Comparative study of environmental impacts related to
wheat production with human-urine based fertilizers
versus mineral fertilizers

Tristan M.P. Martin, Joël Aubin, Enola Gilles, Julie Auberger, Fabien Esculier, Florent Levavasseur, Jennifer Mcconville, Sabine Houot

► To cite this version:

Tristan M.P. Martin, Joël Aubin, Enola Gilles, Julie Auberger, Fabien Esculier, et al. Comparative study of environmental impacts related to wheat production with human-urine based fertilizers versus mineral fertilizers. Journal of Cleaner Production, 2023, 382 (1), pp.135123. 10.1016/j.jclepro.2022.135123. hal-03847046

HAL Id: hal-03847046

https://hal.inrae.fr/hal-03847046v1

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Distributed under a Creative Commons Attribution 4.0 International License
1 Comparative study of environmental impacts related to wheat
2 production with human-urine based fertilizers versus mineral
3 fertilizers

4 Tristan M.P. Martin¹,², Joël Aubin³*, Enola Gilles³, Julie Auberger³, Fabien
5 Esculier²,⁴, Florent Levavasseur¹, Jennifer McConville⁵, Sabine Houot¹

6 ¹Université Paris-Saclay, INRAE, AgroParisTech, UMR ECOSYS, Avenue Lucien
7 Bretignières, 78850, Thiverval-Grignon, France

8 ²École des Ponts ParisTech, UPEC, UMR LEESU, 6-8 Avenue Blaise Pascal, Champs-sur-Marne, 77455 Marne-la-Vallée, France

10 ³UMR SAS, INRAE, Institut Agro, Rennes, 65 rue de Saint Brieuc, 35042, Rennes, France

11 ⁴Sorbonne Universités, CNRS, EPHE, UMR METIS, 4 Place Jussieu, 75005 Paris, France

12 ⁵ Swedish University of Agricultural Sciences, Department of Energy and Technology, SE-
13 750 07 Uppsala, Sweden

14 *Corresponding author: Joël Aubin

15 E-mail address: joel.aubin@inrae.fr

16 E-mail address of the authors:

17 Tristan M.P. Martin [tristan.martin@inrae.fr], Enola Gilles [eno.gilles@gmail.com], Julie Auberger

18 [julie.auberger@inrae.fr], Fabien Esculier [fabien.esculier@enpc.fr], Florent Levavasseur

19 [florent.levavasseur@inrae.fr], Jennifer McConville [jennifer.mcconville@slu.se], Sabine Houot

20 [sabine.houot@inrae.fr].

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**Abstract**

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Most of the fertilizers used in conventional agriculture are derived from nonrenewable resources: natural gas for nitrogen fertilizers and fossil mines for phosphorus and potassium fertilizers. On the other hand, the nutrients from human urine are poorly recycled with regard to agricultural repurposing, despite the fact that urine can be a nutrient resource for agriculture. Human urine may be collected through source separation and treated to produce urine-based fertilizers, resulting in various value chains. However, the environmental impacts and benefits of this fertilization method compared to current fertilization practices are not well known.

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Using a life cycle assessment, we assessed the environmental impacts of three scenarios of wheat production fertilized with three different urine-based fertilizers: stored urine, nitrified concentrated urine and alkalinized dehydrated urine. We compared them to a reference scenario in which mineral fertilizers are used for wheat production and urine is treated in a wastewater treatment plant.

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The environmental impacts for the scenarios using the urine-based fertilizer value chains were lower than those for the reference scenario for climate change, eutrophication, fossil resource and water consumption impact categories. These lower impacts were partly related to the avoided impacts from mineral fertilizer production. Moreover, the avoided impacts from the sanitation system (water consumption, greenhouse gas emissions, and nutrient discharge to the river) were significant and allowed for further and greater reduction of the environmental impacts for the urine-based fertilizer scenarios compared to those for the reference scenario. Nevertheless, stored and alkalinized dehydrated urine can induce higher ammonia volatilization compared to mineral fertilizers, resulting in a higher acidification potential and particulate matter emissions. The electricity consumption values for the nitrified concentrated urine and alkalinized dehydrated urine fertilizers are also higher than that of the reference system. Thus, ammonia volatilization and energy consumption of the urine treatments have been identified as the main environmental hotspots of urine-based fertilizer value chains. In conclusion, the use of urine-based fertilizers completes biogeochemical cycles for the sustainable
47 management of nutrients. It also enables a global reduction of the environmental impacts of
48 fertilization in agriculture and of wastewater treatment.

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## Key words

51 Ammonia volatilization; Energy; Environmental impacts; Human urine; Life cycle assessment; Urine-based fertilizer
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# 1. Introduction

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Approximately 75% of the nitrogen input used in conventional agriculture comes from the Haber-Bosch process, and the remaining 25% corresponds to biological fixation. However, the former process is an energy-intensive process that requires approximately 2% of the global energy consumption (Sutton et al., 2013). Additionally, phosphorus (P) and potassium (K) fertilizers mainly come from mines that have reserves that are limited and concentrated in a few countries (Cordell et al., 2009; U.S. Geological Survey, 2019). Mineral fertilizer may represent a significant economic burden for farmers. For cereal crops such as corn and wheat, fertilizers can account for a third of total operating costs (U.S. Department of Agriculture, 2019).

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In high-income countries, most domestic wastewaters are collected and treated in wastewater treatment plants (WWTPs). Sewage sludge issued from these treatments can be recycled in agricultural practices. For example, approximately 75% of the sewage sludge generated in France is recycled in this way (Collivignarelli et al., 2019). However, the proportion of recycled nitrogen for this type of recycling is very low since the denitrification of nitrogen is considered best-practice in wastewater treatment and usually allows for less than 10% recycling of nitrogen (Esculier et al. 2018). Moreover, denitrification is an energy-intensive treatment (Maurer et al., 2003). In addition, the greenhouse gas nitrous oxide (N₂O) can be emitted from denitrification in high proportions (Kampschreur et al., 2009). The recycling rate can be much higher for phosphorus (typically 80%), but the incineration of sewage sludge leads to a total loss of phosphorus. Finally, a significant proportion of nitrogen and phosphorus and potassium (typically only 2% recycling rate, Esculier 2018) remains and is discharged to the surface water. For example, the removal rate in WWTPs required by European Union regulations in sensitive areas is 70-80% for nitrogen and 80% for phosphorus of the flows entering large WWTPs under usual conditions (Directive 91/271/EEC – urban wastewater treatment), which still leaves more than 20-30% discharge in the receiving waters.

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Most of the nutrients in wastewater come from urine: approximately 80% of the nitrogen, 50% of the phosphorus and 70% of the potassium. When excreted, these nutrients are concentrated in a small
volume of approximately 0.5 m³ per year and per person (Friedler et al. 2013). Pure or slightly diluted urine is a source of nutrients that can be used as fertilizer for agriculture. Source separation is necessary to separate urine from wastewater and avoid nutrient dilution. It can be performed by various types of toilets and urinals (Rossi et al., 2009).

Nitrogen is excreted in urine mainly as urea (Udert et al., 2006). However, urea is rapidly hydrolyzed into ammoniacal nitrogen during storage. After being collected, urine can be treated under different objectives (Martin et al., 2020): (i) Stabilization of nitrogen (e.g., nitrification, alkalinization) in such a way that limits ammonia volatilization, reduces malodor and permits further concentration; (ii) Volume reduction to reduce storage space, heavy transport over long distances and application labor; (iii) Treatment of pharmaceutical residues and/or pathogens to reduce potential health and environmental impacts. All these treatments lead to a wide variety of products with various physico-chemical characteristics (e.g., solid/liquid, basic/acidic) that can be used in agriculture. The name "urine-based fertilizer" (UBF) qualifies these products as resulting from urine treatments.

