Animal Waste Lagoon Water Quality Study
A Research Report by Kansas State University
June 23, 1999
Principal Investigators
J.M. Ham, Department of Agronomy L.N. Reddi, Department of Civil Engineering C.W. Rice, Department of Agronomy
Submitted in partial fulfillment of a contract between Kansas State University and the Kansas Water Office, Topeka, KS. Executive Summary
Animal Waste Lagoon Water Quality Study
J.M. Ham, L.M. Reddi, and C.W. Rice Kansas State University
Report Period: May, 1998 to June, 1999
Anaerobic lagoons are used to collect, treat, and store waste at many concentrated animal operations (CAOs) in Kansas. Lagoons contain nutrients, salts, and other soluble chemicals that, in many cases, are eventually applied to crops as fertilizer. While waste is stored and treated in the lagoons, seepage losses from the sides and bottom of the containment could potentially affect soil and ground water quality. Of primary concern, is possible movement of nitrate-nitrogen into aquifers used to supply drinking water. Bacteria, which also are present in the waste, are another potential source for contamination. A comprehensive environmental assessment of lagoons requires three focus areas: (a) toxicity – what are the constituents in the lagoon waste that pose a threat to water quality and public health? (b) input loading – at what rate does waste seep from a lagoon under field conditions? and (c) aquifer vulnerability – how do soil properties, geology, and water table depth affect the risk of waste movement from the lagoon to the ground water? Researchers at Kansas State University (KSU), in cooperation with the Kansas Water Office, are conducting research to examine these issues. The long- term goal is to determine the best management practices for siting, building, and operating lagoons to adequately protect ground water resources near CAOs. The KSU research project includes: (1) a survey of lagoon effluent chemistry at swine production facilities, cattle feedlots, and dairies; (2) refinement of new measurement techniques to measure whole-lagoon seepage under field conditions; (3) measurement of lagoon seepage and subsurface nitrogen movement at commercial swine and cattle CAOs; (4) laboratory studies of permeability and contaminant transport in soils used to construct lagoon liners; and (5) preliminary computer modeling of water and waste movement in soils beneath lagoons. This report summarizes current research findings in these areas. However, new issues continue to arise as more data becomes available. Thus, this report documents the state of an ongoing project, and certain topics will require additional research before firm conclusions can be reached. 1. Survey of Lagoon Effluent Chemistry. Samples of lagoon effluent were collected from five swine-waste lagoons and four cattle-feedlot runoff lagoons across Kansas (Appendix A). Samples were sometimes collected several times throughout the year to examine seasonal trends. Analysis included + twenty-five chemical and physical characteristics. Ammonium-nitrogen (NH4 -N) accounted for over 99 % of the soluble nitrogen and averaged 673 mg/L (ppm) at swine waste lagoons and 98 mg/L (ppm) at the cattle sites. Ammonium-N typically ranged from 550 to 900 mg/L (ppm) at swine sites and from 20 to 200 mg/L (ppm) at cattle feedlots. The highest ammonium-N concentration, 2000 mg/L (ppm), was observed at swine site in the first stage basin of a two-stage lagoon system (concentration was much lower in the second stage lagoon). Nitrate concentrations were less than 3 mg/L (ppm) at all locations. Total phosphorous averaged 45 mg/L (ppm) across all samples and was similar at the cattle and swine lagoons. On average, sodium was 148 mg/L (ppm) at the cattle feedlots and 270 mg/L (ppm) at the swine sites. Chloride was 275 and 569 mg/L (ppm) at the swine and cattle sites, respectively. Concentrations of nutrients did not vary substantially with depth in the liquid zone above the bottom-sludge layer. However, the organic sludge in the bottom of lagoons did contain higher concentrations of phosphorus. In most cases, strong seasonal patterns in waste chemistry were not evident. At some swine sites, ammonium-N in spring tended to be about 200 mg/L (ppm) higher than that observed in late fall. Results show that waste chemistry is species dependent, with nitrogen concentrations at swine sites being about six times higher than those at cattle feedlots. Conversely, chloride tended to be higher in cattle-feedlot runoff lagoons. The design and management of the waste treatment system (e.g., single-stage vs. multistage lagoons, lagoon volume vs. size of runoff watershed) also affected waste chemistry. The large site-to-site variation in chemical concentrations could affect the risk of ground water contamination and influence decisions regarding the land application of waste. 1 2. Refinement of New Measurement Techniques. Tests were conducted to verify the accuracy of field techniques for measuring whole-lagoon seepage using water balance methods (Chapter 1). Seepage was calculated as the difference between waste level changes (depth) and evaporation when all other lagoon inflows and outflows were precluded. Precision water level recorders, evaporation pans, floating meteorological buoys, and evaporation models were developed and tested to measure the water balance. Results showed that seepage could be measured to within ±0.5 mm/day (0.02 inch/day) over a brief study periods (5 to 10 days) when the evaporation was less than 6 mm/day (0.23 inch/day) During the winter months, when evaporation was small, seepage was estimated to within 0.2 mm/day (0.01 inch/day). Data show that much can be learned about the performance of a lagoon by simply measuring changes in depth over time during the winter. 3. Measurement of Lagoon Seepage and Subsurface Nitrogen Movement. Whole-lagoon seepage rates were measured from seven swine-waste lagoons and two cattle-feedlot runoff collection lagoons (Chapter 2). The earthen lagoons ranged in size from 0.2 to 2.5 ha (0.1 to 5.5 acres) and had waste depths between 1.5 and 6 m (5 to 18 ft.) Seven of the lagoons had waste depths in excess of 5 m (16 ft.). Most lagoons had compacted soil liners between 0.3 and 0.5 m (12 and 24 inch). The average seepage rate from the lagoons was 1.2 mm/day, or 0.05 inch/day (approx. 1/20 inch/day). Among lagoons tested, seepage ranged from 0.2 to 2.4 mm/day (Chapter 3). At some locations, seepage results were combined with data on lagoon geometry and construction methods to estimate the in-situ permeability of the liner. In lagoons built with silt loam liners (no bentonite), permeability's on a whole- lagoon basis were about five times less than those measured from soil cores collected prior to the addition of waste. Results imply that permeability was reduced by organic sludge on the bottom of the lagoons. Field measurements showed that the organic sludge layer was 0.38 m (15 inches) thick in a four-year-old, swine-waste lagoon. Despite the low rates of seepage, calculations showed that subsurface ammonium-N losses from the bottom and sides of swine-waste lagoons could exceed 3000 kg/ha·yr (2,640 lbs./acre·yr) (Chapters 2 and 3). Over twenty years of operation, nitrogen losses at a 2-ha (5-acre) swine-waste lagoon could possibly exceed 110,000 kg (250,000 lbs.) Seepage losses of ammonium-N from cattle feedlot lagoons are much lower because the soluble nitrogen in the effluent is less concentrated. Soil cores were collected in a 6-m zone (20 ft.) beneath an eleven-year-old cattle feedlot lagoon that had been emptied, dried, and cleaned (i.e., sludge removed). Ammonium-N concentrations were near 400 mg/kg, or ppm, near the lagoon “floor” and then deceased rapidly with depth (Chapter 2). About 90 % of the nitrogen found beneath the lagoon was within 3.6 m (12 ft.) of the lagoon. No nitrate-nitrogen was found in any of the soil samples. Data show that ammonium-N, a positively charged ion, was being adsorbed by negatively charged soil particles (i.e., clay minerals). Soils with a large cation exchange capacity, (CEC, a measure of soil ion adsorptive capacity), can retard the movement of ammonium-N and decrease the risk of ground water contamination. However, ammonium-N is not stable and could convert to nitrate, especially if a lagoon is emptied and dried following abandonment or closure. Nitrate is a very mobile in the soil and could potentially move to deeper depths (toward ground water), especially in regions with high rainfall. Additional research is needed to determine the long-term fate of ammonium-N adsorbed by soil directly beneath lagoons. Best management practices should be developed for remediation and closure of lagoon sites. 4 and 5. Laboratory Studies of Soil Permeability and Computer Modeling. Laboratory and modeling studies were conducted to examine the leachability characteristics of compacted soil samples specific to Kansas, and to assess how the properties of a compacted soil liner may affect the movement of ammonium-N in waste seeping from earthen lagoons. Soils were compacted in permeameters in the laboratory and exposed to a lagoon effluent for 2 to 5 months in a leaching experiment (Chapter 4). The waste used was liquid from the upper portion of the lagoon, not the organic bottom-sludge. In some cases, the seepage rate through the soil samples decreased slightly over time. However, biological clogging from microbial exudates did not appear to be a significant factor affecting soil permeability. Data support the premise that the waste-induced reductions in permeability, often observed in these studies, is caused by physical clogging of the soil pores rather than by microbial action. Seepage may be more prevalent along the side embankments of lagoons where organic sludge may not accumulate.
2 Analytical and numerical simulations were used to simulate ammonium-N movement in field- scale liners and to estimate the upper bounds for travel times and end concentrations in the underlying soils (Chapter 5). Results showed that liner thickness and CEC had drastic effects on how fast nitrogen moved through the liner. For example, in one simulation increasing liner thickness from 0.15 to 0.9 m (0.5 to 3 ft.) caused a nine fold reduction in the concentration of ammonium-N exiting the bottom the soil liner and increased the time required for ammonium-N to penetrate the liner from 5 to 65 years. The extent of possible retardation, decay, and saturation levels of ammonium-N, observed in this study, suggests that properties such as CEC and microbial uptake, which influence mass transport of ammonium-N, should be given important consideration in designing liners for animal waste lagoons. Some of the important results and implications of the research project are: · The concentration of ammonium-N was six times higher in swine-waste lagoons than in cattle-feedlot runoff lagoons. Thus, the rate of input loading and the potential for subsurface nitrogen contamination is species dependent. Conversely, phosphorous, the element that often affects the rate at which effluent can be land applied, was the same at cattle and swine facilities. · Field, laboratory, and simulation studies suggest that most animal-waste lagoons in Kansas will have seepage rates less than 2.5 mm/day (1/10 inch/day), well below the historical deign standard of 1/4 inch/day used by the Kansas Dept. of Health and Environment. Lagoons that have engineered soil liners greater than 0.46 m thick (18 inch) often seep at rates near 1 mm/day (less than 1/20th inch/day) even when waste depth exceeds 5 m (16 ft.). However, even at low seepage rates, data show that significant amounts of ammonium-N can be deposited and stored in the soil beneath the lagoons. When lagoons are closed, emptied, and dried, soil-bound ammonium-N could convert to nitrate and more readily move towards the ground water. At many locations, the period following lagoon closure may pose the greatest risk of ground water contamination. Best management practices should be developed for lagoon closure and site remediation. · The rate at which nitrogen is adsorbed and retarded beneath a lagoon is highly dependent on the soil cation exchange capacity (CEC). The CEC of the compacted liner and the underlying native soil should be considered when siting and designing a waste lagoon. Increasing the thickness of soil liners with high-CEC clays could help trap ammonium-N, prevent its downward migration, and simplify closure and remediation procedures (by trapping contaminants close to the surface). More research is needed on the fate and transport of chemicals and bacteria that penetrate the liner. · Future research should focus on chemical and physical factors in the soil that affect transformation and movement of chemicals and microbes between the bottom of the lagoon and the water table. This research could ultimately lead to models of the lagoon system that consider toxicity, input loading, and aquifer vulnerability. Because of the tremendous variation in the physical environment (e.g., depth to water table, soils, climate, etc.) and types of livestock operations, an accurate risk assessment of lagoon use in Kansas must be site and species specific.
Respectfully Submitted,
Jay M. Ham, Ph.D. June 23, 1999
3 Table of Contents
Executive Summary
Table of Contents
Preface
Chapter 1 Measuring Evaporation and Seepage Losses from Lagoons Used to Contain Animal Waste J.M. Ham
Chapter 2 Field Evaluation of Animal-Waste Lagoons: Seepage Rates and Subsurface Nitrogen Transport J.M. Ham and T.M. DeSutter
Chapter 3 Seepage Losses and Nitrogen Export From Swine-Waste Lagoons: A Water Balance Study J.M. Ham and T.M. DeSutter
Chapter 4 Liner Performance – Experimental Investigations L.N. Reddi, H. Davalos, and M. Bonala
Chapter 5 Liner Performance – Modeling Investigations L.N. Reddi, H. Davalos, and M. Bonala
Appendix A Survey of Waste Chemistry in Anaerobic Lagoons at Swine Production Facilities and Cattle Feedlots. T.M. DeSutter, J.M. Ham, and T.P. Trooien
Appendix B References On Animal Waste Lagoons and Related Topics T.M. DeSutter and J.M. Ham
4 Preface
This report was completed in partial fulfillment of a contract between Kansas State University and the Kansas Water Office. The current report covers the period between May 1998 and June 1999. A second year of research has been planned and will be documented in a report scheduled for release in June 2000. Thus, some of the research topics examined in this report will be updated and refined as new data become available. Results from this project build on previous work completed in cooperation with the Kansas Department of Health and the Environment (KDHE). Results from this early work were presented in a report entitled “Evaluation of Lagoons for the Containment of Animal Waste” submitted to KDHE in April 1998. Some of the research findings in Chapters 1, 2, 4 and 5 of this report have been accepted for publication in peer-reviewed scientific journals (Journal of Environmental Quality, and Transactions of Amer. Soc. Agric. Engineers). Some portions of the report have been submitted for publication to Amer. Soc. Civil Engineers Journal of Geotechnical and Geoenvironmental Engineering. Because each chapter was completed separately, there may be small chapter-to-chapter variations in certain calculated parameters. Calculations were refined as improved methods and new data became available.
5 Publication Version (SW-3416)
CHAPTER 1
Measuring Evaporation and Seepage Losses from
Lagoons Used to Contain Animal Waste
Jay M. Ham
May 24, 1999
Article was submitted for publication in February 1999 and approved for publication by the Soil and Water Div. of ASAE in May 1999.
Mention of product name is for information only and does not imply endorsement.
Contribution no. 99-326-J from the Kansas Agric. Exp. Stn., Manhattan, KS.
The author is Jay M. Ham, ASAE Member, Associate Professor, Dept. of Agronomy, Kansas State University, Manhattan, KS 66506.; e-mail [email protected]
1.1 ABSTRACT. Seepage (S) from animal-waste lagoons was estimated using a water balance approach by measuring changes in waste level (i.e., depth) (DD) and evaporation (E) over brief periods (e.g., 6 days) when all other inflow and outflow were precluded. Data were collected at commercial swine and cattle feedlots in southwestern Kansas. Precision waste level recorders, floating evaporation pans, and meteorological models were used to measure each lagoon’s water balance. Different strategies for calculating E and S were compared. Initial work at a 2.5-ha plastic-lined lagoon (S = 0, E » 5.1 mm d-1) showed that E over 6- to 11-day periods could be measured to within ±0.5 mm d-1 with floating evaporation pans using a pan coefficient of 0.81. A bulk-transfer evaporation model, which incorporated real-time measurements of lagoon surface temperature, predicted E to within 6 % when using a transfer coefficient of 2.8x10-3. Evaporation models that did not include surface temperature resulted in significant errors (e.g., >50 %) under certain environmental conditions. The water balance of a soil-lined, cattle-feedlot lagoon over an 11-day period was: DD=2.1; E=1.9, and S= 0.2, all in mm d-1. Additional work over a 6-day period at a soil-lined, swine-waste lagoon resulted in a water balance of: DD = 5.4; E=4.5, and S = 0.9, all in mm d-1. Data suggest that S from lagoons can be determined to within ± 0.5 mm d-1 by making precision water balance measurements over short periods (5 to 10 days), if E is less than 6 mm d-1. Keywords. Feedlot, Instrumentation, Pan Evaporation, Swine, Water Balance.
1.2 Introduction Earthen-lined lagoons have been used extensively since the 1960s to collect, treat, and handle waste from concentrated animal operations (CAOs) (Humenik et al., 1980). In 1992, there were more than 6,600 CAOs in the United States that had more than 1000 animal units, and most of these facilities used lagoons as part of their waste management plan (Kosco and Hall, 1999). The liquid waste in lagoons contains high concentrations of nutrients and salts and, in many cases, is applied to farmland as fertilizer. However, while waste is being stored in the lagoon, seepage losses from the sides and bottom of the basin can impact soil and groundwater quality (Miller et al., 1976; Culley and Phillips, 1989; Huffman and Westerman, 1995; Westerman et al., 1995). Unfortunately, measuring or predicting the rate at which solutes are transported from a lagoon is difficult. Attempts to predict seepage through field-installed earthen liners using laboratory measurements of soil permeability have been disappointing (Daniel, 1984). A host of factors, such as variation in construction methods, weathering of side embankments, and organic sludge (manure) deposits all impact the long-term, in-situ seepage rate (Hills, 1976; Barrington and Madramootoo, 1989; McCurdy and McSweeney, 1993). A review of the literature on this topic is provided by Ham and DeSutter (1999). Although solute transport from a lagoon is a complex process, knowledge of seepage is necessary to calculate chemical flux and nutrient loading. These parameters, coupled with the effect of aquifer vulnerability, are crucial for assessing the site-specific impact of a lagoon on local groundwater quality (Nolan et al., 1997). Additionally, many regulatory agencies have mandated that lagoons must be designed to limit seepage to within a specified range. Thus, improved techniques for the rapid determination of whole-lagoon seepage would benefit both academic and practical aspects of lagoon design and use. Several researchers have estimated seepage from existing animal-waste lagoons using water balance approaches (Hart and Turner, 1965; Davis et al., 1973; Robinson, 1973; Clark, 1975). Typically, seepage was calculated as the difference between changes in waste level and evaporation when waste inputs and outputs were precluded or quantified. Much of this previous work was conducted on miniature, pilot-scale lagoons and may not be representative of the large lagoons used at many modern CAOs. Furthermore, the resolution of the techniques used in many earlier studies was such that water balances had to be conducted for long periods (>30 days) to obtain a useful estimate of seepage. Unfortunately, withholding waste inputs for extended periods is not logistically feasible at many commercial CAOs, because waste must be flushed from the barns (e.g., swine and dairy) on a routine schedule. Recently, Ham and DeSutter (1999) estimated the water balances of three commercial swine-waste lagoons by measuring depth changes and evaporation over measurement cycles as brief as 5 days. Combining these data with measurements of nutrient concentrations in the effluent, they were able to calculate subsurface nutrient export from each facility. Also, seepage results were coupled with data on liner thickness and basin geometry to calculate the apparent in-situ permeability of the compacted soil liner. The objective of this research was to evaluate water-balance strategies for determining whole-lagoon seepage using data collected over brief measurement periods (e.g., 6 days). In a water balance approach, seepage is calculated as a residual, and the adequacy of the method often hinges on the accuracy and resolution of the water level and evaporation measurements. Although there are many ways to measure water level, determining evaporation from small isolated water bodies (i.e., lagoons) is challenging from both theoretical and instrumentation standpoints (Webster and Sherman, 1995). Thus, this study emphasized the measurement of
1.3 evaporation from lagoons, exploring the use of custom-built floating evaporation pans and meteorological models. Data from plastic- and soil-lined animal waste lagoons were used to quantify the expected resolution to which seepage can be determined from a short-term water balance experiment.
METHODS AND EQUATIONS Description of the Lagoons The three lagoons used for the study, hereafter referred to as A, B, and C, were located within 40 km of Ulysses, KS (table 1). Lagoon A , built in 1996, serviced a swine finishing unit and had a 1-mm thick plastic liner over 0.3 m of compacted soil. Lagoon B, located within 3 km of Lagoon A and built in 1995, also serviced a swine finishing unit but had a 0.46-m-thick compacted soil liner. Compaction was performed in 0.15-m lifts by means of six passes with a sheepsfoot roller. Bentonite (9.8 kg m-2) was mixed into the initial 0.15-m compacted layer, followed by 0.3 m of compacted silt loam soil obtained from a barrow area. Lagoon C collected runoff from a cattle feedlot and had a compacted soil liner between 0.46 and 0.6 m thick. Like the swine lagoons, compaction was performed in multiple lifts with a sheepsfoot roller. Lagoon C was 11 years old, but it had been cleaned and a new compacted liner had been constructed three months prior to water balance tests. The capacity (i.e., depth) of the lagoon was increased as part of the cleaning process, so any residual effects on soil hydraulic properties (i.e., sludge effects) probably were negated. The coefficients of permeability for the compacted soil liners at Lagoons B and C were evaluated from cores collected from the bottom and sides of each basin. Six cores from Lagoon B and four cores from Lagoon C were obtained prior to the addition of waste. Permeability was measured using an ELE flexible wall permeameter (Soiltest Products Division, ELE International Inc., Lake Bluff, IL) following ASTM Designation D 5084-90 (1991) (table 1). Dry densities of the soil liners, as determined from the cores, averaged 1887 and 1670 kg m-3 at Lagoons B and C, respectively.
Water Balance Seepage and evaporation from each lagoon was determined by quantifying the water balance,
Qin + P + DD = E + S + Qout (1) where Qin is waste inflow; P is precipitation, E is evaporation; S is seepage loss; Qout is waste outflow (i.e., pumping); and DD is the rate change in storage, all in mm d-1. In equation (1), all variables are considered positive with the exception of DD , which is negative when waste levels are increasing and positive when declining. During periods used for analysis, waste inflow and outflow were stopped and precipitation did not occur (Qin = Qout = P = 0); thus, S from the earthen lagoons (B and C) was calculated as the difference between E and DD (S = DD - E). At the plastic-lined lagoon (Lagoon A), S was assumed to be negligible, so DD was equal to E. Therefore, work at the plastic-lined Lagoon A was used to test methods for measuring E, and work at soil-lined Lagoons B and C was used to evaluate the combined effect of DD and E on the calculation of S.
