— FINAL REPORT— March 3, 2000

A Study of the Lake Chelan Hydroelectric Project Based on Life-Cycle Stressor-Effects Assessment

Prepared for the Public Utility District No. 1 of Chelan County, Washington

Scientific Certification Systems Oakland, California T A B L E O F C O N T E N T S

SECTION 1 — Introduction 1

1.1. Background 1 1.1.1. Environmental Impacts of Electricity Generation 1 1.1.2. Scientific Certification Systems 1 1.1.3. The Chelan County PUD No. 1 2 1.2. Study Overview 2 1.3. Study Goals 3 1.4. Study Deliverables 4

SECTION 2 — Calculation Methodology 5

2.1. LCSEA Technical Framework 5 2.2. LCSEA Protocols 5 2.3. Key Study Indicators 9 2.3.1. Ecosystem Disruption from Direct Physical Disturbance 11 2.3.2. Other Indicators 12 2.3.2.1. Water Resource Depletion 12 2.3.2.2. Fossil Fuel and Uranium Resources Depletion 12 2.3.2.3. Greenhouse Gas Loading 13 2.3.2.4. Acidification and Ground Level Ozone Loadings 13

S ECTION 3 — Goal and Scoping Definition 14

3.1. System Function and Functional Unit 14 3.2. Study Scope 16 3.3. System Description 16 3.3.1. General Description of the Lake Chelan Project 16 3.3.1.1. Ownership and Operation 16 3.3.1.2. Physical Description of the Project 17 3.3.1.3. Location and Geography 18 3.3.2. General Description of the WSCC Power Production Pool 19 3.3.2.1. Geographic Territory 19 3.3.2.2. Electricity Generation 19 3.3.2.3. Electricity Transmission and Delivery 21 3.4. Key Assumption for Calculating the WSCC Indicators 22

Section 4 — Inventory 23

4.1. Inventory Data Sources 23 4.2. Inventory Assumptions and Conventions 23 4.2.1. Raw materials 23 4.2.2. Electricity Inputs (Dam Construction) 23 4.2.3. Fuel Inputs 24 4.2.4. Emissions to Air 24 4.2.5. Discharges to Water 24 4.2.6. Solid and Hazardous Wastes 24 4.2.7. Processes Excluded from Inventory Calculations 25 4.3. Inventory Results 25 4.3.1. Raw Material Resource Requirements 25 4.3.2. Emissions and Wastes 26 4.3.3. Comparing the LCI Profiles of the Lake Chelan Project and the WSCC Average 27

S ECTION 5 — Classification 28

5.1. Identify Potential Stressor Effects Networks 28 5.2. Assign Inputs and Outputs to Identified Networks 28

S ECTION 6 — Energy Resources Depletion 29

S ECTION 7 — Renewable and Mineral Resource Depletion 31

7.1. Water Resource Depletion 31 7.2. Net Mineral and Metal Depletion 31

S ECTION 8 — Ecosystem Disruption 32

8.1. Calculation of the Ecosystem Disruption Indicator 32 8.1.1. Measuring Deviation from Baseline 32 8.1.2. Accounting for Temporal Nature of Disruptions 32 8.1.3. Key Species 32 8.1.4. Calculating the Indicator for General Habitats 33 8.2. Lake Chelan Project Ecosystem Disruption 34 8.2.1. Terrestrial and Aquatic Habitat 34 8.2.2. Key Species 36

S ECTION 9 — Emission Loadings 37

9.1. Greenhouse Gas Loadings 37 9.2. Acidifying Chemical Loading 39 9.3. Ground Level Ozone Loading 40 9.4. Stratospheric Ozone Depletion Loading 41 9.5. Hazardous Chemical (Air) Loading 41 9.6. Eutrophication and TOC Loading 42 9.7. Total Suspended Solids Loading 42 9.8. Hazardous Aquatic Loading 42 9.9 Thermal Loading 42

S ECTION 10 — Residual Hazardous Waste 43

10.1. Ash Wastes 43 10.2. Radioactive Waste 43

S ECTION 11 — Study Results 44

11.1. Summary of LCSEA Results 44 11.2. Conclusions and Recommendations 45 11.2.1. Ecosystem Disruption Indicator 45 11.2.2. Other Indicators 46 11.2.3. Environmentally Preferable Energy Source 47 11.2.4. Recommendations 48

S ECTION 12 — Practitioner Qualifications 49

Bibliography 52

Appendices

1. Life-Cycle Stressor Effects Assessment (LCSEA): 1999 Operational Manual 2. The Lake Chelan Project Ecological Review 3. Peer Review Comments 4. SCS Response to Peer Review Comments Report Acronyms

The following acronyms appear in the study report.