The recovery of all of the human-made nitrogen and phosphorus that is currently not recycled would make it possible to offset 16%-21% and 9-12% of the global inorganic inputs of nitrogen and phosphorus in agriculture, respectively (Trimmer et al. 2019). However, this offsetting depends on geographical conditions and on the farming systems being offset. For example, in the region of Paris (12 million inhabitants, 569 000 ha of arable land cultivated mainly with an intensive cropping system), the nutrients present in the urine of the inhabitants of the metropolis is enough to replace all the nitrogen and phosphorus currently applied in fields (calculated from Esculier et al. 2018 and UNIFA 2018). In less intensive systems such as organic agriculture, coverage rates may be higher, even in less densely populated areas, because the required fertilization inputs are lower. The recycling of urine may be a means to increase the amount of nitrogen usable for organic agriculture and make a large-scale organic scenario possible (Billen et al., 2021).

Considering the entire value chain, from the toilet to the field, including collection, treatment, transport and application on crops, UBF production elicits several environmental impacts, such as greenhouse gas emissions, due to the consumption of fuel for transport, for example, or to the
acidification and eutrophication of the environment resulting from ammonia volatilization. Different
value chains may generate different environmental impacts related to the production of UBFs. Several
studies have aimed to characterize the environmental impacts of different ways of managing urine
using life cycle assessments (LCAs) (Lam et al., 2020). Most studies have focused on different ways
of managing wastewater (e.g., source separation of urine from sewage), and the environmental impacts
of the sanitation system have only taken into account the potential recycling of nutrients from
wastewater to agriculture, e.g. replacing the use of mineral fertilizers (Bisinella de Faria et al., 2015;
Ishii and Boyer, 2015; Kavvada et al., 2017; Landry and Boyer, 2016; Remy and Jekel, 2008a;
Tervahauta et al., 2013). These LCAs find that urine diversion can improve environmental
performance of wastewater treatment, particularly greenhouse gas emissions, relative to conventional
systems (Besson et al. 2021; Hilton et al. 2021; Lam et al. 2020). A few studies have focused on the
production of fertilizer from wastewater and, in particular, from urine (Malila et al., 2019; Spångberg
et al., 2014). The studies also showed an improved environmental performance in different impact
categories compared to the use of mineral fertilizers. To our knowledge, only one other study has
performed a LCA on applied cases of agricultural production, taking into account the technical
constraints for the application of one type of UBF (stored urine, Tidaker et al. 2007). Most previous
studies only considered recycling of stored urine. Recent LCA studies have shown that technologies
for urine concentration (Hilton et al. 2021), struvite precipitation (Hilton et al. 2021; Ishii and Boyer,
2015), and nitrogen recovery from urine using ion exchange (Kavvada et al. 2017) can all reduce
negative environmental impacts. However, no studies have been carried out on two of the UBFs that
have come furthest in technology development (Gunnarsson et al. in press), namely nitrified
concentrated urine or alkalinized dehydrated urine.

The main objective of the study was to assess the comparative environmental performance of the
use of UBFs from an agricultural point of view by studying the impacts of wheat production fertilized
with three different UBFs compared to typical fertilizers using LCAs. The originality of this study is
to compare the entire value chain of three UBFs from the fertilizer production up to grain production,
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and to apply LCA on the grain production, taking into account all the logistic and water treatment

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steps.

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# 2. Method

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## 2.1. Goal and scope of the study

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### 2.1.1. Goal of the study

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We decided to conduct a comparative assessment of the environmental impacts of different ways of producing wheat using mineral fertilizers or various UBFs using a Life Cycle Assessment (LCA) according to the ISO 14000 series standard (ISO, 2006a, 2006b). The study was conducted ‘from the toilet to the farm leaving gate’. We selected three different UBFs: stored urine, nitrified concentrated urine and alkalinized dehydrated urine. They were chosen since they are emerging products on which research is active and because of their various characteristics in terms of treatment, end product and spreading methods (diluted liquid, concentrated liquid and solid, respectively) (Martin et al., 2020).

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The technical constraints of fertilizing wheat (e.g., usable agricultural machinery, dosage to be applied) were taken into account to propose technically possible fertilization practice scenarios. Specific agricultural and urban configurations (peri-urban territory) were chosen to reflect the best plausible scenarios of urine recycling chains.

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### 2.1.2. Functional unit and boundaries of the system

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The functional unit chosen was **1 kg of grain** (15% moisture as the agronomical reference to measure the yield) **leaving the farm**. The different systems studied included the sanitation subsystem and the agricultural subsystem. Their primary function was to produce grain. The UBF scenario also had complementary functions: to use urine as fertilizer (instead of mineral fertilizer) and avoid its treatment as waste. As our scenario only represented a case of approximately one thousand inhabitants, the impacts of the sanitation system were considered upon expansion of the system.

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In the UBF scenarios, all relevant activities 'from the toilet to the farm leaving gate' were considered. This included (i) urine collection (same urine in all scenarios) using a diverting toilet, (ii) urine treatment to produce UBFs, (iii) transport of UBFs to the farm, (iv) storage of UBFs at the farm and (v) all activities associated with grain production (soil preparation, sowing, fertilization, pesticide treatments, harvesting). In the reference scenario, only mineral fertilizer was considered for crop fertilization.

We assumed that urine collection took place in a medium-density urban area. A new district of 1020 inhabitants equipped with source separation was considered. The district was made up of 17 buildings.

The different stages of the scenarios and the input production that were fixed based on the literature review are indicated in Figure 1 (the rest of the parameters come from databases).

## 2.2. Life cycle inventory analysis and description of the scenarios

For each crop and agricultural system, the different scenarios were (i) reference, (ii) stored urine, (iii) nitrified concentrated urine (NCU) and (iv) alkaline dehydrated urine (ADU). The main data taken in the different scenarios are presented in Table 1.

