EVALUATION OF A GREENHOUSE ECOSYSTEM TO MANAGE CRAFT BEVERAGE WASTEWATER By Carley Elizabeth Allison A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Biosystems Engineering – Master of Science 2023 ABSTRACT A greenhouse ecosystem, often referred to as a Living Machine©, is a technology for biological wastewater treatment using plants in a greenhouse structure. It has a small footprint relative to traditional onsite systems, has been shown to manage high strength wastewater, and can provide a high level of treatment to allow for reuse for purposes such as irrigation, toilet flushing, and landscaping. Craft beverage wastewater (water from wineries, breweries, and cideries) is considered high strength and contains chemical oxygen demands (COD) close to 20,000 mg/L, total nitrogen up to 80 mg/L, and total phosphorus up to 70 mg/L. Due to the variability of the wastewater in both flow and composition, it is hard to treat with a conventional wastewater treatment system. The ability of this system to treat craft beverage wastewater is determined through this project. The experimental system consisted of three parallel systems, with one always serving as a control to treat representative synthetic winery wastewater. Each system had three reactors in series with 5 species of plants native to Michigan. The first two reactors had 12-hour aeration cycles. Synthetic wastewater was prepared to test the ability of the system to treat a variety of wastewater characteristics that are found from these sources. Once the performance of the greenhouse ecosystem is understood for various wastewater characteristics, actual wastewater from the three sources will be tested. wastewater characteristics that were routinely measured included pH, dissolved oxygen, and conductivity, as well as visually inspecting the plants. Nitrate, nitrite, ammonia, total nitrogen, COD, and total phosphorus levels were measured weekly. Results show the system is effective within the designed organic and hydraulic loadings. However, extreme events will cause disruptions and potential system failure. An example of an extreme event is having a high concentration of COD. Aeration was found to be a key factor in reducing the odor produced from treating the wastewater. The greenhouse ecosystem can also be scaled up, as the volume and flow are known for a specific loading rate. Next steps include performing an economic analysis, performing optimization experiments to determine the best plants to use, monitoring more sample types (such as total Kjeldahl nitrogen and nitrogen gas), performing microbial analyses, and performing a field scale experiment. ACKNOWLEDGEMENTS Thank you to the Craft Beverage Council (GG 22*1568), and the USDA (MICL04225) for providing funding for this project. Thank you to the winery, brewery, and cidery contacted for providing wastewater for this project. Thank you to the College of Agriculture and Natural Resources Statistics Consulting Center for assisting with the statistical analysis of the data collected in this project. Thank you to my committee members: Dr. Safferman, Dr. Dechand, and Dr. Masten. I have learned so much from all of you and I am grateful for the opportunity to study with you. Thank you to all the undergraduate workers who have helped me with this study: Jackson Hotchkiss, Jordan Dashner, Kate Mann, Ben Bridge, Serena Hurst, Madison Pritchett, and Arianna Fobbs. Thank you to my co-graduate student Greg. Thank you to all my friends and family who have listened to me talk about this project for two years, with a special shoutout to my parents, who pushed me in high school and helped me realize my potential. iii TABLE OF CONTENTS CHAPTER 1: DESIGN OF THE GREENHOUSE ECOSYSTEM ......................................... 1 INTRODUCTION ....................................................................................................................... 1 BIBLIOGRAPHY ....................................................................................................................... 5 CHAPTER 2: CRAFT BEVERAGE WASTEWATER TREATMENT USING A GREENHOUSE ECOSYSTEM .................................................................................................. 6 ABSTRACT ................................................................................................................................ 6 INTRODUCTION ....................................................................................................................... 7 METHODS.................................................................................................................................. 9 COD AND NUTRIENT ANALYSIS ........................................................................................ 19 PLANT HEALTH RESULTS ................................................................................................... 38 CONCLUSIONS ....................................................................................................................... 39 BIBLIOGRAPHY ..................................................................................................................... 41 CHAPTER 3: CRAFT BEVERAGE SITE SPECIFIC WASTEWATER TREATMENT SELECTION TOOL ................................................................................................................... 46 INTRODUCTION ..................................................................................................................... 46 DECISION TOOL ..................................................................................................................... 49 CONCLUSIONS ....................................................................................................................... 51 BIBLIOGRAPHY ..................................................................................................................... 52 CHAPTER 4: FUTURE WORK ............................................................................................... 54 FIELD SCALE .......................................................................................................................... 54 SAMPLE ANALYSIS ............................................................................................................... 54 DECISION SUPPORT TOOL .................................................................................................. 55 PLANT HEALTH AND MICROBIOLOGY ............................................................................ 55 BIBLIOGRAPHY ..................................................................................................................... 57 APPENDIX 1: COD DATA ........................................................................................................ 58 APPENDIX 2: TOTAL PHOSPHORUS DATA ....................................................................... 65 APPENDIX 3: TOTAL NITROGEN DATA ............................................................................. 72 APPENDIX 4: NITRATE DATA ............................................................................................... 79 APPENDIX 5: NITRITE DATA ................................................................................................ 86 APPENDIX 6: AMMONIA DATA ............................................................................................ 93 APPENDIX 7: CODE USED FOR STATISTICS .................................................................. 100 APPENDIX 8: VISUAL REPRESENTATIONS OF STATISTICAL DATA ........................ 111 iv CHAPTER 1: DESIGN OF THE GREENHOUSE ECOSYSTEM INTRODUCTION Craft beverage wastewater refers to winery, brewery, and cidery wastewater and is considered high strength wastewater as it has high amounts of chemical oxygen demand (COD), nitrogen, and phosphorus. Michigan has around 600 craft beverage facilities[1]. The COD in winery wastewater comes from alcohols, sugars, acids, tannins, and lignin[2]. COD in brewery wastewater comes from sugars, starches, ethanol, and fatty acids[3]. There is no literature on the COD components in hard cider wastewater, so it is assumed that it is similar to winery and brewery wastewater. Other components in the wastewater that make it hard to treat are the cleaning/disinfection products. These include sodium hydroxide and potassium hydroxide[4]. Since the cleaning chemicals are caustic and used to clean food processing equipment, including disinfecting, they have a negative impact on the environment and plants. Wastewater treatment systems need to be able to treat high-strength wastewater with substantial amounts of cleaning chemicals. Wineries produce wastewater during several stages, as shown in Figure 1. Most of the wastewater comes from washing the machinery to keep it sterile. The exact amount of wastewater to wine produced changes from winery to winery, but it can range from 0.2L to 14L discharged per liter of wine[5]. Wineries in Michigan can produce over 3.5 million gallons of wine per year[1]. 1 Figure 1: Wine production with locations of wastewater creation [6]. Breweries also produce wastewater at several stages, as shown in Figure 2. Wastewater is produced from cleaning as well, but since brewing requires more steps, there are more types of constituents in the wastewater. Brewery wastewater will also vary between brewery to brewery, but the average is 3.3 L of wastewater per L of beer produced. Breweries in Michigan produce over 1.2 million beer barrels in a year (39 million gallons)[7]. 2 Figure 2: Process flow diagram of beer production [6] In Michigan alone there are 630,000 gallons of hard cider produced in a year[1]. There is currently little to no literature on how much wastewater is produced per liter of hard cider produced but based on the brewery wastewater production (3.3 times as much wastewater as beer produced) and winery wastewater production (up to 14 times as much wastewater as wine produced), it can be assumed that the wastewater produced per liter is similar and varies depending on the season. The US EPA does not currently have a hard cider process flow 3 diagram, but as it is fruit being turned into alcohol, it can be assumed to be similar to the winery production process as both are taking fruits and turning them into alcoholic beverages. The high amount of wastewater produced in the craft beverage industry, as well as the high strength of the pollutants in the wastewater, means that it cannot be easily treated by a typical decentralized wastewater treatment plant. Wastewater treatment plants are designed for a specific range of values, so it is difficult and expensive to send water to wastewater treatment plants. A greenhouse ecosystem, also known as a Living Machine ™ has been used in the past to treat onsite generated municipal and high strength wastewater. This system has been used to treat dairy wastewater[8], blackwater[9], and other types of high strength wastewater[10]. A greenhouse ecosystem has several benefits: less operation/maintenance, chemicals do not need to be added into the system, and it has a smaller footprint. A greenhouse ecosystem works by suspending plants in water media and allowing the microbes around the roots, as well as the plant uptake, to treat the pollutants within the wastewater. However, there is no literature on the use of a greenhouse ecosystem to treat craft beverage wastewater. The need for this research is that there are not many cost effective, small-footprint treatment systems for craft beverage wastewater. The resulting objectives of this research follow.  Design a greenhouse ecosystem for craft beverage wastewater, using literature values for other high-strength wastewater.  Determine the characteristics of winery, brewery, and cidery wastewaters to develop synthetic wastewaters. Synthetic wastewater was used to determine the effect of specific constituents on treatment and plants. Actual winery, brewery, and cidery wastewater was used after the concept was proven to determine if any constituents that could be impactful were not included in the synthetic formulation.  Choose the best native, non-invasive plants to use for the system.  Develop a decision support tool for the selection of the best onsite wastewater treatment technology for site-specific craft beverage producers. 4 BIBLIOGRAPHY [1] Michigan Craft Beverage Council, “Michigan’s Craft Beverage Industry.” [Online]. Available: https://storymaps.arcgis.com/stories/86bd6339343d46a1a8c9740ea67102b0 [2] Z. Jin et al., “Combined process of bio-contact oxidation-constructed wetland for blackwater treatment,” Bioresour Technol, vol. 316, Nov. 2020, doi: 10.1016/j.biortech.2020.123891. [3] S. Bolognesi, D. Cecconet, and A. G. Capodaglio, Agro-industrial wastewater treatment in microbial fuel cells. INC, 2020. doi: 10.1016/B978-0-12-817493-7.00005-9. [4] A. Conradie, G. O. Sigge, and T. E. Cloete, “Influence of Winemaking Practices on the Characteristics of Winery Wastewater and Water Usage of Wineries,” Toward a Sustainable Wine Industry: Green Enology Research, vol. 35, no. 1, pp. 141–162, 2015. [5] E. Melchiors and F. B. Freire, “Winery Wastewater Treatment: a Systematic Review of Traditional and Emerging Technologies and Their Efficiencies,” Environmental Processes, vol. 10, no. 3, pp. 1–22, 2023, doi: 10.1007/s40710-023-00657-4. [6] M. E. Joyce, J. F. Scaief, M. W. Cochrane, and K. A. Dostal, “State of the Art: Wastewater Management in the Beverage Industry.,” 1977. [Online]. Available: https://nepis.epa.gov/Exe/ZyPDF.cgi/9101BCLE.PDF?Dockey=9101BCLE.PDF [7] Better On Draft: Michigan Craft Beer Podcasts News and Reviews, “MI 2022 Brewery Production Numbers.” [Online]. Available: https://www.betterondraft.com/mi-2022- brewery-production-numbers [8] A. M. Keppler and J. F. Martin, “INVESTIGATING THE PERFORMANCE OF A LABORATORY-SCALE ECOLOGICAL SYSTEM TO TREAT DAIRY WASTEWATER,” Trans ASABE, vol. 51, no. 5, pp. 1837–1846, [Online]. Available: www.livingmachines.co [9] L. Tian et al., “Treatment via the Living Machine system of blackwater collected from septic tanks: effect of different plant groups in the systems,” Environ Dev Sustain, vol. 23, no. 12, pp. 1964–1975, 2021, doi: 10.1007/s10668-020-00658-5. [10] Y. Hung, J. F. Hawumba, and L. K. Wang, “Chapter 22: Living Machines,” in Environmental Bioengineering, vol. 11, 2010, pp. 743–772. doi: 10.1007/978-1-60327- 031-1. 5 CHAPTER 2: CRAFT BEVERAGE WASTEWATER TREATMENT USING A GREENHOUSE ECOSYSTEM ABSTRACT A greenhouse ecosystem, often referred to as a Living Machine ™, is a biological wastewater treatment process that uses plants in a greenhouse structure. It has a small footprint relative to traditional onsite wastewater systems, can manage high strength wastewater, and can provide a high level of treatment to allow for non-potable reuse. Craft beverage wastewater (CBW) is considered high strength because of its elevated levels of nitrogen, phosphorus, and biochemical oxygen demand. Due to the variability of the flow and composition, this wastewater is challenging to treat with a conventional onsite wastewater treatment system. A proof-of- concept study was conducted on the applicability of the greenhouse ecosystem to treat CBW. The experimental system had three parallel trains, each with three reactors in series with 5 species of plants native to Michigan. The first two reactors in each train had 12-hour aeration cycles. One train always served as a control, receiving a consistent amount of synthetic winery wastewater (SSWW). The SSWW’s characteristics were altered to represent the variability of craft beverage wastewater in the other two trains. Once the performance of the greenhouse ecosystem was understood, actual wastewater from a winery, cidery, and brewery were tested as to ensure the SSWW was representative. Wastewater characteristics that were measured daily included pH and dissolved oxygen. Visual inspection of the plants also occurred daily. Nitrate, nitrite, ammonia, total nitrogen (TN), chemical oxygen demand (COD), and total phosphorus (TP) levels were measured approximately once a week. Results show that the system is effective at removing the nutrients and COD. The high amounts of COD caused a system failure in the COD spiked wastewater and in the cidery wastewater, but the system was able to recover after additional nutrients were added to COD spiked wastewater. The SSWW moving through Train 1 produced the best plant growth, which the high amounts of COD had the worst plant growth. The highest removal rate of COD was during the cidery and winery wastewater, with 98% removal. The highest removal rate of TN was also in cidery wastewater, with a removal rate of 88%. The highest removal rate of nitrate was 97% in the brewery wastewater. The highest removal rate of nitrite was in the brewery wastewater with over 99% removal. The highest 6 removal rate of ammonia was above 99% removal in the SSWW, the winery wastewater, and the salt and nutrient spike. The highest removal rate of TP was 91% in the brewery wastewater. INTRODUCTION A greenhouse ecosystem (commonly known as a Living Machine ™) for wastewater treatment is a biological system with plants and microbes that are specifically selected to treat wastewater [1], [2] . Plants with large root systems typically have better treatment[3], [4]. The greenhouse ecosystem is intended to be a small-footprint option as well as being a more economical option than conventional onsite wastewater treatment systems for high-strength wastewater and has been demonstrated on blackwater[5][6], dairy wastewater[7], and poultry wastewater[8]. A greenhouse ecosystem typically has anaerobic and aerobic zones to allow for carbon oxidation, nitrification, and denitrification, as well as a clarifier and a final polishing reactor[9]. Plants also contribute to nitrogen uptake. Phosphorus removal is typically achieved by precipitation and plant uptake [10]. The wastewater can be treated to an extent that allows for reuse as irrigation water or toilet flushing. Another benefit of a greenhouse ecosystem is that it does not require any chemicals to be added for treatment. Craft beverage wastewater is high strength wastewater with an average COD of around 3,000 mg/L[11]. Table 1 summarizes these characteristics. Winery and cidery wastewater are also unique in that for about half of the year, there is little to no wastewater being produced. Their main production period is after fall harvest. Beer is different as the wastewater is produced more consistently throughout the year [12]. Craft beverage wastewater includes a wide range of components because it is produced from excess raw products, cleaning equipment, processing, packaging preparation, and off-specification product that is discharged to the drain. Within table 1, the Michigan Department of Environmental Quality [11] had 5 sample collection sites. Skornia et. al. [13] collected wastewater from one site and they noted that the values collected were lower than the average national values but matched values from other studies in Michigan. They also performed a literature review with national averages. Bakare et. al. [14] had 1 sample collection site and used literature to confirm the values. Brito et. al. [15] had 3 sample collection points and had sources they compared the results to. Hard cider wastewater was difficult to characterize as there is little to no literature found with characterization values of the wastewater; most studies examined the solids portions produced. 