Characterization of Low-Volume Meat Processing Wastewater and Impact of Facility Factors
1
Department of Biosystems and Agricultural Engineering, Michigan State University, East Lansing, MI 48824, USA
2
Michigan State University Extension, East Lansing, MI 48824, USA
3
Department of Food Science and Human Nutrition, Michigan State University, East Lansing, MI 48824, USA
*
Author to whom correspondence should be addressed.
†
Current address: Environmental Science Division, Argonne National Laboratory, Lemont, IL 60439, USA.
Water 2024, 16(4), 540; https://0-doi-org.brum.beds.ac.uk/10.3390/w16040540
Submission received: 21 December 2023
/
Revised: 30 January 2024
/
Accepted: 2 February 2024
/
Published: 9 February 2024
(This article belongs to the Special Issue Wastewater Land Treatment System: Research, Designs, and Operation)
Abstract
:Low-volume meat processing facilities often rely on decentralized wastewater treatment due to cost constraints and the lack of access to centralized treatment. Improved characterization of these facilities’ wastewater is crucial for meeting local groundwater discharge permits. This study also directly correlates treatment systems and facility characteristics to the results of the characterization. The total nitrogen (TN), biochemical oxygen demand (BOD), and phosphorus (P) reductions ranged from −15% to 83%, 43% to 95%, and −75% to 62%, respectively. Slaughtering and smoking were found to significantly increase nutrient concentrations. The average TN leaving the slaughterhouses and processing-only facilities was 519 mg/L-N and 154 mg/L-N, respectively. The average BOD produced by the slaughterhouses and processors was 3002 mg/L and 1660 mg/L, respectively. Filtration was found to reduce BOD, chemical oxygen demand (COD), and trace metals. Aeration in a treatment lagoon was found to significantly reduce BOD, COD, and N compounds. The results indicate that even simple decentralized wastewater treatment systems, combined with facility management practices, can substantially reduce permitted wastewater characteristics. The facility with the best BOD removal had an effluent value of 71.3 mg/L, representing a 96% reduction. The facility with the best TN removal had an effluent value of 20 mg/L, representing a 92% reduction prior to discharge.
1. Introduction
Wastewater treatment is a significant challenge for meat processing facilities that use decentralized wastewater treatment, which are typically low-volume processors who may have low revenues and no access to a municipal centralized wastewater treatment plant. Generally, in the U.S., a groundwater discharge permit from their state or regional regulatory agency is required for these low-volume processors. An example of this is Groundwater Discharge General Permit GW1530000 from Michigan’s Department of Environment, Great Lakes, and Energy (EGLE), which governs small-volume (<20,000 gallons of wastewater per day) meat processing facilities. These facilities typically dispose of the wastewater using land application; however, pretreatment is required because of the wastewater’s high strength (Table 1), compared to domestic wastewater (Table 2).
The U.S. Environmental Protection Agency (EPA) is currently researching the impacts of wastewater from the meat slaughter and processing industry. The goal is to develop a better characterization of the wastewater and determine the effectiveness of management approaches to update policy [1].
Table 1.
Characteristics of meat processing wastewater.
| Constituent | Units | Min | Max | Data From |
|---|---|---|---|---|
| TN | mg/L | 49 | 841 | [2,3,4] |
| TKN | mg/L-N | 67 | 1057 | [2] |
| NH3 | mg/L-N | 36 | 75 | [2] |
| NO3 and NO2 | mg/L-N | 0 | 3.3 | [2,5] |
| P | mg/L-P | 15 | 217 | [4,5,6,7] |
| TSSs | mg/L | 200 | 12,000 | [4,7,8] |
| BOD | mg/L | 200 | 4600 | [4,7] |
| COD | mg/L | 200 | 50,665 | [2,3,4,7] |
| Alkalinity | mg/L as CaCO3 | 312 | 872 | [2,5] |
TKN—total Kjeldahl nitrogen; TSSs—total suspended solids; BOD—biological oxygen demand; COD—chemical oxygen demand.
Table 2.
Characteristics of untreated domestic wastewater [9].
Table 2.
Characteristics of untreated domestic wastewater [9].
| Constituent | Unit | Minimum | Maximum |
|---|---|---|---|
| TN | mg/L-N | 26 | 75 |
| P | mg/L-P | 6 | 12 |
| NH3 | mg/L-N | 4 | 13 |
| TSS | mg/L | 155 | 330 |
| BOD | mg/L | 155 | 286 |
| COD | mg/L | 500 | 660 |
| FOG | Mg/L | 70 | 105 |
TKN—total Kjeldahl nitrogen; TSSS—total suspended solids; BOD—biological oxygen demand; COD—chemical oxygen demand; FOG—fats; oil; and grease.
Pretreatment techniques, at a minimum, typically entail storage, settling, and fats, oil, and grease (FOG) flotation in a septic tank, followed by biological treatment in a lagoon prior to land application. Alternatively, a rapid infiltration basin is sometimes used to dispose of the wastewater.
Septic tanks lead to the separation of solids and FOG from wastewater. In domestic systems, this liquid is then discharged into a leach field, mound systems, or drip systems [10]. The materials remaining in the septic tank accumulate into sludge and scum layers that are pumped out as needed. For industrial and commercial systems, this water is often sent to other treatment units, such as a coagulation/flocculation tank or a membrane filter [6]. The addition of optional effluent filters in septic tanks improves the quality of the effluent water, resulting in upwards of 60% solids and 30% 5-day biological oxygen demand (BOD) removals [6,11]. Anaerobic conditions are present in these tanks; however, research is needed to determine if septic tanks also contribute to biological degradation [12]. Other systems are currently used to ensure this biological degradation occurs. The facilities observed in this study use treatment lagoons prior to land application or subsurface discharging.
Treatment lagoons exist in three main forms. Anaerobic lagoons, which are typically 3–6 m deep, rely primarily on anaerobic bacteria that are found in oxygen-deprived environments. Odor is a major concern with these lagoons, and they are typically not recommended if residences are nearby. Anaerobic lagoons also produce more methane, a major greenhouse gas, than the other lagoon types [13]. This methane, however, can be harvested as biogas and used as fuel, although this is likely not economical on a small scale. These lagoons typically have a 4–16-day retention time and can remove upwards of 60–80% of the BOD [7,14]. Aerobic lagoons are shallower, typically only 0.5–1.5 m deep. Algal growth and microorganisms treat the wastewater throughout the entire depth. Oxygen is maintained through a combination of photosynthesis, air diffusion, and mechanical aeration [7]. Facultative lagoons, which are utilized at all sites in this study, are typically up to 3 m deep and combine both aerobic and anaerobic environments. The upper layer of the lagoon is aerobic, while the deeper layer functions as an anaerobic system. BOD and chemical oxygen demand (COD) removal is in the range of 60–90%. These systems typically have a retention time of 30–120 days [7]. In alkaline lagoons, precipitation reactions can occur, resulting in upwards of 50% P removal [15]. Depending on a variety of factors, especially detention time and pH, up to an 80% removal of ammonia (NH3) was observed [15]. A case study focused on piggeries found similar efficacy, with 75% BOD removal and a heavily reduced odor, although this odor can become problematic in the fall and spring [15,16].
The final step for water leaving these facilities is land application. Soil uptake of wastewater pollutants is an important part of the decentralized wastewater treatment process. A substantial amount of BOD can be oxidized if the soil is not overloaded. Land application uses 50–70% less energy and reduces greenhouse gas emissions by an estimated 3000 metric tons per year when compared to activated sludge treatment [17]. Soil treatment is the primary reason that BOD is often regulated based on loading because the land application of high-strength wastewater can cause excessive biofilm growth, leading to high levels of moisture within the porosity of the soil. This can cause wastewater surfacing and anaerobic soil conditions, resulting in poor BOD removal and metal mobilization. Metal mobilization occurs when anaerobic metal-reducing microorganisms predominate and reduce natural soil metals, such as manganese, iron, and arsenic, enabling transport through the soil column [18]. Julien et al. found that soils have a maximum loading before BOD has a major impact on metal leaching [18], which is heavily dependent on soil type. The same study found that at least 12 h of rest between dosing events maximized soil aeration. Metal mobilization was found not to be significant if the volumetric soil moisture content remains below 25–30% [19,20]. This highlights why BOD treatment in lagoons is especially important when looking at these systems.
Nitrate (NO3) is another important wastewater constituent, especially when entering groundwater. High NO3 levels in groundwater can lead to methemoglobinemia, more commonly known as blue baby syndrome. Due to this risk, the U.S. EPA regulates at a 10 mg/L of N limit in groundwater [17]. Denitrification, the conversion of NO3 to N gas, occurs under anaerobic conditions and requires carbon. Achieving denitrification while preventing metal leaching using soil treatment requires careful application [17]. Denitrification will occur in the anaerobic areas of a facultative lagoon, and proper treatment will minimize the risks of high levels of NO3 in groundwater.
The complexity and lack of knowledge concerning the management of decentralized, high-strength wastewater, specifically that originating from meat processing, led to this research, which had the following project components.
- Concentrations and variability in influent and lagoon wastewater constituents.
- Facilities’ characteristics’ impacts on influent and lagoon wastewater constituents, such as facilities that slaughter and process and ones that only process.
- Treatment effectiveness.
- BOD loading compared to concentration for each facility.
2. Materials and Methods
The objective of this research was to characterize wastewater from six representative meat processors. Processors were selected assuming operations would result in typical low-volume meat slaughtering/processing wastewater, and the characterization of such wastewater would support policy development. The data were then analyzed to determine the performance of commonly used treatment units. This allows for the identification of technologies that are best suited for facility- and management-specific considerations. Included are choices between more extensive facility treatment vs. the implementation of enhanced management practices to separate the extreme wastewater components, such as blood and tissue, before land application.