**Table 1. Main data and hypothesis along with the reference used in the different scenarios.** NCU: nitrified concentrated urine, ADU: alkaline dehydrated urine, UBF: urine-based fertilizer, WWTP: wastewater treatment plant. Units expressed as "% of N" or "% of P" denote gaseous losses or treatment efficiency occurring at a specific stage of the value chain; T.km denotes the transport of one ton on one km.
<table><thead><tr><th rowspan="2">Category</th><th rowspan="2">Parameter</th><th rowspan="2">Unit</th><th colspan="4">Scenarios</th><th rowspan="2">Comments and Reference</th></tr><tr><th>Reference</th><th>Stored urine</th><th>NCU</th><th>ADU</th></tr></thead><tbody><tr><td rowspan="10">Urine collection and storage</td><td>Volume urine</td><td>L/cap/day</td><td>1.27</td><td>1.27</td><td>1.27</td><td>1.27</td><td rowspan="3">Friedler et al. (2013)</td></tr><tr><td>N load in urine</td><td>g N/cap/day</td><td>11</td><td>11</td><td>11</td><td>11</td></tr><tr><td>P load in urine</td><td>g P/cap/day</td><td>0.93</td><td>0.93</td><td>0.93</td><td>0.93</td></tr><tr><td>Time at home</td><td>%</td><td>65</td><td>65</td><td>65</td><td>65</td><td>Jönsson (2002)</td></tr><tr><td>Number of flushes at home</td><td>p/cap/day</td><td>4.6</td><td>4.6</td><td>4.6</td><td>4.6</td><td>Spångberg et al. (2014)</td></tr><tr><td>Separation yield at the toilet</td><td>%</td><td>-</td><td>90</td><td>90</td><td>90</td><td>Estimated from Gundlach et al. (2021).</td></tr><tr><td>Flush for urine diverting toilet</td><td>L/flush</td><td>-</td><td>1.5</td><td>1.5</td><td>1.5</td><td>0.1 L urine bowl and 1.4 L feces bowl.</td></tr><tr><td>Flush for urine conventional toilet</td><td>L/flush</td><td>3</td><td>-</td><td>-</td><td>-</td><td>Friedler et al. (2013)</td></tr><tr><td>Number of tanks and their volume(s) per district</td><td>m³</td><td>-</td><td>17 tanks of 6 m³</td><td>Two tanks of 2 m³ and 10 m³</td><td>340 boxes of 0.040 m³</td><td>Polyethylene tank; 1 tank per building for stored urine; 2 per district for nitrified concentrated urine; 1 box per toilet for dehydrated alkalinized urine.</td></tr><tr><td>Ammonia volatilization during storage</td><td>% of N</td><td>-</td><td>0.5</td><td>0.1</td><td>0.5</td><td>Estimated based on Spångberg et al. (2014)</td></tr><tr><td rowspan="4">Urine treatment</td><td>Nitrogen concentration factor</td><td>-</td><td>-</td><td>1</td><td>10</td><td>20</td><td>Estimated based on Etter et al. (2015) for nitrified urine and Senecal and Vinnerås (2017) for alkalinized dehydrated urine.</td></tr><tr><td>Electricity consumption</td><td>kWh/m³ urine entering the treatment</td><td>-</td><td>0</td><td>130</td><td>533.3</td><td>1.92 kWh/toilet/d for alkalinized dehydrated urine, Simha (personal communication); Etter et al. (2015) for nitrified concentrated urine.</td></tr><tr><td>NH₃ volatilization</td><td>% of N</td><td>-</td><td>-</td><td>-</td><td>10</td><td>Simha (2021 and personal communication)</td></tr><tr><td>N₂O emissions</td><td>% of N</td><td>-</td><td>-</td><td>0.5</td><td>-</td><td>Udert (Personal communication)</td></tr><tr><td rowspan="2">UBF transport</td><td>Lime consumption</td><td>g Ca(OH)₂/L urine</td><td>-</td><td>-</td><td>-</td><td>10</td><td>Randall et al. 2016</td></tr><tr><td>Transport from district to farm</td><td>T.km</td><td>3.5</td><td>30.7</td><td>3.1</td><td>1.4</td><td>25 km; Freight lorry (Euro 6): 16-32 ton capacity for stored urine, 7.5-16 ton capacity for nitrified concentration urine and 3.5-7.5 ton capacity for alkalinized dehydrated urine.</td></tr><tr><td rowspan="4">Storage at farm and grain production</td><td>NH₃ volatilization</td><td>% of N</td><td>-</td><td>0.1</td><td>0</td><td>0.1</td><td>Spångberg et al., 2014</td></tr><tr><td>Number of tanks and their volume(s)</td><td>m³</td><td>Neglected</td><td>2 tanks of 250 m³</td><td>50</td><td>Neglected</td><td>2 Flexible Polyvinyl chloride tanks for stored urine and 1 Polyethylene solid tank for nitrified concentrated urine.</td></tr><tr><td>Ammonia volatilization at spreading</td><td>% of N</td><td>2-9*</td><td>7</td><td>0.5</td><td>15</td><td>See text</td></tr><tr><td>Wheat yield</td><td>t/ha</td><td>8.2</td><td>8.2</td><td>8.2</td><td>8.2</td><td>At 15% moisture.</td></tr><tr><td rowspan="7">Treatment at WWTP</td><td>N removal efficiency</td><td>% of N</td><td>62.3</td><td>-</td><td>-</td><td>-</td><td rowspan="2">Esculier et al. (2018)</td></tr><tr><td>P removal efficiency</td><td>% of P</td><td>84</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Energy consumption</td><td>kWh/kg N removed</td><td>2.78</td><td>-</td><td>-</td><td>-</td><td>10MJ/kg N removed for electricity, Maurer et al. (2003)</td></tr><tr><td>Methanol consumption</td><td>kg CH₃OH/kg N removed</td><td>3.4</td><td>-</td><td>-</td><td>-</td><td>SIAAP (2020)</td></tr><tr><td>FeCl₃ consumption</td><td>kg FeCl₃/kg P removed</td><td>9.15</td><td>-</td><td>-</td><td>-</td><td>Bollon et al., (2016)</td></tr><tr><td>N₂O emissions to atmosphere</td><td>% of N</td><td>2.1</td><td>-</td><td>-</td><td>-</td><td>IPCC (2006)</td></tr><tr><td>N₂O to atmosphere from river</td><td>% of N</td><td>0.5</td><td>-</td><td>-</td><td>-</td><td></td></tr></tbody></table>
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## 2.2.1. Common hypothesis and background data

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### a. Geographical considerations

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The scenarios took place in the Paris region (Ile-de-France). The climate is temperate with an average temperature of 10.5 °C and approximately 650 mm of precipitation per year. The main soil type in the region is silty luvisol. It is a farming area that specializes in arable crops (mainly wheat, maize, rapeseed and barley) using intensive agriculture practices. The electricity mix was considered to be the French mix (with more than two-thirds of the electricity produced from nuclear power plants).

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### b. Wastewater treatment plant scenario

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The WWTP considered in this study was based on the main WWTP of the Paris region (Achères – Seine-Aval). Operating data are available for this very large WWTP (5 million inhabitants equivalent, average of 1.5 million m³ treated per day; SIAAP, 2020) in which the treatment of nitrogen consists of nitrification and denitrification, and the phosphorus is treated by precipitation using ferric chloride ($FeCl_3$). Thus, the consumption of $FeCl_3$ was correlated to the amount of phosphorus to be treated. Methanol was used as a carbon source for denitrification, and its consumption was correlated with the quantity of nitrogen to be treated. Denitrification was carried out on biofilters with an $N_2O$ emission rate estimated at 2.1% of the N entering the WWTP (Bollon et al., 2016). The N removal efficiency was 62.3% of the N entering the WWTP, and the P removal efficiency was 84% of the P entering the WWTP. The rest of the nutrients were considered to be released in the river (Esculier, 2018), from which 0.5% of the N released to the river is expected to be emitted later as $N_2O$ (IPCC 2006).

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At the WWTP level, a significant amount of urine must be diverted to incur significant changes in infrastructure and processes (Igos et al., 2017). Thus, impacts such as changes in infrastructure, the quality of sewage sludge or the production of biogas at the WWTP were considered insignificant in the UBF scenarios. The avoided impacts of the sanitation system in terms of reagent consumption (methanol and $FeCl_3$), flushing water and energy were taken into account in the UBF scenarios because the urine was not sent to the WWTP but applied in the field. To simplify the scenarios, no
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sewage sludge recycling was considered. At the regional level, sewage sludge represents a small portion of the nitrogen and phosphorus balances of farms on average (Moinard et al., 2021).