AES Applied Ecological Services CFS Cubic feet per second ECF Environmental characterization factor GWh Gigawatt hour HAPS Hazardous Air Pollutants, Title III of 1990 Clear Air Act Amendments HEP Habitat Evaluation Procedure HSI Habitat Sustainability Index IOU Investor owned utilities IPCC Intergovernmental Panel on Climate Change ISO International Organization for Standardization ISO-14000 Series of environmental management standards developed by international delegates under the auspices of the ISO Technical Committee (TC) 207 ISO-14042 Life-cycle impact assessment standard created as part of the ISO-14000 series LCA Life-cycle assessment LCI Life-cycle inventory LCIA Life-cycle impact assessment LCSEA Life-cycle stressor-effects assessment MW Megawatt NMIR No Measured Indicator Result WSCC Western System Coordinating Council RDF Resource depletion factor SCS Scientific Certification Systems, Inc. SW Soil and Water, Ltd. SCF Stressor characterization factor T/A Tons per annum TOE Tons of oil equivalents, a common unit of measure applied to energy resources

ACKNOWLEDGMENTS

The following individuals made major contributions toward the completion of this study.

Scientific Certification Systems, Inc:

Stanley Rhodes (SCS) Jim Wazlaw (CNEX) Chet Chaffee (SCS) Fjalar Kommonen (Soil and Water, Ltd.) Steve Apfelbaum (Applied Ecological Systems) Linda Brown (SCS)

Chelan County PUD No. 1

Gregg Carrington

Through its support in demonstrating the use of LCSEA methodology in the study of energy generation systems, the Chelan PUD has made an important contribution to the advancement of science-based claims of environmental impacts and achievement in the energy generation sector.

Front cover photo: Mike Barnhart, Lake Chelan Chamber of Commerce Photo Library, www.lakechelan.com The Lake Chelan Project LCSEA Study Report Final Report March 3, 2000

S ECTION 1 — Introduction

1.1. Background

1.1.1. Environmental Impacts of Electricity Generation

The environmental ramifications of electric power generation have been the focus of considerable attention over the past thirty years. Depending on the energy source (e.g., coal, natural gas, hydro-power, nuclear power), a wide array of environmental issues have been raised, such as the release of gases contributing to global warming, acidification and smog; the depletion of mineral resources; the disruption of eco-systems; and the production of non-treatable hazardous wastes. These impacts can vary significantly not only between energy sources, but also among power production systems using the same energy source, depending on differences in the technologies in place, as well as differences in the surrounding environments.

As concern for the environment has grown, so too has interest in comparing and contrasting the relative environmental merits and disadvantages of various power production systems. But while many studies have been conducted and a variety of impacts have been documented, the analyses have varied considerably in terms of methodology, depth and focus, making comparisons between systems difficult to impossible. Nevertheless, government agencies and stakeholder groups have been pressing ahead with the establishment of “green power” criteria and similar initiatives in an attempt to reduce the impacts of electric power production and consumption.

These issues have highlighted the need for a uniform methodology to assess and compare the relative environmental impacts of various power production systems on a level playing field — one that is comprehensive enough in scope to encompass a broad range of environmental issues related to power production.

1.1.2. Scientific Certification Systems

Operating as an established neutral third-party certifier of environmental claims in the U.S., Scientific Certification Systems (SCS) of Oakland, California has studied the issues related to environmental claims and certification in the energy sector. Based on this research, SCS has established a program based on internationally standardized life-cycle impact assessment (LCIA) protocols to evaluate and certify the relative environmental

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merits and tradeoffs of various energy generation systems using a comprehensive, internationally standardized assessment methodology. In addition, SCS independently certifies power generation systems as “Environmentally Preferable” sources, if all relevant indicator results are lower than the comparable indicator results for the regional system power pool average.

