Anaerobic digestion, solid-liquid separation, and drying of dairy manure: Measuring constituents and modeling emission

Anaerobic digestion, solid-liquid separation, and drying of dairy manure: Measuring constituents and modeling emission

Science of the Total Environment 696 (2019) 134059 Contents lists available at ScienceDirect Science of the Total Environment journal homepage: www...

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Science of the Total Environment 696 (2019) 134059

Contents lists available at ScienceDirect

Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Anaerobic digestion, solid-liquid separation, and drying of dairy manure: Measuring constituents and modeling emission Horacio A. Aguirre-Villegas a,⁎, Rebecca A. Larson a, Mahmoud A. Sharara b a b

Biological Systems Engineering, University of Wisconsin-Madison, 460 Henry Mall, Madison, WI 53706, United States Biological and Agricultural Engineering, North Carolina State University, 3100 Faucette Dr, Raleigh, NC, United States

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Manure constituents pre and post processing were measured to model emissions. • VS reduction was lower for digesters feeding only manure vs co-digestion. • Nutrient separation efficiencies were b30% except for TP under centrifugation. • Nearly all ammoniacal nitrogen remained in the liquid fraction after separation. • High efficiency separation can reduce GHG emissions from manure by 60%.

a r t i c l e

i n f o

Article history: Received 29 May 2019 Received in revised form 21 August 2019 Accepted 21 August 2019 Available online 22 August 2019 Editor: Yolanda Picó Keywords: Anaerobic digestion Solid-liquid separation Separation efficiency Dairy manure GHG emissions Ammonia emissions

a b s t r a c t Anaerobic digestion (AD) and solid-liquid separation (SLS) can increase operational flexibility at livestock facilities, but they can also affect environmental impacts during downstream manure handling. In this study, manure was characterized before and after AD, SLS, and drying. The measured data were used as inputs to models to estimate greenhouse gas (GHG) and ammonia (NH3) emissions during manure storage and land application. Nine dairy farms were sampled between each processing component to evaluate total solids (TS), volatile solids (VS), chemical oxygen demand (COD), total nitrogen (TN), total ammoniacal nitrogen (TAN), total phosphorus (TP), and total potassium (TK). AD systems with co-digestion have higher VS reduction than AD systems processing only dairy manure. SLS data indicate that both screw presses and centrifuges achieve higher separation efficiencies (mass in the solids) for TS and VS than for the other manure constituents. The farm with centrifugation achieves the highest separation efficiency for TP. TAN and TK are not well concentrated in the solids fraction for any processing system. TAN remains entirely within the liquid fraction, showing that each constituent has its own separation profile. Drying manure results in moisture, VS, and TAN losses. Since TAN stays with the liquids, these losses are negligible. When analyzing modeling results, most GHGs are emitted during storage as methane. However, land application is the major emitter of nitrous oxide and NH3. Both AD and SLS can reduce GHG emissions, with the combined AD and SLS scenario achieving the highest reduction (41%). AD increases NH3 emissions during storage due to the mineralization process during digestion. SLS alone can achieve significant GHG emission reductions (38%) even greater than AD when using actual performance data from operating systems. Both AD and SLS have the potential to reach higher GHG and NH3 emission reductions with improved technology efficiencies and management.

⁎ Corresponding author. E-mail address: [email protected] (H.A. Aguirre-Villegas).

https://doi.org/10.1016/j.scitotenv.2019.134059 0048-9697/© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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H.A. Aguirre-Villegas et al. / Science of the Total Environment 696 (2019) 134059

© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction Manure can improve soil health and provide valuable nutrients for crop production when land applied. Manure can also lead to negative environmental and health impacts from emissions, runoff and leaching if not managed properly. Manure management represents the second largest source of greenhouse gas (GHG) emissions on a dairy farm after enteric fermentation (Veltman et al., 2018). Methane (CH4), nitrous oxide (N2O), and ammonia (NH3) are emitted during manure storage (Rotz et al., 2018) and N2O and NH3 are emitted after manure land application (Rotz, 2004). Runoff and leaching of nutrients can occur when manure is land applied, which impact water quality and reduce nutrients available to growing crops. Regions with highly concentrated animal operations face the challenge of land applying manure at agronomic rates due to limitations in available land and transport costs. Further, as farmers purchase animal feed off-farm, the net import of nutrients increases the strain on the existing land base (MacDonald et al., 2018). Anaerobic digestion (AD) and solid-liquid separation (SLS) increase operational flexibility in manure management and can reduce environmental impacts and odors. AD produces biogas, a renewable energy source that can be used in direct burn applications, to produce electricity, and for injection into the natural gas grid or compressed and used as a transportation fuel. Most digesters at U.S. dairy farms currently produce electricity for the national electricity grid, reducing nearly 50% of GHG emissions associated with manure management when modeled, mostly in the form of CH4 during storage (Aguirre-Villegas et al., 2014). The digestion process also increases nutrient mineralization, making N more available to crops. The benefits of AD are numerous, but AD is a capital-intensive technology that might only be justified at large farms. Manure SLS systems also have operational and environmental benefits and can be coupled with AD systems to add value and flexibility in managing manure. SLS separates manure into multiple streams with varying characteristics, most commonly divided into two streams known as the liquid and solid fractions. The liquid fraction has reduced total solids (TS) compared to the unseparated manure, is more easily transported via pumps, and reduces NH3 emissions when land applied due to a rapid infiltration (Guilayn et al., 2019). The solid fraction has increased TS and nutrient density that can reduce overall farm hauling costs if managed effectively. This has received interest by producers as they can use separated solids as bedding given the increase in cost and decrease in availability of common bedding materials (Husfeldt et al., 2012), or for use in environmentally sensitive areas when land applied. In addition, studies have reported that SLS systems can reduce GHG emissions related to manure management by 20% when compared to a system with no processing (Aguirre-Villegas et al., 2014). Despite that there is evidence that SLS can reduce emissions from manure management, it is not included in emission guidelines at the national nor regional levels, such as those developed by the Intergovernmental Panel on Climate Change (IPCC). It is important to develop emission factors of manure processing representative of specific practices and regions. Most benefits of both AD and SLS depend on the impacts to the manure composition. Process-based models that predict emissions from manure rely on this composition, which changes after processing (Rotz et al., 2018). For example, during digestion, volatile solids (VS) in manure are reduced, which reduces CH4 emissions downstream (Veltman et al., 2018). When manure is separated, the liquid fraction has a lower percentage of TS which impedes natural crust formation on top of the stored manure, increasing NH3 emissions through wind exposure (Rotz et al., 2018). Manure characteristics are highly variable

