Comparative life cycle assessment of anaerobic co-digestion for dairy waste management in large-scale farms

Journal of Cleaner Production 256 (2020) 120320

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Comparative life cycle assessment of anaerobic co-digestion for dairy waste management in large-scale farms Mohamad Adghim a, d, 1, Mohamed Abdallah a, *, Suhair Saad b, Abdallah Shanableh a, c, Majid Sartaj d, Ahmed Eltigani El Mansouri b a

University of Sharjah, Sharjah, United Arab Emirates Al Rawabi Dairy Company, Dubai, United Arab Emirates Research Institute of Sciences and Engineering, University of Sharjah, Sharjah, United Arab Emirates d University of Ottawa, Ontario, Canada b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 19 February 2019 Received in revised form 19 October 2019 Accepted 28 January 2020 Available online 31 January 2020

Dairy farms typically generate different types of wastes including cow manure, feed waste, sludge, and returned dairy products. In most developing countries, the conventional handling practices of these wastes constitute land application and stockpiling. This study presents a comparative life cycle assessment to evaluate the environmental performance of anaerobic digestion (AD) as an alternative to conventional practices in dairy farms. The analysis was conducted on a large-scale dairy farm (>10,000 cows) that handles manure and other organic feedstocks in a hot arid climate. Various environmental categories, including global warming potential (GWP), acidification potential (AP), eutrophication potential (EP), and photochemical ozone creation potential (POCP) were assessed for the main processes within the dairy farm. The results showed that AD outperformed the conventional practice in all environmental categories; AD reduced GWP, AP, EP, and POCP by 25.7, 49.5, 18.1, and 16.1%, respectively. Accumulation of manure on sand beds was the highest contributor to GWP and POCP in both AD and conventional scenarios due to excessive amounts of methane emissions. The process that contributed the most toward AP in the conventional scenario was the outdoor storage of manure, whereas in the AD scenario, it was fertilization using stockpile and the digestate. Enteric emissions, landfilling, transportation, and feed waste processing had minimal environmental impacts as compared to manure management. The high ambient temperature had a significant negative impact on the overall environmental footprint, while the non-manure feedstock streams did not noticeably affect the results. Based on the sensitivity analysis, it was found that increasing the manure collection efficiency can reduce the environmental footprint in the AD scenario, and varying the methane yield of manure and other feedstocks did not affect the environmental impacts significantly. © 2020 Elsevier Ltd. All rights reserved.

Handling Editor: CT Lee Keywords: Life cycle assessment Anaerobic digestion Dairy waste management Large-scale dairy farms Hot arid climate

1. Introduction Dairy farms generate different types of organic wastes during their operations, such as cow manure, feed waste (forage), returned dairy products, and process water. In many developing countries, the conventional management practices of manure are limited to stockpiling and land application, which overlook the resource value of this waste stream (Fernandez-Lopez et al., 2015). Such practices

* Corresponding author. E-mail address: [email protected] (M. Abdallah). 1 Current affiliation: University of Ottawa, Ontario, Canada. https://doi.org/10.1016/j.jclepro.2020.120320 0959-6526/© 2020 Elsevier Ltd. All rights reserved.

lead to greenhouse gas (GHG) emissions, odors, and soil deterioration. It has been reported that manure from dairy farms typically contribute toward 70e95% of the GHG emissions (Fernandez-Lopez et al., 2016). There are alternative management systems, such as anaerobic digestion (AD), which have proven to be sustainable in dairy waste management (Fernandez-Lopez et al., 2015). AD is a well-established biochemical process, which is capable of manure stabilization, sludge reduction, and energy production (Cantrell et al., 2008). The carbon footprint of an AD system is affected by the biogas potential of the available feedstock, local climate, handling processes, and operating conditions, among others. Hence, an environmental assessment of the overall AD management system covers the various processes and material/energy

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HTP ISO LCA LCI LCIA MAETP ODP POCP TAN TKN UAE VS

List of acronyms AD ADP AP EP ETP FAETP FU FW GHG GWP

Anaerobic digestion Abiotic depletion potential Acidification potential Eutrophication potential Ecotoxicity potential Freshwater aquatic ecotoxicity potential Functional unit Feed waste Greenhouse gas Global warming potential

streams involved during its life cycle phases. One of the most widely used environmental analysis methods is the life cycle assessment (LCA) (International Organization for Standardization, 2006). LCA is a systematic study of all aspects of a process, including raw material extraction, production, use, and disposal, to define the hotspots of the overall process and reduce its € environmental impacts (Ozeler et al., 2006). In the case of manure management, LCA considers the whole life cycle of the manure handling process from collection and transportation to treatment, recovery, and disposal. The main environmental categories affected by the organic waste management processes are global warming potential (GWP), acidification potential (AP), eutrophication potential (EP), photochemical ozone creation potential (POCP), abiotic depletion potential (ADP), and eco-toxicity potential (ETP) (Curran, 2012). Table 1 shows a summary of previous LCA studies that investigated the impacts of AD on several environmental categories. These studies compared one or more scenarios of AD-based manure management with conventional scenarios, which was mostly stockpiling. As dairy farms typically produce other types of organic wastes alongside manure, some studies have also investigated the co-digestion of those waste streams as part of an integrated AD management strategy. Zhang et al., 2013a found that co-digestion of dairy manure with other manures and food waste improved the environmental performance of AD due to the enhanced methane yield. On the other hand, co-digestion of dairy manure with grass in Pehme et al. (2017) reduced the GWP but increased AP and EP due to the increased amounts of nutrients added. However, those studies focused mainly on manure management, thus handling processes of non-manure wastes in the conventional scenario were not covered. Different LCA methodologies, including CML, ReCiPe, TRACI, and EDIP, were used in the reviewed literature. Those methods mainly

