Hybrid life cycle assessment of agro-industrial wastewater valorisation

Journal Pre-proof Hybrid life cycle assessment of agro-industrial wastewater valorisation Wenhao Chen, Thomas L. Oldfield, Sotiris I. Patsios, Nicholas M. Holden PII:

S0043-1354(19)31049-8

DOI:

https://doi.org/10.1016/j.watres.2019.115275

Reference:

WR 115275

To appear in:

Water Research

Received Date: 19 August 2019 Revised Date:

28 October 2019

Accepted Date: 3 November 2019

Please cite this article as: Chen, W., Oldfield, T.L., Patsios, S.I., Holden, N.M., Hybrid life cycle assessment of agro-industrial wastewater valorisation, Water Research (2019), doi: https:// doi.org/10.1016/j.watres.2019.115275. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Conventional WWTP

Sludge

Wastewater

Water

AgroCycle WWTP

Wastewater

Sludge

Water

Single cell protein

1 2

Hybrid life cycle assessment of agro-industrial wastewater valorisation Wenhao Chen1, Thomas L. Oldfield1, Sotiris I. Patsios2, Nicholas M. Holden1

3 4

1

UCD School of Biosystems and Food Engineering, Belfield, Dublin 4, Ireland

5

2

Laboratory of Natural Resources and Renewable Energies, Chemical Process and Energy Resources Institute,

6

Centre for Research and Technology Hellas, Thermi, GR570 01 Thessaloniki, Greece

7 8

Corresponding Email: [email protected]

9 10 11

First hybrid life cycle assessment model for wastewater treatment plant with nutrient

12

valorisation.

13

Gross value added, employment and climate change of wastewater treatment plant were

14

evaluated.

15

Upstream impacts of wastewater have significant influence on sustainability of treatment

16

plant.

17

Novel SCP technology could improve the environmental impact of wastewater treatment

18

plant.

19

Development is required to improve the social-economic impacts of wastewater treatment

20

plant.

21 22 23 24 25 26 27 28 29

1

30 31 32

Abstract:

33

Valorising sugar-rich agro-industrial wastewater may have significant impacts on sustainability

34

of wastewater treatment plant (WWTP). The objective of this study is to evaluate the

35

environmental, economic and social impacts of a novel wastewater valorisation technology. This

36

technology is designed to produce single cell protein (SCP) from wastewater of a fruit juice

37

processing facility. To evaluate the comprehensive sustainability impacts on WWTP and overall

38

background economy, a hybrid life cycle assessment model was developed by combining the

39

multi-regional input-output database (Exiobase) with process-based life cycle inventories of

40

conventional and AgroCycle WWTP. The results indicated the upstream impacts of wastewater

41

could have significant influence on sustainability of WWTP with nutrient valorisation. Therefore

42

the ‘zero burden assumption’ should not be adopted for upstream wastewater. For the

43

sustainability performance, valorising nutrients from WWTP with AgroCycle technology can

44

improve the environmental performance of WWTP. However, the positive social-economic

45

impacts were directly associated with WWTP system, not the whole background economy. The

46

production of SCP could reduce the Gross Value Added (GVA) and employment in the ‘oil

47

seeds sector’. In order to improve the social-economic impacts and promote a circular

48

bioeconomy model in the fruit juice sector, further development is required to improve

49

valorisation productivity and create a better value chain for valorised products.

Wastewater from food processing facilities can have high nutrient valorisation potential.

50 51

Key words: Circular economy, Food waste, Hybrid life cycle assessment, Nutrient valorisation,

52

Sustainability

53 54 2

55

1. Introduction

56 57

Wastewater treatment plants (WWTP) are used to treat municipal and industrial sewage

58

produced by human activities (Wagner et al., 2002). Although WWTP can purify water and

59

reduce the negative environmental impacts of wastewater, they still have adverse environmental

60

impact, such as high energy consumption (McCarty et al., 2011), additional sludge treatment

61

(Yoshida et al., 2013) and spread of antibiotic resistant bacteria (Rizzo et al., 2013). As

62

wastewater contains potentially valuable nutrient resources, research is focusing on extracting

63

value from the treatment process, e.g. to recover nutrients directly (Ward et al., 2018), to

64

generate energy (Shen et al., 2019) and to use the nutrients to support further growth of either

65

crops (Vazquez-Montiel et al., 1995) or more recently algae (Wang et al., 2019). It is necessary

66

to better understand how different options to circularise and extend the use of resources in

67

wastewater can influence the environmental, social and economic impacts of WWTP. This

68

research focuses on how introducing a technology to produce single cell protein (SCP) from the

69

wastewater would influence these impacts of wastewater treatment for a fruit processing facility.

70 71

Life cycle assessment (LCA) is a method that should account for environmental impacts

72

associated with the life cycle (e.g. cradle-to-grave) of a product, process or service (ISO, 2006a).

73

It can be used to make decisions about a system, and to improve system design and operational

74

efficiency (Cocco et al., 2014; Kjaer et al., 2016). LCA has been widely used to quantify the

75

environmental impacts of WWTP (Corominas et al., 2013a; Zang et al., 2015) and downstream

76

activities, such as sludge management (Yoshida et al., 2013). Most wastewater LCA studies have

77

focused on quantifying the environmental impacts of different wastewater treatment systems

78

(Gallego et al., 2008; Hospido et al., 2004; Risch et al., 2015), or investigating management 3

79

strategies and alternative scenarios (Hospido et al., 2012; McCarty et al., 2011; O'Connor et al.,

80

2014; Thibodeau et al., 2014). There have also been studies that have reported the effect of

81

methodological choices including, functional unit and assumed life span (Foley, J.M. et al., 2010;

82

Harder et al., 2014; Rodriguez-Garcia et al., 2011), impact methods (Renou et al., 2008) and

83

regionalization (Hernández-Padilla et al., 2017). Environmental and economic interactions have

84

also been studied (Theregowda et al., 2016; Thibodeau et al., 2014). The importance of data

85

collection and system boundary has been emphasised by Foley, J. et al. (2010) and Yoshida et al.