within and among farms (Aguirre-Villegas et al., 2018). Moreover, manure characteristics have changed over the years because of different practices, such as feeding ration strategies, and technological advances in manure handling and processing. As a result, it is crucial to have detailed and updated inventory data of manure composition before and after operating AD and SLS systems to accurately quantify trade-offs and net impacts of added systems. An assessment of manure composition before and after processing can also allow to evaluate system performance and point out if targeted objectives and efficiencies are being achieved. Studies have analyzed the performance of AD and SLS systems (Moller et al., 2000; Gooch et al., 2005; Moller et al., 2007; Hjorth et al., 2009; Husfeldt et al., 2012; Fournel et al., 2018). Akhiar et al. (2017) characterized digested liquids and solids from 11 co-digestion plants and found a high variability in the characteristics of liquid digestate depending on the substrate and the separation technology. The authors found that centrifuges achieve higher separation efficiencies whereas screw presses achieved the same TS concentrations in the separated liquids than the unseparated digestate. Guilayn et al. (2019) conducted a meta-analysis to determine the efficiency of different mechanical separators. The authors found low and high efficiency separator profiles, where only the latter where able to remove nutrients along with the solids fraction in terms of mass. Overall, studies show that there is great variability in the efficiency of separators and feedstocks, highlighting the need to develop specific characterization data of feedstock before and after processing. This study is one of the first to develop up-to date manure composition inventory data pre and post processing of representative dairy farms and to integrate these real-life system performance indicators into emission models to evaluate their related environmental benefits. The objectives of this study are to i) characterize dairy manure composition pre and post AD, SLS, and drying, ii) estimate and compare processing efficiencies, iii) relate manure characteristics to emissions by using modeling and experimental tools, and iv) compare trade-offs between AD and SLS. 2. Materials and methods A two-part analysis was used to evaluate AD and SLS systems. First, changes to manure characteristics through processing systems were evaluated by sampling manure from dairy farms with AD and/or SLS systems. Second, the results from this characterization were used to estimate GHG and NH3 emissions using modeling tools. 2.1. Assessment of manure processing systems Manure samples from nine dairy farms in southern and eastern Wisconsin (WI) were collected every two weeks from September 2011 to May 2012 for a total of 17 sampling events. These farms varied widely in size (Table 1). All nine farms separated manure into liquid and solid streams using a mechanical separator with eight farms using a screw press and one farm using a centrifuge. Two farms with screw presses used aerobic bedding recovery units (ABRU, comprised of a screw press followed by a rotary drum dryer) and the farm with the centrifuge used a rotary drum dryer but used heat captured from the combined heat and power system (CHP) to dry solids at a higher temperature than the ABRU rotary dryers. Additionally, seven farms used anaerobic digesters prior to solids separation with five farms using mixed plug flow designs and two using complete mix designs. Six farms processed only dairy manure while three farms co-digested dairy manure with by-products from ethanol production, fat, oil, and grease (FOG), paunch manure (partially digested), and food waste. The six digesters represent

H.A. Aguirre-Villegas et al. / Science of the Total Environment 696 (2019) 134059

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Table 1 Summary of each farm's manure management process. Farm ID

No. of animals

AD

SLS

Dryer

Feedstock

1 2 3 4 5 6 7 8 9

N2000 b1000 1000–2000 N2000 N/A N2000 1000–2000 N2000 1000–2000

Mixed plug flow NA Complete mix Mixed plug flow Mixed plug flow Mixed plug flow Mixed plug flow Complete Mix NA

Screw press Screw press Screw press Screw press Screw press Screw press Screw press Centrifuge Screw press

NA Rotary drum NA NA NA NA NA Rotary drum Rotary drum

Dairy manure Dairy manure Dairy manure, food waste Dairy manure Paunch manure, food waste Dairy manure Dairy manure Dairy manure, ethanol byproduct, FOG Dairy manure

AD: anaerobic digestion, SLS: solid-liquid separation, FOG: fat, oil, and grease.

26% of the total digesters in WI processing only dairy manure (USEPA, 2019), highlighting the representativeness of the sampled farms. For all farms, manure was sampled before processing (untreated manure) and after each processing step to isolate the performance of each treatment unit. At farms with a digester, manure was sampled in the reception pit which fed the digester and at the outflow. SLS systems were sampled at the influent reception pit and at the output for the liquids and solids. On farms with dryers, the solid manure was sampled again after the drying process. Manure grab samples were collected every two weeks throughout the sampling period for a total of 17 sampling events: pre-processing, after AD, after SLS and after drying for each farm (n = 476 for all nine farms), kept on ice during transport, and stored at 4 °C before analysis. Samples were analyzed for chemical oxygen demand (COD) at the UW Biowaste laboratory and for TS, VS, total nitrogen (TN), total ammoniacal nitrogen (TAN), total phosphorus (TP), and total potassium (TK) at the UW Soil and Forage Lab in Marshfield, WI. The procedures from both labs followed the A3769 standard: Recommended Methods of Manure Analysis (Peters et al., 2003). Mean and standard deviation values of manure constituents are presented throughout the results section and compared by a t-test (StataCorp, 2011) to evaluate if there are statistical differences (p b 0.005) in manure composition among and between AD and SLS technologies, before and after processing. VS, TS, and COD reduction and the change in TAN after AD were analyzed using Eq. (1). Mass balances for all constituents and farms after AD and SLS were conducted to complement concentration results. Separation efficiencies for all farms were estimated on a mass basis, where the efficiencies represent the percentage of the constituents following the solids fraction. Separation results were also analyzed in terms of separation index (SI), which reflects the ratio of distribution into the solid fraction in relation to the input mass, and removal efficiency (RE), which expresses the relative purification of the liquid fraction (Eqs. (2), (3), and (4)) following Guilayn et al. (2019). The authors classified separators into high and low efficiency categories, where high efficiency separators have SI N 0.62 and RE N 0.53. X reduction ð%Þ ¼

