Science, Irving K. Barber Faculty of (Okanagan)
Biology, Department of (Okanagan)
DSpace
UBCO
Smart, Alyssa
Master of Science - MSc
University of British Columbia
Representation of the impacts of irrigation in agricultural systems is often simplified in life cycle assessment (LCA) to consider only water and energy consumption. This research aimed to understand the importance of irrigation as a contributor to a more comprehensive suite of environmental outcomes of Okanagan cherry orchard production and to elucidate whether employing more efficient irrigation technologies on orchards propagated relevant reductions to environmental impacts. However, how human consumption of freshwater use impacts terrestrial ecosystems is not well understood in life cycle impact assessment and presents a challenge for accurately modelling these pathways. A regionalized life cycle inventory of Okanagan water delivery systems was developed and an LCA was performed to determine the share and distribution of environmental burdens associated with irrigation water, inclusive of processes within the water supply network. To improve our capacity to understand of the impacts of human consumption of freshwater use, a methodological framework was also developed using a literature analysis to model the impacts of freshwater use on terrestrial dryland and semi-arid ecosystems, as applicable to the semi-arid Okanagan. Irrigation contributed a significant portion to most life cycle environmental impacts of cherry production. The highest impact contributions from irrigation were found in the categories water scarcity, human toxicity, freshwater ecotoxicity, and mineral resource use. Employment of more efficient irrigation technologies than the most common irrigation system used on cherry orchards decreased impacts to these categories by 10-16%. Increased carbon dioxide emissions from bicarbonate deposited in orchard soils from irrigation did not make substantial relative impacts to the overall emissions of Okanagan cherry production. When the increased nitrous oxide emissions associated with drip irrigation were considered in the model of cherry orchard production, drip irrigation still outperformed microsprinkler in every impact category except climate change, where higher impacts due to nitrous oxide emissions from drip irrigation were counteracted by the decreased efficiency of microsprinkler systems. Biotic mechanisms for inclusion in the development of a mechanistic impact pathway linking freshwater use to consequent impacts to semi-arid biomes through the use of the “water- web” concept were identified. Mechanistic factors along this impact pathway included invertebrate primary and secondary consumers and phreatophytic plants that propagate water through the biome.
LIFE CYCLE ASSESSMENT OF FRESHWATER USE IN THE SEMI-ARID OKANAGAN: BUILDING THE FRAMEWORK FOR NEW LCIA IMPACT PATHWAYS OF FRESHWATER USE FOR TERRESTRIAL ECOSYSTEMS AND MODELING IRRIGATION WATER INFRASTRUCTURE AND USE IN HORTICULTURE by Alyssa Smart B.Sc., University of British Columbia, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) April 2023 © Alyssa Smart, 2023 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis/dissertation entitled: Life cycle assessment of freshwater use in the semi-arid Okanagan: building the framework for new LCIA impact pathways of freshwater use for terrestrial ecosystems and modeling irrigation water infrastructure and use in horticulture submitted by Alyssa Smart in partial fulfillment of the requirements of the degree of Master of Science . Dr. Nathan Pelletier, University of British Columbia Okanagan Supervisor Dr. Melanie Jones, University of British Columbia Okanagan Supervisory Committee Member Dr. Johannus Janmaat, University of British Columbia Okanagan Supervisory Committee Member Dr. Sean Smukler, University of British Columbia University Examiner iii Abstract Representation of the impacts of irrigation in agricultural systems is often simplified in life cycle assessment (LCA) to consider only water and energy consumption. This research aimed to understand the importance of irrigation as a contributor to a more comprehensive suite of environmental outcomes of Okanagan cherry orchard production and to elucidate whether employing more efficient irrigation technologies on orchards propagated relevant reductions to environmental impacts. However, how human consumption of freshwater use impacts terrestrial ecosystems is not well understood in life cycle impact assessment and presents a challenge for accurately modelling these pathways. A regionalized life cycle inventory of Okanagan water delivery systems was developed and an LCA was performed to determine the share and distribution of environmental burdens associated with irrigation water, inclusive of processes within the water supply network. To improve our capacity to understand of the impacts of human consumption of freshwater use, a methodological framework was also developed using a literature analysis to model the impacts of freshwater use on terrestrial dryland and semi-arid ecosystems, as applicable to the semi-arid Okanagan. Irrigation contributed a significant portion to most life cycle environmental impacts of cherry production. The highest impact contributions from irrigation were found in the categories water scarcity, human toxicity, freshwater ecotoxicity, and mineral resource use. Employment of more efficient irrigation technologies than the most common irrigation system used on cherry orchards decreased impacts to these categories by 10-16%. Increased carbon dioxide emissions from bicarbonate deposited in orchard soils from irrigation did not make substantial relative impacts to the overall emissions of Okanagan cherry production. When the increased nitrous oxide emissions associated with drip irrigation were considered in the model of cherry orchard production, drip irrigation still outperformed microsprinkler in every impact category except climate change, where higher impacts due to nitrous oxide emissions from drip irrigation were counteracted by the decreased efficiency of microsprinkler systems. Biotic mechanisms for inclusion in the development of a mechanistic impact pathway linking freshwater use to consequent impacts to semi-arid biomes through the use of the “water-web” concept were identified. Mechanistic factors along this impact pathway included invertebrate primary and secondary consumers and phreatophytic plants that propagate water through the biome. iv Lay Summary How human consumption of water impacts terrestrial ecosystems is not well understood. Similarly, the role of irrigation as a contributor to the environmental burdens of agricultural systems is often overlooked or considered with insufficient resolution. This research developed new methods for modelling the impacts of water use on terrestrial ecosystems, including drylands and semi-arid biomes such as those of the Okanagan Valley in western Canada. This study also assessed a subset of environmental burdens of irrigation in the context of the “life cycle” (i.e., supply chain) of Okanagan cherry production systems. It was determined that irrigation was a relatively important source of burdens to the environment, including its contribution to climate change. Results showed that the pipes, water treatment, energy use, and water use of water supply networks that supply water to cherry orchards had important consequences for the overall environmental impacts of Okanagan cherry production. It was found that switching to more efficient irrigation technologies used on orchards can reduce these impacts. v Preface A version of the research presented in Chapter 2 will be submitted to the Journal of Cleaner Production. This chapter adapted research previously published in the Journal of Cleaner Production (2019) by Victoria Sanderson, Nicole Bamber, and Dr. Nathan Pelletier. Additional research contained in Chapter 2 was performed by Alyssa Smart and was written by Alyssa Smart and edited by Dr. Nathan Pelletier. The literature analysis presented in Chapter 3 was researched and written by Alyssa Smart, with edits from Dr. Nathan Pelletier, Dr. Anne-Marie Boulay, Dr. Montserrat Núñez, and Dr. Davoud Heidari. All Chapters contained in this thesis were edited by Dr. Nathan Pelletier, Dr. Melanie Jones, and Dr. Johannus Janmaat. vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface ............................................................................................................................................v Table of Contents......................................................................................................................... vi List of Tables ................................................................................................................................ ix List of Figures ............................................................................................................................. xv List of Abbreviations ................................................................................................................. xix Acknowledgements .................................................................................................................... xx Dedication ................................................................................................................................... xxi Chapter 1: Introduction .................................................................................................................1 1.1 Introduction to Life Cycle Assessment (LCA) ............................................................................6 1.1.1 Life Cycle Thinking and Life Cycle Assessment ....................................................................6 1.1.2 Goal and Scope Phase ...........................................................................................................8 1.1.3 Life Cycle Inventory Phase .................................................................................................. 11 1.1.4 Life Cycle Impact Assessment Phase ................................................................................. 14 1.1.5 Interpretation Phase ............................................................................................................ 18 1.2 Addressing freshwater use in semi-arid environments using LCA ........................................ 22 1.2.1 Agricultural freshwater use in the semi-arid Okanagan ...................................................... 22 1.2.2 Using LCA to assess freshwater use in agricultural systems ............................................. 23 1.2.3 Methodologies for addressing freshwater use within LCA .................................................. 25 1.2.4 AWARE indicator from WULCA........................................................................................... 27 1.2.5 Addressing a methodological gap in LCA: Incorporating terrestrial ecosystem demand into midpoint level water scarcity indicators ........................................................................................ 28 vii Chapter 2: LCA of water delivery infrastructure and irrigation technologies employed in Okanagan cherry production .................................................................................................... 31 2.1 Introduction ............................................................................................................................. 31 2.2 Methods .................................................................................................................................. 32 2.2.1 Goal and Scope ................................................................................................................... 32 2.2.2 Life Cycle Inventory ............................................................................................................. 37 2.2.2.1 Overview ........................................................................................................................... 37 2.2.2.2 Delivery Pipes ................................................................................................................... 38 2.2.2.3 Treatment Phase .............................................................................................................. 39 2.2.2.4 Pumping ............................................................................................................................ 41 2.2.2.5 Orchard level emissions and irrigation technologies inventory ........................................ 42 2.2.3 Life Cycle Impact Assessment Methods ............................................................................. 47 2.2.4 Life Cycle Interpretation Methods ........................................................................................ 49 2.2.4.1 Uncertainty Methods ......................................................................................................... 50 2.2.4.2 Sensitivity Analyses .......................................................................................................... 52 2.3 Life Cycle Inventory Results ................................................................................................... 54 2.3.1 Okanagan Water Delivery Systems Overview .................................................................... 54 2.3.2 Delivery pipes ...................................................................................................................... 55 2.3.3 Treatment Processes ........................................................................................................... 59 2.3.4 Pumping ............................................................................................................................... 63 2.3.5 Irrigation Technologies ........................................................................................................ 64 2.3.6 Cherry orchard LCI adjustments of nitrogenous emissions ................................................ 65 2.4 LCIA Results ........................................................................................................................... 66 2.4.1 Okanagan Water Delivery Systems Overview .................................................................... 66 2.4.2 Orchard Overview ................................................................................................................ 68 2.4.3 Treatment comparison ......................................................................................................... 73 viii 2.4.4 Pumping Types .................................................................................................................... 79 2.4.5 Irrigation technology comparisons ....................................................................................... 82 2.4.6 Sensitivity analyses ............................................................................................................. 89 2.5 Discussion ............................................................................................................................... 94 2.5.1 Water supply system comparisons ...................................................................................... 94 2.5.2 Sensitivity analysis ............................................................................................................. 102 2.5.3 Okanagan cherry orchard system comparisons ............................................................... 104 Chapter 3: An interdisciplinary approach to improving LCIA methodologies for freshwater use impacts on terrestrial ecosystem quality.................................................... 110 3.1 Introduction ........................................................................................................................... 110 3.2 Methods ................................................................................................................................ 114 3.3 Results .................................................................................................................................. 117 3.3.1 Unique Water Use Biomes ................................................................................................ 117 3.3.2 Review of drylands and semi-arid biomes for UWUB development ................................. 119 3.3.2.1 Fate factor ....................................................................................................................... 119 3.3.2.2 Effect Factor .................................................................................................................... 121 3.3.2.3 Sensitivity Factor............................................................................................................. 124 3.3.2.4 Damage Factor ............................................................................................................... 125 3.4 Discussion ............................................................................................................................. 125 Chapter 4: Conclusion ............................................................................................................. 129 4.1 Research strengths and limitations ...................................................................................... 133 Bibliography .............................................................................................................................. 136 Appendices ................................................................................................................................ 152 Appendix A ................................................................................................................................ 152 Appendix B ................................................................................................................................ 157 Appendix C ................................................................................................................................ 160 ix List of Tables Table 1 Baseline impact categories most commonly used in LCA according to LCIA method suite CML and adapted from Acero et al. (2016). Corresponding midpoint level indicators and units in IMPACT World+. .............................................................................................................. 48 Table 2 Each reported system from three water purveyors and the attributes associated with each system: total volume of water consumed during the irrigation months April – October (2018), the production proportion, delivery method, water source, and treatment type. ............. 54 Table 3 LCI data for manufacturing, transportation, and installation of 500 mm pipe made of asbestos concrete (AC), concrete, ductile iron, fiberglass reinforced plastic (FRP), galvanized steel, high-density polyethylene (HDPE), reinforced concrete cylinder pipe (RCCP), steel and polyvinyl chloride (PVC) for 5 m of water delivery infrastructure with a lifetime of 50 years. ...... 55 Table 4 Length of each pipe type required to deliver a cubic meter of water to an agricultural user was found by dividing the production weighted average length of each pipe by the average volume of water delivered over a 50-year lifespan. ..................................................................... 59 Table 5 LCI data for the manufacturing, construction and transportation phases of products used in four water treatment methods employed by water delivery systems in the Okanagan. Treatment plant inputs sub-categorized by processes within the plant. Inputs scaled to a m3 of treated water. ................................................................................................................................ 61 Table 6 The delivery method, source, average energy consumed by pumping, and the production weighted average energy consumed by pumping for 14 water delivery systems in the Okanagan. ..................................................................................................................................... 63 Table 7 The type, efficiency, and percent frequency of each irrigation technology employed on Okanagan cherry orchards that used irrigation (n=1422). The input volume describes the volume of water required to deliver a cubic meter of water to the soil for crop uptake and is calculated using the efficiency coefficient. The last column represents the CO2(g) released from x the soil due to bicarbonates delivered by a cubic meter of irrigation water, with irrigation technology efficiency accounted for. ............................................................................................ 65 Table 8 Mass of direct and indirect N2O, NO3-, NH3, and NOx emitted at the orchard level due to synthetic fertilizer application per functional unit of 1 kg of usable cherries. Direct and indirect N2O was combined and entered as one output flow in openLCA. ............................................... 65 Table 9 Life cycle impact assessment results for the production weighted Okanagan water delivery system per functional unit of 1 cubic meter of water delivered to agricultural users. Delivery pipe, treatment, and pumping process results and total system impacts with associated standard deviation (SD) were included for all 14 categories assessed. ...................................... 68 Table 10 Life cycle impact assessment results per functional unit of one kg of usable cherries for the Okanagan cherry production system, using the production weighted Okanagan water delivery system as the irrigation provider. Irrigation, fertilizer application, and energy supply processes results, total system results and associated standard deviation (SD) were included for all 14 impact categories assessed. ......................................................................................... 69 Table 11 Two tailed p-values produced from two-sample T-test (assuming equal variances) for 14 impact categories and between two orchard systems with different irrigation providers: production weighted Okanagan WDS and conventional tap water production in Quebec (original dataset used), where all other processes and inputs were held constant. Bolded p values indicate significance. ..................................................................................................................... 72 Table 12 Single factor analysis of variance (ANOVA) of five groups (treatment at a treatment plant, generated hypochlorite treatment, delivered hypochlorite treatment, delivered chlorine, and no treatment) and for 14 impact categories. Each of the five scenarios were modelled using the production weighted average water supply network with all other inputs and processes held constant except for the inputs associated with five different treatment regimes. Bolded p values indicate significance. ..................................................................................................................... 73 xi Table 13 One tailed p-values, t Stat, and degrees of freedom (df) produced from two-sample T-test (assuming equal variances) between orchard systems with different irrigation inputs: untreated, production weighted irrigation and production weighted irrigation that has been treated at a plant, where all other processes and inputs associated with the water supply network and cherry orchard were held constant in the model. Bolded p values are significant. 79 Table 14 Degrees of freedom (df), F value, and p values of a single factor analysis of variance (ANOVA) of three treatment groups (gravity fed, mixed, and pumped scenarios at the pumping stage) and for 14 impact categories. All other processes and inputs to the water supply network were held constant and were modelled as the production weighted average, so as to compare impact scores among different pumping scenarios. Significance level was Bonferroni corrected to p<0.017 and bolded p values are significant. ........................................................................... 80 Table 15 Degrees of freedom (df), t Stat, and p values according to the two-sample T-test for four different model scenarios of irrigation technologies (SDI, microsprinkler, handline, and the production weighted average irrigation technology) and for the impact category climate change short term. All other processes, inputs, and outputs of the production weighted Okanagan water supply network were held constant other than the irrigation stage so as to make comparisons of impact scores among irrigation technology scenarios. Significance level was Bonferroni corrected to p<0.013 and bolded p values indicate significance. ................................................ 82 Table 16 Degrees of freedom (df), t Stat, and p values according to the two-sample T-test for four different model scenarios of irrigation technologies (SDI, microsprinkler, handline, and the production weighted average irrigation technology) and for the impact category water scarcity. All other processes, inputs, and outputs of the production weighted Okanagan water supply network were held constant other than the irrigation stage so as to make comparisons of impact scores among irrigation technology scenarios. Significance level was Bonferroni corrected to p<0.013 and bolded p values indicate significance. .................................................................... 83 xii Table 17 Single factor analysis of variance (ANOVA) of four Okanagan water supply network model scenarios using different irrigation technologies as inputs to the irrigation process (SDI, production weighted average irrigation, micro-sprinkler, and handline) and for 12 impact categories. All other processes and inputs of the production weighted Okanagan water supply network model were held constant to compare impact scores among irrigation technology scenarios. Bolded p values are significant. .................................................................................. 84 Table 18 Single factor analysis of variance (ANOVA) of orchard model scenarios using different irrigation technologies as inputs to the irrigation process (SDI, production weighted average irrigation, micro-sprinkler, and handline) and for 12 impact categories. All other processes and inputs of the cherry orchard model were held constant to compare impact scores among irrigation technology scenarios. .................................................................................................... 85 Table 19 Single factor analysis of variance (ANOVA) of four scenarios of the production weighted Okanagan water supply network (12% loss at start, 12% loss at end, 23% loss at start, 23% loss at end) and for 14 impact categories. All other processes and inputs were held constant in the model except for the amount and location of water losses occurring within the water supply network. This was achieved by changing the volume of water required to deliver 1 m3 of irrigation water based on the percent losses reported to occur within the water supply network, and by changing the location these losses were accounted for within the model (i.e. before or after water is passed through all stages of the network). Bolded p values indicate significance ................................................................................................................................... 90 Table 20 Degrees of freedom (df), t Stat, and p values according to the two-sample T-test for four different treatment groups of four water delivery system scenarios (12% loss at start, 12% loss at end, 23% loss at start, 23% loss at end and for the impact category climate change (short term). All other processes and inputs were held constant in the model except for the amount and location of water losses occurring within the water supply network. This was achieved by changing the volume of water required to deliver 1 m3 of irrigation water based on xiii the percent losses reported to occur within the water supply network, and by changing the location these losses were accounted for within the model (i.e. before or after water is passed through all stages of the network). The post hoc Bonferroni corrected significance level was p<0.013 and bolded p values indicate significance. .................................................................... 90 Table 21 One tailed p-values produced from two-sample T-test (assuming equal variances) between water delivery systems with different electricity grid providers: Alberta electricity grid and BC electricity grid. This comparison was achieved by changing the provider of electricity in the treatment and pumping stages to either the BC or the Alberta electricity Ecoinvent process provider. All other processes and inputs within the model were held constant. Bolded p values indicate significance. ..................................................................................................................... 92 Table 22 Climatic, soil, vegetative, and hydrological characteristics defining the initial list of four unique water use biomes (UWUBs), primarily based on Baird and Wilby (1999), with additional references. .................................................................................................................................. 118 Table 23 Length and type of pipes used in the distribution network of 12 water supply networks in the Okanagan operated by three distinct water purveyors. ................................................... 153 Table 24 Duteau WTP chemical usage during the irrigation season as reported by the Regional District of North Okanagan. All doses in mg L-1. Note all but caustic 2 are mostly removed from the water before entering the distribution system. ..................................................................... 157 Table 25 Duteau WTP reported volumed of chemicals used during coagulation, flocculation, and clarification processes. Chemical brand names were converted to analogs available in openLCA using active ingredient names listed in the MSDS sheets of each chemical. Calculations and modelling choice justifications are provided in the last column. ................................................ 158 Table 26 Input materials and volumes required by the DE NORA Clortec 750 on-site hypochlorite generator to produce 0.8% hypochlorite solution used during disinfection at the Duteau WTP................................................................................................................................ 159 xiv Table 27 Impact scores (Mean) and standard deviations (SD) for all Okanagan water delivery system scenarios and for all 14 impact categories assessed in the research performed in Chapter 2. Standard deviations were derived from Monte Carlo simulations (n=1000) of each LCIA model scenarios. ................................................................................................................ 160 Table 28 Impact scores (mean) and standard deviations (SD) of Okanagan water delivery system scenarios performed in the sensitivity analyses reported in Chapter 2. Standard deviations were derived from Monte Carlo simulations (n=1000) of each LCIA model scenarios...................................................................................................................................................... 162 Table 29 Impact scores (mean) and standard deviations (SD) across all 14 impact categories and for all Okanagan cherry production system scenarios reported in Chapter 2. For all scenarios except for the Quebec water delivery system (WDS) scenario, a version of the Okanagan water delivery system was used as the provider for irrigation. Standard deviations were derived from Monte Carlo simulations (n=1000) of each LCIA model scenarios. ............ 163 xv List of Figures Figure 1 Flow diagram representing the Okanagan water delivery and cherry product systems. Areas in green and grey represent the ecosphere and technosphere, respectively. Within the technosphere, background and foreground processes are joined along the supply chain with the use of colour coded arrows representing elementary, process, and waste flows (see legend). For a complete methodology and background system description of the cherry product system, see Sanderson et al. (2019). ........................................................................................................ 38 Figure 2 IMPACT World+ life cycle impact assessment results of the production weighted Okanagan water delivery system. Results are broken down by process (delivery, treatment, pumping, and source water) and shown as the percent contribution for each of nine impact categories. ..................................................................................................................................... 67 Figure 3 LCIA results shown as relative percent contributions of each process stage during cherry production across nine impact categories and comparing two orchard systems. Okanagan WDS (water delivery system) model uses the production weighted Okanagan water delivery system as the provider for orchard irrigation. Original model is from Sanderson et al. (2019) and uses Quebec conventional tap water production as the provider for orchard irrigation. *Note that modifications to the published LCA model for Okanagan cherry orchard production were made and are described in section 2.3.6. Differences in total impacts for each system shown are statistically significant (Two-sample T-test; p<0.001). ................................... 71 Figure 4 LCIA results shown as percent contributions on a relative scale across five treatment scenarios: no treatment, delivered chlorine injected at intake source, delivered hypochlorite injected at intake source, chlorine generated on-site and injected at intake source, and the Duteau Creek water treatment plant. Impact results are further broken down into four water delivery phases: piping for delivery, treatment, pumping, and source water usage. All impact categories had statistically significant differences between groups (Single Factor ANOVA; df =4; =0.05; p<0.001). For absolute values across all impact categories, see Appendix C. ............. 76 xvi Figure 5 Percent contribution of irrigation of process stages of an Okanagan cherry orchard, including orchard set up and teardown, fertilizer application, application of irrigation water, and all other orchard maintenance inputs. * Indicates a statistically significant difference between treatment groups (two-sample T-test; p<0.05). ............................................................................ 78 Figure 6 Percent contribution of each pumping regime (pumped, mixed, and gravity fed) across nine impact categories, showing contributions broken down to the process stage (delivery pipe, treatment, pumping, and source water). Note only climate change (short term) and fossil and nuclear energy use have relevant contributions at the pumping stage compared to other processes in the system. Significant differences between groups occur for the terrestrial acidification, climate change and fossil and nuclear energy use impact categories (single factor ANOVA;  = 0.05; df = 2; p<0.05). For absolute values, standard deviations, and all impact categories see Appendix C. .......................................................................................................... 81 Figure 7 Impacts to water scarcity for three irrigation technologies (handline, SDI, and micro-sprinkler) and the production weighted (PW) average. Axis values show the equivalent m3 consumption of water at the world average per m3 of irrigation water delivered to agricultural users. * Indicates statistical significance (Two-sample T-test; p<0.05). Groups that are statistically different are indicated with letters (a, b, and c) ......................................................... 82 Figure 8 Impacts to climate change (short term) for three irrigation technologies (handline, SDI, and micro-sprinkler) and the production weighted (PW) average. * Indicates statistical significance (Two-sample T-test; p<0.05). Groups that are statistically different are indicated with letters (a, b, and c). Percentages indicate the percent each process (treatment, pipe delivery, and pumping) contributes to the system’s total impacts to climate change (short term). Deposited bicarbonate reaches agricultural soils through irrigation water from water sources containing carbonates and accounts for 4% of the water delivery system’s impact to climate change (short term)....................................................................................................................... 84 xvii Figure 9 Percent contribution of the most efficient (SDI), least efficient (handline) and most common (solid-set undertree and micro-sprinkler) irrigation systems to the life cycle impacts of cherry production across nine impact categories, showing contributions broken down to process stages (irrigation, energy supply, fertilizer application, orchard set up and teardown, and all other orchard maintenance). Note for climate change (short term) and terrestrial acidification, different irrigation regimes result in replacing fertilizer as the highest contributor at the orchard level. Statistically significant differences between groups within impact categories did not occur (single factor ANOVA;  = 0.05; df = 3; p>0.05). For absolute values, standard deviations, and all impact categories see Appendix C. ......................................................................................... 87 Figure 10 Relative percent contribution of drip versus micro-sprinkler systems at the orchard level. Nitrogenous GHG emissions are included in fertilizer application as a 29% decrease in emissions produced when using micro-sprinkler over drip irrigation systems. Differences in impacts within the irrigation category are due to an increased water use efficiency in drip compared to micro-sprinkler, therefore resulting in reduced impacts associated with the water delivery system, including bicarbonate deposition into orchard soils from source water. ........... 89 Figure 11 LCIA results for water scarcity (left) and climate change, short term (right) for four water delivery system scenarios. * Indicates statistical significance (two-sample T-test; p<0.05). Groups that are statistically different are indicated with letters (a, b, and c). .............................. 92 Figure 12 Relative percent contribution of two water delivery systems using different electricity providers for the treatment plant phase for 14 impact categories. * Indicates statistical significance between treatment groups (two sample T-test; p<0.05). ......................................... 94 Figure 13 The characterization factor (CF) components along a potential LCIA impact pathway for biomes adjacent to lakes and rivers. The fate factors (FF) link to the human intervention of the withdrawal from surface or groundwater compartments, respectively. The FF is multiplied by the sensitivity factor (SF) – the ecosystem’s buffering response to a change in water availability. The effect factor (EF) at the midpoint is the change in the ecosystem not offset by the SF, xviii represented here as the loss of macrophytes that deposit nutrients at the land-lake interface. The damage factor (DF) at the endpoint is expressed as potentially disappeared fraction of species (PDF•m2•year). .............................................................................................................. 116 xix List of Abbreviations AWARE: Available Water Remaining BWSI: Blue Water Scarcity Index CF: Characterization Factor LCA: Life Cycle Assessment LCIA: Life Cycle Impact Assessment EWR: Environmental Water Requirements (synonymous with EFR – Environmental Flow Requirements) AoP: Areas of Protection, which consist of three endpoint level damage categories in life cycle impact assessment named human health, ecosystem quality, and resource availability FF: Fate Factor – the quantity and duration of the impact the human intervention (water withdrawal – water release, i.e., water consumption) has on water availability in the compartment directly affected and other connected compartments. SF: Sensitivity Factor – also referred to as an exposure factor. In the context of water consumption pathways, it is the ecosystem’s response to a change in water quantity and whether that change will be compensated for through either habitat stability or species’ capacity to utilize alternative water resources EF: Effect Factor – a change in the ecosystem due to a change in water availability (FF) that cannot be offset via the SF. DF: Damage Factor – the severity of the effect factor expressed as an ecosystem quality loss at the endpoint level xx Acknowledgements I would like to thank the employees at Okanagan Basin Water Board, Regional District of Central Okanagan, Regional District of Northern Okanagan, Town of Oliver, City of Kelowna and City of West Kelowna for their cooperation and help providing data imperative to the success of this project. In particular, I am grateful to Ron Fretwell, Anna Warwick Sears, Rob Hillis, Shawn Goode and Skylar Ganz for their effort in data collection that helped me succeed in this research. I am also grateful for Daniel Daly-Grafstein’s expertise and support on my statistical analysis. I would like to thank my committee members Dr. Jones and Dr. Janmaat for providing consistent and thorough support throughout this project. In particular, I would like to give my sincerest gratitude to my supervisor, Dr. Nathan Pelletier, for providing constant and unwavering support and patience throughout my entire degree, and for assuring me the finish line was right around the corner. I would also like to thank my colleagues in the Food Systems PRISM Lab for the times we have shared in both academic frustration and achievement - to each of you I truly wish you success in your degrees, aspirations and careers. In particular, I would like to thank Nicole Bamber, our ‘lab mom’, who has offered her academic knowledge and support to myself and others in this lab where otherwise we may have been lost in a sea of formats and functional units. xxi Dedication To my dad, for instilling in me a restless curiosity about the world and a willingness to search for answers. To Henry, for a lifetime of adventure and companionship that is dearly missed. And to my mom, for showing me what it looks like to persevere through hardship, and for her constant encouragement, unconditional support, and ever evolving friendship. 