86

(2014), with Corominas et al. (2013a) reporting that few studies included the construction phase,

87

which can account for up to 20% of total impacts (Morera et al., 2017; Ortiz et al., 2007; Singh

88

and Kansal, 2018). The system boundary is also a critical decision, with two specific issues to

89

consider: (1) assumptions about the types of replaced activities (e.g. energy or nutrient supply)

90

(Lundin et al., 2000; Vergara et al., 2011), and (2) inclusion of the upstream impact of

91

wastewater. Currently, WWTP LCAs fall into two groups. Most focus on water treatment to

92

improve water quality as an end-of-pipe solution to a waste problem (Foley, J. et al., 2010;

93

Gallego et al., 2008; Hospido et al., 2004; Risch et al., 2015), but some are more interested in the

94

valorisation such as nutrient and energy recovery (Corominas et al., 2013b; Ontiveros and

95

Campanella, 2013; Theregowda et al., 2016). Almost all wastewater LCA studies have adopted

96

the ‘zero burden assumption’ (Ekvall et al., 2007; Schrijvers et al., 2016), which excludes the

97

upstream impact of waste from the waste treatment system boundary (Pradel et al., 2016). Recent

98

studies questioned this approach as the ‘waste’ was derived from technosphere and is assumed to

99

be used as an input remaining in the technosphere (Cleary, 2010; Oldfield and Holden, 2014).

100

This issue is important when considering the value of a technology in the context of circular

101

economy (Ilic et al., 2018). For this work, the upstream environmental impacts have been

4

102

included as the valorised resource could not exist without the original activity having occurred.

103

To reduce the complexity and uncertainty of allocating upstream burden, the source of

104

wastewater needs to be specified and classified.

105 106

Hybrid life cycle assessment (Crawford et al., 2018) was used for capturing the comprehensive

107

impacts of the product supply chains, including in the context of a circular economy (Genovese

108

et al., 2017; Pagotto and Halog, 2015; Reynolds et al., 2015). This approach combines specific

109

data from process-based description of the foreground engineering system with economic value

110

based information from economic input-output database for the background system (Suh and

111

Huppes, 2005). Hybrid LCA can account the impacts of the entire supply chain to avoid

112

truncation error (Suh et al., 2004). Lin (2009) used a waste input–output model (WIO)

113

(Nakamura and Kondo, 2002) to develop a hybrid LCA for WWTP and Stokes and Horvath

114

(2010) also used hybrid LCA to investigate the environmental impact of biogas generation from

115

wastewater. Hybrid LCA has also been applied downstream for sewerage sludge treatment

116

(Ashley et al., 2008) and sewer overflow (De Sousa et al. (2012). The research presented here

117

goes beyond previous WWTP hybrid LCA studies. Since it evaluates the environmental, social

118

and economic impacts of a WWTP with a valorisation step (e.g. SCP production), in the context

119

of Tier 1 life cycle sustainability impact assessment (Chen and Holden, 2018) for a technology at

120

Technology Readiness Level (TRL) 4 (validated in the laboratory) (Mankins, 1995). In addition,

121

no hybrid LCA of WWTP with nutrient valorisation has been found in the literature.

122 123

The objective of this study was to evaluate, in the context of the most important impacts, the

124

consequences of introducing a nutrient (e.g. sugars and simple carbohydrates) valorisation

5

125

technology (e.g. SCP production) in the wastewater treatment process of a fruit processing

126

factory assumed to be in Italy, a major fruit producing country. As the technology was TRL 4, a

127

Tier 1 assessment considered climate change (the preeminent environmental consideration of the

128

time), gross value added (a global and general indicator of contribution to economy) and

129

employment (a key measure of contribution to society). This high-level assessment will indicate

130

key issues before advancing the technology to higher TRL status, and before investing

131

considerable resource for calculating Tier 2 or comprehensive Tier 3 impacts from the inventory

132

collected.

133 134

2. Methods

135 136

The method is structured as follow: (1) description of conventional and AgroCycle wastewater

137

treatment plants; (2) define wastewater treatment scenarios considering upstream impacts in the

138

conventional and AgroCycle systems; the input and output in each scenario was calculated; and

139

(3) introduction of IO analysis and hybridisation of process information with Exiobase.

140 141

2.1 Description of the wastewater treatment plant and processes

142 143

According to the market report by the European Fruit Juice Association, Italy is the fifth largest

144

fruit juice producer in Europe (European Fruit Juice Association, 2019). Based on available data,

145

the WWTP was assumed to treat wastewater from a fruit juice factory in Italy. The treated water

146

will reach the necessary environmental standard and will be discharged to the local river or will

147

be used for irrigation (Barbagallo et al., 2001). For this study, it was assumed that the WWTP is

148

owned and managed by the fruit juice producer and is a necessary part of the facility operation.

149

The owner does not receive any financial income for treating the wastewater and must meet all

150

costs associated with WWTP operation. The juice factory uses three types of fruit (peach, apple, 6

151

kiwi) to produce juice. The annual processing capacity of peach, apple and kiwi were taken as

152

50,000 tonnes, 10,000 tonnes and 3,000 tonnes, respectively. The annual wastewater for

153

processing these fruits is 1,008,000 m3.

154 155

To evaluate the change in impacts of valorising nutrient from the fruit juice wastewater,

156

AgroCycle WWTP (Figure 1) and conventional WWTP (Figure 2) were modelled in this study.

157

The AgroCycle WWTP is a hypothetical system to adopt a nutrient recovery/valorisation

158

technology to produce single cell protein (SCP) (Figure 1). The technology has been tested at

159

pilot scale and these data used to develop a prospective LCA model for full scale deployment. In

160

this case, 10% of wastewater from specific points in the fruit juice facility was assumed to be

161

directed to the SCP production process. The effluent after SCP then combines with the remaining

162

90% of wastewater to enter anaerobic sequencing batch reactor (anaerobic SBR) and aerobic

163

membrane bioreactor (aerobic MBR) processes. The biogas generated from the anaerobic SBR

164

stage was used as thermal energy for the anaerobic SBR. Electricity is also provided in anaerobic

165

SBR process to meet the extra demand of power and thermal in the WWTP facility. The material

166

and energy input per functional unit in the foreground system were derived from the pilot scale

167

study. The conventional WWTP represents the current wastewater treatment technology used in

168

fruit juice factory (Figure 2). The data was collected from a business-as-usual WWTP. Both

169

AgroCycle and conventional WWTP would process 1,008,000 m3 wastewater annually. The two

170

WWTPs have the same capacity, which is defined by the daily wastewater flow rate. However,

171

due to the duration of the fruit harvest season, fruit juice factory normally operates for approx.