X in −X out  100 X in −ðX in  X out Þ

SIX ¼ Rsolid;out 

Rsolid;out ¼

REX ¼ 1−

½X solid;out ½X total;in

½TStotal;in −½TSliquid;out ½TSsolid;out −½TSliquid;out ½X liquid;out ½X total;in

ð1Þ

ð2Þ

ð3Þ

ð4Þ

where: X is the constituent under evaluation (e.g. TS, TAN, etc.); [] indicates concentration (%), in the solid (solid, out), liquid (liquid, out)

fractions after separation, and total before separation (total, in); and Rsolid,out is the ratio of solid fraction in relation to the input mass. 2.2. Modeling of emissions Based on the manure characterization for pre and post processing manure, GHG and NH3 emissions were quantified using the equations presented in Table 2 for all evaluated technologies and farms. These models use several variables that are key to estimate manure emissions under different climatic and management settings. By using the measured results, it is expected that the models can capture the effect of manure processing on emissions. However, there might still be some other interactions not fully captured as most of these emission models have been developed for raw manure and not processed manure. Modeled emission results from this study are key to understand emissions from manure processing, but they must be taken with caution. Emissions of GHG and NH3 reflect only those emissions from manure during storage and after land application. Characterization of GHG emissions consider a 100-year time horizon and are measured in kilograms of carbon dioxide equivalents (kg CO2-eq). Characterized gases include CH4 and N2O, which have a global warming potential of 28 kg CO2-eq and 264 kg CO2-eq, respectively (Myhre et al., 2013). Biotic CO2 is not quantified as it is assumed that the carbon contained in manure has been previously captured by the crops that are part of the dairy diet. It is assumed that raw manure, digestate, and liquid separated manure are stored in uncovered storage basins for six months before surface land applied in spring and fall. Solid manure is also assumed to be stored for six months before land application. As a result, daily emissions represent the yearly average. Average VS reduction rates for AD and separation efficiencies for SLS from the field studies were used to estimate GHG and NH3 emissions for manure storage and land application following processing. Manure emissions from four scenarios were analyzed to evaluate the effect of processing: i) raw manure i) digested manure iii) separated manure and iv) digested and separated manure. As manure processing is most common in permitted dairy facilities (Aguirre-Villegas and Larson, 2017), an assumed dairy farm of 1000 lactating cows, and related maintenance animals, was used for this modeling exercise. A farm this size produces nearly 100 metric tonnes of manure per day (AguirreVillegas et al., 2015b), amount that was used to model emissions from manure along with the characterization described in Tables 3 and A1. 3. Results and discussion 3.1. Manure characterization 3.1.1. Unprocessed manure Descriptive statistics (mean, standard deviation, coefficient of variation, minimum, maximum, and statistical difference between farms) of the analyzed constituents of unprocessed manure for each farm are presented in Table 3. There is variability in all constituents between farms. When unprocessed manure constituents are grouped based on farms

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Table 2 Description of emission factors used to estimate GHG and NH3 emissions from manure storage. Emission and source CH4 Manure storage (raw, liquid, and digestate)

Manure storage solid (raw, separated)

Manure application

N2O direct Manure storage Manure application N2O indirect Ammonia volatilization NH3 Manure storage (raw, liquid, digestate)

Manure application

Equation

Reference

24  VSd  b1 E 24  VSnd  b2 E Þ  eð ln ðAÞ − Þ þ ½ð Þ  eð ln ðAÞ − Þ RT RT 1000 1000 CH4 = methane emissions from storage (g CH4/day); VSd = degradable VSa (g); VSnd = non degradable VS (g); b1 and b2 = rate correcting factors (b1 = 1, b2 = 0.01); A = Arrhenius parameter (g CH4/kgVS/h); ln(A) = 43.33; E = Apparent activation energy (112,700 J/mol); R = constant (8.314 J/K/mol); Tm = manure temperatureb (K) VS  Bo  0:68  MCF CH 4 ¼ 100 CH4 = methane emissions from manure solids (kg CH4/day); Bo = maximum methane producing capacity (0.23 m3 CH4/kg VS); 0.68 = conversion factor; MCF = CH4 conversion factor (0.201*Tm – 0.29, %), Tm = manure temperatureb (°C) CH4 = [(0.17 ∗ FVFA) + 0.026] ∗ Acrop ∗ 0.032 CH4 = methane from application (kg CH4/day); FVFA = volatile fatty acids (mmol/kg manure) = (fraction TAN/(2.02* (9.43-pH)))*e(−0.6939⁎Tm); Acrop = area of application (Ha) = 2.02 Ha/day for permitted facilities based on Aguirre-Villegas and Larson (2017)

(Chianese et al., 2009)

CH 4 ¼ ½ð

(Rotz et al., 2015)

(Chianese et al., 2009)

0.005 kg N2O-N/kg of N solid and liquid manure 0.0176 kg N2O-N/kg of TAN after NH3 losses

(IPCC, 2006) (Chadwick et al., 2011)

0.01 kg N2O-N/kg NH3-N

(IPCC, 2006)

TAN  c  y rMQ NH3 = ammonia emissions NH3 − N (kg/m2/d); TAN = total ammonia nitrogen in manure (kg N/m2); c = time conversion (86,400 s/d); y = manure density (kg/m3); r = resistance of NH3 transport to the atmosphere (s/m), r barn = HSC[1–0.027(20-T)], HSC = housing specific constant (260 s/m), r storage = 75 (manure with crust), 19 (manure without crust), 10 (solid manure); M = manure urine per area of exposed surface (kg/m2); Q = equilibrium coefficient = Kh * Ka, Kh = 10[1478 / (Tm + 273) − 1.69], Ka = 1 + 10[0.09018 + 2729.9 / (Tm + 273) – pH], Tm = manure temperatureb (°C), pH = manure acidity days 17 Þ  ð Þ NH 3 ¼ TAN  ½ð20 þ 5  TSÞ  ð days þ 0:3 14