1.1.3. The Chelan County PUD No.1

The Public Utility District No. 1 of Chelan County (the “Chelan PUD”) owns and operates the Lake Chelan Hydroelectric Project (the “Lake Chelan Project”) located in the city of Chelan, Washington, near the Columbia River. The Chelan PUD operates the hydroelectric project according to the terms and conditions contained in the existing license No. 637, issued by the Federal Energy Regulatory Commission (FERC), which expires in 2004.

1.2. Study Overview

In this study, the Chelan PUD commissioned SCS to conduct an independent assessment of the Lake Chelan Project. The purpose of this assessment was to demonstrate a scientific approach for evaluating and reporting the relative environmental impacts of a specific energy generation source.

For the study, SCS has utilized an assessment technique known as “life-cycle stressor effects assessment” (“LCSEA”), a rigorous life-cycle impact assessment methodology conducted in accordance with international standards (ISO 14042). LCSEA was developed specifically for the purpose of evaluating the environmental performance of industrial systems, calculating environmental impact indicator values for a relevant set of local, regional and global impact indicators, and supporting comparative assertions.

The project was broken down into four major tasks:

Task 1 — Project Scoping, Data Collection, and Preliminary Calculations Task 2 — Data Review and Analysis for Ecosystem disruption (Terrestrial and Aquatic Habitat and Key Species) Indicators

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Task 3 — Preparation of LCSEA Report, including Environmental Performance Rating and Corresponding Eco-Efficiencies Compared to the Regional Power Pool, for Submission to Peer Review 1 Task 4 — Issuance of Final Report, Environmental Performance Rating and Eco- Efficiencies.

This publication of this report represents the completion of Tasks 1-4. ).

1.3. Study Goals

A major goal of the Chelan PUD in commissioning this study was to determine the degree to which the LCSEA methodology can help the power generation industry in general, and the hydropower industry in particular, accurately gauge the relative environmental performance of power generation, and communicate this performance objectively to customers, policy makers, and stakeholders. The study was not intended to duplicate existing environmental research or life-cycle inventory (LCI) research, but rather, to demonstrate how such data can be integrated together under the umbrella LCSEA framework.

Specifically, the following goals were identified:

· Provide the Chelan PUD with an accurate scientific assessment of the environmental footprint of its Lake Chelan Project from a life-cycle perspective. · Support the establishment of a level playing field among energy generators making environmental assertions in marketing territories served by the Chelan PUD. · Provide a scientific framework for responding to inquiries about environmental performance from key customers, stakeholders, and policy makers. · Establish a scientific framework for assessing hydropower in the context of overall power production and delivery in the region and in the U.S. (e.g., coal, nuclear, gas, etc.)

1 The regional power pool referenced in this study is defined as the western U.S. interconnected grid included within the Western System Coordinating Council (WSCC) territory. A further description of this region is contained in Section 3.

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1.4. Study Deliverables

The deliverables of the project included:

· the publication of this study report; · determination of relative environmental performance rating and corresponding eco-efficiencies compared to the benchmark (i.e., the average impacts of the regional power pool); and · decision on certification of the Lake Chelan Project as a source of Environmentally Preferable electricity.

This study has been conducted in a manner intended to satisfy the rigorous public declaration and comparative assertion requirements of the ISO FIS 14042.2

2 International Organization for Standardization (ISO) Final International Standard (FIS) on Life-Cycle Impact Assessment (LCIA)

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S ECTION 2 — Calculation Methodology

2.1. LCSEA Technical Framework

Life-Cycle Stressor-Effects Assessment (LCSEA) is a cradle-to-grave (i.e., from raw material extraction, through product manufacturing, transportation, use, and disposal) assessment technique used to quantify a set of impact indicators representing the local, regional and global impacts of a selected industrial system on the environment. Together, these indicators (e.g., depletion of energy resources, greenhouse gas loading, eutrophication loading) form an Impact Profile of the system.