172

six months (e.g. mid-May to mid-November) in a year. Therefore, the nominal capacity of each

173

plant is 2,016,000 m3/year. The functional unit in this study was 1 m3 wastewater treated by the

174

WWTP facility working at half capacity. The assumed life span for both WWTP was 20 years 7

175

(See Supplementary Information for table of assumptions).

8

176

Figure.1 The foreground system in AgroCycle WWTP

177 9

178 179

Figure.2 The foreground system in conventional WWTP

180

10

181

2.2 Nutrient valorisation scenarios

182 183

In many published LCA studies for WWTP (Foley, J. et al., 2010; Gallego-Schmid and Tarpani,

184

2019; Rodriguez-Garcia et al., 2011), the zero-burden assumption was adopted to avoid having

185

to deal with the complexity of wastewater sources. Most of these studies focused on the impacts

186

of municipal WWTP, for which it is difficult to allocate the upstream impacts, even though some

187

have included nutrient recovery (Corominas et al., 2013b; Guven et al., 2018; Rodriguez-Garcia

188

et al., 2014). In addition, the goals were generally defined in terms of an end-of-pipe solution to

189

a problem, rather than as a valorisation opportunity. However, the commissioning stakeholder

190

perspective for this study was to use a known and quantifiable wastewater as a raw material to

191

create a valuable substance, therefore the zero-burden assumption was neither necessary nor

192

tenable. To investigate the effect of upstream wastewater, the impact of fruit production needs to

193

be accounted in wastewater. A 1% cut-off rule is generally adopted to exclude the impact of

194

small inputs (Bonamente et al., 2014; Humbert et al., 2009; Shen et al., 2010). Therefore, we

195

used the 1% as allocation factor cut-off for upstream of wastewater. If including the upstream

196

impact of wastewater with only 1% of its impacts allocated to WWTP had a significant impact

197

on results, then the upstream input of wastewater need to be included and the zero-burden

198

assumption was not valid in agro-industrial food wastewater valorisation. Four scenarios were

199

modelled with uncertainty around allocation: (1). AgroCycle WWTP without upstream impact

200

(Agro); (2) Conventional WWTP without upstream impact (Conv); (3) AgroCycle WWTP with

201

1% upstream impact allocated to wastewater (Agro-U); and (4) Conventional WWTP with 1%

202

upstream impact allocated to wastewater (Conv-U). The elementary inputs per functional unit for

203

scenarios Agro and Conv (Table 1) were used for Agro-U and Conv-U, with the addition of 1 %

11

204

of the impacts of fruit juice production allocated to wastewater. This was achieved by creating an

205

input demand from the ‘food product nec’ sector in Exiobase (see section 2.3).

206 207

According to the annual wastewater processing capacity, the material and energy consumption

208

for construction of both WWTPs were estimated from Morera et al. (2017) and Risch et al.

209

(2015). The construction inventory included the inputs demand for pumping, pre-treatment, all

210

primary treatments, secondary treatment, sludge treatment, water and electricity network in the

211

wastewater treatment plant.

212 213

Table.1 Inputs demand per m3 wastewater treated in AgroCycle and conventional WWTP without

214

upstream impact scenarios Agro Process stage

Input items

Unit

Quantity

Value(€)

Quantity

Value(€)

Concrete

kg/m

3

0.0063

0.0002

0.0063

0.0002

Sand

kg/m3

0.0338

0.0002

0.0338

0.0002

3

0.4015

0.0024

0.4015

0.0024

Conglomerates and bricks

3

Kg/m

0.0083

0.0010

0.0083

0.0010

Reinforcing steel

kg/m3

0.0005

0.0002

0.0005

0.0002

3

0.0032

0.0001

0.0032

0.0001

3

0.0001

0.0006

0.0001

0.0006

0.0001

0.0002

0.0001

0.0002

0.0016

0.0007

0.0016

0.0007

100

N/A

N/A

N/A

Gravel Construction of civil structure

kg/m

Energy consumption

MJ/m

Plastic consumption

kg/m

Other metal consumption

kg/m3 a

Transport for construction material b

Pre-treatment

Tkm/m 3

kg/m

WW for c WWT

kg/m3

900

N/A

1000

N/A

kwh/m

3

0.21

0.0010

0.23

0.0493

kwh/m

3

0.0110

0.0024

0.085

0.0182

0.0020

0.0035

0.01

0.0175

Tkm/m3

0.0925

0.0500

0.6882

0.3720

NAOH

kg/m3

0.3000

0.1056

N/A

N/A

Water

kg/m3

1.5000

0.0019

N/A

N/A

Electricity for thermal energy

MJ/m3

13.8960

0.2822

N/A

N/A

Electricity Polyacrylate d

Anaerobic SBR

3

WW for SCP

Electricity

Sludge treatment

Conv

kg/m

Transport and handle sludge

12

3

Electricity

Aerobic MBR

SCP production

Secondary treatment Post-treatment

kwh/m3

0.2500

0.0536

N/A

N/A

NAOCL

kg/m3

0.0003

0.0003

N/A

N/A

Water

kg/m3

0.3000

0.0004

N/A

N/A

kwh/m3

0.9900

0.2121

N/A

N/A

Electricity

2

Membrane

m /m3

0.0025

0.1500

N/A

N/A

NH4CL

kg/m3

0.0600

0.0079

N/A

N/A

KH2PO4

kg/m3

0.0500

0.0440

N/A

N/A

MgCl2

kg/m3

0.0220

0.0039

N/A

N/A

Electricity

kwh/m3

0.8500

0.1821

N/A

N/A

Electricity for recovery

kwh/m3

0.2500

0.0536

N/A

N/A

3.9600

0.0804

N/A

N/A

3

Electricity for drying

MJ/m

Electricity

kwh/m3

N/A

N/A

2.58

0.5526

Water

kg/m3

N/A

N/A

0.3

0.0004

NAOCL

kg/m3

N/A

N/A

0.0003

0.0003

Water for irrigation

kg/m3

989.6

N/A

982.2

N/A

Sludge

kg/m3

2.5

0.05

18.6

0.37

SCP

kg/m3

2

0.88

N/A

N/A

Output items

215

a: Tkm=tonne kilometre b: SCP=single cell protein c: WWT=wastewater treatment d: sludge

216

from WWTP send and treated in nearby sludge facility.