(Rotz and Oenema, 2006)

NH 3 volatilization ¼

(Jokela et al., 2004)

100

NH3 = emissions after application (kg NH3); TAN = total ammonia N in manure (kg NH3-N); TS = total solids in manure (%); days = to incorporate manure (if not incorporated, days N7) a b

It is assumed that 50% of VS are degradable (Wilkie, 2005). Manure temperature is assumed to be the same as ambient temperature.

with the same AD and SLS technologies, the difference is not statistically significant (Fig. A1), suggesting that the changes following processing could be attributed to the processing system. The statistical analysis must be taken with caution as the sample size is small and farms with same technologies have different management practices, which might influence the results. The statistical significance in this paper is not conclusive, but rather suggestive of a processing effect on dairy manure constituents. Overall, there is more consistency in TAN and TK between farms. There is also variability among farms, but the variability is higher for COD and TN and lower for TP. Farms 3 and 5 have consistently higher TS, VS and COD results when compared to the rest of farms. Farms 3 and 5 receive mixed substrates and the constituents were more variable over time than farms using manure only. However, farm 8 also receives mixed substrates but the variability is comparable to the rest of farms only receiving dairy manure suggesting a more controlled mixing of substrates in this farm. This is likely due to the addition of feedstock mixing tanks at this facility.

3.1.2. Changes in constituents after AD Detailed characterization results for processed manure for all farms and constituents are presented in Table A1. Results show a significant reduction in TS, VS, and COD and a significant increase in TAN after AD when compared to unprocessed manure for all farms (Fig. 1). A nonsignificant change in TN, TP, and TK is observed, likely due to a sampling error, which is common with grab samples in highly variable samples. This was expected and suggests that the statistical analysis is capturing these differences accurately. TS means are significantly different between manure processed by mixed plug flow and completely mixed digesters (Fig. A2). This might be explained by the reduction in variability in TS processed in mixed plug flow digesters, likely due to the more consistent retention time.

The estimated VS reduction rate ranges from 16% (farm 4) to 59% (farm 5), with the average mean of completely mixed digesters not significantly higher than mixed plug flow digesters. The farms with codigestion (farms 5 and 8) achieve higher VS reduction rates, whereas all farms digesting only dairy manure achieve VS reduction rates between 16% and 31%. The reduction rates of the analyzed digesters in this study are lower than results obtained by other studies analyzing only dairy manure (34% to 42%) (Neerackal et al., 2015; Maldaner et al., 2018; VanderZaag et al., 2018). This is an indication of a low digester efficiency by the sampled digesters. This lower efficiency might be explained by the operation of the systems and not the systems themselves. Specifically, a shorter retention time than designed was likely as a result of increasing farm size over time and build-up of solids common in these systems that also reduces the effective retention time. These results highlight the need to revisit the conditions under which these AD systems are functioning. Operational or management changes might be needed to improve VS reduction as they may represent a simple way to improve performance. AD significantly increased TAN concentration for all farms ranging from 6% (farm 1) to 150% (farm 5), which has also been reported by other studies (6% to 117%) (Amon et al., 2006; Koirala et al., 2013; Page et al., 2014; Neerackal et al., 2015). This increase in TAN content is explained by the mineralization that takes place during the digestion process. Mixed plug flow digesters show a higher mineralization than complete mixed digesters, but this difference between digester types is not statistically significant. Fig. 2 presents mass balances for TS, VS, and TAN for all the farms with AD. The difference in constituents of pre and post processed manure has direct implications in the estimation of environmental impacts of downstream processes such as manure storage and land application. For example, achieved VS reduction rates for some farms are lower than the rates used in modeling studies (Aguirre-Villegas et al., 2014; Rotz et al., 2018; Veltman et al., 2018), which might lead to overestimations

H.A. Aguirre-Villegas et al. / Science of the Total Environment 696 (2019) 134059

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Table 3 Summary of manure constituents for unprocessed manure. Farm Farm 1 Mean±SDa CVb Min-Maxc nd Differencee Farm 2 Mean±SDa CVb Min-Maxc nd Differencee Farm 3 Mean±SDa CVb Min-Maxc nd Differencee Farm 4 Mean±SDa CVb Min-Maxc nd Differencee Farm 5 Mean±SDa CVb Min-Maxc nd Differencee Farm 6 Mean±SDa CVb Min-Maxc nd Differencee Farm 7 Mean±SDa CVb Min-Maxc nd Differencee Farm 8 Mean±SDa CVb Min-Maxc nd Differencee Farm 9 Mean±SDa CVb Min-Maxc nd Differencee Overall Mean±SDa CVb Min-Maxc nd

TS (%)

VS (%)

COD (g/L)

TN (g/kg dry)

TAN (g/kg dry)

TP (g/kg dry)

TK (g/kg dry)