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### c. System boundary and background data

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The temporal boundary was one year from the harvest of the previous crop to the harvest of the crops we studied (Koch et al., 2015). For capital goods, only the additional infrastructures required for the UBF scenarios were taken into account (e.g., tanks for storage at the district and farm levels). The capital goods impacts were divided by the estimated service life. Conventional toilets and urine diverting toilets were considered to be identical in terms of impacts and were not taken into account. Only the avoided flushing due to the use of diverting toilets was taken into account in the UBF scenarios.

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For the life cycle inventory calculation, background data were extracted from the Ecoinvent ® database version 3.4 (Wernet et al., 2016).

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### 2.2.2. Reference scenario of fertilizer production

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Fertilization was based on the use of mineral fertilizers (Figure 2a). Two nitrogen fertilizers (urea ammonium nitrate solution and ammonitrate) were considered. They are produced using the Haber-Bosch process. The energy consumption for their production was respectively 65.90 and 59.79 MJ/kg N, as reported by Ecoinvent ® v 3.4. Phosphorus fertilizer (superphosphate) is derived from fossil phosphorus from mines. The storage of mineral fertilizers at the farm was neglected due to the small space that would need to be occupied for storage, implying that storage infrastructure already exists.

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For each UBF scenario, the amount of urine diverted permitted us to calculate the avoided impacts (e.g., energy consumption) at the WWTP level.

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### 2.2.3. Stored urine

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The stored urine value chain is shown in Figure 2b. In fresh urine, approximately 85% of the nitrogen is excreted as urea, 5% as ammoniacal nitrogen and 10% as other organic N (Rose et al., 2015). Phosphorus is mainly present in a dissolved form as phosphate (Udert et al., 2006). Storage is
considered a urine treatment because there are changes in the composition of the stored urine that lead to the sanitation of the stored urine in terms of potential pathogenic germs (WHO, 2012). Urea is rapidly hydrolyzed into ammoniacal nitrogen due to the ubiquitous presence of the enzyme urease which is secreted by microorganisms (Friedler et al., 2013). Hydrolysis of urea increases the pH of urine to 9.1 (Udert et al., 2006). Ammonia volatilization can occur during the different steps of the value chain and, in particular, during spreading, since the pH is close to 9.2, the pKa of the $NH_4^+/NH_3$ pair. Urine collection is organized at the building level. Urine is separated using urine diverting toilets and directed to storage tanks in the basements of the buildings. The storage tanks are 6 m³ (polypropylene) and emptied every 2 months. Urine is transported to the farm using a freight lorry (16-32 ton capacity). Approximately 450 m³ of stored urine is collected per year at the district level. Considering a capacity of 32 tons for the truck, the volume of stored urine recovered is equivalent to 14 trucks. Urine is stored at the farm level in two different 250 m³ tanks (PVC flexible tank) to allow for at least 1 month of storage, as recommended by the WHO (2012), before application on crops. The increase in pH after urea hydrolysis causes approximately 30% of the phosphorus to precipitate as struvite and hydroxyapatite, forming sludge at the tank bottom or deposits in the urine pipes (Höglund et al., 2000). We considered that the sludge and deposits were mixed with the liquid part. The nitrogen concentration in the UBF corresponding to the applied stored urine is 5.4 g N/L (density is rounded to 1).

### 2.2.4. Nitrified concentrated urine

The nitrified concentrated urine (NCU) value chain is shown in Figure 2c. The start of the value chain is the same as that for stored urine with source separation using diverting toilet, but urine is transported through a pipe to a treatment station at the district level located in the central building. Before the treatment, the urine is stored at the treatment station in a 2 m³ tank (polypropylene, 1.2 m³ of flush diluted urine recovered per day in the district). We assumed that urea hydrolysis occurred in the pipe and storage tank before the treatment, as it occurs quickly in this environment (Udert et al., 2003). The treatment is continuous and consists of biological nitrification of half of the ammoniacal nitrogen of the stored urine by cultivating nitrifying bacteria and supplying air. Nitrification allows for
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the stabilization of nitrogen in the form of nitrate and acidifies the solutions, which limits the potential
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ammonia volatilization and allows for volume reduction by the evaporation of water. Nitrification is
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followed by volume reduction through distillation using a vapor compression vacuum distiller
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(Fumasoli et al. 2016). The entire treatment process is marketed by the company Vuna
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(www.vuna.ch). The nitrogen concentration factor after distillation was set to 10 (53.4 g N/kg in the
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UBF end product) in accordance with the already existing pilots. Energy consumption was set at
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130 kWh/m³ of urine entering the treatment (Etter et al., 2015). As N₂O emissions can occur during
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nitrification, 0.5% of the N entering the nitrifying column is assumed to be emitted as N₂O. Nitrified
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concentrated urine is stored in a 10 m³ tank (polypropylene, emptied every 2 months) before being
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transported to the farm using a freight lorry (6 trucks per year assuming an 8 ton capacity truck).
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Nitrified concentrated urine is stored on the farm in a 50 m³ polyethylene tank (approximately 45 m³
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of nitrified concentrated urine is produced in the district per year with a density of 1.14, corresponding
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to approximately 51 ton per year).

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2.2.5. Alkalinized dehydrated urine

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The alkalinized dehydrated urine (ADU) value chain is shown in Figure 2d. Alkalinization of fresh
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urine above pH 10 occurs by adding a base that inhibits the urease enzyme and stabilizes nitrogen in
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the form of urea (Geinzer, 2017). As with the other scenarios, urine is collected in a urine diverting
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toilet. However, this process is a decentralized treatment at the toilet level. A 20 cm pipe was used to
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bring urine to the drying box. The drying box consists of a polyethylene box (40 L) next to the toilet.
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The drying box was filled with hydrated lime at a rate equivalent to 10 g Ca(OH)₂/L urine. A fan with
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a power of 80 W operated continuously to evaporate the water from urine. Thus, the energy
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consumption per toilet is 1.92 kWh/toilet/day or 533 kWh/m³ of treated urine (Simha, personal
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communication). The process was developed by the Swedish University of Agriculture (SLU, Simha
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et al. 2020). One drying box is installed per toilet per dwelling (340 at the district level). Ammonia
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losses in the drying box are estimated at 10% because 5% of nitrogen in urine is already excreted as
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ammonia and volatizes due to the high pH of lime, and another 5% volatilizes during the rest of the
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treatment. Four times a year, the drying box is emptied (19.8 kg of alkalinized dehydrated urine is
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produced per capita per year), and alkalinized dehydrated urine is transported by truck (4 trucks per year considering a 5 ton capacity truck) to the farm. The nitrogen concentration factor was set at 20 (107 g N/kg in the end product) according to previous studies with this process (Senecal and Vinnerås, 2017). The storage of alkalinized dehydrated urine on the farm is neglected due to the small space that would need to be occupied for storage, implying that storage infrastructure already exists. (20.2 t produced per year in the district).

### 2.2.6. Grain production

We considered an average farm in the Paris region producing field crops. All the urine collected in the studied district could allow for 10.5 to 13.2 ha to be fertilized with UBF depending on the scenarios. The different steps of the technical itineraries are presented in Figure SM-1. The agricultural part of the life cycle inventory was carried out using the MEANS-InOut Software version 2.11 (www.inrae.fr/means, Auberger et al., 2018), which is based on the Agribalyse version 1.4 (Colomb et al., 2015; Koch et al., 2015) and Ecoinvent® version 3.4 (Wernet et al., 2016) databases. Except for ammonia volatilization after UBF applications, which we defined based on previous experiments (Martin, 2020), the other direct emissions in the field ($NH_3$ for mineral fertilizer, $N_2O$ and $NO_x$) were calculated using the IPCC et al. (2006) and EMEP/EEA (2016, 2009) emissions factors. Losses of N and P to water bodies were calculated using the Arvalis (Tailleur et al., 2012) and SALCA models (Gaillard and Nemecek, 2009).