217 218

The chemical, water and energy inputs for all operation stages were measured in situ with the

219

cooperation of the fruit juice factory. The sludge management in the WWTP comprised primary

220

treatment (thickening and drying bed) and the dried sludge was sent to a specialised sludge

221

treatment facility for further treatment (e.g. composting as fertilizer). Therefore, the owner

222

needed to pay for sludge transport and treatment. In this study, it was regarded as a service input

223

for the WWTP operation.

224 225

For implementing the AgroCycle WWTP, a specialized PVDF (polyvinylidene fluoride) and

13

226

polyester membrane (PSH-1650, Koch Membrane Systems) with a life span of 10 year was used,

227

and the membrane was assumed to be replaced during the whole operation time of WWTP (See

228

Supplementary Information for the characteristics of this membrane). Based on the measured

229

dimensions of PVDF and polyester fibre, the volume of PVDF and polyester layer was

230

calculated. The mass was estimated based on the typical density of these two materials. The

231

share of PVDF and polyester accounts approximately 50% of total weight. The rest of the

232

material was estimated from Ioannou-Ttofa et al. (2016). The metallic frame was assumed to

233

account for 35% of total weight and 5% each for other plastics (Table 2). The other minor

234

equipment in WWTP was not included in this study. According to Morera et al. (2017),

235

equipment normally represents only a small share of total impact in WWTP, especially for

236

impact of climate change.

237 238

Table.2 Main components of membrane per m3 wastewater treated in AgroCycle WWTPs Input items

239

Unit

Quantity

Value (€)

Reference

PVDF

kg/m3

0.0004

0.0036

Koch report

Polyester

kg/m3

0.0007

0.0037

Koch report

Stainless steel

kg/m3

0.0008

0.0003

Ioannou-Ttofa et al. (2016)

a

ABS

kg/m3

0.0001

0.0011

Ioannou-Ttofa et al. (2016)

b

PVC

kg/m3

0.0001

0.0002

Ioannou-Ttofa et al. (2016)

c

PE

kg/m3

0.0001

0.0002

Ioannou-Ttofa et al. (2016)

a: ABS= Acrylonitrile butadiene styrene b: PVC= Polyvinyl Chloride c: PE= Polyethylene

240 241

The economic information for the foreground WWTP systems (Table 1 and Table 2) and unit

242

price data (Table 3) were mapped to the corresponding sectors in Exiobase (see section 2.3). The

243

value of energy (electricity and thermal energy) and aluminium in Italy was taken from Statista

244

statistics. The diesel price was from energy report by European Commission. Based on personal 14

245

communication with an expert in logistic, the cost of fuel accounting was assumed to be 20% of

246

transport expenditure. The price of steel was estimated from a steel benchmark report

247

(SteelBenchmarker, 2019) and the historical price of steel from trade organization. The unit price

248

for concrete, brick and sludge management was estimated from survey and communication. The

249

cost of rubber, plastic products and raw plastic material was estimated from the website (Plastics

250

Insight). The average water price in Italy was taken from OECD database (OECD). The average

251

cost of NH4CL and other chemicals was derived from various traders and wholesalers on Alibaba.

252

The shares of chemical cost in total cost of AgroCycle and conventional WWTPs range from

253

11% to 2%. Based on communication with experts in WWTP and report by Tianjiao et al. (2014),

254

the construction cost for the specific scale WWTP in this study was assumed to be €1M. The cost

255

of civil work was calculated by deducting material cost from total construction cost. The

256

conventional WWTPs is net input without income, but the AgroCycle WWTP can create

257

economic income by selling SCP. The SCP was assumed to displace soybean derived animal

258

feed. According to average soybean price from Eurostat and SCP cost on Alibaba, the selling

259

price for SCP from WWTP was assumed to be €0.44/kg.

260 261

Table.3 References of cost information and corresponding sectors in Exiobase Disaggregated components

Sectors in EXIOBASE

Reference

Electricity

Electricity by gas

Statista website

Thermal (natural gas)

Natural gas and service

Statista website

Diesel

Gas/Diesel Oil

Steel

Basic iron and steel and of ferro-alloys

a

European Commission

Report & b trading website

and first products thereof Concrete

Cement, lime and plaster

Sand and gravel

Stone

Brick

Bricks, tiles and construction products,

Survey European Commission report

15

Survey

in baked clay Rubber and plastic products

Rubber and plastic products

Plastics insight website

Plastics material

Plastics, basic

Plastics insight website

Water

Collected and purified water

Transport

Other land transportation services

Sludge treatment

Sewage

sludge

for

OECD database European Commission report

treatment:

Survey

biogasification and land application c

Chemical

Chemicals nec

Alibaba website

Aluminium

Aluminium and aluminium products

Statista website

NH4CL

P- and other fertiliser

Alibaba website

Civil work

Construction work

Survey & Tianjiao et al. (2014)

262

a: Data analysis for weekly oil bulletin by European Commission

263

b: https://www.investing.com/commodities/steel-rebar-historical-data

264

c: including KH2PO4, MgCl2, NAOH, NAOCL

265 266

2.3 Hybridisation of process information and Exiobase

267 268

Input-output (IO) analysis is a powerful tool to describe economic activity within a country,

269

region or even global scale. The general IO analysis uses the Leontief model (equation 1)

270

(Leontief, 1951), where (I-A)-1 is referred to as the Leontief inverse. It indicates the direct and

271

indirect input demands on all other producers, generated by one unit of output. A is the technical

272

coefficient matrix. The element [aij] in A describes the input demand (i) needed by industry (j) to

273

produce a unit monetary output. I is an identity matrix and f is the final demand matrix. X is total

274

output matrix, representing all outputs triggered by the given final demand f.