The first stage of this process was to select processors in Michigan that are representative of the industry. This included key factors such as whether the facility slaughters, smokes meat, comingles human wastewater, uses effluent filters, the detention time in septic tanks, and the lagoon operational strategy. Sample locations for the six selected processors were determined. Thereafter, samples were collected, and data were analyzed. A description of each facility is shown in Table 3.
Generally, two locations were selected at each facility. Influent was collected near the process floor discharge, such as at the first septic tank, unless a high quantity of solids inhibited sampling. The second sample was from a lagoon, which may or may not represent that which was applied to the land, depending on the proximity to the time of lagoon discharge. Where applicable, samples were also collected after the septic tank effluent filter.
Six sampling events occurred: four in the summer, between the start of July and the end of August, and two in the fall, between October and November. All testing methods are listed in Table 4. Methods using HACH testing kits (Loveland, CO, USA) followed EPA methods whenever possible, as indicated in Table 4. The other parameters were measured by a state-certified commercial laboratory, Merit Laboratories (East Lansing, MI, USA). Organic nitrogen (organic N) was calculated by obtaining the difference between total Kjeldahl nitrogen (TKN) and NH3. Total inorganic nitrogen (TIN) was calculated by obtaining the difference between TN and organic N.
Data were analyzed in SAS using PROC GLIMMIX (version 9.4, SAS Inst. Inc., Cary, NC, USA) as a completely randomized design. For all analyses, the processing facility was included as a fixed effect. Treatment least-squares means were separated with the PDIFF option of SAS using a significance level of p ≤ 0.05. The Kenward–Roger approximation was used for estimating denominator degrees of freedom for all analyses.
3. Results and Discussion
3.1. Variability and Concentrations of Influent
Table 5, Table 6, Table 7 and Table 8 show the number of samples (n) and the average (Avg), minimum (Min), maximum (Max), and standard deviation (SD) for all the sampling events at all the facilities.
Table 5 shows the nutrient data. The TN content is the most variable, followed by the TKN content, which includes NH3 and organic N levels. The organic N content also demonstrates a high degree of variability. NO3 and nitrite (NO2−) demonstrate the least variability and are important regarding public health. The Michigan groundwater discharge general permit GW1530000 established a limit of 1.0 mg/L of NO2− for all types of lagoons and infiltration basins [21]. No facilities exceeded this limit, as the highest NO2− lagoon wastewater value, which is ultimately land applied, was 0.21 mg/L. This is expected since NO2− is inherently unstable and rapidly converts to NO3 in most wastewater [22].
Table 5.
Cumulative nutrient data.
| Parameter | n | Avg | Min | Max | SD |
|---|---|---|---|---|---|
| TN Inf (mg/L-N) | 38 | 354 | 51.4 | 1280 | 388 |
| TN Lag (mg/L-N) | 31 | 184 | 3.10 | 818 | 198 |
| Organic N Inf (mg/L-N) | 34 | 134 | 0.00 | 845 | 178 |
| Organic N Lag (mg/L-N) | 31 | 49.3 | 0.00 | 165 | 39.0 |
| NO3 Inf (mg/L-N) | 40 | 3.45 | 0.130 | 10.1 | 2.45 |
| NO3 Lag (mg/L-N) | 35 | 2.27 | 0.087 | 12.4 | 2.48 |
| NH3 Inf (mg/L-N) | 35 | 185 | 14.0 | 1024 | 285 |
| NH3 Lag (mg/L-N) | 31 | 137 | 0.014 | 715 | 186 |
| TKN Inf (mg/L-N) | 42 | 339 | 48.0 | 1260 | 366 |
| TKN Lag (mg/L-N) | 36 | 184 | 2.40 | 813 | 200 |
| TIN Inf (mg/L-N) | 34 | 225 | 17.2 | 1035 | 291 |
| TIN Lag (mg/L-N) | 29 | 134 | 1.80 | 720 | 182 |
| NO2− Inf (mg/L-N) | 27 | 1.12 | 0.025 | 12.5 | 1.12 |
| NO2− Lag (mg/L-N) | 22 | 0.062 | 0.03 | 0.21 | 0.04 |
| P Inf (mg/L-P) | 38 | 50.9 | 17.6 | 265 | 44 |
| P Lag (mg/L-P) | 33 | 41.9 | 3.78 | 264 | 47.5 |
Notes: Inf—influent; Lag—lagoon.
Table 6 contains data pertaining to wastewater carbon, both the BOD and COD. The BOD represents the oxygen consumed in the biological degradation of compounds in the wastewater. The COD utilizes a harsher oxidizer, allowing for the detection of more organic components than only those which are biologically degradable. The COD should always be higher than the BOD, provided that a nitrification inhibitor is used. The BOD-to-COD ratio helps determine how biodegradable the wastewater constituents are. A lower ratio indicates less rapidly biodegradable material is present in the wastewater. For domestic wastewater, the typical BOD/COD ratio is 40% [23]. In the current study, the ratio was higher than for domestic wastewater by the time the water reached the lagoon, which was unexpected. The reason for this difference is unclear but may be attributed to the high variability in the data, especially for the COD.
Table 6.
Cumulative carbon data.
| Parameter | n | Avg | Min | Max | SD |
|---|---|---|---|---|---|
| BOD Inf (mg/L) | 39 | 2396 | 319 | 7900 | 2034 |
| BOD Lag (mg/L) | 36 | 635 | 24.0 | 4740 | 918 |
| COD Inf (mg/L) | 42 | 8880 | 692 | 76,800 | 13,300 |
| COD Lag (mg/L) | 36 | 1317 | 83.0 | 3880 | 1226 |
| BOD/COD Ratio Inf | 27% | ||||
| BOD/COD Ratio Lagoon | 48.2% |
Notes: Inf—influent; Lag—lagoon.
Table 7 and Table 8 contain total suspended solids (TSSs), pH, hardness, alkalinity, FOG, and several metals. The FOG content was greatly reduced at all facilities and did not appear to be an issue for land application or infiltration. One major source of pH variation was the use of caustic and acidic cleaning agents [7,24]. However, the inhibition of the biological process in the lagoon and the corrosion of the treatment infrastructure are unlikely to be a concern within this pH range, especially with the high alkalinity present in the water.
Table 7.
Cumulative commonly measured parameter data.
| Parameter | n | Avg | Min | Max | SD |
|---|---|---|---|---|---|
| TSSs Inf (mg/L) | 29 | 1210 | 44.0 | 15,600 | 2850 |
| TSSs Lag (mg/L) | 25 | 787 | 20.0 | 7050 | 787 |
| pH Inf | 42 | 6.61 | 5.57 | 7.63 | 0.510 |
| pH Lag | 36 | 7.48 | 6.04 | 9.46 | 0.690 |
| Hardness Inf (mg/L) | 42 | 260 | 5.00 | 2440 | 375 |
| Hardness Lag (mg/L) | 36 | 246 | 100 | 384 | 94.7 |
| Alkalinity Inf (mg/L-CaCO3) | 42 | 746 | 170 | 2210 | 620 |
| Alkalinity Lag (mg/L-CaCO3) | 36 | 735 | 240 | 1700 | 458 |
| FOG Inf (mg/L) | 41 | 906 | 1.00 | 9470 | 1850 |
| FOG Lag (mg/L) | 36 | 12.5 | 1.00 | 101 | 21.3 |
Notes: Inf—influent; Lag—lagoon.
Table 8.
Cumulative data on inorganics.
| Parameter | n | Avg | Min | Max | SD |
|---|---|---|---|---|---|
| Calcium Inf (mg/L) | 42 | 166 | 11.5 | 1540 | 253 |
| Calcium Lag (mg/L) | 36 | 62.9 | 21.6 | 131 | 29.7 |
| Sodium Inf (mg/L) | 42 | 299 | 36.0 | 637 | 189 |
| Sodium Lag (mg/L) | 36 | 266 | 66.9 | 731 | 179 |
| Copper Inf (mg/L) | 42 | 0.233 | 0.016 | 0.998 | 0.285 |
| Copper lag (mg/L) | 36 | 0.173 | 0.005 | 1.060 | 0.337 |
| Manganese Inf (mg/L) | 42 | 0.22 | 0.01 | 1.27 | 0.310 |
| Manganese Lag (mg/L) | 36 | 0.133 | 0.024 | 0.429 | 0.134 |
| Chloride Inf (mg/L) | 42 | 386 | 5.00 | 2700 | 474 |
| Chloride Lag(mg/L) | 36 | 332 | 49.0 | 993 | 280 |
| Zinc Inf (mg/L) | 42 | 1.047 | 0.07 | 5.73 | 1.40 |
| Zinc Lag (mg/L) | 36 | 0.268 | 0.005 | 1.67 | 0.478 |
Notes: Inf—influent; Lag—lagoon.
3.2. Impact of Facility Characteristics on Influent and Effluent Wastewater Characteristics
Table 9, Table 10, Table 11, Table 12, Table 13, Table 14, Table 15, Table 16, Table 17, Table 18, Table 19, Table 20, Table 21, Table 22, Table 23, Table 24, Table 25, Table 26, Table 27 and Table 28 focus on the statistical analysis of wastewater for individual facility factors. The statistical analyses used the standard error of the mean (SEM). The factors included are slaughter (compared to processing only), meat smoking, human wastewater comingling, septic tank effluent filtration, and lagoon aeration.