7 Table 1. General Craft Beverage Wastewater Characteristics Parameter (mg/L) Winery [11], [13] Brewery [14], [15] Cidery [16], [17] Average Range Average Range Average Chemical oxygen 3,236 320-296,119 11,214 800- 8,000 >170,000 demand (COD) Biochemical oxygen 2,046 125-130,000 2,746 demand (BOD) pH Sodium 6.2 279 3-12.9 7-470 6.74 Total solids 11,311 1,602-79,635 5,600 Total phosphorus (TP) 5.26 3.3-188.3 Total nitrogen (TN) 7.6 10-415 (TSS) 16-68 8.1 20,000 1,200- 3,600 5-11 N/A 5,100- 8,750 9-50 12-31 4,800 N/A N/A 6,000 N/A N/A The variability of craft beverage wastewater flow and components makes it difficult to use a conventional onsite wastewater treatment system such as a septic tank and drain field. Building a traditional activated sludge or fixed film treatment plant on-site takes a lot of space and is expensive. The Michigan craft beverage industry is extensive, with over 300 breweries, over 200 wineries, and over 90 cideries[18]. This ranks Michigan 6th in the nation for craft breweries, 9th in the nation for wineries[18], and the top producer of hard cider[19]. Michigan craft beverage production facilities are typically in rural areas not served by a centralized wastewater facility. These facilities need an onsite system as trucking the wastewater to a treatment facility is not an economical option. There is much recent attention on the management and regulation of craft beverage wastewater as more facilities are built. Current regulations depend on the region and site-specific factors. The greenhouse ecosystem has been demonstrated on high-strength food processing wastewater but literature on its application to craft beverage wastewater was not found. This research is a proof-of-concept study on its applicability to this industry. The objectives include the following.  Design a greenhouse ecosystem for craft beverage wastewater, using literature values for other high-strength wastewater. 8  Choose the best native, non-invasive plants to use for the system.  Determine the characteristics of winery, brewery, and cidery wastewaters to develop synthetic wastewaters. Synthetic wastewaters were used to determine the effect of specific constituents on treatment and plants. Actual winery, brewery, and cidery wastewater was used after the concept was proven to determine if any constituents that could be impactful were not included in the synthetic formulation.  Develop a qualitative decision support tool for the selection of the best onsite wastewater treatment technology for site-specific craft beverage producers. METHODS This section details the literature gathered for the experimental design, synthetic wastewater recipe, quality assurance and quality control, and the statistical analysis methods for analyzing the data. Experimental Design The desired water loadings were based on chemical oxygen demand (COD) as that is the pollutant of most concern within the wastewater. A loading of 0.61 kg/m3-d was determined from the typical concentration in winery wastewater (Table 1) and literature loading values for a greenhouse ecosystem receiving high-strength wastewater (Table 2). 9 Table 2: Literature Loading Values for High-Strength Wastewater COD Concentration- Loading value Hydraulic residence time Wastewater Technology Reference type 440 +/- 217 mg/L* 5 days Black water 392 +/- 174 mg/L* on startup 723+/- 409 mg/L* once stable 0.004 kg/m2/d 6, 5, 4 days Black water 7, 10, 9 days (compared 3 technologies) Sewage 0.03 kg/m2/d 5 425.7 mg/L* 3.27 days Brewery wastewater Black water 0.092 kg COD/m2/d *These values did not have loading values reported. 3-5 days in septic tank Winery wastewater Bio-contact oxidation; constructed wetland Living Machine ™ 2 biofilters, surface flow wetland, stabilization pond Hydroponics with grass Living Machine ™ Constructed wetland [20] [21] [22] [23] [24] [25] This project had three treatment trains consisting of three reactors each. The reactors were 55-gallon drums with 50 gallons marked. These reactors were found to have a depth adequate for unrestricted root growth for the selected plants. From the selected loading, 0.61 kg/m3-d, the flow rate to each reactor was 5.4 gal/day. Grow lights were used to simulate the greenhouse ecosystem. Figure 1 shows the grow light frame, reactors, and the plants, and Figure 2 is an overhead view of the system. The water is placed into the refrigerator before it goes through the system. This is done to prevent microbial growth from occurring and taking pollutants before the water was pumped through the system. The water was then pumped into each of the train’s Reactor 1. The water was then pumped from Reactor 1 into Reactor 2, and from Reactor 2 to Reactor 3. There was a hole drilled into the side of the Reactor 3’s to allow for the water to come out of the reactor by gravity outflow. This reduced the need for an extra pump. 10 Figure 3: Entire system together with plants, grow lights, and water moving through the system. Refrigerator Figure 4: System Schematic with reactor labels. Arrows show direction of water movement. 11 Plant Decisions The plants were chosen by performing a literature review. Austin et. al. [26] had an extensive list of effective plants that they experimented with, so their list was checked for those native to Michigan, as their experiment took place in Vermont. The following are criteria the plants had to meet to be chosen: cold tolerance (to allow for treatment over winter months), pruning ability, native to Michigan, and non-invasive in the US. The selected plants were Schoenoplectus pungens[27] (three square bulrush, also known as Scirpis pungens), Acorus americanus (American sweetflag), Decodon verticillatus (swamp loosestrife), Typha augustifolia (cattail)[28], [29], Penstemon digitalis (foxglove beard tongue), and a volunteer species (a plant that grew on its own in the system and was not initially planted) Spirodela polyrhiza (duckweed species)[30]. Swamp loosestrife has not been studied widely for wastewater treatment, but a similar plant called purple loosestrife has been. However, purple loosestrife is invasive in Michigan. Foxglove beard tongue also has not been studied widely for wastewater treatment, but it has been studied and determined to be an effective metal accumulator[31]. As some craft beverage wastewaters contain heavy metals, such as copper, it was added to attempt to uptake these heavy metals. These plants were all collected from Cardno Native in Walkerton, Indiana, except the cattail and duckweed, which were collected from a local pond. A plastic chicken wire fence was used to keep the plants anchored in place. The fence was attached to the reactors by drilling holes through the lip of the reactor and using zip ties to keep the fence at the top of the water. The plants arrived in soil media, so as much soil was removed as possible to expose the roots. Holes in the fence were expanded to be slightly smaller than the root ball and the roots were placed into the hole. One of each plant species was placed into every drum. This was done so all drums would have a similar starting point. The plants were monitored visually to determine health. Photos were taken of the plants every day the experiment was operated to allow for comparisons of health over time. The three growth indicators that were monitored were the leaf color, the leaf texture, and plant elongation. Plant leaves will change colors if they are stressed. Common colors are yellow or brown. The leaf texture will change if the plants become dehydrated or absorb too much water. They will become dry and wrinkled if the leaves dry out or become soft if they absorb too much water. 12 Plant elongation is when the plants stretch out because they cannot find enough light. This leads to yellowing leaves near the base of the plant that will be more likely to fall off. Wastewater Characteristics The influent wastewater was stored in a 6-gallon bucket in a refrigerator immediately before being pumped into the first reactor in each train. The pumps used were Cole-Parmer No. 7553-80, 1-100 RPM with Masterflex ® Model 7017-20 pump heads and Masterflex® tubing. The same type of pump was used to transfer the water from the first reactor into the second reactor and second to the third. The pumps operated on different timelines to avoid overtopping the reactors. The first pump ran from 9 AM-7 PM, the second from 9 AM-8 PM, and the third from 9 AM-9 PM. There was a gravity outlet on the side of the third reactor that allows for water to move into a storage carboy. All trains were initally opertated identially. After roughly a month and a half with the original synthetic wastewater to allow the plants to equlibriate. The feed to trains 2 and 3 were then changed to determine the effect of specific wastewater constiuents and simulate the strenths of brewery and cidery wastewater,. Thereafter, actual wastewater was used in trains 2 and 3 to determine if any wastewater consituents that were not in the synthetic formulaiton effected performnace and plant health. Details are provided in Table 3. 13 Table 3: Dates and Phases of Operation Research Phase Phase 1: equilibrium (all trains managed identally, receiving SSWW) 3/20/23- 4/6/23 3/20/23 3/23/23 3/30/23 4/6/23 4/13/23- 5/17/23 5/31/23- 6/22/23 7/14/23- 8/21/23 10/20/23- 11/15/23 Phase 2:  Train 1: SSWW  Train 2: COD spike (winery)  Train 3: Nutrient spike (brewery) Phase 3:  Train 1: SSWW  Train 2: COD and Nutrient spike (winery)  Train 3: Nutrient and Salt spike (cidery) Phase 4:  Train 1: SSWW  Train 2: Acutal winery wastewater  Train 3: Actual cidery wastewater Phase 5:  Train 1: SSWW  Train 2: Actual brewery wastewater  Train 3: SSWW, recovery 4/13/23 4/20/23 4/27/23 5/3/23 5/10/23 5/17/23 5/31/23 6/6/23 6/14/23 6/22/23 7/14/23 7/21/23 7/28/23 8/1/23 8/4/23 8/11/23 8/21/23 10/27/23 11/3/23 11/8/23 Note: SSWW is Standard Synthetic Wastewater, which simulated wastewater from a winery. The extensive literature review (Table 1) conducted to determine the components of winery, cidery, and brewery wastewater was used to develop the synthetic formulation. This formula required 95% ethanol, diluted white grape juice, sodium phosphate, and nitrogen fertilizer. The diluted grape juice had a 1:20 ratio of juice:water. Table 4 is the exact formulation for each phase of the research.The white grape juice and ethanol mimics white wine, since white wine is produced the most often in Michigan. A new batch of wastwater was produced every 3 days and stored at a temperature between 35-40 °F. The wastewater changeover was done in the mornings before 9am before the 14 pumps turn on, or in the evenings after the pumps turn off. Between each batch, the buckets were washed with a phosphate free detergent. Table 4: Synthetic Wastewater Recipes Formulation Standard Synthetic Winery wastewater (SSWW) COD Spike Nutrient Spike COD and Nutrient Spike Salt Spike 95% Ethanol 46 mL Diluted juice 75 mL Nitrogen Fertilizer 1032 mg Sodium Phosphate 170 mg Salt 0 mg 75 mL 46 mL 75 mL 46 mL 570 mL 75 mL 570 mL 75 mL 1032 mg 3100 mg 5160 mg 3100 mg 170 mg 1040 mg 850 mg 1040 mg 0 mg 0 mg 0 mg 15,600 mg The standard synthetic winery wastewater (SSWW) recipe was designed and confirmed with in-house lab testing to obtain an average COD of around 1,000-2,000 mg/L. The amount of nitrogen and phosphorus added were 10 mg/L and 5 mg/L, respectively. The COD spike recipe was designed and confirmed at 5,000-6,000 mg/L. [11]The nutrient spike recipe aimed for 30 mg/L as both nitrogen and phosphorus, as 30 mg/L in the wastewater. The nutrient-salt spike recipe was the same recipe used for the nutrient spike, but added 15,600 mg of salt. The COD in the SSWW was based on the average amount of COD in Michigan wineries. The amount of COD in the COD spiked wastewater comes from the max wastewater COD from 5 Michigan wineries[11]. The COD and nutrient spike was designed to have the same COD as the COD spike alone. The nutrient spike was to mimic brewery wastewater, as brewery wastewater has more excess nutrients than the winery wastewater does. The COD spiked recipe had a COD concentration of up to 5 times greater than the base synthetic wastewater, so the fertilizer and sodium phosphate were also multiplied by 5. The amount of salt comes from 280 mg of sodium in a liter, which is the average amount of salt in Michigan Wineries. There was little to no literature on hard cider wastewater characteristics, so no recipes could be designed for it. Aeration was added after a month of operation to reduce odors. The aeration provided an anaerobic Reactor 1, aerobic Reactor 2, and anoxic Reactor 3. The amount of oxygen in the water was measured using a DO probe. The anaerobic drum being the first drum, even with aeration, is likely because of the high amounts of COD in the wastewater that the aeration is not effective to make an aerobic zone. Having an anaerobic reactor and an aerobic reactor allows for 15 nitrification and denitrification. A 1744 GPM aeration pump was used to provide aeration to the first two reactors in each train. The air pump used was a Simple Deluxe pump, model number LGPUMPAIR110. The air was pumped air through a manifold to the 9 reactors. An aeration stone was placed at the bottom. Each aeration stone provided roughly 194 GPM of air. Train 1 Reactor 3 and Train 3 Reactor 3 had no aeration and Train 2 Reactor 1 and Train 3 Reactor 1 had twice the aeration going into them by having two aeration stones. This was determined as the project continued because of issues with odor and lessening treatment efficiency. The trains were not operated identically because each of the wastewater types have different aeration requirements. The control ran well with only one stone in the first two reactors. There were no odor issues from the control wastewater. However, there were odor complaints about winery and cidery wastewater, so another aeration stone was added to each Reactor 1. Daily sampling for pH, temperature, and dissolved oxygen was performed in each reator. Two probes were used, a HACH pH probe and a HACH LBOD probe. The HACH pH probe is a HACH sensION+ ™ portable pH meter with a Sension+5051T pH electrode[32]. The HACH DO probe is a HACH HQ1130 DO/1 Channel handheld device with an LDO 10101 probe[33]. Measurements until August were used with the HQ40d handheld device. The manufacturer tates these two devices are identical to each other. Water samples were taken on average 3-5 times per sampling phase to measure nitrate, nitrite, total nitrogen (TN), total phosphorus (TP), and COD. HACH TNT kits were used to measure these parameters and a HACH DR6000 UV VIS Spectrophotometer was used to obtain the concentrations. The kit detection limits and methods used are in table 5. 