275 276

X = (I-A)-1× f =L×f

(1)

277 278

From IO analysis, the environmental or social impacts can be calculated by multiplying the total

16

279

output with data for impact intensity per unit output (equation 2). Where B is a row vector. It

280

functions as a satellite account with impact intensities per unit product.

281

S=B×X= B× (I-A)-1× f = B× L× f

282

(2)

283 284

To reflect the technical characteristics of WWTP with a full supply chain, the life cycle

285

inventories of foreground processes in the WWTP scenarios were hybridised with the

286

multi-regional IO (MRIO) table Exiobase (Wood et al., 2014). As Exiobase has a global scope

287

(approximately 89% of global gross domestic product) and detailed information for European

288

countries (Stadler et al., 2014), it was the most suitable MRIO for this hybrid LCA analysis. The

289

first step of hybridisation in Exiobase is the creation of four new sectors in the original technical

290

coefficient matrix, each of which represents the scenarios Agro, Conv, Agro-U and Conv-U

291

(Figure 3).

292 293 294 295 296 297 298 299 300 301

17

302 303

Figure. 3 Hybridisation of life cycle inventories of WWTPs with EXIOBASE MRIO table.

Output

fr1

Xr1

Conv

ar2, r1

ar2, r2

ar2, r3

ar2, r4

ar2, p1

ar2, p2

…

ar2, pn

fr2

Xr2

Agro-U

ar3, r1

ar3, r2

ar3, r3

ar3, r4

ar3, p1

ar3, p2

…

ar3, pn

fr3

Xr3

Conv-U

ar4, r1

ar4, r2

ar4, r3

ar4, r4

ar4, p1

ar4, p2

…

ar4, pn

fr4

Xr4

Sector 1

ap1, r1

ap1, r2

ap1, r3

ap1, r4

ap1, p1

ap1, p2

…

ap1, pn

fp1

Xp1

Sector 2

ap2, r1

ap2, r2

ap2, r3

ap2, r4

ap2, p1

ap2, p2

…

ap2, pn

fp2

Xp2

…

Final demand

ar1, pn

…

Sector n

…

…

ar1, p2

…

Sector 2

ar1, p1

…

Sector 1

ar1, r4

…

Conv-U

ar1, r3

…

Agro-U

ar1, r2

…

Conv

ar1, r1

…

Agro Agro

…

…

…

Intermedia output

Intermedia input

Sector n

apn, r1

apn, r2

apn, r3

apn, r4

apn, p1

apn, p2

…

apn, pn

fpn

Xpn

Var1

Var2

Var3

Var4

Vap1

Vap2

…

Vapn

Eap1

Eap2

…

…

… Ear4

…

… Ear3

…

… Ear2

…

… Ear1

Eapn

Gar4

Gap1

Gap2

…

…

…

…

Gar3

…

…

Gar2

…

…

…

Gar1

…

Employment …

Satellite accounts

Value added

Gapn …

…

…

…

…

…

Greenhouse gas

304 305

The hybrid technical coefficient matrix can be illustrated in Equation 3 with four submatrices

306

(Suh and Huppes, 2005). Where matrix Ar,r is four by four diagonal matrix representing

307

intermediate input coefficients within WWTP. No interaction between different WWTP sectors

308

in four scenarios was assumed. Matrix C represents upstream cut-off flows as input demands by

309

WWTP sectors. C captures downstream cut-off flows from the WWTP systems to background

u

d

18

310

IO table. Matrix A is the same as in the existing product-by-product IO matrix in Exiobase

311

version 3.

312

(3)

313 314

The matrix Ar,r and Cu represent the four WWTP scenarios. To build the bridge between process

315

data with IO table, a concordance was developed based on the mapping relation in Table 3. The

316

concordance was used for constructing Matrix Cu. The Italian ‘food waste for treatment:

317

wastewater treatment sector’ was taken as the reference sector. The corresponding sectors in

318

each region were aggregated into the same cost structure as the cost elements of WWTP in

319

concordance. All the import shares were in the same proportion as in EXIOBASE. A scalar

320

multiplier was developed by dividing the aggregated coefficients in Exiobase with the cost

321

structures of WWTPs. It was multiplied by concordance and the column of Italian ‘food waste

322

for treatment: wastewater treatment sector’ to calculate matrix Cu. For the scenarios with

323

upstream impact, the input demand from ‘food product nec sector’ was created in matrix Cu. To

324

avoid double counting, the inputs already captured in Ar,r were deleted in Cu. For matrix Cd,

325

previous studies found the effect of the downstream sectors on the overall results is minor (Suh,

326

2006), so it can be set to 0 for simplicity (Wiedmann et al., 2011). In this study, C was assumed

327

to have the same downstream distribution as the reference sector in EXIOBASE (Chen et al.,

328

2019).

d

329 330

The second step was developing the new satellite matrix with impact intensities for WWTP

331

scenarios and original Exiobase sectors. For value added in WWTP, no subsidies or tax were

332

applied. The value-added matrix for WWTP sector was calculated by summation of wage and

19

333

operating surplus of WWTP system. In scenarios with upstream impact, the potential value of

334

wastewater was included in operating surplus. For employment, the total working hours required

335

by WWTP was used as indicator. It was calculated by multiplying number of employees in plant

336

and the working hours per person. According to a survey of the current conventional plant, it

337

needs two employees managing the whole plant for approximately 6 months each year for safe

338

operation. Each one works 12 hours per day. Due to the fruit harvest period, the fruit juice

339

factory can only operate half time. In the AgroCycle plant, one more technician was assumed to

340

be required to manage SCP production process. The working time was assumed to be 8 hours per

341

day.

342 343

In this study, as the carbon in the wastewater is from biogenic source (e.g. fruit cultivation).