6.5 ± 0.83 12.8 4.8–8.0 (16)d *,†,‡,§

5.2 ± 0.7 13.5 3.8–6.7 (16) *,†,‡,§

57.4 ± 17.6 30.7 26.8–97.5 (15) †,‡,§

46.9 ± 8.1 17.3 31.8–59.9 (15) *,†,‡,§

26.2 ± 8.2 31.3 20.5–50.8 (13) ‡

7.8 ± 1.4 17.9 5.3–10.0 (14) †,‡,§

35.5 ± 5.2 14.6 25.3–40.8 (7) ‡

7.9 ± 1.2 15.2 4.6–10.2 (15) *,#,¶,∂,Δ,$

6.8 ± 0.7 10.3 5.7–8.3 (13) *,¶,∂,Δ,ƹ,Ʈ,$

68.4 ± 14.7 21.5 40.6–93.9 (15) ¶,∂

48.0 ± 8.8 18.0 31.9–61.1 (13) ¶,∂,Δ,$

19.3 ± 3.2 16.6 14.0–25.3 (13) ∂

7.9 ± 1.4 19.7 6.5–12.0 (14) ¶,∂,Δ,ƹ

34.6 ± 8.4 24.3 19.0–41.9 (7) ∂

9.9 ± 2.0 20.2 5.2–12.6 (14) †,#,%,&,@,¢

7.8 ± 1.6 20.5 3.7–10.0 (14) †,%,&,@,¢

82.8 ± 26.4 31.9 49.3–130.3 (15) †,%,&

44.9 ± 19.2 42.8 20.4–96.8 (11) †,%,&

19.4 ± 9.7 50.0 6.5–46.5 (12) ɸ

8.0 ± 1.7 21.2 5.3–11.8 (13) †,%,&,@,¢

30.8 ± 11.2 36.4 21.3–52.6 (6) ɸ

5.7 ± 0.9 15.8 3.1–7.1 (15) ¶,%,£,¥

4.6 ± 0.9 19.6 2.1–5.8 (15) ¶,%,£,¥

52.3 ± 9.5 18.2 40.8–74.7 (14) ¶,%,£,¥

49.9 ± 8.6 17.2 36.0–66.2 (13) ¶,%,£,¥

25.4 ± 9.5 37.4 14.5–46.4 (11) ʁ,¥

8.8 ± 2.3 26.1 4.4–13.3 (12) ¶,%,£,¥

42.1 ± 6.2 14.7 32.7–48.2 (5) NA

11.0 ± 0.7 6.4 9.2–13.7 (14) ‡,∂,£,μ,ƛ,Ɵ,ƥ

9.1 ± 1.2 13.2 8.0–11.4 (12) ‡,∂,£,μ,ƛ,Ɵ,ƥ

118.7 ± 39.7 33.4 56.7–227.6 (12) ‡,∂,£,μ,ƛ,Ɵ

44.8 ± 10.7 23.9 30.8–64.1 (9) ‡,∂,£,μ,ƛ,Ɵ

9.2 ± 5.9 64.1 2.4–17.9 (9) ‡,∂,ɸ,ƛ,Ɵ,ƥ

10.0 ± 3.3 33.0 7.2–15.1 (11) ‡,∂,£,μ,ƛ,Ɵ,ƥ

10.3 ± 4.0 38.8 4.9–14.5 (4) ‡,∂,ɸ,Ɵ,ƥ

6.5 ± 0.7 10.8 5.2–7.6 (16) Δ,&,μ,Ʀ

5.4 ± 0.6 11.1 4.6–6.5 (15) Δ,&,μ,Ʀ

55.5 ± 22.3 40.2 26.7–114.8 (15) &,μ,Ʀ

40.1 ± 8.4 20.9 29.3–54.6 (13) Δ,&,μ,Ʀ

19.2 ± 3.8 19.8 11.6–24.6 (13) ʂ

7.1 ± 1.6 22.5 4.4–10.5 (14) Ʀ,Δ,&,μ

29.6 ± 7.1 24.0 19.4–37.2 (7) Ʀ

6.9 ± 1.4 20.3 2.8–8.4 (16) @,ƛ,Ʊ

5.4 ± 1.2 22.2 2.0–6.6 (15) ƹ,@,ƛ,Ʊ

65.6 ± 23.2 41.8 17.9–117.9 (14) ƛ

48.8 ± 16.4 33.6 23.9–89.0 (14) ƛ,Ʊ

27.6 ± 6.3 22.8 20.2–41.8 (13) ʁ,ƛ,ʂ

6.7 ± 2.6 38.8 3.1–12.9 (13) ƹ,@,ƛ,Ʊ

38.9 ± 11.7 30.1 27.7–60.7 (7) NA

6.9 ± 1.7 24.6 5.5–11.0 (14) ¢,Ɵ,ƶ

5.4 ± 1.4 25.9 4.1–9.4 (13) Ʈ,¢,Ɵ,ƶ

69.5 ± 31.6 45.5 21.0–142.6 (14) Ɵ

48.7 ± 12.8 26.3 19.3–68.7 (12) Ɵ,ƶ

23.6 ± 6.7 28.4 14.6–38.6 (13) Ɵ

6.9 ± 1.3 18.8 4.7–9.0 (13) ¢,Ɵ,ƶ

31.6 ± 9.0 28.5 19.3–42.1 (7) Ɵ

9.7 ± 0.7 7.2 8.7–10.9 (16) §,$,¥,ƥ,Ʀ,Ʊ,ƶ

8.0 ± 0.6 7.5 7.1–9.2 (16) §,$,¥,ƥ,Ʀ,Ʊ,ƶ

84.1 ± 20.7 24.6 54.2–125.5 (15) §,¥,Ʀ

49.5 ± 7.3 14.7 36.4–59.2 (14) §,$,¥,Ʀ,Ʊ,ƶ

19.5 ± 4.3 22.0 14.4–29.7 (12) ¥,ƥ,Ʀ

7.9 ± 1.0 12.7 6.7–10.8 (13) §,¥,ƥ,Ʀ,Ʊ,ƶ

31.5 ± 6.0 19.0 19.4–38.0 (7) ƥ,Ʀ

7.8 ± 2.1 26.9 2.8–13.7 (136)

6.3 ± 1.8 28.6 2.0–11.4 (129)

71.9 ± 29.6 41.2 17.9–227.6 (129)

47.0 ± 11.6 24.7 19.3–96.8 (114)

21.4 ± 8.1 37.8 2.4–50.8 (109)

7.8 ± 2.1 26.9 3.1–15.1 (117)

32.4 ± 10.5 32.4 4.9–60.7 (57)

a

SD = standard deviation. CV = Coefficient of variation. Minimum-maximum. d Number of samples (n). e Denotes statistical difference between: ⁎ farm 1 and farm 2; † farm 1 and farm 3; ‡ farm 1 and farm 5; § farm 1 and farm 9; # farm 2 and farm 3; ¶ farm 2 and farm 4; ∂ farm 2 and farm 5; Δ farm 2 and farm 6; ƹ farm 2 and farm 7; Ʈ farm 2 and farm 8; $ farm 2 and farm 9; % farm 3 and farm 4; ɸ farm 3 and farm 5 & farm 3 and farm 6; @ farm 3 and farm 7, ¢ farm 3 and farm 8; £ farm 4 and farm 5; ʁ farm 4 and farm 7; ¥ farm 4 and farm 9; μ farm 5 and farm 6; ƛ farm 5 and farm 7; Ɵ farm 5 and farm 8; ƥ farm 5 and farm 9; ʂ farm 6 and farm 7; Ʀ farm 6 and farm 9; Ʊ farm 7 and farm 9; ƶ farm 8 and farm 9. b c

of CH4 reductions during storage. On the other hand, TAN mineralization rates are higher than the 15% average reported in process-based models (Rotz et al., 2018), which might result in underestimations of NH3 emissions during manure storage and land application.