16 HACH TNT Kit Limits Method References Table 5: HACH Kits, Limits and EPA Methods TNT 823 (ultra high range COD) 250-15,000 mg/L COD TNT 826 (low range total nitrogen) 1-16 mg/L N TNT 831 (low range ammonia) TNT 835 (low range nitrate) TNT 839 (low range nitrite) 1-12 mg/L NH3-N 0.23-15.50 mg/L NO3-N 1-60 mg/L NO3 0.015-0.6 mg/L NO2-N 0.05-2 mg/L NO2 TNT 844 (low range phosphorus) 0.5-5.0 mg/L PO4-P 1.5-15.0 mg/L PO4 HACH method: 10212 EPA Reactor Digestion Method HACH method: 10208 Not EPA approved or equivalent HACH method: 10205 EPA method equivalent to 350.1, 351.1, 351.2 HACH method: 10206 EPA method approved, compared to 40 CFR 141 HACH method: 10207 EPA Diazotization method HACH method: 10209/10210 Not EPA approved or equivalent [34] [35], [36] [36], [37] [36], [38] [39] [36], [40] A description of the facilites where the acutal wastewater used in Phases 4 and 5 were collected from follows.  Winery: o Small scale o Current treatment: septic tank (25-30 gal), air treatment, bark bed o Comingled wastewater o No screening o Wastewater collected from the septic tank  Cidery: o Medium scale o Current treatment: two 2,000-gallon septic tanks, then pumped into an aboveground 20,000 gallon tank and shipped away o Non-comingled wastewater o Screening o Wastewater collected from septic tank 17  Brewery: o Large scale o Current treatment: wastewater pumped directly to 20,000 gallon aboveground tank and shipped to a wastewater treatment facility o Non-comingled wastewater o No screening o Wastewater collected from spigot near bottom of 20,000 gallon tank Quality assurance and quality control Water samples were collected by taking a plastic sample collection bottle and putting it into the center of the reactors. Samples were either analyzed immediately or placed into a fridge until analysis could be performed later that day. Analysis was always done on the same day as collection as to avoid having to use a preservative. Three replicates were chosen using a random number generator, representing a 25% replication rate. A standard and blank (DI water) were also performed for all tests (TN, TP, nitrate, nitrite, ammonia, and COD) at a rate of one standard and one blank for every seventeen samples. If the replicates were not within 10% of each and/or the standards and blanks were not within 10% of the expected vale the analyses were rerun. Statistical analysis Data were analyzed using the R statistical software and the code is provided in Appendix 7. A linear mixed effect model was employed to test for the fixed effects of phases, train and drum, and their interactions on response variables using the lme4 package. Responses include COD, total phosphorus, total nitrogen, nitrate, nitrite, and ammonia. Sample ID nested within phases were considered random effects. Model residual diagnostics were performed using residual vs. fitted plots for constant variance, and QQ plots for normality assumptions. Data was log transformed to meet model assumptions. Mean shifting outliers were removed using the car package. If ANOVAs revealed significant main effects at alpha level of 0.05, post hoc tests were conducted using the emmeans package. Results were back transformed to the response scale and estimated marginal means were reported. In each graph, there may be removals of 100% or 0%. The 100% removal occurred when the effluent value was 0 mg/L or not detected. The 0% removal occurred either when there was addition of nutrients (such as nitrate increasing due to nitrification) or when the influent value 18 was marked as not detected and then had an effluent value as 0 (as happened often with ammonia). The average values of the data are presented. The averages below the detection limit are marked as Below Detection Limit (BDL) in the tables. The averages below the detection limit were able to be calculated as the spectrophotometer produces a value for every sample run, but these values cannot be used as it is considered inaccurate. In the statistical analysis for the data BDL, the value midway between 0 and the minimum detection limit was used instead of the spectrophotometer reading. In several cases, BDL values were negative, and a negative concentration does not exist. The percent removal standard deviation was calculated using the propagation of error equation, shown below. As some of the values for the effluent are BDL, the value midway between the value and the detection limit was used. The values used in the standard deviation for the removal percentages are in the tables next to the BDL indicator. 𝑆(cid:3019) = 𝑅(cid:3496) (cid:2870) 𝑆(cid:3032)(cid:3033)(cid:3033). 𝐸𝑓𝑓. 𝑐𝑜𝑛𝑐. (cid:2870) 𝑆(cid:3036)(cid:3041)(cid:3033). 𝐼𝑛𝑓. 𝑐𝑜𝑛𝑐. − COD AND NUTRIENT ANALYSIS (1) The wastewater analysis is broken down into 6 sections: chemical oxygen demand, total nitrogen, nitrate, nitrite, ammonia, and total phosphorus. Within each section, the average influent and effluent values will be displayed for each phase, along with the number of data points, standard deviation, and percent removal. The phases and removals will be compared to each other. The actual wastewaters used will also be compared to each other. The raw data for all constituents is in Appendices 1-6. Appendix 8 has the visual data provided by the statistical analysis, which is what was used to determine if data is statistically significant. Data in the tables is marked as statistically different by using superscripts. Data with no superscripts indicates that there is not a statistical difference between the values. Organic nitrogen estimates do not have statistical differences between the values as no statistical analyses were performed on them. Chemical Oxygen Demand The COD influent, effluent, number of samples taken, and percent removal are shown in Table 6. During Phase 1, the influent wastewater to all trains were the same and had a COD that ranged 19 from 600 mg/L to 3000 mg/L. The broad range was from the wastewater prepping procedure as the COD degraded over the three-day storage period, even though stored at a low temperature. The range of the wastewater is beneficial in proving the concept, however, as this type of range is not unrealistic at actual processors. Regardless, the variability did not prevent the wastewater from being treated to close to or below 250 mg/L (detection limit of the TNT 823 HACH kit) for all trains. The COD was roughly halved between the influent and Reactor 1 for the control train (1100 mg/L to 300 mg/L), and then halved again between reactors 1 and 2 (260 mg/L to 150 mg/L). Reactor 2 and Reactor 3 had similar values (150 mg/L and 140 mg/L, respectively). The average removal percentage across the entire train was 88%. This trend of at least halving the COD between the influent and Reactor 1 and halving again between Reactor 1 and Reactor 2 was observed in all types of wastewaters analyzed. 20 Table 6: COD Analysis Statistics mg/L COD 1118 Mass, kg/d n 9.15E-02 1202 393 4503a 560b 4469a 2.46E-02 8.05E-03 9.22E-02 1.15E-02 Train 1 SSWWW Influent 2.29E-02 19 Train 1 SSWWW Effluent BDL 125 2.56E-03 20 Train 2 SSWWW Influent 4 Train 2 SSWWW Effluent 4 Train 2 COD Spike Influent 6 Train 2 COD Spike Effluent 6 Train 2 COD and Nutrient Spike Influent Train 2 COD and Nutrient Spike Effluent Train 2 Winery Wastewater Influent Train 2 Winery Wastewater Effluent Train 2 Brewery Wastewater Influent Train 2 Brewery Wastewater Effluent Train 3 SSWWW Influent Train 3 SSWWW Effluent Train 3 Nutrient Spike Influent Train 3 Nutrient Spike Effluent 2.22E-02 5.82E-03 2.83E-02 BDL 125b 12981a BDL 125b 7043a 1.44E-01 2.56E-03 6.84E-03 2.56E-03 2.66E-01 4 4 6 334b 7 3 7 3 3 3 1082 284 1384a BDL 125b 1408a BDL 125b 15222a 303b 2.56E-03 2.88E-02 2.56E-03 3.12E-01 6 3 3 7 7 2 2 1137 6.20E-03 2.33E-02 BDL 125 2.56E-03 Train 3 Salt and Nutrient Spike Influent Train 3 Salt and Nutrient Spike Effluent Train 3 Cidery Wastewater Influent Train 3 Cidery Wastewater Effluent Train 3 Recovery Influent Train 3 Recovery Effluent % removal % removal std. 88% 20.9 67% 12.5 88% 80.0 96% 7.8 98% 106.1 97% 11.2 74% 15.9 89% 25.6 90% 25.2 Std. Dev. 754 87 593 145 6039 378 543 10 9074 20 1093 115 691 82 1043 69 1043 23 18495 267 626 12 98% 147.7 89% 16.5 Train 2 had a COD spike and then a COD + Nutrients spike in phases 2 and 3, respectively. During Phase 2, the COD spike representing when a bad batch of wine/beer/cider is dumped down the drain, the average effluent concentration almost doubled that during Phase 1. 21 This was because plant health was rapidly declining during this period (Figure 3). This was considered a system failure, and the loading was too high for this strength of wastewater. Figure 5: Train 2, Reactor 1, COD Spike only. April 25, 2023. Example of unhealthy plant. In Phase 3, additional nutrients were added at the same ratio to the COD (3 times higher than the control winery synthetic wastewater). The treatment efficiency almost instantly increased, and plant health improved. The wastewater at the beginning of the train was around 4500 mg/L and was treated to below detection limit in every analysis. Figure 4 shows the increase in plant health after the change. The cattails grew back the fastest, then the American sweetflag, three-square bulrush, and swamp loosestrife. 22 Figure 6: Train 2, Reactor 1, COD and nutrient spike. June 15, 2023. Plant growth increase upon the addition of nutrients. COD removal is more consistent with higher amounts of nutrients in the water. This is likely due to the microbes and plants having more food to be able to break down the COD. Plant nutrient availability is often expressed as a carbon:nitrogen:phosphorus (C:N:P) ratio. This was calculated from the average values of the COD, TN, and TP from the COD and nutrient spike wastewater. These values were 4469 mg/L COD, 16.6 mg/L TN, and 6.41 mg/L TP. The C:N:P ratio was 697:2.6:1. The C:N ratio was 269:1 and the C:P ratio was 697:1. There have been multiple studies that attribute better COD removal from a higher amount of nitrogen, but with a ratio of up to 5:1 [41]–[43]. Since the COD is so much higher than the N and P, COD removal and plant health will likely be better if there are more nutrients in the wastewater. Phase 2 demonstrated that wastewater with an optimal C:N:P can be effectively treated with the greenhouse ecosystem. The TP, and nitrite were treated to below 5 mg/l and 0.5 mg/L, respectively, TN was consistently above 5 mg/L, but less than 10 mg/L, 88% of the COD spike alone was removed, but 96% was removed when adequate nutrients were provided. During Phase 3, Train 3 had a nutrient spike and a nutrient and salt spike. COD removal was more uniform with just the nutrients, but the removal rates were similar with 89% removal during the nutrient spike and 90% during the salt and nutrient spike. There was no plant health decrease during just the nutrient spike, but there was yellowing and slight curling of the leaves 23 when salt was added. Excess salt leads to a nutrient and mineral imbalance, which impacts the plants by yellowing the leaves, stunting growth, and curling them[44], [45]. The actual winery wastewater had a 98% removal of COD. The highest COD within the winery wastewater was 25,000 mg/L. All values in the winery effluent were below detection limit. This was expected, and it follows the removal patterns for the SSWW. The actual cidery wastewater had a 98% removal of COD as well. The highest COD within the cidery wastewater was 51,000 mg/L and the effluent values were less than 900 mg/L. The highest value of the COD came from the cidery dumping a bad batch of cider into the drain, which has a COD of roughly 91,000 mg/L. This increase of the COD has a negative impact on the plants (Figure 5). The C:N:P ratio (using the highest values for each one) was calculated to be 871:0.77:1. Nitrogen is a limiting factor here (as it is less than the phosphorus amount) and so the plants do not have enough nutrients to effectively remove the pollutants and maintain health. Figure 7: Train 3, August 11, 2023. During hard cider spike. Actual brewery wastewater had a high odor, but it smelled like beer. A very thick film appeared on top of the wastewater in Train 2 Reactor 1 and 2. The plants did not survive in Train 2 Reactor 1. The film appeared within a few days of the brewery wastewater. The aeration stones were changed right before the brewery water was introduced and so were working at full potential. The brewery wastewater was already bubbly during collection due to the carbonation and so adding in aeration increased the amount of bubbles in the system. The bubbles then hardened on top of the water, creating the film. It would likely be a long-term issue and more 24 studies are needed for determination of effect on plants. Films grew on top of the surface of the water in the previous phase so this may have influenced this occurrence with the actual brewery wastewater. Figure 6 shows the film on top of the water in Train 2 Reactor 1 and Figure 7 shows the film on Train 2 Reactor 2. Regardless, excellent COD removal resulted with an overall removal of 97%, with influent values ranging from 12,000-14,000 mg/L. When comparing brewery wastewater to winery and cidery, there seems to be a distinction. Brewery wastewater has a higher overall COD but has less big COD spikes. Winery and cidery wastewater have a lower average COD (7,000 mg/L for winery and 9,000 mg/L for cidery, excluding the data point that came from dumping a batch of hard cider), but the range is much bigger for both (range of 500-25,000 mg/L for winery, and 800-51,000 mg/L for cidery). This is likely because the wastewater is better equalized at the brewery as it is stored in a 20,000- gallon tank, compared to the 30-gallon septic tank and 2,000-gallon septic tank used at the winery and cidery, respectively. The brewery wastewater is also stronger at the sample collection location than the cider location or brewery location. Figure 8: Train 2 Reactor 1 with brewery wastewater film on top of water surface. 25 Figure 9: Train 2 Reactor 2 with brewery wastewater film on top of water surface. Total Nitrogen There were four different nitrogen measurements taken: total nitrogen, nitrate, nitrite, and ammonia. All nitrogen data is expressed as mg/L-N. The constituents in the wastewater that add nitrogen are malt (cereal grains that have been prepared for brewing by soaking in water)[46], adjuncts (other ingredients added to the beer that can be fermented)[47], and the nitric acid used for cleaning[48]. Table 7 shows the total nitrogen data. Regarding Train 1, although data is available for each research phases, the feed was consistent throughout the entire research period and data is supplied for the entire research, which will be used for comparisons. The 64% decrease in the amount of total nitrogen through the system in the control train was due to reduction in the nitrate, nitrite, and ammonia within the system, which will be discussed in more detail in following sections. The removal rate of nitrate was lower consistently in Train 1, the control, than in the other drums. Interestingly, there was also an increase in TN in the control’s Reactor 1 in almost half of the research phases. This could be attributed to an increase in organic matter, which is measured by total Kjeldal nitrogen. This was not measured, but it can be estimated using an equation. 26 Table 7: TN Analysis Statistics. Train 1 SSWWW Influent Train 1 SSWWW Effluent Train 2 SSWWW Influent Train 2 SSWWW Effluent Train 2 COD Spike Influent Train 2 COD Spike Effluent Train 2 COD and Nutrient Spike Influent Train 2 COD and Nutrient Spike Effluent Train 2 Winery Wastewater Influent Train 2 Winery Wastewater Effluent Train 2 Brewery Wastewater Influent Train 2 Brewery Wastewater Effluent Train 3 SSWWW Influent Train 3 SSWWW Effluent Train 3 Nutrient Spike Influent Train 3 Nutrient Spike Effluent Train 3 Salt and Nutrient Spike Influent Train 3 Salt and Nutrient Spike Effluent Train 3 Cidery Wastewater Influent Train 3 Cidery Wastewater Effluent Train 3 Recovery Influent Train 3 Recovery Effluent mg/L N 5.7 2.0 3.7 1.5 16.7 11.5 16.6 Mass, kg/gal-d 1.17E-04 4.19E-05 7.48E-05 3.07E-05 3.43E-04 2.35E-04 3.41E-04 n 19 20 4 4 5 5 4 14.8 3.03E-04 22.2 4.55E-04 17.9 3.66E-04 22.23 4.55E-04 10.51 2.15E-04 2.98 2.03 10.77 2.60 14.59 6.10E-05 4.14E-05 2.21E-04 5.32E-05 2.99E-04 5.28 1.08E-04 45.33 9.28E-04 5.27 1.08E-04 7.02 10.69 1.44E-04 2.19E-04 4 5 7 0 3 4 3 5 5 4 4 6 7 2 2 Std. Dev. 3.6 1.5 3.5 0.4 18.7 8.8 6.5 0.6 2.1 20.5 3.72 9.62 2.62 0.38 7.45 0.46 7.45 2.26 59.28 4.22 3 2 % removal % removal std. 64% 1.17 59% 1.09 31% 1.65 11% 0.18 20% 0.95 53% 1.62 32% 0.49 76% 1.74 64% 1.39 88% 7.95 0% 0.00 Note: For the brewery wastewater influent, the percent removal was calculated from the Train 2 Reactor 1 values. The influent for the brewery wastewater was over the analytical detection limit of 16 mg/L of nitrogen. However, since the effluent total nitrogen was around 10 mg/L, it can be determined that there is effective treatment through the system, although all 3 reactors are needed to achieve this removal. 