344

Therefore, the CO2 from wastewater treatment was not accounted for as part of GHG emission

345

from WWTP, which is different approach in municipal WWTP (Law et al., 2013). The main

346

direct GHG emission from WWTP was N2O. The emission factor for N2O in sludge management

347

was taken from Soda et al. (2010) and N2O from the biogas combustion process was estimated

348

from Doka (2003). The share of chemical cost accounted for a relatively high share of total cost

349

of WWTP, so considering the generality of the chemical sector in Exiobase, the GHG emission

350

of chemicals was estimated from the corresponding emission intensities, which were taken from

351

Ecoinvent database. For the offset GHG emission by SCP, the GHG intensity of soybean meal in

352

Italy was taken from the Agri-footprint database (Durlinger et al., 2014). The inputs already

353

captured for the WWTP sector were discounted in the background IO table. The direct GHG

354

emission from the WWTP sector was calculated using structural path analysis. According to

355

Lenzen and Crawford (2009) and Malik et al. (2015), the total (direct + indirect) GHG emission

20

356

(m) can be enumerated as equation 4. Where matrix q represents the direct GHG emission of

357

WWTP. Therefore, the direct GHG emission q can be calculated by multiplying matrix m with

358

L-1hybrid.

359 360

m = qLhybrid = q(1-Ahybrid)-1= q + qAhybrid + qA2hybrid + qA3hybrid + … + qAnhybrid

(4)

361 362

The final step of the hybrid LCA is defining the final demand f. According to functional unit in

363

this study, 1 m3 treated wastewater was used as the final demand in each scenario. The demand

364

of other sectors was set to ‘0’. The environmental impact of climate change (kgCO2-eq/euro),

365

the economic impact of gross value added (euro/euro) and social impact of employment

366

(hours/euro) were evaluated for all WWTP scenarios per m3 wastewater treated. In addition, the

367

impacts on 200 sectors in each region were aggregated into 15 sector categories (see

368

Supplementary information) and a detailed analysis of aggregated impacts on environmental,

369

social and economic aspects was undertaken.

370 371

3. Results and discussion

372 373

3.1 The total impact of the wastewater treatment plant

374 375

The direct impacts of WWTP and total impacts of wastewater treatment service (Table 4)

376

indicated the WWTP with AgroCycle approach caused greater direct social (employment) and

377

economic (GVA) impact than conventional WWTP. However, the total (direct and indirect)

378

social-economic impacts were greater for the conventional technology. For these two indicators,

379

a greater impact is a positive, or ‘good’ outcome. The AgroCycle WWTP was estimated to cause

380

less direct and total impact on climate change than the conventional WWTP. Comparing 21

381

scenarios with and without upstream impacts suggested the upstream impacts only have a small

382

influence on the direct impact, but a much more important influence on the total impact.

383

Accounting of upstream impact will not influence direct employment and GVA for either

384

WWTP approach. The GVA calculation suggested that even from the point of nutrient

385

valorisation, it would be difficult to add economic value to wastewater in practise. Therefore, the

386

market value of wastewater in Agro-U was also zero. For climate change impact, accounting for

387

the upstream impact of fruit juice increased the direct GHG emission from WWTP. The detailed

388

assessment of the impacts is presented in following sections.

389 390

Table.4 The direct and total impacts of WWTPs in all scenarios Agro

Conv

Agro-U

Conv-U

Direct

Total

Direct

Total

Direct

Total

Direct

Total

GVA (Euro/kg)

-0.4312

-0.1351

-1.0500

-9.4E-8

-0.4312

0.3833

-1.0500

0.5184

Employment (hrs/kg)

0.0058

-0.5284

0.0043

0.0667

0.0058

-0.4866

0.0043

0.1086

GHG (kg CO2-eq./kg)

-2.5139

-1.3587

0.4751

1.6297

-2.3817

-1.0550

0.6072

1.9334

391 392 393

3.2 Economic impacts

394 395

The GVA of WWTP in all scenarios (Figure 4) showed that WWTP was the main contributor

396

among all sector groups. All scenarios had negative added value in WWTP process. The main

397

reason for this was the result indicated that the fruit juice factory could not generate income for

398

processing the wastewater. However, valorisation through SCP production in the WWTP could

399

improve the economic performance. The valorised SCP that can replace soybean meal for animal

400

feed. Therefore, AgroCycle WWTP could reduce the GVA in the oil seeds sector, which leads to

401

the negative value for total GVA of the agricultural and food sector group. Allocating upstream

402

impact of fruit juice to wastewater would create input demand from the ‘food product’ sector. 22

403

This has a significant effect on creation of GVA in the ‘agricultural and food’ sector group. The

404

analysis indicates that a simple assessment of increase or decrease in GVA from process based

405

LCA with predefined system boundary or ‘zero burden assumption’ might be misleading. In

406

addition, decreasing GVA for a very impactful sector (such as fossil fuels) could be regarded as a

407

positive short-term goal. There is certainly a perception that decreasing demand for soybean for

408

animal feed may be worth more than the GVA generated by the sector (Willett et al., 2019).

409 410

An issue to note was the AgroCycle WWTP created more GVA in the sectors ‘fossil-based fuel’

411

and ‘chemical and materials’. This was triggered by the greater demand for electricity, thermal

412

energy and chemical in anaerobic SBR, aerobic MBR and SCP production. A transition to

413

renewable energy would alleviate this antagonistic outcome. However, there is now some

414

concern about the adverse impacts of renewable energy, e.g. hydro-power (Grill et al., 2015).

415

Conventional WWTP had better economic performance in sector for ‘transport,’ ‘administrative

416

and personal service’, ‘business and financial service’, and ‘sludge treatment’. The smaller

417

amount of sludge from AgroCycle WWTPs reduced the added value in the supply chain of

418

sludge treatment service. The offset of soybean by SCP product also contributed to the lower

419

GVA in ‘transport’, ‘administrative and personal service’ and ‘business and financial service’

420

sectors. Although WWTPs with AgroCycle approach caused greater direct GVA, the reduced

421

GVA in the background sectors led to a smaller total GVA per m3 wastewater treated in Agro

422

and Agro-U. To improve the total GVA of AgroCycle WWTP, further development of

423

supporting sectors (e.g. ‘administrative and personal service’ and ‘business and financial service’)

424

for SCP is required. The implication of including upstream GVA in the analysis was, for most

425

sector groups, at least a 10% increase than the GVA compared to non-upstream impact scenarios.