1 Chapter 1: Introduction In 2010, a cohort of concerned scientists estimated the “boundaries for a healthy planet” that, if breached, would threaten the habitability of the Earth (Foley 2010). Among the thresholds of environmental processes that were quantified was the use of freshwater - a resource which has seen a massive increase in demand from agricultural activities in the 20th century, is vital for aquatic and terrestrial ecosystem functioning, and to which stable access is a necessity for food security (UN 2010, 2019). A staggering 70% of our freshwater consumption is for irrigation activity for agricultural purposes, both globally (Foley 2010) and at the regional level within the Okanagan Valley in B.C., Canada (Van Der Gulik et al. 2010). The Okanagan hosts unique and threatened semi-arid ecosystems which rely on the same decreasingly available freshwater from watersheds that is also utilized for human activities (OBWB 2010; Gieselman et al. 2013). The valley’s freshwater resources are heavily relied on to sustain its economically prosperous horticultural and agricultural industry as well as a growing urban population (Van Der Gulik et al. 2010). However, the continual rise in urban and agricultural development within the Okanagan has strained the valley’s limited freshwater resources. Freshwater consumption for irrigation purposes, combined with the decrease in annual snowmelt and rainfall due to climate change, has placed the Okanagan Valley at risk of fully allocating its freshwater resources (Van Der Gulik and Neilsen 2008; Crawford and MacNair 2012). Foley (2010) suggests that Earth’s planetary threshold for freshwater use has not yet been breached, and proposes improving irrigation efficiency as a solution to keep it that way. Could this solution be usefully employed in the Okanagan Valley to avoid a freshwater crisis? To answer this question, one must consider the problem of freshwater use in the Okanagan from a systems level perspective. Life cycle thinking enables taking such a perspective, whereby the environmental, human health, and resource burdens of human activities are 2 assessed through the exploration of the complex interactions between the natural environment and human-mediated technological processes. Life cycle thinking recognizes that systems, such as the irrigation systems used on farms, are embedded in larger networks that provide context across space, time, and technological sectors. Taking this systems-level perspective is necessary for informed decisions at the farm level to ensure net positive environmental outcomes (Bosch et al. 2007). Adopting a systems-level approach to manage freshwater use, in the Okanagan Valley and elsewhere, can enable the discovery of interactions that may have otherwise been left invisible, support identification of management strategies that are adaptive across the life cycle of the system, and is necessary to better manage vital environmental resources. Crop irrigation is an exemplar process that creates environmental trade-offs that often are not accounted for in system level studies. Increasing water use efficiency through employing more efficient irrigation technology, such as switching from micro-sprinkler to drip or sub-surface drip systems, at first glance appears to be an environmental improvement to the crop system. However, evidence suggests trade-offs exist between increasing water use efficiency and increasing release of the greenhouse gas nitrous oxide (N2O) from crop soils (Baram et al. 2018). Irrigation systems can saturate soils and create anaerobic conditions for denitrifers – microbes in the soil that convert nitrates and nitrites into nitrogen (N2) and nitrous oxide (N2O)(Skiba 2008; Fentabil et al. 2016). Studies have shown increased N2O emissions when drip irrigation is used compared to micro-sprinkler or sprinkler systems (as examples, Smart et al. 2011; Fentabil et al. 2016; Baram et al. 2018). This result is likely because of the increased frequency and duration that water filled pore space (volumetric water in the soil with soil density considered) is higher in the soil (Smart et al. 2011). When water filled pore space is high, it results in anaerobic conditions that facilitate the microbial processes responsible for release of N2O (denitrification). 3 Irrigation can also affect soil carbon (C) storage. Midwood et al. (2021) found that woody-perennial crops (such as apple and cherry trees) grown in the semi-arid Okanagan have increased C stocks and organic C concentrations relative to adjacent and unirrigated grassland. It was suggested this outcome is due to higher C inputs as well as decades of irrigation to these crop systems. Further evidence from Emde et al. (2021) suggests that irrigated agriculture can alter soil organic carbon stocks, the magnitude and direction of which is characterized by irrigation methods, aridity, and soil texture. In arid and semi-arid regions, irrigated agriculture can increase soil organic carbon stocks in the 0-10 cm depth and in the 10-20 cm depth of soil. Sprinkler irrigation was correlated to a higher increase of soil organic carbon at the 0-10 cm depth than drip irrigation, and both reduced soil organic carbon in deeper layers of the soil. These studies provide evidence for the nuances that occur between irrigation and organic carbon storage in agricultural soils. Irrigation has also been shown to increase the release of the greenhouse gas carbon dioxide (CO2). Hannam et al. (2016) found that bicarbonate (HCO3) originating in the Okanagan Lake was delivered to orchard soils through irrigation, where a chemical reaction can produce inorganic carbonates and CO2, in the presence of sufficient cations. Production of CO2 is dependent on the amount of irrigation applied, among other factors, and therefore can be influenced by varying water efficiencies of irrigation technologies. The implications of irrigation-induced changes to GHG emissions in crop systems relative to other inputs to the crop system remain an important but as of yet understudied area of research. To understand the consequences and environmental trade-offs of irrigation technologies at a systems level, it is important to take the life cycle of the crop system into account. Assessing the environmental impacts of a system based on life cycle thinking is often accomplished using Life Cycle Assessment (LCA) (Guinée et al. 2001). LCA is a form of systems level impact assessment that seeks to quantify the human health, environmental, and resource burdens associated with a product or service across its life cycle (Guinée et al. 2001). LCA has been previously applied to assess freshwater use and the associated impacts in a 4 variety of contexts. For example, Pradeleix et al. (2018) used LCA to assess the impacts of groundwater irrigation on the Kairouan Plain – a semi-arid region climatically similar to the Okanagan – and found that the depletion of natural resources was the impact category to which freshwater use contributed most. LCA has also been used to compare different farm irrigation technologies, as reported by Eranki et al. (2017) for a natural rubber production system. Further, the water production systems used to supply water to agricultural and household users has also been assessed using LCA in a variety of contexts. For example, Homäki et al. (2003), Landu and Brent (2006), Lassaux et al. (2007), and Lyons et al. (2009) used LCA to understand the different impacts associated with different water delivery methods, as well as to compare the processes and inputs within water delivery systems that have the most significant environmental burdens. Advancements to LCA methodologies for determining the impacts of freshwater use is an ongoing area of research. Pfister et al. (2009) used cotton production as a case study for developing LCA methods for assessing freshwater consumption. Boulay et al. (2011) developed a categorization schema for water in life cycle inventories (data used to create LCAs) that includes, among others, a separate category for agricultural water users. The consensus-based water scarcity indicator AWARE – Available Water Remaining – was a milestone for improving water footprinting methods as it takes a life cycle thinking and regionalized perspective when assessing the demand of freshwater from other users in the region. However, pathways for determining the demand of all users – including aquatic and terrestrial ecosystems – are challenging to develop, hence this remains an ongoing area of research (Kounina et al. 2013). How freshwater is extracted from different water compartments (i.e., atmosphere, lake, etc.) and used by terrestrial ecosystems is a particularly underdeveloped pathway in current life cycle impact assessment models. Understanding how water is propagated through semi-arid ecosystems would enhance the completeness and quality of LCAs for Okanagan production systems that consume freshwater. 5 On the basis of these gaps in current knowledge, this research used LCA to evaluate and compare the environmental impacts of Okanagan water delivery systems that supply to agricultural users, as well as the use of irrigation water on cherry orchards – a common horticultural production system in the Okanagan. The objective of this research was to answer the following questions: (1) How do environmental impacts compare across water delivery systems in the Okanagan and across irrigation technologies employed on cherry orchards? (2) Does irrigation water make a significant relative contribution to the impacts of cherry production in the Okanagan Valley? If so, will employing irrigation technologies that are more water efficient at the orchard level significantly mitigate freshwater consumption as well as other life cycle environmental impacts? (3) Do bicarbonates from irrigated water and differences in nitrous oxide emissions resulting from the employed irrigation technologies have significant implications for the environmental burdens associated with orchard systems? And lastly, (4) what are the potential pathways for assessing the impacts of terrestrial ecosystem freshwater use in semi-arid systems in LCA? In order to answer these research questions, the remainder of this thesis was structured in the following manner. The remainder of chapter one focuses on (1) introducing the concepts and application of life cycle assessment and life cycle thinking in the context of this research and (2) the current state of water scarcity in the Okanagan and the application of water footprinting methodologies pertinent to the LCA performed in this study. The second chapter of this thesis is the first research chapter and describes the life cycle assessment of Okanagan water delivery systems and irrigation technologies employed on Okanagan cherry orchards. The third chapter describes the research performed to develop cause-effect chains of freshwater use in semi-arid terrestrial ecosystems for future use in LCA. The fourth and final chapter summarizes the research performed during this master’s project and will conclude the thesis. 6 1.1 Introduction to Life Cycle Assessment (LCA) 1.1.1 Life Cycle Thinking and Life Cycle Assessment Human activities threaten our health and the health of ecosystems we rely on, yet how to quantify the impacts of these activities is very complex (Perminova et al. 2016). This is because the products and services we use come from a diverse array of interlinked industrial activities, each with exchanges between human processes (the technosphere) and the environment (the ecosphere) that occur throughout the life of the product or service. The method we use to assess human activities must capture this complexity by assessing all exchanges within the system over the lifetime of the product or service, from the procurement of raw materials all the way to the eventual disposal of the product. Without assessing the “life cycle” of the product or system in its entirety, one could erroneously recommend changes that shift environmental burdens within a system rather than reduce them, falsely conclude one product is more environmentally friendly than another, or otherwise fail to produce results that capture reality. In essence, this is what life cycle thinking aims to correct. Life cycle thinking broadens the traditionally narrow scope used to assess systems, with the aim to capture relevant exchanges between the environment and human activities and convey them in an interpretable manner. Life cycle assessment (LCA) was formalized in the 1990s as a methodological approach to evaluate environmental burdens along a product chain (Heijungs et al. 1992). Based on life cycle thinking, it is used to compile and analyze the inputs and outputs associated with the entire life cycle of a product (Guinée et al. 2001) and to estimate their potential resource and environmental effects. This methodological practice was further formalized within the 14040 series of ISO (International Organization for Standardization) standards (ISO 2006a). Together, these documents merged core concepts of life cycle thinking into a standardized practice for life cycle assessment. ISO 14040-44 guidelines for LCA best practices are intended to support a 7 holistic approach to the analysis of product chains, incorporate the use of defined system boundaries and enable the avoidance of burden-shifting (ISO 2006b; Pradeleix et al. 2015). The ISO guidelines define two distinct LCA approaches, both of which follow life cycle thinking. The first is attributional LCA, which is intended to allocate a portion of the global environmental burden to a process or product chain. As Weidema (2003) describes, if the global environmental burden was a pie, then an attributional LCA would seek to measure how big of a slice of that pie is due to a particular process or product. This form of assessment is often used for hot-spot identification (identifying processes within a system that contribute a relatively high degree of damage) and to provide information to consumers (Guinée et al. 2001; Weidema 2003). The second approach is a consequential LCA, which seeks to identify a change in environmental burdens that occurs as a consequence of a decision – which often the study aims to support (Rehl et al. 2012). Consequential LCA is therefore a form of scenario modelling, whereby the criteria for the hypothetical change in the product system is that it must lead to a change in market demand for that product. Central to this approach is the concept that the systems studied should be those actually affected by such a decision or change in demand. Throughout the past two decades both LCA approaches have continued to develop as tools for assessing environmental impacts through the recommendations of LCA practitioners. Practitioners have developed core life cycle inventories (LCI) and LCI databases to hold these data; developed life cycle impact assessment (LCIA) methods, models, and software for use at the LCIA stage; and have provided analysis and possible solutions to several limitations of the current ISO standards. Among these limitations are issues that exist at each stage of a life cycle assessment: discrepancies in handling multifunctionality problems and justifying the boundaries used for a system at the goal and scope phase (Tillman et al. 1994; Pelletier et al. 2015); using unrepresentative data to fill data gaps at the life cycle inventory stage (Björklund 2002); a lack of spatially resolved impact models and indicators at the life cycle impact assessment stage 8 (Kounina et al. 2013); and the methods for handling uncertainty associated with the abovementioned limitations at the interpretation stage (Weidema 2009). Each of these stages and the practical problems that arise within them are defined and discussed in greater detail in the proceeding sections. 1.1.2 Goal and Scope Phase Defining the goal and scope of a life cycle assessment (LCA) is the first and arguably most important step a practitioner must take to achieve a rigorous, transparent, and reproducible LCA. The ingredients necessary to define the goal of the study involve: (1) the research questions the LCA is intended to answer; (2) who the target audience is; (3) the intended application; and (4) the selection of impact categories that will satisfy the first three components of the goal (Guinée et al. 2001; Rebitzer et al. 2004). For example, LCA may be used to answer the question “what change in market demand will result from changing this part of the system?” using a hypothetical scenario (recall this is a consequential analysis); this LCA could be intended as a decision-support tool for policy makers and would therefore include impact categories that are easily interpreted by this audience. The scope is intended to set parameters for the system of study and provide reasoning for such choices, as well as detail the level of analysis and type of modelling that is appropriate to answer the defined questions of interest. Important components of the scope definition include (1) the functional unit, (2) system boundaries and cut-off criteria, (3) specification of the age, geographic resolution and technological coverage of data that is required, (4) the selection of model type to quantify and assess impacts, and (5) the recognition of any potential limitations that may be encountered (Guinée et al. 2001). Defining the functional unit during the goal and scope phase provides a reference unit that quantifies the function of a product system (ISO 2006a). The functional unit therefore can 9 include the specified quantity of a product/service for use over a specified time that meets a specific function. All flows – the output of goods/services from one process in a product chain to the next process – can then be scaled to the functional unit; this is what is referred to as a reference flow. In other words, a reference flow is the output from a process (10 units of material A, for example) that is required to fulfill the function of the functional unit (1 unit of product B used for 1 year with this function). The functional unit is used by LCA practitioners to standardize comparisons of alternative product chains and to quantify the amount of service that is delivered by a studied system (Weidema 2003; Pradeleix et al. 2018). The studied system is defined in the scope phase, which involves selecting which aspects of the supply chain are important in addressing the research goals. Defining the system boundaries – the beginning and end of the supply chain to be studied and the processes within that chain that are deemed relevant – is based on the goal of the study. Cut-off criteria are used to justify the exclusion of inputs and outputs of the processes within a product system of minor importance, and are usually based on mass, energy, or environmental significance (ISO 2006b). Two commonly used terms in LCA to define system boundaries are cradle-to-gate and cradle-to-grave, which refer to beginning your system boundary with the extraction of raw materials and ending at the factory gate (before the consumer) or at the end-of-life disposal, respectively. With the functional unit and system boundaries defined, an LCA practitioner must select the appropriate type of modelling that will best represent the studied system. This includes choosing between the previously defined attributional and consequential LCA approaches, as well as determining the level of analysis of the LCA. The latter refers to utilizing process-based vs screening-level LCA approaches. A process-based LCA is a detailed analysis of the inputs and outputs associated with each process in the supply chain and allows for a complete impact assessment for comparative assertions, hot-spot identification, etc. Process-based LCA is therefore a bottom-up approach which requires specific data for a detailed model unique to that product system. A screening-level LCA provides a quick snapshot of the environmental impacts 10 associated with a product and may also be process-based, though it is differentiated by its use of generic data (Ingwersen et al. 2012). Screening-level LCAs can therefore be useful at the initial stages of a project, as they do not require process-specific data. It is relevant to note that the goal of the LCA influences the scope chosen by providing context for the intended application of the results. I stated previously that the goal and scope phase is the most significant step in conducting an LCA – this is because a poorly constructed goal and scope can introduce error and compromise the study’s capacity to address the intended application. Error can be introduced to an LCA by (as examples) inaccurately prioritizing functions or by defining a functional unit that serves a multitude of functions and introduces false equivalencies (Reap et al. 2008a). Similarly, sub-functions are often overlooked and should be considered in detail; a sub-function of a product or service may be of a lower priority for the consumer than the function defined in the functional unit; however, the consumer may still make purchasing decisions based on that sub-function (Reap et al. 2008a). In this way, sub-functions have important market level consequences and as such must be addressed and resolved when defining the scope of the LCA. The issue of using allocation as a solution for multifunctionality (i.e., how a practitioner may choose to deal with co-products of a system) has received considerable attention as well (Martin et al. 2015). ISO 14040 defines allocation as the distribution of inputs and outputs of a product system between that system and another product system or process (ISO 2006a). ISO 14040 requires a hierarchal approach to allocation, wherein the first step is to avoid allocation altogether through the use of division into sub-processes or expansion of system boundaries to include co-product functions (ISO 2006a). If this is not possible, allocation of inputs and outputs should reflect the physical relationship between the co-products/functions; in cases where the former two avenues are not possible, allocation is most commonly based on the economic value of the products. However, these recommendations encounter criticism as they do not possess 11 definitive terminology and are too general to apply to all modelling formats that LCA has adopted (Pelletier et al. 2015). For instance, whether a practitioner is conducting a consequential LCA or an attributional LCA changes how the practitioner is able to navigate this hierarchal approach (Martin et al. 2015). Several potential issues can also arise when defining the boundaries of the system under analysis. Using highly refined cut-off criteria for quantitative process-based approaches is one method for resolving boundary issues (Reap et al. 2008a). Refining cut-off criteria effectively increases the rigor and replicability of the study, but at the consequence of increasing truncation errors (errors that result from approximations in place of an exact analytical procedure) and expanding data requirements (Reap et al. 2008a; Ward 2018). In part because these avenues for error are not always apparent at first, ISO (2006a) recommends an iterative process of refining the goal and scope of an LCA throughout the subsequent inventory, impact assessment, and interpretation phases. 1.1.3 Life Cycle Inventory Phase The life cycle inventory (LCI) phase of life cycle assessment involves the collection of data representative of the exchange of inputs and outputs associated with the system under study. Defining the system boundaries in terms of flow types, foreground and background systems, and the use of a flow diagram all aid the practitioner at this stage to achieve a comprehensive and robust LCA (Guinée et al. 2001; ISO 2006a). Partitioning inputs and outputs into background and foreground systems is valuable because data quality requirements differ between the two. A foreground system is unique to the particular system that is of focus in the LCA and is considered to be ‘case-specific’ (Guinée et al. 2001). As such, the accuracy and quality of foreground system data is critical for a conclusive LCA and should be collected from 12 primary sources with a high degree of geographical, temporal, and technological resolution (ISO 2001), as required to satisfy the defined goals and scope of the study. The background system includes all other unit processes that input to or output from the study system but are not case-specific nor under the direct control of the decision maker and therefore not within the foreground system (Guinée et al. 2001). Since processes within the background system exchange flows with the foreground system but are not the focal point of the LCA, a practitioner may utilize secondary sources such as third-party inventory databases, including the commonly used LCI database Ecoinvent, or averaged data that are general but comparable to the study system (ISO 2006b). Determining which unit processes form the foreground and background systems enable for a practical execution of the LCI phase. The exchange of flows within and outside the foreground and background systems can be visualized in a flow chart and further described in terms of flow type. Guinée et al. (2001) defines the following flows: (1) elementary flow, (2) product flow, (3) intermediate flow, and (4) waste flow. An elementary flow is any exchange between the ecosphere (raw materials untransformed by humans) and the technosphere (any human technological activity). An elementary flow therefore includes the extraction of raw materials (ecosphere to technosphere) as well as the emission/discarding of human transformed materials (technosphere to ecosphere). A product flow is the exchange of a material within the technosphere which no longer needs to be transformed further and is in itself a product/service (ISO 2006a). Similar to this is an intermediate flow, which exists within the technosphere but still requires some level of transformation to become a final product. Lastly are waste flows, defined as products/waste materials that are inputs to waste management processes; subsequent to management procedures, these waste flows then revert to elementary flows as they leave the technosphere and return to the ecosphere. While terminology discussed above has aided practitioners in developing LCI datasets that are appropriate for addressing the goal and scope of their study, there are a variety of 13 challenges that are still encountered during this phase. One challenge is the lack of access to quality LCI data for proprietary reasons or measurement difficulties (Rebitzer et al. 2004). However, the current trend in development of industrial databases is promising: more and more data is becoming publicly available from the private sector for third party LCA practitioners (Rebitzer et al. 2004). In addition, open access data initiatives are increasing and are managed by associations such as APME (Association of Plastic Manufacturers in Europe) and IISI (International Iron and Steel Institute). To address the need for more consistent LCI datasets EcoSPOLD developed a data documentation format in the 1990s, ILCD emerged as a competing format thereafter, and in 2001 ISO published a technical specification for data formatting. The document, ISO 14048 (2001), specifies the inclusion of relevant metadata in addition to process descriptors and environmental exchanges, modeling and validation, and administrative information (Rebitzer et al. 2004). Finally, it is critical for practitioners at the LCI phase to address uncertainties associated with their datasets. Data or ‘parametric’ uncertainty is the inherent variation that exists in a dataset as well as the uncertainty associated with the varying quality of data sources (de Koning et al. 2009). These uncertainties are commonly dealt with via default inventory ranges, through statistical uncertainty propagation techniques such as Monte Carlo (Groen et al. 2014), and through pedigree estimates like that of Henriksson et al.'s (2013) horizontal averaging. The uncertainty associated with inventory data will be discussed in more detail within the subsequent section detailing the interpretation phase. Due to the iterative structure of life cycle assessment, the LCI phase is expected to be a time consuming and meticulous stage that is necessary to create a complete, precise, and statistically robust life cycle inventory dataset for use at the impact assessment stage. 14 1.1.4 Life Cycle Impact Assessment Phase The life cycle impact assessment (LCIA) phase uses models to convert the inventory data into impact scores that represent the environmental contributions associated with the product or system (Pennington et al. 2004a). LCIA has four successive stages for accomplishing this; the first is to select impact categories and their indicators, as well as the models that will best calculate those impacts in relation to the goal of the LCA (Guinée et al. 2001). Impact categories are the bins into which inventory data is assigned and which represent different forms of damage that can be caused by the inputs and outputs of a process. LCA uses a wide range of possible impact categories which relate to environmental burdens, including climate change, aquatic ecotoxicity, and damage to human health, among others (EC-JRC 2010a). Within impact categories, category indicators can be thought of as a common unit to which all of the inventory data can be aggregated (Guinée et al. 2001). Impact categories can be grouped into either midpoint or endpoint categories, each having their own respective models and indicators to calculate them. Midpoint and endpoint indicators interpret impacts of a product at different points along the cause-effect chain, i.e., along the chain of events that occurs from the initial release of a potential environmental burden all the way to the eventual end result on the environment. A midpoint indicator measures potential impacts that may occur, and as such looks at impacts within the middle of the cause-effect chain (EC-JRC 2010b). Examples of midpoint categories that midpoint indicators would be used to represent include the previously mentioned climate change and aquatic ecotoxicity categories. These categories can be measured using midpoint indicators such as global warming potential of emitted greenhouse gas (GHG) and concentration of toxic pollutants in soil, respectively (EC-JRC 2010b). An endpoint indicator, on the other hand, looks at the environmental impact at the end of the cause-effect chain. There are three broad endpoints that a multitude of endpoint indicators 15 can relate to, and these endpoints are referred to as areas of protection (AoPs). While the nomenclature for AoPs can be variable within the literature (Pennington et al. 2004a; EC-JRC 2010b), they will be referred to here as the impacts on (1) human health, (2) ecosystem quality and (3) resource availability. Mapping the pathway from a midpoint category, such as climate change, to an endpoint category, such as the damage to human health, has the advantage of highlighting environmental burdens and relative benefits of different products in a way that is easily interpreted (Bare et al. 2002). However, endpoint categories typically are burdened with increased uncertainty because there exists data gaps and assumptions that aggregate along the cause-effect chain (Pennington et al. 2004a). Midpoint indicators, on the other hand, have relatively lower uncertainty and offer the capacity to identify trade-offs between different impact categories at a higher resolution. Consider a case of comparing two products: at the midpoint level along the cause-effect chain a trade-off between high human toxicity in one product and high respiratory effects in the other is captured by these two mid-point indicators. At the endpoint of the cause-effect chain, this trade-off is amalgamated into one single impact on human health, without indication of the source. Midpoint categories can therefore provide more nuanced detail at the expense of requiring insight into a multitude of environmental burdens that exist in order to interpret the results. Often understated, it is also important to exclude categories that are irrelevant to the goal and scope that could otherwise shroud important LCIA results. Typically, practitioners will include what are referred to as baseline categories, such as climate change, human toxicity, and acidification (Udo de Haes et al. 1999); baseline impacts are those considered to be among the most relevant to include in an LCA. With the consistent use of baseline impact categories, it follows that baseline models, indicators, and characterization methods have been developed from a variety of LCA resources to support these categories, including ILCD, CML, ReCiPe, and TRACI (Bare et al. 2003; EC-JRC 2010b; Hauschild et al. 2013; Huijbregts et al. 2017). Each of these method suites use a unique approach to measuring the cause-effect chain: for example, 16 land use is considered both a midpoint category and as a component of inventory data used to calculate an endpoint indicator, depending on which methodology you select. While this may seem complicated, it is mentioned here only to highlight the importance of selecting models and their sources that best reflect the goal of the study and are best suited to the inventory data collected. After the initial selection of impact categories, indicators, and models, a practitioner is able to assign relevant elements of the inventory data into each of these categories – a process called classification (ISO 2006a). In such cases when particular inventory data are relevant to place in more than one category, a practitioner should choose a model optimized to partition the input or output values appropriately and avoid double-counting and skewing the analysis. Once the second stage of classifying the inventory data is complete, the characterization stage is conducted to calculate the impact category indicators using characterization factors. Characterization factors are used to convert inventory results to a common unit/reference species used in the category indicator (ISO 2006a). As an example, recall that global warming potential (GWP) is an indicator for climate change at the midpoint. This indicator can be measured by converting all greenhouse gases emitted by the study system into kg of CO2 equivalents. The GWP model uses a characterization factor – in this case, the intensity of an emission relative to the reference species CO2 with respect to radiative forcing– to convert the inventory emissions into kg of CO2 equivalencies. The results now in kg of CO2 equivalents can then be aggregated into a single value – the GWP indicator for climate change – to promote interpretability of LCIA results. There is often a high spatial and temporal variability in LCI datasets, which can translate to a less resolved statement of the specific time, location and relevance of a potential impact (Pennington et al. 2004a). Due to this inventory variability, generic characterisation factors that are unspecific to a given area or point in time are often used in LCIA. Site/temporal-dependent characterisation factors, which are less complex than site/temporal-specific factors, provide an 17 intermediate between generic and specific factors that are more practical and are able to address the problem of achieving spatially/temporally resolved LCA results (Reap et al. 2008b). These factors come from characterization models that are available through published LCA studies and the previously mentioned method suites, such as ReCiPe and CML. The fourth and final stage of LCIA (i.e., after (1) selection of impact categories, indicators, and models; (2) the classification of inventory data into the selected categories; and (3) the characterization of inventory data to convert them to selected category indicators) are the optional normalization and weighting steps. Normalization presents impact assessment results in terms of a reference value and relate these results to a broader context and to the goal of the study (Guinée et al. 2001). The reference value may provide context on either a spatial, temporal, economic, or industry sector level, among others (Pennington et al. 2004a). For example, normalization can transform impact scores of one product system by dividing them by the impact scores of an alternative system; in this way, the difference in environmental contributions between the two systems can be compared easily. The intention of normalization is to provide communicable results that can be compared easily for their relative significance within a reference context (ISO 2006b). However, normalization does not discriminate importance between impact categories, which is why there is an additional (and optional) weighting component to this step. Weighting includes value-based decisions about which impact categories are more relevant with respect to the goal and scope of the study. To weight indicator results a practitioner uses weighting factors based on stakeholder, decision maker, or societal values; ISO 14044 (2006b) recommends the use of several weighting factors and the use of a sensitivity analysis (discussed in the subsequent section) to assess the significance of utilizing any particular weighting method on the overall outcome of the results. Conducting the steps involved at the LCIA stage would be an exhaustive process without LCIA software tools developed for this task. Congruent with the variety of data formats 18 that exist for inventory datasets discussed in the preceding section, there is also a variety of software systems that can be utilized to analyze inventory data at the impact assessment stage. Rebitzer et al. (2004) outlines generic, specialized, and tailored LCA software systems that each have their own attributes and target users. Generic software, such as openLCA and SimaPro, are employed regularly by LCA researchers in the academic field, while specialized and tailored software are often used as an engineering design tool and for firm-specific applications, respectively (Rebitzer et al. 2004). At the end of the LCIA phase, a practitioner should have a statistically robust environmental profile consisting of impact categories and their associated indicators that communicates the main significant results of the impact assessment as they relate to the goal of the study. 1.1.5 Interpretation Phase The life cycle interpretation phase of an LCA includes a final check for consistency, completeness and statistical robustness across all phases of the LCA before conclusions and recommendations are presented (ISO 2006a). Completeness can be qualitatively assessed by the practitioner or an expert to ensure the datasets within the inventory and the parameters within the models used for impact assessment capture the complexity necessitated by the goal and scope. A consistency check ensures whichever methodological choices and assumptions made by the practitioner are consistent with the goal and scope and with each other across all LCA phases (ISO 2006b). After the first check for consistency and completeness, the actual interpretation portion of this phase takes place during the contribution analysis (Guinée et al. 2001). Here, the magnitude and the distribution (along the product chain) of contributions of life cycle stages and their constituent processes to different impacts are analyzed; depending on the goal of the 19 study, this process may be used for hot-spot identification (identifying a process in the product chain that has high contributions) or to make comparisons to similar products (Guinée et al. 2001). The following three analyses – perturbation, sensitivity, and uncertainty – are aspects of the interpretation phase that are used to ensure quality and transparency of LCA results and any subsequent recommendations (Guinée et al. 2001; Pennington et al. 2004a). Perturbation analysis measures the sensitivity of impact results to a change in a particular data set within the inventory (Guinée et al. 2001). For example, if the efficiency rate of an irrigated system was estimated to deliver 60% of water to the root mass with a variance of  10%, would we see a significant difference in impact contributions if we instead ran our models using the conservative end (50%) of that estimate? If quantitatively changing that assumption does impact the results, to what extent? Perturbation, in theory, could help find areas of the product chain that can be minimally changed at (potentially) low cost and that would result in a relatively large difference in environmental contribution. However, systematic perturbation analysis is not commonly utilized in LCA due to a lack of dedicated software, despite being recommended in the LCA Handbook (Guinée et al. 2001). While a perturbation analysis systematically changes inventory values to see how responsive impact results are to that change, a sensitivity analysis measures sensitivity to changes in methodological choices instead (ISO 2006b). A common practice in LCA, sensitivity analyses are recommended, for example, to assess how the practitioner addressed multifunctionality (Pelletier et al. 2015). Recall that if a process or portion of a product chain is shared between two or more coproducts, the practitioner must decide how to allocate the impact contributions across the coproducts, ideally by a physical relationship that exists between them (Guinée et al. 2001). If a practitioner uses mass to allocate inputs and outputs between the coproducts, they may want to use a sensitivity analysis to determine if using a different physical relationship, such as caloric energy content, would change the impact results significantly. Similarly, there are several ways in which a practitioner may choose to quantify the value of 20 each impact category (i.e., there are often a variety of indicators to choose from for each impact category). The unit in which to quantify the indicator for an impact category could greatly influence the outcomes: if a practitioner valuates the human health impact of a system using willingness to pay as a measure, the decisions resulting from the valuation could be very different than if the measure used was quality adjusted life years (Hammitt 2002); further, both of these indicators may use different inventory data to calculate the results, and as such should not be considered to be comparable to each other (Reap et al. 2008b). Another common use of a sensitivity analysis is to evaluate the choice of excluding a process or specific data point due to the poor quality of available data (Guinée et al. 2001); would the impact results differ significantly if these data were included? A sensitivity analysis can show us if the change in results is large enough to merit investing additional effort in order to locate data of sufficient quality to enable including the process or data point in the model. An uncertainty analysis enables quantifying confidence in the model results by calculating uncertainty associated with data, assumptions and choices (Guinée et al. 2001). The ISO guidelines remain vague when discussing how to handle uncertainty, and as a consequence the quantification and acknowledgment (or lack thereof) of uncertainty is criticized in the literature (Reap et al. 2008b; Weidema 2009; McKone et al. 2011). Three overarching issues regarding uncertainty in LCA revolve around (1) the completeness of an uncertainty analysis, (2) the incorporation of uncertainties from different origins, and (3) the modelling of uncertainty (Reap et al. 2008b). To understand the origins of each of these issues, it is first valuable to discuss a subset of the various forms of uncertainty present in an LCA: parametric (including stochasticity and data quality), choice (usually dealt with by a sensitivity analysis), and model uncertainty (Björklund 2002). Stochastic parametric uncertainty – the inherent variability within LCI datasets – is typically represented by use of probability distributions to characterise stochastic variation in input parameters. Parametric uncertainty also encompasses data quality; reductions in data 21 quality occur with technological assumptions, averaging where it reduces accuracy, aged data and use of unrepresentative proxy data at the inventory stage (Björklund 2002; Reap et al. 2008b). The use of data quality indicators (DQIs) is a qualitative way of assessing the representativeness of data through the use of geographical, temporal, and technological metadata, whereby the score can then be integrated with other uncertainties. This is done by translating the qualitative DQIs into quantitative estimates of associated uncertainty, and then aggregating them with stochastic parametric uncertainty. Thereafter, Monte Carlo simulations, which randomly sample from within the distributions for each input data point in the LCI model across multiple runs, are used to generate a distribution of the impact assessment results (and therefore a confidence interval). Some LCA practitioners may estimate and assume probability distributions, however, this should require a sensitivity analysis to ensure reliability of those assumptions. The most abundant source of uncertainty, however, is found at the LCIA stage due to a lack of expert knowledge for some impact areas (Reap et al. 2008b). This means that some models we use weakly represent the biophysical mechanisms that distribute the various impacts to the ecosphere. This model uncertainty is often altogether unacknowledged; a better understanding of those mechanisms, threshold impact levels, and the ultimate fate of environmental burdens is required to reduce the uncertainty present at the impact assessment stage (Reap et al. 2008b). The interpretation is a combinatory step that utilizes both quantitative analysis and qualitative decision making to evaluate the consistency, completeness, statistical robustness, and accuracy of LCIA results in order to have an aggregated, and as such communicative, environmental impact score. 