The LCSEA technical framework was developed by SCS and Soil and Water, Ltd. in international collaboration with other LCA practitioners, environmental assessment professionals, and industry stakeholders. It builds extensively on the work of pioneers in the field of LCA, including many of the contributors to the Nordic Guidelines and the Society for Environmental Toxicology and Chemistry (SETAC) monograms. For instance, the 1993 SETAC document identified important areas of additional research for future technical framework development: 1) setting appropriate boundaries at the outset of the project and iteratively reviewing these boundary conditions; 2) defining stressor- impact chains and their implications along the life-cycle; and 3) identifying additional characterization data needs, including the need to determine whether or not methods developed for human health and environmental risk assessments could be adapted to the practice of LCA. This methodology was made publicly available for comment in initial draft form through ISO/TC 207(SC3/ WG1/ Type III Task Group/N22, 1997).

The LCSEA technical framework is intended to serve the needs of a wide range of industry users, in supporting both internal and external applications. Internal applications include environmental performance evaluation and internal environmental improvement strategies. External applications include environmentally preferable procurement (EPP) and environmental labeling programs.

2.2. LCSEA Protocols

The LCSEA methodology is consistent with the criteria described in ISO-14042, the international standard for life-cycle impact assessment standard, including its provisions for comparative assertions. The LCSEA framework and protocols are described in Appendix 1, and illustrated in Figure 2.1.

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The application of the LCSEA methodology to the current study is included and presented step-by-step in the remaining sections of this report, together with the baseline data and results of the LCSEA assessment.

GoalFunctional and Scoping Analysis

Modeling of Relevant Stressor-Effects Networks/Unit Operations

Selection of Category Indicators and Measurement Endpoints

Collection of Unaggregated LCI & Environmental Data

Calculation of Indicator Results

Generation of LCSEA Impact Profile

Iterative process

Scientific Certification Systems Figure 2.1. — Diagram of the LCSEA Technical Framework

Generally speaking, two types of data are collected: 1) life-cycle inventory (LCI) data that represent the industrial system’s resource and energy inputs, environmental releases, wastes, and product/co-product outputs, and 2) environmental characterization data that can be used to determine the degree to which these input and output streams are affecting the environment. These data are then used to calculate the indicator result for each impact category. For each distinct unit process within the industrial system being studied, the calculation of indicator results involves the following procedure:

1) LCI results are converted to common equivalent units using “stressor characterization factors” (SCFs). These SCFs are based upon the relative potency of various environmental releases contributing to the same environmental mechanism.

2) For each identified stressor-effect network, “environmental characterization factors” (ECFs) are identified. These factors integrate fate and transport data with

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the severity, duration, and reversibility of the measured effect, and are applied to each unit process.

3) Once the results are fully characterized for each unit process, indicator results are aggregated across all unit processes within the system.

In the case of emissions and wastes, SCFs exist for each category indicator, based on the physical, chemical, or biological properties of the emissions/wastes. These factors have been established by government and intergovernmental agencies, and are generally accepted. An example is ozone depleting potentials (ODPs), factors that are commonly applied to distinguish the relative potency of different gases such as CFC-11 and 1,1,1, trichloroethane in depleting stratospheric ozone.

For most resource depletion and ecosystem disruption calculations, there are no SCFs. An exception is the case of energy resources. These resources can be characterized in terms of their relative energy content (e.g., tons of oil equivalents, or “toe”). In the case of resources, the ECF is referred to as the “resource depletion factor,” where:

(å W - N) (DT) + (Rb - R) Resource Depletion Factor (RDF) = ______

R + (å Simisi ) (DT)

å W = Cumulative amount of wastes of a specific resource from all uses N = Rate of natural replenishment, or "gain," of the resource within the defined reserve base. DT = A specified period of time. (T1 = initial date) R = The total amount of resources currently available in the defined reserve base from which the industrial system draws its resources.3 Rb = A baseline reserve base that represents the optimum resource capacity for a defined geographic area at T1, and that serves as a benchmark for 4 R. (For non-accreting resources, Rb = R.)

3 The reserve base is defined as that part of an identified resource that meets the minimum chemical and physical characteristics required to satisfy current mining and production practices. This definition is consistent with definitions proposed by Heijungs et. al. (1992), and by the US Bureau of Mines (1993), which defines economic reserves as that part of a reserve base which can be economically extracted at the time of determination. 4 A discussion of the methods for establishing and calculating the baseline reserve base is contained in Appendix 1.