23

426

The difference in GVA for the AgroCycle WWTP was greater than for conventional WWTP.

427

The most significant changes existed in the sectors ‘agricultural and food’, ‘administrative and

428

personal service’, ‘business and financial service’, and ‘transport’. The differences ranged from

429

23% to 96%. This indicated the importance of allocating upstream impact for the total GVA

430

evaluation, especially for the background sectors.

431

Figure. 4 Sectoral GVA per m3 wastewater treated in all WWTPs

432

433 434

3.3 Social impacts

435 436

The contribution of employment was concentrated mainly in the sector ‘Agricultural and food’

437

(Figure 5). The AgroCycle WWTP caused a marked reduction in employment caused by the

438

reduced labor demand associated with the offset soybean in ‘oil seed’ sector. For agricultural and

439

food supply chains, primary production at farm stage is normally associated with intensive labor

440

demand (Chen and Holden, 2016; Huang et al., 2009). As with the economic impact, there are

441

some important impacts of soybean production that could well be valued far greater than 24

442

employment, but that would depend on specific geographical locations. Comparing the Conv-U

443

and Conv scenarios, the employment by the ‘agricultural and food’ sector group in Conv-U was

444

almost five times greater than employment in Conv. The increase was driven by input demand

445

from the ‘food product’ sector. While, in the two AgroCycle scenarios, the change in the

446

‘agricultural and food’ sector group was small because of the high share of the ‘oil seed’ sector.

447

The marked differences between upstream and non-upstream scenario was also noted for the

448

sectors ‘administrative and personal service’, ‘business and financial service’, ‘transport’ and

449

‘chemical and materials’. The differences ranged from 25% to 89%, so similar to GVA, the

450

employment results also suggested allocation of upstream impact can play an important role in

451

evaluation of employment in the background sectors. In contrast, the foreground WWTP system

452

had no change of employment in upstream and non-upstream scenario. The contribution to total

453

employment was quite small.

454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 25

469

Figure. 5 Sectoral employment (hrs) per m3 wastewater treated in all WWTPs

470 471 472

3.4 Environmental impacts

473 474

WWTP was the main contributor to total GHG emission and thus climate change impact (Figure

475

6). For conventional WWTP, GHG emission from direct inputs to WWTP accounted for 29% to

476

31% of total GHG emissions. For the AgroCycele WWTP, the negative value for direct GHG

477

emissions was almost double the total GHG value. The offset GHG emissions caused by soybean

478

meal production accounted for the dominant share of direct GHG emission in AgroCycle WWTP.

479

This suggested that wastewater valorisation through SCP production could significantly reduce

480

the climate change impact of WWTP. In this study, the GHG intensity of soybean meal in Italy

481

was 2.57 kg CO2.eq-/kg. It is worth noting that the GHG intensity of soybean from other regions

482

could be much lower, so the benefits are not necessarily universal. According to Agri-footprint

483

database, American soybean meal has emission value of 0.8 kg CO2.eq-/kg, so the benefit of 26

484

AgroCycle WWTP in that context would be much smaller.

485

To treat one m3 wastewater, the direct GHG emission from AgroCycle and conventional

486

treatment stages are -4.33 kg CO2.eq- and 0.08 kg CO2.eq-, respectively. Although both WWTPs

487

have N2O and CH4 emission, the SCP in AgroCycle scenarios resulted in a negative value for

488

direct GHG emission from WWTP. The other inputs to the WWTP have positive values for

489

GHG emissions. Table 5 indicates the contribution of inputs to direct GHG emission (excluding

490

the emission from WWTP) in all scenarios. For conventional WWTP, electricity was found to be

491

the main contributor to direct GHG emissions. It accounted for 60% from Conv and 69% from

492

Conv-U. Similar findings were reported in previous WWTP LCA studies (Corominas et al.,

493

2013a; Gallego et al., 2008; Zang et al., 2015). For AgroCycle WWTP, electricity had smaller

494

contribution to direct GHG emission. To process one m3 wastewater in AgroCycle WWTP, the

495

GHG emission from electricity only ranges from 28%-30% in AgroCycle scenarios. However, in

496

AgroCycle WWTP, the GHG emission from fossil fuel was more than double the GHG

497

emissions from electricity generation. This was caused by greater natural gas consumption,

498

which was used as thermal energy for the anaerobic SBR and SCP production stages. Comparing

499

the upstream and non-upstream scenarios, the contribution of upstream impact to direct GHG

500

emissions in Conv-U was greater than Conv scenario. The effect of upstream impact in

501

AgroCycle WWTPs was much smaller, but the results still indicated that upstream impact would

502

have an influence on direct GHG emissions.

503 504

The major sector groups associated with indirect GHG emissions were ‘fossil fuel’, ‘transport’,

505

‘administrative and personal service’, ‘business and financial service’ and ‘manufacture and

506

equipment’ (Figure 6). The indirect GHG emissions accounted for 68% of total GHG in the 27

507

Conv scenario and 71% in the Conv-U scenario. The sectors ‘fossil fuel’ and ‘transport’ were the

508

main indirect GHG contributors for conventional WWTP (about 50% of total indirect GHG), due

509

to the electricity and material consumption used by conventional WWTP. The sector of

510

‘administrative and personal service’ and ‘business and financial service’ also contributed 25%

511

of total indirect GHG from conventional WWTP. For AgroCycle WWTP, the impact

512

contribution from the ‘fossil fuel’ sector as indirect GHG emissions was reduced, while the

513

indirect GHG contribution from ‘administrative and personal service’ and ‘business and financial

514

service’ sector increased. This suggested AgroCycle WWTP shifted indirect GHG from

515

resource-based sectors to service-based sectors. It is worth noting that the total indirect GHG

516

emission from AgroCycle and conventional WWTP was almost the same, with the main

517

difference due to upstream and non-upstream scenarios.