Table 9 shows the impact of slaughtering on wastewater nutrients. Four of the facilities slaughter, while two process only. Slaughtering produced wastewater with greater (p < 0.05) concentrations of every nutrient except TN and influent NO2−. High variability may have contributed to the lack of significant differences, as the values for these characteristics when slaughtering were still higher compared to the values from the facilities that only process meat. NO2− is also typically not stable and is converted quickly to other forms of N.
Table 9.
Slaughter–processing nutrient comparison.
| Parameters (mg/L-N or -P) | Slaughter | Processing | Statistics | ||
|---|---|---|---|---|---|
| SEM | n | p-Value | |||
| TN Inf | 519 a | 154 b | 87.4 | 37 | 0.003 |
| TN Lag | 215 | 89.3 | 69.5 | 24 | 0.132 |
| TKN Inf | 528 a | 88.3 b | 72.6 | 41 | <0.001 |
| TKN Lag | 232 a | 23.4 b | 65.7 | 29 | 0.009 |
| Organic N Inf | 216 a | 41.0 b | 41.2 | 33 | 0.004 |
| Organic N Lag | 59.9 a | 15.7 b | 15.5 | 25 | 0.021 |
| NH3 Inf | 301 a | 50.1 b | 67.7 | 34 | 0.009 |
| NH3 Lag | 179 a | 7.91 b | 60.3 | 25 | 0.021 |
| TIN Inf | 360 a | 53.1 b | 68.1 | 33 | 0.002 |
| TIN Lag | 169 a | 13.4 b | 58.2 | 23 | 0.032 |
| NO3 Inf | 4.12 a | 1.70 b | 0.558 | 39 | 0.002 |
| NO3 Lag | 2.71 | 2.44 | 1.22 | 29 | 0.830 |
| NO2 Inf | 4.55 | 1.84 | 3.03 | 26 | 0.504 |
| NO2 Lag | 0.113 a | 0.051 b | 0.016 | 18 | 0.004 |
| P Inf | 64.1 a | 34.5 b | 10.9 | 37 | 0.003 |
| P Lag | 52.8 | 12.3 | 23.1 | 26 | 0.127 |
Notes: a,b Within a row, least-squares means without a common superscript differ significantly (p < 0.05). Inf—influent; Lag—lagoon.
Table 10 shows the impact of slaughtering on the BOD and COD. The BOD (p = 0.044) and COD (p < 0.001) were both greater in the slaughterhouse wastewater influent compared to the wastewater influent from the facilities that only process. The primary source of this difference is blood, which has a COD of approximately 400,000 mg/L [7,24]. There was no difference (p > 0.05) for the BOD values in the lagoon, indicating that this type of treatment system effectively degrades the biological components in the wastewater, regardless of the differing influent characteristics. The lagoon COD was greater (p = 0.012) for the wastewater from the facilities that slaughtered compared to the only processing facilities. This indicates a greater presence of non-biodegradable compounds in slaughterhouse wastewater compared to wastewater from facilities that process only.
Table 10.
Slaughter–processing carbon comparison.
| Parameters (mg/L) | Slaughter | Processing | Statistics | ||
|---|---|---|---|---|---|
| SEM | n | p-Value | |||
| BOD Inf | 3002 a | 1660 b | 490 | 38 | 0.044 |
| BOD Lag | 871 | 184 | 398 | 29 | 0.136 |
| COD Inf | 12,050 a | 4870 b | 2043 | 41 | <0.001 |
| COD Lag | 1795 a | 334 b | 480 | 29 | 0.012 |
Notes: a,b Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Inf—influent; Lag—lagoon.
Table 11 and Table 12 show the impact of slaughtering on additional common wastewater characteristics and metals. Only pH and alkalinity differed, where both were greater (p < 0.05) in the influent and lagoon in the slaughter wastewater compared to the processing wastewater. No differences (p > 0.05) were observed in the influent or lagoon values for inorganic compounds when comparing the slaughter and processing wastewater.
Table 11.
Slaughter–processing common measurements comparison.
| Parameters (mg/L) | Slaughter | Processing | Statistics | ||
|---|---|---|---|---|---|
| SEM | n | p-Value | |||
| TSSs Inf | 1860 | 308 | 858 | 28 | 0.169 |
| TSSs Lag | 723 | 97.2 | 459 | 19 | 0.242 |
| pH Inf | 6.82 a | 6.46 b | 0.117 | 41 | 0.021 |
| pH Lag | 8.23 a | 7.23 b | 0.222 | 29 | 0.0004 |
| Hardness Inf | 334 | 147 | 90.8 | 41 | 0.123 |
| Hardness Lag | 249 | 249 | 43.6 | 29 | 0.996 |
| Alkalinity Inf | 966 a | 455 b | 141 | 41 | 0.0083 |
| Alkalinity Lag | 890 a | 457 b | 176 | 29 | 0.038 |
| FOG Inf | 645 | 1326 | 450 | 40 | 0.258 |
| FOG Lag | 67.3 | 2.17 | 58.2 | 29 | 0.544 |
Notes: a,b Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Inf—influent; Lag—lagoon.
Table 12.
Slaughter–processing inorganics comparison.
| Parameters (mg/L) | Slaughter | Processing | Statistics | ||
|---|---|---|---|---|---|
| SEM | n | p-Value | |||
| Calcium Inf | 211 | 108 | 61.6 | 41 | 0.209 |
| Calcium Lag | 70.1 | 65.9 | 12.9 | 29 | 0.767 |
| Sodium Inf | 328 | 288 | 45.7 | 41 | 0.508 |
| Sodium Lag | 266 | 387 | 72.1 | 29 | 0.148 |
| Copper Inf | 0.31 | 0.135 | 0.067 | 41 | 0.053 |
| Copper Lag | 0.252 | 0.014 | 0.148 | 29 | 0.162 |
| Manganese Inf | 4.09 | 0.336 | 2.57 | 41 | 0.271 |
| Manganese Lag | 0.160 | 0.139 | 0.058 | 29 | 0.752 |
| Chloride Inf | 419 | 349 | 117 | 41 | 0.647 |
| Chloride Lag | 328 | 477 | 117 | 29 | 0.267 |
| Zinc Inf | 1.12 | 24.2 | 14.8 | 41 | 0.241 |
| Zinc Lag | 0.387 | 0.034 | 0.210 | 29 | 0.142 |
Notes: Inf—influent; Lag—lagoon.
Table 13 shows the impact of smoking meat on wastewater nutrients. Four of the six facilities smoke meat regularly. The facilities with smoking have greater NO3 and P levels in both the influent (p = 0.023 and p = 0.007) and lagoon (p = 0.02 and p = 0.037) samples than the facilities that do not smoke meat. The lagoon samples at these facilities also showed greater TN (p = 0.035), TKN (p = 0.009), and NH3 (p = 0.036) contents. This indicates that smoking meat interferes with the wastewater N treatment cycle.
Table 13.
Smoking–no smoking nutrient comparison.
| Parameters (mg/L-N or -P) | Smoking | No Smoking | Statistics | ||
|---|---|---|---|---|---|
| SEM | n | p-Value | |||
| TN Inf | 409 | 299 | 98.0 | 37 | 0.404 |
| TN Lag | 215 a | 89.3 b | 53.9 | 24 | 0.035 |
| TKN Inf | 399 | 270 | 89.0 | 41 | 0.274 |
| TKN Lag | 254 a | 81.1 b | 48.5 | 29 | 0.009 |
| Organic N Inf | 132 | 143 | 50.8 | 33 | 0.869 |
| Organic N Lag | 55.4 | 38.4 | 14.0 | 25 | 0.341 |
| NH3 Inf | 221 | 145 | 77.3 | 34 | 0.454 |
| NH3 Lag | 188 a | 48.0 b | 50.2 | 25 | 0.036 |
| TIN Inf | 273 | 170 | 78.7 | 33 | 0.331 |
| TIN Lag | 175 | 55.5 | 49.1 | 23 | 0.070 |
| NO3 Inf | 3.87 a | 2.05 b | 0.592 | 39 | 0.023 |
| NO3 Lag | 3.36 a | 1.09 b | 0.724 | 29 | 0.020 |
| NO2 Inf | 1.84 | 4.55 | 3.03 | 26 | 0.503 |
| NO2 Lag | 0.070 | 0.053 | 0.017 | 18 | 0.421 |
| P Inf | 69.5 a | 31.7 b | 9.77 | 37 | 0.007 |
| P Lag | 63.3 a | 20.1 b | 14.9 | 26 | 0.037 |
Notes: a,b Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Inf—influent; Lag—lagoon.
Table 14 shows the impact of meat smoking on the BOD and COD. Both the BOD and COD in the influent (p = 0.007 and p < 0.0001) and lagoon (p = 0.007 and p = 0.006) samples are greater in the facilities that smoke than those that do not smoke meat. A high residual BOD (Table 14) prevents nitrification, which is one explanation for the higher TN and NH3 (Table 13) contents in the lagoons of the smoking facilities [23].
Table 14.
Smoking–no smoking carbon comparison.
| Parameters (mg/L) | Smoking | No Smoking | Statistics | ||
|---|---|---|---|---|---|
| SEM | n | p-Value | |||
| BOD Inf | 3230 a | 1462 b | 453 | 38 | 0.007 |
| BOD Lag | 1050 a | 201 b | 278 | 29 | 0.023 |
| COD Inf | 12,000 a | 4950 b | 2050 | 41 | <0.0001 |
| COD Lag | 1990 a | 681 b | 347 | 29 | 0.006 |
Notes: a,b Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Inf—influent; Lag—lagoon.