3.1.3. Change in constituents after separation Fig. 3 shows the constituents concentration for manure pre SLS, and after SLS for the liquid and solid fractions for all farms. The difference in concentrations is significant when both the liquid and solid fractions are compared to unseparated manure for TS, VS, and TP. As expected, the

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Fig. 1. Mean concentrations of total solids (TS), volatile solids (VS), chemical oxygen demand (COD), total nitrogen (TN), total ammoniacal nitrogen (TAN), total phosphorus (TP), and total potassium (TK) in unprocessed (pre) and processed (post) manure after anaerobic digestion though mixed plug flow (MPF) and complete mix (CM).

concentration in the liquid fraction is reduced and the concentration in the solid fraction is increased for these constituents. On the contrary, the concentration of TAN in the liquid fraction is maintained and is reduced in the solids fraction, indicating that most TAN stays with the separated liquids. These results have important implications when modeling GHG and NH3 emissions from manure storage and land application. For example, N2O emissions from stored manure solids might be negligible as there is nearly no available N in this fraction, as opposed to previously modeled by other studies (Veltman et al., 2018). NH3 emissions from manure liquids might be higher than previously modeled as there is more stored TAN in this fraction. Therefore, assuming the same separation index for TAN as measured for TS is not accurate. Interestingly, the concentration of TK is similar for the liquid and solid fractions when compared to unseparated manure for all farms, indicating a lower separation efficiency for this constituent. When comparing mean concentrations between separator types (screw press vs. centrifuge) TS, VS and TP contents in the liquid fraction are significantly different between separators suggesting that centrifugation might better remove these components from the liquid fraction. However, only TP maintains significance in the solids fraction, where the difference between separators is more noticeable than for any of the other constituents (Fig. A3). Farm

8 with centrifugation achieves the highest TP concentration in the solid fraction. Fig. 4 shows the separation efficiency by mass for all farms and manure constituents. All farms achieve higher separation efficiency values for TS, VS, and TP than for TN, TAN and TK. The separation efficiency for all constituents is relatively consistent for all farms with screw presses except for farms 2 and 3, that have higher separation efficiencies for TS and VS, and for farms 3 and 7 that have higher efficiencies for TP. Of these farms, only farm 3 uses mixed substrates, suggesting that the difference in separation efficiencies might not be attributed entirely to the mix of substrates, but also to other operating parameters or management practices adopted in those farms. Consistently with the concentration results, the farm with centrifugation (farm 8) has the highest separation efficiency for TP (67% of TP follow the solid fraction) despite that the efficiency for TS and VS is comparable to the farms with screw presses. The performance of the centrifuge in this farm is lower than a high efficiency separator as defined by Guilayn et al. (2019). This low performance might be attributed to a high proportion of dairy manure in the substrate mix as centrifugation is not as effective separating fibrous materials, which could even damage the equipment. When comparing mean separation efficiencies between separator types, TP is the only constituent maintaining statistical significance

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Fig. 2. Average mass balance results of a) total solids (TS), b) volatile solids (VS), and c) Total ammoniacal nitrogen (TAN) for the farms with digesters. Numbers in parenthesis show mass balance results for individual farms.

suggesting a better separation performance of centrifugation for this nutrient. TAN and TK are highly soluble and remain in the liquid fraction. As a result, they are not well concentrated in the solids fraction by mechanical separation. Both SI and RE confirm that TAN stays

completely with the liquid fraction in all farms, reaffirming the need to develop different separation profiles for each manure constituent. No farm or separator type fell into the high efficiency category defined by Guilayn et al. (2019) for either RE (RE N 0.53) or SI (SI N 0.62) (Fig. 5). The authors of that study found that fibrous feedstocks, such

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Fig. 3. Mean concentrations of TS: total solids, VS: volatile solids, TN: total nitrogen (TN), TAN: total ammoniacal nitrogen, TP: total phosphorus, TK: total potassium, for manure before separation (pre) and after separated into liquid (liq) and solid (sol) fractions through SP: screw press, and C: centrifuge.

80%

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RE<0.53 low efficiency

8 29 67 45 13

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as dairy manure, are usually associated with low efficiency equipment and that screw presses mainly fall into the low efficiency category, which can be reaffirmed by the results of this study. When comparing constituent means of farms separating digested manure (farms 1, 3, 4, 5, 6, 7) with farms separating raw manure (farms 2 and 9), results suggests that in average constituents are better removed from the liquid fraction (RE) in non-digested manure than digested manure except for TP. This is likely due to the larger particle sizes in non-digested manure. However, except for TN and TK, screw press achieves a lower concentration of the remaining constituents in the solids fraction (SI) for non-digested manure, likely due to degradation of solids and mineralization of nutrients. Again, this is not a conclusive statement as SI and RE results are highly variable between farms but highlights the need for further investigation. The SI of TAN is consistently low for farms with screw press and centrifuge and both digested and raw manure. This nearly non-existent TAN concentration in the solids fraction is maintained despite the different starting TAN concentration of unseparated manure, reflected by the variable RE. When analyzing all the data points (Fig. A4) results show that overall, SI and RE are more variable for centrifuge than for screw press (despite that only one farm used centrifugation). This might be a result of farm 8 mixing substrates. The concentration of TK TAN and TN in the solids fraction (SI) is more consistent, regardless the separator type. Again, this suggests that mechanical separation can only achieve a certain concentration of diary manure constituents in the solids fraction, regardless of the variable starting concentrations in unseparated manure. Results also show that there are some individual observations for the farm with centrifuge that show negative SI numbers, indicating that the constituents were concentrated in the liquid fraction. If the most extreme SI value for TP were to be removed from the analysis, the SI would reach 0.61, confirming that centrifugation better concentrates TP than screw press. 3.1.4. Change in constituents after drying The effect of the drying process was analyzed in the three farms with rotary drum dryers. TS, VS and TAN concentrations are presented before and after the drying process (Fig. A5). The higher concentration of TS after the drying process can be attributed to the loss in moisture, which is 15%, 9%, and 33% for RD1, RD2, and RD3-AD (farm with anaerobic digestion), respectively. The higher moisture removal in RD3-AD is likely due to the higher temperature in the rotary drum dryer at this farm system. VS losses of 3% for RD1, 4% for RD2, and 3% for RD3-AD