Human toxicity potential International organization of standardization Life cycle assessment Life cycle inventory Life cycle impact assessment Marine aquatic ecotoxicity potential Ozone depletion potential Photochemical ozone creation potential Total ammonia nitrogen Total Kjeldahl nitrogen United Arab Emirates Volatile solids

differ in the computed impact categories and weighing factors of certain emissions. As shown in Table 1, in terms of GWP, AD systems generally have a smaller carbon footprint compared to the conventional systems. The impact on other environmental categories, such as AP, EP and POCP, differed among the assessed farms due to the different system boundaries of the LCA studies. Overall, the literature showed that the LCA results could differ from one study to another due to various case-specific inputs and assumptions considered in each study, in addition to system boundaries and limitations. Therefore, it is recommended that different LCA studies are compared qualitatively by evaluating the type and significance of impact caused by the emissions (Battini et al., 2014). Table 1 shows that the reviewed dairy farms were of a smallerscale (100e500 cows), indicating a critical gap in the literature for large-scale farms. The significance of studying large-scale farms lies in the potential environmental impacts of key differences in processes and operations compared to small-scale farms, including: 1) travel distances within the farm, 2) handling and treatment of various waste streams, and 3) amounts of waste handled. All previous studies had been conducted at low ambient temperatures, ranging between 16 and 26
C, that affect the emissions produced from the outdoor activities in those farms. Furthermore, it is noticed that LCA studies have mostly overlooked the minor organic waste streams generated within the farms and do not account for the low collection efficiency of manure. To date, no study has assessed the environmental impacts of AD systems involving manure produced from the different types of cows that are typically raised in dairy farms. The present research aims to address the following gaps in the literature: 1) LCA of large-scale dairy farms (>10,000 cows), 2) LCA of AD systems in which manure is codigested with feed waste, returned dairy product, and sludge streams, 3) LCA of dairy farms under realistic operating conditions, such as different types of cows and the actual manure collection

Table 1 Results from the literature on LCA of anaerobic digestion of dairy manure. Study

Location

Temperature (oC)

Zhang et al., 2013b Zhang et al., 2013a Battini et al. (2014) Zhang et al. (2015) Bacenetti et al. (2016) Pehme et al. (2017) Li et al. (2018)

N/A Canada Italy Canada Italy Estonia China

N/A 26 23 26 23 16 25

a

a

b

Cattle (no.)

AD type

100 100 185 400e500 128 N/A N/A

Co Mono/Co Mono Mono Mono Mono/Co Mono

LCA method

e CML 2001 ReCiPe CML 2001 EPD 2008 EDIP 2003 TRACI

Environmental impact reduction (%)

c

GWP

AP

EP

POCP

22.8e44.6 31.3e92 23.7e36.5 50e65 23 35e40 5.38

N/A 4.2e68.8 (5.5e6.1) 36e55 29 (100e300) 3.29e4.87

28.5e89.3 5.6e51.4 8.1 50 18 (100e900) 11.1

N/A N/A (41.6e42.3) N/A 1.1 N/A N/A

Temperature is considered as average temperature in the warmest months. (Mono) stands for mono-digestion of cow manure, while (Co) stands for co-digestion of cow manure with other feedstock; namely algae biomass in Zhang et al. (2013b), poultry and swine manure and food waste in S. Zhang et al., 2013; Y. Zhang et al., 2013Zhang et al., 2013a, and grass in Pehme et al. (2017). c Values in parenthesis indicate negative impact of AD compared to baseline scenario. b

M. Adghim et al. / Journal of Cleaner Production 256 (2020) 120320

efficiency, and 4) LCA of dairy waste management in the Middle East region where the hot arid climate has a significant heat stress impact on various processes. Such conditions are well represented in the United Arab Emirates (UAE) where the present study takes place. The main objective of this study was to conduct a comparative LCA of the AD systems and the conventional practices for organic waste management in large-scale dairy farms under realistic operating conditions and representative feedstock. The specific objectives were to: 1) identify the processes and streams of organic waste management within the dairy farm, 2) compute the environmental impacts based on the various emissions generated, and 3) conduct sensitivity analysis of the examined scenarios to assess the effects of input variation and uncertainty propagation. This study covers the management processes of different types of cow manures, feed waste, and returned dairy products. The analysis includes four key environmental categories, that is GWP, AP, EP, and POCP, along with other categories related to transportation and energy consumption, particularly marine aquatic eco-toxicity potential (MAETP) and abiotic depletion of fossil (ADP fossil). This work intends to lead large-scale farm operators towards sustainable organic waste management systems and cleaner production of dairy products in hot arid climates. The research outcomes are essential to inform decision-makers about the life cycle environmental footprint of AD systems compared to conventional practices, and to facilitate proper planning and prioritization of climate change mitigation strategies in the agricultural sector. 2. Materials and methods The present study investigated the largest dairy farm in Dubai, UAE, herein denoted as the Dairy Company. The Dairy Company houses a total cattle headcount of 13,600 cows, divided almost equally amongst 85 barns, and consists of a farm, dairy processing facilities, and packaging units. The Dairy Company classifies its different types of cows in the farm into lactating cows, dry cows (pregnant), fresh cows (after delivery), young cows (<16 months), and calves (<2 months). The types of organic wastes generated within the Dairy Company include manure from all cow types, feed waste, returned dairy products, and sludge from the on-site wastewater treatment plant. Similar to a majority of farms in the region, the current waste management strategies followed by the Dairy Company involve outdoor stockpiling of manure, sludge, and feed waste, that are used later as fertilizer. A portion of the returned dairy products is refed to cows, and the remainder is sent to a landfill. The present study assessed the environmental impacts of two dairy waste management scenarios, which are 1) S-Base: the conventional handling of farm wastes, and 2) S-AD: implementation of AD as an alternative management method. S-AD included the anaerobic co-digestion of manure, sludge, feed waste, and returned dairy products. GaBi thinkstep software was used to compute the LCA of the proposed scenarios based on the international organization of standardization (ISO) 14040 guidelines, which include goal and scope definition, life cycle inventory (LCI), life-cycle impact assessment (LCIA), and interpretation (International Organization for Standardization, 2006). The following sections discuss the LCA steps that were followed in the present study. 2.1. Goal and scope definition This step defines the specific study goals, system boundaries, cut-off rules, and the functional unit, based on which the LCA results are reported. The main goal of the study was to assess the environmental impacts of AD as an alternative dairy waste