Table 15 and Table 16 show the impact of meat smoking on additional common wastewater characteristics and metals. The facilities that smoke meat have samples with a greater influent pH (p = 0.002) and lagoon alkalinity (p = 0.0155) than those that do not smoke meat. The sodium content is the only inorganic parameter that was greater (p = 0.015) in the influent in the facilities that smoke meat compared to the facilities that do not smoke meat. The copper (p = 0.036), manganese (p = 0.013), and zinc (p = 0.019) concentrations were greater in the lagoon wastewater from the smoking facilities compared to the non-smoking facilities.
Table 15.
Smoking–no smoking common measurements comparison.
| Parameters (mg/L) | Smoking | No Smoking | Statistics | ||
|---|---|---|---|---|---|
| SEM | n | p-Value | |||
| TSSs Inf | 762 | 1910 | 835 | 28 | 0.310 |
| TSSs Lag | 638 | 490 | 390 | 19 | 0.757 |
| pH Inf | 6.81 a | 6.32 b | 0.1103 | 41 | 0.002 |
| pH Lag | 7.51 | 7.32 | 0.205 | 29 | 0.465 |
| Hardness Inf | 258 | 254 | 93.6 | 41 | 0.974 |
| Hardness Lag | 219 | 297 | 30 | 29 | 0.050 |
| Alkalinity Inf | 853 | 615 | 151 | 41 | 0.235 |
| Alkalinity Lag | 958 a | 543 b | 127 | 29 | 0.0155 |
| FOG Inf | 1377 | 335 | 439 | 40 | 0.079 |
| FOG Lag | 84.6 | 3.64 | 69.3 | 29 | 0.365 |
Notes: a,b Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Inf—influent; Lag—lagoon.
Table 16.
Smoking–no smoking inorganics comparison.
| Parameters (mg/L) | Smoking | No Smoking | Statistics | ||
|---|---|---|---|---|---|
| SEM | n | p-Value | |||
| Calcium Inf | 186 | 143 | 62.7 | 41 | 0.61 |
| Calcium Lag | 66.8 | 66.7 | 66.8 | 29 | 0.991 |
| Sodium Inf | 364 a | 222 b | 42.5 | 41 | 0.015 |
| Sodium Lag | 319 | 246 | 54.2 | 29 | 0.293 |
| Copper Inf | 0.264 | 0.200 | 0.070 | 41 | 0.487 |
| Copper Lag | 0.314 a | 0.022 b | 0.104 | 29 | 0.036 |
| Manganese Inf | 4.27 | 0.202 | 2.57 | 41 | 0.232 |
| Manganese Lag | 0.204 a | 0.076 b | 0.038 | 29 | 0.013 |
| Chloride Inf | 440 | 320 | 116.73 | 41 | 0.435 |
| Chloride Lag | 353 | 367 | 88.6 | 29 | 0.903 |
| Zinc Inf | 1.29 | 23.9 | 14.8 | 41 | 0.250 |
| Zinc Lag | 0.487 a | 0.031 b | 0.144 | 29 | 0.019 |
Notes: a,b Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Inf—influent; Lag—lagoon.
Table 17 shows the impact of comingling domestic wastewater on wastewater nutrients. Four of the six facilities comingled human wastewater, while two did not comingle. Comingling demonstrated no impact (p > 0.05) on the influent water regarding nutrients. Likewise, the lagoon values were similar between the facilities that comingle and do not comingle human wastewater, with the exception of the TKN and NO3 contents, which were both greater (p < 0.05) at the facilities that comingle wastewater compared to those that do not.
Table 17.
Comingling–no comingling nutrient comparison.
| Parameters (mg/L-N or -P) | Comingling | No Comingling | Statistics | ||
|---|---|---|---|---|---|
| SEM | n | p-Value | |||
| TN Inf | 344 | 133 | 163 | 31 | 0.246 |
| TN Lag | 223 | 81.1 | 67.1 | 24 | 0.081 |
| TKN Inf | 341 | 142 | 145 | 35 | 0.220 |
| TKN Lag | 235 a | 81.1 b | 58.6 | 29 | 0.034 |
| Organic N Inf | 116 | 65.5 | 64.4 | 28 | 0.473 |
| Organic N Lag | 54.6 | 42.8 | 16.9 | 25 | 0.549 |
| NH3 Inf | 190 | 95.5 | 151 | 28 | 0.570 |
| NH3 Lag | 169 | 46.2 | 63.5 | 25 | 0.106 |
| TIN Inf | 232 | 108 | 155 | 27 | 0.468 |
| TIN Lag | 157 | 52.3 | 61.6 | 23 | 0.158 |
| NO3 Inf | 3.35 | 1.81 | 1.04 | 33 | 0.188 |
| NO3 Lag | 2.67 a | 1.01 b | 0.595 | 29 | 0.025 |
| NO2 Inf | 4.06 | 0.045 | 5.33 | 23 | 0.501 |
| NO2 Lag | 0.060 | 0.045 | 0.060 | 17 | 0.203 |
| P Inf | 60.1 | 38.8 | 19.2 | 31 | 0.329 |
| P Lag | 55.5 | 22.4 | 18.2 | 26 | 0.144 |
Notes: a,b Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Inf—influent; Lag—lagoon.
Table 18 shows the impact of comingling on the BOD and COD. The COD was greater in both the influent (p < 0.0001) and the lagoon (p < 0.034). The greater organic content puts an extra burden on land application treatment.
Table 18.
Comingling–no comingling carbon comparison.
| Parameters (mg/L) | Comingling | No Comingling | Statistics | ||
|---|---|---|---|---|---|
| SEM | n | p-Value | |||
| BOD Inf | 2700 | 1770 | 900 | 32 | 0.361 |
| BOD Lag | 942 | 192 | 336 | 29 | 0.069 |
| COD Inf | 10,100 a | 6200 b | 2430 | 35 | <0.0001 |
| COD Lag | 1820 a | 711 b | 424 | 29 | 0.034 |
Notes: a,b Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Inf—influent; Lag—lagoon.
Table 19 and Table 20 show the impact of comingling on common wastewater characteristics and metals. The influent TSSs content for the comingled facilities was unexpectedly much lower (p = 0.0098) when compared to the facilities that did not comingle; however, the TSSs content was similar (p > 0.05) in the lagoon samples regardless of comingling practices. The influent pH was greater (p = 0.010) at the facilities that comingle human wastewater compared to those that do not. However, the lagoon pH values were comparable (p > 0.05).
Table 19.
Comingling–no comingling common measurements comparison.
| Parameters (mg/L) | Comingling | No Comingling | Statistics | ||
|---|---|---|---|---|---|
| SEM | n | p-Value | |||
| TSSs Inf | 640 a | 4880 b | 1360 | 24 | 0.0098 |
| TSSs Lag | 664 | 185 | 527 | 20 | 0.413 |
| pH Inf | 6.75 a | 6.15 b | 0.198 | 35 | 0.010 |
| pH Lag | 7.50 | 7.15 | 0.227 | 29 | 0.203 |
| Hardness Inf | 232 | 170 | 166 | 35 | 0.735 |
| Hardness Lag | 236 | 298 | 36.5 | 29 | 0.162 |
| Alkalinity Inf | 738 | 284 | 240 | 35 | 0.095 |
| Alkalinity Lag | 915 a | 532 b | 150 | 29 | 0.039 |
| FOG Inf | 1150 | 812 | 819 | 34 | 0.709 |
| FOG Lag | 73.2 | 3.88 | 81.7 | 29 | 0.476 |
Notes: a,b Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Inf—influent; Lag—lagoon.
Table 20.
Comingling–no comingling inorganics comparison.
| Parameters (mg/L) | Comingling | No Comingling | Statistics | ||
|---|---|---|---|---|---|
| SEM | n | p-Value | |||
| Calcium Inf | 160 | 144 | 111 | 35 | 0.897 |
| Calcium Lag | 67.9 | 67.9 | 11.2 | 29 | 0.999 |
| Sodium Inf | 312 a | 51.3 b | 58.5 | 35 | 0.0003 |
| Sodium Lag | 339 | 199 | 61.8 | 29 | 0.064 |
| Copper Inf | 0.233 | 0.262 | 0.114 | 35 | 0.816 |
| Copper Lag | 0.271 | 0.028 | 0.127 | 29 | 0.114 |
| Manganese Inf | 2.53 | 0.324 | 4.77 | 35 | 0.676 |
| Manganese Lag | 0.185 | 0.077 | 0.047 | 29 | 0.060 |
| Chloride Inf | 375 | 27.2 | 189 | 35 | 0.104 |
| Chloride Lag | 424 | 246 | 102 | 29 | 0.149 |
| Zinc Inf | 14.7 | 0.715 | 27.4 | 35 | 0.646 |
| Zinc Lag | 0.423 | 0.035 | 0.176 | 29 | 0.072 |
Notes: a,b Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Inf—influent; Lag—lagoon.
Three of the six facilities have filters installed. Table 21 shows the impact of an effluent septic tank filter within a system on nutrients. The filter lowered all values, but not significantly (p > 0.05).
Table 21.