Meteorological Instrumentation and Floating Evaporation Pans Much of the instrumentation used in these experiments was similar to that described by Ham and DeSutter (1999). An abbreviated description is provided here. Evaporation in each lagoon was measured using two floating evaporation pans (figure 1). An instrument raft, 1.5 m
1.4 wide and 2.7 m long, was built from two low-profile flotation panels (Superdeck Marketing, Minneapolis, MN) held together by aluminum channel. An area between the flotation panels supported a square evaporation pan (1.09 m by 1.09 m by 0.591 m) made from 0.8-mm-thick galvanized sheet metal or aluminum. The floating pan had the same surface area as a Class-A evaporation pan. When installed, the pan held 0.23 m of waste with a 0.15-m rim extending above the waterline. Waste levels in the pans were monitored continuously using a float-based detector housed in a 0.15-m diameter wet well mounted in the corner of the pan (see figure 2 in Ham and DeSutter, 1999 for a mechanical drawing of the recorder). A linear displacement transducer (LX-PA25, Unimeasure Inc., Corvallis, OR), consisting of a retractable leader and potentiometer, was used to sense changes in float height. A vibrator was attached permanently to the LX-PA25 and activated automatically every 30 min to overcome static friction in the reel mechanism. Water pumps were mounted inside and outside the pans so waste could be added or removed by remote control. Four 0.3-m diameter wheels were mounted on the underside of the floating platform so it could be rolled up or down the lagoon’s side embankment to facilitate installation and retrieval. Waste-level changes, DD, in each lagoon were measured with a float-based recorder identical to the one used in the pans. For work at Lagoon C, a Unimeasure model LX-PA50 was used to measure DD. The recorders were attached to permanent staff gauges already present in each lagoon (usually a vertical steel pipe set in concrete). Meteorological conditions 1 m above the waste surface were monitored using sensors attached to a small mast on one of the floating platforms. Instrumentation included: a three-cup anemometer and wind vane (Wind Sentry, RM Young, Traverse City, MI); a shielded, air temperature and humidity probe (CS500 or HMP35C, Campbell Sci. Inc., Logan, UT); and a pyranometer (LI200, Li-Cor Inc., Lincoln, NE). Water temperatures 0.1 m below the surface were measured inside and outside the evaporation pan with thermistors (107 Probe, Campbell Sci. Inc). Water surface temperatures inside and outside one of the evaporation pans were measured with infrared transducers (IRT, model 4000A, Everest Interscience, Logan, UT). All IRTs were calibrated using the method of Sadler and Van Bavel (1982) and corrected for surface emissivity and downwelling atmospheric radiation using the approach of Ham and Senock (1994). At Lagoons A and C, a single IRT was mounted on an actuator that alternately aimed the transducer at waste inside and then outside one of the evaporation pans on a one minute exchange cycle. Using this approach, the pan’s effect on waste surface temperature could be measured without instrument bias. Sensors on each floating platform were sampled every 10 s, and data were stored as 30-min averages using a datalogger (CR10X, Campbell Sci. Inc.). The datalogger was housed in a weatherproof enclosure on the flotation deck and was powered by a 12-volt battery connected to a 10- or 20-W solar panel. A cellular telephone and modem were used to transfer data from each platform to laboratories in Manhattan, KS. The telephone interface also allowed remote control of the water pumps, which were used to keep the waste surface in the pan to within 25 mm of the lagoon’s surface. The resolution of the water level measurements in the pans and at the staff gauge was 0.16 mm when sampled by the CR10X, except at Lagoon C where the resolution of DD was 0.32 mm. Two floating evaporation pans, the lagoon waste level recorder, and the associated meteorological equipment were deployed at each site. The positions of the floating pans were determined by mapping the lagoon surface into two zones of equal area and then placing a pan in the center of each zone. The floating pans were tethered to the side embankments using steel cables and were at least 50 m from the shoreline in all directions. Waste inputs to the lagoons
1.5 were stopped periodically for 6 to 11 day periods by capping the waste inlet pipes at the swine sites or by damming the lagoon inlet channel at the cattle feedlot.
Evaporation Models Evaporation from the lagoons also was estimated using four meteorological models. An approach that has been used widely to estimate E from open water is the bulk aerodynamic transfer model (hereafter referred to as BT model) * E = CeU r r(qs - qa ) (2) -2 -1 -3 * where E is evaporation (kg m s ); r is air density (kg m ); qs is the saturated specific humidity -1 -1 at water surface temperature (kg kg ), qa is the specific humidity of the air (kg kg ), Ur is the -1 average wind speed at some reference height (m s ), and Ce is the bulk aerodynamic transfer coefficient for vapor transport (dimensionless). An approximation of equation 2 expressed in terms of vapor pressure is
0.622 * E = (es - ea ) U r Ce (3) Rd Ts * where es is the saturation vapor pressure at the temperature of the water surface (Pa), ea is the -1 -1 vapor pressure of the air (Pa), Rd is the gas constant (287.04 J kg K ), Ts is the temperature of the surface (K) and 0.622 is the ratio of the molecular weights of water and dry air. Equations 2 and 3 have been used extensively to estimate E from lakes and ponds (Lakshman, 1972; Quinn 1979; Bill et al., 1980; White and Denmead, 1989). Field studies are often conducted to * determine Ce empirically by measuring (es -ea) and Ur under conditions where E is measured by -3 -3 some other method. Values of Ce range from 1.0x10 to 2.0x10 when Ur and ea are measured 2 to 3 m above the surface (Brutsaert, 1982). However, Ce is dependent on measurement height of Ur and ea, and increases as the reference height decreases. Penman (1948) introduced a combination model for predicting E from open water that avoided the need for Ts D Rn + G g æ 0.622 ö æ ö ç ÷ * E = ç ÷ + ç ÷(ea - ea )U r Ce (4) D + g è L ø D + g è RdTa ø where D is the slope of the saturation vapor pressure curve (Pa K-1), g is the psychrometric constant (Pa K-1), Rn is net radiation (W m-2), G is conduction in the water (W m-2), L is the -1 * latent heat of vaporization (J kg ), Ta is air temperature (K), and ea is the saturation vapor pressure at Ta (Pa). The sign convention for Rn and G is given in equation 8. The advantage of the Penman model is that it can be approximated using basic meteorological data collected in the boundary layer of the surface of interest. However, the determination of Rn+G for shallow water bodies can be difficult. For the lagoons, Rn was approximated as Rn = 0.86Rs - 49 (5) where Rs is global shortwave irradiance (W m-2). This relationship was determined empirically from simultaneous but independent measurements of Rs and Rn over an animal waste lagoon (Ham et al., 1998). The net change in heat storage in the lagoon waste was assumed to be negligible between the start and end of a multiple-day measurement cycle. Thus, G was set to zero in the computations, negating the effect of diurnal patterns in G, but hopefully still preserving the long-term energy balance. Priestley and Taylor (1972) proposed a model for E from extended wet surfaces where the internal boundary layer reached equilibrium with the surface
1.6 D æ Rn + G ö E = a ç ÷ (6) D + g è L ø where a is the empirical Priestley-Taylor coefficient (1.26). Like the Penman equation, Rn was approximated with equation 5 and G was assumed to be negligible over multi-day periods. Even though equation 6 was developed for expansive surfaces where no advection occurs, Stewart and Rouse (1976) used equation 6 to approximate E from small ponds. The Priestley-Taylor model only requires a measurement of Rs and Ta. The last model tested was that of DeBruin (1978) which is a hybrid of the Penman and Priestley-Taylor equations a æ D öæ 0.622 ö ç ÷ * E = ç ÷ç ÷(ea - ea )U r Ce (7) 1-a è D + g øè RdTa ø This approach eliminates the need for the troublesome determination of Rn+G and requires measurements of wind speed, temperature, and humidity. However, equation 7 is sensitive to a, which has been shown to vary seasonally. Evaporation was calculated with each model using environmental data obtained from the floating instrument rafts. When the bulk transfer and DeBruin models (equations 3 and 7) were used, E was calculated every 30 minutes using the short-time-interval meteorological data. Results were then summed over 24 hr to estimate daily E. However, when using the Penman and Priestley-Taylor models (equations 4 and 6), E was calculated on a daily basis using the 24-hr * average of Ta, U, and ea -ea in combination with total Rn accumulated over the same period.
Surface Energy Balance For evaluation of E, it is helpful to quantify the surface energy balance, often expressed as Rn + G + LE + H = 0 (8) where H is sensible heat flux (W m-2). As shown here, all fluxes toward the surface are considered positive whereas all those away from the surface are negative. For example, LE is negative (unless condensation occurs), and G is positive when conduction is upwards toward the surface (e.g., water cooling at night) and negative when conduction is downward (e.g., daytime water heating). The magnitude of H can be modeled with a bulk transfer approach like that used for water vapor in equation (2).
H = rc p (Ta - Ts )U r Ch (9) -1 -1 where cp is the specific heat of air (J kg K ) and Ch is the bulk aerodynamic transfer coefficient for heat transfer. To solve equation 9 at the lagoons, Ch was assumed to be equal to Ce, which is a common procedure in these types of studies (Quinn, 1979; Bill et al., 1980). Once H was computed from equation 9 and Rn and E were calculated using equations 3 and 5, G was calculated as a residual using equation 8. * Equations 2 through 9 include several variables ( e , ea, D, g, r, cp, L) that must be calculated from real-time environmental data. Appendix 1 provides suitable equations for calculating these variables using computerized, data acquisition equipment.
RESULTS AND DISCUSSION Data were collected at the plastic-lined Lagoon A to evaluate the performance of the floating evaporation pans and the evaporation models. Assuming that S was zero, actual E from the whole lagoon was the rate change in lagoon depth (DD) as measured by the waste-level
1.7 recorder on the staff gauge. The waste level recorders were rigorously tested by Ham and DeSutter (1999) and found to be extremely accurate and stable (e.g., ± 0.16 mm). However, the output from the recorders at any given time can be biased by short-term changes in wind speed and direction. Wind drag can cause water levels to rise near the downwind embankment and fall on the upwind side of the lagoon. Sample calculations using the approach of White and Denmead (1989) indicated that errors in DD caused by wind drag would be less than 1 mm. Nevertheless, comparisons between DD , evaporation from the floating pans, and output from the models were made on a cumulative basis or over multi-day periods to minimize bias and discretization error (i.e., effect of instantaneous sensor resolution). Table 2 shows environmental conditions and the cumulative depth changes (or E) at Lagoon A for 9- and 11-day measurement periods in October 1998.
Performance of the Floating Evaporation Pans Table 3 shows E from Lagoon A and the two floating evaporation pans as totaled over six, 3-day periods. The period between day of year (DOY) 284 and 285 was omitted because high wind gusts (>15 m s-1) may have caused some incursion into the pans from large waves. Evaporation in the pans was significantly greater than that in the lagoon. The pan coefficient (Kp), defined as the ratio of lagoon E and pan E (Kp=Elagoon/Epan), ranged from 0.69 to 0.94 during the study. The lowest Kp occurred between DOY 258 and 260, which was a period of high solar irradiance and low wind speeds. Conversely, the largest Kp occurred when wind speeds where higher and global irradiance was slightly lower (DOY 286-288). The coefficients for the two pans were similar in any given period, and both pans had an overall average Kp of 0.81. Figure 2 shows cumulative E from the lagoon and the two pans during the two measurement cycles at Lagoon A. When depth changes in the pans were multiplied by a Kp of 0.81, the pan estimates of E were in excellent agreement with the whole-lagoon measurements. Cumulative E estimated by the pans (Pan Result x Kp) was typically within 5 % of that measured for the lagoon, especially when summed over periods greater than 5 days. The finding that Kp was less than unity but varied according to environmental conditions was consistent with other studies where on-shore, Class-A evaporation pans were compared to actual lake evaporation (Brutsaert, 1982). However, pans floating in a water body should experience a more natural microclimate and have a Kp closer to unity. In this study, the temperature and humidity of air flowing over the waste in the floating pans were essentially the same as those for air over the lagoon. Furthermore, the pan being in direct contact with the lagoon waste should have helped keep waste temperature (and vapor pressure) in the pan closer to lagoon temperature. Figure 3 shows the temperature regime during the first measurement period at Lagoon A. Daytime surface temperatures of the waste in the evaporation pans were greater than those in the lagoon, sometimes by as much as 4 C (figure 3b). The exception was between DOY 264 and 265, when air temperatures dropped as a cold front passed through and conditions were overcast. At night, the pan temperatures were often 2 C less than the lagoon. Pan and lagoon waste water temperatures at 10 cm had a similar pattern (not shown). Divergence in pan and lagoon temperatures was probably caused by a combination of radiant heating and convection on the 0.15-m sidewall of the pan that extended above the water line. Furthermore, the waste water in the pan it did not experience the natural mixing caused by wave action and thermal gradients. Wind speed, which affected convection and surface mixing, was correlated -1 with Kp. For example, between DOY 281 and 288, the average wind speed was 4.7 m s and Kp averaged 0.91. Conversely, between DOY 258 and 260 and between DOY 278 and 280, the
1.8 -1 average wind speed was 2.3 m s and Kp averaged 0.73. A mixture of radiation, convection, and surface mixing effects probably accounted for the variation in Kp observed in table 3. Regardless, because evaporation is strongly governed by the vapor pressure at the surface (equation 2), the elevated pan temperatures during the day caused overall pan evaporation to be greater than lagoon evaporation.
Performance of the Evaporation Models The first step in applying the evaporation models was to empirically determine Ce by * solving equation 3 using measured values of E, Ur, and (e s - ea) from the plastic-lined Lagoon A. -3 Least squares analysis showed that the best choices for Ce were 2.81x10 between DOY 258-267 and 2.83x10-3 between DOY 283-289. These values are higher than reported in similar experiments over lakes (Brutsaert, 1982). However, Ur and ea at the lagoons were measured much closer to the surface than in previous studies (1 m as compared to 2 or 3 m); thus, an increase in Ce would be expected. Furthermore, equation 3 as well as all other methods used to estimate E, did not account for horizontal advection which can increase E from fetch-limited water bodies like lagoons (Itier et al., 1994; Webster and Sherman, 1995). This effect may have -3 amplified Ce. A constant Ce of 2.81x10 was used for all calculations involving equations 3, 4, 6, and 7. Table 4 compares actual E from the plastic-lined Lagoon A with that predicted using four different meteorological models. Data between DOY 278 and 282 were not used because the IRTs were accidentally turned off disallowing the evaluation of equation 3. Of the models tested, the BT model (equation 3) provided the best estimate of E, having a maximum error of ±0.6 mm d-1 within any 3-day period and an overall error of only 0.1 mm d-1. On average, the Penman model (equation 4) also was accurate to within 0.1 mm d-1; however, errors of –3.6 and 2.1 mm d-1 occurred for the measurement periods starting on DOY 264 and 286, respectively. The Priestley-Taylor model (equation 6) tended to underestimate E on most days. The DeBruin model (equation 7) produced mixed results but severely overestimated E between DOY 283 and 288, which was a period of high wind speeds (table 2). Because of this undesirable result, further analysis of the DeBruin model is not presented. Performance of the other models was evaluated further by examining the energy balance of Lagoon A (table 5). Large period-to-period variation occurred in the energy fluxes, especially in H and G, terms that were both sources and sinks of energy. Comparisons of the periods starting on DOY 261 and 264 were particularly interesting, because a transition occurred from warm clear conditions to cool cloudy weather. Average daily Rn decreased from 168 to 61 W m-2 as expected under cloudy skies; however, LE remained essentially constant. Results show that LE between DOY 264 and 266 was driven by massive upward conduction of the heat stored within the liquid waste (G=173.7). Figure 3a shows that the waste temperature was up to 10 C higher than the air temperature, which helped create the upward gradient. Both the Penman and Priestley-Taylor models severely underestimated E during this period as a result of the assumption that G was zero. Conversely, when a warming trend occurred, and G was downward (DOY 286-288), the Penman model overestimated E. Tables 4 and 5 show that energy-based models like the Penman and Priestley-Taylor may result in significant errors during short-term experiments unless G is measured directly. When a water balance is performed to determine S, cumulative E, not daily E, is most critical. Figure 4 shows cumulative E for the BT, Penman, and Priestley-Taylor models during both measurement cycles at Lagoon A. The BT model tracked the pattern of cumulative E with
1.9 remarkable accuracy. After at least 4 days into each measurement cycle, the BT model always predicted cumulative E to within 6 %. The Penman model underestimated E during the cloudy conditions occurring after DOY 265 (figure 4a) as mentioned earlier and overestimated E near the end of the second cycle (figure 4b). The Priestley-Taylor model consistently underestimated E, with the most severe errors occurring during cloudy weather. Data suggest that the BT model is the best choice for predicting E over short periods. Because equation 3 is driven by the vapor pressure difference between the surface and the air, it correctly accounts for the sometimes large differences between the air and lagoon temperatures. The disadvantage of the BT model is that results are sensitive to errors in the measurement of lagoon surface temperature, Ts. For example, E was recalculated using the data in figure 4 after assuming 1.0 C bias (overestimate) in the measurement of Ts. Results showed that the model overestimated cumulative E by 25 %. Given the complexity of calibrating and correcting an IRT, errors of this magnitude could occur routinely in the field. Another possible option would be to suspend a contact thermometer just below the lagoon surface (e.g., 20 mm) to approximate Ts (White and Denmead, 1989).
Estimating Seepage from Soil-Lined Lagoons Results from the plastic-lined Lagoon A indicated that the floating evaporation pans and the BT model were the best techniques for measuring E; however, both approaches were essentially calibrated to Lagoon A through the selection of Kp and Ce. The next step was to test the evaporation methods as components of a water balance experiment at two soil-lined lagoons. The water balance of Lagoon B is summarized in table 6 and figure 5. The two pans and the BT model produced almost identical estimates of E, which resulted in S values of 0.67, 0.93, and 0.74 mm d-1, respectively (table 6). The Penman and Priestley-Taylor models overestimated E, resulting in negative S. Figure 5 shows cumulative DD and E as computed by the various methods. Sample calculations showed that using the pans and the BT model would have produced similar estimates of S after 4 days into the measurement period. Some disparities occurred earlier in the measurement cycle, demonstrating how wind drag, sensor resolution, and measurement errors can bias results if the measurement period is too short. However, Ham and DeSutter (1999) did show that a reasonable approximation of S could be made in a single winter night under ideal weather conditions. At the cattle feedlot (Lagoon C), Pan 1 and the BT model results were in good agreement, resulting in calculated S values of about 0.2 mm d-1 (table 6, figure 6). However, E estimated from Pan 2 was larger, resulting in an S of zero. Mechanical problems with the float-recorder on DOY 326 may have affected Pan 2. Also, weather at Lagoon C was characterized by clear skies and low wind speeds, which were the same conditions that caused low Kp values at Lagoon A. Thus, Kp may have been closer to 0.73 during the test, rather that 0.81. Nevertheless, S was very small, and the two pans and the BT model produced results that agreed to within 0.2 mm d-1. The Penman model overestimated E, resulting in a negative S value of -0.52 mm d-1. The Priestley-Taylor model produced the lowest E estimate, which resulted in a S value of 0.61 mm d-1 (figure 5b, table 6). At all lagoons, the float-based water-level recorders performed extremely well and without failure. In many cases, measuring DD alone can indicate if a lagoon is performing within specification. For example, DD at Lagoon B, which was near capacity, was 5.4 mm d-1, which was less than the state-mandated guideline for S (i.e., 6.4 mm d-1). The rate change in depth at Lagoon C was only 2 mm d-1. Thus, much can be learned about the performance of lagoons by simply measuring DD during periods of low evaporative demand.
1.10 1.11 CONCLUSIONS Floating evaporation pans are a reliable method for estimating E from animal waste lagoons provided the appropriate value for Kp can be identified. In this study, a Kp of 0.81 produced good results in most cases. However, Kp was affected by environmental conditions and ranged between 0.69 and 0.93. Floating pans have the advantage of being adaptable to lagoons of different sizes and shapes and require no assumptions about aerodynamic conditions (e.g., Ce) and energy flux (e.g., G = 0). This could be important when the lagoon surface is several meters below the surface of the surrounding land. Floating pans have the disadvantage of being adversely affected by waves. At lagoons used by Ham and DeSutter (1999) and at Lagoon B in this study, the pans were flooded by waves when the average wind speed was greater than 10 m s-1 and there was more than 75 m of up-wind fetch available for waves to develop. When this occurred, the pans had to be pumped down to normal levels and the water balance restarted. Pans also tend to trap dust and small debris that can coat the water surface and alter evaporation. The BT model (equation 3) in combination with short-time interval environmental data was an excellent method for modeling E from lagoons. This approach performed well in all three -3 lagoons using a constant Ce of 2.81x10 . However, Ce can be affected by lagoon geometry, atmospheric stability, and other factors, so it cannot be assumed constant at all locations. On the other hand, because the BT model uses a measurement of Ts to directly measure the vapor pressure difference between the surface and the atmosphere, it properly accounts for the sometimes large temperature differences between the lagoon and the air. This proved to be its critical advantage over the other models tested. Unfortunately, infrared thermometry is not routine, and small errors in Ts can cause large errors in cumulative E. Also, floating scum occasionally develops on the lagoon surface and adversely affects the measurement of Ts, . The Penman model tended to overestimate E in most cases (e.g., Lagoon B), but underestimated E on some occasions. The Priestley-Taylor model almost always underestimated E. The performance of both models was affected by the changes in heat storage within the liquid waste. As used here, the models assumed that G was zero over a multi-day measurement period. However, results showed that the lagoon liquid could act as a strong sink or source for surface energy. An alternative would be to measure G directly and incorporate these data into the model to more accurately quantify available energy (Rn+G). In general, the Penman or Priestley- Taylor models, as used here, allowed calculation of E and S to within ± 2.5 mm d-1. In summary, S from lagoons can be estimated from measurements of DD and E over 6- to 11-day periods. The reliability of this approach depends on both the accuracy and magnitude of the E measurement. Given that S from many lagoons will often be near 1 mm d-1 (Ham and DeSutter, 1999), performing a water balance test when E is large (e.g., 10 mm d-1) is undesirable because a 10 % error in E could cause a 100 % error in S. In this study, where E was always less than 6 mm d-1, the floating evaporation pans and the BT model both measured E to within about 0.5 mm d-1, but were often accurate to within 0.2 mm d-1. Data suggest that a water balance experiment could be used to determine if S from a lagoon was less than 0.79 mm d-1 (1/32 in. d-1 ) under ideal weather conditions and could easily discern if S was less than 1.6 and 3.2 mm d-1 (1/16 or 1/8 in. d-1 , respectively).
1.12 ACKNOWLEDGEMENTS Research was supported in part by the Kansas Center for Agricultural Resources and the Environment, the Kansas Department of Health and the Environment, and the Kansas Water Office. Technical support was provided by T.M. DeSutter and F.W. Caldwell.
1.13 REFERENCES Barrington, S. F. and C. A. Madramootoo. 1989. Investigating seal formation from manure infiltration into soils. Transactions of the ASAE 32:851-856. Bill, R.G., A.F. Cook, L.H. Allan, and J.F. Bartholic. 1980. Predicting fluxes of latent and sensible heat of lakes from surface water temperature. J. Geophys. Res. 85:507-512. Brutsaert, W. 1982. Evaporation Into the Atmosphere. D. Reidel Publishing, Boston. Clark, R. N. 1975. Seepage beneath feedyard runoff catchments. In Proceedings of the Third International Symposium on Livestock Wastes, 289-295. ASAE, St. Joseph, MI. Culley, J. L. B. and P. A. Phillips. 1989. Retention and loss of nitrogen and solids from unlined earthen manure storages. Transactions of the ASAE 32:677-683. Daniel, D. E. 1984. Predicting hydraulic conductivity of clay liners. J. Geotech. Eng. 110:285- 300. Davis, S., W. Fairbank, and H. Weisheit. 1973. Dairy waste ponds effectively self-sealing. Transactions of the ASAE 16:69-71. DeBruin, H.A.R. 1978. A simple model for shallow lake evaporation. J. Appl. Meteorol. 17:1132-1134. Ham, J.M. and T.M. DeSutter. 1999. Seepage losses and nitrogen export from swine waste lagoons: A water balance study. J. Environ. Qual. (in press) Ham, J.M., L. Reddi, C.W. Rice, and J.P. Murphy. 1998. Evaluation of lagoons for the containment of animal waste. Kansas Center for Agric. Resources and the Environment. Kansas State University, Manhattan, KS. Ham, J.M. and R.S. Senock. 1994. On the measurement of soil surface temperature. Soil Sci. Soc. Am. J. 56:370-377. Hart, S.A. and M. E. Turner. 1965. Lagoons for livestock manure. J. Water Pollut. Cont. Fed. 37:1578-1596. Hills, D. J. 1976. Infiltration characteristics from anaerobic lagoons. J. Water Pollut. Cont. Fed. 48:695-709. Huffman, R. L. and P. W. Westerman. 1995. Estimated seepage losses from established swine waste lagoons in the lower coastal plains of North Carolina. Transactions of the ASAE 38:449-453. Humenik, R.L., M.R. Overcash, J.C. Barker, and P.W. Westerman. 1980. Lagoons: State of the Art. In Livestock Waste: A Renewable Resource, Proceedings of the 4th International Symposium on Livestock Wastes. 211-216. ASAE, St Joseph, MI. Itier, B., Y. Brunet, K.J. McAneney, and J.P. Lagouarde. 1994. Downwind evolution of scalar fluxes and surface resistance under conditions of local advection. Part I. Reappraisal of boundary conditions. Agric. Forest Meteorol. 71:211-225. Kosco, J.A. and W. Hall. 1999. Joint strategy targets farm waste. Resource, ASAE, St Joseph, MI . 6:11-12. Lakshman, G. 1972. An aerodynamic formula to compute evaporation from open water surfaces. J. Hydrology. 15:209-225. McCurdy, M. and K. McSweeney. 1993. The origin and identification of macropores in an earthen-lined dairy manure storage basin. J. Environ. Qual. 22:148-154. Miller, M. H., J. B. Robinson, and D. W. Gallagher. 1976. Accumulation of nutrients in soil beneath hog manure lagoons. J. Environ. Qual. 5:279-282. Murray, F.W. 1967. On the computation of saturation vapor pressure. J. Appl. Meteorol. 6:203- 204.