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management technique compared to current conventional practices, such as, stockpiling and fertilization, and to quantitatively evaluate the environmental footprint of each scenario from cradleto-gate. Typical operations of the Dairy Company include cow grazing at the farm, lactating units (milking parlors), milk processing and packing units, and wastewater handling units. Fig. 1 shows a schematic diagram of the Dairy Company’s operations indicating the boundaries of both scenarios. These operations are relevant to the management of manure as well as feed waste, wastewater sludge, and returned dairy products, which were effectively used as feedstock (co-substrates) for the anaerobic digester (Lateef et al., 2012; Pehme et al., 2017). The main difference between the conventional and AD scenarios is the redirection of waste streams to the anaerobic digester instead of outdoor storage. The introduction of AD did not affect the production units of the farm, cow feeding, as well as the handling of excavated sand bed. The additional processes in the AD scenario have been highlighted in Fig. 1. Cut-off criteria included the production of feed (forage), which is typically outsourced in most local farms due to the high cost and limited arable land, and construction phase, which is a short-term activity compared to the operation life of the digester. Moreover, energy and material streams within the milk processing and packing, as well as the on-site sewage treatment plant operations, were excluded, as they remained unchanged in both scenarios. Furthermore, as this LCA is focused on farm waste management, the environmental impacts of the supply food chain, including product distribution and market processes, were eliminated from the scope; such analyses can be found elsewhere (Sellitto et al., 2018). As multiple operations within the farm may vary frequently, this study considered the average values of operational parameters, such as the number of cows and manure collection efficiency, as reported by the operation engineer of the dairy farm. Parameters and emissions that were difficult to measure on-site were predicted using similar studies conducted previously. In this study, all emissions, material, and energy flows were described in terms of one ton of wet manure, which was selected as the functional unit (FU). The selected FU will facilitate the correlation with the literature and future regional studies. The FU was calculated based on a weighted average of manures generated from different types of cows. Supplementary Table A1 shows the contribution of each cow type to the FU, as reported by the Dairy Company. The cows were classified into lactating, dry, fresh, and young, according to the percentage of the total headcount, average daily manure production, and average daily collected manure. The corresponding quantities of wastewater sludge, feed waste, and returned dairy products to the FU are 3, 84.9, and 20.2 kg/FU, respectively. 2.2. Life cycle inventory The LCI step defined all inputs and outputs involved in the processes, including enteric fermentation, manure accumulation on the sand bed, outdoor storage, fertilization using stockpile and digestate, handling of returned dairy products, anaerobic decomposition in the digester, biogas combustion, transportation, and energy consumption. Multiple empirical equations and experimental results were utilized from the literature to quantify the emissions that could potentially be generated from the examined scenarios. All operational data, such as area of the barns, retention time of manure on sand bed, manure collection efficiency, and other operational data were collected from the Dairy Company. Table 2 presents the LCI of the main processes within the system boundary, as well as the type of emissions, computational method, input parameters/values, and results. The equations used to

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Fig. 1. Processes and system boundaries of the tested scenarios: a) S-Base, and b) S-AD.

compute the outputs presented in Table 2 were compiled from previous studies in the literature, and are provided in the Supplementary Equations section. The study considered the average values of operational parameters as reported by the Dairy Farm, such as the number of cows and manure collection efficiency. The detailed descriptions of the key processes are described in the following sections and are to be read together with Fig. 1 and Table 2.

2.2.1. Manure accumulation on the sand bed In the conventional scenario, all the manure produced, including the scraped manure, was placed on the sand bed. However, in the AD scenario, the scraped manure was vacuumed and transferred to a holding tank before pumping it into the digester. Due to the prolonged storage for three months, manure biodegradation on the sand bed was assumed to be mostly anaerobic.

2.2.2. Outdoor storage The outdoor storage process in the conventional scenario included stockpiling all the manure, scraped sand bed, feed waste, and sludge in an outdoor pile. In the AD scenario, sludge was transferred to the holding tank, feed waste was transported to a grinder prior to the holding tank, and the stockpile (mainly includes scraped sand beds) was maintained for three weeks before being used as fertilizer.

2.2.3. Fertilization using stockpiles In the conventional scenario, sludge and feed waste were stored with manure and scraped sand beds, whereas in the AD scenario, scraped sand beds were stored separately as the collected manure, sludge and feed waste were transferred to the holding tank. Fertilization was assumed to generate only nitrogen emissions; this is because manure remained on the sand bed for a period of four months during which all anaerobic decomposition gases were assumed to have been depleted.

2.2.4. Handling of returned dairy products According to the Dairy Company, the returned dairy products weighing five tons on average, were on a daily basis, as follows: 1) 60% crushed: liquid products were fed to the cows while crushed plastics were transported to a landfill, and 2) 40% were directly transported to the landfill without crushing. Emissions from landfilling the returned dairy products in the conventional scenario were mainly due to anaerobic biodegradation. In the AD scenario, 40% of the returned dairy products were transferred to the digester after crushing instead of being landfilled. The crushed plastics were transported to the landfill. Therefore, no emissions were generated from the returned dairy products in the AD scenario except for those in the digester.