In-facility filter nutrient comparison.
| Parameters (mg/L-N or -P) | Influent | Post-Filter | Lagoon | Statistics | ||
|---|---|---|---|---|---|---|
| SEM | n | p-Value | ||||
| TN | 440 a | 273 ab | 99.5 b | 84.9 | 36 | 0.027 |
| TKN | 332 a | 222 ab | 60.8 b | 67.7 | 36 | 0.027 |
| Organic N | 164 a | 108 ab | 30.3 b | 47.4 | 34 | 0.869 |
| NH3 | 144 a | 90.2 ab | 30.6 b | 37.7 | 36 | 0.120 |
| TIN | 171 a | 108 ab | 36.9 b | 39.6 | 36 | 0.071 |
| NO3 | 3.23 | 2.78 | 2.33 | 0.666 | 36 | 0.643 |
| NO2 | 0.098 | 0.083 | 0.081 | 3.03 | 19 | 0.721 |
| P | 32.5 a | 31.3 a | 14.3 b | 3.48 | 31 | 0.001 |
Notes: a,b Within a row, least-squares means without a common superscript differ (p < 0.05) significantly.
Table 22 shows the impact of an effluent septic tank filter within a system on the BOD and COD. Both parameters demonstrate a significant decrease (p < 0.0001) of 59% and 69%, respectively, between the influent and the post-filter samples. These decreases indicate that filters are an effective pre-treatment for both parameters, which is consistent with findings from others [11].
Table 22.
In-facility filter carbon comparison.
| Parameters (mg/L) | Influent | Post-Filter | Lagoon | Statistics | ||
|---|---|---|---|---|---|---|
| SEM | n | p-Value | ||||
| BOD | 2730 a | 1320 b | 225 c | 266 | 36 | <0.0001 |
| COD | 9275 a | 2846 b | 640 c | 1203 | 36 | <0.0001 |
Notes: a–c Within a row, least-squares means without a common superscript differ (p < 0.05) significantly.
Table 23 and Table 24 show the impact of an effluent septic tank filter within a system on other common wastewater characteristics and metals. The lagoon FOG (p = 0.021), calcium (p = 0.0006), copper (p = 0.001), zinc (p< 0.0001), and sodium (p = 0.005) concentrations were lower than in the influent. The pH of the system showed progressive increases (p = 0.0003) at each sampling facility from the influent to the lagoon. This increase is observed in all the facilities and is likely not due to a filter. Surprisingly, the TSSs content decreased numerically but not significantly (p > 0.05) throughout the system. This is contradictory to the review paper by Mittal, which demonstrated a 50–70% reduction [11]. Data variability may be a factor. A finer filter is one option to increase TSSs removal to prevent finer suspended particles from escaping the filter.
Table 23.
In-facility filter common measurements comparison.
| Parameters (mg/L) | Influent | Post-Filter | Lagoon | Statistics | ||
|---|---|---|---|---|---|---|
| SEM | n | p-Value | ||||
| TSSs | 1160 | 664 | 260 | 582 | 24 | 0.432 |
| pH | 6.81 a | 7.31 b | 7.91 c | 0.198 | 36 | 0.0003 |
| Hardness | 284 | 396 | 281 | 51.3 | 36 | 0.210 |
| Alkalinity | 927 a | 867 a | 521 b | 112 | 36 | 0.032 |
| FOG | 1800 a | 34.4 b | 2.92 b | 494 | 36 | 0.021 |
Notes: a–c Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Inf—influent; Lag—lagoon.
Table 24.
In-facility filter inorganics comparison.
| Parameters (mg/L) | Influent | Post-Filter | Lagoon | Statistics | ||
|---|---|---|---|---|---|---|
| SEM | n | p-Value | ||||
| Calcium | 224 a | 117 b | 69.0 b | 26.0 | 36 | 0.0006 |
| Sodium | 644 a | 489 b | 419 b | 46.4 | 36 | 0.005 |
| Copper | 0.256 a | 0.056 b | 0.012 b | 0.044 | 36 | 0.001 |
| Manganese | 0.251 | 0.118 | 0.110 | 0.060 | 36 | 0.191 |
| Chloride | 871 | 635 | 604 | 84.0 | 36 | 0.061 |
| Zinc | 1.97 a | 0.198 b | 0.031 b | 0.30 | 36 | <0.0001 |
Notes: a,b Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Inf—influent; Lag—lagoon.
Table 25 shows the impact of lagoon aeration on nutrients. Three of the six facilities utilize lagoon aeration. Aeration has a significant effect on TN, with a 67% reduction. The TKN was greater (p = 0.012) in the non-aerated lagoon samples compared to the aerated samples. This was expected since the conversion of TKN to NO3 is an aerobic process. Aerobic denitrification has been demonstrated in mixed microbial communities, like the lagoons observed in this study [25]. Lower amounts of aeration were found to be more efficient than conventional denitrification [25]. This explains the NO3 reductions observed in the aerated lagoons. In addition, the lagoons studied would also have anaerobic pockets where anaerobic denitrification or an alternative pathway such as anammox could occur. Strategic aeration proves to be an effective aid in reducing N levels.
Table 25.
Aeration–no aeration nutrient comparison.
| Parameters (mg/L-N or -P) | Aeration | No Aeration | Statistics | ||
|---|---|---|---|---|---|
| SEM | n | p-Value | |||
| TN Lag | 77.4 a | 233 b | 59.3 | 24 | 0.042 |
| TKN Lag | 72.4 a | 246 b | 53.3 | 29 | 0.012 |
| Organic N Lag | 32.7 | 59.1 | 15.07 | 25 | 0.150 |
| NH3 Lag | 44.1 | 176 | 57.7 | 25 | 0.064 |
| TIN Lag | 52.8 | 164 | 56.4 | 23 | 0.116 |
| NO3 Lag | 0.091 a | 2.79 b | 0.537 | 29 | 0.007 |
| NO2 Lag | 0.053 | 0.057 | 0.008 | 17 | 0.674 |
| P Lag | 20.3 | 58.6 | 16.8 | 26 | 0.078 |
Notes: a,b Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Inf—influent; Lag—lagoon.
Table 26 shows the impact of lagoon aeration on the BOD and COD. Aeration also had an impact on the BOD (p = 0.036) and COD (p = 0.002), with an 83% and 75% reduction, respectively, compared to the non-aerated lagoon samples. A greater reduction in the BOD also has a major and positive impact on N removal, as nitrification does not proceed until most of the BOD is oxidized.
Table 26.
Aeration–no aeration carbon comparison.
| Parameters (mg/L) | Aeration | No Aeration | Statistics | ||
|---|---|---|---|---|---|
| SEM | n | p-Value | |||
| BOD Lag | 167 a | 991 b | 311 | 29 | 0.036 |
| COD Lag | 490 a | 1979 b | 364 | 29 | 0.002 |
Notes: a,b Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Inf—influent; Lag—lagoon.
Table 27 and Table 28 show the impact of lagoon aeration on common other wastewater characteristics and metals. The alkalinity was lower (p = 0.02) in the aerated lagoons, which was expected with improved N removal. The sodium (p = 0.011), manganese (p = 0.032), and zinc (p = 0.047) levels were also lower in the aerated samples compared to the non-aerated samples; however, the cause of this is unknown.
Table 27.
Aeration–no aeration common measurements comparison.
| Parameters (mg/L) | Aeration | No Aeration | Statistics | ||
|---|---|---|---|---|---|
| SEM | n | p-Value | |||
| TSSs Lag | 490 | 636 | 378 | 20 | 0.752 |
| pH Lag | 7.29 | 7.45 | 0.220 | 29 | 0.547 |
| Hardness Lag | 289 | 237 | 34.7 | 29 | 0.228 |
| Alkalinity Lag | 525 a | 937 b | 139 | 29 | 0.020 |
| FOG Lag | 3.78 | 76.7 | 76.9 | 29 | 0.438 |
Notes: a,b Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Inf—influent; Lag—lagoon.
Table 28.
Aeration–no aeration inorganics comparison.
| Parameters (mg/L) | Aeration | No Aeration | Statistics | ||
|---|---|---|---|---|---|
| SEM | n | p-Value | |||
| Calcium Lag | 67.1 | 68.2 | 10.5 | 29 | 0.930 |
| Sodium Lag | 176 a | 356 b | 55.0 | 29 | 0.011 |
| Copper Lag | 0.025 | 0.284 | 0.118 | 29 | 0.080 |
| Manganese Lag | 0.074 a | 0.192 b | 0.043 | 29 | 0.032 |
| Chloride Lag | 272 | 421 | 97.4 | 29 | 0.214 |
| Zinc Lag | 0.033 a | 0.444 b | 0.164 | 29 | 0.047 |
Notes: a,b Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Inf—influent; Lag—lagoon.
3.3. Treatment Effectiveness
Table 29, Table 30, Table 31 and Table 32 show the wastewater treatment effectiveness at each facility. The “Decrease” heading is the percent reduction for the parameters based on the influent and effluent values. A weighted average is used for the Facility D influent, as the slaughter and process streams were collected and analyzed individually. The “Discharge” heading is calculated from the lagoon measurements closest to when the contents within the lagoon were applied to land, as described below.
Facility A: Lagoon drainage occurred soon after the last summer collection. Consequently, the final summer value is assumed to be closest to discharge.
Facility B: Lagoon drainage occurred in the fall after fall collections. Consequently, the average of the fall values was used.
Facility C: The lagoon was pumped over a 5-day period in November. Consequently, the sample collected during this time was assumed to be the discharge value.
Facility D: Lagoon drainage occurred throughout October and November. Consequently, the first fall collection occurred during this period, and that value was used.
Facility E: The lagoon is drained monthly during the summer. Consequently, the average of the summer values was used.
Facility F: The lagoon utilizes an infiltration basin instead of land application. Consequently, the average of all the lagoon samples was used.