22 1.2 Addressing freshwater use in semi-arid environments using LCA 1.2.1 Agricultural freshwater use in the semi-arid Okanagan Water availability is at risk in the semi-arid Okanagan Valley due to climate change induced impacts, such as droughts, and an increasing water demand (Neilsen et al. 2006; White et al. 2007). In 2005, it was estimated that 90% of available water from Okanagan watersheds had been allocated to human users and that it will be fully allocated by the early 2020’s (Van Der Gulik and Neilsen 2008). This places the projected water crisis at present day, while demand from agriculture and urban growth only continues to rise (Van Der Gulik et al. 2010). An estimated 70-75% of water use in the Okanagan is attributed to agricultural irrigation, and the majority of agricultural production in the Okanagan has not historically operated at optimal irrigation efficiencies (Van Der Gulik et al. 2010). Tree fruit production alone accounts for 35%1 of the agricultural water demand in the Okanagan (Van Der Gulik et al. 2010), and access to future water supply is a significant concern for Okanagan tree fruit producers (Crawford and MacNair 2012). According to the 2014/2015 agricultural land use inventory, the majority (87%) of cherry orchards in the Okanagan employ relatively inefficient irrigation systems (i.e., <80% of the water delivered by the system remains in the soil for plant uptake) including solid-set undertree, micro-sprinkler, and sprinkler (B.C. Ministry of Agriculture 2014; BC Ministry of Agriculture 2016). In a report for the Okanagan basin, it was estimated that if all horticultural systems in the Okanagan converted to ‘good’ irrigation technologies, we would see a 16% reduction in agricultural water demand (Van Der Gulik et al. 2010). Reducing agricultural water demand in the Okanagan through increasing irrigation efficiencies is therefore a significant potential pathway for mitigating water consumption. The 1 Includes apple, cherry, and fruit crop water demand based on 2003 data available in the Agricultural Water Demand Model report for the Okanagan Basin (Van Der Gulik et al. 2010). 23 implications of reducing water use for agricultural activities also have the added benefit of potentially reducing environmental impacts associated with the upstream processes involved in water delivery. However, to what extent improving water use efficiencies on orchards impacts the overall life cycle environmental burdens of the orchard system has not yet been explored in the Okanagan. 1.2.2 Using LCA to assess freshwater use in agricultural systems In the last decade, life cycle thinking has been incorporated into the ISO standards for water footprinting methods, making LCA a well suited tool for water use-related impact assessment (ISO 2014). Life cycle assessment has been applied to quantify the resource trade-offs and environmental implications associated with irrigation in agri-food systems in diverse contexts. Second only to fertilizers, Chatzisymeon et al. (2017) found that irrigation was a significant contributor to the majority of life cycle impact categories assessed for both conventional and organic cultivation of field pepper crop systems. Congruent with this, Abeliotis et al. (2013) found that electricity used for irrigation pumping was the major contributor to environmental impacts – including human and ecosystem toxicity, ozone depletion, and global warming, among others – of conventional, integrated, and organic bean production in Greece. In the arid southwestern US, LCA was used by Eranki et al. (2017) to compare flood and drip irrigation for P. argentatum (a shrub used to produce rubber). Drip irrigation introduced additional environmental burdens through the use of sulfuric acid for drip maintenance and energy consumption for pumping, yet outperformed flood irrigation in most categories due to higher water use efficiency. Of note, water application was the largest contributor across all impact categories for both systems and was greater than fertilizer, irrigation and chemical pumping, and chemical use. These studies provide a subset of the available LCA literature highlighting irrigation as a major relative contributor to the impacts of agri-food systems. 24 Milà I Canals et al. (2010) used an LCA case study comparing broccoli cultivation in Spain and the UK to demonstrate the use of a water footprinting derived indicator – the freshwater ecosystem impact (FEI). Incorporating the FEI indicator enabled the life cycle comparison between the two systems to account for higher water scarcity potential in Spain. The potential impact on freshwater ecosystems was higher in Spain than in the UK due to the over exploited aquifer used to supply the broccoli systems. This demonstrated that water source and regional water availability is paramount to an accurate life cycle assessment of freshwater use. Milà I Canals et al. (2010) also emphasized the importance of distinguishing water sources at the agricultural stage for the ability to incorporate regional climate data (including average rainfall and country level evapotranspiration rates) and water loss through infrastructure leaks and evaporation during storage, in order to produce more accurate LCIA results for freshwater consumption. Hospido et al. (2013) tested the irrigation mix concept and the freshwater ecosystem impact (FEI) indicator defined by Milà i Canals et al. (2009) in a case study of lettuce production in Spain. The researchers observed different values for the FEI indicator when a default irrigation mix was used versus inputting the specific water source consumed by the lettuce farms, further indicating that water source plays an important role in determining the magnitude of impact to freshwater ecosystems. Pradeleix et al. (2015) used LCA to contrast diesel-powered versus electric groundwater pumping systems that supply to agricultural systems in the semi-arid to arid region of Tunisia. In this system, diesel-powered pumping was the most environmentally sound option when electricity was derived from coal (rather than natural gas) and when diesel-powered pumping efficiency was greater than 12%. The electricity grid mix used during groundwater extraction can therefore determine whether electric pumping systems will outperform diesel-powered pumping systems. Van der Laan et al. (2012) compared farmer intuition-based irrigation scheduling versus objective irrigation scheduling using LCA and demonstrated that the latter scheduling system was able to reduce total energy input and greenhouse gas emissions by roughly 10%. 25 Interestingly, these savings were made not by reducing total water consumption, but rather by more effectively using green water (rainfall) and so reducing watershed extraction (blue water). The upstream processes used to extract and deliver water (diesel versus electric pumping), as well as the source of water (green versus blue; high versus low demand) used for irrigation can therefore influence the overall impacts of agricultural systems. Unexpected findings in other studies demonstrate that more efficient irrigation systems can increase regional water demand by decreasing irrigation labour requirements and therefore facilitating crop expansion and conversion to more water demanding orchard plantations (Poussin et al. 2008; Pradeleix et al. 2018). General conclusions cannot therefore be drawn about the environmental trade-offs of different on farm irrigation technologies, and instead regionally context-specific assessment of the impacts associated with on farm irrigation technology and management must be made using a life cycle approach. 1.2.3 Methodologies for addressing freshwater use within LCA The LCA community has called for the inclusion of water-related impacts to be addressed more accurately and consistently in LCA, with particular emphasis placed on systems that significantly contribute to global freshwater consumption – including agricultural systems (Milà i Canals et al. 2009; Motoshita et al. 2018). Life cycle assessments of irrigated water use in agricultural systems have historically used a variety of different modelling methods (for example, Pradeleix et al. 2015; Payen et al. 2018). Comprehensive analyses of existing methods for water footprinting in LCA were reported by Kounina et al. (2013), Bayart et al. (2010) and Berger and Finkbeiner (2010), each of which found the general lack of consensus for methods to be problematic. Therefore, the Water Use in Life Cycle Assessment (WULCA) working group, consisting of LCA and hydrology experts, NGOs, government affiliates and industry stakeholders, was created to build a consensus for water footprinting. The mandate of 26 the group was to review currently existing models and to develop a water footprinting method for LCA that was compliant with the ISO 14046 water footprinting guidelines (ISO 2014; Boulay et al. 2018). Kounina et al.'s (2013) paper was the first stage of the WULCA mandate, which followed the ILCD handbook methodologies to (1) describe cause-effect chains, (2) define criteria for evaluation, (3) develop sub-criteria for freshwater use, and (4) review existing freshwater use assessment methods. Kounina et al. (2013) found there was a lack of consensus on the most appropriate midpoint methods to use in freshwater use LCA. This was in part due to a lack of understanding of the physical mechanisms involved between inventory results of freshwater use and the impacts associated with them, which can result in misleading impact scores at the midpoint level (Ridoutt et al. 2010; Ridoutt 2011). This means that water scarcity indicators, sometimes called the water stress index, that are used to calculate endpoint damages do not directly relate to potential (midpoint) impacts. As such, it was an interesting avenue of research to provide evidence that links water deprivation and water scarcity within these indicators to midpoint level impacts. Though water scarcity indicators are used to relate impacts on endpoint AoPs (recall areas of protection are ecosystem quality, human health, and resources), the influence freshwater consumption actually has on these AoPs can be dependent on other factors. For example, Boulay et al. (2011) and Pfister et al. (2009) found that the AoP human health was only impacted by freshwater consumption (measured by the water scarcity indicator) if economic development was insufficient as to provide some compensation plan, such as a backup technology, to meet freshwater needs. Further, freshwater use in LCA has historically only considered the quantity of water inputted to the system, which does not include the quality of water subsequently outputted by the system, and does not characterize the water flow input any further at the LCIA stage (Boulay et al. 2011b; Kounina et al. 2013). Therefore, Boulay et al. (2011) highlighted water quality as an important parameter to include at the midpoint level because it determines functional use. In 27 addition to including water quality parameters and characterizing water flows at the LCIA stage, Pfister et al. (2009) recommended incorporating storage capacity into freshwater use inventories because it changes water deprivation if water evaporates during storage. 1.2.4 AWARE indicator from WULCA The WULCA working group used the findings of Kounina et al. (2013), Boulay et al. (2011), and Pfister et al. (2009), among others, to develop a consensus on new water footprinting methodologies compliant with ISO 14046 and the UNEP-SETAC Life Cycle Initiative. The developed AWARE indicator – which stands for available water remaining – accounts for both quantity and quality issues of water and calculates water consumption at the midpoint level (Boulay et al. 2018). The indicator comes with a characterization factor: the inverse of availability minus demand (AMD-1), which serves to quantify the potential of water deprivation from any user (including both human and ecosystem). The AWARE indicator addresses the question identified by the WULCA working group: “What is the potential to deprive another user when using water in this region?” The characterization factor is normalized (divided by) the reference flow of the world average water consumption, and results in an aggregated value for the AWARE indicator (Boulay et al. 2018). Indicators are calculated at the sub-watershed level on a monthly scale, as recommended by Kounina et al. (2013), and can be scaled up to country and annual scale. Consequently, the characterization factor for AWARE is limited to a range of 0.1-100 (methods for cut-off criteria are available in Boulay et al. 2018), where a value of one corresponds to a region with the same water availability as the world average for a specified area and amount of time. A value <1 signifies less water scarcity problems than the world average, and a value >1 is the multiple of water scarcity compared to the world average. This means if the characterization factor was equal to 10 for a given area, this area has 10x less water available for use, or that it 28 takes 10x more time to regenerate unused water, as compared to the world average (Boulay et al. 2018). To summarize, the new AWARE indicator and AMD-1 characterization factor provides the following novel improvements to assessing impacts of freshwater use: i. It assesses potential deprivation of water at the midpoint level for both human and aquatic ecosystem users ii. It is based on consensus from LCA and hydrology experts and is compliant with ISO 14046 water footprinting standards iii. It includes options at the country and region level and can be based on activity assessed (agricultural vs. non-agricultural) The WULCA working group had an overall goal of providing practitioners, industry stakeholders, and other decision makers with a consensus on best practices for assessing and comparing environmental performance regarding freshwater use in different systems. Among the other areas of focus, the WULCA working group established the need for terrestrial ecosystem impact pathway indicators, which to date include a variety of methods that may complement each other if a midpoint indicator is developed (Kounina et al. 2013; Boulay et al. 2015a). 1.2.5 Addressing a methodological gap in LCA: Incorporating terrestrial ecosystem demand into midpoint level water scarcity indicators Methods discussed by (Boulay et al. 2015b) to calculate the endpoint level impacts to ecosystem quality relate damages to terrestrial biodiversity and freshwater fish species. These damages are interpreted as a loss of aquatic biodiversity through use of turbined water and through reductions in plant biodiversity as a consequence of groundwater withdrawal. However, each of these methods does not use water scarcity indices, but instead complements these indices. These indicators are typically quantified using proxy data like net primary production, 29 observational/theorized loss of biodiversity (Kounina et al. 2013), and occasionally from a mechanistic approach such as Hanafiah et al.'s (2011) use of river discharge impacts on fish species richness. AWARE uses ecosystem water requirement (EWR) to assess the ecosystem water demand. EWR is aggregated into the availability minus demand (AMD) value for all users and can be used to express damages to both human health and ecosystem quality. However, this indicator excludes terrestrial ecosystems because they primarily rely on green water (rainfall) sources, the cause-effect chains for which have not been satisfactorily developed (Kounina et al. 2013). This provides a large gap in the representativeness of using EWR as an indicator for mid-level ecosystem quality, as it neglects terrestrial ecosystems. Other methods have been developed to model specific aspects of the impacts of freshwater consumption in terms of endpoint damages to either ecosystem quality or human health. For example, Hanafiah et al. (2011) applied a mechanistic approach using fish populations to assess aquatic ecosystem damage. Boulay et al. (2011a) reported regionalized results for the impacts of freshwater consumption on human health. Motoshita et al. (2018) proposed characterization factors (CF) for the impacts of freshwater consumption on agricultural water scarcity and resulting food shortages. However, the potential impacts of freshwater use on terrestrial ecosystems remains unsatisfactorily developed in these and other LCIA methods. As such, it is imperative that future work incorporates terrestrial ecosystems into the characterization factors used to quantify demand of freshwater from ecosystem users. Achieving this could take the form of developing cause-effect pathways that characterize green water demand, or through the development of a framework for assessing how different terrestrial ecosystems utilize and propagate water in different ways. Developing cause-effect pathways for semi-arid and arid terrestrial ecosystems would complement future LCAs performed in the Okanagan that include water use in the inventory. In this way, LCAs would improve their capacity to capture water demand from all users, including terrestrial ecosystems, 30 and hence provide a more complete description of how product systems contribute to regional water scarcity. 31 Chapter 2: LCA of water delivery infrastructure and irrigation technologies employed in Okanagan cherry production 2.1 Introduction Demand for freshwater resources is rapidly increasing in the Okanagan Valley of Western Canada due to increased pressures from human activities, in particular agricultural activities (Neilsen et al. 2015). Life cycle inventories of water delivery systems that support these agriculture activities is lacking in currently published Canadian LCA literature. Most LCAs of Canadian agricultural systems therefore rely on previously compiled life cycle inventories of water delivery systems from other regions to model the impacts associated with irrigation. For example, a model of tap water production in Quebec has been used as a proxy for local irrigation supply in LCAs of Okanagan orchards (Sanderson et al. 2019; Bamber et al. 2021). Although modelled in a Canadian context, this dataset – and other, non-Canadian, LCI datasets of water supply – do not accurately represent the variety of treatment, piping and pumping processes used to supply irrigation to Canadian food production systems. Since irrigation inputs can have a significant contribution to the environmental impacts of agricultural activities in some cases (as examples: Chatzisymeon et al. 2017; Pergola et al. 2017), the need for more Canadian LCI datasets that represent regional water supply networks is clear. In addition to the impacts associated with the delivery of water, irrigation also potentially influences GHG emissions from agricultural soils. New findings suggest that bicarbonate found in irrigated water can propagate release of carbon dioxide from orchard soils and is dependent on the type of irrigation technology and source of irrigation water used (Hannam et al. 2016). Further, studies in the Okanagan and other semi-arid regions reveal that nitrogenous GHG emissions are also impacted by the type of irrigation technology employed on farms and orchards (Fentabil et al. 2016; Deng et al. 2018). CO2 emissions from deposited bicarbonate are exacerbated by irrigation technology inefficiencies, as more water and hence more carbonates are deposited into the agricultural system. Yet, in some cases, increased N2O emissions from 32 agricultural soils has been associated with drip irrigation systems that are more efficient at water delivery than micro-sprinklers (Baram et al. 2018). Incorporating this research into the LCIA modelling of Okanagan orchard systems presents a novel approach to understanding the significance of irrigation impacts at the system level, and in understanding the environmental trade-offs that may arise when employing different irrigation technologies. 2.2 Methods 2.2.1 Goal and Scope Goal To achieve ISO 14044 compliance the goal of the LCA must report the research objectives and reasons for the study, target audience, intended application, and whether it will support comparative assertions (ISO 2014). The objective of this study was to answer the following research questions: (1) How do the environmental impacts of each water delivery system (i.e., gravity fed vs. surface pumped, treatment vs. no treatment) compare to each other, and at what phase are the highest impact contributions found? (2) How do the environmental impacts associated with different irrigation technologies compare to each other? (3) Does irrigation make a significant relative contribution to the environmental impacts of cherry production in the Okanagan Valley? Further, will employing irrigation technologies that are more water efficient make relevant reductions in environmental impacts at the orchard level? (4) Does bicarbonate deposited into orchard soils by irrigation have significant implications for the environmental burdens associated with the orchard? 33 (5) Is the difference in nitrous oxide emissions resulting from employing drip vs micro-sprinkler irrigation significant from a systems level perspective? To answer these questions, baseline life cycle inventories and models were developed that characterized the regionally specific environmental contributions of different water delivery systems, as well as the comparative contributions of different irrigation technologies employed at the orchard level. An Okanagan cherry production model system was chosen to quantify the contributions of irrigation water (technology and infrastructure) relative to the environmental impacts associated with the life cycle of cherry fruit production. The LCI models were compliant with the ISO 14044 standards as well as the ISO 14046 standards for water footprint assessment, which are based on and compatible with the methods prescribed in ISO 14044 (ISO 2014). The results highlight for other researchers in LCA the relevancy of including the inputs of different water delivery infrastructure and irrigation technologies in LCI datasets. The life cycle inventories and model were made publicly available on Open Science Framework. This research contributed to the understanding of the impacts of different water delivery and irrigation infrastructure within the Okanagan for decision makers, including water purveyors, policy makers and farmers. This study supported comparative assertions of different technologies available to irrigate cherry orchards, as well as different water delivery systems used to supply agricultural users in the Okanagan. When comparative assertions are being supported by LCA, details of any omissions of the life cycle stages, processes or data in the system boundary must be included, as well as a description of any cut off criteria for initial inclusion of inputs and outputs (ISO 2014). All operational stages, inputs and outputs of the water delivery systems modelled were included. Some studies have shown that piping materials can have significant contributions to the overall LCIA results of water delivery systems (Homäki et al. 2003; Lassaux et al. 2007; Pradeleix et al. 2018). For this reason, piping material was included in this assessment. To be consistent with this, the infrastructure material for the treatment plant was also included. All other construction and decommissioning processes – for 34 example, the energy required to install the pipes and build the treatment plant, or materials used to manufacture pumps – was excluded on the basis that it was not considered in other studies modelling the life cycle of water supply systems (as examples, Lyons et al. 2009; Saad et al. 2019). The application of these assertions may foster new policy to incentivize efficient water use in and supply to agricultural systems on the premise that this change will reduce energy intensity, freshwater consumption, and other environmental impacts of agricultural production in the Okanagan, as has been found in studies in other regions (Bogosh 2011; Pradeleix et al. 2018). The results of this study provided long term benefits to Okanagan Valley residents through elucidating effective strategies to reduce the environmental impacts associated with agricultural water consumption, thereby mitigating the risk of water scarcity caused by climate change and a rising water demand. Scope ISO 14044 and 14046 recognize the scope of an LCA as containing the modelling type, functional unit, system boundaries, allocation procedures, data coverage, assumptions and potential limitations of the study (ISO 2006b, 2014). The life cycle analyses performed were attributional (aLCA) analyses. The aLCA of Okanagan water delivery systems had a cradle-to-gate system boundary which included the withdrawal of water from a ground or surface water source, the treatment and pumping (if applicable), and delivery of water to agricultural users. The scope of this LCA followed the recommendation outlined in the meta-analysis of water supply networks performed by Meron et al. (2016) to include network infrastructure in the analysis of less energy intensive supply networks that rely on surface and ground water sources. Demolition of system infrastructure was only discussed in 6 out of 100 LCA studies of water supply networks analyzed by Meron et al. (2016), as this phase is not considered to have 35 relevant contributions compared to the operational phase. In congruence with this, the deconstruction phase was not included in the scope of the present study. The functional unit of this system was 1 m3 of irrigation water delivered to, but not including, the property water lines of farms in the Okanagan. As discussed in Chapter 1, multifunctionality (how a practitioner chooses to allocate co-products of a system) must be addressed at the goal and scope phase of an LCA. Some water delivery systems included in the inventory provided water to both domestic and agricultural users. In these cases, inputs were allocated on a volume (mass) basis using the ratio between annual domestic and agricultural water consumption, consistent with the second tier of the multifunctionality hierarchy prescribed by ISO 14040 (ISO 2006a). The first approach recommended by ISO 14040 – to expand the system boundaries to incorporate co-product functions – was not chosen because including domestic users does not align with the goal of the study to provide baseline life cycle inventories of water delivery for agricultural systems. To build baseline LCIs of water delivery systems representative of the Okanagan, primary data from multiple district purveyors encompassing different source water types (surface vs ground), pumping methods (gravity flow vs pumped), and treatments were included. The spatial, temporal, and technological coverage of data used to develop the LCIs for these systems and the methods for collecting these data are discussed in the subsequent section – life cycle inventory methods. Additional requirements for the scope of a water footprint analysis according to ISO 14046 include describing the water footprint impact assessment methods and categories, whether the results will have one or more impact indicators, which cause-effect chains are covered by the impact assessment selected and the consequences of excluding other potential environmental impacts (ISO 2014). Impacts related to water are represented by the water scarcity impact category within IMPACT World+, which is compliant with ISO 14044. Details on the potential environmental impacts covered by this category indicator are provided in section 36 2.2.1. The final product (irrigation water) of this system was used as an input to an Okanagan cherry product system, the LCI for which was previously compiled by Sanderson et al. (2019). The Okanagan cherry orchard system was modelled using an attributional LCA with a cradle-to-market system boundary. The LCI of the cherry orchard system therefore included the setup, maintenance, and teardown of a cherry orchard with a lifespan of 20 years and a functional unit of one kg of edible cherries delivered to market gate. For complete goal, scope, and methodology, see Sanderson et al. (2019). The environmental impacts associated with both product systems were modelled using IMPACT World+ using the LCA software openLCA – further details on impact category selection, classification, etc. are provided in a subsequent section: 2.2.3 LCIA Methods. No additional weighting of the impact indicators was implemented in this study. Scenario modelling was performed to quantify changes in environmental impacts that arose from implementing different irrigation technology on the orchard, and from sourcing irrigation water from different water delivery systems. All scenarios are described in section 2.3. No market-mediated effects were foreseen as a response to these changes, firstly because farmers cannot change water purveyors, and second because irrigation water provision is charged on a quota basis rather than per unit of water delivered, and therefore changes in efficiency of irrigation use does not incentivize a change (expansion or reduction) in production. Sensitivity analyses were performed to determine the extent to which environmental impact outcomes of the model were determined by assumptions and value choices made during LCI compilation and are detailed in section 2.2.4.2. 37 2.2.2 Life Cycle Inventory 2.2.2.1 Overview Okanagan water purveyors were sent spreadsheets for data entry on inputs and infrastructure used in water delivery systems that deliver water to agricultural users. Figure 1 presents a flow diagram summarizing these data. Follow up emails were sent to purveyors for clarification of data provided in the spreadsheets (where necessary) and to ask additional questions. Data were collected on delivery pipe types and lengths; treatment processes, including chemical, machinery and transportation inputs; pump electricity consumption; water leaks and losses; annual volume of water delivered to all users; and the volume of water delivered to agricultural users, specifically, during the 2018 irrigation season (April-October). To compare irrigation infrastructure and technologies at the orchard level, a previously published LCI model representative of the Okanagan cherry product system was adapted (Sanderson et al. 2019). The emissions models utilized were changed from IPCC Tier 1 to Tier 2 methods (ECCC 2019a, b; IPCC 2019a) and these changes are described in section 2.2.2.5. The upstream process for irrigation on the orchard was also changed to include multiple scenarios for the Okanagan water delivery system and irrigation technologies, while a copy of the original upstream provider was kept for comparisons. 38 Figure 1 Flow diagram representing the Okanagan water delivery and cherry product systems. Areas in green and grey represent the ecosphere and technosphere, respectively. Within the technosphere, background and foreground processes are joined along the supply chain with the use of colour coded arrows representing elementary, process, and waste flows (see legend). For a complete methodology and background system description of the cherry product system, see Sanderson et al. (2019). 2.2.2.2 Delivery Pipes Okanagan water purveyors were asked to report the type, diameter and length of pipes used to deliver water to agricultural users. Piping used for water mains was included, while pipes used within treatment plants and customer property lines were not included. Pipe types were modelled based on LCI datasets found in published pipe LCAs. To simplify the inventory, copper and polyethylene piping was omitted because of the comparatively negligible length reported, while cast iron pipe was included in the inventory as ductile iron pipe. 39 LCI data reported in pipe LCAs were specific to a given pipe size (diameter). However, data reported by purveyors included multiple sizes for each type of pipe. Therefore, the sizes of each type of pipe reported were scaled to the sizes reported in the LCAs, from which the input materials were derived. Pipe sizes were scaled to the LCA “reference” size using density values reported in industry literature; example calculations and supporting references are provided in Appendix A. Some Ecoinvent providers in the pipe LCI data sets were changed to better represent the Canadian industry for iron and steel manufacturing. These changes are reported in Table 2. Production weighted average pipe length was calculated for each pipe type and reference size. The average length for each pipe type was then scaled to the functional unit of one cubic meter of water delivered by dividing the average annual total volume of water delivered (Table 3) over a lifetime of 50 years, as reported in the pipe LCI data sets (Vahidi et al. 2015; Vahidi et al. 2016; Hajibabaei et al. 2018). 2.2.2.3 Treatment Phase Three Okanagan water purveyors were asked to report the type of treatments applied to water delivered to agricultural users. Testing chemicals and other inputs used to assess water quality were not considered in this model as it was confirmed that these inputs would be negligible when scaled to the functional unit (pers. comm. Shawn Goodsell). Inputs collected from purveyors for each treatment included the material inputs used on-site; electricity and natural gas consumption; supply company names and locations for chemicals used in daily operations; material inputs for large infrastructure or rare materials used in machinery; and the waste materials produced by on-site processes. Water left untreated was assumed to have no inputs at this phase. For the treatment phase, all inputs were reported or scaled to one cubic meter of treated water. The percentage reported for losses and leaks in the 40 system was then used to scale treatment water to the functional unit of one cubic meter of delivered water. If the warehouse location of chemical suppliers was not provided by the purveyors, the location was found by contacting the supply companies directly. For all systems, transport of chemicals was calculated by estimating the two-way distance from the supplier warehouse to the water delivery system using Google Maps (Google n.d.). This value was then multiplied by the weight of delivered chemical in kg m-3 of treated water to get the distance on a kg-km m-3 basis. The chemical supplier Waterhouse Chemicals was contacted to determine the size of truck used in treatment chemical delivery, and whether trucks returned empty. In some cases, annual/irrigation season electricity consumption during treatment was provided by purveyors. To calculate the energy required by the treatment process per m3 of treated water, this value was divided by the annual/irrigation season volume of water extracted at the intake source. In cases where information on the booster pump used during treatment was supplied instead, the energy (in kWh) consumed per m3 of treated water (E) was calculated using Equation 2.2.2.3.1, where p is the horsepower of the pump, c is the capacity of the pump (m3 hour-1), and 0.746 is the conversion factor (kw horsepower-1). (Eq. 2.2.2.3.1) 𝐸 =0.746𝑝𝑐 Two systems used an on-site sodium hypochlorite generator for water disinfection and purification. Electricity, sodium chloride and water diverted from the delivery system was used to make sodium hypochlorite. This process is known as electrochlorination and produces hydrogen gas as a by-product (Eq. 2.2.2.3.2; Water Engineers 2016). (Eq. 2.2.2.3.2) NaCl(s) + H2O(l) + ENERGY → NaOCl(l) + H2(g) For the Miller Road system in the Town of Oliver, inputs of sodium chloride and electricity per functional unit were calculated by dividing the reported annual amount of each treatment input 41 by the annual volume of water extracted from the intake source. For the generator used at the Duteau Creek Water Treatment Plant (DCWTP), the volume of hypochlorite solution produced by the generator per m3 of treated water was provided by the purveyor. Input quantities per volume of hypochlorite solution were supplied by the manufacturing company of the generator, DE NORA. The volume of hydrogen by-product for both systems was calculated using the same salt input to hydrogen output ratio as was reported by DE NORA for the generator used at the DCWTP. With the exception of the DCWTP, the only infrastructure required at the treatment phase were the pump houses where treatment occurred. Construction of these pumphouses was not included in the model because of their long lifespan and comparatively small material inputs. Concrete required during construction of the DCWTP and the man-made reservoirs adjacent to the plant were included in the model. Data on piping internal to the plant was not available. Instruments used within the plant, such as the hypochlorite generator and the UV disinfection device, had such large lifetime capacities that only the materials for the parts needing more frequent replacement was included. Therefore, electrode plates containing titanium used in the generator, and UV bulbs containing mercury used in the UV disinfection machinery, were included in the inventory. 2.2.2.4 Pumping In most cases, water delivery systems supply pressurized water that has been pumped. However, some parts of the supply networks were gravity fed prior to reaching the intake source and pumphouse. Therefore, it is difficult to provide a binary categorization for these delivery systems as they often are a combination of gravity fed and pumped networks. Purveyors were hence asked to specify a percentage breakdown of how much water was pumped versus gravity fed. Systems that used one method for 75% of the water volume were categorized as that 42 method. Systems that either did not have a reported percentage breakdown or that did not meet the above criteria were considered mixed systems. For each system, data for the amount, capacity, and horsepower of pumps used was collected to provide the average energy consumed to pressurize and deliver a cubic metre of water using equation 2.2.2.3.1 (see above – section 2.2.2.3). Construction of the pumphouse, replacement pieces of the pumps, and the material inputs required for the pump itself were not included because they were not considered in other water delivery system LCAs or were reported as having negligible contributions at the impact assessment stage (Lassaux et al. 2007; Lyons et al. 2009). A production weighted average energy consumption per m3 of delivered water was calculated and used in all model scenarios, assuming a 100% efficiency of the pump. 2.2.2.5 Orchard level emissions and irrigation technologies inventory The type of irrigation technology used on Okanagan cherry orchards was collected from the Agricultural Land Use Inventory (B.C. Ministry of Agriculture 2014; BC Ministry of Agriculture 2016) through the Okanagan Basin Water Board. Efficiencies of each technology (i.e., the proportion of irrigation that remains available for crop uptake) was derived from the Okanagan Water Demand Model technical description (Fretwell 2009). The quantity of CO2 (g) released from irrigated soil per m3 of irrigation water due to the bicarbonate contained in the source water was derived from a study on an Okanagan apple orchard (Hannam et al. 2016). For each technology, the amount of CO2 (g) was scaled to the volume of water required to provide 1 m3 of water to the soil for tree uptake (i.e., accounting for losses due to irrigation technology efficiency). In addition to CO2 released from carbonates deposited by irrigation, nitrogenous GHG species resulting from synthetic fertilizer were accounted for. Direct and indirect emissions of 43 dinitrogen oxide (N2O) from synthetic fertilizers applied to the orchard were recalculated using a combination of IPCC tier 1 and tier 2 methods (IPCC 2019a), dependent on availability of sufficient country-specific data . Direct N2O emissions to air was calculated using the IPCC tier 2 equation for managed soils (Eq. 2.2.2.5.1). (Eq. 2.2.2.5.1) N2ODirect-N = ∑i (FSN + FON)i * EF1i + (FCR + FSOM) * EF1 + N2O-NOS + N2O-NPRP Where: N2ODirect–N is the annual direct N2O–N emissions produced from managed soils, kg N2O–N yr-1. FSN is the annual amount of synthetic fertilizer N applied to soils, kg N yr-1. Inventory data for synthetic fertilizer application rates is provided in Sanderson et al. (2019). FON is the annual amount of animal manure, compost, sewage sludge and other organic N additions applied to soils in kg N yr-1. This value was omitted as no manure, compost, etc. was applied to the orchard soils. EF1i is the emission factors developed for N2O emissions from synthetic fertilizer and organic N application under conditions i (kg N2O–N (kg N input)-1); i = 1, …n. This value was obtained from the national inventory report for greenhouse gas sources and sinks in Canada and was specific to the province of BC (ECCC 2019c). FCR is the annual amount of N in crop residues (above-ground and below-ground), including N-fixing crops, and from forage/pasture renewal, returned to soils, kg N yr-1. This value was omitted as it did not apply to the orchard system. FSOM is the annual amount of N in mineral soils that is mineralized, in association with loss of soil C from soil organic matter as a result of changes to land use or management, kg N yr-1. This value was omitted as the changes to land use and management are negligible in cherry orchard systems due to the 20 year average lifetime of the crop (Sanderson et al. 2019). N2O–NOS is the annual direct N2O–N emissions from managed organic soils, kg N2O–N yr-1. This value was omitted as the orchard system modelled did not have organic soils. 