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Table 2 Input parameters, computations, and LCA results of different processes within the system boundary. Process

Enteric fermentation

Manure accumulation on sand bed

Manure accumulation on sand bed and Outdoor storage

Outdoor storage

Fertilization using stockpile

Anaerobic decomposition in digester

Power generation using biogas

Fertilization using digestate

Method/data sources

Input parameters

Input values

Emissions/ requirement (kg/FU)

L ¼ 0.015 g CH4/cow/ CH4 FU, 4540 cows D ¼ 0.038 g CH4/cow/ FU, 2210 cows F ¼ 0.038 g CH4/cow/ FU, 620 cows Y ¼ 0.007 g CH4/cow/ FU, 1480 cows Empirical equations (Aguirre-Villegas and Larson, 2016), and - Weight of manure (W) W ¼ 1,000 kg (S-base), CH4 Farm-specific inputs for [W, A, t] - Area of barn (A) 634 kg (S-AD) - Accumulation time on A ¼ 5,600 m2 t ¼ 90 d T ¼ 34
C sand bed (t) - Average temperature (T) Empirical equations (Aguirre-Villegas and Larson, 2016), and - Weight of manure (W) W ¼ 1000 kg (S-base), N2O Experimental results (Adghim et al., 2019) for [TKN] - TKN of manure (TKN)a 634 kg (S-AD) Nleached L ¼ 10.1 g TKN/kg manure D ¼ 7.0 g TKN/kg manure F ¼ 7.0 g TKN/kg manure Y ¼ 5.4 g TKN/kg manure FW ¼ 7.8 g TKN/kg FW Empirical equations (Kafle and Chen, 2016; Rotz and Oenema, - Weight of manure (W) W ¼ 1,000 kg (S-Base), NH3 634 kg (S-AD) - Density of manure (r) 2006; Triolo et al., 2011), and Experimental results (Adghim - TAN of manure (TAN) et al., 2019) for [pH] r ¼ 1,000 kg/m3 - pH of manure (pH) TAN/TKN ¼ 0.405 - Average temperature (T) pH ¼ 8.2 T ¼ 34
C VS ¼ 160 kg VS/FU (S- CH4 Empirical equations (Aguirre-Villegas and Larson, 2016), and - VS of stockpile (VS) Experimental results (Adghim et al., 2019) for [Bo] - CH4 yield of manure Base), 32 kg VS/FU (S- NH3 AD) (Bo)b N2O - Average temperature (T) Bo ¼ 250 L CH4/kg VS T ¼ 34
C Empirical equations (Curran, 2012) Specific emissions of NH3 ¼ 1.21 g/kg NH3 different nitrogen gases manure N2O N2O ¼ 0.15 g/kg NOx manure Nleached NOx ¼ 0.03 g/kg manure Experimental results (Adghim et al., 2019) for [Bo, VS] - Methane yield of L ¼ 286 L CH4/kg VS; CH4 feedstocksb (Bo) VS ¼ 9.93% - Volatile solids of D ¼ 208 L CH4/kg VS; feedstocks (VS) VS ¼ 12.67% F ¼ 172 L CH4/kg VS; VS ¼ 9.10% Y ¼ 236 L CH4/kg VS; VS ¼ 12.24% FW ¼ 195 L CH4/kg VS; VS ¼ 64.35% RD ¼ 230 L CH4/kg VS; VS ¼ 11.34% S ¼ 198 L CH4/kg VS; VS ¼ 1.52% - CH4:CO2 ratio (R) CO2 R ¼ 1.60 Stoichiometric computationsc - Density of CO2 (r) r ¼ 1.98 kg/m3 c M1 ¼ 13.65 kg CH4/FU CO2 - CH4 combusted (M1) Stoichiometric computations - CH4:CO2 molar mass MR ¼ 16/44 ratio (MR) Empirical equations (Li et al., 2018) Specific emissions of CO ¼ 2.3 kg/m3 biogas CO NOx different combustion gases NOx ¼ 2.96 kg/m3 biogas Fugitive CH4 CH4 ¼ 9.3 kg/m3 NMVOC Energy (kWh/ biogas 3 FU) NMVOC ¼ 2.1 kg/m biogas Empirical equations (Curran, 2012) Specific emissions of NH3 ¼ 1.21 g/kg N2O different nitrogen gases digestate NH3 N2O ¼ 0.15 g/kg Nleached digestate NOx

Empirical equations (Ellis et al., 2007), and Farm-specific inputs Specific emissions for [no. of cows] different cow types a, number of cows.

Scenario SS-AD Base 0.19

0.19

8.33

5.29

0.02 2.49

0.02 0.91

0.09

0.08

0.02 1.49 0.04

0.01 0.08 0.02

1.31 0.16 0.03 9.28

0.77 0.10 0.02 7.05

N/A

13.65

N/A

27.47

N/A

65.01

N/A

0.08 0.10 0.32 0.07 85.80

N/A

0.06 0.49 0.01 0.01

(continued on next page)

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Table 2 (continued ) Process

Method/data sources

Empirical equations (Wilson et al., 2014), and Farm-specific Grinding and Transportation of feed inputs [D, DC] waste

Input parameters

-

Handling of returned dairy products

Experimental results (Adghim et al., 2019) for [Bo, VS], and Farm-specific inputs [W, D, DC,ER, Wplastic]

-

Sludge transportation

Farm-specific inputs

-

Input values

NOx ¼ 0.03 g/kg digestate Weight of disposed feed W ¼ 84.9 kg/FU E ¼ 0.11 kWh/t FW waste (W) Specific energy demand D ¼ 2.7 km DC ¼ 0.3 L/km (E) Transportation distance (D) Diesel consumption (DC) CH4 yield of dairy Bo ¼ 230 L CH4/kg VS products (Bo)b VS ¼ 11.34% of wet VS of the products (VS) sample Weight of disposed dairy W ¼ 20.2 kg/FU products (W) r ¼ 0.656 kg/m3 Density of methane (r) Fl ¼ 10% Uncollected leachate D ¼ 138.3 km (S-Base), factor (Fl) 31.6 (S-AD) DC ¼ 0.3 L/km Distance traveled (D) Diesel consumption (DC) E ¼ 0.124 kWh/kg Energy for crushing plastic/hr Wp ¼ 12 kg (S-Base), bottles (E) Weight of plastic bottles 20.2 kg (S-AD) (Wp) Weight of sludge (W) W ¼ 3 kg sludge/FU Distance traveled (D) D ¼ 2.8 km Diesel consumption (DC) DC ¼ 0.3 L/km