Technical itineraries were developed according to local practices and the recommendation of the local farmer organization (Chambre d'Agriculture de région Ile-de-France; Lefèvre 2015). The technical itinerary of each scenario corresponds to intensive agriculture practices. The grain yield was the same for all scenarios and was set at 8.2 t/ha (at 15% moisture per ha) on the basis of the average yield over 5 years in the region (Chambre d'agriculture de Seine-et-Marne 2017). Only the fertilization hypothesis that differed among the scenarios was that for the grain production step (Table SM-2).
The fertilizing efficiency of stored urine (stored a few months after source separation and before use) has been studied on many crops (Karak and Bhattacharyya, 2011; Pandorf et al., 2019). In general, its fertilizing efficiency is similar to or slightly lower than that of mineral fertilizers (Johansson et al., 2000; Kirchmann and Pettersson, 1995; Viskari et al., 2018). The fertilizing efficiencies of other UBFs have been little studied. However, under controlled conditions (greenhouse) and field conditions, the differences between UBFs appears to be limited (Martin et al., 2021, in press). In the UBF scenarios, we considered that 100% of the nitrogen fertilization was carried out with a UBF. Urine inputs were based on the dose of “available” nitrogen (considering the mineral fertilizer equivalent), as it is the main nutrient for fertilizing field crops. As the mineral fertilizer equivalent of UBFs is high, it was set at 100%, minus the ammonia volatilization that was estimated based on experimentation (Martin, 2020). The best available techniques were considered for the application of urine to limit ammonia volatilization and soil compaction. The availability of P was considered to be 100%.

The mineral nitrogen crop requirement was calculated using the nitrogen balance method of the local farmers' organization (Chambre d'agriculture de Seine-et-Marne, 2017) and local practices in the Paris region (Lefèvre, 2015). It was set at 180 kg N/ha, and inputs were split into three. P fertilization consisted of an application of 50 kg P<sub>2</sub>O<sub>5</sub>/ha using superphosphate. In the UBF scenarios, the difference between the P input from the UBFs and the reference dose was compensated for using superphosphate.

In the reference scenario, the first two N inputs were made using urea ammonium nitrate solution, and the last input was made using ammonitrate according to local practices. The total input of mineral N fertilizer was approximately 0.6 t/ha.

The stored urine nitrogen concentration was low (5.4 g N/L). The stored urine was spread using a tankless slurry spreader fitted with trailing hoses. A tank truck (15 m³) went from the farm to the plot to resupply the tank of the spreader located on the side of the field. The total dose of stored urine was 36.2 t/ha. Ammonia emissions were estimated at 7% of the total N based on climatic conditions at the
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time of spreading and previous experiments (Martin et al., in press; Rodhe et al., 2004). Thus, the
mineral fertilizer equivalent was set at 93%.

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Nitrified concentrated urine (46.9 g N/kg) was spread using a liquid sprayer. Spreading using a
slurry spreader can be easier to manage in terms of volume, but the dose per ha (less than 2 t/ha) is not
suitable for this material. The total dose of nitrified urine was 3.86 t/ha. Due to the low pH and the fact
that half of the nitrogen was in the form of nitrate, ammonia volatilization was estimated to be very
low at 0.5% of the total N. Thus, the mineral fertilizer equivalent was set at 99.5%.

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It was assumed that alkalinized dehydrated urine (107 g N/kg) was applied using a centrifugal solid
spreader. In a real case, transformation into pellet must be considered because the recovered product is
in a powder form. The total dose was 1.93 t/ha. Due to the high pH, the potential volatilization was
expected to be higher than that of other UBFs. We estimated it to be 15% based on the urea emission
factor given by EMEP/EEA (2019, for temperate climate, soil pH < 7). Thus, the mineral fertilizer
equivalent was 85%.

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2.2.7. *Sensitivity analysis*

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A sensitivity analysis was carried out to study the impacts of variations in two key parameters in the
different scenarios, which induce high variations between UBFs performances We considered
transport distance between UBFs production and farm, inducing a difference of factor 22 in ton.km
among the study cases (table 1); and we considered energy use for urine transformation, which ranges
from 0-533.3 kWh/m³ urine (table 1).

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(i) The transport distance of each UBF may have a strong impact on energy consumption and
contribute to climate change through the consumption of fossil fuels. One of the main
differences between the different UBFs is the nutrient concentration. For concentrated
UBFs, impacts should be lower for transport (but higher during treatment). The basic
assumption was that there was a 25 km distance between the district and the farm. Two
other transport distance scenarios were studied. They consist of a “peri-urban” case where

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357 the distance between the neighborhood and the farm was set at 10 km, and a “center of the
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359 (ii) The treatment for the alkalinized dehydrated urine has a high energy consumption, but the
360 process in its present form is not optimized. The basic assumption was one drying unit per
361 toilet. However, we could assume that the design of the apartments that are on the same
362 floor of buildings in the neighborhood could be done in such a way that the toilets of the
363 apartments (4 apartments per floor) are close to each other. Then, all four toilets can be
364 connected to the same drying unit, which is already sized in its current form to handle that
365 amount of urine. The impact of only a single drying unit per floor (1 unit for 4 toilets) was
366 tested.

367 2.3. Life cycle impact assessment

368 The calculated impact categories and the different methods used are presented in Table 2. The main
369 method used was ILCD 2011 Midpoint+, which was developed by the joint research center of the
370 European commission (European Commission et al., 2012). For eutrophication, we used the CML
371 method, as it permits us to combine the impacts of both N and P. The calculations were carried out
372 using Simapro software version 8.5.4.0 (PRé Consultants, 2017).
<table><caption>Table 2. Impact categories studied according to the method used.</caption><thead><tr><th>Category</th><th>Impact categories</th><th>Unit</th><th>Method</th><th>Version</th><th>Reference</th></tr></thead><tbody><tr><td rowspan="4">Environmental impact (at midpoint level)</td><td>Climate change</td><td>kg CO<sub>2</sub> eq</td><td>ILCD 2011 Midpoint+</td><td>1.10</td><td>IPCC (2007)</td></tr><tr><td>Acidification</td><td>mol H<sup>+</sup> eq</td><td>ILCD 2011 Midpoint+</td><td>1.10</td><td>Seppälä et al. (2006); Posch et al. (2008)</td></tr><tr><td>Particulate matter</td><td>kg PM 2.5 eq</td><td>ILCD 2011 Midpoint+</td><td>1.10</td><td>Rabl & Spadaro (2004)</td></tr><tr><td>Eutrophication</td><td>kg PO<sub>4</sub><sup>3-</sup> eq</td><td>CML IA baseline</td><td>3.05</td><td>Heijungs et al. (1992)</td></tr><tr><td rowspan="3">Resource and energy consumption</td><td>Water resource depletion</td><td>m<sup>3</sup> water eq</td><td>ILCD 2011 Midpoint+</td><td>1.10</td><td>Frischknecht et al. (2006)</td></tr><tr><td>Mineral, fossil and renewable resource depletion</td><td>kg Sb eq</td><td>ILCD 2011 Midpoint+</td><td>1.10</td><td>van Oers et al. (2002)</td></tr><tr><td>Cumulative energy demand</td><td>MJ</td><td>Cumulative energy demand</td><td>1.10</td><td>Frischknecht et al. (2007)</td></tr></tbody></table>

The results are presented by impact categories. For a clearer analysis, the contributing factors of the impacts were classified into six types:

(i) **Reference fertilizer (Ref) production and fertilization** includes all impacts related to mineral fertilizers: their production, transport to the farm and application. In most cases, their nutrient concentration is high and application impacts are low, so we grouped fertilizer production and application, unlike for UBFs.