27 Train 3 had a nutrient spike, a salt spike, and the actual cidery wastewater. All the final TN values for the nutrient spike were below 3 mg/L, and it was very consistently treated. The TN removal for the nutrient and salt spike was less consistent, however, was below 6 mg/L. The recovery phase had an increase in the amount of TN, resulting from an increase in Nitrate. As the ammonia decreased, denitrification was inhibited, and the plant must have been contributing to the organic nitrogen level. The experimental system was designed for nitrification, denitrification, and plant uptake. Nitrification is when ammonia is converted to nitrite, then nitrate, and takes place when there are higher amounts of oxygen (aerobic conditions)[49]. Denitrification is when nitrate is converted into nitrogen gas (N2) and happens when there is low oxygen (anaerobic conditions)[49]. Additionally, plant uptake of ammonia and nitrate can also occur. Another pathway of nitrogen removal is anammox, which is when nitrite and ammonia are converted directly into N2. Interestingly, ethanol and other alcohols, as well as high concentrations of nitrite, can inhibit the anammox procedure[50]. Ethanol is the alcoholic component in craft beverages, but in high enough quantities it is used as an antibacterial agent and so can be toxic to the microbes that perform anammox. The most common type of nitrogen within the drums is nitrate. As nitrate is only produced in the water by the process of nitrification, that indicates that there is enough dissolved oxygen in the water to allow this process to occur. The total nitrogen is the highest in the first reactors, but the other forms of nitrogen did not increase in the first reactor. The estimated organic nitrogen increased in the first reactor as well. This could be from N2 fixation, which occurs under anaerobic conditions[51]. The bacteria fix the nitrogen from the air into the first reactors, which could increase the nitrogen, and the plants then uptake that nitrogen. This could be a reasonable explanation as the dissolved oxygen in the first reactors were always the lowest. There is little to no nitrite accumulating in the reactor. There was almost always no nitrite within the system, which is likely due to it being consumed during nitrification. There is also likely little to no N2 or N2O being produced. The nitrogen decreases as the water moves through the train, but based on the concentration of nitrite, the nitrification process is not occurring. There was no nitrite going into the system, and there was no nitrite detected in the other reactors, so it is being used up much faster than it can accumulate. 28 Organic Nitrogen Organic nitrogen can be estimated by using the following equation. The total nitrogen, ammonia, nitrate, and nitrite are all known values, so these can be used to estimate the organic nitrogen. Table 8 shows the estimated organic nitrogen calculated. In several cases, the removal is considered to be 0% as the estimated organic nitrogen increased. 𝑂𝑟𝑔𝑎𝑛𝑖𝑐 𝑁𝑖𝑡𝑟𝑜𝑔𝑒𝑛 = 𝑇𝑜𝑡𝑎𝑙 𝑁𝑖𝑡𝑟𝑜𝑔𝑒𝑛 − 𝑁𝑖𝑡𝑟𝑎𝑡𝑒 − 𝑁𝑖𝑡𝑟𝑖𝑡𝑒 − 𝐴𝑚𝑚𝑜𝑛𝑖𝑎 (2) Based on the values and removal rates estimated, there is a lot of variability in the amount of organic nitrogen. The influent concentrations did not get very high, however. The highest influent concentrations were between 11-13 mg/L. There were three cases when organic nitrogen increased through the system: the COD spike, the winery wastewater, and the brewery wastewater. The COD spike is when the plant health declined rapidly, so the plants decomposed and released the nutrients into the water. This could also be due to microbiological activity increasing within the drums. The increase in organic nitrogen in the winery wastewater is likely due to the lack of screening. The wastewater collected from the septic tank had to be strained to remove the leftover fruit. The fruit that made it into the septic tank was in the process of decomposing, and likely provided food for microbial activity within the drums. Brewery wastewater has a high solids content, which is composed of the dead yeasts from the brewing process. The dead yeast More microbiological testing would be needed to allow for conclusions to be confirmed. 29 Table 8: Estimated Organic Nitrogen Analysis Statistics mg/L ON-N 4.50 1.05 2.63 1.35 0.00 8.47 11.90 n Mass, kg/gal-d 9.21E-05 16 2.16E-05 16 4 5.39E-05 4 2.75E-05 2 0.00E+00 2 1.73E-04 4 2.43E-04 9.39 1.92E-04 10.79 2.21E-04 11.14 2.28E-04 0.00 0.00E+00 10.26 2.10E-04 2.02 1.35 9.81 1.89 13.80 4.13E-05 2.77E-05 2.01E-04 3.87E-05 2.82E-04 1.72 3.51E-05 13.49 2.76E-04 4.22 8.63E-05 5.26 2.99 1.08E-04 6.13E-05 4 6 6 3 3 4 4 2 2 4 4 6 6 2 2 Std. Dev. 4.14 0.51 3.62 0.37 N/A 6.03 5.59 5.45 15.59 22.77 N/A 9.83 2.52 0.99 4.56 0.81 4.56 1.55 20.62 4.90 3 1 % removal % removal std. 77% 1.543 49% 1.103 0% 0.000 21% 0.507 0% 0.000 0% 0.000 33% 0.649 81% 1.267 88% 1.494 69% 4.192 43% 0.537 Train 1 SSWWW Influent Train 1 SSWWW Effluent Train 2 SSWWW Influent Train 2 SSWWW Effluent Train 2 COD Spike Influent Train 2 COD Spike Effluent Train 2 COD and Nutrient Spike Influent Train 2 COD and Nutrient Spike Effluent Train 2 Winery Wastewater Influent Train 2 Winery Wastewater Effluent Train 2 Brewery Wastewater Influent Train 2 Brewery Wastewater Effluent Train 3 SSWWW Influent Train 3 SSWWW Effluent Train 3 Nutrient Spike Influent Train 3 Nutrient Spike Effluent Train 3 Salt and Nutrient Spike Influent Train 3 Salt and Nutrient Spike Effluent Train 3 Cidery Wastewater Influent Train 3 Cidery Wastewater Effluent Train 3 Recovery Influent Train 3 Recovery Effluent Nitrate Table 9 shows all nitrate data. Train 1, the control, had a low influent nitrate level that doubled in Reactor 2 and then substantially decreased in Reactor 3. Although a mass balance of nitrogen is not possible as the levels uptake by the plants and then returned to the waster was not measured, this pattern indicates a microbial nitrification/denitrification pathway was functioning. 30 This is reinforced because the first two reactors are aerated (the first reactor being anaerobic and the second being aerobic), causing nitrification, and reactor 3 was anoxic, resulting in denitrification. However, the removal of nitrate in Phase 1 for Train 1 was substantially less than for Trains 2 and 3. As all were receiving the same wastewater, SSWW, during this phase the reason is unclear. 31 Table 9: Nitrate Analysis Statistics Train 1 SSWWW Influent Train 1 SSWWW Effluent Train 2 SSWWW Influent Train 2 SSWWW Effluent Train 2 COD Spike Influent Train 2 COD Spike Effluent Train 2 COD and Nutrient Spike Influent Train 2 COD and Nutrient Spike Effluent Train 2 Winery Wastewater Influent Train 2 Winery Wastewater Effluent Train 2 Brewery Wastewater Influent Train 2 Brewery Wastewater Effluent Train 3 SSWWW Influent Train 3 SSWWW Effluent Train 3 Nutrient Spike Influent Train 3 Nutrient Spike Effluent Train 3 Salt and Nutrient Spike Influent Train 3 Salt and Nutrient Spike Effluent Train 3 Cidery Wastewater Influent Train 3 Cidery Wastewater Effluent Train 3 Recovery Influent Train 3 Recovery Effluent mg/L NO3-N 0.89 0.58 1.04 BDL 0.115 5.39a 0.23b 4.73 4.85 3.51 8.01 11.50a 0.38b 0.99 BDL 0.115 1.19 0.89 0.79 3.56 5.34 1.11 1.76 7.69 Mass, kg/gal-d Std. Dev. % removal % removal std. n 1.82E-05 18 1.18E-05 19 2.13E-05 2.35E-06 1.10E-04 4.76E-06 9.68E-05 9.92E-05 7.19E-05 1.64E-04 2.35E-04 7.70E-06 2.02E-05 2.35E-06 2.43E-05 1.83E-05 1.61E-05 7.29E-05 1.09E-04 2.27E-05 3.60E-05 1.57E-04 4 4 4 4 4 4 7 7 2 2 4 4 4 4 4 4 7 7 2 2 0.54 0.47 0.24 0.02 2.89 0.11 3.78 5.03 4.58 1.21 1.13 0.10 0.31 0.02 0.60 0.86 0.60 0.94 3.78 0.84 0 1 35% 0.30 83% 0.20 96% 1.21 0% 0.00 0% 0.00 97% 0.36 81% 0.26 25% 0.26 0% 0.00 79% 1.44 0% 0.00 Train 2 had a COD spike, a COD and nutrient spike, actual winery wastewater, and actual brewery wastewater. The nitrate followed similar patterns for the COD spike as it did for the control: increase in the first and second reactors, then decrease to the third reactor. However, a 32 substantially higher removal was observed, 96% compared to 35% in the control. The removal rate of nitrate in Phase 2 specifically of the control was 40%, which is the lowest removal rate observed (the removal rate in Phase 1 of the control was 81%). The nitrate concentration dropped from the influent to Reactor 1 but increased in Reactor 2 and Reactor 3 during the COD and nutrient spike. The overall removal rate was 0 because nitrate increased overall. A similar trend occurred for the actual winery wastewater. This indicates that denitrification or plant uptake of nitrate were not occurring. However, nitrate was 97% removed from the brewery wastewater. The very high COD of the brewery wastewater may have been contributing to an anoxic reactor environment resulting in denitrification of all nitrate that was formed in aerobic zones within the reactors. Train 3 had a nutrient spike, a salt spike, actual cidery wastewater, and recovery from cidery wastewater. Nitrate was not removed in the salt and nutrient spike and recovery phases, indicating that there was enough oxygen to convert the nitrogen forms to nitrate, but there was not enough plant or microbe removal to reduce the amount of nitrate. This makes sense for the salt spike as having a higher salinity water decreases the amount of oxygen. Nitrate was removed in the actual cidery wastewater phase, which had a very high COD, similar to the actual brewery wastewater. Nitrite All influent wastewater had nitrite values below the detection limit of 0.6 mg/L except for actual brewery wastewater, which was 0.85 mg/L N. All nitrite leaving the systems, in all phases, were below the detection limit. This is not unusual for wastewater as nitrite is often the limiting, intermediate compound formed in nitrification, the microbial conversion of ammonia to nitrate. Consequently, when formed, it is immediately converted to nitrate and does not accumulate. Ammonia Train 1 was the control, and the ammonia concentration is shown in Table 10. The detection limit was 1 mg/L. There was no ammonia in the SSWW, and none was produced from ammonification of organic nitrogen in the wastewater or from the plants in any of the reactors. The small amount of ammonia in the influent COD and nutrient spike was not removed through the reactors, for unknown reasons. However, in the actual winery wastewater, all the influent was removed. The poor overall removal in the COD and nutrient spike is unknown. The actual 33 brewery wastewater had high ammonia, which could be related to the amount of yeast within the system, as yeast is a large part of the addition of nitrogen[48].Most of the ammonia was removed in the first reactor. The small amounts of ammonia in the influent of the salt and nutrient spike and cidery wastewater were removed to below detection limits by Reactor 2. 34 Table 10: Ammonia Analysis Statistics Train 1 SSWWW Influent mg/L NH3-N BDL 0.5 Train 1 SSWWW Effluent BDL 0.5 Mass, kg/gal-d Std. Dev. % removal % removal std. n 1.02E-05 20 0.77 1.02E-05 21 0.03 0% 0.00 1.99 0.00 0.00 0.21 4 7 4 7 2 5% 0% 0% 2.95 3.12 1.39 3.25 0.01 7.28 5.48 5.83 92% 6.48a 22.95* 4 4 6 6 6.39E-05 4.70E-04 1.33E-04 1.02E-05 6.04E-05 BDL 0.05b 0.14 0.02 0.26 1.21 1.02E-05 1.02E-05 1.02E-05 1.02E-05 BDL 0.5 Train 2 SSWWW Influent BDL 0.5 Train 2 SSWWW Effluent Train 2 COD Spike Influent BDL 0.5 Train 2 COD Spike Effluent BDL 0.5 Train 2 COD and Nutrient Spike Influent Train 2 COD and Nutrient Spike Effluent Train 2 Winery Wastewater Influent Train 2 Winery Wastewater Effluent Train 2 Brewery Wastewater Influent Train 2 Brewery Wastewater Effluent Train 3 SSWWW Influent Train 3 SSWWW Effluent Train 3 Nutrient Spike Influent Train 3 Nutrient Spike Effluent Train 3 Salt and Nutrient Spike Influent Train 3 Salt and Nutrient Spike Effluent Train 3 Cidery Wastewater Influent Train 3 Cidery Wastewater Effluent Train 3 Recovery Influent Train 3 Recovery Effluent *Note: This value is Train 1, Reactor 1 value as the influent values are much higher than the detection limit. Reactor 1 is not statistically different than Reactor 3, but it is likely that the actual influent value is statistically different than Reactor 3. 1.02E-05 1.02E-05 1.02E-05 BDL 0.5 BDL 0.5 BDL 0.5 1.02E-05 1.02E-05 BDL 0.5 BDL 0.5 0.64 0 0 0.10 0.17 BDL 0.5b 7.70E-05 1.02E-05 2.85E-05 1.02E-05 3.59E-05 1.02E-05 BDL 0.5 BDL 0.5 7 2 2 1.75a 87% 94% 71% 1.92 0.19 0.32 0.32 3.35 0.02 3.76 4 4 0% 0% 0% 6 6 4 2 7 4 0.00 0.00 2.09 0.00 0.14 1.92 35 Total Phosphorus Table 11 shows the phosphorus data for all trains and phases. Train 1 was the control train throughout the experiment. It had the same synthetic wastewater going through every day. The influent wastewater has a variety of phosphorus concentrations, but the effluent levels were always less than the detection limit. Phosphorus in wastewater mainly comes from cleaning chemicals [48]. 36 Table 11: Total Phosphorus Analysis Statistics mg/L P Mass, kg/d Std. Dev. % removal % removal std. n 2.25 4.60E-05 20 BDL 0.25 5.12E-06 21 2.30 4.71E-05 4 BDL 0.25 5.12E-06 4 3.25a 6.66E-05 6 0.69b 1.42E-05 6 6.41a 0.45 0.22 0.03 0.16 3.80 0.51 1.31E-04 4 1.68 3.33E-05 4 0.26 1.92E-04 7 7.51 2.24E-05 7 0.39 1.60E-04 3 2.32 4.74E-05 4 BDL 0.25 5.12E-06 4 1.01E-04 6 2.29E-05 6 4.18 0.24 0.23 2.05 0.89 1.29E-04 4 2.05 4.35E-05 4 0.40 Train 1 SSWWW Influent Train 1 SSWWW Effluent 89.72a 9.40a 1.09b 1.63b Train 2 SSWWW Influent Train 2 SSWWW Effluent Train 2 COD Spike Influent Train 2 COD Spike Effluent Train 2 COD and Nutrient Spike Influent Train 2 COD and Nutrient Spike Effluent Train 2 Winery Wastewater Influent Train 2 Winery Wastewater Effluent Train 2 Brewery Wastewater Influent Train 2 Brewery Wastewater Effluent Train 3 SSWWW Influent Train 3 SSWWW Effluent Train 3 Nutrient Spike Influent 4.94a Train 3 Nutrient Spike Effluent 1.12b 6.31a Train 3 Salt and Nutrient Spike Influent Train 3 Salt and Nutrient Spike Effluent Train 3 Cidery Wastewater Influent Train 3 Cidery Wastewater Effluent Train 3 Recovery Influent Train 3 Recovery Effluent 2.44 2.87 2.99b 7.83b 2.13b 23.90a 79% 0.43 95% 0.32 79% 1.73 75% 0.52 88% 2.19 88% 0.43 77% 0.97 66% 0.57 4.89E-04 7 23.94 88% 4.42 6.11E-05 6 4.99E-05 2 5.87E-05 2 2.16 0 0 0% 0.00 1.84E-03 3 61.20 91% 6.05 The highest amount of phosphorus detected was in the brewery wastewater. 90 mg/L was detected by diluting the sample in distilled water and running the diluted sample. The next highest concentration was in the cidery wastewater. However, both concentrations were treated to below 10 and 3 mg/L, respectively. The phosphorus in the brewery wastewater could have 37 been removed by sedimentation as there is a very high solids content. The phosphorus in the cidery wastewater was likely used by the plants and microbes around the roots as opposed to sedimentation, as the cidery wastewater did not have as many visible solids in it. The phosphorus in Train 3 for the recovery phase was the only phase where there was an increase in the concentration. The phosphorus increased between the influent and Reactor 1, decreased in Reactor 2, and Reactor 3 had a similar concentration to Reactor 2. This could be due to the plants dying in Reactor 1 and the decomposed plant material dropping to the bottom of the drum, then being brought back up with aeration. The removal rates in the trains are similar to each other, and consistently have good treatment. In several cases, the effluent levels of phosphorus are below detection limit, and in the cases where it’s not, the effluent is between 1-2 mg/L, which can be easily polished. Based on the variables within this train, three cells are needed to get the phosphorus below the EPA limit of 1 mg/L. PLANT HEALTH RESULTS P. digitalus did not survive past the equilibrium stage with no wastewater in 6 of the 9 reactors, so it was discarded from all reactors. This may be due to acquiring them and placing them in water in December, which is out of its growing season. S. polyrhiza was the volunteer plant (a plant that grew on its own in the system and was not initially planted) and it grew in most reactors during the equilibrium stage and covered most of the surface of the water. It died in the first reactor in all three trains, most likely due to the increased amount of pollutants, especially in the actual wastewater runs and high COD runs. It grew consistently well in the third reactors in all three trains. However, it died in Train 2 completely during the COD spike but grew back during the COD and nutrient spike. S. polyrhiza was the most sensitive plant in the system. T. augustifolia grew quickly in all reactors. It spread quickly and consistently and had a large root system. It was inhibited during the COD spike and the cidery wastewater as well, but it was the first plant to be revived when a new variable was introduced. It also grew past its growing season in the fall, most likely due to the grow lights and consistent warmth. A. americanus had continuous but slow growth over the course of the experiment. They were very hardy and grew back after the plants were inhibited. They were consistently green 38 even at various