Emissions/ requirement (kg/FU)

Scenario SS-AD Base

Energy (kWh/ N/A 0.83 FU) Diesel

9.30 0.83

CH4 CO2 Diesel Energy (kWh/ FU)

0.01 0.01 41.94 1.49

N/A N/A 9.48 2.50

Diesel

0.84

0.84

(L), (D), (F), and (Y) stand for lactating, dry, fresh, and young cows, respectively. (FW), (RD), and (S) stand for feed waste, returned dairy products, and sludge, respectively. TAN, TKN, and VS are total ammonia nitrogen, total Kjeldahl nitrogen, and volatile solids, respectively. a Due to limited data, fresh cows were given the same specific emissions as dry cows. b The methane yield reported from lab experiments was multiplied by an upscaling factor of 80%. c Stoichiometric equations are based on the chemical equation for combustion of CH4.

2.2.5. Anaerobic decomposition in the digester The anaerobic decomposition of manure and other feedstocks in the digester produced two main outputs: biogas (mainly methane and carbon dioxide) and digestate. The synergistic effects of codigesting the dairy farm wastes were not considered. The total methane production was calculated by adding the biogas production from each cow manure type, feed waste, returned dairy products, and sludge, each with respect to its weight fraction, %VS, and methane yield. 2.2.6. Power generation using biogas The biogas produced from digester was utilized for electricity production through a CHP engine. The amount of electricity produced by the engine was assumed to be utilized within the farm to decrease the electricity demand off the grid. 2.2.7. Fertilization using digestate The digestate produced from the digester is a nitrogen-rich soil conditioner. The amount of digestate was determined based on 60% VS removal within the digester with a 30-day solids retention time (Metcalf and Eddy, 2003). Since the mass flux of feedstocks into the digester was 461.9 kg/d with 94.14 kg VS/d, the amount of digestate produced was 405.4 kg/d. As the end use of the digestate is fertilization, the released nitrogen emissions were calculated similarly to the process mentioned in subsection 2.2.3. 2.2.8. Transportation and energy consumption In LCA studies, transportation and energy consumption are often referred to as external activities. Both conventional and AD scenarios include multiple external activities, which contribute significantly to several environmental categories. To cover the life cycle of these activities, diesel production at the refinery and

energy production at the power station were accounted for and calculated based on the EcoInvent database. The energy requirements of the AD scenario were considerably higher than those of the conventional scenario, due to the additional energyconsuming elements, which are, holding tank and pump station feeding the digester. On the other hand, transportation distances were significantly higher in the conventional scenario due to landfilling of returned dairy products. All vehicles were assumed to run on diesel; in-farm transportation activities were assumed to consume 10 L diesel/h operation, while off-farm transportation was assumed to consume 0.3 L diesel/km traveled. The off-farm freight carriage (ton-kilometer) of wastes was calculated based on a oneway trip, while the on-farm distances used for diesel consumption were measured based on a two-way trip. The speed of vehicles within the farm was limited to 30 km/h, while outside the farm, it was assumed as 60 km/h. 2.3. Life cycle impact assessment In the LCIA step, the environmental impacts of the examined scenarios were assessed based on the CML methodology (Curran, 2012). CML is a predefined LCIA methodology in the GaBi software, in which various emissions within the system boundary are assigned predefined weights. For instance, the impact of 1 kg of CH4 emissions on GWP is equivalent to 25 kg CO2. Table 2 summarizes the LCI data of the examined processes/activities together with the pollutant emissions computed from the LCIA model. The processes in this study were divided into two main types: 1) internal processes, exclusively performed for dairy waste management, and 2) external processes, which include transportation and energy consumption. Both internal and external processes affected GWP, AP, EP, and POCP. On the other hand, external processes affected ozone

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depletion potential (ODP), abiotic depletion potential of elements (ADP elements), abiotic depletion potential of fossil (ADP fossil), freshwater aquatic eco-toxicity potential (FAETP), human toxicity potential (HTP), and marine aquatic eco-toxicity potential (MAETP). 2.4. Interpretation The fourth step was to interpret the results to ensure that the study goals were met. A sensitivity analysis was performed by reassessing the environmental impacts of the AD processes in response to varying selected parameters: the percentage of collected manure and the methane yield. Manure collection was selected because it could be controlled during operation; only 36.6% of manure was collected under the existing conditions, whereas the methane yield parameter was varied to simulate potential positive or negative synergistic effects of co-digesting different types of organic wastes. The analysis can be also used to investigate the propagation of potential uncertainties in the selected parameters, i.e., the impact of the parameter uncertainty on the LCA outcomes. Therefore, four additional scenarios were tested: 1) S-AD/70%/Collect: 70% of total manure produced was collected, 2) S-AD/100%/Collect: all of the produced manure was collected, 3) S-AD/þ15%/Yield: methane yield of feedstocks was increased by 15%, and 4) S-AD/-15%/Yield: methane yield of feedstocks was decreased by 15%. Additionally, the present study had two distinct features that required further investigation, namely the hot-arid climate and multiple non-manure feedstocks. Therefore, the impact of high ambient temperatures was examined by computing the environmental parameters at different temperatures ranging from 15 to 35
C. Whereas, the environmental impacts of non-manure feedstocks on the overall system were examined by varying the quantities of feed waste, returned dairy products, and sludge. The non-manure feedstocks originally constituted around 10% of the functional unit, and the environmental impacts was reassessed after increasing the quantities of those streams by 15%. 3. Results and discussion The discussion is mainly focused on the impacts of internal processes being the most relevant to dairy waste management; nonetheless, the impacts of external processes were also highlighted. The following sections are organized based on impact categories to facilitate the discussion, followed by the results of the sensitivity analysis. All values and figures in the discussion are reported per FU. 3.1. Global warming potential GWP is significantly affected by dairy waste management practices that involve large GHG emissions. The contribution of CH4, N2O, and CO2 emissions toward GWP in each scenario is presented in Fig. 2. CH4 was the highest contributor to GWP in both scenarios, forming 70% of the total GWP in S-Base and 65% of the total GWP in S-AD. CO2 emissions were significantly more than N2O and CH4, but its impact on GWP was less as the GWP indices of N2O and CH4 were 298 and 28 times more than CO2 over a 100-year time horizon, respectively. In the conventional scenario, where manure, sludge, and feed waste were stored outdoor and then used as a fertilizer, the estimated total GWP was 307 kg CO2-eq. This figure was reduced to 228 kg CO2-eq in the AD scenario, which translates to a 25.7% reduction in GWP. This reduction was mainly due to the diversion of the collected fraction of manure (36.6% of the FU) from land application to AD. As shown in Fig. 3, a majority of GWP in S-AD was