44 N2O–NPRP is the annual direct N2O–N emissions from urine and dung inputs to grazed soils, kg N2O–N yr-1. This value was omitted because there were no urine and dung inputs to the orchard system. Indirect emissions of N2O occur through the volatilization of ammonia (NH3) and nitrous oxide (NOx) and the leaching/runoff of nitrate (NO3-). The IPCC tier 2 equation for N2O from atmospheric deposition of N volatized from managed soils was used to calculate indirect emissions resulting from the volatilisation of NH3 and NOx (Equation 2.2.2.5.2). (Eq. 2.2.2.5.2) N2O(ATD)-N = {∑(FSNi * FracGASFi) + [(FON + FPRP) * FracGASM]} * EF4 Where: N2O(ATD)–N is the annual amount of N2O–N produced from atmospheric deposition of N volatilized from managed soils, kg N2O–N yr-1. FracGASFi is the fraction of synthetic fertilizer N that volatilizes as NH3 and NOx under different conditions i, kg N volatilized (kg of N applied)-1. The 2019 updated IPCC default value for FracGASF can be aggregated or disaggregated based on the type of synthetic fertilizer applied (nitrate based, urea, etc.). Since the orchard system modelled utilized a combination of these fertilizers, the default value for aggregated FracGASF was used (IPCC 2019b). FracGASM is the fraction of applied organic N fertilizer materials (FON) and of urine and dung N deposited by grazing animals (FPRP) that volatilizes as NH3 and NOx, kg N volatilized (kg of N applied or deposited)-1. This value was omitted as organic N fertilizer materials and urine and dung N deposited by grazing animals did not apply to the orchard modelled. EF4 is the emission factor for N2O emissions from atmospheric deposition of N on soils and water surfaces, [kg N–N2O (kg NH3–N + NOx–N volatilized)-1]. The emission factor found in the National Inventory Report was derived directly from the IPCC 2006 tier 1 guidelines (ECCC 2019c). Since the Okanagan is considered a dry climate according to IPCC (IPCC 2006), the IPCC tier 1 EF4 representing dry climates at the disaggregate level was used (IPCC 2019b). 45 Indirect emissions from N leaching from managed soils was calculated using IPCC tier 1 methods (Eq. 2.2.2.5.3). (Eq. 2.2.2.5.3) N2O(L)-N = (FSN + FON + FPRP + FCR + FSOM) * FracLEACH * EF5 Where: N2O(L)–N is the annual amount of N2O–N produced from leaching and runoff of N additions to managed soils in regions where leaching/runoff occurs, kg N2O–N yr-1. EF5 is the emission factor for N2O emissions from N leaching and runoff, kg N2O–N (kg N leached and runoff)-1. This factor was provided in the National Inventory Report and derived from the IPCC tier 1 default emission factor for N2O emissions from N leaching and runoff (ECCC 2019c). FracLEACH is the fraction of all N added to/mineralized in managed soils in regions where leaching/runoff occurs that is lost through leaching and runoff, kg N (kg of N additions)-1. A BC specific FracLEACH value was derived using the following equation from Rochette et al. (2008b): (Eq. 2.2.2.5.4) FracLEACH = 0.3247 * P PEi-1 – 0.0247 Where P PEi-1 is the precipitation over potential evapotranspiration of a given ecodistrcit i. The P PE-1 value used in this equation was calculated using the EFeco equation from Rochette et al. (2008b), where EFeco is the annual emission factor at the ecodistrict scale (Eq. 2.2.2.5.5). (Eq. 2.2.2.5.5) EFeco = 0.022 * P PE-1 - 0.0048 (rearranged) P PE-1 = (EFeco + 0.0048) / 0.022 The annual emission factor at the BC specific ecodistrict used in eq. 2.2.2.5.5 was found in Rochette et al. (2008a).The calculated P PE-1 value was then applied to eq. 2.2.2.5.4 to determine the BC specific FracLEACH factor. The direct and indirect emission values from equations 2.2.2.5.1, 2.2.2.5.2, and 2.2.2.5.3 were converted from N2O-N kg year-1 to N2O kg year-1 by multiplying the conversion factor of 46 44/28 (IPCC 2019b). The resulting values were then summed to arrive at the total volume of N2O released from the orchard soils due to synthetic fertilizer application. This volume was then adjusted, as follows, to account for differences in N2O emissions associated with drip and micro-sprinkler irrigation technologies. In a study of apple orchards in the Okanagan, micro-sprinkler irrigation was found to have 29% lower N2O emissions than drip irrigation (Fentabil et al. 2016). The drip irrigation scenario was considered the baseline value (i.e., the unadjusted total direct and indirect N2O emissions without considering irrigation). This is because the regionalized emission factor EF1 used in the eq. 2.2.2.5.1 accounts for irrigation by setting the P PE-1 value within the emission factor to 1 (ECCC 2019a). This means that precipitation (i.e., the amount of irrigation applied) is equal to the potential evaporation that would occur due to the climate. Fentabil et al.’s (2016) methods are consistent with this, as the daily volume of water irrigated in both treatments (micro-sprinkler and drip) was such that P PE-1 = 1. In reality, the volume of water that is delivered to the soil system is less than the volume sent through the irrigation network, due to inefficiencies of the irrigation technology. Since drip is more efficient than micro-sprinkler – 95% vs 78% of water delivered is retained in the soil system for plant uptake (Fretwell 2009) – drip is closer to the scenario of P PE-1 =1. Therefore, drip was considered the baseline scenario and the N2O emissions associated with the micro-sprinkler scenario were modelled as 29% less than the baseline. In addition to calculating the direct and indirect N2O emissions from synthetic fertilizer, NO3, NH3, and NOx were updated in the cherry orchard model. Equation 2.2.2.5.6 was used to calculate the mass of nitrate (mNO3-) in kg emitted to water due to synthetic fertilizer application (FSN). (Eq. 2.2.2.5.6) mNO3- = FSN * FracLEACH / MN * MNO3- 47 Where MN is the molar mass of nitrogen and MNO3- is the molar mass of nitrate used to convert the product of FSN and FracLEACH from kg of N to kg of NO3-. Equation 2.2.2.5.7 was used to calculate the mass of ammonia (mNH3) in kg emitted to the air as a consequence of applying synthetic fertilizer (FSN). (Eq. 2.2.2.5.7) mNH3 = FSN * FracGASF / MN * MNH3 Where MNH3 is the molar mass of ammonia. The FracGASF value for NH3 was an average of the FracGASF NH3 values for the province of BC, reported in the National Inventory Report, from years 2010-2017 (ECCC 2019c). To my knowledge, no NOx specific FracGASF factor has been developed for regions within Canada. Therefore, the mass of NOx in kg (mNOx) was calculated as an expression of the mass of NH3 (eq. 2.2.2.5.8), by rearranging eq. 2.2.2.5.7 and assuming that N volatizes into NH3 and NOx at a 90% - 10% split, respectively (Brentrup et al. 2000). (Eq. 2.2.2.5.8) mNOx = 0.1 ∗ mNH30.9 ∗ MNH3 * MNOx Where MNOx is the molar mass of NOx used to convert kg of N into kg of NOx. The updated values for orchard level emissions of N2O, NO3, NH3, and NOx due to synthetic fertilizer application are reported in section 2.3.6. 2.2.3 Life Cycle Impact Assessment Methods ISO 14044 requires reporting methodologies for impact category selection, classification, characterization, normalization, and weighting (ISO 2006a; for definitions see section 1.1.4). OpenLCA – an open-source life cycle assessment software – was used to model the study system. This software incorporates life cycle inventory data as input and output flows for system processes and models the damages to both midpoint and endpoint impact categories using pre-existing LCIA methodologies. My LCI models were assessed using the IMPACT World+ 48 methodological suite at the midpoint level and included the baseline impact categories recommended by ISO guidelines (Table 1). IMPACT World+ is a regionalized suite of midpoint impact pathways and was launched in 2012 as an updated and combined version of IMPACT 2002+ (Europe), EDIP (Scandinavia), and LUCAS (Canada) (CIRAIG 2012). IMPACT World+ is attractive for the context of this research as it includes site-dependent characterization factors for Canadian ecozones (Toffoletto et al. 2007); (2) assesses impacts to water scarcity at the midpoint level in addition to the baseline categories; and (3) is compatible with openLCA software. The water scarcity midpoint indicator available in IMPACT World+ was used in addition to the baseline categories because water scarcity is a pressing issue in the region this study was performed and improving water use efficiencies was an objective of the goal and scope. The classification and characterization phases of LCIA are accomplished within the IMPACT World+ suite. The LCIA results were not weighted or normalized thereafter. In a meta-analysis of water supply network LCAs, Meron et al. (2016) performed a normalization analysis that revealed marine ecotoxicity, freshwater ecotoxicity, freshwater eutrophication, fossil fuel depletion, human toxicity, and GWP were the most significant impact categories for water supply networks. Each of these impact categories (analogs or proxies) were assessed in this study. Table 1 Baseline impact categories most commonly used in LCA according to LCIA method suite CML and adapted from Acero et al. (2016). Corresponding midpoint level indicators and units in IMPACT World+. Baseline Impact categories Midpoint impact indicators in IMPACT World+ Unit Acidification Freshwater acidification kg SO2 eq Terrestrial acidification kg SO2 eq Climate change Climate change short term kg CO2 eq (short) Climate change long term kg CO2 eq (long Depletion of abiotic resources Mineral resource use kg deprived Fossil energy use MJ deprived Ecotoxicity Freshwater ecotoxicity1 CTUe Eutrophication Freshwater eutrophication lim eq Marine eutrophication kg N N -lim eq Human toxicity Human toxicity cancer CTUh Human toxicity non-cancer CTUh 49 Ozone layer depletion Ozone layer depletion kg CFC-11 eq Photochemical oxidation Photochemical oxidant formation kg NMNOV eq 1Includes marine ecotoxicity at the midpoint level (see Bulle et al. 2019). The following details are provided to achieve compliance with ISO 14046 for water footprint impact assessment methods. The midpoint indicator water scarcity in IMPACT World+ is represented by the proxy-midpoint AWARE (Bulle et al. 2019). AWARE is a consensus based scarcity indicator that stands for available water remaining and is based on the inverse of the difference between water available in a given area and the demand of water in that area (Boulay et al. 2018). It is normalized relative to the world average and assumes that the impact of consuming water in the inventory is proportional to the potential impact on another user. Users accounted for in the characterization factor for AWARE include human and aquatic ecosystem users but excludes terrestrial ecosystem users. The consequences of excluding this cause-effect chain and associated potential environmental impact includes the potential to underrepresent environmental demand in the characterization factor. However, the comparative difference in the water scarcity midpoint across systems studied remains robust and meets the objectives of the goal and scope. 2.2.4 Life Cycle Interpretation Methods The interpretation stage of LCA includes checking the consistency, completeness, and robustness of the inventory, impact assessment methods and associated choices. Parametric uncertainty (a measure of completeness and robustness of the inventory) was accounted for in this study and described in section 2.2.4.1. Model uncertainty was not accounted for in the selected impact assessment suite (Impact World+) and was therefore not included in this assessment. However, it is recommended that only the relative uncertainty is used to make comparisons across product systems in LCA (Henriksson et al. 2015). Provided the LCA models being compared use the same impact assessment suite (which is true in this case), including 50 model uncertainty would only change the absolute uncertainty. The relative uncertainty between each model scenario is influenced only by the accounted for parameter uncertainty. Choice uncertainty was accounted for using sensitivity analyses, as described in the section 2.2.4.2. A contribution analysis was used to interpret the LCIA results. The contribution analysis for water delivery systems aimed to understand the comparative impacts characteristic of (i) surface pumped versus gravity fed irrigation water, (ii) different treatment options, and (iii) the efficiencies of different cherry orchard irrigation technologies. The contribution analysis of cherry orchard scenarios aimed to distinguish the environmental trade-offs between different irrigation technologies – including the difference in associated GHG emissions between drip and micro-sprinkler systems and the potential relevance of sequestered carbonates from lake water under different irrigation regimes – and to assess the environmental significance of irrigation water as it compared to other process associated with the orchard system. 2.2.4.1 Uncertainty Methods Parametric uncertainty was assessed for all foreground system flows for each model using a standard pedigree matrix in combination with base uncertainties derived from either the raw data or from generic sectoral base uncertainty factors (Frischknecht et al. 2005). The pedigree matrix is comprised of five indicators to assess data quality (DQIs): completeness, reliability, temporal correlation, geographic correlation, and technological correlation. The data quality of each foreground flow was assessed on a scale of one (best) to five (worst) for each of the five DQIs. The matrix then generates a lognormal uncertainty distribution for each flow and an associated geometric standard deviation. Base uncertainties were aggregated with the pedigree matrix within openLCA and were calculated from raw inventory data where possible using the following formula: 51 (Eq. 2.2.4.1) 𝑔,𝑤 = exp (√∑ 𝑤𝑖∗(𝑙𝑛(𝐴𝑖)−ln (𝜇𝑔,𝑤))2𝑛𝑖=1∑ 𝑤𝑖𝑛𝑖=1) Where 𝑔,𝑤 is the weighted geometric standard deviation associated with the production weighted average of a system process; 𝑤𝑖 is the weight assigned to each data point (𝐴𝑖) in the inventory dataset; and 𝜇𝑔,𝑤 is the weighted geometric mean calculated using equation 2.2.4.2. (Eq. 2.2.4.2) 𝜇𝑔,𝑤 = exp (∑ 𝑤𝑖∗ln(𝐴𝑖)𝑛𝑖=1∑ 𝑤𝑖𝑛𝑖=1) Where uncertainty (calculated geometric standard deviation) could not be derived from the foreground data, generic sectoral base uncertainties from Frischknecht et al. (2005) were used in combination with the pedigree matrix. Together, the pedigree matrix and base uncertainty (either from generic sectoral values or calculated directly from the inventory datasets) generated a lognormal uncertainty distribution for each foreground flow. Using openLCA, a Monte Carlo simulation with 1000 iterations was then used to generate a lognormal uncertainty distribution for each impact assessment category using the parametric uncertainty calculated from the above methods. Monte Carlo simulations were run for each product system analyzed, which provided a standard deviation for each impact assessment category. The datasets of LCIA results generated by the Monte Carlo simulations were natural log transformed to create a normal distribution for the data. To test for statistical significance between compared production systems, one way analysis of variance (ANOVA) and two sample T-tests of independent variables assuming equal variances were performed on the normalized data using Microsoft Excel (Corporation 2022). The Bonferroni correction method was used to address the multiple comparisons problem for groups with more than two comparisons, where the significance level was divided by the number of test groups. 52 2.2.4.2 Sensitivity Analyses A sensitivity analysis was used to evaluate the consequences of choices made when modelling water losses occurring during delivery of water to agricultural users. The Regional District of North Okanagan (RDNO) provided three estimates for the percent of water extracted at intake source that is lost along the delivery system: 23%, 18%, and 12%. The 23% estimate is based on annual inflows versus annual metered consumption, including main flushing and firefighting, but does not include approximations for theft and water meter malfunctions. The 18% estimate used the same methodology as the former, but included the estimates for theft and water meter malfunctions within the calculation of total annual consumption. A 12% water loss estimate was calculated using the AWWA (American Water Works Association) water audit software and included what is considered unavoidable losses in the calculation of total annual consumption. For all water delivery system scenarios included in the main results (excluding the sensitivity analysis), the 18% estimate was used. This is because the estimate accounts for more uses of water consumption (theft, etc.) than the 23% estimate, meaning it is more accurate at approximating the water losses that are due to main leaks. The 12% estimate was not used because it was estimated for 2019 water consumption patterns and the inventory for the model used 2018 data. However, the AWWA water audit software includes estimate calculations based on the length of mains in the system and in this way appears to provide a more rigorous approximation of the real losses in the system. As such, a sensitivity analysis was warranted to assess the difference in impacts associated with these three estimates: the minimum water loss (12%), maximum water loss (23%), and the estimate used in the models (18%). Choice uncertainty was also introduced to the model when selecting where along the delivery system the water losses occurred. No data existed that specified the locations along the water delivery system that the losses occurred, yet placing water losses at the very end of the 53 delivery system results in higher impacts at the treatment, pumping, and delivery phase. This is because more water is processed at these stages for each cubic meter of water that is received by agricultural users. The alternative modelling choice was to place water losses at the start of the system, before treatment and pumping, or to split water losses by some percentage throughout the system. Because there was no data available to justify a percentage split, the water losses were modelled as though they occurred after all processes in the water delivery system. This resulted in the highest possible impacts associated with the system, as it assumed the largest possible amount of source water required for treatment, pumping, and delivery of one cubic meter of water that reached agricultural users. This was considered as the best modelling option because most of the distribution network, where water losses can occur, was after the treatment phase and after most pump houses. A sensitivity analysis was performed to assess the difference in environmental impacts between a system that assumed water is lost before subsequent processes and a system that assumed water is lost after all processes. As a result, a total of four scenarios were performed for the sensitivity analysis: 12% water loss at the end, 12% water loss at the start, 23% water loss at the end, and 23% water loss at the start. The impact assessment results of these scenarios were compared to each other and to the model used for all water delivery systems scenarios in the LCIA stage (18% water loss at the end). A second sensitivity analysis was performed to assess the significance of using BC electricity as a provider for electricity consumption at the treatment plant. BC electricity is largely comprised of hydro and is considered to have less environmental burdens than average electricity grid mixes. Therefore, the Alberta electricity grid was replaced as the provider for electricity consumption at the treatment plant to assess the comparative difference. 54 2.3 Life Cycle Inventory Results 2.3.1 Okanagan Water Delivery Systems Overview Three water purveyors – the Town of Oliver, the Regional District of North Okanagan (RDNO), and the City of Kelowna – reported on a total of 14 systems that delivered water to agricultural users in the Okanagan. The total volume of water (measured at the intake source) and an overview of each system is provided in Table 2. A total of 11 systems used surface water and three systems used groundwater sources. Five treatment types were captured in this inventory: no treatment (39% of production), treatment at a plant (36%), delivered hypochlorite injected at intake (2%), chlorine generated on-site and injected at intake (<1%), and delivered chlorine injected at intake (23%). The complete life cycle inventories of all Okanagan water delivery system scenarios can be accessed publicly on the Food PRISM Lab Open Science Framework webpage. Table 2 Each reported system from three water purveyors and the attributes associated with each system: total volume of water consumed during the irrigation months April – October (2018), the production proportion, delivery method, water source, and treatment type. System Irrigation Season Volume (m3) Proportion of Production Delivery Method Water Source Treatment Type Kelowna1 7,664,450 23% gravity surface delivered chlorine Oliver 1 – Mud Lake2 1,627,680 5% mix surface none Oliver 1 – Buchanan2 180,520 1% mix ground none Oliver 2 – Black Sage Wells2 721,980 2% pumped ground delivered hypochlorite Oliver 2 - Miller RD2 60,010 0% pumped ground generated hypochlorite Oliver 2B2 404,630 1% pumped surface none Oliver 42 3,542,830 11% mix surface none Oliver 52 1,515,160 5% mix surface none Oliver 62 1,771,430 5% mix surface none Oliver 72 1,434,500 4% mix surface none Goose Lake Non-potable3 662,300 2% pumped surface none King Edward non-potable3 813,080 2% gravity surface none 55 Duteau Non-potable3 805,350 2% pumped surface none Duteau Combined3 11,710,450 36% mix surface plant Total 32,914,360 Average 2,611,610 1From City of Kelowna (pers. comm. Brad Stuart; Hoppe 2019) 2From Town of Oliver (Goodsell 2018) 3From Regional District of North Okanagan (Clark 2018; pers. comm. Skyler Ganz) 2.3.2 Delivery pipes A total of 12 different pipe types were reported across all systems (Appendix A). Two pipe types, polyethylene and copper, were omitted from the inventory due to the negligible lengths reported. Cast-iron was also reported in small numbers and, to simplify the inventory, was included as ductile iron pipe. Therefore, a total of nine pipe types of various diameters were included in the inventory (Table 3). The length of each pipe type required per functional unit of one cubic meter of water delivered to agricultural users was calculated assuming a pipe lifetime of 50 years and a 2.74 X 106 m3 annual average volume of extracted water (Table 4). Table 3 LCI data for manufacturing, transportation, and installation of 500 mm pipe made of asbestos concrete (AC), concrete, ductile iron, fiberglass reinforced plastic (FRP), galvanized steel, high-density polyethylene (HDPE), reinforced concrete cylinder pipe (RCCP), steel and polyvinyl chloride (PVC) for 5 m of water delivery infrastructure with a lifetime of 50 years. Product Process input Value Ecoinvent Unit Process AC1 asbestos, crysotile type kg 254 market for asbestos, crysotile type | asbestos, crysotile type | APOS, U - GLO cement, Portland kg 58 cement production, Portland | cement, Portland | APOS, U - CH concrete, normal kg 580 market for concrete, normal | concrete, normal | APOS, U - CH electricity, high voltage kWh 1.56 X 10-2 electricity production, hard coal | electricity, high voltage | APOS, U - WECC, US only gravel, crushed kg 232 gravel production, crushed | gravel, crushed | APOS, U - CH sand kg 145 gravel and sand quarry operation | sand | APOS, U - CH tap water kg 87 market group for tap water | tap water | APOS, U - RER 56 Product Process input Value Ecoinvent Unit Process transport, freight, lorry t*km 168 market for transport, freight, lorry 3.5-7.5 metric ton, EURO4 | transport, freight, lorry 3.5-7.5 metric ton, EURO4 | APOS, S - GLO transport, truck t*km 12.8 Transport, truck 10-20t, EURO4, 100%LF, empty return Mass - GLO Concrete2 cement, Portland kg 58 cement production, Portland | cement, Portland | APOS, U - CH concrete, normal kg 580 market for concrete, normal | concrete, normal | APOS, U - CH electricity, high voltage kWh 1.56 X 10-2 electricity production, hard coal | electricity, high voltage | APOS, U - WECC, US only gravel, crushed kg 232 gravel production, crushed | gravel, crushed | APOS, U - CH sand kg 145 gravel and sand quarry operation | sand | APOS, U - CH steel, low-alloyed kg 58 market for steel, low-alloyed | steel, low-alloyed | APOS, U - GLOsteel production, electric, low-alloyed | steel, low-alloyed | APOS, S - CA-QC tap water kg 87 market group for tap water | tap water | APOS, U - RER transport, freight, lorry t*km 168 market for transport, freight, lorry 3.5-7.5 metric ton, EURO4 | transport, freight, lorry 3.5-7.5 metric ton, EURO4 | APOS, S - GLO transport, truck t*km 12.8 transport, truck 10-20t, EURO4, 100%LF, empty return Mass - GLO Ductile Iron2 bitumen seal kg 0.69 bitumen seal production | bitumen seal | APOS, U - RER cast iron kg 130 cast iron production | cast iron | APOS, U - RER casting, steel kg 138 market for casting, steel, lost-wax | casting, steel, lost-wax | APOS, U - GLO electricity, high voltage kWh 2.49 X 10-2 electricity production, hard coal | electricity, high voltage | APOS, U - WECC, US only sand kg 6.49 gravel and sand quarry operation | sand | APOS, U - CH transport, freight, lorry t*km 40 transport, freight, lorry 3.5-7.5 metric ton, EURO6 | transport, freight, lorry 3.5-7.5 metric ton, EURO6 | APOS, U - RER FRP3 bitumen adhesive compound kg 2.5 market for bitumen adhesive compound, hot | bitumen adhesive compound, hot | APOS, U - GLO epoxy resin, liquid kg 166 market for epoxy resin, liquid | epoxy resin, liquid | APOS, U - GLO 57 Product Process input Value Ecoinvent Unit Process excavation, hydraulic digger m3 57.5 market for excavation, hydraulic digger | excavation, hydraulic digger | APOS, U - GLO glass fibre kg 1413 market for glass fibre | glass fibre | APOS, U - GLO injection moulding kg 1828 market for injection moulding | injection moulding | APOS, U - GLO laminating service, foil m2 80 market for laminating service, foil, with acrylic binder | APOS, U - GLO sand kg 249 market for sand | sand | APOS, U - GLO transport, freight, lorry t*km 10 market for transport, freight, lorry >32 metric ton, EURO4 | transport, freight, lorry >32 metric ton, EURO4 | APOS, U - GLO Galvanized steel4 bitumen adhesive compound kg 2.85 market for bitumen adhesive compound, hot | bitumen adhesive compound, hot | APOS, S - GLO cement mortar kg 87.5 market for cement mortar | cement mortar | APOS, S - RoW enamelling m2 7.85 market for enamelling | enamelling | APOS, S - GLO excavation, hydraulic digger m3 7.9 market for excavation, hydraulic digger | excavation, hydraulic digger | APOS, S - GLO metal working kg 493 market for metal working, average for steel product manufacturing | metal working, average for steel product manufacturing | APOS, S - GLO steel hot dip galvanized kg 493 Steel hot dip galvanized, including recycling, blast furnace route, production mix, at plant, 1kg, typical thickness between 0.3 - 3 mm. typical width between 600 - 2100 mm. - GLO transport, freight, lorry t*km 191 market for transport, freight, lorry 16-32 metric ton, EURO4 | transport, freight, lorry 16-32 metric ton, EURO4 | APOS, S - GLO transport, freight, lorry t*km 17.5 market for transport, freight, lorry >32 metric ton, EURO4 | transport, freight, lorry >32 metric ton, EURO4 | APOS, S - GLO HDPE3 excavation, hydraulic digger m3 57.5 market for excavation, hydraulic digger | excavation, hydraulic digger | APOS, U - GLO extrusion, plastic pipes kg 3425 market for extrusion production, plastic pipes | extrusion, plastic pipes | APOS, U - GLO polyethylene, high density kg 3425 market for polyethylene, high density, granulate | polyethylene, high density, granulate | APOS, U - GLO 58 Product Process input Value Ecoinvent Unit Process transport, freight, lorry t*km 20 market for transport, freight, lorry >32 metric ton, EURO4 | transport, freight, lorry >32 metric ton, EURO4 | APOS, U - GLO RCCP3 cement cast plaster floor kg 15130 market for cement cast plaster floor | cement cast plaster floor | APOS, U - GLO excavation, hydraulic digger m3 57.5 excavation, hydraulic digger | excavation, hydraulic digger | APOS, U - RER steel, low-alloyed kg 250 market for steel, low-alloyed | steel, low-alloyed | APOS, U - GLO transport, freight, lorry t*km 90 transport, freight, lorry >32 metric ton, EURO4 | transport, freight, lorry >32 metric ton, EURO4 | APOS, U - RER Steel5 bitumen adhesive compound kg 2.85 market for bitumen adhesive compound, hot | bitumen adhesive compound, hot | APOS, S - GLO cement mortar kg 87.5 market for cement mortar | cement mortar | APOS, S - RoW enamelling m2 7.85 market for enamelling | enamelling | APOS, S - GLO excavation, hydraulic digger m3 7.9 market for excavation, hydraulic digger | excavation, hydraulic digger | APOS, S - GLO metal working kg 493 market for metal working, average for steel product manufacturing | metal working, average for steel product manufacturing | APOS, S - GLO steel, low-alloyed kg 493 market for steel, low-alloyed, hot rolled | steel, low-alloyed, hot rolled | APOS, S - GLO transport, freight, lorry t*km 191 market for transport, freight, lorry 16-32 metric ton, EURO4 | transport, freight, lorry 16-32 metric ton, EURO4 | APOS, S - GLO transport, freight, lorry t*km 17.5 market for transport, freight, lorry >32 metric ton, EURO4 | transport, freight, lorry >32 metric ton, EURO4 | APOS, S - GLO PVC3 bitumen adhesive compound kg 2 market for bitumen adhesive compound, hot | bitumen adhesive compound, hot | APOS, U - GLO excavation, hydraulic digger m3 57.5 market for excavation, hydraulic digger | excavation, hydraulic digger | APOS, U - GLO extrusion, plastic pipes kg 3185 market for extrusion production, plastic pipes | extrusion, plastic pipes | APOS, U - GLO 59 Product Process input Value Ecoinvent Unit Process limestone, crushed, washed kg 194 market for limestone, crushed, washed | limestone, crushed, washed | APOS, U - RoW polyvinylchloride kg 2991 market for polyvinylchloride, bulk polymerised | polyvinylchloride, bulk polymerised | APOS, U - GLO transport, freight, lorry t*km 20 market for transport, freight, lorry >32 metric ton, EURO4 | transport, freight, lorry >32 metric ton, EURO4 | APOS, U - GLO 1Asbestos concrete input data approximated using concrete input data (Vahidi et al. 2015) and replacing cement with asbestos cement (ASCE 2017). 2From Vahidi et al. (2015). 3From Vahidi et al. (2016). 4Input data for steel pipe from Hajibabaei et al. (2018) where input for low-alloyed steel was replaced with hot dip galvanized steel. 5From Hajibabaei et al. (2018). Table 4 Length of each pipe type required to deliver a cubic meter of water to an agricultural user was found by dividing the production weighted average length of each pipe by the average volume of water delivered over a 50-year lifespan. Pipe Type Weighted Average Length (km) Length (km m-3) AC 500 18.08 1.4 X 10-7 Concrete 500 16.75 1.3 X 10-7 DI 500 4.30 3.3 X 10-8 HDPE 500 0.02 1.5 X 10-10 FRP 500 0.11 8.6 X 10-10 PVC 500 9.12 7.0 X 10-8 Steel 500 5.81 4.5 X 10-8 Galv 500 0.09 6.5 X 10-10 RCCP 500 2.01 1.5 X 10-8 2.3.3 Treatment Processes A total of five distinct treatment methods were employed: no treatment, treatment at a plant, and treatment at the source intake with trucked in chlorine, trucked in hypochlorite, or hypochlorite generated on-site (Table 5). The City of Kelowna’s separated agricultural irrigation system was treated by trucking in gaseous chlorine supplied by Brenntag Canada and shipped from Langley, B.C. The chlorine was then injected through a 1050 mm transmission line that leaves the intake pond. A booster 60 pump with a power (p) of 5 HP and a capacity (c) of 43.2 m3 hour-1 was used in this process, and electricity consumed per m3 of treated water (E) was calculated using Equation 2.2.2.3.1. Of the four water delivery systems from the RDNO accounted for in the inventory, one was not separated for agricultural users and was therefore treated at the Duteau Creek Water Treatment Plant (DCWTP). The plant treats water first using a process called Dissolved Air Flotation (DAF). DAF separates suspended particles from the water using coagulants and flocculants which clump suspended particles together. The solids are then floated to the surface by rising air bubbles and skimmed off the top of the tanks. The organic matter skimmed off the top is then centrifuged to further separate solids. The resulting solid waste is known as cake and is sent to the Vernon landfill where it is used as a top cover over the pits. The composition of cake is highly variable; therefore, cake was modelled as an output of solid waste. This is consistent with the methodology used in a similar dataset for tap water production in Quebec, Canada (CIRAIG 2012). Centrate – the liquid separated via centrifuge from the cake – has a composition that is also highly variable. The centrate is largely composed of solutes found in the source water and is delivered back to the environment through a series of man-made reservoirs. Negligible quantities of the residual coagulant and flocculant chemicals are present in the centrate. Therefore, centrate was accounted for in the model as an emissions flow of water to a lake (surface water proxy for the man-made reservoir). The water is then treated with 0.8% hypochlorite solution that is generated on-site using electrochlorination. The generator used was the Clortec 750, and all inputs and outputs associated with this process were supplied by the manufacturing company (DeNora 2017). Once treated with hypochlorite, the water undergoes ultraviolet (UV) disinfection. UV bulbs containing mercury are used in this process. It is difficult to estimate the lifetime of a bulb as not all bulbs are used at once – the device operates in accordance with the level of disinfection required to maximize efficiency. Each bulb, according to the manufacturer, has a lifetime of 61 14,000 hours: according to the electrical instrumentation technician at the plant, this works out to an estimated 290 ML of treated water. Therefore, 2.9 X 10-7 bulbs are used per m3 of treated water (Table 5). Two of the nine systems from the Town of Oliver were treated: the Black Sage wells and the Miller Road systems, because they supplied to both domestic and agricultural users. The Black Sage wells system used sodium hypochlorite trucked in from Brenntag Canada in Langley. Annual electricity usage at the pump station where the hypochlorite is added was used to calculate the energy required for this treatment system. The Miller Road system used an on-site sodium hypochlorite generator to treat the water. It was assumed this system trucked in sodium chloride from the same supplier as the Black Sage wells system. Table 5 LCI data for the manufacturing, construction and transportation phases of products used in four water treatment methods employed by water delivery systems in the Okanagan. Treatment plant inputs sub-categorized by processes within the plant. Inputs scaled to a m3 of treated water. Product Process input Value Ecoinvent Unit Process Delivered Chlorine at Intake1 chlorine (g) kg 6.0 X 10-3 market for chlorine, gaseous | chlorine, gaseous | APOS, U - RoW electricity kWh 9.0 X 10-2 market for electricity, medium voltage | electricity, medium voltage | APOS, U - CA-BC transport, truck, >20t kg*km 4.6 transport, truck >20t, EURO1, 80%LF, empty return Mass - GLO Delivered Hypochlorite at intake2 electricity kWh 5.3 X 10-1 market for electricity, low voltage | electricity, low voltage | APOS, U - CA-BC sodium hypochlorite (15%) kg 3.0 X 10-3 sodium hypochlorite production, product in 15% solution state | sodium hypochlorite, without water, in 15% solution state | APOS, U - CA-QC transport, truck, >20 t kg*km 3.2 transport, truck >20t, EURO1, 80%LF, empty return Mass - GLO Generated Hypochlorite at Intake2 electricity kWh 1.7 market for electricity, medium voltage | electricity, low voltage | APOS, U - CA-BC sodium chloride, powder kg 5.9 X 10-3 market for sodium chloride, powder | sodium chloride, powder | APOS, U - GLO transport, truck, >20t kg*km 4.4 transport, truck >20t, EURO1, 80%LF, empty return Mass - GLO hydrogen3 kg -2.5 X 10-5 elementary flows/emission to air/low population density Treatment Plant4 electricity kWh 9.3 X 10-2 market for electricity, medium voltage | electricity, low voltage | APOS, U - CA-BC Construction 62 Product Process input Value Ecoinvent Unit Process concrete, normal m3 8.6 X 10-6 market for concrete, normal | concrete, normal | APOS, U - RoW Coagulation polyaluminium chloride mg 5.8 X 104 market for polyaluminium chloride | polyaluminium chloride | APOS, U - GLO epichlorohydrin mg 1400 market for epichlorohydrin | epichlorohydrin | APOS, U - GLO Flocculation kerosene mg 120 market for kerosene | kerosene | APOS, U - RoW polyacrylamide mg 18.5 market for polyacrylamide | polyacrylamide | APOS, U - GLO pH correction & anti-corrosion sodium hydroxide, without water, in 50% solution state mg 9050 market for sodium hydroxide, without water, in 50% solution state | sodium hydroxide, without water, in 50% solution state | APOS, U - GLO Clarification adipic acid mg 14.2 market for adipic acid | adipic acid | APOS, U - GLO polyacrylamide mg 426 market for polyacrylamide | polyacrylamide | APOS, U - GLO Disinfection ultraviolet lamp Item(s) 2.9 X 10-7 market for ultraviolet lamp | ultraviolet lamp | APOS, U - GLO 0.8% Hypochlorite solution ml 470 On-site hypochlorite generator On-site hypochlorite generator5 sodium chloride kg 1.1 X 10-2 market for sodium chloride, powder | sodium chloride, powder | APOS, U - GLO acetic acid mg 3.7 X 10-2 market for acetic acid, without water, in 98% solution state | acetic acid, without water, in 98% solution state | APOS, U - GLO hydrochloric acid m3 6.7 X 10-4 market for hydrochloric acid, without water, in 30% solution state | hydrochloric acid, without water, in 30% solution state | APOS, U - RoW titanium, primary kg 2.3 X 10-3 market for titanium, primary | titanium, primary | APOS, U - GLO hydrogen gas kg -1.1 X 10-4 emissions to air/low population density, long term Chemical Transport transport, barge ship t*nmi 0.37 transport, barge ship, bulk, 12000t, 80%LF, default Mass - GLO transport, truck >20t kg*km 167 transport, truck >20t, EURO1, 80%LF, empty return Mass - GLO Cake waste to Landfill transport, truck, 10-20t kg*km 1.5 transport, truck 10-20t, EURO1, 80%LF, default Mass - GLO municipal solid waste kg -2.9 X 10-2 treatment of municipal solid waste, sanitary landfill | municipal solid waste | APOS, U - CA-QC Centrate waste to reservoir water, CA kg -1.9 X 10-3 elementary flows/emission to water/lake 1From City of Kelowna (pers. comm. Brad Stuart). 2From Town of Oliver (pers. comm. Shawn Goodsell). 3Outputted hydrogen gas determined using the same ratio of hydrogen output to salt input as the on-site hypochlorite generator used at the Duteua Creek treatment plant. 4From Regional District of North Okanagan for the Duteau Creek treatment plant (pers. comm. Skyler Ganz; pers. comm. Dustin Heidt). Chemical names used in coagulation, flocculation, and clarification processes and known ingredients are listed in Appendix B. 5Input values for on-site hypochlorite generator required to produce 470 ml of 0.8% hypochlorite solution (DE NORA n.d.), which is used to treat a m3 of water (pers. comm. Dustin Heidt). 63 2.3.4 Pumping Of the 14 systems, two were gravity fed, five were pumped, and seven were mixed (Table 6). The production weighted average energy consumed across all delivery systems was 0.19 kWh m-3 (SD 0.13). The production weighted average for gravity fed, pumped, and mixed delivery systems was 0.05 kWh m-3 (SD 0.20), 0.29 kWh m-3 (SD 0.09), and 0.24 kWh m-3 (SD 0.08), respectively. The production weighted average for networks using groundwater sources required 0.31 kWh m-3 (SD 0.01; N=3) and surface water supplied networks required 0.19 kWh m-3 (SD 0.14; N=11). The non-production weighted average energy required for pumping between ground and surface water sources was significantly different (One-tailed two-sample T test; df = 11; t Stat = 2.11; p = 0.03). Table 6 The delivery method, source, average energy consumed by pumping, and the production weighted average energy consumed by pumping for 14 water delivery systems in the Okanagan. System Delivery Method Source Average Energy (kWh m-3) Production Weighted Energy (kWh m-3) Kelowna gravity surface 0.00 0.00 Oliver 1 – Mud Lake mix surface 0.13 6.43 X 10-3 Oliver 1 – Buchanan mix ground 0.33 1.81 X 10-3 Oliver 2 – Black Sage Wells pumped ground 0.30 6.58 X 10-3 Oliver 2 - Miller Road pumped ground 0.30 5.47 X 10-4 Oliver 2B pumped ground 0.32 3.93 X 10-3 Oliver 4 mix surface 0.18 1.94 X 10-2 Oliver 5 mix surface 0.22 1.01 X 10-2 Oliver 6 mix surface 0.14 7.53 X 10-3 Oliver 7 mix surface 0.12 5.23 X 10-3 Goose Lake Non-potable pumped surface 0.16 3.22 X 10-3 King Edward non-potable gravity surface 0.48 1.19 X 10-2 Duteau Non-potable pumped surface 0.38 9.30 X 10-3 Duteau Combined mix surface 0.30 1.06 X 10-1 64 2.3.5 Irrigation Technologies A total of 12 different irrigation technologies were employed on Okanagan cherry orchards (Table 7). Only seven irrigation technologies were modelled as some technologies share the same efficiency coefficient. The two most common technologies were micro-sprinkler and solid-set undertree, which were utilized in 50% and 24% of the irrigated cherry orchards in the survey. Efficiency coefficients were used to calculate the volume of water required to deliver a cubic meter of irrigation into the soil for crop uptake. The value reported by Hannam et al. (2016) for CO2(g) released per cubic meter of irrigated water that was due to bicarbonates in the water was 8.7 x 10-3 kg m-3. The study did not consider irrigation technology efficiencies and measured water volumes from the irrigation infrastructure. Therefore, the amount of CO2(g) per cubic meter of water available for crop uptake was calculated by dividing 1 m3 by the efficiency coefficient and multiplying by 8.7 x 10-3 kg CO2(g) m-3, as shown in Equation 2.3.5.1. Eq. 2.3.5.1 CO2 = 0.0087(ex-1) Where ex is the efficiency coefficient of x irrigation technology. A production weighted average efficiency was calculated to represent the average scenario for the Okanagan. 