were estimated after the drying process by analyzing the difference of the VS to TS ratio before and after the drying process. Results also show that 16%, 28% and 85% of the TAN in the solids fraction are lost for RD1, RD2, and RD3-AD, respectively, again the high TAN loss is likely due to the increased temperatures in the dryer. In addition, the starting TAN content for RD3-AD was significantly higher further increasing losses. Despite results showing TAN losses through drying, the amount of TAN separated within the solids fraction is so small (almost 0.1% of TS), that the TAN losses are negligible when compared to the TAN remaining in the liquid fraction. 3.2. Relating emission and pathogens with AD and SLS technologies 3.2.1. GHG and NH3 emissions Storage and land application GHG and NH3 emissions for each scenario are presented in Fig. 6a. If raw manure were to be stored with no processing, this modeled farm would emit 7252 kg CO2-eq/day from manure, which translates to an emission factor of 72.5 kg CO2eq/t manure (GHG and NH3 emission factors for each scenario are presented in Table A2). More than 60% of GHG emissions are emitted during storage for all scenarios. CH4 losses govern emissions during storage and N2O during land application. Given the low TS content, no crust formation was assumed for either of the scenarios. Thus, the reported N2O emissions from storage of the liquid fraction come from the indirect formation following NH3 volatilization. When manure is separated, the liquid fraction comprises N90% of total GHG emissions for storage and land application. All processing scenarios can reduce total GHG emissions with the combined AD and SLS scenario achieving the highest reduction. These reductions come mostly from CH4 during storage as less VS enter the storage. N2O emissions are increased during land application with AD scenarios achieving the highest increase. This is because N2O emissions after application are directly related to TAN content in the applied manure after NH3 volatilization (Chadwick et al., 2011). Interestingly, SLS can achieve greater GHG emission reductions than AD and are similar to the combined AD and SLS scenario. This might be explained by the low VS reduction in the sampled digesters in this study. With the measured separation efficiencies, SLS alone can reduce GHG emissions from storage by 46%, proving to be a cost-effective GHG emission mitigation strategy. This contradicts the results of other studies that found significant N2O emissions from the solids fraction of separated manure, which exceeded the CH4 benefit from the liquid fraction (Veltman et al.,

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7500 7000 6500 6000 5500 5000 4500 4000 3500 3000 2500 2000 1500 1000 500 0

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Fig. 6. Modeled daily GHG and NH3 emissions during storage and land application of manure a) for the scenarios i) raw manure, ii) anaerobic digestion (AD), iii) solid-liquid separation (SLS), and iv) AD and SLS b) after improvements of VS reduction for AD scenarios and separation efficiency for SLS scenarios. CH4 and N2O emissions from raw and digested manure are classified as liquid.

2018). TN and TAN have different separation efficiencies than TS or VS and follow the liquid fraction, therefore, N2O emissions from separated solids might not be as high as previously modeled. Contrary to GHG emissions, NH3 emissions are increased in scenarios with a digester due to higher TAN content in digestate and given that no cover is assumed during storage. Emission levels of NH3 have been reported to be directly related to TAN content and increase under aerobic conditions, which are created by the lack of a crust on top of manure during storage (Pardo et al., 2015). For all scenarios, N60% of NH3 is volatilized after surface land application. These emissions could be significantly reduced through manure injection or rapid

incorporation (Aguirre-Villegas et al., 2015b). The SLS scenario is the only one that achieves NH3 emission reductions when compared to the raw manure scenario, which shows the added environmental benefits of this technology. For both SLS scenarios, nearly 94% of NH3 is emitted from the liquid fraction, highlighting the importance of improving the management of this fraction through practices both for GHG and NH3 reduction goals. As shown in Fig. 4, no separation technology fell into the high efficiency category established by Guilayn et al. (2019). In addition, the VS reduction rates for AD were lower than previously reported in the literature. We matched the high efficiency rates for VS, TS, TAN and TN