1.14 Nolan, B. T., B. C. Ruddy, K. J. Hitt, and D. R. Helsel. 1997. Risk of nitrate in groundwaters of the United States-A national perspective. Environ. Sci. Technol. 31:2229-2236. Penman, H.L. 1948. Evaporation from open water, bare soil, and grass. Proc. Roy. Soc. London A193:120-146. Priestley, C.H.B. and R.J. Taylor. 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Rev. 100:81-92. Quinn, F. 1979. An improved aerodynamic evaporation technique for large lakes with application to the international field year for the great lakes. Water Resour. Res. 15:935- 940. Robinson, F.E. 1973. Changes in seepage rate from an unlined cattle waste digestion pond. Transactions of the ASAE 16:95-96. Sadler, E.J. and C.H.M. Van Bavel. 1982. A simple method to calibrate an infrared thermometer. Agron. J. 74:1096-1098. Stewart, R.B., and W.R. Rouse. 1976. A simple method for determining the evaporation from shallow lakes and ponds. Water Resource. Res. 12:623-628. Webster I.T., and B.S. Sherman. 1995. Evaporation from fetch-limited water bodies. Irrig. Sci. 16:53-64. Westerman, P. W., R. L. Huffman, and J. S. Feng. 1995. Swine-lagoon seepage in sandy soil. Transactions of the ASAE 38:1749-1760. White, I., and Denmead, O.T. 1989. Point and whole basin estimates of seepage and evaporation losses from a saline groundwater-disposal basin. In Comparisons in Austral Hydrology:Hydrology and Water Resources Symposium. 361-366. University of Canterbury, Christchurch, New Zealand.
1.15 Appendix 1 The following equations are required to calculate environmentally dependent variables appearing in the evaporation models (equations 3-7). Saturation vapor pressure, e*, in kPa can be approximated at temperature, T, in C, using the equation of Murray (1967) æ 17.269T ö e* (T ) = 0.61078expç ÷ (A1.1) è 237.3 + T ø Actual vapor pressure of the air, ea, in kPa, is the product of the e* at air temperature and a simultaneous, collocated measurement of relative humidity. The slope of the saturation vapor pressure curve, D, in kPa K-1, can be calculated as the partial derivative of equation A1.1 æ 17.269 öæ T ö D = e* (T )ç ÷ç1- ÷ (A1.2) è 237.3 + T øè 237.3+ T ø noting that e*(T) is the result from equation A1.1. Atmospheric pressure, P, in kPa, can be approximated from altitude, A, in m, and air temperature, Ta, in C, as æ - 3.42x10-2 A ö P = 101.3expç ÷ (A1.3) è Ta + 273.15 ø The latent heat of vaporization, L, in J kg-1, can be approximated as 6 2 L = 2.5005x10 - 2.359x10 (Ta + 273.15) (A1.4) -1 Heat capacity of air, cp, in J kg K , can be expressed as
æ 0.522ea ö c p = 1004.7ç +1÷ (A1.5) è P ø Density of moist air, r, in kg m-3, is P æ 0.378e ö r = ç1- a ÷ (A1.6) Rd (Ta + 273.15) è P ø -1 -1 where Rd is the gas constant (287.04 J kg K ). The psychrometric constant, g, in kPa K , can be approximated as 1.61c P g = p (A1.7) L
1.16 Table 1. Description of the lagoons used for the study.
Lagoon Characteristic A B C
Period of study Sept.-Oct. April Nov.-Dec.
Type Swine waste Swine waste Cattle feedlot
Max. capacity, m3 110,000 110,000 96,000
Surface area during study, ha 2.1 2.1 1.8
Depth during study, m 5.8 5.3 2.3
Liner type or thickness, m plastic* 0.46 0.46-0.6
Liner texture Silty clay + loam bentonite
Liner permeability, cm s-1 4.44 x 10-8 7.54 x 10-8
Depth to water table, m 30 58 85
* 1.0-mm thick, high density polyethylene.
1.17 Table 2. Environmental conditions and cumulative evaporation during two measurement cycles at the plastic-lined Lagoon A. Included are minimum and maximum air temperatures (Ta) and
* vapor pressure deficit (ea -ea), average water temperature at 0.1 m (Tw), average wind speed (U), and daily global irradiance (Rs). Also provided is the cumulative change in depth (i.e., evaporation, SE) over each cycle.
* DOY Tair ea -ea Tw U Rs SE Min Max Min Max (C) (kPa) (C) (m s-1) (MJ m-2) (mm)
258 13.6 25.2 0.0 1.7 22.6 2.3 17.5 4.7 259 13.1 27.9 0.1 2.2 24.6 1.4 20.3 8.9 260 13.7 29.4 0.1 2.7 23.8 2.1 21.0 13.2 261 16.2 31.3 0.2 3.3 23.3 3.8 21.7 18.9 262 16.4 34.9 0.3 4.7 23.4 3.8 21.9 26.5 263 14.2 27.2 0.1 1.9 22.4 2.9 21.8 31.0 264 9.5 20.1 0.0 0.6 21.6 4.3 12.5 36.7 265 8.6 13.2 0.0 0.3 9.4 3.5 5.0 44.3 266 11.1 28.7 0.0 2.1 20.2 4.8 15.8 48.5
278 5.4 17.0 0.1 1.3 17.1 2.8 11.3 5.30 279 3.5 20.0 0.1 1.8 16.6 2.9 19.3 10.6 280 4.5 22.6 0.1 1.9 17.6 2.3 18.4 14.3 281 8.2 26.3 0.2 2.6 17.6 5.0 18.0 21.0 282 10.7 27.3 0.4 2.7 16.9 3.6 17.8 25.5 283 9.9 28.8 0.2 3.0 16.8 5.0 17.6 31.8 284 9.7 22.6 0.1 2.1 16.1 3.6 18.1 38.5 285 3.6 22.2 0.1 1.6 16.0 5.0 17.1 42.4 286 11.4 27.1 0.1 4.2 18.2 3.1 16.9 44.7 287 10.1 32.8 0.0 2.0 17.8 4.9 15.7 48.0 288 15.2 28.2 0.2 2.5 17.0 7.6 15.0 54.5
1.18 Table 3. Comparison of total evaporation (E) from the plastic-lined Lagoon A to evaporation
from two floating evaporation pans (Epan). Also included are the pan coefficeints, Kp.
Pan 1 Pan 2 Period E Epan Kp Epan Kp (DOY) (mm) (mm) (mm)
258-260 13.2 17.6 0.75 19.2 0.69 261-263 17.8 22.8 0.78 22.3 0.80 264-266 17.5 21.9 0.80 21.7 0.81 278-280 14.3 19.0 0.75 19.5 0.73 281-283 17.5 19.5 0.90 19.2 0.91 286-288 12.1 13.8 0.88 12.9 0.94
Avg. 15.4 19.1 0.81 19.1 0.81
1.19 Table 4. Comparison of actual evaporation (E) from the plastic-lined Lagoon A to that predicted using four different models. Data are shown as average daily evaporation in mm d-1 for five,
3-day periods. The numbers in parenthesis represent model error (Emodeled -Eactual) for each block-model combination, also in mm d-1.
Evaporation Model*
Period E EBT Epen EPT EDB (DOY) (mm d-1)
258-260 4.4 3.8 (-0.6) 4.5 (0.1) 4.6 (0.2) 3.8 (-0.6) 261-263 5.9 6.3 (0.6) 6.7 (0.8) 5.5 (-0.4) 8.3 (2.6) 264-266 5.8 6.1 (0.3) 2.4 (-3.6) 1.8 (-4.0) 3.7 (-2.1) 283-285 5.6 5.4 (-0.2) 5.6 (0.0) 3.8 (-1.8) 11.5 (5.9) 286-288 4.0 4.2 (0.2) 6.1 (2.1) 3.3 (-0.7) 12.4 (8.4)
Avg. 5.1 5.2 5.1 3.8 7.9
* BT, Bulk Transfer Model; pen, Penman Model; PT, Priestley-Taylor Model; DB, DeBruin Model
1.20 Table 5. Average daily (24-h) energy balances of Lagoon A.
Period Rn LE H G
(DOY) (W m-2)
258-260 146.6 -108.6 -9.2 28.8 261-263 168.1 -180.2 8.1 -4.0 264-266 61.3 -180.0 -55.1 173.7 283-285 126.4 -154.0 39.6 -12.0 286-288 109.3 -119.2 77.2 -67.3
1.21 Table 6. Water balances of a soil-lined Lagoons B and C. Shown are evaporation and the
calculated seepage rate when the floating evaporation pans and three different models were used
to calculate evaporation. The measurement periods for lagoons B and C where 6 and 11 days,
respectively.
Location DD Evaporation Method E S (mm) (mm) (mm d-1) (mm) (mm d-1) Lagoon B 24.5 Pan 1 20.5 3.42 4.0 0.67 (swine) Pan 2 19.0 3.16 5.5 0.92 Bulk Transfer 20.1 3.35 4.4 0.74 Penman 32.1 5.35 -7.6 -1.27 Priestley-Taylor 27.6 4.60 -3.1 -0.52
Lagoon C 22.8 Pan 1 21.0 1.91 1.8 0.16 (cattle) Pan 2 22.8 2.07 0.0 0.0 Bulk Transfer 20.5 1.86 2.3 0.21 Penman 28.6 2.58 -5.8 -0.52 Priestley-Taylor 16.1 1.46 6.7 0.61
1.22 List of Figures
Figure 1. Photograph showing one of the rafts used to measure pan evaporation and meteorological conditions at the lagoons.
Figure 2. Cumulative depth change (i.e., evaporation) in the plastic-lined Lagoon A and two floating evaporation pans between (a) DOY 258-267 and (b) DOY 278-289. Depth changes recorded in the pans were multiplied by a pan coefficient, Kp, of 0.81 to approximate actual evaporation from the lagoon (table 3).
Figure 3. Temperature regime at the plastic-lined Lagoon A between DOY 258-267. Shown are
(a) air temperatures at 1.0 m and waste temperatures at the surface and 0.1 m, and (b) the difference in waste surface temperatures inside and outside the evaporation pans (Tlagoon-Tpan).
Figure 4. Comparison of cumulative evaporation at the plastic-lined Lagoon A to that calculated using three different models (see equations 3, 4,and 6). Data are shown for two experimental periods: (a) DOY 258-267; (b) DOY 283-289.
Figure 5. Cumulative change in depth and evaporation at soil-lined Lagoon B (swine waste) between DOY 108 and 114, 1998. Evaporation was estimated with (a) two floating evaporation pans and (b) meteorological models. The overall water balance is given in table 6.
Figure 6. Cumulative change in depth and evaporation at soil-lined Lagoon C (cattle feedlot) between DOY 325 and 336, 1998. Evaporation was estimated with (a) two floating evaporation pans and (b) meteorological models. The overall water balance is given in table 6.
1.23 Figure 1.
1.24 50 a Lagoon 40 (Pan1) x Kp (Pan2) x Kp Kp=0.81 30
20
10
Cumulative Depth Change (mm) 0 258 259 260 261 262 263 264 265 266 267 268 Day of Year, 1998
60 b 50
40
30
20
10
Cumulative Depth Change (mm) 0 278 280 282 284 286 288 290 Day of Year, 1998
Figure 2.
1.25 40 Air a 35 Lagoon Surface Lagoon, 0.1 m 30
25
20
15 Temperature (C)
10
5 258 259 260 261 262 263 264 265 266 267 Day of Year, 1998
5 b 4 T -T 3 in out 2 1 0 -1 -2 -3 Temperature Difference (C) -4 258 259 260 261 262 263 264 265 266 267 Day of Year, 1998
Figure 3.
1.26 50 Depth Change (measured E) a Bulk Transfer 40 Penman Priestley-Taylor 30
20
10 Depth Change or Evaporation (mm) 0 258 259 260 261 262 263 264 265 266 267 268 Day of Year, 1998
40 b
30
20
10 Depth Change or Evaporation (mm) 0 283 284 285 286 287 288 289 290 Day of Year, 1998
Figure 4.
1.27 35 a Depth Change (E + S) 30 (Pan1) x Kp (Pan2) x Kp 25 Kp=0.81 20
15
10
5 Depth Change or Evaporation (mm) 0 108 109 110 111 112 113 114 115 Day of Year, 1998
35 b Depth Change (E + S) 30 Bulk Transfer Penman 25 Priestley-Taylor 20
15
10
5 Depth Change or Evaporation (mm) 0 108 109 110 111 112 113 114 115 Day of Year, 1998
Figure 5.
1.28 30 a Depth Change (E + S) (Pan1) x Kp (Pan2) x Kp 20 Kp=0.81
10 Depth Change or Evaporation (mm) 0 325 327 329 331 333 335 337 Day of Year, 1998
30 b Depth Change (E + S) Bulk Transfer Penman 20 Priestley-Taylor
10 Depth Change or Evaporation (mm) 0 325 327 329 331 333 335 337 Day of Year, 1998
Figure 6.
1.29 Chapter 2
Field Evaluation of Animal-Waste Lagoons: Seepage Rates and Subsurface Nitrogen Transport
J.M. Ham and T.M. DeSutter
Department of Agronomy, Kansas State University, Manhattan, KS 66506
Abstract: Earthen lagoons are an integral part of the waste management plan at many concentrated animal-feeding operations. Lagoon waste contains high concentrations of N, P, salts, and other nutrients that, in many cases, are applied to farmland as liquid fertilizer. However, while the waste is being stored and treated in the lagoon, subsurface seepage losses may affect soil and water quality near the facility. Water balance methods were used to study seepage losses and nitrogen export from soil- lined lagoons at nine different swine and cattle feedlots in Kansas. Lagoons ranged in size from 0.2 to 2.5 ha and had waste depths between 1.2 and 5.8 m. Compacted-soil liners were between 0.30 to 0.46 m thick and built with native soil or, in some cases, a soil-bentonite mixture. Whole lagoon seepage was measured using the methods of Ham (1999) and Ham and DeSutter (1999). Seepage rates from the lagoons ranged from 0.2 to 2.4 mm/d, with an overall average of rate of 1.2 mm/d . Analysis of lagoon + effluent showed that Ammonium-nitrogen (NH4 -N) accounted for over 99 % of the soluble nitrogen + and averaged 673 mg/L at swine waste lagoons and 98 mg/L at the cattle sites. The NH4 -N typically ranged from 550 to 900 mg/L at swine sites and from 20 to 200 mg/L at cattle feedlots. Nitrate - (NO3 -N) concentrations were less than 3 mg/L at all locations. Analysis of soil cores collected + beneath an 11-year-old cattle-waste lagoon showed that a large fraction of the NH4 -N in the leachate remained in a shallow (e.g., 5 m) adsorption zone directly beneath the lagoon. When lagoons are + - closed, emptied, and dried; NH4 -N could convert to NO3 -N and more readily move towards the + ground water. More information is needed regarding the fate of NH4 -N deposited in soil (vadose zone) beneath lagoons. Data suggest that risk of ground water contamination will be location and species dependent. Introduction Anaerobic lagoons are an integral part of the waste management system at many concentrated animal operations (CAOs). Lagoon waste contains high concentrations of nitrogen, phosphorus, salts, and other nutrients that are usually applied to farmland as liquid fertilizer. However, while the waste is being stored and treated in the lagoon, subsurface seepage losses may affect soil and water quality near the facility. Of particular concern is the movement of nitrate-N into local drinking water supplies. Kansas State University is conducting research to determine the relationship between the lagoon use and ground water quality. This report provides an update on the field component of the research effort. Emphasis is placed on lagoon effluent chemistry, whole-lagoon seepage rates, and the fate and transport of material in the subsoil (vadose zone) directly beneath the lagoons. Results represent progress to date and are not final conclusions. A more thorough synthesis of these concepts will be presented in later reports. Evaluating the potential impact of animal waste lagoons on ground water quality requires the consideration of three focus areas: a) toxicity – what are the constituents in the lagoon waste that pose a threat to water quality and public health? (b) input loading – at what rate does waste seep from a lagoon under field conditions? and (c) aquifer vulnerability – how do soil properties, geology, and water table depth affect the risk of waste movement from the lagoon to the ground water? These concepts are outlined in Table 1 and provide a framework for the remainder of the report.
Lagoon Effluent Chemistry and Toxicity Lagoon effluent was analyzed from five swine-waste lagoons and four cattle-feedlot runoff lagoons in Kansas (DeSutter et al., 1999; Appendix A). Samples were sometimes collected several times throughout the year to examine seasonal trends. Analysis included twenty-five chemical and + physical characteristics. Ammonium-nitrogen (NH4 -N) accounted for over 99 % of the soluble nitrogen and averaged 673 mg/L at swine-waste lagoons and 98 mg/L at the cattle sites. Total phosphorous averaged 45 mg/L across all samples and was similar at the cattle and swine lagoons. On average, sodium was 148 mg/L at the cattle feedlots and 270 mg/L at the swine sites. Chloride was 275 and 569 mg/L at the swine and cattle sites, respectively. In most cases, strong seasonal patterns in + waste chemistry were not evident. At some swine sites, NH4 -N in spring tended to be about 200 mg/L higher than that observed in late fall. Results show that waste chemistry was species dependent, with nitrogen concentrations at swine sites being about six times higher than those at cattle feedlots. Conversely, chloride tended to be higher in cattle-feedlot runoff lagoons. The chemistry and toxicity of lagoon effluent are species dependent. The mechanisms behind differences in chemistry are primarily caused by how the waste is managed rather than by inherent differences in the biology of the animals. Because “pull-plug” swine lagoon systems must accept all the waste produced in the barn, concentrations in the waste entering the lagoon are very high. For + example, NH4 -N in waste entering a swine lagoon is often greater than 3,500 mg/L (Ni et al., 1998). Ammonia-N volatilization from the lagoon surface and some dilution by precipitation reduces long-term concentrations in the lagoon to the 500 to 900 mg/L range. Conversely, at a cattle feedlot, most wastewater entering a lagoon is runoff from precipitation that falls on the pens. In this case, most + of the NH4 -N stays on the pen surface, trapped in manure and the soil surface. This process keeps nitrogen in a cattle feedlot lagoon more dilute. However, ions that easily leach through the soil, such as chloride, will accumulate in the runoff as it flows downhill over a long swath of open pens. Therefore, chloride concentrations are often much higher in cattle lagoons than at swine sites (Table 2). There are also site-to-site differences in chemistry among lagoons used for the same species. + DeSutter et al. (1999) found that NH4 -N ranged from 550 to 900 mg/L at swine sites and from 20 to 200 mg/L at cattle feedlots. The highest concentration, 2000 mg/L, was observed at swine site in the first stage basin of a two-stage lagoon system (concentration was much lower in the second stage + lagoon). Conversely, the NH4 -N concentrations at one of the cattle feedlots never exceeded 70 mg/L 2.2 and was often less than 30 mg/L. Data show that site-to-site differences in waste chemistry will affect the toxicity and the potential for input loading (Table 1).
Whole-lagoon Seepage Rates and Subsurface Nitrogen Export Regulations in Kansas stipulate that soil-lined lagoons used for animal waste should be constructed so that seepage is less than 6.4 or 3.2 mm/d (1/4 or 1/8 inch per day), depending on where and when the facility was built. Whole-lagoon seepage rates were measured from seven swine-waste lagoons and two cattle-feedlot runoff collection lagoons. Measurements were made using the methods of Ham (1999) and Ham and DeSutter (1999). Descriptions of the lagoons and details of the water balance calculations are provided in Appendix 2A (Water Balance Worksheets). The earthen lagoons ranged in size from 0.2 to 2.5 ha and had waste depths between 1.2 and 5.8 m. Five of the lagoons had waste depths in excess of 4.9 m. Most lagoons had compacted soil liners between 0.3 and 0.5 m thick. The average seepage rate from the lagoons was 1.2 mm/day (Table 3). Among lagoons tested, seepage ranged from 0.2 to about 2.4 mm/day. At some locations, seepage results were combined with data on lagoon geometry and construction to estimate the in-situ permeability of the liner. In lagoons built with silt loam liners (no bentonite) having initial permeability’s greater than 3.5x10-7 cm/s, seepage rates on a whole-lagoon basis were about two to five times less than those predicted from soil core data collected prior to the addition of waste (Table 4). Results imply that permeability of the liner was reduced by organic sludge on the bottom of the lagoons. Field measurements showed that the organic sludge layer was 0.38 m thick on the bottom in a four-year-old, swine-waste lagoon. Sludge depth was approximately 0.15 m thick on the side embankments, which where constructed with a 4:1 slope. Measurements of whole-lagoon seepage where combined with waste chemistry data to estimate the rate of nitrogen movement into the surrounding soil (Table 5). Ammonium-N losses ranged from 0.2 to 0.5 kg/m2 ·yr (130 to 4453 lbs./acre·yr). Nitrogen export was much lower at the cattle feedlots + compared to swine sites because the NH4 -N concentrations in the waste were more dilute. Although only two cattle feedlots are represented in the current study, one would expect nitrogen export (nitrogen input loading, Table 1) to be five to ten times lower at cattle feedlots compared to swine sites.