parts of the year (fall and winter), which suggests they could continue growing for multiple years at a time. D. verticillatus grew consistently over the course of the experiment. It was the first plant to start growing in the system though all plants were planted at the same time. D. verticillatus is a flowering plant and was specifically chosen for its flowers. It had consistent growth, but it did not flower over the course of the experiment, likely due to being stressed. CONCLUSIONS The main objective of this research was to determine the ability of a greenhouse ecosystem to treat craft beverage wastewater. Results show the system is effective within the designed organic and hydraulic loadings. However, extreme events will cause disruptions and potential system failure. Particularly, COD spikes caused a system failure. COD with no extra nutrients essentially starved the plants of oxygen and their health declined. Plants use oxygen for aerobic respiration and if the oxygen is completely depleted for a long enough period of time, which differs based on one plant to another, then the cells will start to die[52]. Another possibility is that the microbes living around the roots are depleting the nutrients before the plant can uptake them[53]. The plants took a long time to recover during the recovery phase, in some cases over two months. Based on the COD results, only two reactors are needed. However, all three reactors are needed for nitrogen removal phosphorus removal. Aeration is also recommended for all the reactors to minimize odors and pests. Odors resembling rotting fruit, likely due to the grape juice in the synthetic wastewater, were observed after 3 weeks. The smell improved within a week after adding aeration. The air bubbles also helped keep the water moving and made the trains more aerobic than they were before adding aeration. As this greenhouse ecosystem has known flowrate, volume, and loading values, it can be scaled up to fit different facilities. However, there is a point at which the system will be too big. At that point, it is more economical to build a conventional treatment plant, or other type of treatment. Calculations and a life cycle assessment need to be performed to determine the most flow the greenhouse ecosystem could treat. The treatment methods for the volatile organic carbon pollutants (in the case of craft beverage wastewater, it is ethanol) are microbial degradation, volatilization, and 39 phytoremediation[54]. In natural systems, it is quickly volatilized at the water-surface interface and has a half-life of only 5 days[55]. Ammonia is also volatilized from wastewater, which is not desirable. Volatilization could possibly be an important removal mechanism of ethanol, especially since the hydraulic residence time is 9 days. However, the greenhouse ecosystem was not designed for volatilization and volatilization of ethanol was not studied within this research. The main limitation of this project was the low budget. The quality of the instruments could’ve been upgraded if the budget was higher, as well as more sample analyses performed. However, the low budget was beneficial as most craft beverage facilities have lower budgets, and since the concept was proven with a lower budget, it will still be effective if there is a higher budget used. There were a few lessons learned over the course of this project. The first is to include aeration at the very beginning of the project instead of waiting until the system had already been deprived of oxygen. This likely had an impact on all of the future analyses. The second is that there is a long start up period and so this needs to be factored into design plans when being used at an actual facility. 40 BIBLIOGRAPHY [1] US EPA, “Wastewater Technology Fact Sheet: The Living Machine,” 2001. [2] J. Todd, E. J. G. Brown, and E. Wells, “Ecological design applied,” Ecol Eng, vol. 20, no. 5, pp. 421–440, 2003, doi: 10.1016/j.ecoleng.2003.08.004. 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Tang, “A new theory of plant-microbe nutrient competition resolves inconsistencies between observations and model predictions,” Ecological Applications, vol. 27, no. 3, pp. 875–886, 2017, doi: 10.1002/eap.1490. 44 [54] E. M. Seeger, N. Reiche, P. Kuschk, H. Borsdorf, and M. Kaestner, “Performance evaluation using a three compartment mass balance for the removal of volatile organic compounds in pilot scale constructed wetlands,” Environ Sci Technol, vol. 45, no. 19, pp. 8467–8474, Oct. 2011, doi: 10.1021/es201536j. [55] Ohio Department Of Health: Bureau of Environmental Health and Radiation Protection, “Ethanol: Answers to Frequently Asked Health Questions,” 2016. [Online]. Available: http://www.cdc.gov/niosh/npg. 45 CHAPTER 3: CRAFT BEVERAGE SITE SPECIFIC WASTEWATER TREATMENT SELECTION TOOL INTRODUCTION This chapter introduces a decision tool and the methodology that went into designing it to use for craft beverage wastewater treatment, with an emphasis on the greenhouse ecosystem. Data to collect before using the decision tool includes climate, land availability, land cost, hauling cost to process offsite, location within the community, highest daily flow rate, and pollutant characteristics. There are several treatment methods that have been shown to treat craft beverage wastewater (CBW). The following is a list of the biological treatment processes that were compared in this chapter: anaerobic digestion, land application, constructed wetlands, drain fields, and treatment lagoons. Physical and chemical treatments can also be used to treat CBW, but they are more expensive and less developed than biological treatments, which is why only covering biological methods were compared. A brief overview of each treatment chosen is given in the following sub-sections and then a simplified decision tool is presented in the next section. Anaerobic digestion Anaerobic digestion is used to treat high strength wastewater and has the added benefit of potentially producing green energy. Anaerobic digestion has been shown to treat winery and brewery wastewater[1]–[4], removing over 90% of COD, even with influent values of over 300,000 mg/L [2], [4]. Recurring expenses to operate anaerobic digestion may be more economical than aerobic treatments because there is no aeration and less sludge, as well as being able to reuse the biogas, but capital costs may be higher. Operation and maintenance costs are more expensive because a skilled operator is needed as the digesters are highly sensitive to temperature and pollutants, and there is a high hazard of dangerous gases being produced. Anaerobic digesters can cost from as low as $500,000 to as high as $12 million[5]. In addition to the high cost, the wastewater also may need an aerobic polishing system after anaerobic digestion. Land application Land application is a treatment process where wastewater is sprayed over a section of land. This is a common practice for multiple types of food processing wastewater. Nitrogen and phosphorus within the wastewater can be used as a fertilizer, which reduces the cost of buying fertilizers[6]. Certain soil structures are preferred, and good management practices are needed to 46 reduce biofilm production/soil fouling [7] and natural soil metal mobilization[8]. A leaching area is needed so salt buildup does not occur[7], a potential problem with winery wastewater because of its high salt content. However, there is a limit on the amount of BOD that can be applied per acre per day (50 lb. COD/ac/day in Michigan), which means that with the high amount in CBW, more land would need to be used. Treatment Lagoons There are four types of treatment lagoons: facultative, aerated, aerobic, and anaerobic. The main difference between lagoons and wetlands is the presence of plants. Wetlands have plants and lagoons typically do not[9]. Facultative lagoons have an aerobic zone at the top and an anaerobic zone at the bottom, and a facultative zone in the middle. The facultative zone has microorganisms that can use oxygen or not use it. The entire lagoon can treat the wastewater in the presence or absence of oxygen[10]. It is the most used type of lagoon. Aerated lagoons are aerated. They are more costly as they use electricity to power the aeration mechanism within the lagoon. These typically require less land area and have better treatment[10]. Aerobic lagoons are shallower, which allows sunlight to get to the bottom of the lagoon. The oxygen is distributed equally through the entire lagoon. They have a larger land requirement than the other lagoons as it is shallower. Anaerobic lagoons have no oxygen at all within the system. They are typically used for high strength, industrial wastewaters. They are deeper and have less sludge produced than aerobic lagoons[6]. Constructed wetland There are multiple types of constructed wetlands that can be used to treat craft beverage wastewater, including free water surface wetlands, vertical flow, and horizontal flow[11]. Constructed wetlands use the soil, plants, and microbes to treat the pollutants in the water. Constructed wetlands are slightly more economical compared to a conventional treatment system and the operation costs are much lower than a conventional treatment system, depending on the wastewater’s characteristics and flow [12]–[14]. All constructed wetlands require pretreatment to reduce the number of solids entering. This pretreatment step would be something as simple as a septic tank. 47 Free water surface wetlands are wetlands where all of the water is above media (such as soil, sand, clay, etc.) and has plants surrounding it. Aboveground wetlands treat wastewater very well and are very inexpensive, but can have problems with odors or pests, and have high detention times[6]. Aeration is not typically used in these constructed wetlands, so they are more economical than vertical or horizontal flow constructed wetlands. However, these wetlands are not as effective in winter as the water will freeze and treatment will be reduced, especially in states such as Michigan. Vertical flow constructed wetlands are lined, filled with media (such as sand, gravel, or manufactured media), covered with few plants, and the water flows down into the cells and then pumped out through the bottom [15]. They have a smaller footprint than aboveground wetlands since the treatment is vertically into the soil and not spread out on top and is well suited for high- strength wastewater in cold temperatures[15]–[17]. Horizontal flow wetlands are similar to vertical flow, but instead of the water trickling down from the top of the cells, the water enters in at one side of the cell and collected at the other side. Horizontal flow wetlands are different than free water surface wetlands because all water is below the top of the media. In a free water surface wetland, the water is all above the media. These constructed wetlands require more surface area than vertical flow constructed wetlands. Both vertical flow and horizontal flow wetlands may have aeration, although it is not always required. Drain Fields Drain fields are one of the oldest methods of wastewater treatment and are typically connected to septic tanks. The septic tanks remove solids and floating fats, oil, and grease. Then the water runs through distribution pipes in the soil and slowly infiltrates into the soil. The soil quality must not be too permeable (such as sand) and not too impermeable (clays) and the water table deep compared to the location of the drain field[18]. They are simple to operate, but they do not provide the highest quality effluent and are prone to failure from too many solids, excessive biofilm growth, or clogging the drain field[19]. 48 DECISION TOOL The decision tool (Figure 1) was designed based on cost, size, nuisance possibilities, and marketability. This entire project assumes that centralized treatment is not possible and so decentralized methods need to be determined. The tool is then divided into space limitations, the soil conditions (poor or good), and the poor soil conditions are broken into nuisance amount (based on aesthetics and odor). The severe space limitation has three possible treatment methods: wetlands, the greenhouse ecosystem, and anaerobic digestion. Wetlands have the lowest cost, lowest hydraulic retention time, but the highest surface area of the three choices. The greenhouse ecosystem has a cost in the middle between wetlands and anaerobic digestion and a smaller footprint than wetlands, but higher cost than wetland. If there is no space limitation, soil conditions becomes a critical factor. If the soil is adequate for wastewater treatment, then land application or drain fields are the best options. If it is not adequate for wastewater treatment, then other methods are needed. The poor soil conditions are marked into three categories based on nuisance level. Common nuisances are poor aesthetic or, more importantly, the odor produced during treatment. Anaerobic digesters and wetlands are the highest nuisance regarding odor. The low and moderate nuisances are then selected based on their marketability, regarding showcasing sustainability at the craft beverage facility. Aerated lagoons and other types of lagoons are less marketable. The greenhouse ecosystem is more marketable as it has decorative plants and could be used as a tourist attraction. 49 Figure 10: Decision tool for craft beverage wastewater treatment selection. 50 CONCLUSIONS There are multiple treatment methods for craft beverage wastewater: wetlands, lagoons, anaerobic digestion, land application, drain fields, and greenhouse ecosystems. They have all been shown to effectively treat high strength wastewater. There are also new technologies being developed to treat wastewater biologically, such as a microbial fuel cell. To select the best, site- specific technology it is essential to discuss and tour other craft beverage facilities within the region and consult with an experienced industry professional. 51 BIBLIOGRAPHY [1] W. M. Kaira, E. Kimpiab, A. B. Mpofu, G. A. Holtman, A. Ranjan, and P. J. Welz, “Anaerobic digestion of primary winery wastewater sludge and evaluation of the character of the digestate as a potential fertilizer,” Biomass Convers Biorefin, vol. 13, no. 12, pp. 11245–11257, 2023, doi: 10.1007/s13399-022-03087-8. [2] D. Rawalgaonkar, Y. Zhang, S. Walker, P. Kirchman, Q. Zhang, and S. J. Ergas, “Recovery of Energy and Carbon Dioxide from Craft Brewery Wastes for Onsite Use,” Fermentation, vol. 9, no. 9, p. 831, 2023, doi: 10.3390/fermentation9090831. [3] [4] L. A. Ioannou, G. L. Puma, and D. Fatta-Kassinos, “Treatment of winery wastewater by physicochemical, biological and advanced processes: A review,” J Hazard Mater, vol. 286, pp. 343–368, 2015, doi: 10.1016/j.jhazmat.2014.12.043. E. Melchiors and F. B. Freire, “Winery Wastewater Treatment: a Systematic Review of Traditional and Emerging Technologies and Their Efficiencies,” Environmental Processes, vol. 10, no. 3, pp. 1–22, 2023, doi: 10.1007/s40710-023-00657-4. [5] AgSTAR, “Funding On-Farm Anaerobic Digestion,” U.S Environmental Protection Agency, no. September, p. 5, 2012, [Online]. Available: www.epa.gov/agstar/tools/funding/index.html [6] N. R. Metzger, “Industrial wastewater landscapes: ecological design at the craft brewery,” Masters of Landscape Architecture , University of Missouri, 2015. [7] N. Di Stefano, W. Quayle, M. Arienzo, R. Zandona, J. Blackwell, and E. Christen, “A low cost land based winery wastewater treatment system(cid:3031): Development and preliminary results,” Science (1979), no. June, 2008. [8] R. Julien and S. Safferman, “Evaluation of food processing wastewater loading characteristics on metal mobilization within the soil,” J Environ Sci Health A Tox Hazard Subst Environ Eng, vol. 50, no. 14, pp. 1452–1457, 2015, doi: 10.1080/10934529.2015.1074477. [9] Ph. D. Bruce E. Rittmann, Environmental Biotechnology: Principles and Applications. McGraw Hill, 2020. [Online]. Available: https://www.accessengineeringlibrary.com/content/book/9781260441604/back- matter/appendix2 [10] Environmental Protection Agency, “Principles of design and operations of wastewater treatment pond systems for plant operators, engineers, and managers,” vol. 12, no. 3, pp. 289–293, 2009, [Online]. Available: https://www.epa.gov/sites/default/files/2014- 09/documents/lagoon-pond-treatment-2011.pdf [11] K. Skrzypiecbcef and M. H. Gajewskaad, “The use of constructed wetlands for the treatment of industrial wastewater,” Journal of Water and Land Development, vol. 34, no. 1, pp. 233–240, 2017, doi: 10.1515/jwld-2017-0058. 