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reported in Guilayn et al. (2019) and increased the VS reduction rate to 50%, which is the percentage of degradable VS in manure (Rotz et al., 2018), to evaluate the effect of these improvements and the role of performance on GHG and NH3 emissions (Fig. 6b). The increase in VS reduction nearly tripled the reduction in GHG emissions when compared to raw manure from 12% to 33%, with the reduction coming in the form of CH4 from storage. Again, N2O emissions after land application are increased in AD scenarios due to the higher manure TAN content. The higher separation efficiency also reduced emissions from separated solids and liquids with a combined reduction of 59% (from 38%) when compared to raw manure. As the separation efficiency increases, N2O emissions from the solids fraction also increase, having a more important role on overall GHG and NH3 emissions from stored manure. The AD and SLS scenario combines both improvements in VS reduction and separation efficiencies, achieving the highest GHG emission reduction (60%) and highlighting the potential for improvement. Moreover, storage GHG emissions are reduced by nearly 80%, making manure land application the greater contributor to emissions in this combined scenario. These results are under characterization factors for a time horizon of 100 years for both CH4 and N2O. Under a 20-year time horizon (characterization factors of 84 and 264 for CH4 and N2O, respectively), total GHG emissions from unprocessed raw manure are 19,200 kg CO2-eq/day, with CH4 representing 92% of these emissions. These results show the impact of CH4 emissions in the shorter term and the importance to target this gas to address GHG reduction strategies. Overall, NH3 emissions are increased for AD scenarios. However, NH3 losses are reduced for the SLS scenario as TAN in the solid fraction is less susceptible to volatilization. NH3 emissions from the separated solids are still important (45%) given the improved TAN separation efficiency. Emissions of GHG and NH3 downstream from AD and SLS are directly related to environmental factors, such as temperature, and management practices adopted during storage and land application. For example, NH3 emissions can be significantly reduced by injecting manure instead of surface applying it and by covering the manure storage. If this cover is sealed and biogas captured, CH4 emissions from storage can be almost eliminated (Veltman et al., 2018). Moreover, if manure solids are quickly used as fertilizer of soil amendment on or off-farm, N2O emissions from separated solids can be eliminated. Therefore, the results of this paper must be taken with caution as they cannot be generalized to all regions or practices. This study showed that SLS can reduce GHG emissions from manure management and could surpass the GHG emission reductions achieved by AD when achieving high separation efficiencies. SLS can also achieve NH3 emission reductions when reaching higher TAN separation efficiencies. However, AD has many other benefits that are not analyzed in this study, such as the production of renewable energy, and the avoided use of fossil-based energy on-farm, which makes this an invaluable technology to promote the sustainability and profitability of dairy farms. Regardless, SLS has an important role for nutrient management, facilitating the economic transport of nutrients from regions with nutrient surplus to nutrient deficient regions, and has added benefits in terms of GHG and NH3 emission reductions. 4. Conclusions Manure constituents before and after AD, SLS, and drying were characterized in nine dairy farms of WI. Results show that AD systems with co-digestion are achieving higher VS reduction rates than AD systems digesting only dairy manure, which has implications for energy production and GHG emissions. Separation results show that each manure constituent has a specific profile. In general, TS and VS are better separated along with the solids fraction than the rest manure constituents, highlighting the need for use of individual separation efficiencies for each constituent in modeling. The farm with centrifugation achieves the highest separation efficiency for TP on a mass basis. TAN and TK

11

achieve low separation efficiencies for all farms and separator types. Moreover, nearly all TAN follows the liquid fraction. Drying increases TS and VS concentration but results in TAN losses. However, the amount of TAN in manure solids is so small following separation that these losses are negligible. When using these results to model emissions from manure storage and land application, most GHGs are emitted during storage as methane. Both AD and SLS can reduce GHG emissions from manure, with the combined AD and SLS scenario achieving the highest reduction. AD can increase NH3 emissions during storage due to the mineralization process in the digester. SLS alone can achieve significant GHG emission reductions even greater than AD. An increase in VS reduction nearly tripled the reduction in GHG emissions after AD when compared to raw manure. However, SLS still achieved the highest reduction of both GHG and NH3 emissions after increasing separation efficiencies. CRediT authorship contribution statement Horacio A. Aguirre-Villegas:Conceptualization, Formal analysis, Investigation Methodology, Writing original draft, Writing review & editing. Rebecca A. Larson:Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Validation, Writing review & editing. Mahmoud A. Sharara:Data curation, Validation, Visualization, Writing original draft. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This material is based upon work supported by the U.S. Department of Agriculture, National Institute of Food and Agriculture, under grant number 2017-67003-26055. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.134059. References Aguirre-Villegas, H.A., Larson, R.A., 2017. Evaluating greenhouse gas emissions from dairy manure management practices using survey data and lifecycle tools. J. Clean. Prod. 143. https://doi.org/10.1016/j.jclepro.2016.12.133. Aguirre-Villegas, H.A., Larson, R.A., Reinemann, D.J., 2014. From waste-to-worth: energy, emissions, and nutrient implications of manure processing pathways. Biofuels Bioprod. Biorefin. 8, 770–793. https://doi.org/10.1002/bbb.1496. Aguirre-Villegas Horacio, A., Passos-Fonseca, T.H., Reinemann, D.J., Armentano, L.E., Wattiaux, M.A., Cabrera, V.E., Norman, J.M., Larson, R.A, 2015b. Green cheese: Partial life cycle assessment of greenhouse gas emissions and energy intensity of integrated dairy production and bioenergy systems. J. Dairy Sci. 98, 1571–1592. https://doi.org/ 10.3168/jds.2014-8850. Aguirre-Villegas, Horacio A., Passos-Fonseca, T.H., Reinemann, D.J., Armentano, L.E., Wattiaux, M.A., Cabrera, V.E., Norman, J.M., Larson, R.A., 2015a. Green cheese: partial life cycle assessment of greenhouse gas emissions and energy intensity of integrated dairy production and bioenergy systems. J. Dairy Sci. 98, 1571–1592. https://doi.org/ 10.3168/jds.2014-8850. Aguirre-Villegas, H.A., Sharara, M.A., Larson, R.A., 2018. Nutrient variability following dairy manure storage agitation. Appl. Eng. Agric. 34, 909–918. Akhiar, A., Battimelli, A., Torrijos, M., Carrere, H., 2017. Comprehensive characterization of the liquid fraction of digestates from full-scale anaerobic co-digestion. Waste Manag. 59, 118–128. https://doi.org/10.1016/j.wasman.2016.11.005. Amon, B., Kryvoruchko, V., Amon, T., Zechmeister-Boltenstern, S., 2006. Methane, nitrous oxide and ammonia emissions during storage and after application of dairy cattle slurry and influence of slurry treatment. Agric. Ecosyst. Environ. 112, 153–162. https://doi.org/10.1016/j.agee.2005.08.030. Chadwick, D., Sommer, S., Thorman, R., Fangueiro, D., Cardenas, L., Amon, B., Misselbrook, T., 2011. Manure management: implications for greenhouse gas emissions. Anim. Feed Sci. Technol. 166–167, 514–531. https://doi.org/10.1016/j. anifeedsci.2011.04.036.

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