Subsurface Nitrogen Losses Into Soil Under Lagoons The movement of effluent-nitrogen into the soil surrounding the lagoon is not only dependent on the seepage rate and the nitrogen concentration, but also is affected by the chemical and physical + properties of the soil. Ammonium-N (NH4 ion) has a positive charge, while clay particles in soil are + negatively charged. Objects with opposite charge attract; thus, NH4 ions that leak from a lagoon are often adsorbed onto the surface of clay particles in the soil profile. Conversely, negatively charged ions, such as chloride (Cl-), are not attracted to soil particles and tend to move through the soil profile unimpeded. The ability of a soil to adsorb positively charged ions is described by the Cation Exchange Capacity (CEC). Soils with high clay contents have CECs near 30 cmol/kg and very sandy soils have CECs near 5 cmol/kg. If two lagoons were seeping at the same rate, but one was built above a sandy + soil and the other above a clayey soil, one might expect the NH4 to travel six times farther from the lagoon built at the sandy site. This is not exactly what happens in the field because other factors affect solute transport, but it does demonstrate the importance of soil CEC. To gain a better understanding of subsurface nitrogen dynamics, soil cores were collected from the bottom of an 11-year-old cattle-feedlot lagoon in southwestern Kansas. The lagoon had been dried and the organic sludge removed prior to sampling. A direct-push soil coring unit was used to sample + four locations to a depth of 5 m. Figure 1 shows the average NH4 -N and chloride concentration profiles. Ammonium-N concentrations were near 400 ppm near the original bottom of the lagoon and then decreased rapidly to about 30 ppm at 5 m. The shape of the concentration curve demonstrates + how NH4 -N was adsorbed in the soil profile. There were essentially no nitrates in any of the soil 2.3 + samples. Thus, almost all the nitrogen that had been lost from the lagoon was still in the NH4 -N form and about 90% of that nitrogen was still within 3 m of the soil liner. However, in one area of the + lagoon the subsoil was very sandy, and NH4 concentrations were 66 ppm at 5 m. This shows how a lower CEC allowed nitrogen to move to lower depths. Chloride concentrations did not decrease with depth (Fig. 1) because these ions have a negative charge and were not adsorbed by clay particles. In summary, preliminary data suggest that nitrogen losses through a lagoon liner will, in many cases, be + deposited as NH4 -N in a rather shallow soil zone near the periphery of the lagoon liner. Ammonium-N could potentially move directly into the ground water at sites built above shallow aquifers in sandy soils. The amount of nitrogen and size of the deposit will be dependent on the seepage rate, concentrations of nitrogen in the waste, CEC of the underlying soil, local geology, and lagoon age.
Aquifer Vulnerability Aquifer vulnerability is one of the most critical factors affecting the risk of ground water contamination. In many cases, depth to ground water is the most important geologic feature correlated with vulnerability. Although data are limited, previous research shows that ground water contamination from lagoons usually occurs at locations with high rainfall and water tables less than 7.6 m deep (Table 6). Kansas is a state that has tremendous geographic variation in hydrology, soils, and agricultural practices. Thus, we would expect large regional variation in aquifer vulnerability. To demonstrate this point, Table 7 compares conditions in Washington Co. (northeast) and Grant Co. (southwest) – regions with intense livestock production. Grant Co. has low precipitation, high evaporation, and a aquifer median aquifer depth over 70 m. Thus, there is a thick vadose zone between the lagoons and the water table. Under these conditions, soil beneath lagoons becomes unsaturated just a few meters from the bottom of the basin. Unsaturated conditions are beneficial because they greatly retard the movement of water, nutrients, and bacteria that may have seeped from the lagoon (Schafer et al., 1998). In contrast, Washington Co. has almost twice the rainfall of Grant Co. and median ground water depths near 12 m. Clearly, the aquifer is more vulnerable in the northeastern portion of the state. This does not imply that lagoons in northeastern counties are polluting ground water. The comparison was made to simply show that large differences in vulnerability exist between regions and that any changes in regulations, permitting, and prescribed management practices should consider these differences.
Lagoon Closure Field measurements have shown that seepage losses from many lagoons occur very slowly. However, over 20 to 40 years of operation, even a low seepage rate can deposit a large mass of nitrogen beneath a lagoon. For example, Ham and Desutter (1999) showed that the total nitrogen deposited in soil beneath a 2.2-ha (5-acre) swine lagoon could potentially exceed 110,000 kg (250,000 lbs.) over a 20 year period. When a lagoon is eventually emptied and closed, the nutrient-laden zone of soil under the lagoon will tend to become dry and aerobic , especially in western Kansas where + potential evaporation is much greater than precipitation. Under dry soil conditions the NH4 -N may - convert to NO3 -N, which is very mobile in the soil (Figure 2). Over time, seasonal precipitation and intermittent water movement (drainage) through the soil profile could transport this newly formed - NO3 -N toward the ground water. However, a fraction of the nitrogen may be converted to harmless N2 gas and released into the atmosphere (denitrification). It is difficult to predict the ultimate fate of nitrogen in the NH4-laden soil surrounding lagoons. It may be feasible to phytoremediate the soil profile with plants. Salt tolerant crops like barley or perhaps constructed wetlands might be capable of absorbing large portions of the nitrogen and also stimulate denitrification. Furthermore, it is not clear if the nutrient-laden soil under a lagoon poses a significant risk to the groundwater, especially when the depth to ground water is large (e.g., 100 ft). Much of the nitrogen may be lost to the atmosphere even without a phytoremediation plan. In summary, older lagoons that are closed and abandoned will + initially have a deposit of NH4 nitrogen in the soil under the facility. Additional research is needed to 2.4 determine if this nitrogen will affect ground water quality, and how the risk of contamination is affected by soil and geologic conditions. Best management practices for lagoon closure should be explored.
2.5 References Ciravolo, T.G., D.C. Martens, D.L. Hallock, Jr. E.R. Collins, E.T. Kornegay, and H.R. Thomas. 1979. Pollutant movement to shallow ground water tables from anaerobic swine waste lagoons. J. Environ. Qual. 8:126-130. DeSutter T.M., J.M. Ham, and T. Trooien. 1999. Survey of waste chemistry in anaerobic lagoons at swine units and cattle feedlots. Technical Report. Department of Agronomy, Kansas State University, Manhattan, KS 66506. (Animal waste lagoon water quality study, Appendix A) Ham, J.M. 1999. Measuring evaporation and seepage losses from lagoons used to contain animal waste. Trans. of the ASAE (in press) Ham, J.M. 1999. Seepage losses and nitrogen export from animal waste lagoons: A water balance study. J. Environ. Qual. (in press) Ham, J.M., L. Reddi, C.W. Rice and J.P. Murphy. 1998. Evaluation of lagoons for the containment of animal waste. A report submitted to the Kansas Department of Health and the Environment. Kansas Center for Agric. Resources and the Environment. Kansas State University, Manhattan, KS 66506. Krapac, I.L., W.S. Dey, C.A. Smyth, and W.R. Roy. Impacts of bacteria, metals, and nutrients on groundwater at two hog confinement facilities. Proceedings from Animal Feeding Operations and Ground Water: Issues, Impacts, and Solutions – A Conference for the future, Presented by National Ground Water Assoc., Nov. 4-5, 1998, St. Louis, Missouri. pp. 29-50. Ni, J., Heber, A.J., Lim, T.T., Duggirala, R., Haymore, B.L., Diehl, C.A., and A.L. Sutton. 1998. Ammonia emissions from a large mechanically ventilated swine building during warm weather. ASAE paper 984051. ASAE International Meeting, Orlando, Florida. ASAE, St. Joseph, MI Quade, D.J., R.D. Libra, and L.S. Seigley. 1996. Ground water monitoring at an earthen manure storage structure. Iowa Geological Survey, Bureau Guidebook Series No. 18. Schafer, A., Ustohal, P., Harms, H., Stauffer, F., Dracos, T., and A. Zehnder. 1998. Transport of bacteria in unsaturated porous media. J. Contaminant Hydrology. 33:149-169. Sewell, J.I. 1978. Dairy lagoon effects on groundwater quality. Trans. of the ASAE. 21:948-952. Westerman, P.W., R.L. Huffman, and J.S. Feng. 1995. Swine lagoon seepage in a sandy soil. Trans. of the ASAE 38:1749-1760. Wise, C.F. 1998. Regulatory control of groundwater impacts at animal feeding operations. Proceedings from Animal Feeding Operations and Ground Water: Issues, Impacts, and Solutions – A Conference for the future, Presented by National Ground Water Assoc., Nov. 4-5, 1998, St. Louis, Missouri. pp. 14-23.
2.6 Table 1. Factors that must be considered when assessing the potential impact of animal waste lagoons on local ground water quality. The impact of each factor is often dependent on livestock species, characteristics and design of the waste handling system, and location.
Toxicity: Constituents in an anaerobic waste lagoon that could potentially affect human health by contaminating ground water.
a. Inorganic Constituents (nitrate-N, ammonium-N, Chloride, Phosphorous, metals) b. Bacteria (Fecal Coliform, Fecal Strep.) c. Enteric viruses ? d. Pharmaceuticals ?
Input Loading: The rate at which waste and potential contaminates seep from the lagoon into the soil profile surrounding the earthen basin.
a. Seepage Rate (properties of soil liner, depth of waste, sludge accumulation) b. Concentration of constituents in the waste (species, management)
Aquifer Vulnerability: Properties of the zone between the lagoon and the water table that determine if seepage losses will reach the ground water.
a. Depth to Ground water b. Soil Properties (rate of contaminant transport, biochemical transformations) c. Geology (geologic layers that inhibit or promote contaminant movement)
2.7 Table 2. Selected chemical characteristics of waste in anaerobic lagoons. Data are averages of five swine sites and four cattle feedlots in Kansas, U.S.A. (DeSutter et al., 1999)
Measured Lagoon Type parameters Swine Cattle mg L-1 Nitrate-Nitrogen 1 0.5 Ammonium-Nitrogen 673 98 Total N 792 184 Organic N 119 86 Total P 43 48 Sodium 270 148 Chloride 276 569 pH 8.1 7.7 BOD* 1726 370
* BOD, Biological Oxygen Demand
2.8 Table 3. Whole-lagoon seepage rates from nine animal waste lagoons in Kansas. Data used to calculate the seepage rates are presented in Appendix 2A.
Waste Lagoon Seepage Max. Seepage Lagoon Species Depth Area Rate Rate* m (ft.) ha (acre) mm/d (in./d) mm/d (in./d)
1 swine 5.5 (18) 0.7 (1.7) 1.4 (0.06) 1.5 (0.06) 2 swine 5.8 (19) 2.3 (5.7) 2.0 (0.08) 2.2 (0.09) 3 swine 5.3 (17) 2.2 (5.5) 0.8 (0.03) 0.9 (0.03) 4 swine 5.4 (18) 2.2 (5.5) 0.9 (0.03) 1.0 (0.04) 5 swine 4.9 (16) 2.9 (7.2) 1.5 (0.06) 1.6 (0.06) 6 swine 2.1 (7) 0.5 (1.2) 1.3 (0.05) 3.0 (0.11) 7 swine 4.4 (14) 1.5 (3.7) 0.6 (0.02) 1.9 (0.07) 8 cattle 2.3 (8) 1.8 (4.5) 0.2 (0.01) 0.5 (0.02) 9 cattle† 1.2 (4) 2.8 (7.0) 2.4 (0.09) 4.3 (0.17)
Mean 4.1 (13) 1.9 (4.6) 1.2 (0.05) 1.9 (0.07)
* estimate of whole lagoon seepage at maximum capacity (i.e., maximum waste depth).
† measurements made using different techniques and equipment than employed at lagoons 1 through 9.
2.9 Table 4. Properties of the compacted soil liner at six of the lagoons in the study. Also shown is a comparison of the mesured seepage rate with that predicted from soil cores collected prior to the addition of waste.
Predicted Measured Soil Texture CEC* Ks* Seepage Seepage Lagoon % Sand % Silt % Clay cmol/kg cm/s mm/d
1 40.0 30.0 30.0 32 7.6E-7† 5.4 1.4 2 52.5 21.3 26.3 26 3.7E-7‡ 5.2 2.0 3 58 20 22§ 15 4.4E-8† 0.4 0.8 4 52.5 21.3 26.3 26 <1.0E-8‡ 0.1 0.9 5 75 11.3 13.8 15 5.0E-7‡ 3.8 1.5 8 54 26 20 14 7.5E-8† 0.4 0.2
* CEC, cation exchange capacity; Ks, coefficient of permeability.
† Average of four to six soil samples collected from the liner after lagoon construction but prior to the addition of waste.
‡ Measured from a single recompacted core of soil slated for use during construction (not sampled from the actual compacted liner).
§ Liner was 0.46 m thick, but the lowermost 0.15-m layer was augmented with bentonite. Soil used for texture and CEC analysis was collected from the uppermost 0.38 m and may not accurately reflect the effect of the bentonite.
2.10 Table 5. Rate of subsurface ammonium-nitrogen (NH4-N) export from lagoons into the underlying soil. Export rates were computed based on the measured seepage rates (Table 3) and NH4-N concentrations in the effluent .
Lagoon Species, Type NH4-N Conc. NH4-N Export Rate mg/L kg/m2·yr lbs./acre·yr
1 swine, nursery 565 0.289 2574 2 swine, sow 685 0.500 4453 3 swine, finshing 824 0.241 2143 4 swine, sow 685 0.225 2004 5 swine, finishing 824 0.451 4018 8 cattle feedlot 200 0.015 130
2.11 Table 6. Examples of research studies where significant ground water contamination has been detected in close proximity (< 30 m, <100 ft.) to animal waste lagoons.
Location Lagoon Depth to Reference Type Ground Water
North Carolina Swine 0.6-3 m (2-10 ft.) Westerman et al., 1995 Virgina Swine 3-6 m (10-20 ft.) Ciravolo et al., 1979 Virgina Swine 3-6 m (10-20 ft.) Collins et al., 1975 Illinois Swine 3-7 m (10-23 ft.) Krapac et al., 1998 Texas (central) Cattle 4.3m (14 ft.) Wise, 1998 Iowa Swine < 7.6 m (25 ft.) Libra and Quade, 1998 Tennessee Dairy 3.5 m (11 ft.) Sewell, 1978
2.12 Table 7. Comparison of agricultural and geophysical statistics of Grant Co. (southwestern Kansas) and Washington Co. (northeastern Kansas).
County ______Parameter Grant Washington ______
All cattle and calves† 189,900 64,700 All hogs and pigs† 77,150†† 97,000 Corn for silage, 1,457 (3,600) 688 (1,700) ha (acres )† Corn production, 263,225 (10,341,000) 63,280 (2,486,000) metric tons (bushels)† Wheat production, 126,357 (4,633,100) 147,510 (5,408,700) metric tons (bushels)† Annual precipitation, 429 (16.9) 807 (31.8) mm (inches)† Average temperature, 12.9 (55.2) 11.8 (53.3) C (oF)† Potential evaporation, 1,270 (50) 991 (39) mm (inches)‡ Median depth to ground 71.6 (235) 12.2 (40) water, m (feet)§ Ratio of acres of land 0/368,000 59,900/515,200 having a Cropland Erodibility Index for Water Erosion greater than 8.0, signaling highly erodible land to acres of cropland¶ Ground water recharge by 2.5 (0.1) 8.4 to 12.7 (0.33 to 0.5) precipitation into designated High Plains Aquifer# Dakota Aquifer aquifer, mm (inches)
† Reference-Kansas Agricultural Statistics Service ‡ Reference- Approximated using weather data from Dodge City and Topeka, KS § Reference-Calculated from the Kansas Geological Survery’s WIZARD and WWC5 databases ¶ Reference-1992 National Resources Inventory, USDA Natural Resources Conservation Service # Allen MacFarlane, Kansas Geological Survey (personal communication) †† Calculated value due to incomplete data
2.13 Ammonium Concentration in Soil (ppm) 0 100 200 300 400 0
2
4
6
8
10 Depth (ft) Collected beneath an 11-year old 12 cattle-feedlot runoff lagoon 14
16
18
Chloride Concentration in Soil (ppm) 0 10 20 30 40 50 60 70 0
2
4
6
8
10 Depth (ft)
12
14
16
18
Figure 1. Concentrations of ammonium-nitrogen and chloride in the soil under an 11-year old cattle-feedlot lagoon.
2.14 Lagoon Closure Issues
Empty (Dry) Lagoon
Sludge Compacted Soil Liner Unsaturated NH4 Adsorption Zone + - 2NH4 + 3O2 = NO2 +2H20 +4H - - 2NO2 +O2 = 2NO3
NO - Vadose Zone 3 - NO3
- NO3 - Ground Water NO3
Figure 2. Potential conditions after lagoon closure.
2.15 Appendix for Chapter 2
Water Balance Worksheets
Note: Water balance worksheets provide a portion of the information used to calculate whole-lagoon seepage. The examples shown do not represent all data collected at a specific site. In many cases, water balance measurements were repeated over several multi-day measurement cycles during a 30 to 60 day period. The measurements of seepage were very similar between measurement cycles at the same location. The most reliable data are shown – depicting the best estimate of seepage.
2.16 Lagoon Seepage Worksheet, Lagoon 1 March 23, 1999
Lagoon ID 1 Period of Study, DOY 48-56 Depth During Study, m 5.5 Lagoon Area, ha 0.7 Maximum Depth, m 6.1 Liner Thickness, m 0.46 Depth to Groundwater, m 58 Lagoon Age, yr. 3 yr
0
5
10
15
Total Depth Change: 19.0 mm
Cumulative Depth Change (mm) Precipitation: 2.0 mm (DOY 49)
20 Rate Change in Depth (seepage + evaporation): 3.0 mm/day or 0.12 in./day
data from DOY 51.4 to 52.4 excluded because of waste addition
25 48 49 50 51 52 53 54 55 56 57
Day of Year, 1998
Water Balance and Seepage Calculations
Case 1:Using Floating Lysimeters Case 2:Using Meteorological Models To Estimate Evaporation to Estimate Evaporation Depth Change, mm 19.0 Depth Change, mm 19.0 Precipitation, mm 2.0 Precipitation, mm 2.0 Evaporation, mm 11.1 Evaporation, mm 14.2 Seepage, mm 9.9 Seepage, mm 6.8 Seepage Rate, mm/d 1.4 Seepage Rate, mm/d 1.0 Seepage Rate, in./d 0.06 Seepage Rate, in./d 0.04
Notes: A description of the methods used to measure and calculate the water balance are provided by Ham (1999) and Ham and DeSutter (1999). The lysimeter-based estimates of evaporation were calculated using a pan coefficient of 0.81. A bulk-transfer meteorological model (see Ham (1999) Eq. 2) was used to calculate evaporation in Case 2. The period used for analysis was selected based on the following criteria: (1) optimal weather conditions - negligible precipitation and no sustained high winds that flooded the lysimeters; (2) knowledge that no waste inflow occurred; and (3) no equipment failures. In some instances,
2.17 Lagoon Seepage Worksheet, Lagoon 2 March 23, 1999
Lagoon ID 2 Period of Study, DOY 80-86 Depth During Study, m 5.5 Lagoon Area, ha 2.3 Maximum Depth, m 6.1 Liner Thickness, m 0.3 Depth to Groundwater, m 70 Lagoon Age, yr. 4 yr
0
2
4
6
8
10
12
14 Total Depth Change: 18.0 mm Cumulative Depth Change (mm)
16 Rate Change in Depth (seepage + evaporation): 3.6 mm/day or 0.14 in./day 18
20 80.4 81.8 83.2 84.6 86.0
Day of Year, 1998
Water Balance and Seepage Calculations
Case 1:Using Floating Lysimeters Case 2:Using Meteorological Models To Estimate Evaporation to Estimate Evaporation Depth Change, mm 18.0 Depth Change, mm 18.0 Precipitation, mm 0 Precipitation, mm 0 Evaporation, mm 8.3 Evaporation, mm 12.5 Seepage, mm 9.7 Seepage, mm 5.5 Seepage Rate, mm/d 2.0 Seepage Rate, mm/d 1.1 Seepage Rate, in./d 0.08 Seepage Rate, in./d 0.04
Notes: A description of the methods used to measure and calculate the water balance are provided by Ham (1999) and Ham and DeSutter (1999). The lysimeter-based estimates of evaporation were calculated using a pan coefficient of 0.81. A bulk-transfer meteorological model (see Ham (1999) Eq. 2) was used to calculate evaporation in Case 2. The period used for analysis was selected based on the following criteria: (1) optimal weather conditions - negligible precipitation and no sustained high winds that flooded the lysimeters; (2) knowledge that no waste inflow occurred; and (3) no equipment failures. In some instances,
2.18 Lagoon Seepage Worksheet, Lagoon 3 March 23, 1999
Lagoon ID 3 Period of Study, DOY 108-115 Depth During Study, m 5.3 Lagoon Area, ha 2.2 Maximum Depth, m 6.1 Liner Thickness, m 0.45 Depth to Groundwater, m 32 Lagoon Age, yr. 2
0
10
20 Total Depth Change: 24.5 mm
Rate Change in Depth (seepage + evaporation): 4.1 mm/day or 0.16 in./day Cumulative Depth Change (seepage +evapration) (mm)
30 108 109 110 111 112 113 114 115
Day of Year, 1998
Water Balance and Seepage Calculations
Case 1:Using Floating Lysimeters Case 2:Using Meteorological Models To Estimate Evaporation to Estimate Evaporation Depth Change, mm 24.50 Depth Change, mm 24.5 Evaporation, mm 19.72 Evaporation, mm 20.1 Seepage, mm 4.78 Seepage, mm 4.4 Seepage Rate, mm/d 0.80 Seepage Rate, mm/d 0.73 Seepage Rate, in./d 0.03 Seepage Rate, in./d 0.03
Notes: A description of the methods used to measure and calculate the water balance are provided by Ham (1999) and Ham and DeSutter (1999). The lysimeter-based estimates of evaporation were calculated using a pan coefficient of 0.81. A bulk-transfer meteorological model (see Ham (1999) Eq. 2) was used to calculate evaporation in Case 2. The period used for analysis was selected based on the following criteria: (1) optimal weather conditions - negligible precipitation and no sustained high winds that flooded the lysimeters; (2) knowledge that no waste inflow occurred; and (3) no equipment failures. In some instances,
2.19 Lagoon Seepage Worksheet, Lagoon 4 June 13, 1999
Lagoon ID 4 Period of Study, DOY 318-322 Depth During Study, m 5.4 Lagoon Area, ha 2.2 Maximum Depth, m 6.1 Liner Thickness, m 0.45 Depth to Groundwater, m 70 Lagoon Age, yr. 4
0
5
10
15
Total Depth Change: 20.4 mm Precipitation: 0 mm C umulati v e Depth Change (mm) 20 Rate Change in Depth (seepage + evaporation): 4.8 mm/day or 0.19 in./day
25 318 319 320 321 322 323
Day of Year, 1998
Water Balance and Seepage Calculations
Case 1:Using Floating Lysimeters Case 2:Using Meteorological Models To Estimate Evaporation to Estimate Evaporation Depth Change, mm Depth Change, mm 20.4 Evaporation, mm Evaporation, mm 16.7 Seepage, mm Seepage, mm 3.7 Seepage Rate, mm/d Seepage Rate, mm/d 0.9 Seepage Rate, in./d Seepage Rate, in./d 0.034
Notes: A description of the methods used to measure and calculate the water balance are provided by Ham (1999) and Ham and DeSutter (1999). The lysimeter-based estimates of evaporation were calculated using a pan coefficient of 0.81. A bulk-transfer meteorological model (see Ham (1999) Eq. 2) was used to calculate evaporation in Case 2. The period used for analysis was selected based on the following criteria: (1) optimal weather conditions - negligible precipitation and no sustained high winds that flooded the lysimeters; (2) knowledge that no waste inflow occurred; and (3) no equipment failures. In some instances,
2.20 Lagoon Seepage Worksheet, Lagoon 5 June 13, 1999
Lagoon ID 5 Period of Study, DOY 127-138 Depth During Study, m 4.9 Lagoon Area, ha 2.9 Maximum Depth, m 6.1 Liner Thickness, m 0.45 Depth to Groundwater, m 35 Lagoon Age, yr. 4
0
10
20
30
Total Depth Change: 44.4 mm
Cumulative Depth Change (mm) Precipitation: 0 mm
40 Rate Change in Depth (seepage + evaporation): 4.0 mm/day or 0.16 in./day
50 127 128 129 130 131 132 133 134 135 136 137 138
Day of Year, 1999
Water Balance and Seepage Calculations
Case 1:Using Floating Lysimeters Case 2:Using Meteorological Models To Estimate Evaporation to Estimate Evaporation Depth Change, mm Depth Change, mm 44.4 Evaporation, mm Evaporation, mm 28.1 Seepage, mm Seepage, mm 16.3 Seepage Rate, mm/d Seepage Rate, mm/d 1.5 Seepage Rate, in./d Seepage Rate, in./d 0.06
Notes: A description of the methods used to measure and calculate the water balance are provided by Ham (1999) and Ham and DeSutter (1999). The lysimeter-based estimates of evaporation were calculated using a pan coefficient of 0.81. A bulk-transfer meteorological model (see Ham (1999) Eq. 2) was used to calculate evaporation in Case 2. The period used for analysis was selected based on the following criteria: (1) optimal weather conditions - negligible precipitation and no sustained high winds that flooded the lysimeters; (2) knowledge that no waste inflow occurred; and (3) no equipment failures. In some instances,
2.21 Lagoon Seepage Worksheet, Lagoon 6 March 23, 1999
Lagoon ID 6 Period of Study, DOY 337-343 Depth During Study, m 2.1 Lagoon Area, ha .47 Maximum Depth, m 5.5 Liner Thickness, m 0.45 Depth to Groundwater, m 12 Lagoon Age, yr. 5
0
1
2
3
4
5
6
7 Total Depth Change: 9.47 mm Precipitation: 1.86 mm (DOY 340) Cumulative Depth Change (mm)
8 Rate Change in Depth (seepage + evaporation): 1.61 mm/day or 0.06 in./day 9
10 337 338 339 340 341 342 343 344 345
Day of Year, 1998
Water Balance and Seepage Calculations
Case 1:Using Floating Lysimeters Case 2:Using Meteorological Models To Estimate Evaporation to Estimate Evaporation Depth Change, mm Depth Change, mm 9.5 Precipitation, mm Precipitation, mm 1.9 Evaporation, mm Evaporation, mm 2.6 Seepage, mm Seepage, mm 8.8 Seepage Rate, mm/d Seepage Rate, mm/d 1.3 Seepage Rate, in./d Seepage Rate, in./d 0.05
Notes: A description of the methods used to measure and calculate the water balance are provided by Ham (1999) and Ham and DeSutter (1999). The lysimeter-based estimates of evaporation were calculated using a pan coefficient of 0.81. A bulk-transfer meteorological model (see Ham (1999) Eq. 2) was used to calculate evaporation in Case 2. The period used for analysis was selected based on the following criteria: (1) optimal weather conditions - negligible precipitation and no sustained high winds that flooded the lysimeters; (2) knowledge that no waste inflow occurred; and (3) no equipment failures. In some instances,
2.22 Lagoon Seepage Worksheet, Lagoon 7 March 23, 1999
Lagoon ID 7 Period of Study, DOY 56-63 Depth During Study, m 1.2 Lagoon Area, ha 1.5 Maximum Depth, m 4.6 Liner Thickness, m 0.45 Depth to Groundwater, m 30 Lagoon Age, yr. 3 mo.