65 Table 7 The type, efficiency, and percent frequency of each irrigation technology employed on Okanagan cherry orchards that used irrigation (n=1422). The input volume describes the volume of water required to deliver a cubic meter of water to the soil for crop uptake and is calculated using the efficiency coefficient. The last column represents the CO2(g) released from the soil due to bicarbonates delivered by a cubic meter of irrigation water, with irrigation technology efficiency accounted for. Irrigation Used Percent Efficiency Input volume (m3 m-3) CO2(g) (kg m-3) Drip 3% 0.92 1.09 0.0095 Handline 2% 0.70 1.43 0.0124 Microspray 10% 0.88 1.14 0.0099 Micro-sprinkler 50% 0.78 1.28 0.0112 Overtreedrip 0.4% 0.92 1.09 0.0095 SDI 0.1% 0.95 1.05 0.0092 Ss. Overtree* 2% 0.70 1.43 0.0124 Ss. Sprinkler* 1% 0.72 1.39 0.0121 Wheelline 0.1% 0.72 1.39 0.0121 Blank 0.1% 0.72 1.39 0.0121 Sprinkler 8% 0.72 1.39 0.0121 Ss. Undertree* 24% 0.74 1.35 0.0118 PW Average - 0.78 1.29 0.0112 *ss. = solid-set 2.3.6 Cherry orchard LCI adjustments of nitrogenous emissions Adjustments made to the life cycle inventory of the Okanagan cherry orchard model are provided in Table 8. Emissions of nitrogenous species from synthetic fertilizer applied on cherry orchards were recalculated to reflect IPCC tier 2 methods where data was available. N2O emissions associated with the orchard system using micro-sprinkler irrigation was 29% (7.53 X 10-5 kg of N2O kg-1 of usable cherry yield) less than the recalculated value reflected in Table 8. All other orchard level system comparisons, including the drip irrigation system at the orchard level, used the amounts described in Table 8. Table 8 Mass of direct and indirect N2O, NO3-, NH3, and NOx emitted at the orchard level due to synthetic fertilizer application per functional unit of 1 kg of usable cherries. Direct and indirect N2O was combined and entered as one output flow in openLCA. GHG Species Amount Unit Ecoinvent Unit Process N2O - Dinitrogen monoxide 1.06 X 10-4 kg Emission to air/unspecified Direct N2O (– air) 9.02 X 10-5 kg Indirect N2O (NOx + NH3 – air) 4.87 X 10-6 kg 66 GHG Species Amount Unit Ecoinvent Unit Process Indirect N2O (NO3- – water) 1.10 X 10-5 kg NH3 - non combustion 6.08 X 10-4 kg Emission to air/unspecified NO3- - Nitrate 4.13 X 10-3 kg Emission to water/unspecified NOx - non combustion 1.19 X 10-4 kg Emission to air/unspecified 2.4 LCIA Results 2.4.1 Okanagan Water Delivery Systems Overview All life cycle impact assessment results and associated standard deviations derived from Monte Carlo uncertainty assessments for the production weighted Okanagan water delivery system are shown at the process level in Table 9. Treatment is responsible for 80% of the impacts on freshwater ecotoxicity associated with the system (Figure 2). Pumping had a relatively small role across impact categories compared to other processes in the system. Detailed analysis and comparison of different treatment, irrigation, and pumping technologies and inputs are found in the subsequent sections. 67 Figure 2 IMPACT World+ life cycle impact assessment results of the production weighted Okanagan water delivery system for each of nine impact categories. Results are broken down by process (delivery, treatment, pumping, and source water) and shown as the percent contribution, where 100% is the total impact score of the water supply network model for each impact category. Of the nine impacts categories shown in Figure 2, piping had the largest contribution across process stages for climate change (short term; 50%), fossil and nuclear energy use (53%), freshwater eutrophication (53%), human toxicity (cancer and non-cancer; 52%), and mineral resource use (85%). Piping was responsible for 85% of the impacts of mineral resource use. This was largely due to various forms of steel production for steel, ductile iron, and concrete piping (see Table 3 in the inventory results section), accounting for a total of 63% (7.20 X 10-3 kg deprived m-3) of the system’s impacts for this category. Delivery piping was responsible for 53% (1.40 MJ m-3) of fossil and nuclear energy use, where 21% (0.54 MJ m-3) of the total impacts of the system were from steel casting used in ductile iron pipe manufacturing, and 12% (0.33 MJ m-3) from polyvinylchloride used in PVC pipe manufacturing. Delivery piping accounted for 53% (3.04 X 10-7 kg PO4 P-lim eq m-3) of freshwater eutrophication impacts, 52% of human toxicity impacts (2.72 X 10-8 CTUh m-3 and 3.53 X 10-8 CTUh m-3 for cancer and non 0%20%40%60%80%100%% ContributionDelivery pipe Treatment Pumping Source water68 cancer, respectively), and 50% (0.10 kg CO2 eq m-3) of short term climate change impacts – the highest contributions to which were from steel manufacturing processes. Table 9 Life cycle impact assessment results for the production weighted Okanagan water delivery system per functional unit of 1 cubic meter of water delivered to agricultural users. Delivery pipe, treatment, and pumping process results and total system impacts with associated standard deviation (SD) were included for all 14 categories assessed. Impact Category Unit Delivery Pipe Treatment Pumping Total SD Climate change, long term kg CO2 eq (long) 9.42 X 10-2 7.08 X 10-2 1.80 X 10-2 1.83 X 10-1 1.68 X 10-1 Climate change, short term kg CO2 eq (short) 1.01 X 10-1 8.13 X 10-2 1.87 X 10-2 2.01 X 10-1 1.84 X 10-1 Fossil and nuclear energy use MJ deprived 1.40 9.64 X 10-1 2.79 X 10-1 2.64 2.51 Freshwater acidification kg SO2 eq 2.49 X 10-15 2.62 X 10-15 2.75 X 10-16 5.38 X 10-15 4.99 X 10-15 Freshwater ecotoxicity CTUe 7.89 X 102 3.75 X 103 1.36 X 102 4.68 X 103 2.06 X 104 Freshwater eutrophication kg PO4 P-lim eq 3.04 X 10-7 2.52 X 10-7 1.45 X 10-8 5.70 X 10-7 5.13 X 10-7 Human toxicity cancer CTUh 2.72 X 10-8 2.37 X 10-8 1.45 X 10-9 5.23 X 10-8 2.14 X 10-7 Human toxicity non cancer CTUh 3.53 X 10-8 2.95 X 10-8 2.77 X 10-9 6.76 X 10-8 3.87 X 10-7 Marine eutrophication kg N N-lim eq 7.38 X 10-6 1.35 X 10-5 5.07 X 10-7 2.14 X 10-5 1.64 X 10-5 Mineral resources use kg deprived 9.79 X 10-3 1.39 X 10-3 2.80 X 10-4 1.15 X 10-2 1.84 X 10-2 Ozone layer depletion kg CFC-11 eq 6.43 X 10-9 1.46 X 10-8 1.44 X 10-9 2.24 X 10-8 1.44 X 10-8 Photochemical oxidant formation kg NMVOC eq 3.18 X 10-4 3.03 X 10-4 3.42 X 10-5 6.55 X 10-4 5.59 X 10-4 Terrestrial acidification kg SO2 eq 1.57 X 10-9 1.66 X 10-9 1.77 X 10-10 3.41 X 10-9 3.13 X 10-9 Water scarcity m3 world-eq 8.98 X 10-2 8.25 X 10-2 1.94 X 10-1 8.46 1.90 X 101 2.4.2 Orchard Overview The production weighted Okanagan water delivery system (WDS) was used as the provider of irrigation water for the orchard system. Across most impact categories, irrigation water had the largest contribution per unit of cherries produced, followed either by fertilizer application or energy supply (Table 10). The exceptions to this were marine and freshwater eutrophication impact categories, where fertilizer was the largest contributor, and photochemical oxidant formation, where mulch application had the second highest impact (1.22 X 10-4 kg NMVOC eq kg-1 usable cherries) rather than fertilizer application or energy supply. Irrigation was responsible for 30% (6.57 X 10-2 kg CO2 eq kg-1 usable cherries) of the orchard’s impacts to 69 climate change (short term), compared to 27% (5.95 X 10-2 kg CO2 eq kg-1 usable cherries) due to fertilizer application. For a detailed comparison of life cycle impacts across orchard processes see Sanderson et al. (2019). Table 10 Life cycle impact assessment results per functional unit of one kg of usable cherries for the Okanagan cherry production system, using the production weighted Okanagan water delivery system as the irrigation provider. Irrigation, fertilizer application, and energy supply processes results, total system results and associated standard deviation (SD) were included for all 14 impact categories assessed. Impact Category Unit Irrigation Fertilizer application Energy supply Total SD Climate change, long term kg CO2 eq (long) 6.0 X 10-2 5.8 X 10-2 2.3 X 10-2 2.1 X 10-1 2.1 X 10-2 Climate change, short term kg CO2 eq (short) 6.6 X 10-2 5.9 X 10-2 2.4 X 10-2 2.2 X 10-1 2.2 X 10-2 Fossil and nuclear energy use MJ deprived 8.3 X 10-1 5.1 X 10-1 3.4 X 10-1 2.9 X 100 8.2 X 10-2 Freshwater acidification kg SO2 eq 1.7 X 10-15 1.3 X 10-15 4.5 X 10-16 6.0 X 10-15 2.1 X 10-16 Freshwater ecotoxicity CTUe 1.5 X 103 1.1 X 102 5.1 X 102 2.3 X 103 3.8 X 103 Freshwater eutrophication kg PO4 P-lim eq 1.8 X 10-7 2.2 X 10-7 2.1 X 10-8 8.8 X 10-7 4.0 X 10-8 Human toxicity cancer CTUh 1.6 X 10-8 9.8 X 10-10 2.7 X 10-9 2.3 X 10-8 6.9 X 10-9 Human toxicity non cancer CTUh 2.1 X 10-8 5.1 X 10-9 7.1 X 10-9 4.1 X 10-8 6.3 X 10-8 Marine eutrophication kg N N-lim eq 6.7 X 10-6 4.6 X 10-4 7.6 X 10-7 4.8 X 10-4 3.5 X 10-4 Mineral resources use kg deprived 3.6 X 10-3 5.8 X 10-4 4.1 X 10-4 6.2 X 10-3 3.1 X 10-4 Ozone layer depletion kg CFC-11 eq 7.0 X 10-9 4.1 X 10-9 2.0 X 10-9 2.2 X 10-8 1.7 X 10-9 Photochemical oxidant formation kg NMVOC eq 2.0 X 10-4 9.6 X 10-5 4.6 X 10-5 8.2 X 10-4 2.0 X 10-5 Terrestrial acidification kg SO2 eq 1.1 X 10-9 9.2 X 10-10 2.9 X 10-10 4.0 X 10-9 1.4 X 10-10 Water scarcity m3 world-eq 2.6 X 100 6.2 X 10-2 2.3 X 10-1 3.0 X 100 2.2 X 10-1 The Okanagan WDS orchard model was compared to the cherry orchard system using the original irrigation provider used by Sanderson et al. (2019) and derived from a Quebec dataset for tap water production (Sanderson et al. 2019)(Figure 3). For both orchard systems, irrigation was the highest contributing input process at the orchard level for the following impact categories: fossil and nuclear energy use, freshwater acidification, human toxicity (cancer), human toxicity (non-cancer), mineral resource use, ozone layer depletion, and water scarcity. Fertilizer application (5.95 X 10-2 kg CO2 eq kg-1 usable cherries) was the largest contributor to the impacts to climate change (short term) in the original orchard system, whereas irrigation (6.57 X 10-2 kg CO2 eq kg-1 usable cherries) was the largest contributor in the Okanagan WDS 70 model. When the Okanagan WDS was the provider, irrigation also had the highest contribution to impacts on freshwater ecotoxicity (1.46 X 103 CTUe kg-1 usable cherries), displacing energy supply (5.10 X 102 CTUe kg-1 usable cherries); photochemical oxidant formation (2.05 X 10-4 kg NMVOC eq kg-1 usable cherries), displacing mulch application (1.22 X 10-4 kg NMVOC eq kg-1 usable cherries); and terrestrial acidification (1.07 X 10-9 kg SO2 eq kg-1 usable cherries), displacing fertilizer application (9.21 X 10-10 kg SO2 eq kg-1 usable cherries). 71 Figure 3 LCIA results shown as relative percent contributions of each process stage during cherry production across nine impact categories and comparing two orchard systems. 100% is the impact score of the scenario which had the highest impacts between the two scenarios for a given impact category. The Okanagan WDS (water delivery system) model used the production weighted Okanagan water delivery system as the provider for orchard irrigation. The original model was from Sanderson et al. (2019) and used Quebec conventional tap water production as the provider for orchard irrigation. All other inputs and processes were held constant within the two scenarios, except for the irrigation provider. Note that modifications to the published LCA model for Okanagan cherry orchard production were made and are described in section 2.3.6. * Indicates statistical significance (Table 11). * ** ** * * **90% 91%57%90%53%72% 71%96%27%0%20%40%60%80%100%Okanagan WDSOriginal modelOkanagan WDSOriginal modelOkanagan WDSOriginal modelOkanagan WDSOriginal modelOkanagan WDSOriginal modelOkanagan WDSOriginal modelOkanagan WDSOriginal modelOkanagan WDSOriginal modelOkanagan WDSOriginal modelClimatechange, shorttermFossil andnuclear energyuseFreshwaterecotoxicityFreshwatereutrophicationHuman toxicitycancerHuman toxicitynon-cancerMineralresources useTerrestrialacidificationWater scarcity% ContributionFertilizer application Irrigation water Energy supplyPlant protection product application Mulch application Harvesting containersMachinery use Orchard teardown Orchard setup72 The impact categories that showed the largest differences between the two systems were water scarcity, human toxicity (cancer), and freshwater ecotoxicity. For impacts on water scarcity, the Okanagan WDS orchard deprived 2.97 (SD 0.2) m3 world eq kg-1 usable cherries compared to 10.9 (SD 3.1) m-3 world eq kg-1 usable cherries for the original model. This was largely due to the higher impact factor associated with the elementary water flow used in the original data set. The original orchard model had 57% of the impacts on freshwater ecotoxicity compared to the Okanagan WDS model. This was largely due to the use of polyaluminium chloride (37% of total orchard impacts; 852.16 CTUe kg-1 usable cherries) and treatment of municipal solid waste (11%; 257.68 CTUe kg-1 usable cherries) for the treatment plant. The original orchard system had 53% of the impact to human toxicity (cancer) compared to the Okanagan WDS model. 37% (8.51 X 10-9 CTUh kg-1 usable cherries) of the Okanagan WDS orchard system’s impact to human toxicity (cancer) was due to delivery piping, within which the highest impacts are associated with steel production (15% of orchard impacts or 3.39 X 10-9 CTUh kg-1 usable cherries). 32% of the Okanagan WDS orchard system’s impact to human toxicity (cancer) was from treatment, within which polyaluminium chloride production was the largest contributor (30% of orchard impacts or 6.87 X 10-9 CTUh kg-1 usable cherries). Differences at the orchard level between the two irrigation providers were statistically significant for all impact categories assessed, with the exception of marine eutrophication (Table 11). Table 11 Two tailed p-values produced from two-sample T-test (assuming equal variances) for 14 impact categories and between two orchard systems with different irrigation providers: production weighted Okanagan WDS and conventional tap water production in Quebec (original dataset used), where all other processes and inputs were held constant. Bolded p values indicate significance. Impact Category df t stat p value Climate change, long term 1996 -45.40 <0.001 Climate change, short term 1996 -47.95 <0.001 Fossil and nuclear energy use 1996 -119.2 <0.001 Freshwater acidification 1996 -111.87 <0.001 Freshwater ecotoxicity 1996 -24.86 <0.001 Freshwater eutrophication 1996 -47.86 <0.001 Human toxicity cancer 1996 -44.18 <0.001 73 Impact Category df t stat p value Human toxicity non cancer 1996 -21.02 <0.001 Marine eutrophication 1996 -1.037 0.30 Mineral resources use 1996 -108.1 <0.001 Ozone layer depletion 1996 -57.71 <0.001 Photochemical oxidant formation 1996 -103.9 <0.001 Terrestrial acidification 1996 -109.2 <0.001 Water scarcity 1892 -131.9 <0.001 2.4.3 Treatment comparison As illustrated in Figure 4, treatment plays a significant role in the overall impacts associated with the production weighted water delivery system in most impact categories. The exceptions to this include the water scarcity category – where extraction of source water accounts for most of the impacts – and mineral resource use, where piping components contributed far more than treatment. Treatment accounted for the majority of the water delivery systems’ impacts on freshwater ecotoxicity. Among all impact categories there was a significant difference among treatment types (Table 12). Generally, delivered chlorine and delivered hypochlorite had relatively low impacts compared to generated hypochlorite and the treatment plant (Figure 4). Table 12 Single factor analysis of variance (ANOVA) of five groups (treatment at a treatment plant, generated hypochlorite treatment, delivered hypochlorite treatment, delivered chlorine, and no treatment) and for 14 impact categories. Each of the five scenarios were modelled using the production weighted average water supply network with all other inputs and processes held constant except for the inputs associated with five different treatment regimes. Bolded p values indicate significance. Impact category df (within groups) df (between groups) F value P value Climate change, long term 4986 4 2031.1 <0.001 Climate change, short term 4986 4 1978.2 <0.001 Fossil and nuclear energy use 4986 4 1925.8 <0.001 Freshwater acidification 5983 4 1540.00 <0.001 Freshwater ecotoxicity 4984 4 1150.00 <0.001 Freshwater eutrophication 4984 4 837.7 <0.001 Human toxicity cancer 4989 4 660.5 <0.001 Human toxicity non cancer 4986 4 680.4 <0.001 Marine eutrophication 4984 4 1759.5 <0.001 Mineral resources use 4989 4 155.5 <0.001 74 Ozone layer depletion 4984 4 3079.1 <0.001 Photochemical oxidant formation 4984 4 1444.4 <0.001 Terrestrial acidification 4984 4 1563.5 <0.001 Water scarcity 3536 4 162.9 <0.001 The water delivery system using a treatment plant had the highest impacts compared to all other water treatment regimes across all impact categories except climate change (both short and long term), fossil and nuclear energy use, mineral resource use and water scarcity (Figure 4). The treatment plant contributed 10,200 CTUe m-3, or 92% of the delivery system’s impacts to freshwater ecotoxicity; 6.84 X 10-7 kg PO4 P-lim eq m-3, or 68% of the impacts to freshwater eutrophication; 6.49 X 10-8 CTUh m-3 (69%) and 7.83 X 10-7 CTUh m-3 (67%) to human toxicity, cancer and non-cancer, respectively; and 4.43 X 10-9 kg SO2 eq m-3, or 72% of the impacts to terrestrial acidification. Within the treatment plant, 45% of the impacts on freshwater eutrophication were due to polyaluminium chloride production, which was used during the coagulation process, and 25% is due to the treatment of municipal solid waste at a sanitary landfill (a proxy for the organic solid waste that is sent to the Glenmore landfill). For freshwater ecotoxicity, polyaluminium chloride production was responsible for 74% of the impacts associated with the treatment stage and 22% is due to the treatment of the solid waste sent to the landfill. Polyaluminium chloride also accounted for 94%, 79%, and 66% of the treatment plant’s impact on human toxicity (cancer), human toxicity (non-cancer), and terrestrial acidification, respectively. Generated hypochlorite treatment had 2% higher impacts to climate change (short term) than the treatment plant scenario. LCIA results for climate change (short term) between generated hypochlorite treatment (3.45 X 10-1 kg CO2 eq m-3, SD 0.19) and the treatment plant (3.37 X 10-1 kg CO2 eq m-3. SD 0.21) were statistically significant using the Bonferroni corrected significance level of p<0.01 (Two-Sample T-Test; df =1994; t Stat = 16.73; p<0.001). Treatment with hypochlorite generated on-site was responsible for 65% (0.23 kg CO2 eq m-3) of the total water delivery system’s contribution to climate change (short term). Of that, 99% was due to the electricity consumption used to create hypochlorite out of sodium chloride. For the treatment plant scenario, treatment was 75 responsible for 64% of the water delivery system’s impacts on climate change (short term), 55% of which was due to polyaluminium chloride production. The LCIA results for the generated hypochlorite treatment scenario were 16% larger than the treatment plant scenario’s impacts to fossil and nuclear energy use; this difference was statistically significant using the Bonferroni corrected significance level of p<0.01 (Two-Sample T-Test; df = 1994; t Stat = 24.15; p<0.001). Similar to the climate change (short term) impact category, the majority of impacts to fossil and nuclear energy use were associated with electricity consumption for the generated hypochlorite scenario, and polyaluminium chloride for the treatment plant. LCIA results for this impact category between generated hypochlorite and delivered hypochlorite treatments were also statistically significant, where the significance level was adjusted to p<0.01 using the Bonferroni correction (Two-Sample T-Test; df = 1994; t Stat = 66.38; p<0.001). Generated hypochlorite treatment had the highest impacts to water scarcity (2.21 m3 world-eq m-3; 21% of system impacts) compared to all other treatments (Figure 4), with electricity consumption having been almost solely responsible for the treatment’s contribution to this category. Delivered hypochlorite treatment had the second highest contribution to water scarcity, but accounted for only 8% of the water delivery system’s associated impact to water scarcity. Generated hypochlorite was therefore the only treatment type with relatively important implications for this category and was significantly different from the impact results of delivered hypochlorite treatment, with the Bonferroni corrected significance level of p<0.01 (Two-Sample T-Test; df = 993; t Stat = -11.82; p<0.001). 76 Figure 4 LCIA results shown as percent contributions on a relative scale across five treatment scenarios: no treatment, delivered chlorine injected at intake source, delivered hypochlorite injected at intake source, chlorine generated on-site and injected at intake source, and the Duteau Creek water treatment plant. 100% is the impact score for the treatment scenario with the highest impacts to a given impact category, which all other scenarios are scaled relative to. All inputs and processes occurring in every stage of the water supply network other than the treatment stage were held constant so as to compare impact scores between treatment stage scenarios. Impact results were further broken down into four water delivery phases: piping for delivery, treatment, pumping, and source water usage. * Indicates statistically significant differences among groups according to a single factor ANOVA (Table 12) and bars that share the same letter were not significantly different according to a Bonferroni adjusted post hoc test (p<0.01). Impact scores between the generated hypochlorite treatment and the treatment plant were statistically significant for climate change, short term (Two-Sample T-Test; df =1994; t Stat = 16.73; p<0.001) and fossil and nuclear energy use (Two-Sample T-Test; df = 1994; t Stat = 24.15; p<0.001). Impact scores for water scarcity between generated hypochlorite and delivered hypochlorite were statistically significant (Two-Sample T-Test; df = 993; t Stat = -11.82; p<0.001). For absolute values across all impact categories, see Appendix C.a b a0%20%40%60%80%100%No treatmentDelivered chlorineDelivered hypochloriteGenerated hypochloriteTreatment plantNo treatmentDelivered chlorineDelivered hypochloriteGenerated hypochloriteTreatment plantNo treatmentDelivered chlorineDelivered hypochloriteGenerated hypochloriteTreatment plantNo treatmentDelivered chlorineDelivered hypochloriteGenerated hypochloriteTreatment plantNo treatmentDelivered chlorineDelivered hypochloriteGenerated hypochloriteTreatment plantNo treatmentDelivered chlorineDelivered hypochloriteGenerated hypochloriteTreatment plantNo treatmentDelivered chlorineDelivered hypochloriteGenerated hypochloriteTreatment plantNo treatmentDelivered chlorineDelivered hypochloriteGenerated hypochloriteTreatment plantNo treatmentDelivered chlorineDelivered hypochloriteGenerated hypochloriteTreatment plantClimate Change,short termFossil andnuclear energyuseFreshwaterecotoxicityFreshwatereutrophicationHuman toxicitycancerHuman toxicitynon-cancerMineralresources useTerrestrialacidificationWater scarcity% contributionDelivery pipe Treatment Pumping Source Water* ********b a b 77 At the orchard level, irrigation input with no treatment was compared to irrigation treated at the treatment plant (Figure 5). The difference in LCIA results of the two systems were statistically significant for the fossil and nuclear energy use and the ozone layer depletion impact categories (Table 13). Of the impact categories shown in Figure 5, freshwater ecotoxicity showed the largest difference in impact results between no water treatment, 1.15 X 103 (SD 3.54 X 103) CTUe kg-1 usable cherries, and treatment at a plant, 4.34 X 103 (SD 3.99 X 103) CTUe kg-1 usable cherries. This equated to a 73% reduction in freshwater ecotoxicity impacts from an orchard when irrigation water is not treated. There was 56% and 43% reduction in impacts to human toxicity cancer and non-cancer, respectively, a 29% reduction in impacts to terrestrial acidification, and a 26% reduction in impacts to climate change, (short term) when treatment of irrigation water was avoided. 78 Figure 5 Percent contribution to impact scores of process stages within the Okanagan cherry orchard model, including orchard set up and teardown, fertilizer application, application of irrigation water, and all other orchard maintenance inputs. Two treatment regimes of the Okanagan water supply network were compared to each other, with all other inputs and processes held constant between the two model scenarios. 100% is the total impact score for the scenario with the largest impact to a given impact category. * Indicates a significant difference between impact scores of the orchard with treated versus untreated irrigation water (Table 13).*0%20%40%60%80%100%No TreatmentTreatment PlantNo TreatmentTreatment PlantNo TreatmentTreatment PlantNo TreatmentTreatment PlantNo TreatmentTreatment PlantNo TreatmentTreatment PlantNo TreatmentTreatment PlantNo TreatmentTreatment PlantNo TreatmentTreatment PlantClimateChange, shorttermFossil andnuclear energyuseFreshwaterecotoxicityFreshwatereutrophicationHuman toxicitycancerHuman toxicitynon-cancerMineralresources useTerrestrialacidificationWater Scarcity% ContributionFertilizer Application Irrigation Water Orchard Set up & Teardown Other Orchard Maintenance79 Table 13 One tailed p-values, t Stat, and degrees of freedom (df) produced from two-sample T-test (assuming equal variances) between orchard systems with different irrigation inputs: untreated, production weighted irrigation and production weighted irrigation that has been treated at a plant, where all other processes and inputs associated with the water supply network and cherry orchard were held constant in the model. Bolded p values are significant. Impact Category df t Stat p value Climate change, long term 1996 -1.33 0.09 Climate change, short term 1996 -1.32 0.09 Fossil and nuclear energy use 1996 -2.08 0.02 Freshwater acidification 1996 -0.65 0.26 Freshwater ecotoxicity 1996 1.07 0.14 Freshwater eutrophication 1996 -1.63 0.05 Human toxicity cancer 1995 -0.59 0.28 Human toxicity non cancer 1995 -0.34 0.37 Marine eutrophication 1996 -0.04 0.48 Mineral resources use 1996 0.69 0.25 Ozone layer depletion 1996 -1.85 0.03 Photochemical oxidant formation 1996 -0.91 0.18 Terrestrial acidification 1996 -0.57 0.28 Water scarcity 1579 -0.01 0.50 2.4.4 Pumping Types Statistically significant differences among pumping types occurred for the following impact categories: climate change (both short and long term), fossil and nuclear energy use, and ozone layer depletion (Table 14). All three pumping types had only electricity as an input to the process stage; electricity production therefore embodied 100% of the contributions to LCIA results at the pumping stage. Climate change and fossil and nuclear energy use were the only impact categories where pumping regimes had relevant contributions (i.e., >10% of the impacts of the system). Pumping accounted for 17% (3.6 X 10-2 kg CO2 eq m-3) of the total impacts to climate change (short term), compared to 3% (6.2 X 10-3 kg CO2 eq m-3) for gravity fed systems (Figure 6); the difference between pumped and gravity fed was significant (Bonferroni post hoc test; df = 1994; t Stat = 5.39; p<0.001). A post hoc Bonferroni test revealed the difference between pumped and mixed systems for climate change was not significant (df = 1995; t Stat = 80 0.52; p=0.60) and the difference between mixed and gravity fed was significant (df = 1995; t Stat = 4.95; p<0.001). Pumping contributed 19% (5.4 X 10-1 MJ deprived) of the total impacts for fossil and nuclear energy use, while gravity fed contributed 4% (9.3 X 10-2 MJ deprived); statistical significance was found between the pumped and gravity fed scenarios (Bonferroni post hoc test; df = 1994; t Stat = 5.61; p<0.001). Pumped and mixed systems for fossil and nuclear energy use were not significantly different (Bonferroni post hoc test; df = 1995; t Stat = 0.57; p=0.57), while the difference between mixed and gravity systems was significant (Bonferroni post hoc test; df = 1995; t Stat = 5.14; p<0.001). For absolute values and standard deviations across all impact categories, see Appendix C. Table 14 Degrees of freedom (df), F value, and p values of a single factor analysis of variance (ANOVA) of three treatment groups (gravity fed, mixed, and pumped scenarios at the pumping stage) and for 14 impact categories. All other processes and inputs to the water supply network were held constant and were modelled as the production weighted average, so as to compare impact scores among different pumping scenarios. Significance level was Bonferroni corrected to p<0.017 and bolded p values are significant. Impact category df (between groups df (within groups) F value P value Climate change, long term 2 2992 20.55 <0.001 Climate change, short term 2 2992 18.66 <0.001 Fossil and nuclear energy use 2 2992 20.19 <0.001 Freshwater acidification 2 2992 3.28 0.04 Freshwater ecotoxicity 2 2993 0.12 0.88 Freshwater eutrophication 2 2992 1.24 0.29 Human toxicity cancer 2 2992 1.06 0.35 Human toxicity non cancer 2 2992 2.84 0.06 Marine eutrophication 2 2993 0.51 0.60 Mineral resources use 2 2993 0.64 0.53 Ozone layer depletion 2 2992 15.05 <0.001 Photochemical oxidant formation 2 2992 3.96 0.02 Terrestrial acidification 2 2992 3.52 0.03 Water scarcity 2 2734 2.98 0.05 81 Figure 6 Percent contribution of each pumping regime (pumped, mixed, and gravity fed) across nine impact categories, showing contributions broken down to the process stage (delivery pipe, treatment, pumping, and source water). All other inputs and processes within the water supply network model were held constant so as to compare scenarios at the pumping stage. 100% is equivalent to the impact score for the scenario with the highest impacts within the impact category. Note only climate change (short term) and fossil and nuclear energy use had relevant contributions at the pumping stage compared to other processes in the system. * Indicates statistical significance (Table 14) and bars that share the same letter were not significantly different according to a Bonferroni adjusted post hoc test (p<0.017). For climate change, the gravity fed scenario was significantly different from the mixed (Bonferri post hoc test; df = 1995; t Stat = 4.95; p<0.001) and pumped (Bonferroni post hoc test; df = 1994; t Stat = 5.39; p<0.001) scenarios; the difference between pumped and mixed scenarios was not significant (Bonferroni post hoc test; df = 1995; t Stat = 0.52; p=0.60). For fossil fuel, the gravity fed scenario was significantly different from the mixed (Bonferroni post hoc test; df = 1995; t Stat = 5.14; p<0.001) and pumped (Bonferroni post hoc test; df = 1994; t Stat = 5.61; p<0.001) scenarios; the difference between pumped and mixed scenarios was not significant (Bonferroni post hoc test; df = 1995; t Stat = 0.57; p=0.57). For absolute values, standard deviations, and all impact categories see Appendix C.*, a*, a*, b*, a *, a*, ba a a0%20%40%60%80%100%PumpedMixedGravity fedPumpedMixedGravity fedPumpedMixedGravity fedPumpedMixedGravity fedPumpedMixedGravity fedPumpedMixedGravity fedPumpedMixedGravity fedPumpedMixedGravity fedPumpedMixedGravity fedClimateChange, shorttermFossil andnuclear energyuseFreshwaterecotoxicityFreshwatereutrophicationHuman toxicitycancerHuman toxicitynon-cancerMineralresources useTerrestrialacidificationWater Scarcity% ContributionDelivery pipe Treatment Pumping Source watera a a a a a a a a a a a a a a a a a 82 2.4.5 Irrigation technology comparisons Eight irrigation technologies employed on Okanagan cherry orchards were assessed across 14 impact categories. A subset of the technologies, including the most and least efficient (SDI and handline, respectively), most common (micro-sprinkler), and the production weighted averaged (PW average), were used to test for statistical significance. Significant differences among groups were found for all impact categories (Figure 7; Figure 8; Table 17). Figure 7 Impacts to water scarcity due to the water supply network in four irrigation technology scenarios: handline, SDI, micro-sprinkler, and the production weighted (PW) average irrigation technology. All other processes and inputs to the Okanagan water supply network model were held constant. Axis values show the equivalent m3 consumption of water at the world average per m3 of irrigation water delivered to agricultural users. * Indicates statistical significance (Table 17) and bars that share the same letter were not significantly different according to a Bonferroni post hoc test (p<0.013). Within the climate change (short term) impact category, all combinations of irrigation technologies were statistically significant with the exception of the PW average and micro-sprinkler (Table 15). This trend was found within the water scarcity impact category as well, where the difference between PW average and micro-sprinkler was not significant and all other pairings were (Table 16). Table 15 Degrees of freedom (df), t Stat, and p values according to the two-sample T-test for four different model scenarios of irrigation technologies (SDI, microsprinkler, handline, and the production weighted average irrigation technology) and for the impact category climate change short term. All other processes, inputs, and outputs of the production weighted Okanagan water supply network were held constant other than the irrigation stage so as to make comparisons of impact scores among irrigation technology 10.48.511.610.40 2 4 6 8 10 12 14MicrosprinklerSubsurface Drip (SDI)HandlinePW Averagem3 world average m-3 water delivered to agricultural usersSource water Delivery Pipe Pumping Treatment*, b*, a *, a *, c 83 scenarios. Significance level was Bonferroni corrected to p<0.013 and bolded p values indicate significance. Pairs df t Stat p value PW average – Handline 1996 -4.15 <0.001 PW average – SDI 1996 11.27 <0.001 PW average – Microsprinkler 1995 1.51 0.066 Handline – SDI 1996 14.38 <0.001 Handline – Microsprinkler 1995 5.68 <0.001 SDI – Microsprinkler 1995 -9.83 <0.001 Table 16 Degrees of freedom (df), t Stat, and p values according to the two-sample T-test for four different model scenarios of irrigation technologies (SDI, microsprinkler, handline, and the production weighted average irrigation technology) and for the impact category water scarcity. All other processes, inputs, and outputs of the production weighted Okanagan water supply network were held constant other than the irrigation stage so as to make comparisons of impact scores among irrigation technology scenarios. Significance level was Bonferroni corrected to p<0.013 and bolded p values indicate significance. Pairs df t Stat p value PW average – Handline 1823 -2.70 0.003 PW average – SDI 1820 5.57 <0.001 PW average – Microsprinkler 1818 0.82 0.207 Handline – SDI 1809 8.56 <0.001 Handline – Microsprinkler 1807 3.56 <0.001 SDI – Microsprinkler 1804 -4.79 <0.001 84 Figure 8 Impacts to climate change (short term) from the production weighted averagr Okanagan water supply network for three irrigation technology scenarios: handline, SDI, micro-sprinkler and the production weighted (PW) average. All other processes and inputs to the water supply network model were held constant. * Indicates statistical significance (Table 15) and bars that share the same letter were not significantly different according to a Bonferroni post hoc test (p<0.013). Percentages indicate the percent each process (treatment, pipe delivery, and pumping) contributed to the systems’ total impacts to climate change (short term). Deposited bicarbonate reached agricultural soils through irrigation water from water sources containing carbonates and accounted for 4% of the water delivery system’s impact to climate change (short term). Across all impact categories, the percentage difference in total impacts between each irrigation technology was the same. SDI performed the best and had a 18% and 19% reduction in impacts compared to the micro-sprinkler irrigation and PW average, respectively. Handline had the largest impact across all categories and had 12% and 11% higher impacts than the micro-sprinkler irrigation and PW average, respectively, and 36% higher impacts than SDI irrigation. Bicarbonate deposited into agricultural soils from source water accounted for 4% of the impacts to climate change (short term). Table 17 Single factor analysis of variance (ANOVA) of four Okanagan water supply network model scenarios using different irrigation technologies as inputs to the irrigation process (SDI, production weighted average irrigation, micro-sprinkler, and handline) and for 12 impact categories. All other processes and inputs of the production weighted Okanagan water supply network model were held constant to compare impact scores among irrigation technology scenarios. Bolded p values are significant. Impact category df (within groups) df (between groups) F value P value Climate change, long term 3991 3 87.00 <0.001 Fossil and nuclear energy use 3991 3 77.00 <0.001 Freshwater acidification 3990 3 86.00 <0.001 Freshwater ecotoxicity 3991 3 34.00 <0.001 Freshwater eutrophication 3991 3 83.00 <0.001 4%4%4%4%,39%39%39%39%48%48%48%48%9%9%9%9%0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35MicrosprinklerSubsurface Drip(SDI)HandlinePW Averagekg CO2 eq m-3 of water delivered to agricultural usersDeposited Carbonate Treatment Delivery Pipe Pumping*, a*, a*, b*, c85 Impact category df (within groups) df (between groups) F value P value Human toxicity cancer 3991 3 43.00 <0.001 Human toxicity non cancer 3991 3 31.00 <0.001 Marine eutrophication 3991 3 108.00 <0.001 Mineral resources use 3991 3 42.00 <0.001 Ozone layer depletion 3990 3 116.00 <0.001 Photochemical oxidant formation 3991 3 95.00 <0.001 Terrestrial acidification 3990 3 87.00 <0.001 Within the orchard system, no significant differences were found according to the one-way ANOVA between the most efficient (SDI), least efficient (handline) and most common (solid-set undertree and micro-sprinkler) irrigation systems across all 14 impact categories per kg of usable cherries (Table 18). However, trends between irrigation systems within impact categories were observed (Figure 9). Water scarcity showed the largest difference between irrigation types at the orchard level: SDI has 24%, 20%, and 16% reductions in water scarcity compared to handline, solid-set undertree, and micro-sprinkler, respectively. SDI irrigation reduced human toxicity (cancer) impacts by 19%, 16%, and 13% compared to handline, solid-set undertree, and micro-sprinkler systems, respectively. Within the freshwater ecotoxicity category, impacts were reduced by 17%, 14%, and 11% when SDI replaced handline, solid-set undertree, and micro-sprinkler, respectively. For the climate change (short term) and terrestrial acidification impact categories, irrigation contributed more than fertilizer application, unless SDI irrigation was used (Figure 9). Table 18 Single factor analysis of variance (ANOVA) of orchard model scenarios using different irrigation technologies as inputs to the irrigation process (SDI, production weighted average irrigation, micro-sprinkler, and handline) and for 12 impact categories. All other processes and inputs of the cherry orchard model were held constant to compare impact scores among irrigation technology scenarios. Impact category df (within groups) df (between groups) F value P value Climate change, long term 3992 3 0.87 0.46 Climate change, short term 3992 3 0.89 0.45 Fossil and nuclear energy use 3992 3 1.18 0.31 Freshwater acidification 3992 3 0.61 0.61 Freshwater ecotoxicity 3992 3 1.11 0.34 Freshwater eutrophication 3992 3 0.44 0.72 Human toxicity cancer 3992 3 1.70 0.17 86 Impact category df (within groups) df (between groups) F value P value Human toxicity non cancer 3992 3 0.33 0.80 Marine eutrophication 3992 3 0.44 0.72 Mineral resources use 3992 3 0.40 0.75 Ozone layer depletion 3992 3 0.54 0.65 Photochemical oxidant formation 3992 3 1.06 0.37 Terrestrial acidification 3992 3 0.74 0.53 Water scarcity 3524 3 1.19 0.31 87 Figure 9 Percent contribution of the most efficient (SDI), least efficient (handline) and most common (solid-set undertree and micro-sprinkler) irrigation systems to the life cycle impacts of cherry production across nine impact categories, broken down to process stages (irrigation, energy supply, fertilizer application, orchard set up and teardown, and all other orchard maintenance). All other processes and inputs to the cherry orchard were held constant other than the irrigation technology stage to compare impact scores across different irrigation technology scenarios. 100% is the the impact score of the model scenario with the highest impacts within a given impact category. Note that for climate change (short term) and terrestrial acidification, different irrigation regimes resulted in replacing fertilizer as the highest contributor at the orchard level. An analysis of variance (ANOVA) revealed statistically significant differences between groups within impact categories did not occur (Table 18). For absolute values, standard deviations, and all impact categories see Appendix C.0%20%40%60%80%100%HandlineMicrosprinklerSDISolid-set undertreeHandlineMicrosprinklerSDISolid-set undertreeHandlineMicrosprinklerSDISolid-set undertreeHandlineMicrosprinklerSDISolid-set undertreeHandlineMicrosprinklerSDISolid-set undertreeHandlineMicrosprinklerSDISolid-set undertreeHandlineMicrosprinklerSDISolid-set undertreeHandlineMicrosprinklerSDISolid-set undertreeHandlineMicrosprinklerSDISolid-set undertreeClimateChange, shorttermFossil andnuclear energyuseFreshwaterecotoxicityFreshwatereutrophicationHuman toxicitycancerHuman toxicitynon-cancerMineralresources useTerrestrialacidificationWater Scarcity% ContributionFertilizer Application Irrigation Energy Supply Orchard Set up & Teardown Other Orchard Maintenance88 Nitrogenous emissions associated with fertilizer application were reduced by 29% for micro-sprinkler irrigation and compared to drip irrigation, which was considered the baseline emissions scenario. Results for climate change, both short and long term, were almost equal between irrigation systems when their effect on emissions were considered: drip had 2.11 X 10-1 (SD 2.20 X 10-2) kg CO2 eq kg-1 usable cherries versus 2.13 X 10-1 (SD 1.69 X 10-2) kg CO2 eq kg-1 usable cherries for climate change short term (Figure 10). Fertilizer application reduced from 28% (5.95 X 10-2 kg CO2 eq kg-1 usable cherries) to 24% (5.03 X 10-2 kg CO2 eq kg-1 usable cherries) of the orchard’s impacts to climate change (short term) with a change from drip to microsprinkler irrigation. Concurrently, irrigation increased from 26% (5.55 X 10-2 kg CO2 eq kg-1 usable cherries) to 31% (6.52 X 10-2 kg CO2 eq kg-1 usable cherries) of the cherry orchard’s impacts to climate change, short term. Across all other impact categories drip outperformed micro-sprinkler (see Appendix C, Table 29, for impact scores and standard deviations for all impact categories assessed). 