518 519

Figure. 6 Sectoral greenhouse gas emission per m3 wastewater treated in all WWTPs

520 521 522 28

523

Table.5 The contribution of different sector groups to the direct GHG in different scenarios Agro-U Agro

Conv-U Conv

Fossil based fuel

64%

68%

4%

4%

Transport of construction material

0%

0%

1%

1%

Other transport

4%

4%

10%

11%

Agricultural and food

4%

0%

10%

1%

Administrative and personal service

0%

0%

2%

2%

Business and financial service

0%

0%

2%

2%

Manufacture & Equipment

0%

0%

1%

1%

Sludge treament

0%

0%

3%

4%

Other waste management

0%

0%

2%

2%

Chemical and Materials

2%

1%

2%

1%

Metals, mineral and wood products

1%

1%

2%

2%

Electricity

24%

25%

60%

69%

Water

0%

0%

0%

0%

Other sectors

0%

0%

0%

0%

524 525 526

3.5 Sustainability implication

527 528

Based on the economic, social and environmental results above, agro-industrial wastewater

529

valorisation can improve the environmental performance as indicated by climate change impact,

530

but will have less influence on social-economic performance as indicated by employment and

531

GVA. The offsetting of the impact of soybean meal by valorised SCP offers more carbon credit

532

than the additional GHG emissions from the increased demand for thermal energy and chemical

533

and material consumption. A transition to renewable energy and bioeconomy may increase the

534

relative benefits further. The negative GHG emissions for the AgroCycle WWTP was due to the

535

carbon reduction associated with decreased demand for soybean production. Its relative value

29

536

also depends on the GHG intensity of the offset soybean meal. This will vary through space and

537

time as well.

538 539

AgroCycle WWTP can create more value for the plant owner and WWTP system, but

540

conventional WWTP can add more GVA for background sectors. The decreased demand for

541

sludge treatment and transport can be regard as an improvement of resource efficiency. In term

542

of sustainability, it was positive indicator and should be supported. To further improve the

543

economic performance over the background sectors, more development is required in the

544

‘administrative and personal service’ and ‘business and financial service’ sectors. Possible

545

solutions include supporting more research activities on valorised SCP and developing better

546

‘reverse supply chains’ for valorised products (Ülkü and Hsuan, 2017). Another option for

547

increasing total GVA using the AgroCycle WWTP technology is to valorise more SCP and other

548

products (e.g. pectin) per unit wastewater (Galanakis et al., 2010). This would generate a greater

549

demand for technology and thus inputs from the ‘administrative and personal service’ sector and

550

leads to greater GVA.

551 552

3.6 Limit and future research

553 554

For employment, valorising nutrients from wastewater reduced working hours. The reduction of

555

working hours on farms producing soybean will need to be valued against the other adverse

556

impacts caused by such farms. And the steady development of new technology that is normally

557

associated with improvement of agricultural productivity and reduction of labor intensity in

558

agriculture sector (Espey and Thilmany, 2000; Ruttan, 2002), which would probably reduce

30

559

employment hours anyway. The SCP can provide the same function as soybean meal, but with

560

much less labor demand. Improving the labor efficiency of protein production (through SCP

561

valorisation) could be regard as positive feature. As the nature of labor is changing, it should not

562

only be evaluated by working hours from a quantity point of view in the future. AgroCycle

563

WWTP is worth promoting, unless regional stakeholders have a very high priority associated

564

with employment hours.

565 566

The assumed allocation factor for upstream impact to wastewater (1%) was equal to the

567

magnitude of a mass/energy flow that could be excluded using the LCA cut off rule (Shen et al.,

568

2010). This rule imposed the upstream impact could be excluded. However, the economic, social

569

and environmental results all suggested that upstream impacts, as small as only 1%, had a

570

marked influence on the direct and total impacts, so could not be ignored. An accurate allocation

571

method for upstream impact is required for WWTP with nutrient recovery or any other

572

valorisation technology. The zero burden assumption should not be adopted in this case, as

573

suggested for other valorisation technologies by Oldfield and Holden (2014). Perhaps the most

574

important limitation of this study was absence of an accurate allocation method for the upstream

575

impact from fruit juice to wastewater. In future work, the allocation of upstream impact could be

576

based on the nutrient content, such as sugar content in fruit juice and wastewater. Upstream

577

impacts can also be allocated to corresponding sector (e.g. food product) in the Exiobase

578

input-output table. Finally, the rebound effect caused by displacing soybean with SCP

579

technology on the soybean market was excluded. This is the normal approach hybrid LCAs with

580

small functional unit (Acquaye et al., 2011; Genovese et al., 2017; Malik et al., 2015). Because

581

at sectoral intervention level, the annual SCP production from the WWTP is around 2,000 t,

31

582

which would be expected to have a negligible effect on soybean meal price. For sectoral effect,

583

future studies should estimate the potential SCP from the whole juice sector, which requires the

584

information about sectoral juice productivity. The SCP productivity from different types of

585

agro-industrial wastewater should be investigated to fully understand the impact implications of

586

this technology.

587 588

4. Conclusion

589

Agro-industrial wastewater has high nutrient value. Valorising wastewater nutrients could

590

increase the added value of the treatment plant and influence the sustainability of the food

591

processing facility. This study was the first attempt to use hybrid LCA to evaluate the

592

comprehensive environmental, social and economic performance of a wastewater treatment plant

593

for a fruit juice factory. The results indicated that valorising agro-industrial wastewater streams

594

using SCP technology can improve the environmental performance (indicated by climate change)

595

and resource use efficiency. However, the current valorisation technology could only drive

596

positive social-economic impact to the WWTP system, and not the whole background economy,

597

especially for social aspect. The AgroCycle WWTP can contribute to technical transformation of

598

agricultural labor, however, it is not a suitable option for those situations that prioritise

599

employment. Hybrid LCA can capture the impacts, which could be cut-off by inappropriate

600

system boundary placement in conventional process LCA. In order to improve the

601

social-economic impacts and promote a circular bioeconomy model in the fruit juice sector,

602

further development is required to better understand valorisation productivity and develop better

603

reverse supply chains to create more social-economic value for the products. This study provides

604

comprehensive sustainability information for stakeholders in wastewater treatment and the fruit

32

605

juice sectors. The implication can be used for making management or pollical decision with

606

consideration of three aspects of sustainability.

607 608

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Declaration of interests ☒ 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. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: