) 29 ( (9.3) 12 ( (8.2)
23 ( (4.7) 7.0 ( (1.5) 13 ( (4.2) 12 ( (2.7)
57 ( (9.5) 24 ( (5.9) 100 ( (32) 63 ( (21)
35 ( (6.9) 15 ( (5.8) 24 ( (7.5) 16 ( (2.0)
31 ( (14) 57 ( (36) 150 ( (24) 31 ( (23)
46 ( (21) 28 ( (8.2) 70 ( (11) 33 ( (12)
76 ( (7.6) 110 ( (20) 530 ( (88) 170 ( (73)
50 ( (12) 57 ( (20) 130 ( (21) 42 ( (7.0)
DDM-1 DDM-2 DDM-3
13 ( (3.2) 21 ( (5.5) 27 ( (26)
2.6 ( (0.6) 4.7 ( (0.5) 5.5 ( (2.4)
11 ( (3.1) 19 ( (1.9) 18 ( (7.7)
31 ( (11) 11 ( (1.6) 12 ( (2.3)
54 ( (47) 13 ( (7.5) 80 ( (80)
10 ( (8.6) 2.9 ( (1.2) 16 ( (2.5)
40 ( (32) 12 ( (4.3) 49 ( (5.4)
110 ( (90) 7.2 ( (3.2) 20 ( (4.6)
DDS-1 DDS-2
25 ( (5.6) 5.8 ( (6.7)
4.6 ( (1.2) 2.3 ( (2.9)
18 ( (1.0) 2.8 ( (1.6)
8.2 ( (3.2) 2.7 ( (3.2)
12 ( (3.0) 37 ( (41)
2.2 ( (0.5) 12 ( (11)
8.6 ( (2.8) 19 ( (15)
3.7 ( (1.1) 14 ( (12)
DHP-1 DHP-2 DHP-3
0.8 ( (0.5) 1.7 ( (0.9) 1.9 ( (0.6)
4.0 ( (1.7) 2.7 ( (1.5) 2.1 ( (0.4)
8.5 ( (4.3) 16 ( (5.3) 16 ( (2.7)
1.6 ( (0.9) 2.9 ( (1.4) 2.3 ( (0.5)
2.5 ( (0.0) 5.6 ( (4.3) 5.5 ( (3.0)
12 ( (2.5) 8.7 ( (5.8) 5.8 ( (1.4)
26 ( (3.1) 53 ( (33) 47 ( (21)
5.2 ( (1.0) 9.3 ( (5.8) 6.4 ( (2.2)
a Values are expressed on wet basis (ppb) and as a mass ratio to the macronutrients nitrogen (N), phosphorus (P), and potassium (K). Standard deviations are in parentheses. Abbreviations are the same as those used in Figure 2.
FIGURE 3. Mean 17β-estradiol (E2) to macronutrient ratios for all facility types sampled over summer and winter sampling events, 190 samples total. Abbreviations are the same as those used in Figure 2. DHP systems; E2:K ratios in DDM were significantly higher (R ) 0.05) than in either DDS or DHP (Figure 3). Estrone to macronutrient ratios (Figure 4) followed a similar trend in dairies, while 17R-estradiol:macronutrient ratios (data not shown) were greater in DDM than in either DDS or DHP. The naturally occurring estrogen, 17R-estradiol (in contrast to the synthetic hormone 17R-ethinyl estradiol) may be assumed to have a lower potency than either E1 or E2 based upon its responses in a yeast reporter strain assay (unpublished) and the lack of literature describing the effect of 17Restradiol on fish. In light of this, it appears that liquid and solid dairy storage types do not have greatly different impacts on the estrogen pollution potential of dairy wastes. Differences were observed in the ratios of E2 to macronutrients in swine finishing wastes (SFIL and SFIH), but these differences were only statistically significant (R ) 0.05) for
E2:P. In contrast, E1 to macronutrient ratios were all significantly higher (R ) 0.05) in swine finishing lagoons than in swine finishing hoop structures (Figure 4), suggesting that hoop systems may reduce the potential for estrogen pollution due to land application of swine wastes. When considering E2:N ratios, SFAP were significantly lower than SFAL (R ) 0.05). This relationship reversed with the E2:P ratio, but the difference was not statistically significant. The E2:K ratios were virtually identical between these facility types. Estrone followed a different trend, with E1:N and E1:P ratios not being significantly different (R ) 0.05), but with E1:K ratios being significantly higher in SFAP than in SFAL; intra-type variability confounded these results (Table 1). (d) Spatial Heterogeneity. In swine lagoons and dairy holding ponds, estrogen concentrations varied slightly across VOL. 38, NO. 13, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 4. Mean estrone (E1) to macronutrient ratios for all facility types sampled over both summer and winter sampling events, representing all samples collected at each facility type, 222 samples total. Abbreviations are the same as those used in Figure 2. the four surface sampling locations (mean coefficient of variation was 32%). In 15 of the 21 samplings of these liquid systems, E2 concentrations were positively correlated with depth (r > 0.5). In these 15 data sets, fitting a straight line to normalized concentration versus normalized depth (depth/ maximum depth) data yielded a mean slope of 0.46, indicating that E2 is approximately 1.5× more concentrated at the bottom of these systems than at the surface. Estrone showed similar results: in 8 of 12 samplings with detectable levels of E1, normalized concentration correlated positively with depth (r > 0.5). The observed correlations with depth probably reflect the association of estrogenic compounds with solid particles and the partitioning of solids in the slurry column. It suggests that estimates of the estrogen content within a liquid holding system need to be made either after thorough agitation or at several depths through the system to determine a reasonable mean value. Ratios of E2:K were used to assess the biodegradation in dairy dry-stack systems, under the assumption that K was well conserved in these systems. The highly moist (and probably