89 Figure 10 Relative percent contribution of drip versus micro-sprinkler systems at the orchard level. Nitrogenous GHG emissions are included in fertilizer application as a 29% decrease in emissions produced when using micro-sprinkler over drip irrigation systems. Differences in impacts within the irrigation category are due to an increased water use efficiency in drip compared to micro-sprinkler, therefore resulting in reduced impacts associated with the water delivery system, including bicarbonate deposition into orchard soils from source water. The difference between drip and microsprinkler was not statistically significant for the impact categories climate change, short term (two sample T-test; df = 1996; t Stat = -0.93; p=0.35) and water scarcity (two sample T-test; df = 1777; t Stat = 0.89; p=0.37) 2.4.6 Sensitivity analyses The amount and location of water losses (due to leaks, etc.) along the water delivery system were compared to understand the significance of this modelling choice. Statistically significant differences occurred among scenarios for all impact categories (Table 19). For all impact categories, the scenario using a 23% water loss at the end of the water delivery system (i.e., after pumping and treatment) resulted in the highest impacts. When compared to the highest impact scenario (‘23% loss at end’), the ‘12% loss at end’, ‘12% loss at start’, and ‘23% loss at start’ scenario had 88%, 77%, and 77% of the impacts, respectively, across all impact categories except water scarcity. The ‘12% loss at start’ and ‘23% loss at start’ scenarios therefore had no differences between them and performed the best of all scenarios for all impact categories except water scarcity. The statistical analysis of climate change (short term) was provided in Table 19 as an example. The 12% loss at start and 23% loss at start was the only scenario comparison that did not yield a significant difference between groups (two sample T test; df = 1995; t stat = 0.77; p value = 0.44; Table 20). 0%20%40%60%80%100%DripMicrosprinklerDripMicrosprinklerClimate Change, short term Water Scarcity% Contribution Fertilizer ApplicationIrrigationOrchard Set up & TeardownOther Orchard Maintenance90 Table 19 Single factor analysis of variance (ANOVA) of four scenarios of the production weighted Okanagan water supply network (12% loss at start, 12% loss at end, 23% loss at start, 23% loss at end) and for 14 impact categories. All other processes and inputs were held constant in the model except for the amount and location of water losses occurring within the water supply network. This was achieved by changing the volume of water required to deliver 1 m3 of irrigation water based on the percent losses reported to occur within the water supply network, and by changing the location these losses were accounted for within the model (i.e. before or after water is passed through all stages of the network). Bolded p values indicate significance Impact category df (within groups) df (between groups) F value P value Climate change, long term 3991 3 91.0 <0.001 Climate change, short term 3991 3 93.0 <0.001 Fossil and nuclear energy use 3991 3 84.0 <0.001 Freshwater acidification 3991 3 95.0 <0.001 Freshwater ecotoxicity 3991 3 37.0 <0.001 Freshwater eutrophication 3991 3 91.0 <0.001 Human toxicity cancer 3992 3 44.0 <0.001 Human toxicity non cancer 3991 3 50.0 <0.001 Marine eutrophication 3991 3 124.0 <0.001 Mineral resources use 3992 3 43.0 <0.001 Ozone layer depletion 3991 3 127.0 <0.001 Photochemical oxidant formation 3991 3 98.0 <0.001 Terrestrial acidification 3991 3 96.0 <0.001 Water scarcity 3634 3 8.0 <0.001 Table 20 Degrees of freedom (df), t Stat, and p values according to the two-sample T-test for four different treatment groups of four water delivery system scenarios (12% loss at start, 12% loss at end, 23% loss at start, 23% loss at end and for the impact category climate change (short term). All other processes and inputs were held constant in the model except for the amount and location of water losses occurring within the water supply network. This was achieved by changing the volume of water required to deliver 1 m3 of irrigation water based on the percent losses reported to occur within the water supply network, and by changing the location these losses were accounted for within the model (i.e. before or after water is passed through all stages of the network). The post hoc Bonferroni corrected significance level was p<0.013 and bolded p values indicate significance. Pairs df t Stat p value 12% loss at end – 12% loss at start 1995 4.89 <0.001 12% loss at end – 23% loss at end 1996 -9.38 <0.001 12% loss at end – 23% loss at start 1996 5.62 <0.001 12% loss at start – 23% loss at end 1995 -13.82 <0.001 12% loss at start – 23% loss at start 1995 0.77 0.44 23% loss at start – 23% loss at end 1996 14.41 <0.001 The sensitivity analysis revealed that the assumed percentage of water lost was more important than the assumed location of the loss for impacts on water scarcity (Figure 11). Both 91 scenarios with only 12% water loss had less impacts to water scarcity than the 23% loss scenarios, resulting in a reduction in impacts of 11-13%. Water scarcity impacts of the ‘12% loss at end’ scenario was significantly different from the highest contributing scenario – ‘23% loss at end’ (two-sample T-test; df = 1810; t stat = -2.55; p=0.005) – and the second highest contributing scenario – ‘23% loss at start’ (two-sample T-test; df = 1824; t stat = -4.38; p<0.001). The ‘12% loss at start’ scenario was also significantly different from the ‘23% loss at end’ (two-sample T-test; p=0.02) and ‘23% loss at start’ (two-sample T-test; p<0.001) scenarios for water scarcity impacts. ‘23% loss at start’ had only 1% less impacts than ‘23% loss at end’ and the difference was not statistically significant (two-sample T-test; p=0.08). Similarly, water scarcity impacts between ‘12% loss at end’ and ‘12% loss at start’ were not statistically different (two sample T-test; p=0.28). 92 Figure 11 LCIA impact scores for water scarcity (left) and climate change, short term (right) for four water delivery system scenarios. All other processes and inputs were held constant in the model except for the amount and location of water losses occurring within the water supply network. This was achieved by changing the volume of water required to deliver 1 m3 of irrigation water based on the percent losses reported to occur within the water supply network, and by changing the location these losses were accounted for within the model (i.e. before or after water is passed through all stages of the network). * Indicates statistical significance (Table 20) and bars that share the same letter were not significantly different according to a Bonferroni post hoc test (p<0.013). The relevance of using the BC electricity grid versus that of another province, for example Alberta, within the treatment plant of the water delivery system was assessed using a sensitivity analysis to account for the importance of electricity grid mix composition. The difference in LCIA results between water delivery systems treated at a plant using the Alberta electricity grid mix versus the BC electricity grid mix was statistically significant for most impact categories (Table 21). Table 21 One tailed p-values produced from two-sample T-test (assuming equal variances) between water delivery systems with different electricity grid providers: Alberta electricity grid and BC electricity grid. This comparison was achieved by changing the provider of electricity in the treatment and pumping stages to 7.84 7.778.91 8.82567891012% Lossat End12% Lossat Start23% Lossat end23% Lossat StartWater Scarcity (m3-world average m-3)*,a *,a*,b*,b0.190.160.210.1600.050.10.150.20.2512% Lossat End12% Lossat Start23% Lossat end23% Lossat StartClimate change, short term (kg CO2 eq m-3)*,a *,b *,c *,b 93 either the BC or the Alberta electricity Ecoinvent process provider. All other processes and inputs within the model were held constant. Bolded p values indicate significance. Impact category df t Stat p value Climate change, long term 1995 3.88 <0.001 Climate change, short term 1995 4.15 <0.001 Fossil and nuclear energy use 1995 8.24 <0.001 Freshwater acidification 1995 -4.23 <0.001 Freshwater ecotoxicity 1995 -0.28 0.781 Freshwater eutrophication 1995 5.01 <0.001 Human toxicity cancer 1996 -0.46 0.643 Human toxicity non cancer 1995 -0.02 0.987 Marine eutrophication 1995 2.69 0.007 Mineral resources use 1996 4.42 <0.001 Ozone layer depletion 1995 10.73 <0.001 Photochemical oxidant formation 1995 1.73 0.083 Terrestrial acidification 1995 -3.77 <0.001 Water scarcity 1460 3.64 <0.001 The largest difference between scenarios was found in the climate change (short and long term) and terrestrial acidification impact categories, with BC electricity resulting in 76, 78% and 79% of the impacts of the Alberta electricity scenario for each category, respectively (Figure 12). There was no difference in the impacts to freshwater eutrophication and mineral resource use (see Appendix C for absolute values). 94 Figure 12 Relative percent contribution of two water delivery systems that used different electricity providers for the treatment plant phase for 14 impact categories. 100% is the impact score of the scenario with the highest impacts to a given impact category. This comparison was achieved by changing the provider of electricity in the treatment and pumping stages to either the BC or the Alberta electricity Ecoinvent process provider. All other processes and inputs within the model were held constant. * Indicates statistical significance between treatment groups (Table 21). 2.5 Discussion 2.5.1 Water supply system comparisons The LCAs performed in this research were intended to answer the five research questions outlined in the goal and scope. The first was to identify the environmental trade-offs associated with different water supply systems and which processes within the supply systems had substantial impacts. Piping contributed significantly to the overall impacts of the production weighted average water supply system, with particular relevance to the impact category mineral resource use, where it contributed 85% of the impacts from the water delivery system (Figure 2). Piping also accounted for 46-53% of the impacts to terrestrial acidification (1.57 X 10-9 kg SO2 eq m-3), freshwater acidification (2.49 X 10-15 kg SO2 eq m-3), photochemical oxidant * * * * * * * * * *0%20%40%60%80%100%% ContributionAlberta Electricity BC Electricity95 formation (3.18 X 10-4 kg NMVOC eq m-3), climate change (1.01 X 10-1 kg CO2 eq (short) m-3; 9.42 X 10-2 kg CO2 eq (long) m-3), human toxicity (cancer 2.72 X 10-8 CTUh m-3; non-cancer 3.53 X 10-8 CTUh m-3), fossil and nuclear energy use (1.40 MJ deprived m-3), and freshwater eutrophication (3.04 X 10-7 lim eq m-3)(Appendix C). This was congruent with the meta-analysis of potable water supply network LCAs performed by Meron et al. (2016), which described impacts associated with materials and construction of the distribution network as making a significant contribution to the impacts of water supply networks. Materials and construction for the distribution network have been reported to contribute 10-40% of the Global Warming Potential 100-year time frame (GWP-100) of water supply systems (Frischknecht et al. 2005; Lane et al. 2011; Godskesen et al. 2013; Slagstad and Brattebø 2014; Uche et al. 2014; Meron et al. 2016). The contribution of piping material and construction was higher for the production weighted average Okanagan delivery system, contributing 50% of the impacts to climate change (Figure 2). The higher contribution of piping materials in this study compared to other water supply system LCAs could be explained, in part, by the varying lifespans reported for different piping materials. The lifespan selected for piping in this study was 50 years based on the lifespans provided by the life cycle inventories from which piping materials and construction data were derived. However, the lifespan of pipes can vary based on environmental factors – as examples, asbestos concrete has an approximate lifespan of 50-60 years, and PVC piping can sometimes last for 80-100 years (pers. comm Shawn Goodsell). The impacts associated with piping may therefore be artificially higher in this LCA in cases where piping outlives the assumed 50-year lifespan. For impact categories where piping had a significant contribution (>46%), the largest contribution within piping was due to steel production for RCCP, steel, ductile iron, galvanized steel, and concrete pipes, and polyvinyl chloride production for PVC piping. This was congruent 96 with Lane et al. (2011), who found that piping networks were responsible for 66% of the human toxicity potential – predominantly due to steel production. The LCI from Vahidi et al. (2015) was used to model the production of ductile iron, concrete, and asbestos concrete pipes. With the functional unit of 20 in of pipe for 1 km of sewer infrastructure, ductile iron had the highest impacts for most environmental areas assessed, including fossil fuel use, while concrete was the most impactful to ecotoxicity. Vahidi et al. (2016) was used to create the LCI for RCCP, PVC, FRP, and HDPE pipes in the current study. For a functional unit of one km of piping, PVC had the highest contribution to global warming and carcinogenics while RCCP had the lowest impact. FRP had the highest contribution of all pipe systems for acidification and eutrophication categories, while RCCP had the lowest. FRP also had the highest impact to ecotoxicity while HDPE had the lowest. However, HDPE had the highest impact to fossil fuel depletion, followed by RCCP and PVC, with FRP performing the best in this category. The LCA performed by Vahidi et al. (2016) therefore elucidated the complexity of environmental trade-offs involved in selecting piping infrastructure. In both studies, the production phase accounted for the majority of impacts compared to the transportation, installation, and use phases. As new technologies improve the efficiencies of water supply networks and reduce environmental burdens in other stages, the significance of including piping infrastructure in LCAs of water supply networks will become proportionally larger (Jeong et al. 2015). The piping materials used in the systems under study vary in their capacity for pressurized water volumes and their longevity and suitability are dependent on local temperature and geological conditions. The priority when selecting pipe materials is meeting the human health and environmental objectives for minimizing the level of hazardous compounds, compatibility with soil and groundwater chemistry, and reducing energy usage (Vahidi et al. 2016). However, it is recommended that environmental impact priorities are outlined in detail prior to new pipe system development due to the trade-offs present in this selection process. Where appropriate 97 alternatives are available, ductile iron and HDPE piping should be avoided if fossil fuel use is deemed an environmental priority, while PVC should be avoided to reduce impacts to global warming. Finally, FRP should be avoided to reduce impacts to acidification, eutrophication, and ecotoxicity. Significant environmental concerns for many piping materials and across many categories arised from steel manufacturing. Thus, environmental improvements to steel manufacturing processes could alleviate a significant portion of the environmental burdens associated with piping infrastructure in water delivery systems. Electricity consumption for abstraction, distribution, and treatment had significant contributions across environmental impacts in water supply network LCAs (Lundie et al. 2004; Lemos et al. 2013; Jeong et al. 2015; Meron et al. 2016). However, many studies did not report the impacts from electricity consumption for pumping in the distribution network as a standalone metric (Lassaux et al. 2007; Godskesen et al. 2013; Jeong et al. 2015; Meron et al. 2016). In this study, the additional electricity required for pumping contributed <10% to all impact categories for the production weighted average water delivery system (Appendix C), with the exception of climate change long term (10% of system impacts) and fossil and nuclear energy use (11% of system impacts) (Figure 6). In Del Borghi et al.'s (2013) assessment of a potable water supply network in Sicily, Italy, electricity for pumping and for network maintenance combined accounted for only 3% of the system’s total Global Warming Potential (GWP). However, by far the most impactful stage of the system Del Borghi et al. (2013) studied was the desalination plant (74% of the total GWP). Thus, in a system that does not have the energy intensive requirement of a desalination process, one would expect electricity for pumping to have a higher percent contribution to GWP. The pumped and gravity fed scenarios were significantly different, as well as the mixed and gravity fed scenarios for both climate change (long term) and fossil and nuclear energy use (Table 14). 17% of the systems impacts to climate change (long term) was due to electricity consumption for distribution in the pumping scenario and only 3% of the systems impacts were 98 due to pumping in the gravity fed scenario. Similarly, pumping contributed 19% of the fossil and nuclear energy impact for the pumping scenario, and only 4% in the gravity fed scenario. For a pumped system in the hilly terrain of Iasi City, Romania, energy consumption was 0.35 kwh m-3 during peak times (Barjoveanu et al. 2014). Commensurate to this, the production weighted average energy consumption for pumped systems in the Okanagan was 0.29 kWh m-3. The City of Copenhagen was supplied by a network that used a groundwater source with simple treatment methods, where the abstraction, treatment, and distribution combined consumed 0.27 kWh m-3 of drinking water (Godskesen et al. 2013). In the Walloon Region of Belgium, the mostly ground water sourced supply network consumed an average of 0.39 kWh m-3 across the abstraction, treatment, and distribution stages. In comparison, pumping alone consumed 0.19 kWh m-3 for the production weighted average supply network in the Okanagan. In the ground water and mostly gravity fed water supply system analyzed by Barjoveanu et al. (2014), 3.35% of total energy used was due to pumping. This is congruent with the 4% contribution pumping had to the fossil and nuclear energy use impact category for the Okanagan gravity fed system scenario. In some cases, groundwater sources were reported as less energy intensive when source water was of high quality, thus reducing treatment requirements (Amores et al. 2013), and when paired with a gravity delivery system (Barjoveanu et al. 2014). However, in this study groundwater supply networks required 0.31 kWh m-3 and surface water supplied networks required 0.19 kWh m-3. Energy consumption in the sub systems of water supply networks, and the resulting contributions to GWP, are associated with high variation (Meron et al. 2016) and are dependent on soil chemistry, geography and topology. Further, the total distance of the distribution varies across water supply networks, and while most LCAs of water supply networks look at systems that deliver to urban areas, this study focused on agricultural users presumably spanning larger distances and thus requiring a longer distribution network. The condition of the water supply network also has a considerable impact on the total energy consumption of the 99 distribution network, as older systems consume more energy per functional unit of water delivered to the end user due to leaks in the system (Homäki et al. 2003; Barjoveanu et al. 2014). Finally, the total water volumes contribute to the efficiency of distribution networks, where an increase in volume increases the energy efficiency per cubic meter of water delivered (Del Borghi et al. 2013). Thus, it is not unexpected to see a wide range of energy requirements across systems and for system level contributions of the pumping phase across LCAs of water supply networks. This further illustrates the importance of selecting background databases for water supply based on relevance to the system under study. Treatment was shown to be a significant phase of the water supply networks in previous LCAs due to energy and treatment chemical inputs (Buckley et al. 2011; Bonton et al. 2012; Jeong et al. 2015). In the Okanagan water supply network, the production weighted average treatment phase contributed over 40% of the system’s impacts to the following categories: climate change (short term), freshwater acidification, freshwater ecotoxicity, freshwater eutrophication, human toxicity (cancer and non-cancer), marine eutrophication, ozone layer depletion, photochemical oxidant formation, and terrestrial acidification (Appendix C). For 10 of the 14 impact categories assessed, the treatment plant scenario had the highest impact followed by generated hypochlorite, delivered hypochlorite, delivered chlorine, and no treatment. For climate change (short and long term), fossil and nuclear energy use, and water scarcity the generated hypochlorite treatment scenario had higher impacts than the treatment plant due to energy consumption (Figure 4). Though the Duteau water treatment plant used the same process for on-site hypochlorite generation, it operated at a larger scale with higher water volumes, which has been found to reduce electricity requirements per functional unit (Del Borghi et al. 2013). The different options for technologies used during the production phase is considered the most significant differentiator for impacts to terrestrial acidification, eutrophication, and ozone layer depletion in water supply networks (Meron et al. 2016). Across all five treatment 100 scenarios, the treatment plant contributed the highest impacts to the aforementioned categories. In the treatment plant scenario, polyaluminium chloride used as a coagulant in the DAF process was responsible for 45-94% of the impacts to freshwater eutrophication and ecotoxicity, cancer and non-cancer human toxicity, and terrestrial acidification. Polyaluminium chloride production emits nitrous oxides and sulfur oxides that are respiratory inorganics and contribute to human health impacts, acidification and eutrophication (Sharaai et al. 2010). In an LCIA performed on three treatment technologies in Malaysia, Sharaai et al. (2010) also found polyaluminium chloride was the primary contributor to impacts from the DAF treatment, and suggests the replacement of polyaluminium chloride with aluminum sulphate to significantly reduce impacts to human health. Freshwater ecotoxicity and marine ecotoxicity were the most relevant impact categories in Barjoveanu et al.'s (2014) assessment of water supply to Iasi City, Romania, where treatment comprised 20-25% of the contribution of the water supply system up to the tap use phase. Impacts to freshwater and marine ecotoxicity in both the treatment and distribution phase of Iasi City’s water supply network were due to energy production. The high energy consumption due to an actively pumped distribution network operating with poor efficiency (i.e., leaks) resulted in the distribution phase outweighing the treatment phase for these impact categories. In the production weighted average Okanagan water supply network that does not rely solely on active pumping, treatment accounted for 80% of the impacts to freshwater ecotoxicity due to polyaluminium chloride use in the treatment plant (Figure 2). The treatment plant scenario had a large impact on freshwater ecotoxicity compared to the other four treatment scenarios, where 92% of the scenario’s impact was due to the treatment plant. 74% of the treatment plant’s contribution to freshwater ecotoxicity was due to polyaluminium chloride. No other treatment scenarios utilize polyaluminium chloride; consequently, the impacts to freshwater ecotoxicity for the other treatment scenarios were 12-27% that of the treatment plant scenario (Figure 4). The generated hypochlorite treatment scenario was the second highest contributor to freshwater 101 ecotoxicity primarily due to energy production, congruent with findings from other water network LCAs (Lundie et al. 2004; Lemos et al. 2013). Generated hypochlorite also had significant contributions to terrestrial acidification and non-cancer human toxicity due to energy production. Generated hypochlorite required 1.6 kWh more electricity than delivered hypochlorite treatment per functional unit, thus resulting in higher impacts to all categories (Table 55). At the Miller Rd water delivery system in the Town of Oliver, electrochlorination was used to generate hypochlorite by passing an electric current through saltwater. In contrast, the sodium hypochlorite that was delivered to the Town of Oliver’s Black Sage wells was produced by capturing gaseous chlorine in a sodium hydroxide solution, using additional inputs of electricity and heat. Not only do the two processes use different inputs and technologies, but the amount of sodium hypochlorite used per cubic meter of treated water is also variable. This is because the input volume of treatment chemicals is dependent on the quality of source water, which fluctuates daily and across different systems (i.e., Miller Rd versus Black Sage wells). Further, the amount of inputs used to treat one cubic meter of water is based on annual input volumes of treatment chemicals, electricity consumption, and source water extracted. It is therefore challenging to compare the impact assessment results of these two systems directly, as source water quality and flow rates fluctuate. Environmental tradeoffs related to ground and surface water sources has been discussed in other water supply network studies (Stokes and Horvath 2006; Lyons et al. 2009; Barjoveanu et al. 2014) and was found to have high predictive power with respect to system-wide impacts (Meron et al. 2016). For example, Barjoveanu et al. (2014) found surface water had higher environmental impacts than ground water from increased treatment and energy requirements related to source water quality and pumping, respectively, compared to the mostly gravity fed groundwater system. This was not the case in the LCI of Okanagan water networks, as only 2 of the 11 surface water systems required treatment – those which were not separated 102 for agricultural and domestic users. Further, there was no clear relationship between source water and whether the system was pumped, gravity fed, or a combination of both; thus, energy required for the distribution network was not corelated to source water. An embodied energy model based on input-output analysis performed by Mo et al. (2011) revealed that groundwater and surface water supply systems had comparable energy consumption per functional unit, though groundwater used more direct energy due to additional pumping requirements, while the surface water system used more indirect energy through the increased use of treatment chemicals. The findings of the present study are congruent with this trend, as groundwater sources required more energy to pump on average than surface water sources. However, it is important to note that the inventory had a small sample size (n=3) for groundwater sourced systems, and none were mostly gravity fed – unlike the groundwater system in Barjoveanu et al. (2014), for example. 2.5.2 Sensitivity analysis Water losses across the distribution networks of water supply systems range between 9-55% (Twort et al. 2009). Water loss in the Okanagan water production LCA model was modelled as an estimated 18% at the end of the system (i.e., after all stages of the water supply network). Justification for this modelling choice is provided in section 2.2.4.2. A sensitivity analysis was performed to determine the impact of this choice over other estimates for water loss provided by RDNO (12% and 23%), as well as the choice to model the water losses as occurring at the end of the supply network (i.e., all water withdrawn is processed at every stage before being lost to leaks) rather than at the beginning. Results indicated significantly different outcomes between scenarios, verifying water loss and the presumed location of these losses along the supply network are important parameters to include in LCA modelling of water supply networks (Figure 11). In some cases, water losses are responsible for the majority of 103 environmental impacts of water supply networks. This is observed in particular in LCAs of water networks in poorer countries – for example the 25% water loss observed by Homäki et al. (2003) in Hanoi, Vietnam – or of older supply networks like that of Iasi City, Romania, where Barjoveanu et al. (2014) estimated a 41% water loss along the distribution network. The background data for water supply networks available to LCA practitioners – i.e., Ecoinvent data originating from Canada or Switzerland – is skewed towards well-developed countries which, partially due to the aforementioned trend, yield lower emissions than average values determined from a meta-analysis of LCA water supply networks (Weidema et al. 2014; Meron et al. 2016). This further demonstrates the importance of regionalized LCAs and access to background data that is reflective of the scope of the LCA performed and serves to caution practitioners to the relevance of LCIA results that utilize background data for water supply where water supply is a major input to the study system. A sensitivity analysis was also performed to determine the predictive power electricity grid selection had on the outcome of the LCIA. The Alberta electricity grid was used as a provider for energy inputs to the treatment plant stage as an alternative to the BC electricity grid used in all other analyses. The BC electricity grid was equal to or outperformed the Alberta electricity grid for all impact categories except water scarcity, as expected based on the higher percentage of hydro used in BC’s electricity grid mix. The largest differences between these two scenarios were found in the climate change and terrestrial acidification categories, where the impacts of the treatment plant using the BC grid were 76-79% that of the treatment plant if it were using the Alberta electricity grid (Figure 12). These findings indicate that when selecting background data for water supply inputs for an LCA, selection of a process that uses a comparable electricity grid mix to that of the region the LCA is analyzing has high predictive power for providing accurate results. This is an intuitive outcome following the previously discussed importance of energy consumption as a determinant for water supply network LCIA results. 104 2.5.3 Okanagan cherry orchard system comparisons The orchard level LCIA analysis in the present study did not yield as many statistically significant differences, despite obvious trends, as the water delivery system LCIA analysis. This is due to the increased uncertainty introduced with a more complex system linked to larger background datasets. Despite this, trends in the data are still relevant to digest. The life cycle assessment of cherry orchard production in the Okanagan addressed four main research questions. As was previously found by Hannam et al. (2016), bicarbonate deposited into orchard soils by irrigation in the Okanagan altered soil chemistry such that an increase of CO2 released from soils is observed. However, the results of the LCA indicated this phenomenon does not prove to significantly impact the environmental burdens associated with Okanagan cherry orchard production at the system level. Deposited bicarbonate from irrigation accounted for only 1% (2.63 X 10-3 kg CO2 eq kg-1 usable cherries) of the orchard’s overall short-term impacts to climate change. The next research question was to determine if irrigation makes a significant contribution to the environmental impacts of cherry production and whether employing more efficient irrigation technologies can make relevant reductions in environmental impacts at the orchard level. Of all inputs to cherry orchard production and for all impact categories other than marine and freshwater eutrophication and photochemical oxidant formation, irrigation had the largest contribution per unit of cherries produced (Table 10). Irrigation contributed 71%, 63%, and 58% of the impacts to human toxicity (cancer effects), freshwater ecotoxicity, and mineral resource use, respectively, mostly due to pipe manufacturing used for delivery and polyaluminium chloride used in treatment (Figure 3). The contribution of irrigation to the overall impacts of cherry orchard production in other published LCAs, input-output analyses, and energy analyses is highly variable, though a review of studies on the environmental impact assessment of fruit production systems by Cerutti et al. (2011) revealed that irrigation is one of the six major field 105 operations in orchards with impacts to the environment. Electricity demand from irrigation and fertigation accounted for 82% of overall consumption in an input flow analysis of cherry production in the Apulia region of Italy (Tricase et al. 2017). In the Apulia region, the pre-production and production phases consumed 0.13 kWh of electricity kg-1 of cherries, and 0.15 m3 kg-1 cherries (Tricase et al. 2017). The volume of water used in the production phase of Okanagan cherry production was 0.38 m3 kg-1 of usable cherries. Despite the Apulia region orchard system having consumed half the amount of water as the Okanagan orchard system per functional unit, Tricase et al. (2017) highlight irrigation for the electricity and water usage as a major input to the environmental management of cherry orchards. Similarly, in an energy and GHG emissions analysis of cherry production located in or near Natura 2000 sites in Europe, Litskas et al. (2011) found that irrigation in conventional orchards was the major input to energy consumption, and was the second highest input next to fertilizer in organic orchards. Cappelletti et al. (2015) also compared conventional and organic cherry orchard systems and found that the additional water and electricity required in the organic system (to supply water to the cover crop) was responsible for a higher GWP impact score when compared to the conventional system. Shen et al. (2021) assessed an open-field cherry production system that used sprinkler irrigation and found irrigation accounted for 29.1% of total energy use and contributed 19.2% of the system’s GWP. In the present study, irrigation from the production weighted Okanagan water supply network contributed 28.6% and 29.7% to the orchard’s total impact to climate change long term and short term, respectively (Figure 3). It is clear from these studies as well as the present study that irrigation makes a significant contribution to the overall energy consumption and GWP of cherry production systems. In contrast, a few cherry production LCAs indicate irrigation makes only a small contribution to total energy consumption (Kizilaslan 2009; Gaspar et al. 2021), or do not indicate irrigation as a significant predictor for environmental impacts of cherry orchard systems (Bravo et al. 2017; Tassielli et al. 2018). In all of these studies, irrigation as an input was only considered as the water consumed 106 and the electricity consumed to pump either from a private groundwater source or to pump along the on-farm irrigation network. In this way, the background processes of water production were not considered. This is a justifiable methodological choice where water is being derived from a private pump. However, this was not the case in the Okanagan, as many agricultural users were supplied by water purveyors with large distribution networks and often with some water treatment involved prior to arriving at the farm gate. This is a major reason the impacts of irrigation in the present study accounted for a larger proportion of the environmental impacts of the orchard system than is observed in other studies. Irrigation has higher impacts in the present study compared to the original Okanagan cherry production model from Sanderson et al. (2019), which used the Ecoinvent database for Quebec tap water production. The largest increase in impacts were found in freshwater ecotoxicity, human toxicity, and mineral resource use (Figure 3). This is predominantly due to the additional impacts associated with polyaluminium chloride used in the treatment of water in the Okanagan water delivery system in the case of impacts to freshwater ecotoxicity and human toxicity, and the production of steel for delivery pipes that contributed impacts to human toxicity and mineral resource use. The difference in environmental burdens of the orchard in these two scenarios which used different background datasets for irrigation reflect the importance of selecting regionalized life cycle inventory data where available. It also underscores that country-specific datasets are not always accurate in representing the relevant inputs to a system under study in an LCA. The water scarcity footprint of producing one kg of cherries in the Okanagan using the production weighted irrigation and water delivery system was 2.97 m3 world average. Recall from the description of the AWARE indicator for water scarcity in Chapter 1 that m3 world average is the unit used by the water scarcity impact score. It is a reference flow that compares the impact score scaled by the world average consumption. The AWARE method takes the volume of water consumed in the life cycle inventory and multiplies it by a characterization factor that represents the inverse of the 107 availability minus demand for freshwater in the region my system is in. This is interpreted as the potential to deprive another user of water in this region as being directly proportional to the amount of water consumed (from the inventory) and inversely proportional to the available water remaining (based on the characterization factor for the region). This explains why the resulting score is seemingly high: in reality, it does not require 2.87 m3 of water to produce one kg of cherries. It means that the consumption of water in the inventory to produce one kg of cherries (0.38 m3) is equivalent to consuming 2.97 m3 of water in this region, given the demand for freshwater in the area and the surface time to regenerate unused water. In comparison, the tap water production dataset – used in the original cherry orchard model by Sanderson et al. (2019) – yielded a footprint of 10.9 m3 world average. The difference is due to the characterization factors associated with the different elementary water flows used in the datasets. The Impact World+ impact factors for the lake and river water elementary flows used in the Quebec dataset was 42.95 m3. The elementary flow of lake water from Canada was used in the production weighted Okanagan water delivery system, which had an impact factor of 6.58 m3. From the life cycle inventory, the Quebec dataset and the system used in this study required 0.25 m3 and 0.38 m3 of water, respectively, to produce a kg of usable cherries. The system modeled for tap water production in Quebec is therefore more efficient than the production weighted Okanagan system, before characterization factors are assigned. The original model of Okanagan cherry production from Sanderson et al. (2019) included employee transport of foreign employees in the production phase. However, when employee transport was included in the orchard production phase, the relative impact of irrigation was decreased in 7 out of 12 categories, due to the high environmental impacts of air travel. Most LCAs do not include foreign employees in the production phase of orchards, thus rendering a comparison to current literature less relevant. As such, employee transport was not included as an input in the present study. Impact results of the orchard were compared between irrigation with no treatment applied to the water delivery system and with treatment at a treatment plant. The largest differences were 108 observed in the freshwater ecotoxicity and human toxicity (cancer) categories, where treatment at a plant more than tripled (276% increase) and more than doubled (129% increase) the orchards impacts, respectively, compared to irrigation with no treatment (Figure 5). Impacts to mineral resource use, freshwater eutrophication, fossil and nuclear energy use, climate change, photochemical oxidant formation, terrestrial acidification, freshwater acidification, ozone layer depletion, and human toxicity (non-cancer) were increased by 20-76%. Therefore, whether the water delivery system linked to irrigation was treated or not prior to arriving at the orchard makes substantial changes to the impacts associated with the orchard. The present study appeared to be the only LCA of cherry orchard production that considered this important aspect of the background data for irrigation water inputs. This study sought also to answer whether changing irrigation methods employed on orchards to more efficient technologies makes relevant reductions in overall cherry orchard impacts. If the most commonly used irrigation technology (i.e., microsprinkler), which 50% of Okanagan cherry orchards employed during a survey conducted in 2016, was switched to the most efficient irrigation technology used on Okanagan cherry orchards (i.e., SDI), substantial reductions in some impact categories would occur. Impacts from cherry orchard production in the Okanagan to water scarcity, human toxicity (cancer), freshwater ecotoxicity, and mineral resource use were decreased by 16%, 13%, 11%, and 10%, respectively (Figure 9). No direct comparison of environmental impacts between irrigation technologies has been studied in other cherry production LCAs. However, Shen et al. (2021) reported the use of sprinkler irrigation in the open-field cherry production studied in their analysis, where electricity consumption from irrigation accounted for 29.1% of the orchard’s energy use. In contrast, irrigation water using a more efficient drip technology on a cherry orchard studied by Gaspar et al. (2021) was responsible for only 4.3% of the total energy consumption of the orchard. Though these two study systems are not directly comparable due to varying energy consumptions of other inputs 109 during the cherry production phase, it is likely that irrigation efficiency contributed to some degree to the difference between relative contributions of irrigation. Studies conducted by Fentabil et al. (2016) and Baram et al. (2018) motivated the research question in the present study to determine whether the increase in N2O emissions resulting from drip over microsprinkler irrigation systems make significant differences to the overall GHG emissions of cherry orchard production. This LCA found that drip outperformed microsprinkler in every impact category except climate change even when the decrease in N2O emissions from agricultural soil due to employing microsprinkler were accounted for (Figure 10). For most impact categories, these reductions were minor. Decreased impacts ranged from 0-9% out of the total orchard impacts. Similar to the trends observed in switching from microsprinkler to SDI discussed previously, reductions in water scarcity and human toxicity (cancer) were greater than 10%. For climate change, the 29% decrease in N2O emissions resulting from using microsprinkler over drip irrigation was cancelled out by the 18% higher usage of water due to the lower efficiency of microsprinkler technology (thus requiring more water usage which consequently resulted in higher emission impacts to climate change originating from the water delivery system). The conclusion of this analysis was that the benefit of using the more efficient drip irrigation technology was offset by the consequently increased soil emissions of N2O for the climate change impact category. However, the efficiency of drip irrigation resulted in observable environmental benefits in other impact categories. 