(ii) **UBF production** takes into account all the impacts 'from the toilet to the farm entrance gate': urine storage, treatment and transport.

(iii) **UBF fertilization** takes into account all the impacts of UBFs at the farm level: storage and application.

(iv) **Direct field emissions** includes emissions coming from the soil or fertilizer to the atmosphere and hydrospheres of nitrous oxide (N<sub>2</sub>O), nitrogen oxides (NO<sub>x</sub>) and ammonia (NH<sub>3</sub>). It also includes emissions of nitrogen and phosphorus in water bodies.
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(v) **Other field operations** groups together the impacts linked to crop protection (pesticides and their application), tillage, sowing and harvesting.

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(vi) **Avoided impacts for the sanitation system** represents the consumption and impact avoided because the urine was not treated in the WWTP but instead spread onto the crops. It includes the consumption of water, energy, chemicals (methanol and $FeCl_3$) and emissions to the atmosphere ($NH_3$, $N_2O$) and to water bodies (N and P).

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All results were expressed using the functional unit: the production of 1 kg of grain (at 15% moisture).

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# 3. Results and discussion

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## 3.1. Contribution analysis

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Three main drivers determine the levels in the selected impact categories and the differences between the scenarios for the production of 1 ton of wheat. The first driver is the production of the fertilizers (mineral and urine based). It is the major cause of differences between the scenarios in cumulative energy demand, particulate matter emission and mineral, fossil and renewable resource use. Higher impacts are associated to the ADU scenario, and the lowest to the stored urine scenario. The second driver is the direct field emissions. It is a main contributor to acidification, eutrophication, particulate matter emission, and climate change. It explains a good part of the difference between the scenarios in acidification and particulate matter emission, due to the high levels induced by ADU scenario and low levels induced by NCU scenario. The third driver is the avoided impact of water sanitation due to the reduced amount of nitrogen entering the wastewater plant. We propose to classify it in third position, as it is an indirect consequence associated to a methodological choice of the study. This contributor is major in climate change, eutrophication, water resource depletion, and sensitive in mineral, fossil and renewable resource use. In particular, the avoided impact of sanitation over compensated the impact of the UBF scenarios, and induced negatives values in climate change, eutrophication and water resource depletion, for them. The high value of this avoided impact for ADU
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leads to a reclassification of the UBF scenarios in climate change, as it ranks better than NCU
scenario.

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**3.2. Results by impact categories**

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The results of the different impact categories are presented in Figure 3.

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**3.2.1. Cumulative energy demand**

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The cumulative energy demand was 2.2 MJ/kg grain for the reference scenario and ranged from -1.3 to 34.1 MJ/kg grain for the UBF scenarios (Figure 3a). In the reference scenario, energy consumption was mainly linked to the production of mineral fertilizers. The energy consumption for the transport of mineral fertilizers and their applications and for the other field operations was very low. The energy consumption values for the UBF scenarios were mainly driven by the electricity consumption of each treatment and were high for NCU and ADU. The avoided impacts of the WWTP were low and related to the avoided production of methanol. With the exception of stored urine for which no energy was required for treatment, the energy consumption of the other UBF scenarios was above the reference scenario. No major impact on the energy consumption due to the large amount of fertilizer to transport or apply for stored urine was observed. The energy balance of the stored urine scenario was negative because of the avoided impacts at the WWTP.

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**3.2.2. Climate change**

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The climate change impact was 376 g CO₂ eq/kg grain for the reference scenario and ranged from -309 to 149 g CO₂ eq/kg grain for the UBF value chains (Figure 3b). In the reference scenario, the production of mineral fertilizers and direct field emissions were the main contributing factors. The treatments to produce NCU and ADU had higher impacts than the production of mineral fertilizer due to the high consumption of electricity and, to a lesser extent, due to N₂O emissions during the treatment for NCU and lime consumption for ADU. For stored urine, despite high transport needs, the impact was minor, as was the impact related to its application. The impacts of transport and application were negligible for NCU and ADU. Direct field emissions (mainly N₂O emissions)

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contributed to a large part of the climate change impact, and the other field operations had minor
contributions. The avoided impacts at the WWTP represented a significant part of the total climate
change category impact values due to the avoided N₂O emissions and ultimately offset the impacts of
the UBF scenarios. Thus, the balances of the UBF scenarios were negative and lower than that in the
reference scenario.

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### 3.2.3. Acidification

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The acidification impact for the reference scenario was 7.4 mmol H⁺/kg grain and ranged from 1.5 to 24.5 mmol H⁺/kg grain for the UBF scenarios (Figure 3c). The main contributing factors were direct field emissions and especially ammonia volatilization (15% of the N applied at the field level for ADU). The impacts of fertilizer production were negligible for stored urine and NCU (low NH₃ losses during the treatment) and low for the synthesis of mineral fertilizers (0.002% in Ecoinvent® V3.4 database). The production of ADU was a very important contributing factor due to the volatilization of ammonia during the treatment (10% of total N diverted). The avoided impacts at the WWTP were low. The impacts related to the application and other field operations were minor. Compared to the reference scenario, the balance for stored urine was similar and the balance for NCU was approximately 5 times lower. However, the impact was approximately 3.6 times higher for ADU than for the reference scenario.

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### 3.2.4. Particulate matter

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The particulate matter impact for the reference scenario was 296 mg PM 2.5 eq/kg grain and ranged from 49 to 608 mg PM 2.5 eq/kg grain for the UBF scenarios (Figure 3d). For acidification, the impacts were mainly attributable to the volatilization of ammonia (secondary particles). However, in the reference scenario, the production of mineral fertilizers represents approximately half of the impact values (mainly due to the chemicals and the energy needed for their production). In the stored urine scenario, the impact of the application was small but not negligible. The avoided impacts at the WWTP were low. For stored urine, transport to the farm and ammonia emissions at the neighborhood level account for most of the impacts of the UBF production category. Most of the particulate matter
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for NCU production came from the electricity consumed during processing. In the ADU scenario, the
high impacts were related to UBF production (electricity but also NH₃ volatilization during treatment)
and direct field emissions. The balances for stored urine and NCU were lower than the reference
scenario but the balance for ADU was higher.

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**3.2.5. Eutrophication**

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The eutrophication impact was 2.8 g PO₄³⁻ eq/kg grain for the reference scenario and ranged from -2.0 to 1.0 g PO₄³⁻ eq/kg grain for the UBF scenarios (Figure 3e). Eutrophication was mainly due to direct field emissions. The models used to simulate the nitrate emissions to water bodies did not depend on the amount of nitrogen applied but were based on the time between sowing and harvesting and soil cover in winter; thus, for all scenarios, the modeled nitrate emissions were the same and contributed at the same level to eutrophication. Part of the direct field emissions was, however, related to ammonia volatilization, so the impact was greater for ADU. The impacts of the treatments were low except for ADU. The avoided N and P emissions at the WWTP were high and made it possible to offset the impacts of the value chain. Thus, the impacts of all UBF scenarios were lower than in the reference scenario and were negative. Indeed, the separate collection of urine avoids the impacts on freshwater in the WWTP (904 kg of N and 32 kg of P per year, approximately 0.9 kg N and 32 g P/cap/y, respectively). The impacts from other sources were minor.