52 [12] V. Phillips, “Anatomy of a Constructed Wastewater Wetland,” Restoration and Reclamation Review, vol. 2, no. 4, pp. 1–6, 1997. [13] D. Gkika, G. D. Gikas, and V. A. Tsihrintzis, “Construction and operation costs of constructed wetlands treating wastewater,” Water Science and Technology, vol. 70, no. 5, pp. 803–810, 2014, doi: 10.2166/wst.2014.294. [14] J. Vymazal, “Constructed wetlands for wastewater treatment,” Water (Switzerland), vol. 2, no. 3. MDPI AG, pp. 530–549, Sep. 01, 2010. doi: 10.3390/w2030530. [15] E. R. Rozema, L. R. Rozema, and Y. Zheng, “A vertical flow constructed wetland for the treatment of winery process water and domestic sewage in Ontario, Canada: Six years of performance data,” Ecol Eng, vol. 86, pp. 262–268, 2016, doi: 10.1016/j.ecoleng.2015.11.006. [16] E. L. Campbell and S. I. Safferman, “Design criteria for the treatment of milking facility wastewater in a cold weather vertical flow wetland,” Transactions of the ASABE, vol. 58, no. 6. pp. 1509–1519, 2015. doi: 10.13031/trans.58.11068. [17] K. Skornia, S. I. Safferman, L. Rodriguez-Gonzalez, and S. J. Ergas, “Treatment of winery wastewater using bench-scale columns simulating vertical flow constructed wetlands with adsorption media,” Applied Sciences (Switzerland), vol. 10, no. 3, 2020, doi: 10.3390/app10031063. [18] C. W. Fetter, W. E. Sloey, and F. L. Spangler, “Potential Replacement of Septic Tank Drain Fields by Artificial Marsh Wastewater Treatment Systems,” Ground Water, vol. 14, no. 6, pp. 1–23, 1976, [Online]. Available: https://ngwa.onlinelibrary.wiley.com/doi/abs/10.1111/j.1745-6584.1976.tb03133.x [19] D. Butler and J. Payne, “Septic tanks: Problems and practice,” Build Environ, vol. 30, no. 3, pp. 419–425, 1995, doi: 10.1016/0360-1323(95)00012-U. 53 CHAPTER 4: FUTURE WORK FIELD SCALE In the future, a field scale demonstration is needed to determine if the greenhouse ecosystem can be run for multiple years in a greenhouse (compared to this pilot-scale project which lasted Jan-Dec in a controlled laboratory environment). The pilot-scale system was not difficult to maintain, but that is likely due to the small size and easy access. The field scale operated by craft beverage facility personnel could have maintenance issues the lack of access to tools that would be needed to troubleshoot and solve the maintenance problem. There are also unknown impacts to running this system with this type of wastewater over the course of multiple seasons. It is unknown whether the plants will have a continuous degradation of health and treatment ability after one year or one pressing season. Related, is plant maintenance, including pruning to harvest phosphorus but as not to damage the plant. Brewery wastewater had the highest amount of sludge produced and so would require more sludge removal than the other types of wastewater. A field scale study will also allow for optimization. This research was not optimized as it was a proof-of-concept study to determine if a greenhouse ecosystem could treat a specific type of wastewater. An optimization study would ideally examine the treatment ability of different plants, different combinations of plants, and run for at least two consecutive years. Results would be both the optimization of system size and maintenance protocols. SAMPLE ANALYSIS Total nitrogen values were often much higher than the other nitrogen forms put together. However, total Kjeldahl nitrogen was not analyzed, nor was nitrogen gas (N2). Measurement of both components to determine if the difference between nitrogen forms is due to organic matter (from decaying plant leaves, algae, or components in the wastewater) or if it was fixed to become N2. If it is N2, then this would be good information as to how the nitrogen is removed from the system. Phosphorus values in winery and brewery wastewater were much higher than in other wastewater types. The plant nutrient components were not measured, and neither were the nutrients within the settled sludge at the bottom of the reactors. It would be beneficial to measure the nutrient components of both to determine the main pathway of phosphorus removal. If plant uptake is the main pathway, then that is beneficial as it will more permanently remove the 54 phosphorus from the wastewater if the plants are harvested. The plants can then be used in compost and placed next to the plants. DECISION SUPPORT TOOL A quantifiable, interactive decision support tool with estimated sizing and life-cycle costs to help craft beverage facilities decide on a wastewater treatment system would be of benefit. This decision support tool would require more literature to compare the treatment systems (an extension on Chapter 3), as well as considering other variables to customize the system to better fit the area. Other variables would include the groundwater level, how close are neighbors, the highest flowrate, the space available, the characteristics of the wastewater, the cost that the craft beverage facilities could afford for treatment, and others. An economic analysis is needed for further development of the decision tool. The economic analysis will allow for better comparisons of the different treatment systems. While keeping the same wastewater, the loading and flowrate values can be used to determine the design for each type of treatment method. This will allow for determination of both the area needed for the treatment and the materials needed for building. The area needed and amount of materials will have costs associated with them and so can be included in the analysis. An effective treatment system could possibly have too big of an area needed and as such too many materials needed, and so a different treatment system should be used. PLANT HEALTH AND MICROBIOLOGY The plants chosen in this study were chosen mainly for their ability to treat wastewater and if they were native to Michigan. Other plants may be as effective but potentially be healthier in the long term. In addition, this study did not monitor viruses or fecal matter in comingled wastewater, but this is important to look at if the treated wastewater is discharged where exposure is possible. Constructed wetlands have been shown to remove fecal coliforms[81], but it is unknown if the plants used in this study can also remove fecal coliforms. The plant exudates could interact with each other as well. Plant exudates are chemicals that the plant produces around the roots. The interactions between the exudates of different plants could have a negative impact on water treatment. Two plants that have been shown to treat wastewater in monocultures could have a reduced treatment ability when placed together. There 55 have been studies on the role of plants in wastewater treatment and in wetlands, but it has been difficult to determine the interactions between plants because there is not enough literature[37]. The microbes grow around the roots of the plants and on the sides of the reactor. An option to possibly increase the removal efficiency of the greenhouse ecosystem is to add engineered media to the bottom of the drum. Adding engineered media increases the surface area available for the microbes to grow on. The increased surface area means more microbe growth, which means more organisms for treatment. These studies would need to look at what type of engineered media to use – a media to absorb the pollutants or a media that provides surface area. 56 BIBLIOGRAPHY [1] R. Alufasi, W. Parawira, A. I. Stefanakis, P. Lebea, E. Chakauya, and W. Chingwaru, “Internalisation of Salmonella spp. by Typha latifolia and Cyperus papyrus in vitro and implications for pathogen removal in Constructed Wetlands,” Environmental Technology (United Kingdom), vol. 43, no. 7, pp. 949–961, 2022, doi: 10.1080/09593330.2020.1811395. [2] N. M. Kulshreshtha, V. Verma, A. Soti, U. Brighu, and A. B. Gupta, “Exploring the contribution of plant species in the performance of constructed wetlands for domestic wastewater treatment,” Bioresour Technol Rep, vol. 18, no. March, p. 101038, 2022, doi: 10.1016/j.biteb.2022.101038. 57 APPENDIX 1: COD DATA Table 12: COD Raw Data Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Inf. 1 2098 651 1079 861 2087 905 615 763 3091 458 732 1385 420 433 1936 1646 125 393 1343 350 T1D1 T1D2 T1D3 320 954 446 817 336 619 125 298 125 344 125 323 125 271 125 263 125 250 125 125 125 125 125 125 125 125 125 125 125 125 125 125 125 125 125 125 125 125 125 125 125 125 380 125 125 125 125 125 125 125 125 125 125 284 125 125 125 125 125 125 Inf. 2 T2D1 T2D2 T2D3 1354 2079 1280 768 1013 983 652 978 560 2111 1115 5063 1760 4392 6128 5019 4156 6039 3041 4395 710 5023 1055 4446 935 3939 1846 1472 1017 780 25044 588 781 8592 1305 592 11642 1845 467 335 14024 4438 13074 3662 11844 7247 552 688 611 488 294 285 403 1439 2628 2471 387 290 343 125 125 125 125 125 125 125 766 706 1768 125 309 496 535 329 257 293 411 1147 921 125 125 125 125 125 125 125 125 125 125 402 125 399 Inf. 3 2034 573 1153 570 1984 876 575 612 3239 1018 1919 1624 680 870 835 8212 17538 51224 25888 1990 T3D1 1088 935 787 306 366 125 125 270 394 542 350 1544 125 125 125 4351 14411 14415 12864 15556 T3D2 T3D3 463 550 609 125 125 125 125 281 252 125 382 125 125 125 125 125 2688 2282 2028 3953 125 285 367 314 258 125 125 125 125 125 125 125 125 125 125 125 303 302 342 859 1579 694 305 307 125 125 125 125 Note: If the value is 125, then it is equivalent to BDL/2 to get the midpoint between the BDL and 0. 58 Wastewater Type Table 13: Train 1 Influent COD Average Min Phase n Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Overall SSWW 1 2 3 4 4 6 3 6 19 1172 1320 846 1017 1118 value 651 458 420 350 350 Max value Standard deviation 641 1044 492 710 754 2098 3091 1385 1936 3091 Table 14: Train 1 Reactor 1 COD Wastewater Type Phase n Average Min Max value Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Overall SSWW 1 2 3 4 672 264 145 85 4 6 3 7 20 265 value 298 130 121 52 52 954 344 171 116 954 Table 15: Train 1 Reactor 2 COD Wastewater Type Phase n Average Min Max value Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Overall SSWW 1 2 3 4 317 4 121 6 135 3 7 75 20 146 value 165 50 95 49 49 446 202 207 105 446 Standard deviation 285 75 25 23 252 Standard deviation 116 65 63 19 109 Table 16: Train 1 Reactor 3 COD Wastewater Type Phase n Average Min Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Overall SSWW 1 2 3 4 228 4 125 6 108 3 7 107 20 137 value 151 37 84 59 37 Max value 380 237 151 284 380 Standard deviation 107 75 37 79 87 59 Table 17: Train 2 Influent COD Wastewater Type Phase n Average Min Std. syn. Winery WW COD spike COD+N spike Winery wastewater Brewery wastewater 1 2 3 4 5 4 6 3 7 3 1202 4503 4469 7043 12981 value 768 2111 3939 467 11844 Max value 2079 6039 5023 25044 14024 Table 18: Train 2 Reactor 1 COD Wastewater Type Phase n Average Min Equilibrium stage COD spike COD+N spike Winery wastewater Brewery wastewater 1 2 3 4 5 4 6 3 7 3 1075 2793 900 1000 5116 value 652 560 710 335 3662 Max value 1354 6128 1055 1846 7247 Table 19: Train 2 Reactor 2 COD Wastewater Type Phase n Average Min Equilibrium stage COD spike COD+N spike Winery wastewater Brewery wastewater 1 2 3 4 5 4 6 3 7 3 585 1253 340 169 1080 value 488 285 290 136 706 Max value 688 2628 387 219 1768 Table 20: Train 2 Reactor 3 COD Wastewater Type Phase n Average Min Equilibrium stage COD spike COD+N spike Winery wastewater Brewery wastewater 1 2 3 4 5 4 6 3 7 3 393 560 190 135 334 value 234 257 179 105 202 Max value 535 1147 199 169 402 Standard deviation 593 6039 543 9074 1093 Standard deviation 318 2093 175 613 1886 Standard deviation 85 1094 49 32 597 Standard deviation 145 378 10 20 115 60 Table 21: Train 3 Influent COD Wastewater Type Phase n Average Min Std. syn. Winery WW Nutrient Spike Salt spike Cidery wastewater Recovery 1 2 3 4 5 4 6 3 7 2 1082 1384 1408 15222 1137 value 570 575 680 835 694 Max value 2034 3239 1919 51224 1579 Standard deviation 691 1043 647 18495 626 Table 22: Train 3 Reactor 1 COD Wastewater Type Phase Equilibrium stage Nutrient Spike Salt spike Cidery wastewater Recovery 1 2 3 4 5 n 4 6 3 7 2 Average Min 779 330 673 8840 306 value 306 182 125 137 305 Max value 1088 542 1544 15556 307 Standard deviation 338 132 763 7011 1 Table 23: Train 3 Reactor 2 COD Wastewater Type Phase n Average Min Equilibrium stage Nutrient Spike Salt spike Cidery wastewater Recovery 1 2 3 4 5 4 6 3 7 2 464 184 205 1609 129 value 234 58 110 93 112 Max value 609 281 382 3953 146 Table 24: Train 3 Reactor 3 COD Wastewater Type Phase n Average Min Equilibrium stage Nutrient Spike Salt spike Cidery wastewater Recovery 1 2 3 4 5 4 6 3 7 2 284 148 138 303 129 value 172 70 124 94 120 Max value 367 258 164 859 137 Standard deviation 165 87 153 1532 24 Standard deviation 82 69 23 267 12 61 Phase 1 COD 3/20/2023 3/23/2023 3/30/2023 4/6/2023 Figure 11: Phase 1 COD for all 3 trains Phase 2 COD 2500 2000 1500 1000 500 0 7000 6000 5000 4000 3000 2000 1000 0 4/13/2023 4/20/2023 4/27/2023 5/3/2023 5/10/2023 5/17/2023 Figure 12: Phase 2 COD for all 3 trains 62 6000 5000 4000 3000 2000 1000 0 55000 50000 45000 40000 35000 30000 25000 20000 15000 10000 5000 0 Phase 3 COD 5/31/2023 6/6/2023 6/14/2023 Figure 13: Phase 3 COD for all 3 trains Phase 4 COD 7/14/2023 7/21/2023 7/28/2023 8/1/2023 8/4/2023 8/11/2023 8/21/2023 Figure 14: Phase 4 COD for all 3 trains 63 Phase 5 COD 16000 14000 12000 10000 8000 6000 4000 2000 0 Influent 2 T2D1 T2D2 T2D3 Influent 3 T3D1 T3D2 T3D3 10/27/2023 11/3/2023 11/8/2023 Figure 15: Phase 5 COD for all 3 trains 64 APPENDIX 2: TOTAL PHOSPHORUS DATA Table 25: Total Phosphorous Raw Data Phase 2 Phase 3 Total P Phase 1 Inf. 1 T1D1 T1D2 T1D3 2.25 2.17 2.20 2.51 2.22 2.43 2.19 2.77 2.59 2.15 2.18 2.27 1.56 1.70 Phase 4: Winery and Cidery 2.43 2.51 0.97 0.25 2.60 2.20 3.03 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.52 0.91 1.17 1.37 1.45 1.26 1.34 0.88 0.99 0.68 0.92 0.82 0.63 0.82 0.92 1.06 3.67 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.65 0.80 0.77 0.66 0.68 0.64 0.80 0.25 0.53 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.64 0.25 1.50 2.30 2.25 1.95 2.06 1.42 1.98 2.42 2.04 1.50 1.85 2.39 2.07 1.59 1.79 1.58 1.45 1.82 0.25 0.25 0.25 Phase 5 Inf. 3 T3D1 T3D2 T3D3 Inf. 2 T2D1 T2D2 T2D3 0.25 0.25 0.98 2.33 0.25 1.57 2.32 0.25 0.55 0.25 0.66 2.19 0.25 0.25 2.28 0.25 0.25 0.25 0.25 2.65 0.25 0.25 2.33 0.25 0.25 0.25 0.93 2.10 0.25 0.85 2.28 0.25 0.25 0.25 2.25 2.38 0.25 2.08 2.19 0.25 0.60 0.66 2.46 3.04 0.25 1.59 3.16 0.25 0.25 1.61 4.31 4.35 0.55 2.07 3.40 1.09 0.82 2.47 5.02 7.40 1.01 3.48 3.44 1.98 1.90 3.59 6.39 5.46 0.51 2.03 3.80 1.69 2.53 3.66 6.66 7.01 1.57 4.40 3.53 3.75 2.55 2.86 4.59 6.66 1.87 5.65 7.83 3.67 2.40 1.59 4.28 7.54 1.84 6.49 7.76 3.33 1.74 1.65 2.67 4.34 1.43 3.62 4.39 2.13 1.82 1.18 4.10 6.72 1.37 5.90 5.66 2.32 1.43 1.63 5.01 8.95 0.25 6.16 3.50 1.99 1.55 1.49 5.13 8.70 1.09 5.91 8.38 3.29 1.56 3.85 1.46 7.44 0.90 16.50 7.35 2.02 36.24 18.36 9.75 3.02 1.11 2.03 7.88 2.34 50.30 29.75 11.49 3.29 1.13 2.51 15.70 8.96 58.52 38.92 15.48 0.25 1.41 19.50 13.48 2.84 39.78 18.30 7.07 0.78 1.63 2.69 2.67 8.43 0.25 0.25 0.25 0.25 6.08 19.60 14.75 8.73 2.94 1.98 5.41 117.2 63.36 15.51 4.80 2.46 2.80 3.34 5.47 132.4 107.2 42.63 12.60 2.42 Note: If the value is 0.25, then it is equivalent to BDL/2 to get the midpoint between the BDL and 0. 