Fig. 2. Specific greenhouse gas emissions from the conventional and AD scenarios.

due to the accumulation of manure on the sand bed (67.1%). In terms of power, biogas combustion generated 75.3 kW, which was significantly higher than that required by the farm (<5 kW), resulting in an overall positive net energy system. However, the same process contributed to 9.0 kg CO2-eq, that is, 4% of the total GWP due to fugitive CH4. In S-Base, manure accumulation on the sand bed was found to be the highest contributor to GWP (69.7%), followed by fertilization using stockpile (15.8%). The GWP of the manure accumulation process was driven by CH4 and N2O emissions, and by N2O in the fertilization process. Landfilling of the returned dairy products in S-Base had a minor effect on GWP, although expired milk can produce up to 230 L CH4/ kg VS, which is close to the methane yield of manure (Fantozzi et al., 2015). The GWP of landfilling, excluding the associated transportation, was less than 1% of the total GWP in all scenarios. When transportation of the returned dairy products was considered, the overall contribution summed up to 6.5% of the total GWP in S-Base, and less than 2% in S-AD. Another low contributing process was enteric fermentation with 1.5 and 2.4% of the total GWP in S-Base and S-AD scenarios, respectively. Its higher contribution in S-AD can be attributed to lower emissions from the overall system, while enteric emissions remained unchanged. The extended outdoor storage did not affect the GWP significantly as the CH4 emissions were already exhausted while accumulating on the sand bed. However, some of the nitrogen was oxidized forming N2O during outdoor storage, resulting in 10.2 kg CO2-eq in S-Base and 4.8 kg CO2-eq in S-AD. The overall results highlight the most critical processes affecting the GWP. Such negative impacts could be mitigated by preventing anaerobic conditions by frequently mixing the sand bed or reducing the duration of manure accumulation. Since those processes were also practiced in S-AD for the uncollected manure (63.4% of total produced manure), the overall reduction in GWP was in line with other similar studies in the literature including Battini et al. (2014) and Pehme et al. (2017), who obtained around 20e40% reduction in GWP by applying AD.

3.2. Acidification potential AP is affected by several processes, particularly those involving NH3, NOx, and SO2 emissions. In both S-Base and S-AD scenarios, such emissions were present when the organic wastes were handled outdoor during fertilization, land application, or accumulation of manure on the sand bed. Although both scenarios shared the same processes affecting acidification, AP of S-AD was around 50% lower than that of S-Base, due to reduction in the amount of organics handled in the open air, which reduced the associated

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Fig. 3. Global warming potential of the conventional and AD scenarios.

emissions. As shown in Fig. 4, the total AP in S-Base was 4.75 kg SO2-eq, and the main contributing processes were outdoor storage (50.1%) and fertilization using stockpile (44.6%). In the S-AD scenario, outdoor storage and fertilization using stored manure resulted in 57% of the total AP. This difference was due to the diversion of the collected manure to the digester instead of using it as fertilizer. Moreover, the mass of manure in S-AD was reduced as 60% of the volatile solids were converted to biogas, consequentially reducing the amount of digestate used for fertilization. AP originating from the handling of the returned dairy products processes was negligible due to low nitrogen emissions. The overall AP of these processes contributed to only 1.6 and 0.3% of the total AP in S-Base and S-AD, respectively. This impact was due to the transportation and energy consumed for crushing the products rather than landfilling. Similar studies, such as Battini et al. (2014) and Pehme et al. (2017), had shown that AP in the AD scenarios increased due to the addition of other organic matter with high nitrogen content. However, these studies did not consider the

handling of the organic matter in their baseline scenarios. On the other hand, the AP results of the present study are in agreement with the findings of Li et al. (2018) who found that AP is reduced whenever AD was used instead of composting, storage, or land application. 3.3. Eutrophication potential EP is caused by nutrients loadings of mainly nitrogenous and phosphorus compounds, in soil, water, or air. The emissions of nutrients within the system boundary were mostly due to the processes of manure accumulation on the sand bed and fertilization. AD outperformed the conventional scenario by demonstrating a lower EP risk. As shown in Fig. 5, the overall EP of S-Base was 8.5 kg PO4-eq, mainly produced from fertilization using stockpile (52.1%) and manure accumulation on the sand bed (41.7%). The overall EP of S-AD was 7.0 kg PO4-eq, which was 18.1% lower than that of S-Base. The most significant processes contributing to EP in

Fig. 4. Acidification potential of the conventional and AD scenarios.

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Fig. 5. Eutrophication potential of the conventional and AD scenarios.