Table 29 compares the concentrations of nutrients between the facilities. The NO2− values were similar (p > 0.05) in the lagoon. These values are also well below the 1.0 mg/L limit, as specified in the GW1530000 permit for Michigan. Facility C had the highest value for NO2− leaving the facility, and that still demonstrated a 99% reduction. Consequently, NO2− is not of concern in these meat processing facilities. Most facilities experienced a decrease in TN, ranging from 65 to 26%, most likely indicating that an effective nitrification/denitrification process is occurring in the lagoons. However, microbial processes can also produce nitrous oxide (N2O), a gas that has a major impact on global warming. This is likely not a concern in these systems, as Harper et al. found that on a swine production farm, 43% of the N entering the wastewater treatment system was lost through lagoons as N2. Only 0.1% was emitted as N2O, and 8% of losses occurred due to NH3 volatilization [26].
Table 29.
Nutrients by location.
| Parameter (mg/L -N or -P) | Facility | Statistics | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| A Co, F, Sm | B Ae *, Co *, F, S | C Co, F, S, Sm | D-Proc Sm | D-Slau S, Sm | E Ae, S | F Ae, Co | SEM | n | p-Value | |
| TN Inf | 254 c | 626 b | 1064 a | 116 c | 370 bc | 133 c | 71.7 c | 128 | 38 | <0.0001 |
| TN Lag | 89.3 c | 110 c | 484 a | 229 b | 59.8 c | 80.2 c | 44.5 | 31 | <0.0001 | |
| TN Decrease | 65% | 83% | 54% | 26% | 55% | −15% | ||||
| TN Discharge | 20.0 | 101 | 489 | 264 | 57.9 | |||||
| TN Discharge Decrease | 92% | 84% | 54% | 15% | 56% | |||||
| TKN Inf | 91.7 c | 572 b | 1021 a | 107 c | 377 b | 142 c | 61.8 c | 68.5 | 42 | <0.0001 |
| TKN Lag | 23.4 d | 97.0 c | 482 a | 258 b | 67.9 cd | 56.8 cd | 18.9 | 36 | <0.0001 | |
| TKN Decrease | 74% | 62% | 53% | 2% | 52% | −1% | ||||
| TKN Discharge | 3.9 | 85.5 | 484 | 302 | 57.1 | |||||
| TKN Discharge Decrease | 96% | 85% | 53% | −15% | 60% | |||||
| Organic N Inf | 59.0 b | 292 a | 167 ab | 23.8 b | 301 a | 65.5 b | 35.4 b | 77.3 | 34 | 0.0189 |
| Organic N Lag | 15.7 c | 41.9 abc | 80.4 a | 78.2 ab | 34.8 bc | 28.9 c | 16.2 | 31 | 0.0144 | |
| Organic N Decrease | 73% | 79% | 58% | 68% | 47% | 6% | ||||
| Organic N Discharge | 3.76 | 33.2 | 29.8 | 83.6 | 28.5 | |||||
| Organic N Discharge Decrease | 94% | 89% | 82% | 66% | 56% | |||||
| NH3 Inf | 32.6 c | 255 b | 877 a | 87.4 c | 57.8 c | 95.5 c | 29 c | 58.7 | 35 | <0.0001 |
| NH3 Lag | 7.91 c | 53.3 c | 419 a | 173 b | 38.4 c | 26 c | 24.4 | 31 | <0.0001 | |
| NH3 Decrease | 76% | 79% | 51% | −151% | 60% | −4% | ||||
| NH3 Discharge | 0.14 | 52.3 | 479 | 218 | 28.6 | |||||
| NH3 Discharge Decrease | 99% | 80% | 45% | −216% | 70% | |||||
| TIN Inf | 38.4 c | 302 b | 905 a | 92.1 cd | 194 bc | 108 cd | 44.2 cd | 61.1 | 34 | <0.0001 |
| TIN Lag | 13.4 c | 60.3 c | 417 a | 176 b | 46 c | 49 c | 26.7 | 29 | <0.0001 | |
| TIN Decrease | 67% | 80% | 54% | −58% | 57% | −5% | ||||
| TIN Discharge | 12.6 | 53.8 | 484 | 266 | 29.5 | |||||
| TIN Discharge | 37% | 82% | 47% | −139% | 73% | |||||
| NO3 Inf | 2.99 bcd | 3.46 bc | 6.92 a | 1.34 de | 4.76 b | 1.81 cde | 0.340 e | 0.73 | 40 | <0.0001 |
| NO3 Lag | 2.71 ab | 1.95 b | 4.94 a | 2.43 ab | 0.698 b | 0.600 b | 0.969 | 35 | 0.0222 | |
| NO3 Decrease | 10% | 43% | 35% | 40% | 61% | −130% | ||||
| NO3 Discharge | 0.455 | 1.37 | 3.61 | 0.985 | 0.677 | |||||
| NO3 Discharge Decrease | 85% | 60% | 48% | 76% | 63% | |||||
| NO2 Inf | 0.113 b | 0.091 b | 6.71 a | 0.052 b | 0.061 b | 0.045 b | 0.047 b | 1.17 | 26 | 0.0011 |
| NO2 Lag | 0.07 | 0.07 | 0.046 | 0.052 | 0.046 | 0.045 | 0.022 | 22 | 0.0642 | |
| NO2 Decrease | 38% | 23% | 99% | 2% | 0% | 20% | ||||
| NO2 Discharge | 0.07 | 0.07 | 0.05 | 0.07 | 0.035 | |||||
| NO2 Discharge Decrease | 38% | 23% | 99% | −32% | 24% | |||||
| P Inf | 32.8 c | 32.3 c | 82.9 ab | 45.5 bc | 108 a | 38.8 bc | 20.0 c | 17.3 | 38 | 0.0014 |
| P Lag | 12.3 c | 15.9 c | 72.75 ab | 99.6 a | 23.4 bc | 33.3 bc | 18.6 | 27 | 0.0022 | |
| P Decrease | 62% | 51% | 12% | 9% | 40% | −75% | ||||
| P Discharge | 3.78 | 17.2 | 82.5 | 84.8 | 21.5 | |||||
| P Discharge Decrease | 88% | 47% | 0% | 23% | 45% | |||||
Notes: a–e Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Ae = aerated lagoon; Co = comingled; F = filter; S = slaughterhouse; Sm = smoking. * Facility B added aeration and comingling during the sample collection period. Inf—influent; Lag—lagoon.
Table 30 compares the BOD and COD between each location. The BOD varied (p < 0.05) in both the influent and lagoon samples. Facility C had greater (p < 0.05) BOD values than all the other facilities, which were similar (p > 0.05). Facilities C and D showed an increase in the discharge value when compared to the average lagoon values. This likely resulted from the variability in the values and the continuous addition of wastewater to the lagoon. Facility A demonstrated the greatest removal for both BOD and COD, likely due to the long treatment time in the second holding lagoon where, wastewater is not continually added.
The COD varied (p < 0.05) by facility, and the two influent wastewater flows at facility D - one was from the slaughter operation and the other was from processing. The discharge value of facility A is abnormal, as the COD is expected to be higher than the BOD.
Table 30.
Carbon by location.
| Parameter (mg/L) | Facility and Characteristics | Statistics | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| A Co, F, Sm | B Ae *, Co *, F, S | C Co, F, S, Sm | D-Proc Sm | D-Slau S, Sm | E Ae, S | F Ae, Co | SEM | n | p-Value | |
| BOD Inf | 3509 ab | 1955 bc | 4928 a | 602 c | 3803 a | 1774 c | 556 c | 651 | 39 | <0.0001 |
| BOD Lag | 184 b | 275 b | 2113 a | 856 b | 139 b | 243 b | 255 | 36 | <0.0001 | |
| BOD Decrease | 95% | 86% | 57% | 72% | 92% | 42% | ||||
| BOD Discharge | 119 | 273 | 3720 | 1060 | 71.3 | |||||
| BOD Discharge Decrease | 97% | 86% | 25% | 65% | 96% | |||||
| COD Inf | 11,723 b | 6828 d | 9010 c | 1090 g | 26,156 a | 6206 e | 1117 f | 1770 | 42 | <0.0001 |
| COD Lag | 334 c | 945 c | 3188 a | 2443 b | 435 c | 553 c | 225 | 36 | <0.0001 | |
| COD Decrease | 97% | 86% | 65% | 88% | 93% | 63% | ||||
| COD Discharge | 83 | 640 | 2950 | 3200 | 364 | |||||
| COD Discharge Decrease | 99% | 91% | 67% | 84% | 94% | |||||
Notes: a–g Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Ae = aerated lagoon; Co = comingled; F= filter; S = slaughterhouse; Sm = smoking. * Facility C added aeration and comingling during the sample collection period. Inf—influent; Lag—lagoon.
Table 31 and Table 32 compare other commonly measured wastewater characteristics and metals. The TSSs content was similar (p > 0.05) between the facilities in the lagoon treatment. Facilities B and C demonstrated an interesting increase in TSSs, attributed to the high quantity of algae when compared to the other facilities. The pH values varied (p < 0.05) between the facilities in both the influent and lagoon samples but had a consistent, slight increase at all the facilities. This increase is assumed to be caused by microbial denitrification, as evident by the reduction in TN. The alkalinity levels increased at some facilities, likely because of the varying levels of denitrification and nitrification between the facilities. The cleaning products used at each facility also contribute to the overall alkalinity of the system, as many cleaners and sanitizers contain sodium hydroxide. The influent FOG content was highly variable but different (p < 0.05), but it was consistently low in the lagoon samples.
Table 31.