110 Chapter 3: An interdisciplinary approach to improving LCIA methodologies for freshwater use impacts on terrestrial ecosystem quality 3.1 Introduction Increased pressures on freshwater systems due to human-induced impacts have endangered the health of roughly four billion people who annually experience severe water scarcity (UN 2019), undermined potential economic gains and food security (World Bank Group 2002; FAO 2011) and precipitated negative and lasting effects on aquatic and terrestrial biodiversity (EPA 2018). It is therefore crucial to develop methods that will enable better understanding and management of the impacts of freshwater consumption (IPCC 2007). Over the last decade, the assessment of water consumption in LCA has advanced from “first generation indicators” – those of the volumetric water footprints that express total consumed water for a product – towards “second generation indicators” (Núñez et al. 2018). Second generation indicators provide a level of detail more appropriate for use in life cycle assessment (LCA) by accounting for regional differences in water demand and temporal variation in water availability (FAO 2011; Núñez et al. 2018). Examples include water scarcity metrics such as the Available Water Remaining (AWARE) model (Boulay et al. 2018), the Blue Water Scarcity Index (BWSI) (Hoekstra et al. 2012), and the Water Stress Index (WSI) (Pfister et al. 2009), all of which quantify the potential for depriving human users of freshwater at midpoint level. The second generation of indicators are considered “proxy” midpoint indicators (Verones et al. 2017) as they do not explicitly lie along the cause-effect chain of freshwater use impacts (i.e., the environmental impact or damage pathway along which impacts propagate). As such, they are not well-positioned to characterize environmental damages to the areas of protection (AoPs), including human health, ecosystem quality, and resource availability, which is the final aim of LCA (Damiani et al. 2018). Furthermore, when accounting for regional variability in the demand for freshwater from both ecosystem and human users, both AWARE and BWSI use 111 only environmental water requirement (EWR) for aquatic ecosystems as a measure of total ecosystem demand for freshwater (Richter et al. 2012; Pastor et al. 2014). Terrestrial ecosystem demand and the consequences for terrestrial ecosystems resulting from withdrawing freshwater therefore remains unaccounted for in these second generation indicators. Some suggest the use of a mechanistic approach to impact pathway development for the freshwater use impact category (Núñez et al. 2018). A mechanistic approach to develop characterization factors starts with linking the human intervention to the subsequent fate, sensitivity, effect and damage factors lying directly along the impact pathway. The fate factor (FF) quantifies the quantity and duration of the effect the human intervention has on water availability in other compartments, thereby linking the inventory to the impact assessment stage. A sensitivity factor (SF, also referred to as an exposure factor) is the ecosystem’s response to a change in water availability and whether that change will be compensated for, through either physical habitat stability or through a species’ capacity to utilize alternative water resources. This can be measured, for example, in distance travelled for a mobile species, but should encompass several taxonomic groups and ecosystem types (Henderson et al. 2011; Damiani et al. 2018). The effect factor (EF) is the resulting change in the ecosystem due to a change in water availability that cannot be offset by mechanisms defined in the SF. To convert the EF at the midpoint level to the endpoint level, the damage factor (DF) is used to measure the severity of the effect factor expressed as an ecosystem quality loss, typically in potentially disappeared fraction of species (PDF•m2•year). A pressing challenge faced by LCA practitioners when seeking to incorporate terrestrial ecosystems into freshwater use impact pathways is the unclear link between the consumption of surface and groundwater (i.e., blue water) and the subsequent impacts on terrestrial ecosystems, which primarily use precipitated moisture from soil (green water) (Kounina et al. 2013; Boulay et al. 2018). How the withdrawal of blue water ultimately changes water availability in the atmospheric compartment, and whether that change is felt by nearby or distant terrestrial 112 ecosystems, depends on complex eco-physiological and hydrological phenomena. The link between blue water consumption and terrestrial ecosystem impacts is challenging in part because terrestrial ecosystems vary in their acquisition of freshwater depending on biotic attributes specific to the ecosystem as well as on temporal variation in water availability (Baird and Wilby 1999). Available LCIA models that do account for the impact of water consumption on terrestrial ecosystems are diverse in their choice of indicators and pathways. Pfister et al. (2009) assessed water shortage-related damages to the endpoint indicator ecosystem quality using net primary production (NPP) as a proxy for potentially disappeared fraction of species (PDF). PDF is a common unit to express impacts to ecosystem quality and is frequently assessed using the vulnerability of vascular plants. Pfister et al. (2009) found that net primary production was correlated to the vulnerability of vascular plants and could therefore be used as a proxy for PDF in order to include water consumption impacts on ecosystem quality. To determine how water limits NPP, they adopted Nemani et al.'s (2003) indices quantifying plant growth limitations due to climatic factors, including water availability as measured by vapor pressure deficit. The resulting characterization factor includes the fraction of NPP that is vulnerable to a shortage of water, weighted by precipitation in the given area, and is therefore based on green water (the atmospheric water compartment). However, the characterization factor is used to quantify the impacts of a blue water (surface or ground water) withdrawal, assuming blue water consumption indirectly impacts terrestrial ecosystems by impacting riparian vegetation within the same watershed. This method therefore: (1) precludes characterizing the interactions between green and blue water compartments; (2) does not use a mechanistic characterization factor comprised of fate, effect, and exposure factors representing the physical cause-effect pathway; and (3) does not include impacts on animal species which support ecosystem functioning. 113 Other characterization factors built to quantify the impacts of water consumption on ecosystems use theorized loss of biodiversity, as estimated using varying taxa, including birds (Verones et al. 2013), vascular plants (van Zelm et al. 2011), and fish species (Hanafiah et al. 2011). Each has a different structure and position of the characterisation factor components (i.e., fate, effect, and exposure factors) along the pathway (Núñez et al. 2016). Despite the importance of terrestrial ecosystems for the good functioning and biodiversity that determines overall ecosystem quality, only van Zelm et al. (2011) provides characterization factors for terrestrial ecosystems. Further, van Zelm et al.'s (2011) characterization factors are not truly operational for LCA, generally, as they are applicable only to the Netherlands. While these methodologies do represent a valuable step forward, development of robust and broadly accepted methods for modelling terrestrial ecosystem damage caused by freshwater use is necessary and remains a pressing methodological challenge in LCIA (Damiani et al. 2018; Quinteiro et al. 2018). Currently, there is no method for developing the cause-effect pathway for how blue water consumption impacts terrestrial ecosystems that takes a mechanistic approach (i.e., defines characterization factor components) and combines hydrological and habitat methods for determining impacts. Terrestrial biome types utilize water in different ways and from different compartments (Baird and Wilby 1999). This variability hence necessitates identifying biome-specific impact pathways that enable differentiating the consequences of freshwater use on distinctive terrestrial ecosystems. Identification and modelling of these biome-specific pathways for use in LCIA will improve upon the environmental relevancy of current techniques. Therefore, we propose using an interdisciplinary approach to resolve the challenge of incorporating terrestrial ecosystems into the freshwater use impact category by defining unique water use biomes (UWUBS). UWUBs will serve as a framework to structure the development of more environmentally relevant, robust, and less uncertain impact pathways for freshwater use on terrestrial ecosystems. The description of the parameters involved in the cause-effect chain for 114 every UWUB is intended to serve as a cookbook to avoid missing important aspects when developing UWUBs-specific indicators for LCIA. Towards this end, the aim of this literature review was to take an interdisciplinary approach to answering the following questions: (1) What initial list of UWUBs can be defined using previously defined classification schema of biomes that are based on climatic, vegetative and hydrological interactions, but with a particular emphasis on the eco-hydrological interactions unique to each biome (2) Using one of the defined UWUBs as an illustrative example, what damage pathways, approaches and models from outside of current LCA literature have the potential to be usefully incorporated into freshwater use life cycle impact assessment pathways for terrestrial ecosystem quality? 3.2 Methods Literature spanning the fields of ecology, eco-hydrological modelling, and watershed and water vapour dynamics of terrestrial ecosystems were reviewed to assemble a non-exhaustive list of biomes that utilize water distinctly from each other (UWUBs). Peer reviewed journal articles, book chapters and gray literature publications published within a 30-year time frame spanning 1990-2020 were consulted. Titles, abstracts and conclusions of identified sources were analyzed to determine whether they fell within the scope of the review. The literature search was conducted using the JSTOR Collection database and used combinations of the following keywords: ("eco-hydrology” OR “terrestrial hydrology”) AND/OR (“freshwater use” OR “freshwater consumption”), terrestrial ecosystem, (“ecosystem damage” OR “ecosystem quality”), (“impact*” OR “impact assessment” OR “cause-effect chain”), soil, climate, biome. An exception was made to exclude the search term “(“impact*” OR “impact assessment” OR “cause-effect chain”) in order to expand the results to contain literature that did not use this terminology, which is more common within the field of LCA and LCIA. The literature search was 115 adjusted to provide results specifically relating to semi-arid and dryland ecosystems to obtain the results pertaining to the second aim of the study. The literature search was changed to include (“semi-arid” AND/OR “arid” AND/OR “dryland”), and to exclude the last three search terms, i.e. “soil”, “climate”, and “biome”. The search results were analyzed to compare and contrast the eco-hydrological interactions that occur in different biomes and to create a preliminary list of UWUBs based on their distinct eco-hydrological dynamics. Biomes are partitioned based on climatic, vegetative and soil attributes. These attributes were used to partition UWUBs as each possess modifying influences on eco-hydrological interactions: physical soil traits like permeability and available water capacity can influence green water retention in an ecosystem, surface runoff, and groundwater recharge (Dugan et al. 1990); trees can impact soil moisture regimes through evapotranspiration and plants can control lake water levels (Baird and Wilby 1999); and the climatic patterns of a biome influence seasonal shifts in water availability, biodiversity, and net primary productivity (Yuan et al. 2021). The varying and complex interconnections between the physical, biological, and hydrological properties of ecosystems necessitates the inclusion and consideration of these properties when defining UWUBs and biomes more generally. UWUBs are therefore additionally identified based on biome attributes both at the interface of the hydrological cycle and in the differences in how water is biotically propagated throughout the ecosystem. The unique attributes of water uptake and propagation in each biome define the mechanisms that are essential for inclusion in characterization factor development. These attributes include the acquisition of water, which may occur predominantly in one or more compartments of the hydrological cycle (i.e., atmosphere, groundwater, etc.), the biological mechanism for extraction and delivery of water to the biome (i.e., root uptake, canopy runoff, etc.) and the mechanism for propagation, retention, and recycling of water within the biome (to account for feedback systems). A pre-existing schema that accounted for the eco-hydrological phenomena discussed above as being necessary for UWUB definition was developed by Baird and Wilby (1999) and 116 identified during the literature review. Their biome classification schema was therefore well suited to base the preliminary classification of UWUBs and further justification of the use of this schema as a basis for the UWUB framework is provided in the results section. An example schema of a potential impact pathway is depicted in Figure 13. Further, intra-annual variability should also be considered so as to have spatially and temporally resolved impact assessment results, because shares of water sources could vary per season, which in turn can change the response of terrestrial vegetation in the cause-effect chain. Figure 13 The characterization factor (CF) components along a potential LCIA impact pathway for biomes adjacent to lakes and rivers. The fate factors (FF) link to the human intervention of the withdrawal from surface or groundwater compartments, respectively. The FF is multiplied by the sensitivity factor (SF) – the ecosystem’s buffering response to a change in water availability. The effect factor (EF) at the midpoint is the change in the ecosystem not offset by the SF, represented here as the loss of macrophytes that deposit nutrients at the land-lake interface. The damage factor (DF) at the endpoint is expressed as potentially disappeared fraction of species (PDF•m2•year) (© 2023 Alyssa Smart). To provide an example of using UWUBs as a framework for developing impact pathways for freshwater use on terrestrial ecosystems, the concept was applied to the semi-arid and dryland UWUB. Towards this end, literature relating to semi-arid and dryland biomes were analyzed to identify the eco-hydrological interactions as well as the biotic interactions that 117 propagate water in these regions. For each mechanistic characterization subfactor (i.e., fate, effect, sensitivity and damage factors), relevant eco-hydrological interactions and ecosystem attributes identified for the semi-arid and dry UWUB were categorized and described. 3.3 Results 3.3.1 Unique Water Use Biomes A non-exhaustive initial list of four UWUBs were identified for consideration and described in Table 22: drylands and semi-arid biomes, freshwater wetlands, woodlands and biomes adjacent to lakes and rivers. This is based on the categories described by Baird and Wilby (1999) in their account of the eco-hydrological interactions occurring in these respective biomes and the hydrological models that best represented terrestrial and aquatic ecosystems at the time of publication. Baird and Wilby (1999) took a thorough approach to identifying plant-water relations that influence hydrological predictions, beyond the typical biome properties – such as evapotranspiration and interception – used in hydrology. Similar categorization of biomes in the context of hydrology were used elsewhere (for example, Rockström et al. 1999). The choice to partition biomes into these four categories was because these biomes constitute a range of environments that showcase the modifying influences vegetation has on the interactions between hydrological and ecological phenomena. Further, this initial list served as a starting point that can be expanded upon identification of impact pathways that are operational in LCA and are sufficiently unique to those developed for the abovementioned biomes. Future work may, for example, consider the expansion of this initial list to include biomes not included in Baird and Wilby’s (1999) essays, including high latitude environments like the tundra and mid-latitude grasslands. As an additional way to partition vegetative characteristics of biomes into the UWUBs for respective modelling pathways, the use of Plant Function Types (PFTs) can be employed. PFTs 118 are used in land-surface models and are detailed vegetation categories based on phenologies that affect energy, water and carbon cycles (Zhu et al. 2016; Jiao et al. 2017). PFTs can be usefully included in the UWUB framework for LCIA modelling as a predictor of vegetative response to interventions in climate and hydrology. Table 22 Climatic, soil, vegetative, and hydrological characteristics defining the initial list of four unique water use biomes (UWUBs), primarily based on Baird and Wilby (1999), with additional references. Biome Soil Attributes Climate Attributes Vegetative Attributes Hydrological Attributes Drylands and semi-arid biomes - low soil fertility2 - low clay formation and coarse texture, i.e., sand and loam3 - often saline, sodic, calcic, or gypsic3 - Low organic matter - prone to soil degradation and desertification - High frequency of storm events - Low (> 700 mm year-1), infrequent, or highly variable rainfall - high radiation receipt - high potential evaporation, low evapotranspiration1 - high temperatures - low biological potential - plant cover <30% - above ground biomass between 300-700 g m-2 - strong seasonal and inter-annual variability - thorns, bushes, grasses, and savannah - High variability in water availability - dominant control on growth and maintenance of plants - fog and dew significant contribution to water balance Freshwater wetlands -aquic soil moisture regime4 - poor natural drainage - predominantly waterlogged hydric soil - high precipitation: evaporation ratio - >1200 mm year-1 - cool temperatures - predominantly hydrophytic - stable vegetation in permanent wetlands, ephemeral species that temporarily colonize in seasonal wetlands - variable water table induced by loss and recharge Woodlands - often high organic matter in the soil relative to other biomes - wide range of textures, including - decreased solar radiation with increased canopy density - wide range of average temperatures - trees and/or woody vegetation present - except in shrublands, forest cover height exceeds 2 m - deep root systems - exploitation of atmospheric and ground water sources - yields high quality freshwater (low concentrations of dissolved materials) Biomes adjacent to lakes and rivers - hydrosoils periodically saturated at the land-lake boundary - anaerobic sediment conditions - includes temperate and tropical climates - emergent and floating macrophytes - water intake achieved through precipitation, surface influents (runoff) from drainage basin, and groundwater seepage or springs 1Due to a combination of high radiation receipt, low water availability, and low surface cover. 2From Sahrawat et al. (2010). 3From Osman (2018). 119 4Defined as the anaerobic conditions in soil due to prolonged water saturation such that the growing season is dominated by this condition (Osman 2018). 3.3.2 Review of drylands and semi-arid biomes for UWUB development A review of eco-hydrological interactions in dry and semi-arid systems was performed to provide an example of how the UWUB framework can function as a roadmap to arriving at cause-effect chains for how human water uses impact terrestrial systems. Using the framework, interactions between soil, climate, vegetation, hydrology, and animals were considered through the lens of a mechanistic impact pathway (i.e., how interventions in different water compartments propagate through dry and semi-arid systems was used to provide insight on fate, effect, sensitivity, and damage factor development). Growing evidence was found to support the idea of water as a trophic currency that drives ecological interactions at the community and species level in arid regions (Nippert and Knapp 2007; Mccluney and Sabo 2009; Allen et al. 2014). In fact, across all but the most northern latitudes, patterns of species richness are correlated to hydrological variables more than energy (Hawkins et al. 2003). Unlike other ecosystems, dryland food webs can shift between water limited to energy limited trophic structures depending on water availability (Sabo et al. 2008). A promising approach to developing the cause-effect chain for arid ecosystems is therefore to identify how interventions in blue water compartments are propagated up the trophic ladder of an arid ecosystem’s “water web”. The subsequent sections aim to provide a possible road map to achieve this by describing relevant hydro-ecological phenomena to consider and incorporate into each characterization sub factor. 3.3.2.1 Fate factor Changes to surface water flow regimes and groundwater levels through dam construction and groundwater withdrawal are the most common human interventions to the 120 hydrological cycle in dryland regions (Okello 2019). It was therefore recommended that fate factors for these human interventions are developed for the semi-arid and drylands UWUB. Dams have been linked to ecosystem and biodiversity loss in downstream arid ecosystems through changing flow regimes such that low flows occur in the wet season or high flows occur in the dry season (reversing seasonality), through the trapping of sediments, and through altering average monthly flows (Tullos et al. 2009; Moore et al. 2010). Dry regions also face compounding impacts of groundwater withdrawal as it can cause disproportionately large reductions in groundwater levels due to low precipitation rates (Dragoni and Sukhija 2008). The fate factors developed for dam construction and for groundwater extraction should account for these phenomena. The trophic structure is unique for groundwater and surface water sources in semi-arid and arid regions (Sabo et al. 2008). Direct impacts on animal communities via a hydrological intervention occur primarily in the surface water compartment, while interventions in the groundwater compartment are typically propagated up the trophic ladder to animal communities via vegetation (Allen et al. 2014). Water availability can also directly influence interactions between primary and secondary consumers (Mccluney and Sabo 2009). A decrease in surface water availability for animals is usually met with the physiological response of decreasing water losses (discussed further in the sensitivity factor section) or the behavioural response of increasing consumption of moist foods (Mccluney and Sabo 2009). McCluney and Sabo’s (2009) study of invertebrate response to water availability in the San Pedro River in Arizona, USA revealed that removal of a surface water source (i.e., stream drying) would likely drive predator invertebrates to consume more prey to meet water needs. These prey invertebrates (aka primary consumers) increased their consumption of moist vegetation when access to surface water was unavailable, which was a source of groundwater uptake. This behaviour change therefore resulted in primary and secondary consumers shifting from surface water to 121 groundwater sources, the implications of which scaled up the trophic ladder as these invertebrates were considered a water currency for dryland animal communities. Even where surface water is available in natural rivers during the dry season, groundwater contained in plants is a significant trophic currency for primary consumers in dry regions (Allen et al. 2014). Therefore, developing a fate factor that links the dryland ecosystem’s utilization of groundwater sources to a direct human intervention within the groundwater compartment or indirectly within the surface water compartment would represent the trophic structure of these ecosystems well. As noted above, groundwater levels are also common human interventions and the magnitude of these withdrawals is exacerbated by the dry climate (Dragoni and Sukhija 2008; Okello 2019). While many vegetative groups present in the dryland community are sources of groundwater through the consumption of their leaves, phreatophytic plants – deep-rooted plants that access water in the phreatic zone below the water table – are capable of delivering permanent and deep groundwater sources to surface communities year-round (Sabo et al. 2008). Phreatophytic plants can therefore provide the first rung of the trophic ladder for incorporation into the cause-effect chain. 3.3.2.2 Effect Factor The decline of phreatophytic cottonwood-willow forests in the arid San Pedro river system in the USA and Mexico is prominent in watersheds that have experienced water table lowering and reduced water flows due to human activities (Shafroth et al. 2000; Webb and Leake 2006). Reduction of water tables results in community restructure towards non-native species dominance (Busch and Smith 1995; Horton et al. 2001; Amlin and Rood 2002; Lite and Stromberg 2005). Stromberg et al. (1996) found depth to groundwater as the most influential variables on riparian vegetative composition in semi-arid regions. The implications of a change 122 in water table depth were therefore both a change in composition as well as a loss of endemic biodiversity which can be reflected in the effect factor. At the midpoint level, small changes in groundwater can impact size and productivity of plant species (Stromberg et al. 1996). Even species that are resilient to changes in groundwater depth in terms of mortality will experience declines in foliage area, xylem water potential and growth rate with a reduction in groundwater (Stromberg et al. 1993). Reductions in size and productivity of overstory trees (i.e., phreatophytic trees) reduces the positive effect they have on the ecosystem through increasing soil fertility, moderating extreme temperatures, and increasing surface soil moisture (Cross 1991). Impacts on phreatophytic overstory trees as a result of a reduction in groundwater are therefore propagated to other species in the plant community that rely on these positive effects. Declines in canopy vigor and radial growth as well as tree mortality in semi-arid forested regions have resulted from a decrease in groundwater greater than 1 m (Stromberg et al. 1996); these forested areas typically do not occur in areas where the groundwater depth has reduced to more than 3 m below the surface. These values can provide a tipping point for impact assessment modelling if over time hydrological changes occurring in the inventory could result in either an overall change in depth greater than 1 m, or a reduction in groundwater below 3 m from the surface. Stromberg et al. (1996) also identified indicator plant species based on their sensitivities to groundwater decline. Shallow groundwater herbaceous species declined severely in abundance when groundwater was lower than 0.25 m below the surface. Stromberg and Patten (1996) found juvenile Salix gooddingii (willow species) and Populus fremontii (cottonwood) to be the most sensitive woody indicator species, both of which are found in semi-arid regions of the US and Mexico. The different sensitivities of plant species suggest a range of taxa should be included in the effect factor, inclusive of both sensitive herbaceous and woody plant species with shallow groundwater rooting depths. 123 While the reduction in size and productivity in phreatophytic trees has negative impacts on plant communities, it is also linked to animal communities via a reduction in leaf moisture content (Mccluney and Sabo 2009). To link the trophic web from plant to animal communities, invertebrate species are good candidates as they represent the first and second trophic levels and are typically similar in structure across different arid regions (Allen et al. 2014). As an abundant invertebrate taxon in desert floodplain forests, field crickets (G. alogus) link groundwater collected by phreatophytic plants to higher trophic levels, including spiders, lizards, birds and mammals (Sabo et al. 2008). One study found that cricket abundance increased in response to ground water additions through increased moisture in leaf litter (Allen et al. 2014). It therefore may follow that abundance would be decreased over time as moisture content in leaves becomes limiting and competition increases. Further, a reduction in groundwater can be met with an increase in consumption of primary consumers by secondary consumers (Mccluney and Sabo 2009). The effect factor should therefore include the decrease in abundance of primary consumers due to increased intra-trophic level competition and increased preying from higher trophic levels. As invertebrate species can be considered a water currency in drylands for higher trophic levels (Sabo et al. 2008), this effect can then be propagated up the trophic ladder and therefore constitutes a pathway for cause-effect chain development. While the relationships described between primary and secondary consumers were present in both the monsoon and dry season, the treatment effect was significantly less in the monsoon season when water was not always the limiting factor for the trophic web (Nippert and Knapp 2007; Mccluney and Sabo 2009; Allen et al. 2014). Temporal variation in ecological responses is therefore an important consideration for cause-effect chain development. Because consumption of the fresh leaf litter of these plants by primary consumers is increased when other sources of water are low, these ecological interactions can also be considered a buffering capacity of drylands: as surface water availability decreases, uptake of groundwater stored in fresh leaves can be increased. 124 3.3.2.3 Sensitivity Factor Semi-arid and dry region ecosystems possess adaptive strategies to combat water limitations that are important considerations in sensitivity factor development. The impacts of reduced water availability on ecosystems is mediated by a reduction in competition observed both in plant and animal communities (Sorenson et al. 1991). Historically, sensitivity factors developed for terrestrial ecosystems in LCIA suggest that the ability of some species to travel to a new source of water provides a buffer to the impacts of water resource reductions (Damiani et al. 2018). This is a poor variable for ecosystem resilience because only a small subset of terrestrial animals are capable of relying on more than one watershed due to physiological or habitat fragmentation constraints. Switching surface water sources within a watershed also does not reduce water demand as pressures are propagated through the contiguous water network. Further, water loss in animals is associated with increased locomotion which would increase the water deficit of mobile individuals (Mccluney and Sabo 2009). Instead of considering the ability to find new sources of water when water becomes less available in one area, an appropriate sensitivity factor for drylands could instead be the physiological adaptations many arid dwelling species possess. Plant communities in arid regions also possess the capacity to more efficiently capture green water through physiological adaptations that increase stemflow and consequently reduce evaporative water loss (Gebauer and Ehleringer 2000; Yuan et al. 2019). As an example of mammalian adaptations, desert ungulates are capable of reducing their total evaporative water loss (TEWL) by lowering their resting metabolic rate and switching to nocturnal activity patterns in response to long-term water limitation (Ostrowski et al. 2006). These adaptations can mitigate body mass losses due to water restrictions, which if left unmanaged could lead to mortality. 125 3.3.2.4 Damage Factor The impacts of a sustained decline in groundwater depth in phreatrophytic forested areas would propagate from lower moisture content in leaves, through linkage to the water trophic web to animal communities, to death of juvenile plant species, followed by the death of aged trees (Stromberg and Patten 1996; Allen et al. 2014). This transition represents the severity of the effect of a change in water availability, i.e., the damage factor. Desert floodplain forests, like the phreatrophytic cotton-wood willow forests in Southern USA and Mexico, provide oases for the inhabitants of adjacent and often large stretches of dryland, but can only relieve water limitation stress if there are expansive forests across wide floodplains (Sabo et al. 2008). This occurs indirectly through phreatophytic trees rather than through direct alleviation via a river (Sabo et al. 2008). Consequently, long term impacts of hydrological interventions to rivers in arid systems to consider in the damage factor is the width reduction of these desert floodplains if death of aged trees occurred. If the forested area narrows (an attribute of degraded channels) to a degree that hinders their capacity to alleviate water stress in the desert community, biodiversity across all taxa within the trophic system would be lost. 3.4 Discussion To support filling a gap in current LCIA methods, this literature review aimed to introduce and demonstrate the use of the UWUB framework to develop mechanistic impact pathways of water consumption on terrestrial ecosystems. The initial list of biomes defined using this review comprised drylands and semi-arid biomes, freshwater wetlands, woodlands, and biomes adjacent to lakes and rivers. These four UWUB categories represent a preliminary framework for modelling terrestrial ecosystem impacts from freshwater use based on their unique use, biotic propagation, and cycling of freshwater resources. Many alternative biome schema are 126 available to categorize terrestrial environments using traditional methods of defining biomes based on biotic and abiotic factors (as examples, Prentice et al. 1992; Rutherford 2004; Pennington et al. 2004b). The UWUB framework incorporated these methods in addition to placing emphasis on interactions at the eco-hydrological interface. Some biome classifications have been produced using dynamic global vegetation models, which rely on plant functional types to differentiate biomes (Poulter et al. 2011; Dallmeyer et al. 2019). Incorporating plant functional types into the UWUB framework may be a promising expansion to the methodology, provided emphasis is placed on the different functional roles vegetation has at the eco-hydrological interface. The UWUB framework can be built on existing LCIA pathways that address terrestrial ecosystem demand in water scarcity indicators. Pfister et al.'s (2009) methods assess the potential disappearance of vascular plants from a ground or surface water withdrawal at a 0.5 grid native spatial scale. However, they do not define a fate, sensitivity, or effect factor at the native spatial scale. This methodology could therefore be improved by adding UWUB-specific sub-characterization factors to represent the response of various taxa within each terrestrial environment. Van Zelm et al.'s (2011) methodology includes a fate and effect factor for modelling the potentially disappeared fraction of vascular plants due to a groundwater withdrawal. Similarly, the UWUB framework can be applied here to expand the use of the developed characterization factors beyond the Netherlands, by incorporating sub-characterization factors representative of unique biomes. Verones et al.'s (2013) methodology includes both plant and bird taxa and can be usefully incorporated into the freshwater wetlands UWUB. Species of semi-arid and arid environments thrive in harsh and often water limited conditions due to special adaptations. Because of this, the semi-arid and dryland UWUB was chosen as the example biome to illustrate the function of the UWUB framework as the species impacted along the pathway have phenologies that directly relate to changes in water 127 availability. Drylands and semi-arid biomes are typically sustained by a permanent groundwater source that is accessed by phreatophytic plants and propagated up trophic levels and through a ‘water web’ (Sabo et al. 2008). As such, changes in groundwater level or interventions of surface water sources that are linked to groundwater can be linked to arid animal communities through impacts on phreatophytic plants in the fate factor. These impacts can include changes in canopy cover, size and productivity of phreatophytic overstory trees, leading to an increase in non-native plant abundance, decrease in soil fertility and surface soil moisture, and a reduction in resilience to extreme temperatures (Cross 1991; Amlin and Rood 2002; Lite and Stromberg 2005). The animal community, beginning with primary and secondary consumers, can respond to these changes in a variety of ways. Changes in invertebrate communities include a reduction in species abundance and behavioural changes of secondary consumers to consume more prey (Mccluney and Sabo 2009). Because invertebrate community structure is similar across different arid communities and because invertebrates are considered a water currency in dry regions (Sabo et al. 2008; Allen et al. 2014), they are good candidates to represent the trophic level linking phreatophytic plants in the fate factor to animal communities higher up the trophic ladder in the effect factor. The effects on invertebrate abundance due to a reduction in water availability are propagated up to other taxa, including birds, reptiles and mammals (Sabo et al. 2008). However, both plants and animals in semi-arid and drylands possess unique abilities to resist water loss during water limited periods. Ungulate species are capable of reducing water loss through physiological and behavioural changes (Ostrowski et al. 2006), while plants possess physiological adaptations for increased water retention and decreased evaporative loss (Tao and Zhang 2012). These unique adaptations should be incorporated into the sensitivity factor of the mechanistic impact pathway for the semi-arid and dryland UWUB. Midpoint and endpoint impacts to consider include the loss of oases habitat due to reductions in surface and groundwater that are critical for surrounding arid dwelling animal communities’ survivorship (Li et al. 2021). 128 This review addressed a methodological gap as outlined in Núñez et al.'s (2016) review of freshwater use impact assessment methods in LCA. The work builds on Núñez et al.'s (2016) review by focusing on terrestrial ecosystems and lays the foundation for a unique framework for developing impact pathways of freshwater use. Through an interdisciplinary approach, core elements of the dryland and semi-arid UWUB were identified for characterization factor development. Expansion of the UWUB framework and further development of UWUB impact pathways will provide robust, representative LCIA impact pathways for terrestrial ecosystem damage due to freshwater use. 129 Chapter 4: Conclusion Human consumption of freshwater puts pressure on our freshwater resources. How we manage freshwater consumption and assess the impacts to human health and the environment is an evolving science. The research detailed in this thesis sought to increase our understanding of how freshwater use in horticulture systems impacts human health and the environment. This research also aimed to address the current gap in understanding as to how to better represent the impacts on terrestrial ecosystems due to human freshwater consumption in LCIA. Four main research questions were outlined in Chapter 1. The first research question was to determine the environmental impacts associated with water supply networks in the Okanagan that distribute irrigation for agricultural uses and how processes within each supply network vary in their environmental performance. The LCA performed in this research showed that pipes used in the distribution network made substantial contributions to the environmental impacts associated with the water supply network, in particular with respect to mineral resource use, freshwater eutrophication, human toxicity, and fossil and nuclear energy use. This was predominantly due to steel and PVC manufacturing. This is notable given that some LCAs of water supply networks do not include pipe infrastructure in the analysis based on the assumption that it would make negligible contributions. In contrast, this research underscores that pipe infrastructure is important to include in future LCAs of water supply networks. This is particularly true for supply networks that deliver to agricultural users, where treatment requirements are reduced and wastewater (which typically contributes a large portion of the overall impacts of water supply networks) is not collected, thereby increasing the relative contribution of pipe infrastructure to the environmental performance of the network. Treatment at a plant to produce potable drinking water that is then diverted to agricultural users produces unnecessary and increased environmental impacts. The treatment 130 plant phase of the Okanagan water delivery system nearly quadrupled the freshwater ecotoxicity impacts of cherry orchard production, and increased other impacts by 2%-129% compared to the scenario without treatment. RDNO does not have a fully separated agricultural irrigation water system, meaning water is unnecessarily treated at a plant prior to delivery to some agricultural users. Having separated agriculture and potable drinking water networks is therefore a meaningful way to substantially reduce the environmental burdens associated with both the irrigation water in North Okanagan agricultural systems and of the water supply network operated by RDNO. The impacts of irrigation in agricultural systems are often attributed to energy consumption, and influenced by factors such as climate conditions, water source, irrigation type, etc. The research presented herein contests water source as being a deterministic factor for environmental outcomes, as the difference in environmental impacts associated with ground or surface water are dependent on multiple factors including water quality and the energy consumed along the distribution network. Where water is being treated to be potable, the source of water is deterministic only through water quality, where ground water generally requires less treatment than surface water. However, in the case of agricultural use of water supply networks where treatment isn't required and is not occurring, surface water may not have higher impacts compared to groundwater since energy and chemical consumption for treatment is not occurring. It may follow that surface water, in this case, is more environmentally friendly than groundwater due to the increased energy required to pump (out of a deep well, for example) associated with some groundwater sources. Additionally, the energy required along the distribution network of a water supply system is sometimes interpreted as a difference between surface and groundwater sources. The differences here between sources are reflective of topography and whether or not a large portion of the distribution network can be gravity-fed. Whether the water supply network is ground or surface water sourced is circumstantial and is not directly causal of the observed trends between impact assessment results. 