S-AD were manure accumulation on the sand bed (43.2%), fertilization using stockpile (32.4%), and fertilization using digestate (23.4%). The AP and EP results of the present study differed from other studies in the literature (Battini et al., 2014; Pehme et al., 2017). However, these literature studies did not consider the handling of sewage sludge, feed waste, and returned dairy products in their baseline scenarios, though they were included as cosubstrates or inocula in the AD scenario.

in S-Base resulted in 22.4% of the total POCP, which was mainly due to the long-haul distance between the farm and disposal site. Since this trip no longer existed in S-AD, the contribution of returned dairy products handling processes was reduced by 86.2%. Power generation from biogas contributed to 27.7% of the overall POCP of S-AD due to the combustion emissions.

3.4. Photochemical ozone creation potential

External processes mainly refer to transportation and electricity generation. Energy demand was computed as electricity produced from the combustion of natural gas, while the inputs for the transportation processes were the distance traveled by the freight cargos (ton-kilometer) and the fuel consumption. The conventional scenario involved more transportation activities with longer travel distances than the AD scenario. The main contributing transportation process in S-Base, which was the transportation of returned dairy products to the disposal site, was omitted in S-AD.

POCP evaluates the potential of forming ozone at the ground level due to the emissions of VOC, CH4, CO, and SO2. Fig. 6 shows the distribution of POCP in each scenario. The net POCP result for SBase was 0.077 kg ethene-eq and for S-AD was 0.065 kg ethene-eq. The process that contributed the most to POCP was the accumulation of manure on the sand bed, with 64.9 and 49.2% in S-Base and S-AD, respectively. The returned dairy products handling processes

3.5. Impacts of external processes

Fig. 6. Photochemical ozone creation potential of the conventional and AD scenarios.

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The two categories that were severely impacted by the external processes were MAETP and ADP fossil. The reduction in transportation activities had significantly reduced the MAETP from 3520 kg DCB-eq in S-Base to 1690 kg DCB-eq in S-AD. Moreover, the ADP fossil decreased from 3120 MJ in the conventional scenario to 1580 MJ in the AD scenario; these reductions translate to 52 and 46% of the MAETP and ADP fossil, respectively. S-Base included only one process that required energy, that is crushing of returned dairy products. On the other hand, S-AD required more energy for operation even though it was a net positive energy scenario. Impacts from electricity generation in S-AD were significantly higher than those in S-Base, and the AD scenario remained favorable due to the overall reduced emissions. The reduced impacts of external processes due to AD on other environmental categories, such as ODP, ADP elements, FAETP, and HTP, were also around 50%. 3.6. Sensitivity analysis The definitions of the sensitivity analysis scenarios were outlined in section 2.4. Table 3 summarizes the LCA results for S-Base and S-AD in comparison with additional sensitivity analysis scenarios. All environmental assessment parameters in the S-AD/70%/ Collect and S-AD/100%/Collect scenarios were enhanced compared to S-AD. Increasing the manure collection efficiency (up to 70 and 100%) significantly reduced the impacts of manure accumulation, outdoor storage, and fertilization. The GWP of S-AD/70%/Collect and S-AD/100%/Collect were 159 and 97 kg CO2-eq, respectively, which translated to a 48.2 and 68.4% reduction as compared to SBase. Moreover, AP and EP were reduced by the biochemical conversion in the digester, thus reducing the emissions produced during fertilization. Compared to S-Base, treating 70 and 100% of the produced manure in the digester led to a decrease in AP by 53 and 56%, respectively, as well as in EP by 36.8 and 54.1%, respectively. While improving the manure collection process significantly increased the biogas production, the 70% and 100% collection scenarios decreased the net POCP by 26.7e43.5%, compared to S-Base. This is because the major contributor to POCP was the methane emissions from manure accumulation on the sand bed, which was significantly reduced in S-AD/70%/Collect and completely omitted in S-AD/100%/Collect. Varying the methane yield as a simulation of different synergistic effects in the digester did not cause significant variations from S-AD. GWP for S-AD/-15%/Yield was 225 kg CO2-eq/FU and for S-AD-/þ15%/Yield, it was 230 kg CO2-eq/FU. This minor change was mainly due to the fugitive CH4 from the digesters. Moreover, CO2 emissions from the digesters were considered to be from a biogenic source that would not have the same impact as that emitted from fossil fuel combustion. Both AP and EP have not changed from the S-AD scenario as the oxidation of CH4 did not significantly vary the emissions of NH3, NOX, SO2, or the nitrogenous and phosphorus compounds. Conversely, increasing the methane yield in S-AD/ þ15%/Yield increased the POCP to 0.068 kg ethane-eq (12.5% less than S-Base) due to higher biogas production, whereas it decreased to 0.062 kg ethane-eq in S-AD/-15%/Yield (19.6% less than S-Base).