Commonly measured characteristics by location.
| Parameter (mg/L) | Facility and Characteristics | Statistics | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| A Co, F, Sm | B Ae *, Co *, F, S | C Co, F, S, Sm | D-Proc Sm | D-Slau S, Sm | E Ae, S | F Ae, Co | SEM | n | p-Value | |
| TSSs Inf | 600 | 695 | 333 | 318 | 1614 | 4875 | 140 | 1900 | 29 | 0.1954 |
| TSSs Lag | 97.2 | 746 | 482 | 1196 | 185 | 193 | 475 | 24 | 0.36 | |
| TSSs Decrease | 84% | −189% | −45% | 7% | 97% | −8% | ||||
| TSSs Discharge | 64 | 293 | 298 | 1070 | 53.3 | |||||
| TSSs Discharge Decrease | 89% | 58% | 11% | 17% | 99% | |||||
| pH inf | 6.80 ab | 6.42 bc | 6.80 ab | 7.19 a | 6.47 bc | 6.14 c | 6.42 bc | 0.172 | 42 | 0.0037 |
| pH Lag | 8.23 a | 7.58 abc | 6.97 c | 7.33 bc | 7.05 c | 7.73 ab | 0.24 | 36 | 0.0068 | |
| pH Decrease | −21% | −18% | −12% | −10% | −15% | −17% | ||||
| pH Discharge | 9.46 | 7.8 | 7.4 | 7.59 | 6.91 | |||||
| pH Discharge Decrease | −40% | −22% | −9% | −14% | −13% | |||||
| Hardness Inf | 105 | 463 | 83.3 | 223 | 621 | 170 | 151 | 143 | 42 | 0.087 |
| Hardness Lag | 249 a | 311 a | 117 c | 345 a | 286 ab | 166 c | 21.2 | 36 | <0.0001 | |
| Hardness Decrease | −7% | 32% | −66% | 33% | −68% | −4% | ||||
| Hardness Discharge | 164 | 296 | 124 | 368 | 304 | |||||
| Hardness Discharge Decrease | −56% | 36% | −49% | 29% | −79% | |||||
| Alkalinity Inf | 429 d | 1305 b | 1880 a | 708 c | 396 d | 284 d | 220 d | 95.2 | 42 | <0.0001 |
| Alkalinity Eff | 458 cd | 583 c | 1468 a | 1101 b | 501 cd | 299 d | 83.0 | 36 | <0.0001 | |
| Alkalinity Decrease | −6.8% | 55% | 22% | −126% | −76% | −55% | ||||
| Alkalinity Discharge | 268 | 600 | 1550 | 1086 | 516 | |||||
| Alkalinity Discharge Decrease | 38% | 54% | −5% | −122% | 81% | |||||
| FOG-Inf | 3556 a | 44 c | 42.5 c | 107 bc | 1890 ab | 811 bc | 53.6 c | 651 | 41 | 0.0009 |
| FOG-Lag | 2.16 b | 3.67 b | 14.0 ab | 37 a | 4.16 b | 18.2 ab | 8.56 | 35 | 0.0476 | |
| FOG decrease | 100% | 92% | 67% | 98% | 99% | 83% | ||||
| FOG Discharge | 1 | 2 | 12 | 101 | 3.5 | |||||
| FOG Discharge Decrease | 100% | 92% | 72% | 95% | 100% | |||||
Notes: a–d Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Ae = aerated lagoon; Co = comingled; F= filter; S = slaughterhouse; Sm = smoking. * Facility C added aeration and comingling during the sample collection period. Inf—influent; Lag—lagoon.
Table 32.
Inorganic Compounds by location.
| Parameters (mg/L) | Facility | Statistics | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| A Co, F, Sm | B Ae *, Co *, F, S | C Co, F, S, Sm | D-Proc Sm | D-Slau S, Sm | E Ae, S | F Ae, Co | SEM | n | p-Value | |
| Calcium Inf | 215 ab | 233 ab | 20.3 b | 61.1 b | 447 a | 144 b | 42.4 b | 93.3 | 42 | 0.0364 |
| Calcium Lag | 70.1 b | 67.7 b | 25.0 c | 105 a | 68.6 b | 40.9 c | 6.66 | 36 | <0.0001 | |
| Calcium Decrease | 67% | 71% | −23% | 70% | 52% | 23% | ||||
| Calcium Discharge | 44.8 | 67 | 24.1 | 32.8 | 73.8 | |||||
| Calcium Discharge Decrease | 79% | 71% | −19% | 91% | 49% | |||||
| Sodium Inf | 456 ab | 522 a | 257 c | 418 b | 323 c | 51.3 d | 63.9 d | 32.0 | 42 | <0.0001 |
| Sodium Lag | 387 a | 450 a | 214 b | 357 a | 78.7 b | 109 b | 47.4 | 36 | <0.0001 | |
| Sodium Decrease | 15% | 14% | 17% | −3% | −53% | −80% | ||||
| Sodium Discharge | 234 | 357 | 226 | 353 | 84.4 | |||||
| Sodium Discharge Decrease | 49% | 32% | 12% | −2% | −65% | |||||
| Copper Inf | 0.273 b | 0.238 bc | 0.032 c | 0.04 c | 0.01 a | 0.262 bc | 0.08 bc | 0.08 | 42 | <0.0001 |
| Copper Lag | 0.014 b | 0.011 b | 0.022 b | 0.904 a | 0.035 b | 0.053 b | 0.026 | 36 | <0.0001 | |
| Copper Decrease | 95% | 95% | 30% | −72% | 87% | −55% | ||||
| Copper Discharge | 0.005 | 0.006 | 0.015 | 0.017 | 0.05 | |||||
| Copper Discharge Decrease | 98% | 97% | 53% | 97% | 81% | |||||
| Manganese Inf | 0.115 bc | 0.386 ab | 0.103 bc | 0.061 c | 0.530 a | 0.32 abc | 0.02 c | 0.109 | 42 | 0.0140 |
| Manganese Lag | 0.139 b | 0.081 bc | 0.087 bc | 0.385 a | 0.074 bc | 0.028 c | 0.027 | 36 | <0.0001 | |
| Manganese Decrease | −21% | 79% | 15% | 7% | 77% | −24% | ||||
| Manganese Discharge | 0.036 | 0.07 | 0.081 | 0.969 | 0.070 | |||||
| Manganese Discharge Decrease | 69% | 82% | 21% | −134% | 78% | |||||
| Chloride Inf | 442 ab | 828 a | 150 b | 495 ab | 673 a | 27.1 b | 83.7 b | 166 | 42 | 0.0089 |
| Chloride Lag | 477 b | 730 a | 138 c | 445 b | 62.0 c | 136 c | 61.8 | 36 | <0.0001 | |
| Chloride Decrease | −8% | 12% | 8% | 30% | −128% | 36% | ||||
| Chloride Discharge | 303 | 768 | 146 | 2700 | 68.3 | |||||
| Chloride Discharge Decrease | 31% | 7% | 3% | −324% | −152% | |||||
| Zinc Inf | 2.61 a | 1.34 ab | 0.187 b | 0.119 b | 2.25 a | 0.713 b | 0.112 b | 0.440 | 42 | 0.0004 |
| Zinc Lag | 0.034 b | 0.028 b | 0.133 b | 1.29 a | 0.039 b | 0.079 b | 0.046 | 36 | <0.0001 | |
| Zinc Decrease | 99% | 98% | 29% | 23% | 94% | −15% | ||||
| Zinc Discharge | 0.005 | 0.024 | 0.082 | 3.64 | 0.045 | |||||
| Zinc Discharge Decrease | 100% | 98% | 56% | −117% | 94% | |||||
Notes: a–d Within a row, least-squares means without a common superscript differ (p < 0.05) significantly. Ae = aerated lagoon; Co = comingled; F= filter; S = slaughterhouse; Sm = smoking. * Facility B added aeration and comingling during the sample collection period. Inf—influent; Lag—lagoon.
Table 29, Table 30, Table 31 and Table 32 demonstrate noticeable differences between the discharge values and the average lagoon values collected over the entire sampling period. Facility A demonstrated the ideal situation, as the treatment progresses throughout the entire summer in the second lagoon that does not receive wastewater. The lagoon is land-applied in the early fall after the maximum amount of treatment is achieved. The average TN removal at this facility for all sampling events was 65%, but the value closest to when it was land applied was 92%. This was also true for the TKN, NO3, and P concentrations, which were 96%, 84%, and 88%, respectively, prior to land application compared to 74%, 10%, and 62%, respectively, over the entire sampling period. In contrast, Facility D demonstrated the opposite trend for TIN and NH3 concentrations, most likely due to the sample location and the continuous influent into the lagoons even immediately before discharging. The only notable difference in BOD and COD was in Facility C, which went from 57% to a 25% BOD removal. These examples highlight the necessity of basing regulations on the sampling closest to discharge.
3.4. BOD Concentration and Loading
In addition to concentrations, limits for loadings are also specified in permits. GW1530000 specifies a limit of 50 lb of BOD/acre-day (56 kg/ha-day) for land application to prevent metal mobilization [21]. This is calculated by multiplying the concentration and flow and dividing by the amount of land the wastewater is applied to. Table 33 lists the kg of BOD produced at each facility per day, but data on the area of the land it was applied to were not available. The importance of considering loadings is observed by comparing Facilities A and D. Both have similar average BOD concentrations, but the differences in flow rates result in a loading of 13.3 kg of BOD/day being produced by Facility A and 135.2 kg of BOD/day for the slaughter side of Facility D.