131 The research outcomes of Chapter 2 and the literature analysis of water supply network LCAs revealed three important questions to consider when developing life cycle inventory models and assessing the impacts of irrigation systems. (1) Is irrigation derived from a groundwater source on or near the farm? If so, the model need only incorporate the water volume and electricity/diesel consumption for pumping and distributing irrigation to the crops (the dominant choice in many tree fruit production LCAs), which avoids inflating the impact results by incorporating a background dataset of water supply that is not relevant to the study system. If this is not the case, it is likely that background data for a water distribution network is necessary, and selecting a representative upstream process is possible by answering the next questions. (2) Is the water arriving at the study system treated? The accuracy of the choice of background data is dependent on this answer, as choosing a supply network that treats water will inflate the environmental impact assessment results, and vice versa. (3) Is the supply network mostly gravity fed? Answering this question will determine whether to select an upstream provider for irrigation that models energy use along the distribution network. Further, changing the inputs of electricity use within the water supply network dataset to the electricity grid representative of the study system was shown in the sensitivity analysis in Chapter 2 to have meaningful consequences for the LCIA results. Following this procedure in the goal and scope and life cycle inventory phase will aid LCA purveyors in arriving at representative LCA models of agricultural systems. The second research question was to determine if irrigation makes a significant relative contribution to the impacts of Okanagan cherry production and whether employing irrigation technologies that are more water efficient can mitigate life cycle environmental impacts. Through the LCA performed in Chapter 2 it was found that irrigation does have substantial relative contributions to most environmental impacts assessed. Climate change is expected to continue to increase the growing season in the Okanagan and increase the arable land for agricultural use at higher elevations (Neilsen et al. 2001; Rayne et al. 2011). Already the 132 Okanagan is seeing a rapid increase in land devoted to wine grape and tree fruit production (Crawford and MacNair 2012). In tandem, studies indicate crop water demand will increase with an increasing evapotranspiration rate and longer growing season, coinciding with a decrease in annual surface water flows (and hence freshwater supply) (Neilsen et al. 2006; Crawford and MacNair 2012). This has the potential to create water supply shortages in the near future, making adaptive horticultural management strategies an imperative component of the growing Okanagan wine grape and tree fruit production industry. The research outcomes of Chapter 2 show a meaningful reduction to the impact to water scarcity and other environmental impact categories can be achieved by switching to more efficient irrigation technologies, such as drip and SDI. Existing Okanagan tree fruit production systems as well as new developments can switch to or select the most efficient irrigation technologies as a relevant adaptive management strategy that mitigates the water demand and environmental impacts associated with irrigation. The third research question was prompted by previous work that showed drip irrigation can result in an increased release of the greenhouse gas N2O compared to microsprinkler irrigation (Fentabil et al. 2016). However, the analysis presented herein showed that the increased contribution to the climate change impact category due to this phenomenon was balanced by the increased efficiency of the technology, thereby reducing the upstream emissions associated with the water supply network. In all other impact categories, drip still outperformed microsprinkler irrigation for this reason. The research outcomes of Hannam et al. (2016) on the role of bicarbonates found in irrigation water leading to increased CO2 emissions from agricultural soils was also incorporated into the present research. Including the increased emissions of CO2 as a consequence of irrigation in the LCA of Okanagan cherry orchard production revealed that, at the system level inclusive of the life cycle of cherry production, these emissions were negligible. These outcomes showcase the value of performing a system level environmental impact analysis like LCA, as new research on agricultural greenhouse gas emissions can be incorporated into the analysis of the entire life cycle of agricultural products. 133 The Okanagan valley is categorized as semi-arid (Cohen et al. 2001) and developing a cause-effect chain that characterizes the impacts of freshwater use in this environment is pertinent to the research performed in Chapter 2. The final research goal was therefore to determine the potential pathways for assessing the impacts of freshwater use in semi-arid terrestrial ecosystems in LCIA. The conclusions made in Chapter 3 highlight the “water-web” concept as having important implications for the methodological development of an impact pathway. A “water-web” is analogous to the trophic levels represented in a “food-web” in biomes that are considered more water limited than energy limited, as is the case in semi-arid and dryland ecosystems. The preliminary work presented in Chapter 3 provided the basis for a new methodological framework that incorporated the concepts of a water-web and unique water use biomes (UWUBs) for development of mechanistic pathways detailing how the impacts of freshwater consumption are propagated through terrestrial environments. This framework and the review of ecological mechanisms that propagate water, mitigate the effects of water stress, and ultimately assess damage to terrestrial ecosystems lays the groundwork for methodological advancement in LCIA. Future research building on these concepts would provide relevant applications to LCA purveyors performing research on human systems within water-stressed environments such as the Okanagan. When the effects of freshwater consumption on semi-arid and dryland biomes are better represented, adaptive environmental management strategies are better positioned to provide valuable solutions for the critical and growing issue of freshwater scarcity. 4.1 Research strengths and limitations The midpoint category particulate matter formation – which expresses the impact of inhalation of fine particulate matter as it relates to the endpoint damage to human health – was not included in this study because it is not considered a baseline impact category in LCA and was not reported as a significant impact category for water supply networks (Acero et al. 2016; 134 Meron et al. 2016). However, in a study of a water distribution network in the Walloon Region of Belgium, Lassaux et al. (2007) found that the second most significant impact category of their LCA was respiratory effects due to particulate matter formation. Impacts to this category were associated with the manufacturing process of pipes used for the distribution network. Thus, including particulate matter formation in the present study may have elucidated substantial impacts to this category deriving from the Okanagan water delivery system. However, because the water supply network LCA performed in this research was intended to answer research questions relevant to the agricultural system under study, the choice to not include particulate matter formation is unlikely to have significant repercussions for the overall conclusions. Uncertainty in LCIA results was introduced during the treatment phase, due to the assumptions and proxies used when modelling the treatment chemicals. Some chemical product names provided by purveyors were not listed in Ecoinvent, nor were the compounds that constitute these products well described in MSDS and other product information sheets. Therefore, the best analogs available in Ecoinvent for the chemical families disclosed in product information sheets were used to model these products. Data from the City of Kelowna had been converted from a rudimentary system inherited by the city. Therefore, some data from this source should be considered extrapolations and estimates. The results of this study provided long term benefits to Okanagan Valley residents through elucidating effective strategies to reduce the environmental impacts associated with agricultural water consumption, thereby mitigating the risk of water scarcity caused by climate change and a rising water demand. 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The length of each pipe type was then production weighted based on the annual irrigation water volume of each water supply network transported (Table 1). To build the inventory consisting of the raw material inputs for each pipe type, life cycle inventories datasets from existing LCAs were used and are reported in Section 2.3. These LCAs reported raw materials for different pipe types at a specific diameter. The raw data collected from Okanagan water purveyors included several diameter sizes for each pipe type and therefore had to be converted to a reference diameter (the diameter reported in the LCAs). The following equation was used to calculate the production weighted average length (L) for each pipe type (x): 𝐿𝑥 = ∑ 𝑙𝑖 × 𝑉𝑖 Where (li) is the length of pipe type (x) for each supply network, and (Vi) is the production contribution for each supply network to the total irrigation water volume of all purveyors. The density scaled production weighted average (Ls) was found by multiplying the production weighted average length of pipe x (Lx) by the density of the pipe x (dx) and dividing by the density of the reference size for pipe x (dR). 𝐿𝑠 = ∑ 𝐿𝑥 (𝑑𝑥𝑑𝑅) The density scaled production weighted average (Ls) for each pipe type is the length of the reference pipe that equates to the same mass of materials of Lx. The Ls values for each pipe type were then summated. The result is the total length of each reference pipe reported in Section 2.3.2 in Table 3. These values, as shown in Table 3, were then divided by the lifetime capacity of the pipe, which was estimated by multiplying the lifespan of the pipe (50 years) by the annual average water consumption of the water supply networks in the LCI (2.6 X 106 m3). The result is the length of the 153 reference pipe scaled to the functional unit of one cubic meter. A sample calculation arriving at the length of the asbestos concrete reference diameter pipe (AC 500) required per the functional unit is provided below: 𝐿𝐴𝐶 50 = ∑( 0.74 × 0.013, 0.68 × 0.37, 0.11 × 0.03, 0.082 × 0.02) = 0.27 𝐿𝑠,𝐴𝐶 500 = ∑ (0.27 ×3.70.4, 𝐿𝐴𝐶 75 ×𝑑𝐴𝐶 750.4, … ) = 18.08 𝑇𝑜𝑡𝑎𝑙 𝑒𝑞𝑢𝑖𝑣𝑎𝑙𝑒𝑛𝑡 𝑙𝑒𝑛𝑔𝑡ℎ 𝑜𝑓 𝐴𝐶 500 𝑓𝑢−1 =18.0850 × 2.6 𝑋 106 Table 23 Length and type of pipes used in the distribution network of 12 water supply networks in the Okanagan operated by three distinct water purveyors. Pipe Type Density (kg m-1) Oliver 1 (km) Oliver 2 (km) Oliver 2B (km) Oliver 4 (km) Oliver 5 (km) Oliver 6 (km) Oliver 7 (km) Kelowna (km) Duteau Combined (km) Duteau non-potable Goose Lake King Edward Lx (km) Ls (km) AC 501 3.7 X 100 - - 7.4 X 10-1 - - - - - 6.8 X 10-1 1.1 X 10-1 8.2 X 10-2 - 2.7 X 10-1 1.1 X 10-2 AC 751 6.0 X 100 - - - - - - - - 4.3 X 10-1 - 4.4 X 10-2 - 1.6 X 10-1 1.1 X 10-2 AC 1001 1.0 X 101 1.5 X 100 3.0 X 10-1 7.6 X 10-1 4.1 X 100 1.3 X 100 1.1 X 100 1.1 X 100 8.5 X 100 3.4 X 101 3.1 X 100 2.4 X 100 5.1 X 10-1 1.6 X 101 1.7 X 100 AC 1501 1.6 X 101 3.3 X 100 9.9 X 10-1 1.0 X 100 3.4 X 100 1.0 X 100 2.6 X 100 1.0 X 100 1.9 X 101 6.7 X 101 2.0 X 100 2.2 X 100 1.9 X 100 3.0 X 101 5.4 X 100 AC 2001 3.2 X 101 1.4 X 100 1.1 X 100 - 2.5 X 100 9.4 X 10-1 7.5 X 10-1 8.2 X 10-1 7.3 X 100 2.0 X 101 8.1 X 10-1 2.9 X 10-1 8.6 X 10-1 9.8 X 100 3.4 X 100 AC 2501 3.2 X 101 7.4 X 10-1 - - 1.3 X 100 4.0 X 10-1 6.4 X 10-1 1.6 X 100 5.2 X 100 9.5 X 100 7.5 X 10-1 4.1 X 10-1 3.2 X 10-1 5.1 X 100 1.8 X 100 AC 3001 3.6 X 101 1.8 X 100 - - 6.8 X 10-1 1.6 X 100 1.6 X 100 6.6 X 10-1 4.2 X 100 9.5 X 100 1.9 X 100 1.9 X 100 4.8 X 10-1 5.0 X 100 2.0 X 100 AC 3501 4.6 X 101 - - - 2.9 X 10-1 - 9.7 X 10-1 1.4 X 100 4.5 X 100 3.6 X 100 1.8 X 10-1 1.5 X 100 - 2.6 X 100 1.3 X 100 AC 4001 6.3 X 101 1.7 X 100 - - 1.5 X 100 5.5 X 10-1 1.8 X 10-1 - 3.3 X 100 4.7 X 100 - 1.3 X 100 - 2.9 X 100 2.0 X 100 AC 4501 7.4 X 101 - - - 3.6 X 10-1 5.0 X 10-1 - 2.0 X 10-1 1.2 X 100 - - 8.1 X 10-1 - 3.8 X 10-1 3.1 X 10-1 AC 5001 9.0 X 101 - - - - - - - - - - - - - - AC 6001 1.1 X 102 - - - 2.1 X 10-1 - - - - - - - - 2.3 X 10-2 2.9 X 10-2 Concrete Cylinder 5002 2.4 X 102 - - - - - - - - - - - - - - Concrete Cylinder 7502 4.1 X 102 - - - - - - - - 1.6 X 101 3.6 X 10-3 3.4 X 10-2 - 5.9 X 100 1.0 X 101 Concrete Cylinder 9002 5.1 X 102 - - - - - - - - 5.5 X 100 8.5 X 10-2 - - 2.1 X 100 4.5 X 100 154 Concrete Cylinder 12702 8.7 X 102 - - - - - - - - 1.2 X 100 3.2 X 100 - - 5.5 X 10-1 2.0 X 100 Cast Iron 503 6.6 X 100 - - - - - - - - 1.8 X 10-1 - - - 6.8 X 10-2 3.9 X 10-3 Cast Iron 1003 1.5 X 101 - - - - - - - - 4.3 X 100 - 4.1 X 10-2 - 1.6 X 100 2.1 X 10-1 Cast Iron 1503 2.3 X 101 - - - - - - - - 3.5 X 100 - - - 1.3 X 100 2.7 X 10-1 DI 1004 1.7 X 101 - - - - - - - - 9.3 X 10-2 - - - 3.5 X 10-2 5.1 X 10-3 DI 1504 2.5 X 101 - - - - - - - - 6.2 X 100 5.1 X 10-1 4.9 X 10-2 - 2.3 X 100 5.0 X 10-1 DI 2004 3.3 X 101 - - - - - - - - 1.3 X 100 - - - 4.7 X 10-1 1.3 X 10-1 DI 2504 4.3 X 101 - - - - - - - - 2.7 X 10-1 - - - 1.0 X 10-1 3.8 X 10-2 DI 3004 5.4 X 101 - - - - - - - - 2.2 X 100 - - - 8.2 X 10-1 3.8 X 10-1 DI 3504 6.8 X 101 - - - - - - - - 7.9 X 10-1 - 1.4 X 10-2 - 2.9 X 10-1 1.7 X 10-1 DI 4004 8.3 X 101 - - - - - - - - 2.3 X 100 - 7.9 X 10-3 - 8.6 X 10-1 6.1 X 10-1 DI 4504 9.9 X 101 - - - - - - - - 5.6 X 10-1 - 2.3 X 100 - 2.6 X 10-1 2.2 X 10-1 DI 5004 1.2 X 102 - - - - - - - - 1.4 X 100 - 2.5 X 100 2.4 X 100 6.3 X 10-1 6.3 X 10-1 DI 6004 1.5 X 102 - - - - - - - - 1.4 X 100 - 3.9 X 100 - 6.1 X 10-1 8.1 X 10-1 DI 7504 2.1 X 102 - - 1.7 X 10-1 - - - - - 4.4 X 10-1 - - - 1.7 X 10-1 3.1 X 10-1 HDPE 255 3.6 X 10-1 - - - - - - - 3.6 X 10-3 - - - - 8.8 X 10-4 3.8 X 10-6 HDPE 505 1.2 X 100 - - - - - - - 4.0 X 10-2 1.1 X 100 - - - 4.2 X 10-1 6.0 X 10-3 HDPE 755 2.5 X 100 - - - - - - - - 1.1 X 100 1.7 X 10-2 - - 4.0 X 10-1 1.2 X 10-2 HDPE 5005 8.3 X 101 - - - - - - - - - - - - - - HDPE 12005 2.7 X 102 - - - - - - - - - 1.6 X 10-2 - - 4.1 X 10-4 1.3 X 10-3 FRP 1506 5.7 X 100 - - - - - - - - 6.0 X 10-2 - - - 2.2 X 10-2 5.1 X 10-3 FRP 2006 7.0 X 100 - - - - - - - - 3.9 X 10-1 - - - 1.5 X 10-1 4.1 X 10-2 FRP 3006 9.0 X 100 - - - - - - - - 4.9 X 10-1 - - - 1.8 X 10-1 6.6 X 10-2 FRP 5006 2.5 X 101 - - - - - - - - - - - - - - PVC 254 2.4 X 10-1 - - - - - - - 1.0 X 100 - - - - 2.5 X 10-1 1.4 X 10-3 PVC 324 3.3 X 10-1 - - - - - - - 3.3 X 10-1 - - - - 8.1 X 10-2 6.1 X 10-4 155 PVC 384 4.2 X 10-1 - - - - - - - 2.3 X 100 - - - - 5.6 X 10-1 5.4 X 10-3 PVC 504 6.4 X 10-1 1.5 X 100 1.6 X 100 - 3.8 X 10-1 6.3 X 10-2 4.2 X 10-1 2.1 X 10-1 4.0 X 100 4.6 X 100 1.0 X 100 6.6 X 10-1 - 2.9 X 100 4.3 X 10-2 PVC 754 1.4 X 100 5.2 X 10-1 5.8 X 10-1 - 3.7 X 10-1 9.4 X 10-2 3.5 X 10-1 7.6 X 10-1 2.3 X 10-1 3.5 X 100 9.3 X 10-2 5.9 X 10-1 - 1.5 X 100 4.7 X 10-2 PVC 1004 2.2 X 100 - - - - - 1.1 X 10-2 - 6.1 X 100 4.1 X 100 2.2 X 10-1 4.7 X 10-1 - 3.1 X 100 1.6 X 10-1 PVC 1504 4.8 X 100 2.0 X 10-1 - 9.4 X 10-1 4.5 X 10-1 - 2.8 X 10-1 - 2.1 X 101 3.4 X 101 6.5 X 10-1 4.8 X 10-1 9.5 X 10-3 1.8 X 101 1.9 X 100 PVC 2004 8.1 X 100 - - - - - - - 1.0 X 101 3.6 X 101 - 1.9 X 10-2 4.7 X 10-1 1.6 X 101 2.9 X 100 PVC 2504 1.3 X 101 - - 7.5 X 10-1 - - - - 3.5 X 100 1.2 X 101 1.9 X 100 - 9.2 X 10-1 5.4 X 100 1.6 X 100 PVC 3004 1.8 X 101 - - 1.3 X 10-1 - - - - 3.8 X 100 8.9 X 100 - - - 4.3 X 100 1.7 X 100 PVC 3504 2.1 X 101 - - 9.0 X 10-2 - - 3.7 X 10-2 - - 3.5 X 10-3 - 7.1 X 10-1 2.0 X 10-2 9.7 X 10-3 PVC 4004 2.8 X 101 - - 4.0 X 10-1 - - - - 5.4 X 10-1 2.4 X 10-2 - 2.8 X 10-1 5.8 X 10-1 1.7 X 10-1 1.1 X 10-1 PVC 4504 3.6 X 101 - - - - - - - - - 3.1 X 10-2 - 6.6 X 10-4 5.4 X 10-4 PVC 5004 4.4 X 101 - - - - - - - - 4.3 X 10-1 - - - 1.6 X 10-1 1.6 X 10-1 PVC 7504 8.0 X 101 - - - - - - - 7.6 X 10-1 - - - - 1.9 X 10-1 3.4 X 10-1 PVC 9004 1.1 X 102 - - - - - - - - - 1.2 X 100 - - 3.2 X 10-2 8.1 X 10-2 POLY 254 2.4 X 10-1 - - - - - - - 9.0 X 10-2 - - - - 2.2 X 10-2 1.2 X 10-4 POLY 384 4.2 X 10-1 - - - - - - - 1.8 X 10-1 - - - - 4.3 X 10-2 4.1 X 10-4 POLY 504 6.4 X 10-1 - - - - - - - 2.2 X 10-1 - - - - 5.3 X 10-2 7.8 X 10-4 Steel 1007 1.0 X 101 - - - - - - - 8.7 X 10-2 3.2 X 10-1 1.4 X 10-2 1.7 X 10-2 - 1.4 X 10-1 1.1 X 10-2 Steel 1507 1.9 X 101 - - - - - - - 9.8 X 10-3 3.2 X 100 1.2 X 10-2 2.5 X 10-3 - 1.2 X 100 1.6 X 10-1 Steel 2007 3.3 X 101 - - - - - - - - 8.6 X 10-2 2.2 X 10-2 3.1 X 10-3 - 3.3 X 10-2 9.3 X 10-3 Steel 2507 4.2 X 101 - - - - - - - - 1.4 X 10-1 - - - 5.3 X 10-2 1.9 X 10-2 Steel 3007 5.0 X 101 - - - - - - - - 3.2 X 10-1 2.2 X 10-3 1.0 X 10-2 - 1.2 X 10-1 5.2 X 10-2 Steel 3507 6.8 X 101 - - - - - - - - 2.3 X 10-2 - 7.2 X 10-3 - 8.9 X 10-3 5.1 X 10-3 Steel 4007 7.8 X 101 - - - - - - - - 2.1 X 100 - - - 7.9 X 10-1 5.3 X 10-1 Steel 4507 8.8 X 101 - - - - - - - - 3.5 X 10-2 - - - 1.3 X 10-2 9.9 X 10-3 Steel 5007 1.2 X 102 - - - - - - - - 2.0 X 10-3 - - 7.1 X 10-3 9.3 X 10-4 9.3 X 10-4 Steel 6007 1.4 X 102 - - - - - - - - 2.2 X 10-3 - 4.3 X 10-3 - 9.1 X 10-4 1.1 X 10-3 156 Steel 7507 2.3 X 102 - - - - - - - - 2.1 X 10-3 - - - 8.0 X 10-4 1.6 X 10-3 Steel 8507 2.7 X 102 - - - - - - - 4.0 X 10-1 - - - - 9.7 X 10-2 2.2 X 10-1 Steel 9007 2.8 X 102 - - - - - - - 8.4 X 10-1 - - - - 2.1 X 10-1 5.0 X 10-1 Steel 10007 3.3 X 102 - - - - - - - - 4.9 X 10-2 - - - 1.8 X 10-2 5.1 X 10-2 Steel 10507 3.5 X 102 - - - - - - - 5.6 X 100 2.1 X 10-3 - - - 1.4 X 100 4.1 X 100 Steel 12007 4.3 X 102 - - - - - - - - 5.1 X 10-2 1.5 X 10-1 - - 2.3 X 10-2 8.5 X 10-2 Steel 20007 9.2 X 102 - - - - - - - - - 9.4 X 10-2 - - 2.4 X 10-3 1.9 X 10-2 GALV 508 2.7 X 100 - - - - - - - 7.8 X 10-2 - - - - 1.9 X 10-2 6.7 X 10-4 GALV 628 5.8 X 100 - - - - - - - 7.3 X 10-2 - - - - 1.8 X 10-2 1.3 X 10-3 GALV 1008 1.1 X 101 - - - - - - - 1.5 X 10-1 - - - - 3.7 X 10-2 5.1 X 10-3 GALV 1509 1.9 X 101 - - - - - - - 4.5 X 10-3 - - - - 1.1 X 10-3 2.7 X 10-4 GALV 2009 2.9 X 101 - - - - - - - 8.7 X 10-1 - - - - 2.1 X 10-1 7.8 X 10-2 GALV 6009 9.5 X 101 - - - - - - - - - - - - - - RCCP 4504 2.2 X 102 - - - - - - - 1.7 X 100 - - - - 4.1 X 10-1 2.9 X 10-1 RCCP 5004 3.0 X 102 - - - - - - - 1.2 X 100 - - - - 3.1 X 10-1 3.1 X 10-1 RCCP 6004 3.9 X 102 - - - - - - - 1.5 X 100 - - - - 3.7 X 10-1 4.9 X 10-1 RCCP 7504 5.7 X 102 - - - - - - - 2.0 X 100 - - - - 4.9 X 10-1 9.2 X 10-1 1 Density values retrieved from Taylor Walraven (ND) 2 Density values for concrete cylinder pipes retrieved from Forterra (2020) 3 Density values for cast iron pipes retrieved from Cooper Industries (2020) 4 Density values for ductile iron (DI), PVC, POLY, and RCCP pipes retrieved from Fei et al. (2013). PVC 38 and 75 density values were based on PVC 40 and 80 values in the source material, respectively. 5 Density values for high-density polyethylene (HDPE) pipes retrieved from JM Eagle (2013) 6 Density values for fiberglass reinforced plastic (FRP) pipes retrieved from Future Pipe Industries (2017) 7 Density values for steel pipes retrieved from Pipe Flow Calculations (2020) 8 Density values for galvanized steel (GALV) pipes retrieved from (ANS Steel Co. 2008) 157 Appendix B Chemical names and volumes used in the coagulation, flocculation, and clarification processes at the Duteau water treatment plant (WTP) were provided by the Regional District of North Okanagan (RDNO) and reported in Table 24. An average of the doses in mg L-1 for each chemical in Table 24 was used to determine the volume of chemicals used per functional unit of one cubic meter of treated water (second column, Table 25). All other chemicals not listed in Table 24 were provided as averages from the 2018 irrigation season. Transformations to these data are described in Table 25 and Table 26. Table 24 Duteau WTP chemical usage during the irrigation season as reported by the Regional District of North Okanagan. All doses in mg L-1. Note all but caustic 2 are mostly removed from the water before entering the distribution system. Chemical: polyaluminium chloride Magnafloc LT-7990 1) Caustic 50% NaOH 2) Caustic 50% NaOH Flocculation polymer HF 708E Supplier : Kemira-Washington BASF Chemical company Univar from China Univar from China Waterhouse chemicals 2017 May 77 3.8 4.9 7.5 0.5 June 59 3 4.8 6.9 0.4 July 57 2.5 4.8 6.2 0.3 Aug 58 2.5 4.9 5.5 0.3 Sept 57 2.3 4.5 5.5 0.3 2018 May 72 3.5 5.4 5 0.6 June 65 3.2 5.6 5 0.5 July 61 3 5.3 3 0.35 Aug 58 3 5.7 3 0.3 Sept 57 3 6 3 0.3 2019 May 53 2.6 2.4 10 0.4 June 47 2.3 2.7 4 0.4 July 47 2.3 2.8 1.5 0.3 Aug 49 2.5 3.1 1.7 0.3 Sept 48 2.4 3.6 1.5 0.3 158 Table 25 Duteau WTP reported volumed of chemicals used during coagulation, flocculation, and clarification processes. Chemical brand names were converted to analogs available in openLCA using active ingredient names listed in the MSDS sheets of each chemical. Calculations and modelling choice justifications are provided in the last column. Chemical name Volume reported (mg FU-1) Active ingredient Source Analog available in openLCA Volume in inventory (mg FU-1) Explanation Magnafloc LT-7990 2.8 X 103 49-51% 1,2-Ethanediamine, polymer with (chloromethyl)oxirane and N-methylmethanamine Magnafloc LT-7991 safety data sheet epichlorohydrin 1.4 X 103 MSDS describes chemical nature as an “aqueous epichlorohydrin amine”. The average of the 49-51% concentration of the active ingredient was 50%. Therefore the volume used in the inventory was 50% of the volume reported for Magnafloc LT-7990 Flocculation Polymer HF 708E 3.7 X 102 20-45% ICP solvent (kerosene - jet fuel type); 5% cationic polyacrylamid co-polymer W-Hyrdofloc 708E safety datasheet from Waterhouse Environmental Services Corporation; ICP safety datasheet from Supleco kerosene 1.2 X 102 Average of 20-45% ICP (kerosene) is 32.5%. Therefore the volume used in the inventory was 32.5% of the volume reported for Flocculation Polymer HF 708E. polyacrylamide 1.9 X 101 Polyacrylamide is within the same organic polymer family as polyacrylamid co-polymer listed in MSDS. The volume used in inventory is 5% of the volume reported for Flocculation Polymer HF 708E. Zetag 8165 4.7 X 10-1 copolymer of acrylomide quarternized cationic monomer; 90% ethaniminium; 3% adipic acid Zetag 8165 safety data sheet polyacrylamide 4.3 X 102 No occurrence of ethaniminium or its chemical synonyms were found in the openLCA nexus. Zetage8165 safety data sheet describes the chemical nature as a “copolymer of acrylomide and quarternized cationic monomer”. Polyacrylomide is a polymer of acrylomide. Therefore polyacrylamide was used as an analog at 90% of the volume of Zetag 8165 reported Adipic acid 1.4 X101 The volume used in the inventory was 3% of the volume reported for Zetag 8165 Polyaluminium chloride 5.8 X 104 polyaluminium chloride ((Aln(OH)mCl(3n-m))x) Kemira-Washington polyaluminium chloride 5.8 X 104 - (1) and (2) Caustic 50% NaOH 9.1 X 103 Sodium hydroxide (NaOH) in aqueous solution Univar sodium hydroxide, without water, in 50% solution state 9.1 X103 - 159 Table 26 Input materials and volumes required by the DE NORA Clortec 750 on-site hypochlorite generator to produce 0.8% hypochlorite solution used during disinfection at the Duteau WTP. On-site hypochlorite Generator Amount (m-3 of 0.8% Hypochlorite solution) Source Active ingredient available in openLCA Amount (m-3 of treated water) Explanation 0.8 % hypochlorite solution (m3) -1 DE NORA; RDNO 0.8% hypochlorite solution 4.70 X 10-4 3.75 mg L-1 of 100% hypochlorite was reported as the average dosage for treatment at the Duteau WTP. This equates to 468.75 mg L-1 at 0.8% solution. Converting mg L-1 to m3 m-3 yields 4.7 X 10-4 m3 of 0.8% solution m-3 of treated water. Sodium chloride (NaCl) (kg) 24 DE NORA Sodium chloride (NaCl) 1.1 X 10-1 The DE NORA ClorTec 750 on site generator consumes 3 kg NaCl kg-1 free active chlorine (FAC). 0.125 m3 of 0.8% hypochlorite solution is produced per kg of FAC. Therefore, 24 kg of NaCl were required to produce 1 m3 of 0.8% hypochlorite solution (3 x 0.125). 4.7 X 10-4 m3 of 0.8% solution is required to treat 1 m3 of water (see above row). Therefore, the product of multiplying 24 kg of NaCl by 4.7 X 10-4 m3 yields the amount of salt required per m3 of treated water. Hydrogen gas (kg) -2.3 X 10-1 DE NORA Hydrogen gas (emission to air) -1.1 X 10-4 The DE NORA ClorTec 750 on site generator produces 2.3 X 10-2 lbs. of Hydrogen gas per 15 gallons of hypochlorite solution. This equates to 1.3 X 10-2 kg of hydrogen gas. Converting gallons to cubic meters by a constant of 3.8 X 10-3 and dividing this number from 1.3 X 10-2 kg of hydrogen gas results in 2.3 kg of hydrogen gas m-3 of output solution. This value was multiplied by the amount of 0.8% hypochlorite solution required per m3 of treated water (4.7 X 10-4 m3) and resulted in the amount of hydrogen gas emitted per m3 of treated water. Vinegar / Muriatic acid (m3) 2.3 X 10-6 RDNO; acetic acid safety data sheet 98% acetic acid (mg) 7.9 X 101 The amount of vinegar and muriatic acid used for cleaning the generator were provided as one volume. Thus it was assumed that each agent was used in equal proportions. Vinegar is comprised of 5-8% acetic acid. The average of this (6.5%) was calculated and used as the concentration of acetic acid in vinegar. Thus the volume of acetic acid was (2.3 X 10-6/2)*0.065. The product of this calculation was then converted to a 98% concentration as that was the concentration of the acetic acid flow available in openLCA. The volume was further converted to mg using a density of 1048 kg m-3. RDNO; hydrochloric acid safety data sheet 30% hydrochloric acid (mg) 8.7 X 10-7 Muriatic acid ranges from 14.5%-31.5% HCl. The average (23%) was assumed to be the concentration of HCl. The total value for vinegar and muriatic acid was divided evenly between the two (0.5x2.2e-6). This volume was then multiplied by 0.35 to calculate the volume of HCl. The volume of 100% HCl was then converted from m3 to mg using a density of 6.1 X 10-7 m3 mg-1. This calculation yields 1.42 mg of HCl, which was then converted to a 30% concentration for input into openLCA. titanium plates (kg) 1.0 X 102 DE NORA titanium, primary 2.32 X 10-3 The lifespan of titanium plates used in the ClorTec 750 on site generator is 10 years at an 80% capacity. The weight of the plates was divided by the lifespan of the plates times the annual volume of solution passing through the generator (4.7 X 10-4 m3 of hypochlorite solution m-3 of treated water x average annual volume of water treated at Duteau WTP). 160 Appendix C Table 27 Impact scores (Mean) and standard deviations (SD) for all Okanagan water delivery system scenarios and for all 14 impact categories assessed in the research performed in Chapter 2. Standard deviations were derived from Monte Carlo simulations (n=1000) of each LCIA model scenarios. Climate change, long term (kg CO2 eq) Climate change, short term (kg CO2 eq) Fossil and nuclear energy use (MJ deprived) Freshwater acidification (kg SO2 eq) Freshwater ecotoxicity (CTUe) Freshwater eutrophication (lim eq) Human toxicity cancer (CTUh) Human toxicity non cancer (CTUh) Marine eutrophication (kg N N -lim eq) Mineral resources use (kg deprived) Ozone Layer Depletion (kg CFC-11 eq) Photochemical oxidant formation (kg NMVOC eq) Terrestrial acidification (kg SO2 eq) Water scarcity (m3 world -eq) Production Weighted Okanagan Average Mean 1.8E-1 2.0 X 10-1 2.6 X 10+0 5.4 X 10-15 4.7 X 10+3 5.7 X 10-7 5.2 X 10-8 6.8 X 10-8 2.1 X 10-5 1.1 X 10-2 2.2 X 10-8 6.6 X 10-4 3.4 X 10-9 8.5 X 10+0 SD 1.7 X 10-1 1.8 X 10-1 2.5 X 10+0 5.0 X 10-15 2.1 X 104 5.1 X 10-7 2.1 X 10-7 3.9 X 10-7 1.6 X 10-5 1.8 X 10-2 1.4 X 10-8 5.6 X 10-4 3.1 X 10-9 1.9 X 101 No Treatment Mean 1.1 X 10-1 1.2 X 10-1 1.7 X 100 2.8 X 10-15 9.3 X 102 3.2 X 10-7 2.9 X 10-8 3.8 X 10-8 7.9 X 10-6 1.0 X 10-2 7.9 X 10-9 3.5 X 10-4 1.7 X 10-9 8.4 X 100 SD 1.6 X 10-1 1.8 X 10-1 2.4 X 100 4.9 X 10-15 2.6 X 104 5.0 X 10-7 1.3 X 10-7 2.5 X 10-7 3.4 X 100 1.9 X 10-2 1.3 X 10-8 5.3 X 10-4 3.0 X 10-9 9.1 X 100 Delivered Chlorine Treatment Mean 1.3 X 10-1 1.4 X 10-1 2.0 X 100 3.3 X 10-15 1.3 X 103 3.5 X 10-7 3.1 X 10-8 4.5 X 10-8 9.0 X 10-6 1.0 X 10-2 1.4 X 10-8 4.1 X 10-4 2.1 X 10-9 8.5 X 100 SD 1.7 X 10-1 1.8 X 10-1 2.5 X 100 5.1 X 10-15 1.3 X 105 5.2 X 10-7 8.2 X 10-8 9.1 X 10-7 1.6 X 10-5 1.8 X 10-2 1.4 X 10-8 5.5 X 10-4 3.2 X 10-9 9.8 X 100 Delivered Hypochlorite Treatment Mean 1.9 X 10-1 2.0 X 10-1 2.8 X 100 4.3 X 10-15 2.6 X 103 4.0 X 10-7 3.7 X 10-8 6.2 X 10-8 1.1 X 10-5 1.2 X 10-2 2.0 X 10-8 5.1 X 10-4 2.7 X 10-9 9.1 X 100 SD 1.7 X 10-1 1.9 X 10-1 2.6 X 100 6.0 X 10-15 2.4 X 105 5.5 X 10-7 3.7 X 10-7 3.5 X 10-7 1.6 X 10-5 1.8 X 10-2 1.7 X 10-8 5.6 X 10-4 3.8 X 10-9 1.7 X 101 Generated Chlorine Treatment Mean 3.3 X 10-1 3.4 X 10-1 4.9 X 100 6.2 X 10-15 3.0 X 103 5.1 X 10-7 4.7 X 10-8 7.8 X 10-8 1.4 X 10-5 1.4 X 10-2 2.5 X 10-8 7.7 X 10-4 4.0 X 10-9 1.1 X 101 SD 1.7 X 10-1 1.9 X 10-1 2.6 X 100 4.9 X 10-15 3.0 X 105 6.1 X 10-7 3.1 X 10-7 1.5 X 10-6 1.6 X 10-5 1.8 X 10-2 1.7 X 10-8 5.7 X 10-4 3.1 X 10-9 4.8 X 101 Treatment Plant Mean 3.0 X 10-1 3.4 X 10-1 4.2 X 100 9.8 X 10-15 1.1 X 104 1.0 X 10-6 9.4 X 10-8 1.2 X 10-7 4.5 X 10-5 1.4 X 10-2 4.5 X 10-8 1.2 X 10-3 6.2 X 10-9 8.6 X 100 SD 2.0 X 10-1 2.2 X 10-1 2.9 X 100 6.0 X 10-15 1.2 X 105 5.9 X 10-7 3.2 X 10-7 4.4 X 10-7 2.3 X 10-5 1.9 X 10-2 2.2 X 10-8 6.6 X 10-4 3.8 X 10-9 1.2 X 101 Pumped Mean 2.0 X 10-1 2.2 X 10-1 2.9 X 100 5.6 X 10-15 4.8 X 103 5.8 X 10-7 5.4 X 10-8 7.0 X 10-8 2.2 X 10-5 1.2 X 10-2 2.4 X 10-8 6.9 X 10-4 3.6 X 10-9 8.6 X 100 SD 1.6 X 10-1 1.8 X 10-1 2.5 X 100 4.9 X 10-15 2.2 X 104 5.2 X 10-7 1.1 X 10-7 4.3 X 10-7 1.6 X 10-5 1.6 X 10-2 1.5 X 10-8 5.3 X 10-4 3.1 X 10-9 1.1 X 101 Mixed (Pumped and Gravity fed) Mean 1.9 X 10-1 2.1 X 10-1 2.8 X 100 5.5 X 10-15 4.8 X 103 5.8 X 10-7 5.3 X 10-8 6.9 X 10-8 2.2 X 10-5 1.2 X 10-2 2.3 X 10-8 6.8 X 10-4 3.5 X 10-9 8.6 X 100 SD 1.6 X 10-1 1.8 X 10-1 2.5 X 100 4.8 X 10-15 1.1 X 105 5.0 X 10-7 1.4 X 10-6 1.0 X 10-6 1.6 X 10-5 1.6 X 10-2 1.4 X 10-8 5.4 X 10-4 3.0 X 10-9 8.9 X 100 Gravity fed Mean 1.7 X 10-1 1.9 X 10-1 2.5 X 100 5.2 X 10-15 4.6 X 103 5.6 X 10-7 5.1 X 10-8 6.6 X 10-8 2.1 X 10-5 1.1 X 10-2 2.1 X 10-8 6.3 X 10-4 3.3 X 10-9 8.3 X 100 SD 1.8 X 10-1 1.9 X 10-1 2.9 X 100 5.2 X 10-15 5.6 X 104 5.4 X 10-7 1.6 X 10-7 3.5 X 10-7 1.7 X 10-5 2.0 X 10-2 1.4 X 10-8 6.2 X 10-4 3.3 X 10-9 1.1 X 101 Mean 2.5 X 10-1 2.7 X 10-1 3.4 X 100 6.9 X 10-15 6.0 X 103 7.4 X 10-7 6.8 X 10-8 8.7 X 10-8 2.8 X 10-5 1.5 X 10-2 2.9 X 10-8 8.5 X 10-4 4.4 X 10-9 1.1 X 101 161 Climate change, long term (kg CO2 eq) Climate change, short term (kg CO2 eq) Fossil and nuclear energy use (MJ deprived) Freshwater acidification (kg SO2 eq) Freshwater ecotoxicity (CTUe) Freshwater eutrophication (lim eq) Human toxicity cancer (CTUh) Human toxicity non cancer (CTUh) Marine eutrophication (kg N N -lim eq) Mineral resources use (kg deprived) Ozone Layer Depletion (kg CFC-11 eq) Photochemical oxidant formation (kg NMVOC eq) Terrestrial acidification (kg SO2 eq) Water scarcity (m3 world -eq) Production Weighted Irrigation SD 2.4 X 10-1 2.6 X 10-1 3.7 X 100 8.6 X 10-15 1.3 X 105 8.8 X 10-7 1.6 X 10-7 2.2 X 10-6 2.3 X 10-5 2.6 X 10-2 3.1 X 10-8 8.5 X 10-4 5.4 X 10-9 1.2 X 101 Handline Irrigation Mean 2.7 X 10-1 3.0 X 10-1 3.8 X 100 7.7 X 10-15 6.7 X 103 8.2 X 10-7 7.5 X 10-8 9.7 X 10-8 3.1 X 10-5 1.6 X 10-2 3.2 X 10-8 9.4 X 10-4 4.9 X 10-9 1.2 X 101 SD 2.4 X 10-1 2.6 X 10-1 3.5 X 100 7.1 X 10-15 4.5 X 104 7.4 X 10-7 3.5 X 10-7 1.0 X 10-6 2.2 X 10-5 3.0 X 10-2 2.0 X 10-8 8.4 X 10-4 4.4 X 10-9 1.2 X 101 Sprinkler Irrigation Mean 2.7 X 10-1 2.9 X 10-1 3.7 X 100 7.5 X 10-15 6.5 X 103 7.9 X 10-7 7.3 X 10-8 9.4 X 10-8 3.0 X 10-5 1.6 X 10-2 3.1 X 10-8 9.1 X 10-4 4.7 X 10-9 1.2 X 101 SD 2.4 X 10-1 2.6 X 10-1 3.7 X 100 7.5 X 10-15 4.0 X 104 9.2 X 10-7 1.9 X 10-7 9.2 X 10-7 2.5 X 10-5 2.6 X 10-2 3.1 X 10-8 1.2 X 10-3 4.8 X 10-9 1.4 X 101 Microspray Irrigation Mean 2.2 X 10-1 2.4 X 10-1 3.0 X 100 6.1 X 10-15 5.3 X 103 6.5 X 10-7 6.0 X 10-8 7.7 X 10-8 2.4 X 10-5 1.3 X 10-2 2.6 X 10-8 7.5 X 10-4 3.9 X 10-9 9.6 X 100 SD 1.7 X 10-1 1.9 X 10-1 2.8 X 100 5.1 X 10-15 2.8 X 105 5.4 X 10-7 2.1 X 10-7 5.7 X 10-7 1.6 X 10-5 1.8 X 10-2 1.5 X 10-8 5.9 X 10-4 3.2 X 10-9 1.2 X 101 Microsprinkler Irrigation Mean 2.5 X 10-1 2.7 X 10-1 3.4 X 100 6.9 X 10-15 6.0 X 103 7.3 X 10-7 6.7 X 10-8 8.7 X 10-8 2.7 X 10-5 1.5 X 10-2 2.9 X 10-8 8.4 X 10-4 4.4 X 10-9 1.1 X 101 SD 6.5 X 10-1 6.9 X 10-1 1.0 X 101 2.8 X 10-14 2.1 X 105 2.7 X 10-6 2.1 X 10-7 2.0 X 10-6 5.0 X 10-5 2.8 X 10-2 9.1 X 10-8 2.6 X 10-3 1.8 X 10-8 1.4 X 101 Solid-set undertree Irrigation Mean 2.6 X 10-1 2.8 X 10-1 3.6 X 100 7.3 X 10-15 6.3 X 103 7.7 X 10-7 7.1 X 10-8 9.1 X 10-8 2.9 X 10-5 1.5 X 10-2 3.0 X 10-8 8.8 X 10-4 4.6 X 10-9 1.1 X 101 SD 2.3 X 10-1 2.5 X 10-1 3.8 X 100 6.7 X 10-15 5.4 X 104 7.4 X 10-7 6.2 X 10-7 7.0 X 10-7 2.2 X 10-5 2.4 X 10-2 2.0 X 10-8 8.2 X 10-4 4.2 X 10-9 1.3 X 101 Drip Irrigation Mean 2.1 X 10-1 2.3 X 10-1 2.9 X 100 5.9 X 10-15 5.1 X 103 6.2 X 10-7 5.7 X 10-8 7.4 X 10-8 2.3 X 10-5 1.2 X 10-2 2.4 X 10-8 7.1 X 10-4 3.7 X 10-9 9.2 X 100 SD 1.9 X 10-1 2.0 X 10-1 3.1 X 100 5.6 X 10-15 3.7 X 104 6.4 X 10-7 2.9 X 10-7 3.4 X 10-7 1.7 X 10-5 1.9 X 10-2 1.7 X 10-8 6.7 X 10-4 3.5 X 10-9 1.1 X 101 SDI Irrigation Mean 2.0 X 10-1 2.2 X 10-1 2.8 X 100 5.7 X 10-15 4.9 X 103 6.0 X 10-7 5.5 X 10-8 7.1 X 10-8 2.3 X 10-5 1.2 X 10-2 2.4 X 10-8 6.9 X 10-4 3.6 X 10-9 8.9 X 100 SD 2.1 X 10-1 2.3 X 10-1 3.1 X 100 6.0 X 10-15 4.4 X 104 6.3 X 10-7 1.6 X 10-7 8.5 X 10-7 2.0 X 10-5 2.3 X 10-2 1.8 X 10-8 6.9 X 10-4 3.8 X 10-9 1.4 X 101 162 Table 28 Impact scores (mean) and standard deviations (SD) of Okanagan water delivery system scenarios performed in the sensitivity analyses reported in Chapter 2. Standard deviations were derived from Monte Carlo simulations (n=1000) of each LCIA model scenarios. Climate change, long term (kg CO2 eq) Climate change, short term (kg CO2 eq) Fossil and nuclear energy use (MJ deprived) Freshwater acidification (kg SO2 eq) Freshwater ecotoxicity (CTUe) Freshwater eutrophication (lim eq) Human toxicity cancer (CTUh) Human toxicity non cancer (CTUh) Marine eutrophication (kg N N -lim eq) Mineral resources use (kg deprived) Ozone Layer Depletion (kg CFC-11 eq) Photochemical oxidant formation (kg NMVOC eq) Terrestrial acidification (kg SO2 eq) Water scarcity (m3 world -eq) 12% Loss at End Mean 1.7 X 10-1 1.9 X 10-1 2.4 X 100 5.0 X 10-15 4.3 X 103 5.3 X 10-7 4.9 X 10-8 6.3 X 10-8 2.0 X 10-5 1.1 X 10-2 2.1 X 10-8 6.1 X 10-4 3.2 X 10-9 7.8 X 100 SD 1.5 X 10-1 1.7 X 10-1 2.3 X 100 4.6 X 10-15 4.7 X 104 4.7 X 10-7 2.8 X 10-7 3.4 X 10-7 1.4 X 10-5 1.5 X 10-2 1.3 X 10-8 5.0 X 10-4 2.9 X 10-9 1.1 X 101 12% Loss at Start Mean 1.5 X 10-1 1.6 X 10-1 2.1 X 100 4.4 X 10-15 3.8 X 103 4.6 X 10-7 4.3 X 10-8 5.5 X 10-8 1.7 X 10-5 9.3 X 10-3 1.8 X 10-8 5.3 X 10-4 2.8 X 10-9 7.8 X 100 SD 1.8 X 10-1 1.9 X 10-1 2.5 X 100 5.3 X 10-15 2.1 X 104 5.2 X 10-7 1.6 X 10-7 8.3 X 10-7 1.6 X 10-5 1.8 X 10-2 1.6 X 10-8 5.7 X 10-4 3.3 X 10-9 9.4 X 100 23% Loss at end Mean 1.9 X 10-1 2.1 X 10-1 2.8 X 100 5.7 X 10-15 4.9 X 103 6.0 X 10-7 5.5 X 10-8 7.1 X 10-8 2.3 X 10-5 1.2 X 10-2 2.4 X 10-8 6.9 X 10-4 3.6 X 10-9 8.9 X 100 SD 1.9 X 10-1 2.1 X 10-1 2.8 X 100 5.7 X 10-15 1.1 X 105 5.9 X 10-7 2.2 X 10-7 5.1 X 10-7 1.8 X 10-5 2.1 X 10-2 1.6 X 10-8 6.2 X 10-4 3.6 X 10-9 1.1 X 101 23% Loss at Start Mean 1.5 X 10-1 1.6 X 10-1 2.1 X 100 4.4 X 10-15 3.8 X 103 4.6 X 10-7 4.3 X 10-8 5.5 X 10-8 1.7 X 10-5 9.3 X 10-3 1.8 X 10-8 5.3 X 10-4 2.8 X 10-9 8.8 X 100 SD 1.5 X 10-1 1.7 X 10-1 2.3 X 100 4.5 X 10-15 2.0 X 104 4.5 X 10-7 1.9 X 10-7 3.3 X 10-7 1.4 X 10-5 1.6 X 10-2 1.4 X 10-8 5.1 X 10-4 2.8 X 10-9 7.2 X 100 Alberta Electricity at Treatment Plant Mean 3.9 X 10-1 4.3 X 10-1 5.3 X 100 1.2 X 10-14 1.2 X 104 1.0 X 10-6 1.1 X 10-7 1.4 X 10-7 4.7 X 10-5 1.4 X 10-2 4.7 X 10-8 1.3 X 10-3 7.8 X 10-9 8.5 X 100 SD 1.7 X 10-1 1.8 X 10-1 2.4 X 100 5.5 X 10-15 7.3 X 104 5.5 X 10-7 4.5 X 10-7 1.7 X 10-6 1.9 X 10-5 2.0 X 10-2 2.0 X 10-8 6.3 X 10-4 3.5 X 10-9 9.1 X 100 BC Electricity at Treatment Plant Mean 3.0 X 10-1 3.4 X 10-1 4.2 X 100 9.8 X 10-15 1.1 X 104 1.0 X 10-6 9.4 X 10-8 1.2 X 10-7 4.5 X 10-5 1.4 X 10-2 4.5 X 10-8 1.2 X 10-3 6.2 X 10-9 8.6 X 100 SD 2.0 X 10-1 2.2 X 10-1 2.9 X 100 6.0 X 10-15 1.2 X 105 5.9 X 10-7 3.2 X 10-7 4.4 X 10-7 2.3 X 10-5 1.9 X 10-2 2.2 X 10-8 6.6 X 10-4 3.8 X 10-9 1.2 X 101 163 Table 29 Impact scores (mean) and standard deviations (SD) across all 14 impact categories and for all Okanagan cherry production system scenarios reported in Chapter 2. For all scenarios except for the Quebec water delivery system (WDS) scenario, a version of the Okanagan water delivery system was used as the provider for irrigation. Standard deviations were derived from Monte Carlo simulations (n=1000) of each LCIA model scenarios. Climate change, long term (kg CO2 eq) Climate change, short term (kg CO2 eq) Fossil and nuclear energy use (MJ deprived) Freshwater acidification (kg SO2 eq) Freshwater ecotoxicity (CTUe) Freshwater eutrophication (lim eq) Human toxicity cancer (CTUh) Human toxicity non cancer (CTUh) Marine eutrophication (kg N N -lim eq) Mineral resources use (kg deprived) Ozone Layer Depletion (kg CFC-11 eq) Photochemical oxidant formation (kg NMVOC eq) Terrestrial acidification (kg SO2 eq) Water scarcity (m3 world -eq) Production Weighted Okanagan Average WDS Mean 2.1 X 10-1 2.2 X 10-1 2.9 X 100 6.0 X 10-15 2.3 X 103 8.8 X 10-7 2.3 X 10-8 4.1 X 10-8 4.8 X 10-4 6.2 X 10-3 2.2 X 10-8 8.2 X 10-4 4.0 X 10-9 3.0 X 100 SD 2.1 X 10-2 2.2 X 10-2 8.2 X 10-2 2.1 X 10-16 3.8 X 103 4.0 X 10-8 6.9 X 10-9 6.3 X 10-8 3.5 X 10-4 3.1 X 10-4 1.7 X 10-9 2.0 X 10-5 1.4 X 10-10 2.2 X 10-1 Quebec WDS Mean 1.9 X 10-1 2.0 X 10-1 2.6 X 100 5.8 X 10-15 1.3 X 103 7.9 X 10-7 1.2 X 10-8 3.0 X 10-8 4.7 X 10-4 4.4 X 10-3 2.0 X 10-8 7.3 X 10-4 3.8 X 10-9 1.1 X 101 SD 2.5 X 10-2 2.5 X 10-2 1.5 X 10-1 4.3 X 10-16 3.8 X 104 5.2 X 10-8 1.9 X 10-8 2.6 X 10-7 3.5 X 10-4 6.2 X 10-4 3.0 X 10-9 3.6 X 10-5 2.7 X 10-10 3.1 X 100 Okanagan WDS with No treatment Mean 1.9 X 10-1 2.0 X 10-1 2.6 X 100 5.2 X 10-15 1.2 X 103 8.0 X 10-7 1.6 X 10-8 3.2 X 10-8 4.7 X 10-4 5.7 X 10-3 1.7 X 10-8 7.3 X 10-4 3.4 X 10-9 2.9 X 100 SD 2.2 X 10-2 2.3 X 10-2 8.1 X 10-2 2.3 X 10-16 3.5 X 103 3.9 X 10-8 1.1 X 10-8 3.6 X 10-7 3.6 X 10-4 3.4 X 10-4 1.8 X 10-9 1.9 X 10-5 1.5 X 10-10 2.3 X 10-1 Okanagan WDS with Treatment plant Mean 2.5 X 10-1 2.6 X 10-1 3.4 X 100 7.4 X 10-15 4.3 X 103 1.0 X 10-6 3.6 X 10-8 5.7 X 10-8 4.8 X 10-4 6.9 X 10-3 2.9 X 10-8 9.8 X 10-4 4.8 X 10-9 3.0 X 100 SD 2.4 X 10-2 2.4 X 10-2 8.4 X 10-2 2.3 X 10-16 4.0 X 103 3.9 X 10-8 4.3 X 10-9 6.1 X 10-8 3.7 X 10-4 3.1 X 10-4 1.7 X 10-9 2.1 X 10-5 1.5 X 10-10 2.2 X 10-1 Handline Irrigation Mean 2.2 X 10-1 2.3 X 10-1 2.9 X 100 6.2 X 10-15 2.5 X 103 9.0 X 10-7 2.5 X 10-8 4.4 X 10-8 4.8 X 10-4 6.6 X 10-3 2.3 X 10-8 8.4 X 10-4 4.1 X 10-9 3.3 X 100 SD 2.6 X 10-2 2.6 X 10-2 7.9 X 10-2 2.2 X 10-16 4.9 X 103 3.9 X 10-8 6.9 X 10-9 1.5 X 10-7 3.3 X 10-4 3.2 X 10-4 1.7 X 10-9 2.0 X 10-5 1.4 X 10-10 2.4 X 10-1 Microsprinkler irrigation Mean 2.1 X 10-1 2.2 X 10-1 2.9 X 100 6.0 X 10-15 2.3 X 103 8.8 X 10-7 2.3 X 10-8 4.1 X 10-8 4.8 X 10-4 6.1 X 10-3 2.2 X 10-8 8.2 X 10-4 3.9 X 10-9 2.9 X 100 SD 2.2 X 10-2 2.2 X 10-2 8.1 X 10-2 2.2 X 10-16 2.9 X 103 4.0 X 10-8 7.5 X 10-9 6.8 X 10-8 3.2 X 10-4 3.2 X 10-4 1.9 X 10-9 2.0 X 10-5 1.4 X 10-10 2.2 X 10-1 SDI Irrigation Mean 2.0 X 10-1 2.1 X 10-1 2.7 X 100 5.7 X 10-15 2.1 X 103 8.5 X 10-7 2.0 X 10-8 3.7 X 10-8 4.8 X 10-4 5.5 X 10-3 2.1 X 10-8 7.8 X 10-4 3.8 X 10-9 2.5 X 100 SD 2.3 X 10-2 2.3 X 10-2 9.2 X 10-2 2.5 X 10-16 3.2 X 103 3.9 X 10-8 5.0 X 10-9 6.1 X 10-8 3.3 X 10-4 3.2 X 10-4 1.7 X 10-9 2.2 X 10-5 1.6 X 10-10 2.2 X 10-1 Solid-set undertree irrigation Mean 2.1 X 10-1 2.2 X 10-1 2.9 X 100 6.1 X 10-15 2.4 X 103 8.9 X 10-7 2.4 X 10-8 4.2 X 10-8 4.8 X 10-4 6.3 X 10-3 2.2 X 10-8 8.3 X 10-4 4.0 X 10-9 3.1 X 100 SD 2.2 X 10-2 2.2 X 10-2 7.9 X 10-2 2.3 X 10-16 1.7 X 103 3.8 X 10-8 9.9 X 10-9 1.4 X 10-7 3.3 X 10-4 3.0 X 10-4 1.7 X 10-9 2.2 X 10-5 1.5 X 10-10 2.3 X 10-1 Drip Irrigation with N2O emissions Mean 2.0 X 10-1 2.1 X 10-1 2.7 X 100 5.8 X 10-15 2.1 X 103 8.5 X 10-7 2.1 X 10-8 3.8 X 10-8 4.8 X 10-4 5.6 X 10-3 2.1 X 10-8 7.9 X 10-4 3.8 X 10-9 2.6 X 100 SD 2.2 X 10-2 2.2 X 10-2 8.1 X 10-2 2.3 X 10-16 4.7 X 103 3.9 X 10-8 7.3 X 10-9 8.1 X 10-8 4.2 X 10-4 3.2 X 10-4 1.8 X 10-9 1.9 X 10-5 1.5 X 10-10 2.3 X 10-1 Microsprinkler irrigation with N2O Emissions Mean 2.0 X 10-1 2.1 X 10-1 2.9 X 100 6.0 X 10-15 2.3 X 103 8.8 X 10-7 2.3 X 10-8 4.1 X 10-8 4.8 X 10-4 6.1 X 10-3 2.2 X 10-8 8.2 X 10-4 3.9 X 10-9 2.9 X 100 SD 1.7 X 10-2 1.7 X 10-2 8.1 X 10-2 2.2 X 10-16 2.9 X 103 3.8 X 10-8 4.6 X 10-9 8.5 X 10-8 3.6 X 10-4 3.3 X 10-4 1.8 X 10-9 1.9 X 10-5 1.4 X 10-10 2.2 X 10-1
Thesis/Dissertation
eng
Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Pelletier, Nathan
Life cycle assessment of freshwater use in the semi-arid Okanagan : building the framework for new LCIA impact pathways of freshwater use for terrestrial ecosystems and modeling irrigation water infrastructure and use in horticulture
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