anaerobic) DDM systems appeared to have low rates of biodegradation, with a mean E2:K difference between new and old parts of less than 1%. In contrast, the two DDS systems exhibited a mean decrease in the E2:K ratio of 70%, perhaps reflecting aerobic degradation of the steroidal estrogens. Although a similar trend was observed in E1 degradation (based upon E1:K ratios), the observations were more scattered and the data was inconclusive. Per Capita Excretion Estimates. In contrast to earlier work examining estrogen abundances in animal manures (reviewed in refs 6 and 20), this study did not examine manure as excreted but instead focused on manure in storage structures. Because of this, direct computation of per capita excretion rates is not possible. However, because ratios of estrogen to macronutrients were computed and because macronutrient excretion rates per unit live animal weight (LAW) are readily available (21), it was possible to estimate per capita excretion rates as assayed within the structure itself. To do this, the estrogen to macronutrient concentration was multiplied by the macronutrient excretion rate for the appropriate animal. Predictably, these values varied depending on the macronutrient selected and on the structure type (Table 2). Because degradation of some estrogens undoubtedly occurs in waste treatment and storage structure, 3572
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TABLE 2. Mean As-Collected Estrogen Emission Factors for Dairy and Swine by Facility Typea estimated estrogen emission factors, mg d-1 (1000 kg live animal weight)-1 facility type
17β-estradiol
estrone
17r-estradiol
SFIL SFIH SFAL SFAP DDM DDS DHP
3.5 2.5 9.7 8.2 2.9 1.2 1.1
14 3.4 16 28 7.0 2.6 3.3
bdl bdl bdl 2.0 8.7 1.9 2.5
a bdl, below detection limit. Abbreviations are the same as those used in Figure 2.
these numbers cannot be considered as-excreted values; instead, they should be interpreted as mean as-collected values associated with a particular facility type. Although the values in Table 2 fall within the broad ranges of as-excreted emission estimates provided by Hanselman et al. (6) in their literature review of manure borne estrogens, further surveys are needed to solidify these data. The 1.0 × 107 milk cows and 4.3 × 107 swine in the United States (22) represent standing biomass of 6.1 × 109 and 1.4 × 109 kg LAW, respectively. We previously used data from other investigators (6, 23) to estimate a total estrogen excretion rate of 1 mg d-1 cow (7) or approximately 1.7 mg (1000 kg LAW d)-1, which is in good agreement with the E2 emission estimate provided for dairy systems in Table 2. However, if total environmental estrogenicity is taken as the sum of E2 and one-half E1 (7, 24), then the estrogen emission of dairy cows ranges from 3 to 6 mg (1000 kg LAW d)-1, roughly 2-4× greater than we previously estimated. Using the estimated emission factors presented in Table 2, total daily emissions ranging from 10 to 30 kg of E2 and from 20 to 80 kg of E1 are produced by the combination of these two animal types in the United States, with roughly equal total amounts coming from each. These mass emission rates are well over an order of magnitude greater than the estimated mass flow of estrogen from wastewater treatment plants in the United States (7). Although the large land areas over which manures are distributed mitigates their potentially serious environ-
mental impact, care must be taken to monitor these compounds in catchments heavily populated by farm animals and to develop management strategies that minimize risk to the environment, especially in light of increasing global demand for livestock products (25).
Acknowledgments The authors gratefully thank David Smith and Craig Wagoner for their assistance in constructing and testing the remotely piloted sample collection boat; Michael Newman for his statistical expertise; Galina Melnichenko and Lara Moody for their assistance with the non-estrogen water quality analyses; and Renea Dyer, Henry Dowlen, Jeff Dowlen, John Goddard, Dr. Bruce Greene, Cortis Jarvis, Gordon Jones, Jeff Lannom, Dean Northcut, David Qualls, Dr. Bobby Simpson, Bob Sliger, and Ronnie Wyatt for their gracious assistance with sampling. This material is based upon work supported by the Cooperative State Research, Education and Extension Service, U.S. Department of Agriculture, under Agreement 99-35102-8179. Additional support for the work was provided by the Tennessee Agricultural Experiment Station, under Project TN146/S-1000, and by the Waste Management Research and Education Institute at The University of Tennessee.
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Received for review November 26, 2003. Revised manuscript received March 22, 2004. Accepted April 6, 2004. ES0353208
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