4. Conclusions
Facility A, which carries out meat processing only, had superior wastewater nutrient constituent removals, with a TN reduction of 92% and an effluent value of 20 mg/L, prior to land application. The TKN, NO3, and P levels were reduced by 96%, 84%, and 88%, respectively. These results were achieved with a septic tank effluent filter and no lagoon aeration. A unique factor of this facility, however, is the batch configuration of the lagoons. While one was fed, the other was idle to allow time for treatment. This leads to long detention times without any additional wastewater entering the treating lagoon. This supports the findings of Vendramelli et al., who state that long periods of isolation improve N removal in lagoons [27]. Applying this method is difficult and expensive for existing facilities that are space-limited.
Facility E had the second lowest concentrations of TN (57.9 mg/L) and the lowest BOD (71.3 mg/L) prior to discharge, and both were comparable to those in domestic wastewater. This was surprising, as the statistical analysis demonstrated that the facilities that slaughtered had significantly higher influent values when compared to those that did not. The lagoon at this facility also had a relatively shorter detention time than the others. Two factors are believed to contribute to this success. This facility had substantial lagoon aeration, both on the surface and in the subsurface. The monthly electricity cost for their aeration is approximately USD 88, but this value is highly dependent on the aeration schedule and lagoon size. This facility also implemented the most blood management among the facilities that slaughtered. With an approximate COD of 400,000 mg/L and a high organic N content, minimizing blood entry into the wastewater substantially reduces the strength of the wastewater [7].
Facility C demonstrates the highest concentrations of TN in the lagoon sample, primarily in the form of organic N and NH3 (TKN). There are two major contributing factors. The first is the high volume of blood that enters the wastewater at this facility. The wastewater collected from the septic tank was observed to have a red coloration. While a 50% reduction was observed in TN, there was still a substantial concentration of TKN in the lagoon. This facility does not use aeration, which would be expected to achieve further nitrification, improving the progression of the N cycle.
Facility D showed the smallest reduction in TN. In examining other N compounds, it can be observed that TKN did not change between the influent and the lagoon. Breaking the TKN content down further, organic N decreased while NH3 increased, leading to a net zero change in the TKN content. The conversion of NH3 to NO3 is an aerobic process. Due to the N cycle locking up at the NH3 stage, adding aeration to this lagoon should be investigated.
There are several options for the improved treatment of meat processing wastewater that must be considered. First, the facility’s wastewater must be fully understood. A once-per-year sampling event will not accurately describe the state of a system or lagoon. For those who apply wastewater to land, samples should be taken from the lagoon immediately before or during discharge; otherwise, decisions will be based on inaccurate values. Once the characteristics of the facility-specific wastewater are understood, a typical question is would it be more effective to improve blood and waste collection during operations so that these materials do not enter the wastewater stream to be ultimately land-applied? This option would add costs for disposing of high-strength industrial wastewater, possibly labor, and cleaning supplies, but reduce the level of pretreatment before land application. The other option is to improve pretreatment before land application, which would require greater capital and operational cost but would save resources needed for in-plant blood separation. However, improving pretreatment is complex. Adding aeration will add a major capital cost and require optimization to balance electrical costs and treatment improvements. This includes an improvement in TN reduction as well as BOD. Another example of enhanced pretreatment is maintaining two lagoons, with one receiving the wastewater and the other discharging it, which does not receive fresh wastewater during an extended treatment time. This option, however, can have a significantly higher capital cost when compared to aeration, especially if a geotextile liner is required, but it reduces reoccurring operation costs, such as electricity for aeration. Filters are another option to consider. Filters will improve the BOD and COD in the treatment but incur a capital cost and increase the operational requirements. Operators will need to determine what level of filtration is necessary to meet treatment goals. A reduced pore size will filter out more suspended pollutants, up to the maximum level, but is more susceptible to clogging and requires more labor for maintenance.
In designing a meat processing land application system, the soil’s treatment capacity must be considered. This capacity varies greatly depending on the wastewater’s characteristics, soil characteristics, and application scheduling. The capacity of the soil can be significant and should not be ignored if it is not saturated and overloaded with organic materials. This capacity should be considered by regulatory agencies in setting permit values. One example of this capacity is the soil’s ability to handle FOG. High values of FOG can clog the soil and inhibit wastewater treatment. The high reduction in FOG at all the facilities indicates that this will not be an issue at any of the facilities studied. Reductions in BOD will also be beneficial for land-applied soil and minimize the risk of metal leaching due to low-oxygen environments.
Author Contributions
Conceptualization, G.R., S.I.S., J.P.S. and A.J.G.; methodology, G.R., S.I.S., J.P.S. and A.J.G.; software, A.J.G.; validation, G.R., S.I.S., J.P.S. and A.J.G.; formal analysis, G.R. and A.J.G.; investigation, G.R.; resources, G.R., S.I.S., J.P.S. and A.J.G.; data curation, A.J.G.; writing—original draft preparation, G.R.; writing—review and editing, S.I.S., J.P.S. and A.J.G.; visualization, G.R., S.I.S., J.P.S. and A.J.G.; supervision, G.R. and S.I.S.; project administration, S.I.S. and J.P.S.; funding acquisition, J.P.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Michigan Alliance for Animal Agriculture (M-AAA), grant number AA-21-176.
Data Availability Statement
Data is contained within the article.
Acknowledgments
Sam Dougherty and Oluyemi Adetule, who aided in the sample collection and data analysis.
Conflicts of Interest
The authors declare no conflicts of interest. The sponsors had no role in the design, execution, interpretation, or writing of the study.
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Table 3.
Facility characteristics.
| Facility | Aeration | Comingled Human Wastewater | Septic Tank Filter | Slaughter-House | Smokes Meat | Special Characteristics |
|---|---|---|---|---|---|---|
| A | ✓ | ✓ | ✓ | Two non-aerated lagoons in batch operation. One is actively filled, while the other provided detention time. | ||
| B | ✓ | ✓ | ✓ | ✓ | Lagoon aeration, 6–10 h/day, began partway through the sampling period. Commingling began partway through the sampling period. | |
| C | ✓ | ✓ | ✓ | ✓ | Septic tank effluent filter but not accessible for sample collection. | |
| D | ✓ | ✓ | Separate processing and slaughter wastewater samples were able to be collected. Two lagoons are used; only the first one is regularly used, as the second lagoon is a backup in case of overflow in the first. | |||
| E | ✓ | ✓ | Subsurface aeration occurs year-round, and surface aeration occurs during spring, summer, and fall. | |||
| F | ✓ | ✓ | Wash water from loading bay mixed in. Two infiltration lagoons in series; only second lagoon is aerated. |
Table 4.
Wastewater analytical methods.
| Parameter | Method |
|---|---|
| TN (mg/L-N) | HACH 10208 |
| Organic N (mg/L-N) | Calculated |
| NO3 (mg/L-N) | 40 CFR 141, HACH 10206 |
| NH3 (mg/L-N) | EPA 350.1, 351.1, 351.2, HACH 10205 |
| TKN (mg/L-N) | 4500-N(Org) C. Semi-Micro-Kjeldahl |
| TIN (mg/L-N) | Calculated |
| NO2− (mg/L-N) | HACH 10207 |
| P (mg/L-P) | EPA365.1, 365.3, HACH 8190 |
| BOD (mg/L) | SM 5210 B |
| COD (mg/L) | E410.4 |
| TSSs (mg/L) | EPA 160.2 |
| pH | HACH Lange 50 50 T Probe |
| Hardness (mg/L) | SM 2340 C |
| Alkalinity (mg/L-CaCO3) | SM 2320 B |
| FOG (mg/L) | E1664A |
| Ca (mg/L) | E200.8 |
| Na (mg/L) | E200.8 |
| Cu (mg/L) | E200.8 |
| Mn (mg/L) | E200.8 |
| Cl (mg/L) | E200.8 |
| Zn (mg/L) | E200.8 |
Table 33.
Influent BOD concentration and loading.
| Concentration (mg/L) | Loading (kg/Day) | |||||
|---|---|---|---|---|---|---|
| Facility | Avg | Min | Max | Avg | Min | Max |
| A | 3510 | 1206 | 4920 | 13.3 | 4.58 | 18.6 |
| B | 1960 | 660 | 3000 | 14.8 | 4.98 | 22.7 |
| C | 4930 | 1740 | 7900 | 41.4 | 14.6 | 66.2 |
| D-Proc | 602 | 460 | 966 | 7.12 | 5.44 | 11.4 |
| D-Slau | 3803 | 651 | 7470 | 135 | 23.1 | 265 |
| E | 1770 | 1020 | 3300 | 1.67 | 2.11 | 6.80 |
| F | 319 | 1050 | 556 | 12.1 | 39.7 | 21.0 |
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Rouland, G.; Safferman, S.I.; Schweihofer, J.P.; Garmyn, A.J. Characterization of Low-Volume Meat Processing Wastewater and Impact of Facility Factors. Water 2024, 16, 540. https://0-doi-org.brum.beds.ac.uk/10.3390/w16040540
AMA Style
Rouland G, Safferman SI, Schweihofer JP, Garmyn AJ. Characterization of Low-Volume Meat Processing Wastewater and Impact of Facility Factors. Water. 2024; 16(4):540. https://0-doi-org.brum.beds.ac.uk/10.3390/w16040540
Chicago/Turabian StyleRouland, Gregory, Steven I. Safferman, Jeannine P. Schweihofer, and Andrea J. Garmyn. 2024. "Characterization of Low-Volume Meat Processing Wastewater and Impact of Facility Factors" Water 16, no. 4: 540. https://0-doi-org.brum.beds.ac.uk/10.3390/w16040540
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