65 Wastewater Type Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Overall SSWW 1 2 3 4 4 6 4 6 Table 26: Train 1 Influent Total Phosphorus Average Min Phase n 2.28 value 2.17 Max value 2.51 Standard deviation 0.16 2.39 2.15 2.77 0.25 1.93 1.56 2.27 0.35 2.29 0.97 3.03 0.70 20 2.25 0.97 3.03 0.45 Table 27: Train 1 Reactor 1 Total Phosphorus Wastewater Type Phase n Average Min 0.66 value 0.11 Max value Standard deviation 0.60 1.50 Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Overall SSWW 1 2 3 4 4 6 4 7 1.99 1.42 2.30 1.95 1.50 2.42 1.81 1.45 2.39 21 1.67 0.11 2.42 Table 28: Train 1 Reactor 2 Total Phosphorus Wastewater Type Phase n Average Min 0.12 value 0.02 Max value Standard deviation 0.13 0.30 Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Overall SSWW 1 2 3 4 4 6 4 7 1.11 0.52 1.45 0.97 0.68 1.34 1.26 0.63 3.67 21 0.95 0.02 3.67 66 0.31 0.38 0.33 0.62 0.35 0.28 1.07 0.75 Table 29: Train 1 Reactor 3 Total Phosphorus Wastewater Type Phase n Average Min 0.23 value 0.08 Max value 0.47 Standard deviation 0.18 Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Overall SSWW 1 2 3 4 4 6 4 7 0.50 0.09 0.80 0.29 0.70 0.64 0.80 0.07 0.44 0.33 0.53 0.06 21 0.46 0.08 0.80 0.22 Table 30: Train 2 Influent Total Phosphorus Wastewater Type Phase n Average Min Std. syn. Winery WW COD spike COD+N spike Winery wastewater Brewery wastewater 1 2 3 4 5 4 6 4 7 3 2.30 3.25 6.41 9.40 value 2.28 2.19 4.39 2.03 Max value 2.33 3.80 7.83 19.50 Standard deviation 0.03 3.80 1.68 7.51 90 20 132 61 Table 31: Train 2 Reactor 1 Total Phosphorus Wastewater Type Phase n Average Min 1 Equilibrium stage 2 COD spike 3 COD+N spike Winery wastewater 4 Brewery wastewater 5 4 6 4 7 3 0.82 2.61 5.42 8.66 62 value 0.39 1.59 3.62 6.16 15 Max value 1.57 4.40 6.49 13.48 107 Standard deviation 0.54 1.09 1.25 2.31 46 67 Table 32: Train 2 Reactor 2 Total Phosphorus Wastewater Type Phase n Average Min 1 Equilibrium stage 2 COD spike 3 COD+N spike 4 Winery wastewater Brewery wastewater 5 4 6 4 7 3 0.25 1.55 2.86 2.52 22 value 0.25 0.25 2.13 1.99 9 Max value 0.25 3.75 3.67 3.29 43 Standard deviation 0.03 1.26 0.75 0.25 18 Table 33: Train 2 Reactor 3 Total Phosphorus Wastewater Type Phase n Average Min Equilibrium stage COD spike COD+N spike Winery wastewater Brewery wastewater 1 2 3 4 5 4 6 4 7 3 0.25 0.69 1.63 1.09 8 value 0.25 0.17 1.37 0.40 5 Max value 0.25 1.57 1.87 1.63 13 Standard deviation 0.16 0.51 0.26 0.39 4 Table 34: Train 3 Influent Total Phosphorus Wastewater Type Phase n Average Min Std. syn. Winery WW Nutrient Spike Salt spike Cidery wastewater Recovery 1 2 3 4 5.0 4 6 4 7 2 2.32 4.94 6.31 23.90 2.4 value 2.10 2.38 4.34 0.78 2.4 Max value 2.65 7.40 7.54 58.52 2.5 Standard deviation 0.24 2.05 1.38 23.94 0.0 Table 35: Train 3 Reactor 1 Total Phosphorus Wastewater Type Phase n Average Min Equilibrium stage 1 2 Nutrient Spike Salt spike 3 Cidery wastewater 4 5 Recovery 4 6 4 7 2 0.77 4.51 3.91 20.63 5.44 value 0.50 2.25 2.67 5.01 5.41 Max value 0.98 6.66 4.59 39.78 5.47 Standard deviation 0.23 1.88 0.85 15.53 0.04 68 Table 36: Train 3 Reactor 2 Total Phosphorus Wastewater Type Phase n Average Min Equilibrium stage 1 2 Nutrient Spike Salt spike 3 Cidery wastewater 4 5 Recovery 4 6 4 7 2 0.25 2.06 1.82 8.51 2.7 value 0.25 0.35 1.18 1.46 2.0 Max value 0.25 3.66 2.86 18.30 3.3 Standard deviation 0.11 1.43 0.72 7.08 1.0 Table 37: Train 3 Reactor 3 Total Phosphorus Wastewater Type Phase Equilibrium stage 1 2 Nutrient Spike Salt spike 3 Cidery wastewater 4 5 Recovery n 4 6 4 6 2 Average Min 0.25 1.12 2.13 2.99 2.87 value 0.25 0.25 1.74 1.43 2.80 Max value 0.55 2.53 2.55 7.07 2.94 Standard deviation 0.23 0.89 0.40 2.16 0.10 Phase 1 TP 3 2.5 2 1.5 1 0.5 0 -0.5 3/20/2023 3/23/2023 3/30/2023 4/6/2023 Figure 16: Phase 1 TP for all 3 trains 69 Phase 2 TP 8 7 6 5 4 3 2 1 0 Influent 1 T1D1 T1D2 T1D3 Influent T2D1 T2D2 T2D3 Influent T3D1 T3D2 T3D3 2 3 4/13/2023 4/20/2023 4/27/2023 5/3/2023 5/10/2023 5/17/2023 Figure 17: Phase 2 TP for all 3 trains Phase 3 TP 9 8 7 6 5 4 3 2 1 0 5/31/2023 6/6/2023 6/14/2023 6/22/2023 Figure 18: Phase 3 TP for all 3 trains 70 Phase 4 TP 70 60 50 40 30 20 10 0 Influent 1 T1D1 T1D2 T1D3 Influent T2D1 T2D2 T2D3 Influent T3D1 T3D2 T3D3 2 3 7/14/2023 7/21/2023 7/28/2023 8/1/2023 8/4/2023 8/11/2023 8/21/2023 Figure 19: Phase 4 TP for all 3 trains Phase 5 TP 140 120 100 80 60 40 20 0 Influent 2 T2D1 T2D2 T2D3 Influent 3 T3D1 T3D2 T3D3 10/27/2023 11/3/2023 11/8/2023 Figure 20: Phase 5 TP for all 3 trains 71 Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 APPENDIX 3: TOTAL NITROGEN DATA Table 38: Total Nitrogen Raw Data Inf. 1 T1D1 T1D2 T1D3 Inf. 2 T2D1 T2D2 T2D3 Inf. 3 1.58 0.70 0.60 1.33 4.41 0.74 0.83 1.15 1.83 5.60 5.87 2.02 5.28 4.88 5.07 1.50 9.37 7.49 7.43 4.38 7.69 4.91 4.64 6.26 7.53 7.50 6.10 22.15 8.28 26.20 4.92 2.17 6.66 13.10 14.40 4.94 6.20 15.30 9.27 4.82 9.44 15.30 12.40 4.91 7.41 14.55 17.90 6.58 4.07 14.00 18.80 5.81 1.56 12.45 13.50 5.87 2.67 10.80 12.50 5.42 5.13 10.50 56.40 1.37 9.45 3.68 0.5 9.40 9.95 1.56 64.20 23.54 16.30 9.31 8.28 2.15 9.50 5.41 21.50 0.5 0.5 0.5 5.24 3.59 0.5 0.5 8.80 6.44 0.5 0.5 2.24 0.65 2.53 0.65 1.57 6.26 3.37 7.06 9.79 6.60 6.30 18.80 22.30 13.80 47.10 4.78 1.42 11.10 25.80 12.80 14.80 28.90 10.58 59.70 15.40 35.10 20.10 12.30 21.10 57.20 47.00 0.5 0.5 32.00 22.00 36.61 22.33 41.35 25.63 27.20 18.00 0.5 23.70 0.5 25.00 0.5 T3D1 T3D2 T3D3 1.85 2.19 1.71 2.12 1.52 3.60 1.76 5.66 5.61 6.61 4.21 6.10 5.50 3.48 13.10 5.67 11.15 5.28 13.10 7.76 11.90 9.09 9.24 2.90 5.35 2.97 3.33 4.76 2.58 3.46 2.11 3.70 0.5 5.55 0.5 160.60 35.97 8.68 0.5 2.44 1.94 1.70 2.59 2.13 2.19 2.85 3.23 3.38 4.04 8.48 5.23 3.79 3.01 2.78 2.85 3.91 43.23 22.05 6.02 35.53 32.92 14.50 0.5 0.5 5.92 6.70 9.16 8.49 1.06 0.74 1.15 1.97 3.26 10.02 11.60 12.05 14.50 21.80 27.70 18.80 16.70 7.72 11.60 13.60 9.69 9.16 8.00 7.88 7.57 8.36 24.50 1.35 1.44 1.66 1.29 5.76 3.51 3.99 4.43 5.14 4.51 5.70 5.46 3.68 2.56 2.41 2.00 1.76 1.83 2.35 2.41 0.5 0.5 0.5 1.29 1.69 2.23 1.12 2.08 1.87 1.61 3.25 1.42 2.60 2.20 2.43 1.08 1.91 1.83 1.39 0.49 7.72 1.24 1.55 0.5 0.5 0.5 0.5 12.30 9.09 Note: If the value is 0.5, then it is equivalent to BDL/2 to get the midpoint between the BDL and 0. 72 Wastewater Type Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Overall SSWW 1 2 3 4 4 5 4 6 Table 39: Train 1 Influent Total Nitrogen Average Min Phase n 3.09 value 0.60 Max value 5.87 Standard deviation 2.77 5.06 2.17 7.43 1.95 5.53 4.82 6.58 0.83 8.07 1.37 16.30 5.10 19 5.69 0.60 16.30 3.60 Table 40: Train 1 Reactor 1 Total Nitrogen Wastewater Type Phase n Average Min 3.28 value 1.58 Max value 5.28 Standard deviation 1.85 Table 41: Train 1 Reactor 2 Total Nitrogen Wastewater Type Phase n Average Min 1.43 value 1.29 Max value 1.66 Standard deviation 0.16 Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Overall SSWW 1 2 3 4 Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Overall SSWW 1 2 3 4 4 5 4 7 4 5 4 7 7.91 6.66 9.37 1.00 6.78 4.07 9.44 2.25 3.72 1.56 9.31 2.77 20 5.29 1.56 9.44 2.83 4.57 3.51 5.76 0.90 4.84 3.68 5.70 0.93 2.19 1.76 2.56 0.32 20 3.16 1.29 5.76 1.56 73 Table 42: Train 1 Reactor 3 Total Nitrogen Wastewater Type Phase n Average Min Std. syn. Winery WW 1 Std. syn. Winery WW 2 Std. syn. Winery WW 3 Std. syn. Winery WW 4 Overall SSWW 4 5 4 7 20 1.58 2.05 2.08 2.30 2.05 value 1.12 1.42 1.08 0.5 0.5 Max value 2.23 3.25 2.60 7.72 7.72 Standard deviation 0.49 0.72 0.68 2.43 1.47 Table 43: Train 2 Influent Total Nitrogen Wastewater Type Phase n Average Min Std. syn. Winery WW COD spike COD+N spike Winery wastewater Brewery wastewater 1 2 3 4 5 4 5 4 5 0 3.65 16.74 16.65 22.23 N/A value 0.5 1.42 10.58 20.10 0 Max value 7.06 47.10 25.80 25.63 0 Standard deviation 3.48 18.70 6.47 2.09 N/A Note: The N/A values mean that the measurements were too high to use data and so an average and STD. could not be calculated. Table 44: Train 2 Reactor 1 Total Nitrogen Wastewater Type Phase n Average Min Equilibrium stage COD spike COD+N spike Winery wastewater Brewery wastewater 1 2 3 4 5 4 5 4 7 3 2.43 11.65 34.13 36.24 22 value 1.57 4.78 12.80 12.30 18 Max value 3.37 18.80 59.70 57.20 25 Standard deviation 0.75 5.16 19.47 14.46 4 Table 45: Train 2 Reactor 2 Total Nitrogen Wastewater Type Phase n Average Min Equilibrium stage COD spike COD+N spike Winery wastewater Brewery wastewater 1 2 3 4 5 4 5 4 7 3 1.23 10.29 21.25 9.66 13 value 0.74 3.26 16.70 7.72 8 Max value 1.97 14.50 27.70 13.60 25 Standard deviation 0.52 4.24 4.78 2.21 10 74 Table 46: Train 2 Reactor 3 Total Nitrogen Wastewater Type Phase n Average Min Equilibrium stage 1 COD spike 2 3 COD+N spike Winery wastewater 4 5 Brewery wastewater 4 5 4 7 3 1.50 11.49 14.79 17.87 11 value 1.15 4.38 14.00 8.28 4 Max value 2.02 26.20 15.30 64.20 22 Standard deviation 0.38 8.84 0.63 20.47 10 Table 47: Train 3 influent Total Nitrogen Wastewater Type Phase n Average Min 1 Std. syn. Winery WW 2 Nutrient Spike Salt spike 3 Cidery wastewater 4 5 Recovery 4 5 4 6 2 2.98 10.77 14.59 45.33 7 value 0.70 4.91 9.27 5.41 5 Max value 5.60 22.15 18.80 160.60 9 Standard deviation 2.62 7.45 4.54 59.28 3 Table 48: Train 3 Reactor 1 Total Nitrogen Wastewater Type Phase n Average Min Equilibrium stage Nutrient Spike Salt spike Cidery wastewater Recovery 1 2 3 4 5 4 5 4 6 2 3.39 8.49 7.72 21.11 8 value 2.12 5.50 2.90 3.46 7 Max value 5.66 13.10 13.10 43.23 8 Standard deviation 1.66 3.41 5.55 18.97 1 Table 49: Train 3 Reactor 2 Total Nitrogen Wastewater Type Phase n Average Min Equilibrium stage Nutrient Spike Salt spike Cidery wastewater Recovery 1 2 3 4 5 4 5 4 7 2 1.71 4.85 7.86 11.03 8 value 1.52 3.48 5.35 2.11 6 Max value 1.85 5.67 9.24 32.92 9 Standard deviation 0.14 0.96 1.80 11.88 2 75 Table 50: Train 3 Reactor 3 Total Nitrogen Wastewater Type Phase n Average Min Equilibrium stage Nutrient Spike Salt spike Cidery wastewater Recovery 1 2 3 4 5 3 5 4 7 2 2.03 2.60 5.28 5.27 11 value 1.70 2.13 3.38 2.78 9 Max value 2.44 3.23 8.48 14.50 12 Standard deviation 0.38 0.46 2.26 4.22 2 Phase 1 TN 8 7 6 5 4 3 2 1 0 50 45 40 35 30 25 20 15 10 5 0 3/20/2023 3/23/2023 3/30/2023 4/6/2023 Figure 21: Phase 1 TN for all 3 trains Phase 2 TN Influent 1 T1D1 T1D2 T1D3 Influent T2D1 T2D2 T2D3 Influent T3D1 T3D2 T3D3 2 3 4/20/2023 4/27/2023 5/3/2023 5/10/2023 5/17/2023 Figure 22: Phase 2 TN for all 3 trains 76 Phase 3 TN 5/31/2023 6/6/2023 6/14/2023 6/22/2023 Figure 23: Phase 3 TN for all 3 trains Phase 4 TN 70 60 50 40 30 20 10 0 180 160 140 120 100 80 60 40 20 0 Influent 1 T1D1 T1D2 T1D3 Influent T2D1 T2D2 T2D3 Influent T3D1 T3D2 T3D3 2 3 7/14/2023 7/21/2023 7/28/2023 8/1/2023 8/4/2023 8/11/2023 8/21/2023 Figure 24: Phase 4 TN for all 3 trains 77 Phase 5 TN 30 25 20 15 10 5 0 Influent 2 T2D1 T2D2 T2D3 Influent 3 T3D1 T3D2 T3D3 10/27/2023 11/3/2023 11/8/2023 Figure 25: Phase 5 TN for all 3 trains 78 Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 APPENDIX 4: NITRATE DATA Table 50: Nitrate Raw Data T1D2 T1D3 0.115 0.115 0.115 0.25 0.115 0.115 0.115 0.115 0.115 0.27 0.38 1.25 1.17 3.58 0.98 4.32 0.84 4.16 1.58 5.05 1.62 3.83 0.69 3.32 0.53 1.61 0.34 1.11 0.30 1.30 0.33 1.25 0.23 1.19 0.29 1.42 0.76 1.61 0.115 0.115 0.115 0.115 Inf. 2 1.02 1.31 1.11 0.74 1.62 6.89 4.81 8.25 7.44 8.41 2.42 0.65 4.67 0.74 13.20 3.72 0.67 1.10 0.50 0.115 10.70 T2D1 T2D2 T2D3 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.115 0.24 0.27 0.26 0.115 0.115 0.115 0.115 0.35 0.63 0.38 0.48 0.55 0.32 0.65 0.64 2.94 0.43 0.48 4.13 1.88 0.43 12.00 9.99 0.44 9.77 4.38 0.96 7.77 4.62 0.92 7.52 9.12 0.76 7.43 5.67 0.98 7.22 5.82 0.94 6.70 4.07 1.22 9.64 17.20 0.59 0.115 0.115 0.115 0.31 0.54 2.83 Inf. 1 0.91 1.25 1.10 0.41 1.60 1.09 0.70 1.20 1.09 1.15 0.42 0.115 1.24 1.85 1.46 0.115 0.25 0.115 0.115 0.115 0.115 T1D1 0.115 0.115 0.115 0.115 0.27 0.115 0.115 0.115 0.115 1.73 0.72 1.52 0.115 0.36 3.94 2.64 0.46 2.32 0.23 0.115 0.115 Inf. 3 T3D1 T3D2 0.115 0.115 0.68 0.115 0.115 1.18 0.115 0.115 1.33 0.115 0.115 0.78 0.23 0.25 1.91 0.40 0.115 0.91 2.73 0.115 0.52 2.57 0.25 1.42 6.72 0.28 1.30 7.32 0.32 1.21 5.78 0.115 0.42 4.88 0.83 0.115 1.71 0.59 0.99 1.42 0.52 0.97 1.25 0.93 4.00 0.83 2.13 7.28 0.86 2.27 7.64 1.28 3.60 11.40 1.46 3.93 5.11 0.115 0.115 0.115 2.19 0.36 1.84 T3D3 0.115 0.115 0.115 0.115 0.24 0.115 1.29 1.92 2.62 2.89 4.45 4.30 2.34 1.95 1.63 0.36 0.36 0.47 0.68 0.115 8.71 Note: If the value is 0.115, then it is equivalent to BDL/2 to get the midpoint between the BDL and 0. 79 Table 51: Train 1 Influent Nitrate Wastewater Type Phase n Average Min Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Overall SSWW 1 2 3 4 4 4 4 6 18 0.92 1.15 0.69 0.83 0.89 value 0.41 0.70 0.12 0.115 0.115 Max value 1.25 1.60 1.15 1.85 1.85 Standard deviation 0.36 0.37 0.51 0.78 0.54 Table 52: Train 1 Reactor 1 Nitrate Wastewater Type Phase n Average Min Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Overall SSWW 1 2 3 4 4 4 4 7 19 0.115 0.115 1.04 1.45 0.83 value 0.115 0.115 0.21 0.21 0.07 Max value 0.115 0.27 1.73 3.94 3.94 Standard deviation 0.02 0.08 0.71 1.50 1.09 Table 53: Train 1 Reactor 2 Nitrate Wastewater Type Phase n Average Min Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Overall SSWW 1 2 3 4 0.115 4 2.35 4 4.09 4 7 1.35 19 1.90 value 0.115 0.27 3.32 1.11 0.14 Max value 0.25 4.32 5.05 1.61 5.05 Standard deviation 0.05 1.91 0.73 0.20 1.61 Table 54: Train 1 Reactor 3 Nitrate Wastewater Type Phase Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Overall SSWW 1 2 3 4 n 4 4 4 7 Average Min 0.115 value 0.115 Max value 0.115 Standard deviation 0.03 0.68 0.115 1.17 0.46 1.18 0.69 1.62 0.49 0.40 0.23 0.76 0.18 19 0.58 0.15 1.62 0.47 80 Table 55: Train 2 Influent Nitrate Wastewater Type Phase n Average Min Std. syn. Winery WW 1 2 COD spike 3 COD+N spike 4 Winery wastewater 5 Brewery wastewater 4 4 4 7 2 1.04 5.39 4.73 3.51 12 value 0.74 1.62 0.65 0.50 11 Max value 1.31 8.25 8.41 13.20 12 Standard deviation 0.24 2.89 3.78 4.58 1 Table 56: Train 2 Reactor 1 Nitrate Wastewater Type Phase n Average Min Equilibrium stage 1 2 COD spike 3 COD+N spike 4 Winery wastewater Brewery wastewater 5 4 4 4 7 2 0.115 0.41 0.50 0.91 value 0.115 0.115 0.43 0.59 Max value 0.115 0.63 0.64 1.22 Standard deviation 0.02 0.21 0.10 0.19 4 3 4 1 Table 57: Train 2 Reactor 2 Nitrate Wastewater Type Phase n Average Min Equilibrium stage 1 2 COD spike 3 COD+N spike 4 Winery wastewater Brewery wastewater 5 4 4 4 7 2 0.115 0.31 3.24 7.27 value 0.115 0.115 0.43 4.07 Max value 0.115 0.48 9.99 17.20 Standard deviation 0.02 0.14 4.55 4.70 1 1 1 0 Table 58: Train 2 Reactor 3 Nitrate Wastewater Type Phase n Average Min 1 Equilibrium stage 2 COD spike 3 COD+N spike 4 Winery wastewater Brewery wastewater 5 4 4 4 7 2 0.115 0.23 4.85 8.01 0.38 value 0.115 0.115 0.32 6.70 0.31 Max value 0.115 0.38 12.00 9.77 0.45 Standard deviation 0.02 0.11 5.03 1.21 0.10 81 Table 59: Train 3 Influent Nitrate Wastewater Type Phase n Average Min Std. syn. Winery WW Nutrient Spike Salt spike Cidery wastewater Recovery 1 2 3 4 5 4 4 4 7 2 0.99 1.19 0.79 5.34 2 value 0.68 0.52 0.22 0.97 2 Max value 1.33 1.91 1.30 11.40 2 Standard deviation 0.31 0.60 0.55 3.78 0 Table 60: Train 3 Reactor 1 Nitrate Wastewater Type Phase n Average Min Equilibrium stage Nutrient Spike Salt spike Cidery wastewater Recovery 1 2 3 4 5 4 4 4 7 2 0.115 0.115 0.40 2.00 0.29 value 0.115 0.115 0.115 0.52 0.115 Max value 0.115 0.25 0.83 3.93 0.36 Standard deviation 0.01 0.12 0.29 1.40 0.10 Table 61: Train 3 Reactor 2 Nitrate Wastewater Type Phase n Average Min Equilibrium stage 1 2 Nutrient Spike 3 Salt spike Cidery wastewater 4 5 Recovery 3 4 4 7 2 0.115 1.48 6.18 1.26 2.10 value 0.115 0.23 4.88 0.83 2.01 Max value 0.115 2.73 7.32 1.71 2.19 Standard deviation 0.02 1.35 1.07 0.32 0.13 Table 62: Train 3 Reactor 3 Nitrate Wastewater Type Phase n Average Min Equilibrium stage 1 2 Nutrient Spike Salt spike 3 Cidery wastewater 4 5 Recovery 4 4 4 7 2 0.115 0.89 3.56 1.11 7.69 value 0.115 0.115 2.62 0.36 6.68 Max value 0.115 1.92 4.45 2.34 8.71 Standard deviation 0.02 0.86 0.94 0.84 1.44 82 Phase 1 Nitrate 3/20/2023 3/23/2023 3/30/2023 4/6/2023 Figure 26: Phase 1 Nitrate for all 3 trains Phase 2 Nitrate 1.4 1.2 1 0.8 0.6 0.4 0.2 0 9 8 7 6 5 4 3 2 1 0 Influent 1 T1D1 T1D2 T1D3 Influent T2D1 T2D2 T2D3 Influent T3D1 T3D2 T3D3 2 3 4/13/2023 4/20/2023 4/27/2023 5/3/2023 5/10/2023 5/17/2023 Figure 27: Phase 2 Nitrate for all 3 trains 83 Phase 3 Nitrate 5/31/2023 6/6/2023 6/14/2023 6/22/2023 Figure 28: Phase 3 Nitrate for all 3 trains Phase 4 Nitrate 14 12 10 8 6 4 2 0 20 18 16 14 12 10 8 6 4 2 0 Influent 1 T1D1 T1D2 T1D3 Influent T2D1 T2D2 T2D3 Influent T3D1 T3D2 T3D3 2 3 7/14/2023 7/21/2023 7/28/2023 8/1/2023 8/4/2023 8/11/2023 8/21/2023 Figure 29: Phase 4 Nitrate for all 3 trains 84 Phase 5 Nitrate 14 12 10 8 6 4 2 0 Influent 2 T2D1 T2D2 T2D3 Influent 3 T3D1 T3D2 T3D3 11/3/2023 11/8/2023 Figure 30: Phase 5 Nitrate for all 3 trains 85 APPENDIX 5: NITRITE DATA Table 63: Nitrite Raw Data Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Inf. 1 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 T1D1 0.0075 0.0075 0.0075 0.0075 0.053 0.061 0.03 0.0075 0.0075 0.0725 1.49 1.36 0.0075 0.069 0.231 0.207 0.016 0.041 0.0075 0.0075 0.0075 T1D2 T1D3 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.48 0.3295 0.944 0.181 0.0075 0.0075 0.0075 0.0075 0.047 0.018 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 Inf. 2 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0725 0.148 0.031 0.147 0.025 0.06 0.086 0.0075 0.3745 1.32 T2D1 0.0075 0.0075 0.0075 0.0075 0.045 0.107 0.94 0.0075 0.103 0.09 0.123 0.05 0.117 0.197 0.1 0.146 0.189 0.214 0.0075 0.229 0.355 T2D2 0.0075 0.0075 0.0075 0.0075 0.0075 0.029 0.122 0.054 0.05 0.03 0.629 0.594 0.099 0.03 0.049 0.05 0.0325 0.044 0.0075 0.0075 0.016 Inf. 3 T2D3 T3D1 T3D2 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.025 0.025 0.0075 0.0075 0.0075 0.788 1.05 0.0075 0.0075 0.015 0.028 0.0075 0.0075 0.131 0.0075 0.0075 0.0075 0.102 0.0075 0.0075 0.0075 0.199 0.0075 0.509 1.24 0.045 0.0075 0.0075 0.0075 0.93 0.0075 0.0075 1.155 0.024 0.0075 0.153 0.0075 0.062 0.049 0.0075 0.063 0.0075 0.0075 0.0075 0.0075 0.134 0.037 0.124 0.0075 0.133 0.087 0.0075 0.136 0.041 0.0075 0.12 0.2995 0.22 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.045 0.0075 0.0075 0.036 0.067 0.259 T3D3 0.0075 0.0075 0.0075 0.0075 0.0075 0.642 0.158 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.219 0.0075 0.0075 0.016 Note: If the value is 0.0075, then it is equivalent to BDL/2 to get the midpoint between the BDL and 0. 