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**3.2.6. Water resource depletion**

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The water consumption for the reference scenario was 0.5 L/kg grain and ranged from -3.7 to -2.2 L/kg grain for the UBF scenarios (Figure 3f). Some water was used for the production of mineral fertilizers and for UBF during source separation at the toilet level (0.1 L per flushing in the urine part of the toilet). The differences among the UBF scenarios were mainly due to the different amounts of fertilizers used (due to their different mineral fertilizer equivalents, etc.) and the electricity consumption for the treatment. However, the avoided impacts for the sanitation system largely outweigh the positive consumption of the scenarios. Thus, all UBFs scenarios had a negative balance and saved water compared to the reference scenario. Indeed, with our hypothesis (1.5 L for flushing in

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diverting toilets versus 3 L in conventional toilets), approximately 2570 m³ per year were saved at the
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district level (approximately 2.5 m³ per year and per person and 8.4 L of water per L of urine
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collected).

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### 3.2.7. Mineral, fossil and renewable resource depletion

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The resource consumption for the reference scenario was 22.1 mg Sb eq/kg grain and ranged from 6.8
to 19.9 mg Sb eq/kg grain for the UBF scenarios (Figure 3g). The use of phosphate fertilizer (in the
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fertilizer production category) was the main contributing factor (approximately two-thirds) in the
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reference scenario, and the remainder corresponded to the production of mineral nitrogen fertilizers.
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The P mineral fertilizer impact was also significant in the UBF scenarios, as the substitution of
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mineral P by UBFs was not complete. The impact of the production of stored urine was mainly due to
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its transport from the district to the farm. Most of the impact values for NCU and ADU production
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relate to electricity consumption during the treatment. The impact of UBF application was significant
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only for stored urine (diesel consumption by slurry spreader). For other field operations, most of the
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impacts were related to the materials used in the agricultural machinery (e.g., tractor, combine
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harvester) and some of the pesticides used. The avoided impacts for the sanitation system were mainly
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linked to the avoided consumption of iron chloride and, to a lesser extent, methanol and water. The
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consumption values of the stored urine and NCU scenarios were approximately 3 times lower than that
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in the reference scenario. The impacts of ADU remained higher compared to the reference scenario.
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## 3.3. Sensitivity analyses

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The impacts of the sensitivity analyses for the different impact categories are presented in Figure 4.

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### 3.3.1. Transport distance

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Among the different UBFs, the effect of the transport distance was high only for stored urine (Figure 4a). In addition, the differences are especially significant for 100 km transport on the following impact categories: cumulative energy demand (+64%), mineral, fossil and renewable resource depletion (+37%), climate change (+21%) and, to a lesser extent, particulate matter (+12%). In the 10 km
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scenario, the relative impact compared to the 25 km scenario decreases by 13% for the cumulative energy demand but decreases by less than 10% for the other impact categories. Even if the impact for climate change, for example, was 21% higher in the 100 km scenario, the overall impacts considering the entire scenario are limited, as shown in Figure SM-3. For this impact category, the direct field emissions remain the main contributing factor. For NCU and ADU, the impacts of transport were very limited and not significant. The decrease from 25 to 10 km reduces all the impacts to less than 1%, while the 100 km scenario increases the mineral, fossil and renewable resource depletion impact value to 7% for NCU and 5% for ADU.

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### 3.3.2. Number of drying units for the ADU scenario

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The energy consumption for ADU was high, but the process was not fully optimized, and the energy consumption can be decreased. The relative impact of the number of drying units is illustrated in Figures SM-4 and SM-5. The drying unit was already dimensioned to potentially treat 4 times more urine than recovered in the initial scenario and is technically feasible. Thus, the installation of a single drying unit per floor (instead of 4) makes it possible to reduce approximately 66% of the water resource and mineral, fossil and renewable resource depletion and 75% of the cumulative energy demand (from 38 to 10 MJ/kg grain). The impact on climate change was also significant but not major, with a reduction of 25%, as most of the electricity comes from nuclear plants in France. The reduction of the impact was significant, as shown for the mineral, fossil and renewable resource depletion in Figure SM-4, for which ADU with only one drying unit per floor was the least impacting UBF scenario. The other impact categories depend mainly on volatilization and are little affected by the reduction in the number of drying units. As shown for the cumulative energy demand in Figure SM-5, using only one drying unit per floor can reduce energy consumption to the same amount as the NCU. However, energy consumption was still higher than the reference or stored urine scenarios.
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**3.4. Discussion**

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**3.4.1. Avoided impacts at WWTP**

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The impacts of all UBF scenarios are much lower than those in the reference scenario for climate change, eutrophication and water resource depletion due to the high avoided impacts for the sanitation system. These are in line with results reported from (Hilton et al. 2021; Lam et al. 2020). N₂O emissions at the WWTP level were an environmental hotspot. In other studies, the impact of stored urine scenarios versus mineral fertilizer use on climate change was not as important because the hypothesis on greenhouse gas emissions at the WWTP was lower or not taken into account (Spångberg et al., 2014; Tidaker et al., 2007). Knowledge of the importance of the N₂O factor in WWTPs is quite recent (The IPCC, 2019, emission factor correspond to 1.6% of the nitrogen entering the water line or membrane bioreactor, Bisinella de Faria et al., 2015).

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**3.4.2. Energy consumption for UBF treatment**

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Our study highlighted the energy consumption of UBF treatment as a hotspot. For mineral, fossil and renewable resource consumption, the impacts of the stored urine and NCU scenarios are lower than those in the reference scenario, but greater for ADU due to the high electricity consumption. Optimization of the energy consumption in these emerging technologies is a need that has also been highlighted in other studies (Gunnarsson et al. 2022). For the cumulative energy demand, only the impact of the stored urine scenario was lower than that of the reference scenario. They were higher in the other UBF scenarios due to the high electricity consumption for the treatments. However, since the French energy mix was mainly based on nuclear energy, the impact of electricity consumption on climate change was limited. The same electricity consumption with a more carbon-intensive mix for electricity production could have a larger climate change impact. The energy consumption of each scenario strongly depends on the boundaries of the study. The stored urine scenario usually shows lower energy consumption than using mineral fertilizer and urine treatment in WWTPs (Jönsson 2002; Tidaker et al. 2007; Remy and Jekel 2008).
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### 3.4.3. Ammonia volatilization

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Ammonia volatilization was also highlighted as a hotspot. Some of the environmental impact categories are directly related to the volatilization of ammonia: acidification, particulate matter and eutrophication. For these impact categories, stored urine and, even more so, ADU had higher impacts than NCU due to higher volatilization at the field level and at the treatment level for ADU. The data available for robust and accurate modeling of ammonia emissions are still insufficient. Further research to better assess N volatilization at the different stages for the three scenarios is needed. Moreover, technical itineraries that could strongly limit volatilization; for example, incorporation before maize sowing should strongly reduce the environmental impacts.

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### 3.4.4. Limitations and perspectives

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Comparison with the literature was not always easy because the system boundaries are not completely the same as the method used to assess the environmental impacts. For stored urine, the results of our study were in line with previous studies. With similar boundaries, Tidaker et al. (2007), Malila et al. (2019) and Medeiros et al. (2020) observed a reduction in eutrophication but an increased acidification potential due to ammonia volatilization compared to the reference scenarios studied (mineral fertilizers and WWTP or septic tank).