0
5
10
15
Total Depth Change: 21.4 mm
Cumulative Depth Change (mm) Precipitation: 1.1 mm (DOY 61)
20 Rate Change in Depth (seepage + evaporation): 3.2 mm/day or 0.13 in./day
25 56 57 58 59 60 61 62 63 64
Day of Year, 1998
Water Balance and Seepage Calculations
Case 1:Using Floating Lysimeters Case 2:Using Meteorological Models To Estimate Evaporation to Estimate Evaporation Depth Change, mm Depth Change, mm 21.4 Precipitation, mm Precipitation, mm 1.1 Evaporation, mm Evaporation, mm 18.1 Seepage, mm Seepage, mm 4.4 Seepage Rate, mm/d Seepage Rate, mm/d 0.63 Seepage Rate, in./d Seepage Rate, in./d 0.025
Notes: A description of the methods used to measure and calculate the water balance are provided by Ham (1999) and Ham and DeSutter (1999). The lysimeter-based estimates of evaporation were calculated using a pan coefficient of 0.81. A bulk-transfer meteorological model (see Ham (1999) Eq. 2) was used to calculate evaporation in Case 2. The period used for analysis was selected based on the following criteria: (1) optimal weather conditions - negligible precipitation and no sustained high winds that flooded the lysimeters; (2) knowledge that no waste inflow occurred; and (3) no equipment failures. In some instances,
2.23 Lagoon Seepage Worksheet, Lagoon 8 March 23, 1999
Lagoon ID 8 Period of Study, DOY 325-336 Depth During Study, m 2.3 Lagoon Area, ha 1.8 Maximum Depth, m 5 Liner Thickness, m 0.45 Depth to Groundwater, m 85 Lagoon Age, yr. 3 mo.
0
5
10
15 Total Depth Change: 22.8
Rate Change In Depth
Cumulative Depth Change (mm) (seepage+evaporation) : 2.1 mm/day or 0.08 in./day 20
25 325 327 329 331 333 335 337
Day of Year, 1998
Water Balance and Seepage Calculations
Case 1:Using Floating Lysimeters Case 2:Using Meteorological Models To Estimate Evaporation to Estimate Evaporation Depth Change, mm 22.8 Depth Change, mm 22.8 Evaporation, mm 22.3 Evaporation, mm 20.5 Seepage, mm 0.5 Seepage, mm 2.3 Seepage Rate, mm/d 0.05 Seepage Rate, mm/d 0.21 Seepage Rate, in./d 0.002 Seepage Rate, in./d 0.008
Notes: A description of the methods used to measure and calculate the water balance are provided by Ham (1999) and Ham and DeSutter (1999). The lysimeter-based estimates of evaporation were calculated using a pan coefficient of 0.81. A bulk-transfer meteorological model (see Ham (1999) Eq. 2) was used to calculate evaporation in Case 2. The period used for analysis was selected based on the following criteria: (1) optimal weather conditions - negligible precipitation and no sustained high winds that flooded the lysimeters; (2) knowledge that no waste inflow occurred; and (3) no equipment failures. In some instances,
2.24 Publication Version
Seepage Losses and Nitrogen Export from Swine-Waste Lagoons:
A Water Balance Study
Jay M. Ham* and Tom M. DeSutter Department of Agronomy, Kansas State University, Manhattan, KS 66506
January 28, 1999
Journal of Environmental Quality
J.M. Ham and T.M. DeSutter, Dep. of Agronomy, Throckmorton Hall, Kansas State University, Manhattan, KS 66506. Contribution no. 99-138-J from the Kansas Agric. Exp. Stn., Manhattan, KS. Received 12 Oct. 1998. *Corresponding author ([email protected]). Abbreviations: DOY, day of year; CST, central standard time; IRT, infrared transducer; CEC, cation exchange capacity; EAF, exchangeable ammonium fraction. Mon, Jun 28, 1999
Abstract Seepage losses from animal-waste lagoons could affect ground water quality. Water balance methods were used to study seepage and nitrogen export from three swine-waste lagoons in southwestern Kansas, USA. Lagoons ranged in size from 0.8 to 2.2 ha and had an average waste depth of 5.6 m. Compacted-soil liners were 0.30 to 0.46 m thick and built with native soil or, in one case, a soil-bentonite mixture. Seepage was calculated from measurements of evaporation and changes in depth when the addition or removal of waste was precluded or quantified. Seepage rates were 1.1 , 1.1, and 0.8 mm d-1 from the three lagoons, with the lowest rate occurring at the site with a 0.46-m liner augmented with bentonite. The in-situ coefficient of permeability of the soil liners ranged from 7.8x10-8 and 1.5x10-7 cm s-1. In two lagoons built with silt loam liners (no bentonite), permeabilities on a whole-lagoon basis were about five times less than those measured from soil cores collected prior to the addition of waste. Results imply that permeability was reduced by organic sludge on the bottom of the lagoons. The average ammonium-N concentration in the lagoons was 665 mg L–1, accounting for almost all of the soluble N. Calculations indicate that the ammonium-N export rates were between 2187 and 2726 kg ha yr-1, but more information is needed regarding the fate of N deposited in the soil beneath lagoons.
2 Mon, Jun 28, 1999
Earthen lagoons are integral components of many concentrated animal operations. Lagoon waste often contains high concentrations of N, P, salts, and other nutrients that are eventually applied to farmland as liquid fertilizer. However, while the waste is being stored and treated in the lagoon, subsurface seepage losses may affect soil and water quality near the facility. Of particular concern is the movement of nitrates into local drinking water supplies. An integrated assessment of lagoon use requires consideration of aquifer vulnerability and the rate of chemical input loading at each facility (Nolan et al., 1997). For waste lagoons, input loading represents the rate at which ammonium, nitrate, and other soluble compounds flow from the containment into the underlying soil. Thus, accurate measurements or numerical models of seepage from lagoons are needed to determine the risk of groundwater contamination. Furthermore, an understanding of the factors governing seepage under field conditions is needed to improve the design, construction, and management of lagoons. Earthen lagoons are typically engineered to limit seepage to some target level by considering the hydraulic properties of the soil and the basin design (i.e., depth, thickness of the compacted soil liner). Most modern lagoons have compacted soil liners 0.3 to 0.5 m thick. However, older lagoons typically do not have a constructed liner, relying on native soil properties and self-sealing processes to retard flow. When a constructed soil liner is used, seepage through the compacted layer is often approximated by a simplified form of Darcy’s law; where flow rate is proportional to the coefficient of permeability of the soil liner, almost inversely proportional to liner thickness, and almost proportional to the depth of waste above the liner. Given that liner thickness and maximum waste depth are engineered parameters, the permeability of the compacted soil material is the critical variable that affects lagoon design and the engineered seepage rate. Prior to lagoon construction, the permeability of the liner is usually estimated in the laboratory from recompacted samples of the soil material slated for use during construction. However, liner permeabilities and lagoon seepage rates in the field can differ from those predicted from laboratory measurements. Daniel (1984) showed that whole-lagoon permeabilities at industrial sites were often 10 to 1000 times larger than would have been predicted from laboratory tests. It is difficult to sample and compact the soil in the laboratory to realistically emulate the highly variable conditions that exist in the field. Furthermore, in the field, biological and environmental factors can increase liner permeability. Wetting and drying and freezing and thawing appear to be the main factors causing cracks and weaknesses in the soil liner (Kim and Daniel, 1992; Miller and Mishra, 1989; Parker et al., 1995). McCurdy and McSweeny (1993) showed that the soil on the side embankment undergoes a process of pediogenesis and macropore formation because of biological (e.g., roots, earthworms, rodents) and physicochemical mechanisms. In large lagoons, erosion by waves may also disrupt the seal along the embankment. Although many factors may increase seepage from earthen basins, an assumption exists that liner permeability in animal waste lagoons decreases as a layer of organic sludge (manure) and sediment accumulates on the bottom of the lagoon. Several modes of sealing have been investigated: (1) physical clogging of soil pores by waste particles; (2) biological sealing of pores by polysaccharides and other microbial byproducts; and (3) chemical sealing from salt-induced dispersion of clays. Researchers have used laboratory soil cores and miniature test lagoons to explore how these processes affect the permeability of a soil liner (Avnimelech and Nevo, 1964; Barrington et al., 1987a, 1987b; Barrington and Broughton, 1988; Barrington and Madramootoo, 1989; Chang et al., 1974; DeVries, 1972; Hills, 1976; Roswell et al., 1985; Tollner et al., 1983; Travis et al., 1971). Although results varied, marked reductions in permeability typically occurred
3 Mon, Jun 28, 1999
between 30 d and 12 wk after the addition of waste. A common conclusion was that physical clogging of pores was the primary sealing mechanism, with biological processes playing a secondary role. Researchers have evaluated lagoon performance at a more integrated scale by measuring whole-lagoon water balances and soil water conditions in the field. Unfortunately, these measurements have been rare and often limited to small pilot-scale research lagoons that may not accurately represent long-term conditions at commercial facilities. Meyer et al. (1972) and Miller et al. (1985) measured soil water chemistry and soil water content to evaluate seepage under cattle-waste lagoons. Other researchers have measured seepage by quantifying the water balance of miniature research lagoons containing either dairy, cattle-feedlot, or swine waste (Clark, 1975; Davis et al., 1973; Hart and Turner, 1965; Robinson, 1973). Like the laboratory studies, field research has shown that seepage in new lagoons decreases rapidly during the first 4 to 6 months of operation; presumably as the result of the accumulating bottom sludge. For example, Davis et al. (1973) measured the water balance of newly constructed dairy-waste lagoons built on sandy loam soil with waste about 3 m deep. Seepage decreased from 58 mm d-1 after about 2 wk of operation to 5 mm d-1 after 4 mo. The multitude of complex factors affecting seepage from earthen lagoons makes it difficult to predict their ability to contain waste under a given set of conditions. Furthermore, water balance measurements of whole-lagoon seepage and bulk N export have not been made at commercial swine facilities. However, quantifying seepage and mass transport of soluble compounds into the soil surrounding the lagoon is a critical first step in estimating how N and other compounds may impact long-term soil and water quality. Measurements of seepage combined with knowledge of solute concentrations in the leachate provide an estimate of the chemical-flux boundary condition at the interface between the liquid waste and the soil. Knowing this boundary condition improves the performance of numerical models that predict the fate and transport of chemicals in the vadose zone. This report is a case study of seepage losses from three commercial swine lagoons in southwestern Kansas, USA. The objectives of the research were: (1) to develop instrumentation for measuring whole-lagoon seepage using the water balance method; (2) to measure seepage from commercial swine-waste lagoons having soil liners that differ in thickness and composition; (3) to compare measured values of whole-lagoon permeability to that predicted from laboratory measurements of the liner material; and (4) to quantify waste chemistry and the ammonium-N export rate from each lagoon.
MATERIALS AND METHODS Description of the Lagoons The three lagoons used for the study, hereafter referred to as A, B, and C, were located within 40 km of Ulysses, KS and had been built in the mid 1990s. Sites were selected with soil liners that had different thickness’ and compositions. Each lagoon served a different type of swine operation (i.e., nursery, sow, finishing). All locations used pit-recharge, pull-plug systems to handle the waste. The manure slurry was drained from the barns (i.e., under-floor pits) into the lagoons every 5 to 10 days. Detailed data on lagoon design, construction, and soil properties were provided by an engineering firm that supervised construction of all three facilities. Table 1 provides descriptions of the three lagoons and conditions during the study. In all cases, the depth of waste at the deepest point in the lagoons was within 0.8 m of the maximum working depth of 6.1 m. Long-term measurements of waste depth, made at biweekly intervals by the lagoon
4 Mon, Jun 28, 1999
operator, showed that it varied between 4.6 and 5.8 m throughout the year. No seasonal trends in depth were evident. Soil liners for Lagoons A and B were 0.46 m and 0.3 m thick, respectively, and constructed from soil native to each location. Compaction was performed in 0.15-m lifts by means of six passes with at sheepsfoot roller. The 0.46-m liner for Lagoon C was built in a similar manner except that bentonite (9.8 kg m-2) was mixed into the initial 0.15-m compacted layer, followed by 0.3 m of compacted silt loam soil obtained from a nearby barrow area. Optimum soil water was maintained during compaction, and final density to 95% Standard Proctor Density was verified by field measurement. The coefficients of permeability for the compacted soil liners on Lagoons A and C were evaluated from intact cores collected from the bottom and sides of each basin. Four cores from Lagoon A and six cores from Lagoon C were obtained prior to the addition of waste. Permeability was measured using an ELE flexible wall permeameter (Soiltest Products Division, ELE International Inc., Lake Bluff, IL) following ASTM Designation D 5084- 90 (1991). Dry densities of the liners, as determined from the intact cores, were 1743 and 1887 kg m-3 on Lagoons A and C, respectively . For Lagoon B, the permeability of the liner material was estimated prior to construction from laboratory-compacted soil using a high-pressure miniature permeameter (model K-670A, Soiltest Products Division, ELE International, Inc.).
Water Balance and Meteorological Measurements Seepage, on a whole-lagoon basis, was determined by quantifying the water balance,
Qin + P =Q out +E + S + DD [1] where Qin is waste inflow; P is precipitation, E is evaporation; S is seepage loss; Qout is waste outflow (i.e., pumping for irrigation); and DD is the rate change in storage, all in mm d-1. In Eq. [1], all variables are considered positive with the exception of DD, which is negative when water levels are declining. During periods when waste inflow and outflow were stopped and precipitation did not occur (Qin=Qout =P = 0), S was calculated as the difference between E and DD. Lagoon water balances were evaluated consecutively, starting with Lagoon A in Feb. 1998 and ending with Lagoon C in April 1998. Evaporation was measured using two floating lysimeters (Fig. 1). The chassis of each platform was Al channel supporting two low-profile flotation panels (Superdeck Marketing, Minneapolis, MN). An area between the flotation panels supported a square lysimeter pan (1.09 m by 1.09 m by 0.305 m) made from 0.8-mm-thick galvanized sheet metal. When installed, the lysimeter held 0.2 m of waste with a 0.1-m rim extending above the waterline. Water levels in the lysimeter were monitored continuously using a float-based detector housed in a wet well mounted adjacent to the pan (Fig. 2). A section of 9.5-mm diam. tubing provided a hydraulic connection between the pan and the wet well. A linear displacement transducer (LX-PA25, Unimeasure Inc., Corvallis, OR), consisting of a retractable leader and potentiometer, was used to sense changes in float height. A vibrator built from a small fan (Orion Fan, BP402012H, Newark Electronics, Chicago, IL), was attached permanently to the LX-PA25 and activated automatically every 30 min to overcome static friction in the retractable reel mechanism. Water pumps (Atwood Corp., Lowell, MI) were mounted inside and outside the lysimeter so waste could be added or removed from the pan. Pumping was required periodically to replace evaporation losses or remove excess water added by precipitation or wind-driven waves. Waste-level changes in each lagoon, DD, were measured by a float-based recorder identical to the one used in the lysimeter. The detector was attached to the staff gauge already
5 Mon, Jun 28, 1999 present in each lagoon, typically located about 15 m from the shoreline. The staff gauge consisted of a vertical pipe set in concrete. A 60-m cable routed signals from the staff gauge recorder to one of the floating platforms. Meteorological conditions 1 m above the waste surface were monitored using sensors attached to a small mast on each floating platform. Instrumentation included: a three-cup anemometer and wind vane (Wind Sentry, RM Young, Traverse City, MI); a shielded, air temperature and humidity probe (CS 500, Campbell Sci. Inc., Logan, UT); and a pyranometer (LI200, Li-Cor Inc., Lincoln, NE). Water temperatures 0.1-m below the surface were measured inside and outside the lysimeter pan with thermistors (107 Probe, Campbell Sci. Inc). During measurements at Lagoon C, water surface temperature inside and outside the lysimeter pan was measured with two infrared transducers (IRT, model 4000A, 4 degree field of view, Everest Interscience, Logan, UT). Both IRTs were calibrated with the same temperature standard using the method of Sadler and Van Bavel (1982). A tipping-bucket rain gauge (Sierra Misco 2501, Nova Lynx Corp., Grass Valley, CA) was installed on the lagoon embankment and monitored with an event recorder (Onset Computer Corp., Pocasset, MA). Sensors on each floating platform were sampled every 10 s, and data stored as 30-min averages using a datalogger (CR10X, Campbell Sci. Inc.). The datalogger was housed in a weatherproof enclosure on the flotation deck and was powered by a 12-volt battery connected to a 20-W solar panel. A cellular telephone and modem were used to transfer data from each platform to laboratories in Manhattan, KS on a daily basis. The telephone interface also allowed remote control of the water pumps. The resolution of the water-level measurements in the lysimeter and at the staff gauge was 0.16 mm when sampled by the CR10X. At each measurement site, the two floating lysimeters were deployed so that each was positioned in the center of zones representing one half of the total water surface. The cooperator halted waste additions by installing caps on the outlets of pipes used to route waste into the lagoon. The lagoon’s recirculating pumps, normally used to mix the effluent and recharge the under-floor pits, were disconnected. Every 7 to 10 d, the cooperator temporarily removed the caps to flush waste from the barns into the lagoon. After 1 or 2 days, the caps preventing waste input were reinstalled. Data were collected for about 3 weeks at each location.
Waste Chemistry Analysis Waste samples from each lagoon were collected on two dates; once during the water balance measurements and again between 29 Aug. and 9 Sept. 1998. On the first sampling date, waste was obtained at multiple depths using a 1.2-L Kemmerer sampler. During the summer sampling period, samples were collected only near the surface. Samples were chilled to near 4 C and - + transported to a commercial laboratory for analysis that included: NO3 -N, NH4 -N, total N, organic N, K+, Na+, Ca2+, and Mg 2+. Analysis was performed using EPA approved methods taken from the “Standard Methods for the Examination of Waters and Wastewaters”, 18th edition, 1989.
Pan Coefficient for the Floating Lysimeter The floating platforms were used to estimate evaporation on a daily basis from measurements of depth changes in the lysimeter pan. Aerodynamic transport and water surface temperature inside the lysimeter pan were not identical to those of the lagoon water surface. Thus, evaporation from the lagoon (E) was estimated from the measurements of pan evaporation (Ep) as
6 Mon, Jun 28, 1999
E = K p E p [2] where Kp is the pan coefficient. The value of Kp for the floating lysimeter was determined using the pan conversion method (Kohler, 1954). This approach requires measurements or estimates of water surface temperature, vapor density of the air, and the aerodynamic transfer coefficient for both the pan water and the open water in the lagoon (Brutsaert, 1982; p. 253; Eq. [11.34]). Prior to the lagoon experiments, the aerodynamic effect of the pan was quantified in a wind tunnel at the USDA-ARS Wind Erosion Laboratory, Manhattan, KS. Results showed that the bluff body effect of the pan rim reduced the aerodynamic transfer coefficient by 22%, and that the fraction of attenuation was not a strong function of wind speed. We assumed that this same relationship held for evaporation when the lysimeter was in a lagoon. Measurements in April at Lagoon C showed that daily Kp ranged from 0.7 to 1.15, with a mean value of 0.96. Daily measurements of Kp were not available at Lagoons A and B because IRT measurements of water surface temperature were not available. Thus, the average value of Kp determined from Lagoon C, was used to adjust pan measurements in Lagoons A and B. The importance of the pan coefficient was diminished at Lagoons A and B, because evaporation in February and March represented a smaller component of the water balance.