86 Table 64: Train 1 Influent Nitrite Wastewater Type Phase n Average Min Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Overall SSWW 1 2 3 4 0.0075 4 0.0075 4 0.0075 4 5 0.0075 18 0.0075 value 0.0075 0.0075 0.0075 0.0075 0.0075 Max value 0.0075 0.0075 0.0075 0.0075 0.0075 Standard deviation 0.05 0.02 0.01 0.01 0.03 Table 65: Train 1 Reactor 1 Nitrite Wastewater Type Phase n Average Min Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Overall SSWW 1 2 3 4 0.0075 4 0.03 4 0.73 4 6 0.09 18 0.20 value 0.0075 0.0075 0.0075 0.0075 0.0075 Max value 0.0075 0.06 1.49 0.23 1.49 Standard deviation 0.04 0.04 0.80 0.10 0.45 Table 66: Train 1 Reactor 2 Nitrite Wastewater Type Phase n Average Min Std. syn. Winery WW 1 Std. syn. Winery WW 2 Std. syn. Winery WW 3 Std. syn. Winery WW 4 Overall SSWW 0.0075 0.36 0.02 0.0075 4 3 4 6 17 0.06 value 0.0075 0.0075 0.0075 0.0075 0.0075 Max value 0.0075 0.94 0.05 0.0075 0.94 Standard deviation 0.04 0.52 0.02 0.00 0.23 Table 67: Train 1 Reactor 3 Nitrite Wastewater Type Phase n Average Min Std. syn. Winery WW 1 Std. syn. Winery WW 2 Std. syn. Winery WW 3 Std. syn. Winery WW 4 Overall SSWW 4 4 4 6 18 0.0075 0.20 0.0075 0.0075 0.04 value 0.0075 0.0075 0.0075 0.0075 0.0075 Max value 0.0075 0.48 0.0075 0.0075 0.0075 Standard deviation 0.04 0.25 0.00 0.00 0.14 87 Table 68: Train 2 Influent Nitrite Wastewater Type Phase n Average Min 1 Std. syn. Winery WW 2 COD spike 3 COD+N spike Winery wastewater 4 5 Brewery wastewater 0.0075 0.0075 0.02 0.08 1 value 0.0075 0.0075 0.0075 0.03 0 4 4 4 6 2 Max value 0.0075 0.0075 0.07 0.15 1 Standard deviation 0.04 0.09 0.04 0.05 1 Table 69: Train 2 Reactor 1 Nitrite Wastewater Type Phase n Average Min 1 Equilibrium stage 2 COD spike COD+N spike 3 Winery wastewater 4 5 Brewery wastewater 0.0075 0.27 0.09 0.16 0.29 value 0.0075 -0.03 0.05 0.10 0.23 4 4 4 6 2 Max value 0.0075 0.94 0.12 0.21 0.36 Standard deviation 0.04 0.45 0.03 0.05 0.09 Table 70: Train 2 Reactor 2 Nitrite Wastewater Type Phase n Average Min Equilibrium stage COD spike COD+N spike Winery wastewater Brewery wastewater 1 2 3 4 5 4 4 4 6 2 0.0075 0.05 0.33 0.05 0.0075 value 0.0075 0.0075 0.03 0.03 0.0075 Max value 0.0075 0.12 0.63 0.10 0.02 Standard deviation 0.00 0.05 0.33 0.03 0.01 Table 71: Train 2 Reactor 3 Nitrite Wastewater Type Phase n Average Min Equilibrium stage COD spike COD+N spike Winery wastewater Brewery wastewater 1 2 3 4 5 4 4 4 6 2 0.0075 0.01 0.55 0.0075 0.0075 value 0.0075 0.0075 0.01 0.0075 0.0075 Max value 0.0075 0.03 1.24 0.01 0.0075 Standard deviation 0.04 0.02 0.63 0.00 0.001 88 Table 72: Train 3 Influent Nitrite Wastewater Type Phase n Average Std. syn. Winery WW Nutrient Spike Salt spike Cidery wastewater Recovery 1 2 3 4 5 4 4 4 6 2 0.0075 0.0075 0.0075 0.11 0.0075 Max Min value value 0.0075 0.0075 0.0075 0.0075 0.0075 0.01 0.14 0.06 0.0075 0.0075 Standard deviation 0.04 0.02 0.01 0.04 0.001 Table 73: Train 3 Reactor 1 Nitrite Wastewater Type Phase n Average Equilibrium stage Nutrient Spike Salt spike Cidery wastewater Recovery 1 2 3 4 5 4 4 4 6 2 0.01 0.01 0.42 0.12 0.041 Max value Min value 0.0075 0.03 0.0075 0.02 0.0075 1.16 0.30 0.01 0.045 0.036 Standard deviation 0.02 0.01 0.54 0.10 0.006 Table 74: Train 3 Reactor 2 Nitrite Wastewater Type Phase n Average Equilibrium stage Nutrient Spike Salt spike Cidery wastewater Recovery 1 2 3 4 5 4 4 4 6 2 0.0075 0.52 0.06 0.05 0.163 Max Min value value 0.03 -0.08 0.10 1.05 0.0075 0.20 0.0075 0.22 0.259 0.067 Table 75: Train 3 Reactor 3 Nitrite Wastewater Type Phase n Average Equilibrium stage Nutrient Spike Salt spike Cidery wastewater Recovery 1 2 3 4 5 4 0.0075 4 0.20 4 0.0075 6 0.04 2 0.010 Max value Min value 0.0075 0.01 0.0075 0.64 0.0075 0.01 0.0075 0.22 0.0075 0.016 Standard deviation 0.04 0.48 0.09 0.09 0.136 Standard deviation 0.05 0.31 0.01 0.09 0.009 89 Phase 1 Nitrite 0.04 0.02 0 -0.02 -0.04 -0.06 -0.08 -0.1 -0.12 3/20/2023 3/23/2023 3/30/2023 4/6/2023 Figure 31: Phase 1 Nitrite for all 3 trains Phase 2 Nitrite 1.2 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 Influent 1 T1D1 T1D2 T1D3 Influent T2D1 T2D2 T2D3 Influent T3D1 T3D2 T3D3 2 3 4/20/2023 4/27/2023 5/3/2023 5/10/2023 5/17/2023 Figure 32: Phase 2 Nitrite for all 3 trains 90 Phase 3 Nitrite 5/31/2023 6/6/2023 6/14/2023 6/22/2023 Figure 33: Phase 3 Nitrite for all 3 trains Phase 4 Nitrite 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 -0.2 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 -0.05 Influent 1 T1D1 T1D2 T1D3 Influent T2D1 T2D2 T2D3 Influent T3D1 T3D2 T3D3 2 3 7/14/2023 7/21/2023 7/28/2023 8/1/2023 8/4/2023 8/11/2023 8/21/2023 Figure 34: Phase 4 Nitrite for all 3 trains 91 Phase 5 Nitrite 1.4 1.2 1 0.8 0.6 0.4 0.2 0 Influent 2 T2D1 T2D2 T2D3 Influent 3 T3D1 T3D2 T3D3 11/3/2023 11/8/2023 Figure 35: Phase 5 Nitrite for all 3 trains 92 APPENDIX 6: AMMONIA DATA Table 76: Ammonia Raw Data Phase 1 Phase 2 Phase 3 Phase 4 Phase 5 Inf. 1 T1D1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 2.25 0.5 0.5 0.5 0.5 0.5 2.25 2.28 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1.6 0.5 0.5 0.5 3.23 0.5 3.35 1.88 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 T1D2 T1D3 0.5 0.5 0.5 0.5 1.94 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 Inf. 2 T2D1 T2D2 T2D3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1.33 1.74 1.43 8.00 4.37 12.9 0.5 0.5 5.33 8.15 13.9 0.5 17.8 28.1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 8.3 0.5 19.2 0.5 4.76 20.4 0.5 1.26 0.5 10.05 0.5 13.1 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 2.80 0.5 6.18 17.8 0.5 0.5 0.5 1.12 0.5 0.5 0.5 0.5 0.5 0.5 4.42 17.9 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 2.99 0.5 11.7 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 2.75 Inf. 3 T3D1 T3D2 T3D3 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 13.05 2.69 1.23 8.89 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1.09 3.01 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 5.55 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1.71 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 3.03 0.5 11 11.6 0.5 0.5 1.91 0.5 0.5 0.5 0.5 1.01 0.5 0.5 0.5 0.5 Note: If the value is 0.5, then it is equivalent to BDL/2 to get the midpoint between the BDL and 0. 93 Wastewater Type Table 77: Train 1 Influent Ammonia Phase n Average Min Std. syn. Winery WW 1 Std. syn. Winery WW 2 Std. syn. Winery WW 3 Std. syn. Winery WW 4 Overall SSWW 4 6 4 6 20 0.5 0.5 0.5 0.5 0.5 value 0.5 0.5 0.5 0.5 0.5 Max value 0.5 0.5 2.25 2.28 2.28 Standard deviation 0.12 0.13 1.05 1.06 0.77 Table 78: Train 1 Reactor 1 Ammonia Wastewater Type Phase n Average Min Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Std. syn. Winery WW Overall SSWW 1 2 3 4 0.5 4 0.5 6 1.46 4 7 0.5 21 0.5 value 0.5 0.5 0.5 0.5 0.5 Max value 0.5 3.23 3.35 0.5 3.35 Standard deviation 0.33 1.32 1.46 0.18 1.04 Table 79: Train 1 Reactor 2 Ammonia Wastewater Type Phase n Average Min Std. syn. Winery WW 1 Std. syn. Winery WW 2 Std. syn. Winery WW 3 Std. syn. Winery WW 4 Overall SSWW 0.5 4 0.5 6 0.5 4 7 0.5 21 0.5 value 0.5 0.5 0.5 0.5 0.5 Max value 0.5 1.94 0.5 0.5 1.94 Standard deviation 0.02 0.81 0.01 0.01 0.43 Table 80: Train 1 Reactor 3 Ammonia Wastewater Type Phase n Average Min Std. syn. Winery WW 1 Std. syn. Winery WW 2 Std. syn. Winery WW 3 Std. syn. Winery WW 4 Overall SSWW 4 6 4 7 21 0.5 0.5 0.5 0.5 0.5 value 0.5 0.5 0.5 0.5 0.5 Max value 0.5 0.5 0.5 0.5 0.5 Standard deviation 0.02 0.06 0.01 0.01 0.03 94 Table 81: Train 2 Influent Ammonia Wastewater Type Phase n Average Min Std. syn. Winery WW 1 2 COD spike 3 COD+N spike 4 Winery wastewater 5 Brewery wastewater 0.5 0.5 3.12 6.48 4 6 4 7 2.000 22.950 value 0.5 0.5 1.33 0.5 17.800 Max value 0.5 0.5 8.00 13.90 28.100 Standard deviation 0.14 0.26 3.25 5.48 7.283 Table 82: Train 2 Reactor 1 Ammonia Wastewater Type Phase Equilibrium stage 1 2 COD spike 3 COD+N spike 4 Winery wastewater Brewery wastewater 5 n 4 6 4 7 1 Average Min 0.5 0.5 7.00 5.36 value 0.5 0.5 0.5 0.5 Max value 0.5 0.5 19.20 20.40 Standard deviation 0.09 0.24 8.97 7.53 13.100 13.100 13.100 N/A Note: The STD could not be calculated as only 1 value was available. Table 83: Train 2 Reactor 2 Ammonia Wastewater Type Phase Equilibrium stage 1 2 COD spike 3 COD+N spike 4 Winery wastewater Brewery wastewater 5 n 4 6 4 7 2 Average Min 0.5 0.5 6.20 0.5 value 0.5 0.5 0.5 0.5 Max value 0.5 2.80 17.80 1.12 Standard deviation 0.05 1.14 8.20 0.40 11.158 4.415 17.900 9.535 Table 84: Train 2 Reactor 3 Ammonia Wastewater Type Phase n Average Min 1 Equilibrium stage 2 COD spike 3 COD+N spike 4 Winery wastewater Brewery wastewater 5 4 6 4 7 2 0.5 0.5 2.95 0.5 1.391 value 0.5 0.5 0.5 0.5 0.5 Max value 0.5 2.99 11.70 0.5 2.750 Standard deviation 0.02 1.21 5.83 0.01 1.923 95 Table 85: Train 3 Influent Ammonia Wastewater Type Phase n Average Min 1 Std. syn. Winery WW 2 Nutrient Spike 3 Salt spike Cidery wastewater 4 5 Recovery 4 6 4 7 2 0.5 0.5 3.76 1.75 0.5 value 0.5 0.5 0.5 0.5 0.5 Max value 0.5 0.5 13.05 8.89 0.5 Standard deviation 0.10 0.32 6.19 3.35 0.021 Table 86: Train 3 Reactor 1 Ammonia Wastewater Type Phase n Average Min Equilibrium stage Nutrient Spike Salt spike Cidery wastewater Recovery 1 2 3 4 5 4 6 4 7 2 0.5 0.5 5.76 0.5 0.5 value 0.5 0.5 0.5 0.5 0.5 Max value 0.5 3.03 11.60 1.91 0.5 Standard deviation 0.07 1.19 6.40 0.62 0.078 Table 87: Train 3 Reactor 2 Ammonia Wastewater Type Phase n Average Min Equilibrium stage Nutrient Spike Salt spike Cidery wastewater Recovery 1 2 3 4 5 4 6 4 7 2 0.5 0.5 0.5 0.5 0.5 value 0.5 0.5 0.5 0.5 0.5 Max value 0.5 3.01 0.5 5.55 0.5 Standard deviation 0.10 1.16 0.01 2.07 0.069 Table 88: Train 3 Reactor 3 Ammonia Wastewater Type Phase n Average Min Equilibrium stage Nutrient Spike Salt spike Cidery wastewater Recovery 1 2 3 4 5 4 6 4 7 2 0.5 0.5 0.5 0.5 0.5 value 0.5 0.5 0.5 0.5 0.5 Max value 0.5 0.5 0.5 1.71 0.5 Standard deviation 0.17 0.19 0.02 0.64 0.009 96 Phase 1 Ammonia 3/20/2023 3/23/2023 3/30/2023 4/6/2023 Figure 36: Phase 1 Ammonia for all 3 trains Phase 2 Ammonia 0.8 0.6 0.4 0.2 0 -0.2 -0.4 3.5 3 2.5 2 1.5 1 0.5 0 -0.5 Influent 1 T1D1 T1D2 T1D3 Influent T2D1 T2D2 T2D3 Influent T3D1 T3D2 T3D3 2 3 4/13/2023 4/20/2023 4/27/2023 5/3/2023 5/10/2023 5/17/2023 Figure 37: Phase 2 Ammonia for all 3 trains 97 Phase 3 Ammonia 25 20 15 10 5 0 -5 5/31/2023 6/6/2023 6/14/2023 6/22/2023 Figure 38: Phase 3 Ammonia for all 3 trains Phase 4 Ammonia 25 20 15 10 5 0 -5 Influent 1 T1D1 T1D2 T1D3 Influent T2D1 T2D2 T2D3 Influent T3D1 T3D2 T3D3 2 3 7/14/2023 7/21/2023 7/28/2023 8/1/2023 8/4/2023 8/11/2023 8/21/2023 Figure 39: Phase 4 Ammonia for all 3 trains 98 Phase 5 Ammonia 30 25 20 15 10 5 0 -5 Influent 2 T2D1 T2D2 T2D3 Influent 3 T3D1 T3D2 T3D3 11/3/2023 11/8/2023 Figure 40: Phase 5 Ammonia for all 3 trains 99 APPENDIX 7: CODE USED FOR STATISTICS --- title: "Carley_with_phase5" author: "Rabin KC" date: "`r Sys.Date()`" output: html_document --- ```{r} #Carley data analysis------ #Rabin KC #SCC rm(list = ls()) getwd() #set working directory--- setwd("C:/Users/rabin/OneDrive - Michigan State University/MSU/STAT_CONSULTING/SCC_2.0/10182023_Carley/") #load libraries------ library(tidyverse) library(readxl) library(lme4) library(ggResidpanel) 100 library(car) library(MASS) library(emmeans) dt <- read_excel("Carley_long_format_data_with_phase_5_V2.xlsx") dim(dt) str(dt) head(dt) dt[,1:7] <- lapply(dt[,1:7], factor) ############################################################################## ### #For COD---- dt_cod <- subset(dt, response == "COD") head(dt_cod) xtabs(~ phase + train_drum, dt_cod) xtabs(~phase+ sample_name, dt_cod) #samples nested within phases. (No data for train_drum, rank deficient issues) head(dt_cod) #model------- m1 <- lmer(value ~ phase * train_drum + (1|sample_name), dt_cod) 101 summary(m1) joint_tests(m1) outlierTest(m1) m1.cleaned <- lmer(value ~ phase * train_drum + (1|sample_name), dt_cod[- c(213,185,177,165),]) resid_panel(m1.cleaned) #logging the response--- m1.log <- lmer(log1p(value) ~ phase * train_drum + (1|sample_name), dt_cod) joint_tests(m1.log) resid_panel(m1.log) outlierTest(m1.log) m1.log.clean <- lmer(log1p(value) ~ phase * train_drum + (1|sample_name), dt_cod[- c(193,16,178,166),]) resid_panel(m1.log.clean) outlierTest(m1.log.clean) joint_tests(m1.log.clean)# Anova---- cod.emm<- emmeans(m1.log.clean, ~train_drum|phase, type = "response") cod.emm plot(cod.emm, comparisons = T) 102 ############################################################################## ####### #total P--------- dt_Total_P <- subset(dt, response == "Total_P") head(dt_Total_P) xtabs(~ phase + train_drum, dt_Total_P) xtabs(~phase+ sample_name, dt_Total_P) #samples nested within phases. head(dt_Total_P) #model------- m1 <- lmer(value ~ phase * train_drum + (1|sample_name), dt_Total_P) summary(m1) joint_tests(m1) outlierTest(m1) resid_panel(m1) m1.cleaned <- lmer(value ~ phase * train_drum + (1|sample_name), dt_Total_P[- c(237,225,249,250,201),]) resid_panel(m1.cleaned) #logging the response--- m1.log <- lmer(log1p(value) ~ phase * train_drum + (1|sample_name), dt_Total_P) joint_tests(m1.log) 103 resid_panel(m1.log) outlierTest(m1.log) m1.log.clean <- lmer(log1p(value) ~ phase * train_drum + (1|sample_name), dt_Total_P[- c(249,240,201),]) resid_panel(m1.log.clean) outlierTest(m1.log.clean) joint_tests(m1.log.clean)# Anova---- Total_P.emm<- emmeans(m1.log.clean, ~train_drum|phase, type = "response") Total_P.emm plot(Total_P.emm, comparisons = T) ############################################################################## ## #total N--------- dt_Total_N <- subset(dt, response == "Total_N") head(dt_Total_N) xtabs(~ phase + train_drum, dt_Total_N) xtabs(~phase+ sample_name, dt_Total_N) #samples nested within phases. head(dt_Total_N) #model------- 104 m1 <- lmer(value ~ phase * train_drum + (1|sample_name), dt_Total_N) summary(m1) joint_tests(m1) outlierTest(m1) resid_panel(m1) m1.cleaned <- lmer(value ~ phase * train_drum + (1|sample_name), dt_Total_N[-c(213,224),]) resid_panel(m1.cleaned) #logging the response--- m1.log <- lmer(log1p(value) ~ phase * train_drum + (1|sample_name), dt_Total_N) joint_tests(m1.log) resid_panel(m1.log) outlierTest(m1.log) m1.log.clean <- lmer(log1p(value) ~ phase * train_drum + (1|sample_name), dt_Total_N[- c(201),]) resid_panel(m1.log.clean) outlierTest(m1.log.clean) joint_tests(m1.log.clean)# Anova---- Total_N.emm<- emmeans(m1.log.clean, ~train_drum|phase, type = "response") Total_N.emm 105 plot(Total_N.emm, comparisons = T) ############################################################################## ######## #Nitrate--------- dt_Nitrate <- subset(dt, response == "Nitrate") head(dt_Nitrate) xtabs(~ phase + train_drum, dt_Nitrate) xtabs(~phase+ sample_name, dt_Nitrate) #samples nested within phases. head(dt_Nitrate) #model------- m1 <- lmer(value ~ phase * train_drum + (1|sample_name), dt_Nitrate) summary(m1) joint_tests(m1) outlierTest(m1) resid_panel(m1) m1.cleaned <- lmer(value ~ phase * train_drum + (1|sample_name), dt_Nitrate[- c(223,173,140,139),]) resid_panel(m1.cleaned) #logging the response--- 106 m1.log <- lmer(log1p(value) ~ phase * train_drum + (1|sample_name), dt_Nitrate) joint_tests(m1.log) resid_panel(m1.log) outlierTest(m1.log) m1.log.clean <- lmer(log1p(value) ~ phase * train_drum + (1|sample_name), dt_Nitrate[- c(173,139),]) resid_panel(m1.log.clean) outlierTest(m1.log.clean) joint_tests(m1.log.clean)# Anova---- Nitrate.emm<- emmeans(m1.log.clean, ~train_drum|phase, type = "response") Nitrate.emm plot(Nitrate.emm, comparisons = T) ############################################################################## ###### ############################################################################## ######## #Nitrite--------- dt_Nitrite <- subset(dt, response == "Nitrite") head(dt_Nitrite) 107 xtabs(~ phase + train_drum, dt_Nitrite) xtabs(~phase+ sample_name, dt_Nitrite) #samples nested within phases. head(dt_Nitrite) #model------- m1 <- lmer(value ~ phase * train_drum + (1|sample_name), dt_Nitrite) summary(m1) joint_tests(m1) outlierTest(m1) resid_panel(m1) #logging the response--- m1.log <- lmer(log1p(value) ~ phase * train_drum + (1|sample_name), dt_Nitrite) joint_tests(m1.log) resid_panel(m1.log) outlierTest(m1.log) m1.log.clean <- lmer(log1p(value) ~ phase * train_drum + (1|sample_name), dt_Nitrite[- c(78,142,116),]) resid_panel(m1.log.clean) outlierTest(m1.log.clean) joint_tests(m1.log.clean)# Anova---- 108 Nitrite.emm<- emmeans(m1.log.clean, ~train_drum|phase, type = "response") Nitrite.emm plot(Nitrite.emm, comparisons = T) ############################################################################## ############### ############################################################################## ######## #--------- dt_Ammonia <- subset(dt, response == "Ammonia") head(dt_Ammonia) xtabs(~ phase + train_drum, dt_Ammonia) xtabs(~phase+ sample_name, dt_Ammonia) #samples nested within phases. head(dt_Ammonia) #model------- m1 <- lmer(value ~ phase * train_drum + (1|sample_name), dt_Ammonia) summary(m1) joint_tests(m1) outlierTest(m1) resid_panel(m1) 109 #logging the response--- m1.log <- lmer(log1p(value) ~ phase * train_drum + (1|sample_name), dt_Ammonia) joint_tests(m1.log) resid_panel(m1.log) outlierTest(m1.log) m1.log.clean <- lmer(log1p(value) ~ phase * train_drum + (1|sample_name), dt_Ammonia[- c(225),]) resid_panel(m1.log.clean) outlierTest(m1.log.clean) joint_tests(m1.log.clean)# Anova---- Ammonia.emm<- emmeans(m1.log.clean, ~train_drum|phase, type = "response") Ammonia.emm plot(Ammonia.emm, comparisons = T) ``` 110 APPENDIX 8: VISUAL REPRESENTATIONS OF STATISTICAL DATA Figure 41: COD Visual Results. x-axis is concentration, y-axis is the location of the samples. 111 Figure 42 Total Phosphorus Visual Results. x-axis is concentration, y-axis is the location of the samples. 112 Figure 43 Total Nitrogen Visual Results. x-axis is concentration, y-axis is the location of the samples. 113 Figure 44: Nitrate Visual Results. x-axis is concentration, y-axis is the location of the samples. 114 Figure 45: Nitrite Visual Results. x-axis is concentration, y-axis is the location of the samples. 115 Figure 46: Ammonia Visual Results. x-axis is concentration, y-axis is the location of the samples. 116