In general, higher methane yield translated to more energy production, and the associated environmental impacts were not significant. Optimizing the digester to produce more methane reduced the demand of energy from fossil fuel, thereby reducing the indirect emissions of CO2 from fossil fuel combustion. It is important to mention that all the reported GWP results in this study were from direct emissions, and carbon emissions savings were not included. Nevertheless, the energy produced from the S-AD, AD/þ15%/Yield, AD/-15%/Yield, AD/70%/Collect, and AD/100%/Collect can reduce CO2 emissions from fossil fuels by 16.7, 19.2, 14.2, 30.5, and 24.0 kg CO2-eq, respectively. The external processes in all the modified AD scenarios, except S-AD/100%/Collect, did not show significant variations in their environmental impacts as compared to S-AD. This is because, in SAD/100%/Collect, all manure was internally transferred to the digester, which led to significantly less transportation; MAETP was reduced by 72% and ADP fossil was reduced by 60%, compared to SBase. In order to assess the effect of non-manure feedstocks on the environmental impacts of the overall system, a sensitivity analysis was conducted on the quantities of feed waste, returned dairy products, and sludge. The results showed that a 15% increase in the non-manure fractions of the overall feedstock could increase the GWP, AP, EP, POCP, MAETP, and ADP fossil by 1.0, 5.0, 3.5, 0.6, 1.8, and 2.4%, respectively. This insignificant impact emphasizes that the main polluting processes are the manure-related ones, specifically the uncontrolled outdoor activities. To compare the environmental impact of dairy farm activities at high and low ambient temperatures, the LCA model of the AD scenario was reassessed at different temperatures between 15 and 35
C, which covered the range reported in the literature (Fig. 7). Overall, the corresponding effect on all impact categories was found to be proportional to temperature. However, the impact was more noticeable on GWP and POCP, while the AP and EP were reduced by a maximum of 9.63 and 0.65%, respectively between 35 and 15
C. As shown in Fig. 7, the GWP and POCP at 15
C were 49.34 and 38.03% lower than those at 35
C. These major reductions occurred due to the temperature-driven drops in emissions produced from uncontrolled outdoor activities, particularly manure accumulation and outdoor storage. 3.7. Limitations and implications The specific considerations, limitations, and implications of the present study are summarized as follows:  In order to facilitate comparative assessments, the outcomes of the present LCA were reported in terms of a general FU (equivalent ton of manure), and the internal/external processes were detailed and individually analyzed. It should be emphasized that LCA studies are typically compared qualitatively due to specific conditions of the analysis.  The present LCA study is unique in terms of the large size of the dairy farm, multiple feedstock streams involved in the analysis,

Table 3 Summary of key findings for the tested scenarios. Scenario*

Base

AD

AD/þ15%/Yield

AD/-15%/Yield

AD/70%/Collect

AD/100%/Collect

GWP (kg CO2-eq/FU) AP (kg SO2-eq/FU) EP (kg PO4-eq/FU) POCP (kg ethane-eq/FU) MAETP (kg DCB-eq/FU) ADP Fossil (MJ/FU)

307 4.75 8.50 0.077 3520 3120

228.00 2.40 6.96 0.065 1690 1680

230.00 2.40 6.96 0.068 1700 1690

225.00 2.39 6.96 0.062 1690 1660

159.00 2.21 5.37 0.057 1760 1770

96.90 2.08 3.90 0.044 986 1250

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Fig. 7. GWP and POCP impacts under different ambient temperatures.









and high ambient temperature of the study area. These distinct factors have affected the results at different degrees, which was indicated through a comparison with the literature or the sensitivity analysis. Despite an increase in number of cows, the main environmental impacts of the analyzed scenarios remained comparable to those of smaller-scale studies. This is mainly because the computations, compiled from the literature and other wellestablished databases that are typically used in all LCA studies, do not take the scale of the process into account as process parameters are normalized to the functional unit. Moreover, emissions from the construction phase, which was excluded from the scope of this study and similar previous studies, could have resulted in different impacts between small- and largescale farms. Although the contribution of waste streams and processes relevant to the co-digested feedstocks to the overall environmental impacts were noticeable, it was much less significant than that of manure. This suggests that while inclusion of nonmanure feedstocks is more representative, it would not play a major role in the generated emissions, and hence less effort should be made to reduce their specific impacts. This study highlighted the processes that had the highest environmental impacts, namely manure accumulation on sand beds and outdoor storage. These hotspots can be significantly mitigated by increasing the frequency and efficiency of manure collection. Improving manure collection is possible by increasing the time during which the cows spend on hardscapes where manure can be effectively collected, or by screening the sand bed to adequately separate manure from sand. Alternatively, the emissions from the outdoor storage of manure can be mitigated through controlled indoor storage where air filtering and leachate collection systems can be installed. High ambient temperature at which the dairy farm was operated affects the manure characteristics due to heat stress on the cows. Elevated temperatures increased the emissions of the outdoor processes, leading to higher environmental impacts compared to colder climates.

The present study was conducted in the UAE as a representative of the climatic conditions and conventional practices in the Middle East. The AD technology is rarely implemented in the region due to the additional capital and operating costs involved, compared to the low-cost traditional procedures. The Middle Eastern countries are not obliged to cut their carbon emissions, and therefore there is no urgency at the private or governmental levels to bear the cost of

high-tech eco-friendly management techniques. A local lifecycle costing study can assess if revenues from electricity generation and digestate sales can overcome the high capital and operating costs of AD system. In addition to financial aspects, the regional application of AD systems in dairy waste management must be backed by a comprehensive legislative framework, particularly for the utilization and trading of the produced energy and digestate. 4. Conclusion This study compared the environmental impact of a proposed AD-based dairy waste management system to that of the existing conventional practice. The assessment took place in a large-scale dairy farm by treating multiple feedstocks under high ambient temperature. The results of the life cycle assessment showed that the AD scenario had outperformed the conventional scenario in all the examined environmental impact categories. The reduction in GWP, AP, EP, and POCP due to AD were computed as 25.7, 49.5, 18.1, and 16.1%, respectively. Manure accumulation on the sand bed was the highest contributor to GWP and POCP in both scenarios, due to the excessive amount of CH4 emissions. In the conventional scenario, the processes that contributed the most to AP was the outdoor storage of manure, followed by fertilization using stockpile. In the AD scenario, the processes that contributed the most to AP were fertilization using stockpile and digestate. The impacts on MAETP and ADP fossil by the external processes of the AD scenario, that is, transportation and energy generation, were 52.1 and 49.2% lower than those of the conventional scenarios. A sensitivity analysis was conducted to evaluate the effect of methane yield or manure collection efficiency on the findings. It was found that varying the methane yield did not affect AP and EP as compared to S-AD, whereas varying the methane yield had a minor effect on GWP and POCP. The collection efficiency was inversely proportional to all impact categories. Overall, this life cycle assessment proved that under the tested local conditions, particularly high ambient temperature and co-digestion of manure with other farm wastes, AD can be considered an environmentally favorable alternative to the current dairy waste management practices in large-scale farms. 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.

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