Calculating Seepage Data collected at each site consisted of evaporation measurements from the two lysimeters, a measurement of changes in water depth (evaporation + seepage) collected from the staff gauge recorder, and associated meteorological data. When waste inflows and outflow had been stopped, seepage was calculated as the difference between the change in lagoon depth and the average, corrected evaporation measured by the two floating lysimeters. Ideally, water balance determinations should encompass the full period between sequential waste additions (7 to 10 d). Integrating over longer time periods improves the resolution of the water balance and seepage calculations. However, high winds sometimes caused waves that made the output from the water-level recorders in the lysimeters and at the staff gauge “noisy.” Average wind speeds greater than 10 m s-1 for sustained periods caused waves that flooded the pan lysimeters. This required restarting the test after liquid in the lysimeters had been pumped down to normal levels. Disruptions caused by the weather (wind storms, rain) coupled with the periodic waste additions by the cooperator tended to break up the water balance measurement cycle. Nevertheless, the resolution of the equipment was adequate such that only several days of fair weather were required to estimate evaporation and seepage. More detail on the resolution of the seepage calculations will be presented in the results section. In addition to the lysimeter-based measurements, S also was calculated when E was modeled from weather data using the equation of DeBruin (1978),
a æ D öæ 0.622 ö ç ÷ç ÷ ED = ç ÷ç ÷(es - ea )U r Ce [3] 1-a è D +g øè RdTa ø
-2 -1 -1 where ED is evaporation (kg m s ); Ta is air temperature (K), u is wind speed (m s ); es-ea is - the vapor pressure deficit (Pa) at Ta; D is the slope of the saturation vapor pressure curve (Pa K
7 Mon, Jun 28, 1999
1 -1 ); g is the psychometric constant (Pa K ); Ce is the bulk transfer coefficient for water vapor (dimensionless); a represents the Priestly Taylor coefficient (1.28, dimensionless); Rd is the gas constant (287.04 J kg-1 K-1); and 0.622 is the ratio of the molecular weights of water and dry air -3 (dimensionless). A value of 1.9 x 10 was used for Ce, which is a typical value for open water when wind speed is measured at 1 m (Brutsaert, 1982; p. 126). The DeBruin equation was used to calculate evaporation using meteorological data from the floating platforms. Like the lysimeter based approach, seepage was calculated as the residual of the water balance (Eq. 1), except that E was calculated instead of measured. The modeled values of E were used to help verify the lysimeter-based calculations of S and demonstrate that it may be possible to estimate S without floating lysimeter data.
Calculating the In-Situ, Whole-Lagoon Permeability Seepage losses determined from the water balance must be equivalent to the total flow through the submerged area of the soil liner. The apparent coefficient of permeability for the -1 lagoon liner, Ks, in m s ,was calculated by solving the water balance, assuming that flow through the liner can be described using Darcy’s Law,
D SA = K ( +1)dA [4] ò s L where S is the seepage rate (m s-1); A is the surface area of lagoon (m2); L is the path length of the flow through the compacted liner (m); and D is the waste depth (m). Operationally, the right hand side of Eq. [4] was expanded to include a separate expression for each side panel and the bottom of the lagoon. This was critical because the side embankments represented between 39 and 79 % of the submerged liner areas. The average waste depth for the side panels was about half of that at the lagoon bottom. Also, we assumed that flow was vertical; thus, L was slightly larger on the sloping side panels than on the bottom. This assumes that the designed liner thickness, 0.3 or 0.46 m, was maintained normal to the slopes during construction (Table 1). The matric potential at the bottom of the liner was assumed to be zero, which is a reasonable approximation when Ks is much smaller in the compacted liner than in the underlying subsoil. The apparent Ks for each lagoon was calculated by solving Eq. [4] using the water balance measurements of S on the left hand side and the unique geometry and construction dependent parameters on the expanded right hand side. Factoring Ks from Eq. [4] assumed that hydraulic properties were homogenous over the entire liner area.
Calculating Nitrogen Export and Ammonium Adsorption + The mass of NH4 -N transported from the lagoon liquid into the underlying soil was + calculated as the product of: the lagoon area, seepage rate, NH4 -N concentration in the waste, and the time interval of interest. The equivalent depth of soil (Zeq), in m, required to adsorb the + NH4 -N lost during seepage was calculated as
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+ [NH 4 - N]St Z eq = [5] r b (CEC)(EAF)
+ -3 where NH4 -N is the ammonium concentration (cmol m ); St is total seepage over the time -3 period of interest (m); rb is bulk density of the soil (kg m ); CEC is the cation exchange capacity (cmol kg-1); and EAF is the exchangeable ammonium fraction (i.e., fraction of the exchange sites + occupied by NH4 -N). The competing effects of Mg and Ca on EAF were calculated following the methods of Lance (1972). Calculations of Zeq were tabulated using an EAF of 0.87, a CEC -1 -3 of 10 cmol kg , and a rb of 1300 kg m .
RESULTS AND DISCUSSION Water Balance Estimates of Seepage The water level recorders in both the lagoons and the floating lysimeters provided high- resolution measurements of DD. Figure 3 shows an example of depth changes over a 5-d period at Lagoon C when skies were mostly clear and the evaporative demand was similar on each day. Depth in the floating lysimeters decreased about 0.20 m over the 5 days, and diurnal patterns of E were evident. Lagoon depth, measured by the staff-gauge recorder, decreased steadily at about 4.7 mm d-1 in a linear manner. Because the recorder was mounted about 15 m from the bank, changes in wind direction caused subtle changes in float height as water was “pushed” away from or toward the staff gauge. Furthermore, wave height increased with wind speed and duration, causing a slight vertical oscillation in the float mechanism. Wind and waves also caused the tethered floating platform to tilt and bob slightly, which could have caused small errors in the lysimeter-depth measurements. Therefore, when calculating total changes in depth over a time interval, the 0600 CST (predawn) readings were used because this tended to be the time of day with the lowest wind speeds. However, winds were not always calm at this hour and caution must be used when interpolating over short time periods between points. The time intervals used for the water balance calculations of seepage were carefully selected such that the beginning and end of the period, DOY 109 and 114 in this case, were characterized by wind speeds less than 2 m s-1. Table 2 shows the water balances of all three lagoons when the floating lysimeters where used to measure E. The rate changes in depth , the sum of S and E, ranged from 1.9 mm d-1 at Lagoon A to 4.7 mm d-1 at Lagoon C. Evaporation increased from 0.8 mm d-1 in February to 3.9 mm d-1 in April. Evaporation from the two lysimeters, when in the same lagoon and averaged over the multi-day measurement period, agreed to within 0.3 mm d-1 (Table 2). Seepage, calculated as a residual, was 1.1 mm d-1 for Lagoons A and B, and 0.8 mm d-1 for Lagoon C. Seepage rates were also estimated from Lagoons A and B by solely examining the rate change in depth over a single 24-h period when evaporative demand was low. Figure 4 shows depth changes in the lagoons on DOY 45 and 73 when global irradiance was less than 7.4 MJ d-1 and the average vapor pressure deficit was less than 0.3 kPa . The rate changes in depth were –
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2.27 mm d-1 and -1.99 mm d-1 for lagoons A and B, respectively, and were linear throughout the period. Evaporation rates from the floating lysimeters were 1.2 and 0.3 mm d-1 ; thus, calculated values of S were 1.1 and 1.7 mm d-1 for Lagoons A and B, respectively. Evaporation rates calculated from the DeBruin method (Eq. [3]) over the same periods were 0.5 and 0.2 mm d-1, from Lagoons A and B, resulting in a calculated S of 1.8 mm d-1 from both lagoons. The DeBruin equation may have underestimated E during winter conditions, because it did not account for the elevated temperature of the lagoon waste. Regardless of how E was estimated, results show that S calculated from a single 24-h sample under low evaporative conditions produced results that were comparable to the independent results in Table 2. The water balances of Lagoons A and C were also estimated over 17-d periods using measurements of P and DD and calculated values of E. Waste additions, which occurred only once during each interval, were estimated from the short-term increase in depth detected by the staff gauge recorder (Fig. 5). The resolution of the water-level recorder was adequate to detect the magnitude and duration of small precipitation events. Results show that S values were 0.8 and 0.4 mm d-1 from Lagoons A and C, respectively (Table 3). These results are within 0.4 mm d-1 of those in Table 2, which is remarkable considering the potential errors in the E estimation. A long-term water balance calculation was not attempted on Lagoon B because runoff from melting snow could not be quantified during certain periods. The water balance analyses show that S values from the three lagoons tested were between 0.4 and 1.8 mm d-1, regardless of the method employed (Tables 3 and 4, Fig. 4). For each lagoon, S calculated using the three methods always agreed to within 0.6 mm d-1 and, in some cases agreed to within 0.3 mm d-1 (i.e., Lagoon A). All estimates were within a very narrow range of values, especially when one considers that the maximum allowable S for the state of Kansas was 6.3 mm d-1 when these lagoons were constructed. Thus, all three facilities were easily within state-mandated guidelines. The lowest S was observed at Lagoon C, which had a 0.46-m thick liner made from a mixture of silt loam and bentonite. Soil samples collected from an excavated area adjacent to Lagoon C showed that the texture of the subsoil beneath the liner was 72% sand, 12% silt, and 16% clay, which probably offered little resistance to saturated flow. Thus, data indicate that very low seepage rates can be achieved with a properly constructed soil liner, even when the region beneath the lagoon is highly permeable.
Permeability of the Earthen Liners Laboratory analysis of soil cores showed that Ks for Lagoon C, having the bentonite– augmented liner, was substantially lower than that observed in the other two lagoons (Table 4). Substituting the laboratory-derived Ks into a simple model like Eq. [3], and accounting for depth and geometry of each lagoon, the calculated values of S were 5.4, 5.2, and 0.4 mm d-1 from Lagoons A, B, and C, respectively. Water balance measurements showed that actual S from lagoons A and B was about 1.1 mm d-1, significantly less than that predicted from the soil cores (Table 2). Conversely, S derived from soil cores at Lagoon C was slightly less than that observed in the field.
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-8 -1 In-situ liner Ks values, calculated using Eq. [4], ranged from 7.83x10 cm s at Lagoon -7 –1 B to 1.54x10 cm s at Lagoon A (Table 4). The low Ks at Lagoon B was partially the result of using a 0.3–m thick liner in the calculations as opposed to a 0.46-m liner used for the other two locations. The Ks of the compacted liners were probably not uniform with depth as assumed in Eq. [4], especially if the sludge affected hydraulic properties at the soil-liquid interface. Thus, some caution must be exercised when interpreting the ranking of the whole-lagoon Ks values. Nevertheless, the assumptions used to calculate in-situ liner Ks are consistent with those used to evaluate Ks in the laboratory. Thus, comparison of laboratory- and field-derived Ks values is warranted. Permeabilities on a whole-lagoon basis were 4.9 and 4.7 times less than that derived from soil core analysis for Lagoons A and B, both built from silt loam soil (Table 4). At Lagoon C, Ks on a whole-lagoon basis was larger than that determined from postconstruction soil cores. Differences between the whole-lagoon and soil-core results could have been caused by sampling or measurement errors. However, the large reductions in liner Ks at Lagoons A and B were probably caused by the sealing effect of organic sludge that accumulated on the bottom of each basin. These results are very similar to the soil-column studies of Hills (1976), who found that the addition of dairy waste significantly reduced infiltration in medium- and coarse-textured soils but had little effect on infiltration in clay loam soils. Hills showed that waste additions to a silt loam soil caused a sevenfold decrease in Ks over 6 mo. If we assume that the soil core analysis represents the initial permeability of the liner, waste additions to Lagoons A and B caused a similar reductions in Ks on a whole-lagoon basis.
Waste Chemistry and Nitrogen Export Selected chemical concentrations in the liquid waste are shown in Table 5. Initially, samples were collected at multiple depths, but concentrations showed no significant signs of vertical stratification. Thus, most samples were collected within 1 m of the surface. Lack of vertical concentration gradients probably resulted from the action of mixing pumps, which were operated continuously throughout the year in each lagoon. Concentration differences between the samples collected in the winter and summer were small, and no seasonal trends were evident. Other researchers have found that N concentrations decrease in the warmer summer months and increase in the fall and winter (e.g., Dickey and Vanderholm, 1977). Ammonium-N ranged from 568 to 749 mg L-1 and represented almost all the soluble N. In both the winter and summer + samples, NH4 -N was highest at the finishing operation and lowest at the nursery. Nitrates were present only at trace levels, as would be expected under anaerobic conditions. Organic N represented about 16 % of total N. Concentrations of Ca2+ and Mg2+ were 77 and 28 mg L-1, respectively. The concentrations of these cations are important, because as components of the + leachate, they can effectively compete with NH4 -N for exchange sites in the soil beneath the lagoon liner. Concentrations of K+ and Na+ were also significant, and to a lesser extent, may also + affect how NH4 -N is distributed beneath the lagoon. Predicting N losses from the lagoons on an annual basis requires assumptions regarding the relationship between our measurements and long-term averages. As mentioned earlier, researchers have shown that S from earthen lagoons tends to stabilize after about six months of
11 Mon, Jun 28, 1999 operation. Thus, the Ks values of the soil liners in the lagoons tested are probably quite stable. Also, long-term records showed that waste depth in the lagoons did not vary substantially during the year. Thus, annual seepage losses from Lagoons A, B, and C are probably comparable to the rates derived from our short-term measurements (Table 2). Lack of variation in winter and summer waste samples suggests that the chemical concentrations in Table 5 are reasonable + estimates of the long-term averages. Given these assumptions, NH4 -N flux densities were similar from all three lagoons, averaging 2398 kg ha-1 yr-1 (Table 6). A lower S at Lagoon C + + was offset by higher NH4 -N concentrations in the effluent. The highest NH4 -N export, on both area and site bases, occurred at Lagoon B. This lagoon’s large size coupled with a seepage -1 + -1 of 1.1 mm d and a NH4 -N concentration of 679 mg L resulted in an annual export of 5698 kg site-1 yr-1 . + The equivalent depths of soil required to adsorb annual NH4 -N seepage losses ranged from 0.13 to 0.17 m yr-1. These results demonstrate that a rather small volume of soil is required + to adsorb the NH4 -N exported in the seepage losses. However, Eq. [5] does not account for a host of factors that affect solute transport under field conditions (e.g., preferential flow, + dispersion, transformations). Thus, the distribution of NH4 -N beneath a lagoon cannot be + predicted using Eq. [5]. Nevertheless, several researchers have shown that NH4 -N concentrations are elevated directly beneath the compacted liner and then abruptly decrease to trace levels at lower depths (Miller et al., 1976; Culley and Phillips, 1989). However, broad + NH4 -N seepage plumes have been observed at swine-waste lagoons built in sandy soils with shallow water tables (Ciravolo et al., 1979; Huffman and Westerman, 1995; Westerman et al., 1995).
CONCLUSIONS Results suggest that S from earthen lagoons can be determined by the water balance method over short time periods if depth is measured with sufficient resolution (e.g., 0.16 mm). The floating lysimeters provided a viable method for estimating E in lagoons but required a significant technical investment. More research will be required to determine the accuracy of the lysimeters under a wide range of environmental conditions and lagoon types. Results in Table 3 and Figs. 4 and 5 indicate that it may be possible to estimate S by simply measuring lagoon depth changes and calculating E from meteorological data. The accuracy of this approach will improve during periods of low evaporative demand when errors in E have a minimal impact. During cool overcast conditions, a reasonable estimate of S can be obtained in a single 24-h period (Fig. 4). Improved models for predicting E from lagoons are needed to lend confidence to the water balance approach. Unfortunately, lagoons are fetch-limited bodies in which the atmospheric boundary-layer is subjected to a step-change in surface conditions (wet-dry). Advection and other factors make it difficult to predict E from these systems (Webster and Sherman, 1995). Nevertheless, at each lagoon tested in this study, S values calculated using the three water balance methods (Tables 2 and 3, Fig. 4) always agreed to within 1 mm d-1 , which may be a sufficient level of resolution for many applications.
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Despite differences in liner thickness and composition, S values from the three lagoons tested were similar. The overall average S was 1.0 mm d-1, which is lower than that observed in the few whole-lagoon tests published previously. However, much of the previous work was conducted on lagoons that did not have compacted-soil liners and, in many cases, were not full– scale commercial lagoons. Also, new technology allowed us to measure changes in depth with an accuracy that was not possible in the past. Our data show that S less than 1.6 mm d-1 (1/16 in. d- 1) can be achieved with soil liners 0.3 to 0.46 m thick when proper soils and construction methods are used. Seepage and in-situ Ks of the two lagoons built with silt loam liners were about four to five times less than would have been predicted from laboratory analysis of soil cores. Thus, there is evidence some degree of self sealing resulted from the mat of organic sludge deposited at the bottom of the basin. Conversely, at Lagooc C with the bentonite-augmented liner, seepage was slightly greater than that predicted from the soil core analysis. At this site, the initial Ks of the liner may have been so low that the sludge had a minimal impact, but weathering processes may have increased S along the side embankments. Thus, sludge may reduce the permeability of liners that are initially rather “porous”, while weathering of the side embankments may increase flow through a liner that initially had a very low permeability. These two processes may combine to reduce the importance of initial liner characteristics and reduce variablity in seepage rates from a large population of lagoons. In general, data suggest that the Ks of intact or recompacted soil cores collected prior to the addition of waste may have limited value for predicting the long-term seepage rate from lagoons. The soil pore distribution or some other physical property that characterizes the liners susceptibility to clogging may be a better indicator of how well an earthen basin will contain animal waste (Barrington and Madramootoo, 1989). Although the total S was quantified, questions remain regarding the spatial variability of flow through the liner, especially along the side embankments. Previous research suggests that solutes tend to be more concentrated in soil near the shoreline (Parker et al., 1995). Also, measurements from a large number of lagoons are needed to characterize the distribution of S that may exist within a geographical region. Research on spatial variably at the field scale suggests that soil hydraulic properties fit a log normal distribution (e.g., Lauren et al., 1988). It is possible that S from lagoons may also follow this trend. However, compaction of the liner and clogging by sludge may significantly reduce the coefficient of variation in a large sample. Ammonium-N concentrations in the lagoon effluent were high, averaging 665 mg L-1 + across all sites. Concentrations of NH4 -N in the swine waste lagoons were over five times higher than the average of nearby cattle-feedlot runoff lagoons (Ham et al., 1998). Thus, there could be large differences in the N exported from different types of animal operations even if S values from the lagoons were similar. Although S values from the three lagoons tested were quite + small, a significant mass of NH4 -N could be exported into the soil at the periphery of the + compacted liner. If quasi steady-state conditions persisted at these sites over 20 yr, the NH4 -N moving into the subsoil would range from 32,840 to 113,960 kg site-1 (Table 6). It is likely that + a large fraction of the NH4 -N will be adsorbed and remain in close proximity to the lagoon, especially in areas of the profile that remain water-saturated (anaerobic) and have a high CEC. However, conversion to the more mobile nitrate form could occur where the profile is + unsaturated. A potentially large fraction of NH4 -N could convert to nitrate when a lagoon is
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emptied and dried for long periods. This potentially hazardous process suggests the need for research on protocols for lagoon closure and soil N removal or remediation. Basic research is needed to understand the key processes affecting the long-term fate and transport of N deposited beneath earthen lagoons. These results represent a small number of samples from swine operations in western Kansas. Additional measurements are needed in geographic areas with different climates, soils, and types of animal operations.
ACKNOWLEDGEMENTS Funding was provided by the Kansas Agric. Experiment Station and a grant from the Kansas Dept. of Health and the Environment. Appreciation is extended to G. Pierzynski, G.J. Kluitenberg, and L.R. Stone for their assistance with theoretical aspects of the manuscript. Technical assistance was provided by F.W. Caldwell and M. Craft.
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REFERENCES ASTM D 5084-90. 1991. Standard test method for measurement of hydraulic conductivity of saturated porous materials using a flexible wall permeameter. Annual Book of ASTM Standards 4.08: 1070-1077. Avnimelech, Y. and Z. Nevo. 1964. Biological clogging of sands. Soil Sci. 98:222-226. Barrington, S. F. and R. S. Broughton. 1988. Designing earthen storage facilities for manure. Can. Agric. Eng. 30:289-292. Barrington, S. F., P. J. Jutras, and R. S. Broughton. 1987a. The sealing of soils by manure. I. Preliminary investigations. Can. Agric. Eng. 29:99-103. Barrington, S. F., P. J. Jutras, and R. S. Broughton. 1987b. The sealing of soils by manure. II. Sealing mechanisms. Can. Agric. Eng. 29:105-108. Barrington, S. F. and C. A. Madramootoo. 1989. Investigating seal formation from manure infiltration into soils. Trans. ASAE 32:851-856. Brutsaert, W. 1982. Evaporation Into the Atmosphere. D. Reidel Publishing, Boston. 299 pp. Chang, A. C., W. R. Olmstead, J. B. Johanson, and G. Yamashita. 1974. The sealing mechanism of wastewater ponds. J. Water Pollut. Cont. Fed. 46:1715-1721. Ciravolo, T. G., D. C. Martens, D. L. Hallock, Jr. E. R. Collins, E. T. Kornegay, and H. R. Thomas. 1979. Pollutant movement to shallow ground water tables from anaerobic swine waste lagoons. J. Environ. Qual. 8:126-130. Clark, R. N. 1975. Seepage beneath feedyard runoff catchments. In: Managing Livestock Wastes, Proceedings of the Third International Symposium on Livestock Wastes, ASAE, St. Joseph, MI, 49085. pp. 289-295. Culley, J. L. B. and P. A. Phillips. 1989. Retention and loss of nitrogen and solids from unlined earthen manure storages. Trans ASAE 32:677-683. Daniel, D. E. 1984. Predicting hydraulic conductivity of clay liners. J. Geotech. Eng. 110:285- 300. Davis, S., W. Fairbank, and H. Weisheit. 1973. Dairy waste ponds effectively self-sealing. Trans. ASAE 16:69-71. DeBruin, H.A.R. 1978. A simple model for shallow lake evaporation. J. Appl. Meteorol. 17:1132- 1134. De Vries, J. 1972. Soil filtration of wastewater effluent and the mechanism of pore clogging. J. Water Pollut. Control Fed. 44:565-573. Dickey, E. C. and D. H. Vanderholm. 1977. Feedlot runoff holding ponds: Nutrient levels and related management aspects. J. Environ. Qual. 6:307-312. Ham, J.M., L. Reddi, C.W. Rice and J.P. Murphy. 1998. Evaluation of lagoons for the containment of animal waste. Kansas Center for Agric. Resources and the Environment. Kansas State University, Manhattan. 152 pp. Hart, S. A. and M. E. Turner. 1965. Lagoons for livestock manure. J. Water Pollut. Cont. Fed. 37:1578-1596. Hills, D. J. 1976. Infiltration characteristics from anaerobic lagoons. J. Water Pollut. Cont. Fed. 48:695-709. Huffman, R. L. and P. W. Westerman. 1995. Estimated seepage losses from established swine waste lagoons in the lower coastal plains of North Carolina. Trans of the ASAE 38:449- 453. Kim, W. H. and D.E. Daniel. 1992. Effects of freezing on hydraulic conductivity of compacted clay. J. Geotech. Eng. 118:1083-1097.
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Kohler, M.A. 1954. Lake and Pan Evaporation. p. 127-148 In Water Loss Investigations: Lake Hefner Studies, Tech, Report. Prof. Paper 269. Geol. Survey. U.S. Dept. Of Interior. Lance, J. C. 1972. Nitrogen removal by soil mechanisms. J. Water Pollut. Cont. Fed. 44:1352- 1361. Lauren, J.G., R.J. Wagenet, J. Bouma, and J.H.M. Wosten. 1988. Variability of saturated hydraulic conductivity in a glossaquic hapludalf with macropores. Soil Sci. 145:20-28. McCurdy, M. and K. McSweeney. 1993. The origin and identification of macropores in an earthen-lined dairy manure storage basin. J. Environ. Qual. 22:148-154. Meyer, J. L., E. Olson, and D. Baier. 1972. Manure holding ponds found self sealing. California Agriculture 26:14-15. Miller, C. J. and M. Mishra. 1989. Modeling of leakage through cracked clay liners-I: State of the art. Water Res. Bull. 25:551-556. Miller, M. H., J. B. Robinson, and D. W. Gallagher. 1976. Accumulation of nutrients in soil beneath hog manure lagoons. J. Environ. Qual. 5:279-282. Miller, M. H., J. B. Robinson, and R. W. Gillham. 1985. Self-sealing of earthen liquid manure storage ponds: I. A case study. J. Environ. Qual. 14:553-538. Nolan, B. T., B. C. Ruddy, K. J. Hitt, and D. R. Helsel. 1997. Risk of nitrate in groundwaters of the United States-A national perspective. Environ. Sci. Technol. 31:2229-2236. Parker, D. B., J. A. Nienaber, D. E. Eisenhauer, and D. D. Schulte. 1995. Unsaturated seepage from a feedlot runoff storage pond. ASAE Paper No. 95-1765. ASAE, St. Joseph, MI 49085 . Robinson, F. E. 1973. Changes in seepage rate from an unlined cattle waste digestion pond. Trans. of the ASAE 16:95-96. Roswell, J. G., M. H. Miller, and P. H. Groenevelt. 1985. Self-sealing of earthen liquid manure storage ponds: II. Rate and mechanism of sealing. J. Environ. Qual. 14:539-542. Sadler, E.J., and C.H.M. Van Bavel. 1982. A simple method to calibrate an infrared thermometer. Agron. J. 74:1096-1098. Standard Methods For Examination of Water and Waste Water. 1989. 18th Ed. American Public Health Assoc., New York, NY. Tollner, E. W., D. T. Hill, and C. D. Busch. 1983. Manure effects on model lagoons treated with residue for bottom sealing. Trans. ASAE 26:430-435. Travis, D. O., W. L. Powers, L. S. Murphy, and R. I. Lipper. 1971. Effect of feedlot lagoon water on some physical and chemical properties of soils. Soil Sci. Soc. Amer. Proc. 35:122-126. Webster I.T., and B.S. Sherman. 1995. Evaporation from fetch-limited water bodies. Irrig. Sci. 16:53-64. Westerman, P. W., R. L. Huffman, and J. S. Feng. 1995. Swine-lagoon seepage in sandy soil. Trans ASAE 38:1749-1760.
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Table 1. Descriptions of the lagoons used for the study.
Lagoon Characteristic A B C
Type of facility Nursery Sow Finishing Construction date 1995 1995 1996 Period of Study, DOY 44-63 64-90 91-125 Max. capacity, m3 27,619 101,352 109,983 Inside slopes 3:1 3:1 4:1 Max. working depth, m 6.1 6.1 6.1
Depth of waste during study, m † 5.5 5.8 5.3
Surface area during study, ha 0.72 2.09 2.15
Liner thickness, m 0.46 0.30 0.46 Liner texture Silt Loam Silt Loam Silty Clay+bentonite‡ Depth to water table, m 58 70 32 Soil Ulysses Richfield Dalhart † Represents the depth of waste at the deepest point in the lagoon. ‡ Approximately 9.8 kg m-2 of bentonite was mixed into the lowermost 0.15-m layer of the compacted liner. Silty clay used for the liner was not native to the site of construction.
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Table 2. Water balance measurements of whole-lagoon seepage. Shown are the average rate change in depth and evaporation as measured over the evaluation period. Seepage was calculated as a residual.
Lagoon Parameter A B C Average
Dates used for analysis, DOY 48-55† 80-85 109-114 Rate change in depth, mm d-1-1.9 -3.3 -4.7 -3.3 Evaporation, mm d-1 Lysimeter 1 1.0 - 3.8 Lysimeter 2 0.7 2.2 4.0 Average 0.8 2.2 3.9 2.3 Seepage, mm d-1 1.1 1.1 0.8 1.0
† DOY 51 and 52 were excluded because of waste additions from the confinement barns.
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Table 3. Water balances of Lagoons A and C measured over 17-day periods. The change in depth, waste additions, and precipitation were measured directly. Evaporation was modeled using Eq. [3] and seepage was calculated as a residual using Eq. [1]. Numbers in parentheses are seepage or evaporation expressed as mm d-1.
Parameter Lagoon A Lagoon C
mm
Net change in depth -27.4 -55.5 Input: Waste additions 33.6 59.4 Precipitation 9.5 6.0 Output: Pumping† 0.0 27.9 Evaporation 53.8 (3.2) 86.6 (5.1) Seepage 16.7 (1.0) 6.4 (0.4)
† After waste was flushed from the barns, some liquid from Lagoon C was pumped into the below- floor pits as an odor control measure. Fresh water was used for this purpose at Lagoon A, so no pumping output resulted.
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Table 4. Permeability of the compacted-soil liners measured from soil cores in the laboratory and that calculated on a whole-lagoon basis from water balance data (Eq. [4]). Also shown are the ratios of the soil-core and water-balance derived permeabilities.
Coefficient of permeability Lagoon Soil Cores ±SE Whole-lagoon Ratio
cm s-1
A 7.59x10-7 ± 1.95x10-7 1.54x10-7 4.9 B 3.70x10-7† 7.83x10-8 4.7 C 4.44x10-8 ± 3.19x10-8 1.00x10-8 0.4
† Permeability was estimated from a single recompacted core, thus SE could not be approximated.
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Table 5. Selected chemical characteristics of the liquid waste contained in the lagoons. Nitrogen and chloride concentrations are the average of two sampling dates, winter and summer. Calcium, Mg, K, and Na were measured only in the summer. Lagoon Measured ______parameters A B C Average
mg L-1 - NO3 -N 1 1 <1 1 + NH4 -N 568 679 749 665 Total N 701 778 910 796 Organic N 134 98 161 130 Ca2+ 90 70 71 77 Mg2+ 25 15 19 28 K+ 678 628 792 699 Na+ 198 340 282 273
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Table 6. An estimate of ammonium-N exported into the soil beneath the lagoons and the calculated equivalent-depth of soil required to adsorb the annual exported ammonium. Lagoon areas, seepage rates, and chemical concentrations used for the calculations are shown in Tables 1, 2 and 5.
Equivalent depth Lagoon NH4-N Flux of adsorption Instantaneous Annual per Site kg m-2 s-1 kg ha-1 y-1 kg site-1 y-1 m A 7.23x10-9 2281 1642 0.14 B 8.64x10-9 2726 5698 0.17 C 6.94x10-9 2187 4702 0.13
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List of Figures
Fig. 1. Photograph of a floating-pan lysimeter and meteorological platform used to measure evaporation and environmental conditions at the lagoons. Fig 2. Diagram of the apparatus used to measure changes in water level in the lagoons and in the floating-pan lysimeters. The components of the detector are: (a) ellipsoid float, (b) aluminum tube and support shaft, (c ) lead ballast disk, (d) linear displacement transducer and spring-loaded retractable reel mechanism, (e) vibrator, (f) retractable tether line, (g) strain relief, (h) multiconductor signal cable, (i) removable cap, (j) 0.15 m-diam. pipe, (k) acrylic support platform, and (l) vent. Fig. 3. Cumulative evaporation and change in waste depth at Lagoon C between DOY 109 and 114, 1998. Symbols on the evaporation curve mark 0600 CST, which was considered the best time to evaluate evaporation and depth changes between days. Fig. 4. Relative changes in depth at Lagoons A and B under conditions of low evaporative demand (i.e., cloudy skies, low vapor pressure deficits). Model estimates of evaporation from Lagoons A and B over these periods were 0.5 and 0.2 mm d-1, respectively. Fig. 5. Changes in depth over 17-d periods at Lagoons A and C as measured with a float-based water–level recorder (Fig. 1). The abrupt increases in depth on DOY 51 and DOY 105 were caused by the addition of waste from the production barns. Other small increases in depth were from precipitation (snow or rain). Losses were the result of seepage and evaporation. Some waste from Lagoon C was removed by pumping on DOY 105, 106, and 107. The complete water balances of both lagoons are provided in Table 3.
23 Mon, Jun 28, 1999
Figure 1.
Figure 2.
24 Mon, Jun 28, 1999
Figure. 3
25 Mon, Jun 28, 1999
26 Mon, Jun 28, 1999
27 Chapter 4
LINER PERFORMANCE – EXPERIMENTAL INVESTIGATIONS
Principal Investigator
Lakshmi N. Reddi, Ph.D., P.E.
Research Assistants
Hugo Davalos Mohan Bonala
Abstract
This task of research project involved two phases. In the first phase, presented in Chap 4, compacted specimens of Kansas soils were tested with animal waste as the influent. The key objective of this phase of research was to assess the range of seepage quantities and the transport characteristics of nitrogen in the ammonium form (NH4-N) through the compacted soils. Results from this phase indicated steady increase of microbial counts in the effluent. However, biological clogging did not appear to be prominent during the time period it took for NH4-N breakthrough. The results indicate significant differences in microbial uptake of NH4-N among samples of the same soil type. In the second phase, presented in Chap 5, analytical and numerical solutions were used to simulate ammonium transport in the field-scale liners and to estimate upper bound values of NH4-N travel times and end concentrations in the underlying soils. Results from this phase showed drastic differences in travel times and end concentrations of NH4-N among liners prepared from the same soil type. The potential for significant retardation, decay, and saturation levels of NH4-N in clay liners, suggests that liner thickness is an important parameter. It is concluded that mass transfer characteristics of liner material, cation exchange capacity (CEC) and microbial uptake in particular, should be important considerations in the design of animal waste lagoon liners.
Introduction
Animal waste lagoons constructed at sites with coarse-grained soils are often provided with a compacted clay liner to prevent excessive seepage from the lagoon and limit ground water contamination below the lagoon. At present, many states in the US are sharply divided on the design standards for waste lagoons. The regulatory standards in general restrict the maximum seepage through the lagoon; however, this seepage rate varies from 0.64 cm/day (0.25 inch/day) in the states of Kansas and Nebraska to 0.04 cm/day (1/56 inch/day) in the states of Minnesota and Missouri. The inconsistency in the regulations is primarily because some state regulatory agencies accept the notion that the liner seals in due course of time as a result of particulate and biological clogging. Some regulatory agencies support the usage of natural/compacted soils with permeability as high as 1 x 10-6 cm/sec in anticipation of time-dependent liner sealing.
A common practice of evaluating the suitability of soils as lagoon liner materials is to characterize the soils into four groups based on the fines content of the soils and their Atterberg limits (USDA, 1997) (Table 1). The National Resources Conservation Service (NRCS) suggested this grouping based on a database of permeability tests performed on over 1,100 compacted soil samples. The permeability of the soils in each of these groups was believed to vary in a relatively narrow margin. It was indicated that natural soils classified under groups III or IV usually have permeabilities that result in acceptable seepage losses. According to the NRCS, soils in Group I require a liner to be placed, and “soils in Group II may or may not require a liner. Documentation through laboratory or field permeability testing or by other acceptable alternatives is advised.” NRCS cautions that these guidelines are only qualitative and that site-specific hydrogeological conditions may sometimes require liners even for soil groups III and IV.
One of the important design considerations that differentiates the lagoon liners from landfill liners is the extent to which fate and transport of contaminants are controlled by the operation of the facility. Unlike a sanitary waste landfill liner, the lagoon liner might be exposed to the environment whenever the lagoon is dried for clean- up operations. Animal waste lagoons lose their volume at about 1% per year and at even faster rate with cattle manure than with swine waste manure, because of the sedimentation and formation of organic sludge at the bottom of the lagoon (Sweeten et al. 1980). Periodic emptying for clean up of the lagoon bottom is often recommended. The top section of the liner within which NH4-N was accumulated could be removed during the clean up process and used as a fertilizing resource. Timely placement of a new section could provide a new buffer zone for accumulation of NH4-N during the next cycle of anaerobic phase. The extent to which NH4-N is retarded and decayed in the liner is thus an important consideration. The fate and transport characteristics of NH4-N in the liner material could be used to make important recommendations in design and operation of the lagoon.
The specific objectives addressed in this phase of the investigation are: i) to assess the leachability characteristics of compacted clay samples, specific to soil types in the state of Kansas and to the NH4-N, and ii) to assess the extent to which liner properties control the transport of NH4-N during the anaerobic phase of lagoon operation. The first objective was achieved through laboratory investigations on selected soils from Kansas and waste streams from animal feeding operations in Iowa and Kansas. To fulfill the second objective, both analytical and numerical solutions (obtained with an existing software package) were used. The laboratory tasks of the investigation are presented in this chapter. Chapter 5 deals with the modeling aspects.
Experimental Materials and Methods
In view of the importance of southwestern region of Kansas for swine industries, soil samples were acquired from three different areas in Stevens County of that region. Native soils are typically used in lagoon liner construction in that region, and these soils are believed to be representative of soils used in existing la goon liners. Table 2 presents the general characteristics of these soils and their USDA grouping. Soil No. 1, which was a coarser material, was acquired with the intent of creating a modified soil by mixing bentonite with it. Soil No. 1B refers to the mixture, 94% Soil No. 1 and 6% commercial bentonite. The fines contents (using Hydrometer and Sieve analyses) and the Atterberg limits reported in Table 2 were determined using the ASTM standard tests, D-422, D- 421, and D-4318, respectively. All of the three soil types belonged to Group II, hence the emphasis on the laboratory component of this study to check if the soils meet the seepage standards imposed by the Kansas Department of Health and Environment (KDHE). According to the USDA Soils Survey, the three soil types originated from Dalhart-Otero fine sandy loams (Dx), Vona loamy fine sand (Vo), and Vona-Tivoli loamy fine sands (Vx) (Soil No. 1), and from Richfield silt loam (Rm) and Ulysses silt loam (Ua) (Soil Nos. 2 and 3).
Figure 1 shows the compaction characteristics of the three soil types. The optimum molding water content varied from 10% to about 23%, and the peak dry densities exhibited a wide range, from 15.5 kN/m3 to 19.3 kN/m3. Of importance in these results is the behavior of the bentonite-mixed soil sample, which had a higher maximum dry density than the other two mixtures. As will be evident from the seepage results presented subsequently, modifying coarse soils with nominal percentages of bentonite is a viable alternative to natural borrow materials. Soil No. 3 was obtained in two different batches from the field site. This allowed an examination of the natural variability of the material. As seen in Fig. 1, the compaction curves for the two soils differed to some extent. However, this difference is not significant enough to cause major differences in hydraulic conductivity, as seen later.
After the compaction characteristics of the soils were identified, soil samples were prepared in the same molds with selected molding water contents, for determining the seepage behavior of soils. There were two main reasons for selecting this method to determine the permeability as opposed to the several existing methods including flexible wall permeameter. First, compaction permeameters enable a simple and quick determination of leachate quantity and quality, as opposed to flexible wall permeameters (Shackelford and Daniel, 1991a, 1991b; Daniel 1994). Second, although they are known to suffer from sidewall leakage and overestimation of the permeability, the estimates will be on the conservative side for the purpose of this study.
A number of samples belonging to the three soil types were permeated with water (for permeability experiments) or the waste liquid (for leachate quality determination) and subjected to gradients with the help of pressured air lines. The effluent from the permeameters was closely monitored at regular time intervals. Prior to application of gradients, the soil samples in the permeameters (each 5-cm in thickness) were allowed to saturate under standing liquids. In all the cases, permeabilities were determined at the gradients corresponding to the maximum operating head of lagoons, 6.1 m (20 ft). The coefficient of permeability (k) of the soil samples was obtained using Darcy’s law:
Q k = (1) iADt
where Q = total volume of effluent collected; i = pressure gradient applied; A = area of cross-section of the soil sample; and Dt = time period of effluent collection.
Because the seepage characteristics of compacted clays are known to exhibit variability, a number of reproducibility experiments were conducted on duplicate soil samples. Also, a few experiments were duplicated by a different research assistant to account for possible human errors. Permeability experiments were also conducted at several points on the compaction curve, including optimum water content, wet of optimum, and dry of optimum.
After a steady flow rate of effluent was observed in each of the experiments, the permeability experiments were terminated, and water on the top soil samples was replaced by samples of swine or livestock waste acquired from various facilities in both Kansas and Iowa. About ten gallons of waste suspension were transported to the laboratory and subsamples from the suspension were pipetted into the permeameters. Although the waste suspension was thoroughly agitated prior to pipetting, it was felt that the suspended solid quantities in the subsamples were less than that of the main suspension. This was not viewed as a limitation of the study, since under-representation of particles in the suspension can only give an upper bound value of leachate quantities. The leachate quantities were collected in 5 ml sample bottles and were periodically sent for chemical and biological analyses (primarily Cl, NH4-N, NO3-N, and microbial counts). The influent was replenished at approximately two-week intervals. At the time of each replenishment, control samples of the waste influent were sent for analytical testing.
Results and Discussion
The permeabilities obtained for the three soil types are shown in Table 3. The permeability for the three soil types varied in a range of two orders of magnitude with the maximum coefficient of permeability being 4.95 X 10-7 cm/s and the minimum being 4.75 X 10-9 cm/s. The corresponding seepage rates in the field for a liner of thickness d = 0.9 m (3 ft), varied between 0.33 and 0.0032 cm/day. The effect of d on seepage rate is shown in Fig 2. For the range of k observed in the present laboratory investigation, the field lagoon liners will meet the KDHE standards even if the liner thickness is as low as 0.5 m (1.6 ft). This fact becomes more relevant as the transport characteristics of NH4-N are discussed in the next chapter.
For the three samples which were tested with swine manure (1B.14, 2.22.2, and 3.24.2), the permeabilities declined somewhat immediately after the introduction of the waste liquid and then leveled off, as shown in Fig. 3. In all of the cases, the reduction occurred in the initial time periods, hence it was attributed to the physical clogging of samples caused by fine particles in the influent suspension. The local variations in the permeabilities seen in Fig. 3 are attributed to the sample collection scheme, which involved minor variations in emptying the small polyethylene tube connecting the vial to the effluent port of the permeameters. Unfortunately, these samples had to be disassembled due to lack of cooperation from the farms supplying the soils and waste streams. Further discussion will be restricted to samples belonging to Soil Type 3.
Permeability variations for samples subjected to livestock waste and swine waste from Southwestern Kansas are shown in Figures 4 and 5, respectively. The permeabilities started declining after being steady during the first two months. The exception is Sample 3.18.2, which maintained low permeabilities throughout. Microbial counts for the effluent samples during the testing period are shown in Fig. 6. In spite of the sharp increases in microbial counts observed in all the three cases during the first two months, the biomass growth inside the sample did not seem to be sufficient to bring down the permeability of the samples. As will be seen later, the fastest breakthrough of NH4-N occurred right around two months for the laboratory sample. This indicates that it may not be a conservative design practice to account for time-dependent reductions in permeability of clay liners due to biological clogging. Literature is sharply divided on the issue of whether biological clogging is prominent in lagoon liners. Although a 3-fold reduction in permeability in the case of clays and a 25-fold reduction in the case of sands was reported in the literature (Barrington et al. 1987a, 1987b; Barrington and Broughton, 1988; Barrington and Madramootoo, 1989; Rowsell et al. 1985), much of the reduction was attributed to the mat of organic sludge and sediment settled on the top of the liner material. Physical clogging involving sealing of the pores by particulates was in general considered as the primary sealing mechanism. When one considers the fact that the sides of the lagoon may not allow for the formation of a sealing layer, it appears prudent not to consider time-dependent reductions in permeability of the liner in the design process.
Chloride and NH4-N concentrations in the effluent are shown in Figs. 7 and 8 for the three samples tested with livestock waste from Kansas farms. In general, high concentrations of ammonium and chloride were observed in the effluents of all the three samples. Considering the average concentrations in the influent of Ammonia (235 mg/L) and Chloride (1020 mg/L), it is clearly seen that breakthrough was achieved relatively faster in the case of Chloride whereas it was significantly retarded and decayed in the case of NH4-N. The decay of NH4-N is because of its consumption by microorganisms. + - Nitrogen is available as a source to the organisms in two forms – NH4 and NO3 . The effluent concentrations of NO3-N, not shown here, are all steady at about 0.5 mg/l. When + nitrogen is present in both forms, organisms prefer the ionic source of N in NH4 for + - protein synthesis. Preferential uptake of NH4 over NO3 was demonstrated in several studies (see Paul and Clark, 1996, p. 209).
The retardation of ammonium is expected because of the cation adsorptive capacity of negatively charged clay particles. Significant retardations observed in Fig. 8 can be understood in terms of the potential NH4 saturation rates of soils. The adsorption of ammonium is primarily dependent on the cation exchange capacity (CEC) of the soil and the concentration of the other competing cations in the waste (e.g., calcium and magnesium). Using representative concentrations of the various chemical constituents from waste lagoons in the state of Kansas and the lower bound CEC of soils at the site where the soils of this study were taken from (10 cmol/kg), Ham et al. (1998) showed that annual ammonium losses into the bottom soils from lagoon could be adsorbed in an equivalent depth of soil only 7.5 inches thick. Such low cation saturation rates were also documented by Barrington et al. (1987b) who reported that NH4 in natural dairy and hog slurries could be adsorbed at the rate of 0.01 to 0.04 m/year in clays with a CEC of 25 cmol/kg. Thus, it is possible that the clay liners may be used as reservoirs to conserve nitrogen in the form of NH4 for possible use as fertilizer when the lagoon is emptied and cleaned.
References
Barrington, S.F. and R.S. Broughton. 1988. Designing earthen storage facilities for manure. Can. Agric. Eng. 30:289-292.
Barrington, S.F., P.J. Jutras, and R.S. Broughton. 1987a. The sealing of soils by manure. I: Preliminary investigations. Can. Agric. Eng. 29:99-103.
Barrington, S.F., P.J. Jutras, and R.S. Broughton. 1987b. The sealing of soils by manure. II: Sealing mechanisms. Can. Agric. Eng. 29:105-108.
Barrington, S.F. and C.A. Madramootoo. 1989. Investigating seal formation from manure infiltration into soils. Trans. of the ASAE 32:851-856.
Daniel, D.E. 1994. State-of-the-art: Laboratory hydraulic conductivity tests for saturated soils. In: Hydraulic conductivity and waste contaminant transport in soil, edited by Daniel, D.E. and Trautwein, S.J. American Society for Testing and Materials (ASTM), STP 1142, pp. 30-78.
Ham, J.M., L.N. Reddi, C.W. Rice, and J.P. Murphy. 1998. Evaluation of lagoons for containment of animal waste. A Research Report submitted to the Kansas Department of Health and Environment, April 28, 1998.
Paul, E.A. and F.E. Clark. 1996. Soil Microbiology and Biochemistry. 2nd edition. Academic Press, Inc. San Diego, California. Page 209.
Rowsell, J.G., M.H. Miller, and P.H. Groenevelt. 1985. Self-sealing of earthen liquid manure storage ponds: II. Rate and mechanism of sealing. J. Environ. Qual. 14:539-542.
Shackelford, C.D., and Daniel, D.E. 1991a. Diffusion in saturated soil. I: Background. Journal of Geotechnical Engineering, ASCE, 117(3):467-484.
Shackelford, C.D., and Daniel, D.E. 1991b. Diffusion in saturated soil. II: Results for compacted clay. Journal of Geotechnical Engineering, ASCE, 117(3):485-506.
Sweeten, J.M., L.M. Safley, and S.W. Melvin. 1980. Sludge removal from lagoons and holding ponds: case studies. In: Livestock Waste: A Renewable Resource, Proceedings of the 4th International Symposium on Livestock Wastes. ASAE, St. Joseph, MI 49085. Pp. 204-210.
U.S. Department of Agriculture (USDA). 1997. Geotechnical design and construction guidelines, 651.0703. Draft Report. Natural Resources Conservation Service.
Chapter 5
LINER PERFORMANCE – MODELING INVESTIGATIONS
Principal Investigator
Lakshmi N. Reddi, Ph.D., P.E.
Research Assistants
Hugo Davalos Mohan Bonala
Introduction
As discussed in Chapter 4, seepage rates obtained from the laboratory experiments indicated that the Southwest Kansas soils satisfy the Kansas regulated seepage rate limit (0.25 in/day). Even though the quantity of the contaminants leaking from the lagoon is limited, the quality of the ground water below the lagoon depends on the concentration of contaminants present in the effluents. The fate and transport of contaminants in the liner and in the subsurface is a complex problem involving the interactions among the soil, contaminants, and bacteria present in the animal waste. The specific objectives addressed in this chapter are:
· Estimation of contaminant transport parameters · Prediction of contaminant concentrations vs. time in the subsurface
Estimation of Solute Transport Parameters
In view of the numerical simulations needed to understand the impact of leachate on groundwater quality, the transport characteristics of chloride and ammonium were obtained fitting the solution of an advection-dispersion type equation to the experimental data. The governing differential equation for the solute transport in the compacted soil column can be written as (see van Genuchten 1981; Shackelford 1993):
¶c D ¶2c v ¶c = h - s - lc (2) ¶ 2 ¶ t R d¶x R d x where c = concentration of the solute, Dh = hydrodynamic dispersion coefficient, Rd = retardation coefficient, vs = seepage velocity in the soil column, and x and t = space and time co-ordinates, respectively. The analytical solution for the above differential equation and for the initial condition, c = 0 (x >=0) and boundary conditions, c = c0 (x=0) and dc/dx = 0 (x = ) is given by Ogata and Banks (1961):
c(x, t) 1 é (vs - U)x ù é Rdx - Ut ù 1 é(vs + U)xù é R d + Ut ù = expê ú erfcê ú + exp ê ú erfcê ú (3) c0 2 ë 2Dh û ëê2 DhR dt ûú 2 ë 2D h û ëê2 DhRdt ûú
2 1/ 2 where U = (vs + 4Dh Rdl) .
A program was written in Matlab to compute the squared differences between experimental observations and the analytical solution (Eq. 2) for chosen values of Rd, Dh, and l. In the case of chloride, Rd was set equal to 1 and l was set equal to zero because of it being a conservative solute. The best-fit analytical solution using Eq. 2 is plotted with the experimental data in Figures 1-3 and in Figures 4-6 for NH4-N and Cl, respectively. The values of Rd, Dh, and l, ultimately selected (shown in Table 1) were those corresponding to the minimum squared differences. It is important to note the significant differences in the decay coefficient among the three samples of the same soil type. It is apparent that the microorganism uptake of NH4-N may depend on the pore structure, which is influenced by the molding water content. The decay coefficient plays an important role in NH4-N transport, as seen later. Pore scale examination of microbial colony formation, which is beyond the scope of the present work, may lead to useful insight on how molding water contents influence microbial activity.
The best-fit estimates shown in Tables 1 and 2 were in turn used in the analytical solution (Eq. 2) to obtain possible breakthrough curves for field-scale liners. The results for liners of three different thicknesses made of Soil Type 3.22 are shown in Fig. 7. It is clear that maximum NH4-N loading into the natural soils could be decreased by 90% of the lagoon source and the time for its breakthrough could be greater than 10 years for a liner thickness of 0.9 m (3 ft). Removal of top few cm of NH4-N saturated layer prior to this period (during clean up of the lagoon bottom) and its replacement with a clean soil might significantly improve these numbers; however, this scenario requires extensive modeling and is beyond the scope and intent of this investigation.
The impact of NH4-N exiting the liner on groundwater quality will depend on a host of hydrogeological parameters such as the depth to the groundwater table, and physicochemical and biological characteristics of the unsaturated soils underlying the lagoon liner. Although the leachate quantities are far below the Kansas regulated limit of 0.64 cm/day (0.25 inch/day), the high concentrations of NH4-N (particularly in the case of swine facilities) may not be attenuated enough in the unsaturated domain to bring the concentrations at groundwater level to be within the drinking water quality standards. An upper bound estimate of the impact of NH4-N transport on groundwater quality was obtained using numerical simulations described below.
Numerical Simulations Using SWMS-2D model
The program SWMS-2D is a finite element model (Simunek et al. 1994) developed originally for simulating two-dimensional water and solute movement in variably saturated media. The program solves the Richard’s equation for saturated and unsaturated water flow and Fickian based advection-dispersion equation for solute transport. The transport equation includes provisions for linear equilibrium adsorption, zero-order production, and first-order degradation. The program may be used to analyze water and solute movement in unsaturated, partially saturated, or fully saturated porous media. The governing equations are solved using a Galerkin type linear element method applied to a network of triangular elements. Integration in time is achieved using an implicit finite difference scheme for both saturated and unsaturated conditions. The resulting equations are solved in an iterative fashion by linearization.
The numerical model was used to simulate the NH4-N transport in a two-layer system – the lagoon liner and the underlying natural soils as shown in Figure 8. Only an upper bound estimate of NH4-N concentrations were sought in terms of the liner properties. For the purpose of this hypothetical simulation, a sandy soil conducive to faster transport (with a high saturated permeability of 7 m/day and longitudinal 2 dispersivity of 450 cm /year) of NH4-N with no retardation and decay were chosen. The problem is idealized using the following assumptions: i) the lagoon head is constant throughout the simulation period, ii) percolation in the unsaturated zone underlying the liner begins under a constant suction head of 5 cm in the domain at time t = 0; iii) initial NH4-N concentration in the natural soil = 0, iv) the hydrodynamic dispersion, retardation factor, and decay coefficients in the liner are constant throughout the simulation, v) pressure head in the lagoon liner varies linearly between suction head of the natural soils at the bottom and the lagoon head at the top at time t = 0, and vi) mass transfer + - mechanisms, nitrification (conversion of NH4 to NO3 in particular), are ignored in order to estimate the maximum possible NH4 concentrations at groundwater table elevation.
Modeling Results and Discussion
The sensitivity analyses conducted using the numerical model have all shown predictable trends as the properties of the two layers are varied. The retarded and decayed transport of NH4-N is demonstrated in comparison with the transport of a conservative tracer Cl in Fig. 9. Although an increase in the operating head of the lagoon quickens the arrival of the contaminant at the groundwater table, its effect is not as significant as the effect of increasing the liner thickness. As seen in Fig. 10, a 0.9 m (3 ft) thick liner subjected to a load of 6.10 m (20 ft) has its Cl breakthrough delayed by 10 years when compared with the case of a 0.3 m (1 ft) liner subjected to an operating head of 3.05 m (10 feet).
The drastic effect of the engineering properties of liners is demonstrated in Fig. 11. Sample 3.20.1 has a higher permeability and a lower decay coefficient compared with Sample 3.18.2. Accordingly, at field scale, liners corresponding to Sample 3.20.1 are associated with faster travel times and higher end concentrations, as shown in Fig. 11. The permeabilities of both 3.18 and 3.20 are such that the seepage rates remain well within the state stipulated limit of 0.64 cm/day (0.25 inch/day) for a maximum operating head of 6.10 m (20 ft) and for liner thicknesses as low as 0.5 m (1.6 ft). However, when one considers the mass transfer characteristics of NH4-N in the liners, it is obvious from Fig. 11 that the engineering properties of liners play larger role in the travel times and the end concentrations than in seepage rates. A broad range in the normalized concentrations of 0.7 to 0.025 is possible with liners molded from the same soil type.
The effect of liner thickness on travel times and end concentrations of NH4-N, is equally significant. Fig. 12 shows this effect for three thicknesses (0.15, 0.3, and 0.9 m) and for liner material 3.20. The concentrations of NH4-N, normalized with respect to the influent concentration, have reduced from 0.9 to 0.1, and the travel times increased from 5 to 65 years, as the liner thickness is increased from 0.15 to 0.9 m. For clays with high CEC (leading to high retardation and saturation potential), providing thicker liners will yield much larger time periods for safe operation of anaerobic lagoons. The extent of possible retardation, decay, and saturation levels of NH4-N in clay liners, observed in this study, suggests that properties such as CEC and microbial uptake, which influence mass transfer of NH4-N, should be given an important consideration in designing liners for animal waste lagoons.
Summary and Conclusions
In addition to documenting the leachability behavior of compacted samples of Southwestern Kansas soils, this study has resulted in an understanding of the mass transfer and transport characteristics of nitrogen in the form of ammonium (NH4-N), a key constituent in animal waste lagoons. In general, the natural soils available in the region under consideration were found to be capable of meeting the KDHE standard of 0.64 cm/day (0.25 inch/day). Considering the side liners of lagoon facilities which may offer no opportunity for particulate clogging or organic sludge formation, it may not be appropriate to assume reductions in permeability. Furthermore, the results from this study indicate that biological clogging may not be prominent during the time period it takes for breakthrough of NH4-N. Transport of NH4-N is associated with significant retardation, decay, and saturation rates in compacted clays. The results indicate significant differences in microbial uptake of NH4-N among samples of the same soil type. Considering the beneficial effects of a high decay coefficient on groundwater quality, it may be useful for future studies to focus on the effects of soil structure on microbial colony formation and NH4-N uptake by microorganisms. Potential for excessive sorption of ammonium in the liner and its microbial uptake make the thickness of the liner an important variable. Although the seepage quantity may be less than the regulated limit for thinner liners, providing a thicker liner may significantly increase the time periods for periodic clean up of animal waste lagoon bottoms. In general, mass transfer characteristics of liner material, CEC and microbial uptake in particular, should be important considerations in the design of animal waste lagoon liners.
References
Ogata, A. and Banks, R.B. 1961. A solution of the differential equation of longitudinal dispersion in porous media, US Geol. Surv. Prof. Paper 411-A.
Shackelford, C.D. 1993. Contaminant transport. Chapter 3 In: Geotechnical Practice for Waste Disposal, Edited by D.E. Daniel. Chapman & Hall, London, UK. Pp. 33- 65.
Simunek, J., T. Vogel, and M. Th. van Genuchten. 1994. The SWMS-2D Code for Simulating Water Flow and Solute Transport in Two-Dimensional Variably Saturated Media. Version 1.21. Research Report No. 132, US Salinity Laboratory, Agricultural Research Service, US Department of Agriculture, Riverside, California. van Genuchten, M. Th. 1981. Analytical solutions for chemical transport with simultaneous adsorption, zero-order production, and first-order decay. J. Hydrology, 49:213-233. Table 1. Best-fit estimates of fate and transport characteristics of NH4-N, and Cl.
Appendix A
Survey of Waste Chemistry in Anaerobic Lagoons at Swine Production Facilities and Cattle Feedlots.
Tom M. DeSutter1, Jay M. Ham1, and Todd P. Trooien2 1Department of Agronomy, Kansas State University, Manhattan, KS 66506 2Southwest Research and Extension Center, Garden City, KS 67846
Samples of lagoon waste were collected from four swine units and five cattle feedlots in Kansas from October, 1997 to December, 1998. Samples were obtained using zero-contamination methods and analyzed for various chemical and physical parameters by Servi-Tech Laboratories, Dodge City, KS or by the Kansas State University Soil Testing Laboratory, Manhattan, KS (Table 1). Some lagoons were sampled at multiple depths and at multiple times to determine spatial and seasonal variability. General site characterizations of the lagoons are reported in Tables 2 and 3. Chemical and physical parameters of the waste waters from swine and feedlot lagoons are reported in Tables 4, 5, 6, and 7. Table 1. Laboratory methods. ______Laboratory Methods Measured ______Parameters Servi-Tech Laboratories† KSU Soil Testing Laboratory ______- -1 - NO3 -N (mg L ) 4500-NO3 F cadmium reduction + -1 NH4 +NH3-N (mg L ) 4500-NH3 E colorimetric analysis Total N (mg L-1) 4500-N org C salicylic digest Organic N (mg L-1) calculated calculated Boron 3120 B Sulfur 3120 B Calcium 3120 B Magnesium 3120 B Potassium 3120 B Sodium 3120 B pH 4500 HB direct measurement Total P (mg L-1) 200.7‡ salicylic digest Chloride (mg L-1) 4500-Cl- E colorimetric analysis BODa (mg L-1) 5210 B CODb (mg L-1) 5220 D TSSc (mg L-1) 2540 D ECd (mmho cm-1) 2510 B direct measurement Carbonate 2310 B Bicarbonate 2320 B Alkalinity (Calc.) 2320 B Hardness (Calc.)(mg L-1) 2340 B Hardness (Calc.)(grains gal-1) 2340 B TDSe By EC SARaf calculated SARg calculated ______†Methods taken from the “Standard Methods for the Examination of Waters and Wastewaters”, 18th ed., 1989. ‡Method taken from the “EPA 600 Methods for Chemical Analysis of Waters and Waste”. aBOD = Biological Oxygen Demand bCOD = Chemical Oxygen Demand cTSS = Total Suspended Solids dEC = Electrical Conductivity eTDS = Total Dissolved Solids fSARa = Adjusted Sodium Adsorption Ratio gSAR = Sodium Adsorption Ratio Table 2. Descriptions of swine lagoons sampled for waste water analysis. ______Lagoon ______
Characteristic Nursery Sow(A) Finishing Sow(B) ______Construction date 1995 1995 1996 1995 Max. Capacity, m3 27,619 101,352 109,983 113,721 Inside slopes 3:1 3:1 4:1 3:1 Max. working depth, m 6.1 6.1 6.1 6.1 Surface area when sampled, ha 0.72 2.09 2.15 2.4 Liner thickness, m 0.46 0.30 0.46 0.46 Liner texture silt loam silt loam silty clay+bentonite† 6silt loam Location‡ 32 km from Ulysses, KS 47 km from Ulysses, KS 32 km from Hugoton, KS 47 km from Ulysses, KS ______
† Approximately 9.8 kg m-2 of bentonite was mixed into the lowermost 0.15-m of the compacted liner. Silty clay used for the liner was not native to the site of construction. ‡ Represents a relative location from the specified town in southwestern Kansas. Table 3. Descriptions of cattle feedlot lagoons sampled for waste water analysis. ______Lagoon ______Characteristic A B C D E ______Construction date 1970's 1990 NA 1998‡ NA Max. capacity, m3 NA 9,465 NA 96,174 NA Inside slopes 3:1 4:1 NA 3:1 NA Max. working depth, m 2.5 3.6 NA 4.8 NA Surface area when sampled, ha 3.0 0.55 NA 2.0 NA Liner thickness, m NA NA NA NA NA Liner texture NA NA NA loam NA Depth to water table, m 27.4 27.4 56 56 NA Area soils clay loam clay loam silt loam silt loam silt loam Location† 47 kma 47 kma 32 kmb 32 kmb 32 kmc ______† Represents a relative location from a specified town in southwestern Kansas: a=Dodge City; b=Ulysses; c=Garden City Table 4. Selected chemical and physical characteristics of lagoon waste water from swine nursery, sow, and finishing units. ______Swine Unit ______Measured Parameters Nursery Sow (A) Finishing Sow (B) ______Sampling Date 2-12-98 9-2-98 3-6-98 9-2-98 4-2-98 9-10-98 6-12-98
- -1 NO3 -N (mg L ) 2.2 <0.1 1.7 <0.1 1.4 0.5 0.6 + -1 NH4 +NH3-N (mg L ) 577.0 554.0 702.0 656.0 711.0 786.0 698.0 Total N (mg L-1) 739.2 662.0 797.7 758.0 858.4 962.5 780.6 Organic N (mg L-1) 160.0 108.0 94.0 102.0 146.0 176.0 82.0 Calcium (mg L-1) — 90.0 — 70.0 — 71.0 88.0 Magnesium (mg L-1) — 25.0 — 15.0 — 19.0 18.0 Potassium (mg L-1) — 678.0 — 628.0 — 792.0 490.0 Sodium (mg L-1) — 198.0 — 340.0 — 282.0 261.0 pH 7.9 — 8.1 — 8.5 — 7.9 Total P (mg L-1) 44.9 32.5 45.4 38.1 31.2 40.0 54.1 Chloride (mg L-1) 291.0 278.0 294.0 302.0 231.0 327.0 241.0 BODa (mg L-1) 2370.0 — 2129.0 — 1697.0 — 708.8 CODb (mg L-1) 5095.0 1036.0 4695.0 992.0 3760.0 2143.0 1550.0 TSSc (mg L-1) 200.0 — 140.0 — 400.0 — 260.0 ECd (mmho cm-1) 7.9 8.2 7.5 9.2 8.8 8.0 7.5 Hardness (Calc.)(mg L-1) — 326.3 — 236.0 — 255.5 296.3 Hardness (Calc.)(grains gal-1) — 19.1 — 13.8 — 14.9 17.3 ______aBOD = Biological Oxygen Demand bCOD = Chemical Oxygen Demand cTSS = Total Suspended Solids dEC = Electrical Conductivity Table 5. Selected chemical and physical characteristics of lagoon waste water from cattle feedlots. ______Measured Parameter Feedlot (A) Feedlot (B) Feedlot (C) Feedlot (D) ______Sampling Date 10-15-97 11-6-97 2-12-98 2-12-98 9-2-98 12-9-98
- -1 NO3 -N (mg L ) 0.2 0.3 1.0 1.0 <0.1 <1.0 + -1 NH4 +NH3-N (mg L ) 50.4 49.3 84.0 111.9 86.0 200.0 Total N (mg L-1) 117.0 114.0 159.9 191.5 207.0 310.0 Organic N (mg L-1) 66.4 64.4 75.9 78.6 121.0 110.0 Boron (mg L-1) — — — — — <1.0 Sulfur (mg L-1) — — — — — 40.0 Calcium (mg L-1) — — — — 128.0 190.0 Magnesium (mg L-1) — — — — 130.0 72.0 Potassium (mg L-1) — — — — 763.0 500.0 Sodium (mg L-1) — — — — 212.0 120.0 pH 7.6 7.7 7.9 7.7 — 7.6 Total P (mg L-1) 42.5 41.5 44.4 37.2 50.4 75.0 Chloride (mg L-1) 1120.0 980.0 822.0 665.0 540.0 255.0 BODa (mg L-1) — — 245.6 495.2 — — CODb (mg L-1) — — 1710.0 2338.0 1734.0 — TSSc (mg L-1) — — 240.0 280.0 — — ECd (mmho cm-1) 4.0 3.5 5.5 4.8 5.7 3.9 Hardness (Calc.)(mg L-1) — — — — 855.6 — Hardness (Calc.)(grains gal-1) — — — — 50.0 — TDSe (mg L-1) — — — — — 2502.0 SARaf — — — — — 6.1 SARg — — — — — 1.9 ______aBOD = Biological Oxygen Demand; bCOD = Chemical Oxygen Demand; cTSS = Total Suspended Solids; dEC = Electrical Conductivity; eTDS = Total Dissolved Solids; fSARa = Adjusted Sodium Adsorption Ratio; gSAR = Sodium Adsorption Ratio Table 6. Selected chemical and physical characteristics of lagoon waste water from cattle feedlots. ______Measured Parameter Feedlot (E) ______Sampling Date 3-6-98 7-17-98 7-31-98 8-21-98 9-1-98
- -1 NO3 -N (mg L ) 1.2 0.3 0.3 <0.1 1.1 + -1 NH4 +NH3-N (mg L ) 67.0 25.8 18.4 30.6 20.1 Total N (mg L-1) 119.2 67.2 89.3 51.0 85.3 Organic N (mg L-1) 51.0 41.0 70.6 20.4 64.1 Boron (mg L-1) 0.25 0.28 0.31 0.31 0.37 Sulfur (mg L-1) 27.6 30.6 35.4 32.4 37.2 Calcium (mg L-1) 105.0 119.0 119.0 115.0 126.0 Magnesium (mg L-1) 57.0 59.0 59.0 64.0 68.0 Potassium (mg L-1) 336.0 349.0 383.0 428.0 467.0 Sodium (mg L-1) 94.0 108.0 105.0 118.0 130.0 pH 8.0 7.8 7.6 7.6 7.9 Total P (mg L-1) 35.0 29.6 29.7 33.4 32.5 Chloride (mg L-1) 355.7 395.9 363.0 433.1 503.0 ECa (mmho cm-1) 2.9 2.5 2.7 2.9 3.6 Carbonate (mg L-1) <1.0 <1.0 <1.0 <1.0 <1.0 Bicarbonate (mg L-1) 900.0 812.0 793.0 870.0 940.0 Alkalinity (Calc.) (mg L-1) 738.0 665.8 650.0 713.3 770.8 Hardness (Calc.)(mg L-1) 495.6 539.1 539.8 549.9 595.4 Hardness (Calc.)(grains gal-1) 29.0 31.5 31.6 32.2 34.8 TDSb (mg L-1) 1875.2 1625.6 1728.0 1856.0 2304.0 SARac 4.6 5.1 4.9 5.5 6.0 SARd 1.8 2.0 2.0 2.2 2.3 ______aEC = Electrical Conductivity; bTDS = Total Dissolved Solids; cSARa=Adjusted Sodium Adsorption Ratio; dSAR=Sodium Adsorption Ratio Table 7. Average values of selected chemical and physical characteristics of lagoon wastewater from swine units and cattle feedlots. ______Measured Parameters Swine Cattle ______- -1 NO3 -N (mg L ) 1.0 0.5 + -1 NH4 +NH3-N (mg L ) 672.8 98.3 Total N (mg L-1) 792.4 184.2 Organic N (mg L-1) 118.8 85.6 Boron (mg L-1) — 0.4 Sulfur (mg L-1) — 36.3 Calcium (mg L-1) 79.8 144.9 Magnesium (mg L-1) 19.3 87.8 Potassium (mg L-1) 647.0 551.9 Sodium (mg L-1) 270.3 147.7 pH 8.1 7.7 Total P (mg L-1) 42.5 47.5 Chloride (mg L-1) 275.6 568.8 BODa (mg L-1) 1726.2 370.4 CODb (mg L-1) 2602.6 1927.3 TSSc (mg L-1) 250.0 260.0 ECd (mmho cm-1) 8.1 4.3 Carbonate (mg L-1) — <1.0 Bicarbonate (mg L-1) — 863.0 Alkalinity (Calc.)(mg L-1) — 732.3 Hardness (Calc.)(mg L-1) 278.5 297.0 Hardness (Calc.)(grains gal-1) 16.3 31.8 TDSe (mg L-1) — 2189.9 SARaf — 5.7 SARg — 2.0 ______aBOD = Biological Oxygen Demand bCOD = Chemical Oxygen Demand cTSS = Total Suspended Solids dEC = Electrical Conductivity eTDS = Total Dissolved Solids fSARa = Adjusted Sodium Adsorption Ratio gSAR = Sodium Adsorption Ratio
*Some noteworthy differences are highlighted. Appendix B
References on Animal Waste Lagoons and Related Topics
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T.M. DeSutter and J.M. Ham, Dept. of Agronomy, Kansas State University
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