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The urine treatments studied are still evolving and not fully optimized in their current state of development, and their environmental impacts may be more or less easily reduced. For example, the sensitivity analysis shows that the technical solutions of implementation of urine collection influences greatly the environmental performances in ADU. Significant room for improvement exist and can reorder the environmental performances of the different treatment technologies. The sensitivity analysis showed that logistic might influence the energy efficiency as shown by other authors (Kavvada et al., 2017). However, the transportation of stored urine could induce a significant increase in fuel consumption and related impact beyond 100 km of distance. In this case, ADU can be preferred. Other setups for urine treatment can be designed, such as transporting stored urine in a nitrification plant, which can save energy due to a scaling effect. Thus, the aim of this study was to assess the global trends and the environmental hotspots of these UBF scenarios. Furthermore, some of
the mentioned issues, such as eutrophication or volatilization, can be managed independently of the
UBF scenarios at the field level (e.g., nitrogen catch crops, spreading techniques).

The LCA methodology is based on technical criteria and focuses only on the environmental impacts. However, many other criteria must be taken into account for the implementation of the UBF value chains. For example, stored urine appears to be the least impacting scenario for many impact categories. However, since it was a nonconcentrated UBF, the volume to be applied was substantial compared to concentrated UBFs (e.g., 36.2 t/ha vs. 2.0 t/ha for ADU). Thus, much more work should be necessary for the fertilization step at the farm level, and it may not be suitable for the farming systems currently in place in the study region, as well as the collection in densely populated areas, due to lack of space. Other aspects, such as the odors of UBFs, the acceptability of their use by farmers and consumers (e.g., Lienert and Larsen 2010; Segrè Cohen et al. 2020) or the contaminant content, are also important to consider. These aspects should be addressed in future studies.

In a previous LCA, the substitution of mineral fertilizers by other organic products (e.g., compost) reduced the overall impact linked to the production of mineral fertilizer. However, for some urine-based fertilizers, the impacts linked to the treatment of organic products may counterbalance the positive effects on some impact categories (Avadí et al., 2021; Martínez-Blanco et al., 2009).

## 4. Conclusion

We assessed the environmental impacts of producing wheat with three different urine-based fertilizers compared to the use of mineral fertilizer using an LCA. The impacts of all urine-based fertilizer scenarios were lower than those of the reference system with mineral fertilizers for the climate change, eutrophication, fossil resource and water consumption impact categories. In addition to the avoided emissions linked to the avoided fertilizers use, we underlined that the avoided impacts for the sanitation system (water consumption, greenhouse gas emissions, and nutrient discharge to the river) were significant and allowed for a great reduction in the environmental impacts of crop production compared to the reference scenario. In contrast, few impact categories were higher for the UBF scenarios: acidification, particulate matter emissions and the cumulative energy demand. Acidification
and particulate matter emissions were directly related to ammonia volatilization, which may be higher than the reference scenario for the stored and alkalinized dehydrated urine scenarios. Electricity consumption for the energy production necessary to produce nitrified concentrated urine and alkalinized dehydrated urine was another important parameter. Thus, ammonia volatilization and energy consumption of the treatment have been identified as the main environmental hot spots of the urine-based fertilizer scenarios. More research is needed to reduce these impacts of urine-based fertilizer production. In addition to environmental impacts, other aspects, such as the organization of value chains, technical constraints linked to the urban context and the agricultural system, or the acceptability of the use of urine-based fertilizers by farmers and consumers, must be taken into account. Nevertheless, our study clearly shows the potential environmental improvement of human urine use in agriculture and should help in the design of relevant urine source separation value chains that contribute to the socioecological transition of agrofood systems.

628

## 629 Acknowledgments

This study is part of the AGROCAPI project (www.leesu.fr/ocapi). This work was supported by ADEME (Agence de l'environnement et de la maîtrise de l'énergie); SEDE VEOLIA; SIAAP (Syndicat interdépartemental pour l'assainissement de l'agglomération parisienne); and the IDEX Paris-Saclay, ANR-11-ISEX-0003-02. We would like to thank them for their financial support. We would like to thank Bastian Etter and Prithvi Simha for providing us with the data necessary to perform the LCA.

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## Figure captions

Figure 1. Boundaries and foreground data of the systems studied. Only the main emissions and input are presented. The yellow arrows show the urine, and the blue arrows show the mineral fertilizers. The functional unit is 1 kg of grain.

Figure 2. Upstream analysis and application method for the different scenarios: a. Reference; b. Stored urine; c. Nitrified concentrated urine; d. Alkalinized dehydrated urine.

Figure 3. Environmental impacts of the different scenarios of wheat fertilization calculated for 1kg of wheat grain: a. Cumulative energy demand; b. Climate change; c. Acidification; d. Particulate Matter; e. Eutrophication; f. Water resource depletion; g. Mineral, fossil and renewable resources. The red dot and the number above the bars indicate the balance between impacts due to wheat production and urine treatment and avoided impacts at the WWTP. NCU: nitrified concentrated urine. ADU: alkalinized dehydrated urine.

Figure 4. Results for the sensitivity analyses: a. Relative impacts compared to the main scenario (25 km, base 100%, black line) for the transport of stored urine; b. Relative impacts of the number of drying units for ADU (alkalinized dehydrated urine) compared to the main scenario (1 drying unit per toilet, base 100%, black line). Only positive impacts (without the avoided impact of sanitation) are taken into account in the calculation.
Urine

Water

Collection in
diverting toilet

Electricity

Reagents

Treatment of
urine

Transport of
urine-based
fertilizer

Storage at
farm

N₂O

NH₃

NH₃

NH₃

Avoided
impacts

Methanol

Energy

Water

FeCl₃

Treatment at
WWTP

N₂O to
atmosphere

Phosphorus to
surface water

Nitrogen to
surface water

N₂O to
atmosphere

Seed

Pesticides

Production and transport
of mineral fertilizer

Grain
production

NH₃

Resources and
infrastructures

Emissions to the
environment

1 kg of grain
b. Stored urine

<figure><img src="image_2.png" /><figcaption>Diverting toilet</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Liquid transport to
the farm</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Farm storage</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Tankless slurry spreader with
trailing hoses</figcaption></figure>

c. Nitrified concentrated urine - NCU

<figure><img src="image_6.png" /><figcaption>Diverting toilet</figcaption></figure>

<caption>District
storage</caption>

<caption>Nitrification
column + distiller</caption>

<figure><img src="image_7.png" /><figcaption>Liquid transport
to the farm</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Farm storage</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Liquid sprayer</figcaption></figure>

d. Alkalinized dehydrated urine - ADU

<figure><img src="image_10.png" /><figcaption>Diverting toilet + Urine drying box</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Recovery of ADU</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Solid transport to
the farm</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Centrifugal solid spreader</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>b. Climate change</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>c. Acidification</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>d. Particular matter</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>e. Eutrophication</figcaption></figure>

<figure><img src="image_18.png" /><figcaption>f. Water resource depletion</figcaption></figure>

<figure><img src="image_19.png" /><figcaption>g. Mineral, fossil and renewable resource</figcaption></figure>

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>
a. Transport distance (stored urine)

<figure><img src="image_22.png" /></figure>

b. Number of drying unit (ADU)

<figure><img src="image_23.png" /></figure>
Relative difference of urine-based fertilizers scenarios of wheat production compared to the actual fertilization practices

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>