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å Si = Cumulative amount of recyclable stock — that is, processed resources recovered for subsequent sequential uses (secondary, tertiary, etc.). mis = Performance equivalency factor reflecting the proportional loss in material integrity (mi) in each subsequent reuse of a processed resource.

The ECFs used for calculating emission loadings have three aspects:

1) First, any emission released at a level that does not exceed the observable effects threshold for that receiving environment is assigned a value of zero (ECF = 0), while any emission that exceeds such a threshold is assigned a value of one (ECF = 1).

2) Next, for those emissions that are released at a level that does exceed the observable effects threshold in a given receiving environment, the percentage of emission that deposits in areas of exceedance is determined by fate and transport modeling, such that ECF = 1 * (x%).

3) Data permitting, the ECF may be further adjusted to reflect the duration and/or severity of exceedance, as in the case of greenhouse gas loadings.

In the case of the ecosystem disruption, the ECF for habitat lost is generally set at ECF=1, but may be further refined in the future to facilitate comparisons between depleted habitats that are still somewhat intact and habitats that have been completely removed. Key species are broken out as separate sub-indicators (see below). For habitat gain, the ECF is generally set at 0, unless the habitat gain has unique value to the region or enlarges existing habitats without causing any measurable loss of integrity to those habitats. In such a case, the ECF can approach 1, although it cannot equal 1, due to the fact that any conversion in habitat inevitably involves some loss even though it may not be measurable.

Work toward the development of a more refined habitat equivalency to serve as the basis for this ECF is ongoing. For instance, it is conceivable that in some instances, the ECF could exceed 1, reflecting the fact that the new habitat was valued more highly than the habitat it replaced (based on a habitat equivalency system such as that developed by the U.S. Fish and Wildlife Service).

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The indicator result is the product of the unaggregated LCI result, also referred to as the “stressor value”, multiplied by the appropriate ECFs and SCFs. The cumulative indicator result is the sum of indicator values aggregated for all unit processes. It is critical that the category indicator be additive across all unit processes (Figure 2.2) in order to retain the integrity of the system LCA.

Net Resource Net Resource Net Resource Depletion Depletion Depletion

Emission Emission Emission Loadings Loadings Loadings

Figure 2.2. Linking unit processes under LCSEA

The calculation of the indicator values is expressed by the following equation:

1. For each unit process:

Indicator Stressor Stressor Environmental Result Value Char. Factor Char. Factor (IRi) = [SV] x [SCF] x [ECF]

n 2. Cumulative Indicator Result = å IRi across unit processes (n) i =1

2.3. Key Study Indicators

Tables 2.1.a and 2.1.b presents a summary of the indicators evaluated in this study. By and large, these indicators are common to all industrial systems.

In the case of hydroelectric power generation, the key indicator of interest is ecosystem disruption to terrestrial and aquatic habitats and to key species. Additional indicators of interest include water resource depletion, and greenhouse gas loadings, and off-site energy-production related indicators, such as fossil fuel and uranium resources depletion, acidification loadings and ground level ozone loadings.

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Table 2.1.a — LCSEA Category Indicators Relevant to Electric Power Generation – Resources and Ecosystem Disruption

INDICATORS ASSUMPTIONS AND PROTOCOLS

E NERGY RESOURCE DEPLETION Coal Depletion Coal is assumed to be a global resource. Biomass Depletion Resources are assumed to be regionally-based resources. Oil Depletion Oil is assumed to be a global resource, and includes not only fuels for fleet but also petroleum products such as process oils, lubricants, etc. Natural Gas Depletion Natural gas resources are assumed to be regionally based resources. Uranium Depletion Uranium is assumed to a global resource.

R ENEWABLE RESOURCE DEPLETION Water Resources See Appendix 1

E COSYSTEM DISRUPTION FROM DIRECT PHYSICAL DISTURBANCE Terrestrial & Aquatic Habitat Direct alteration caused by physical or mechanical industrial activity. (due to physical disruption) Key Species Direct alteration caused by physical or mechanical industrial activity.

Table 2.1.b — LCSEA Category Indicators – Emissions and Wastes

INDICATORS ASSUMPTIONS AND PROTOCOLS

E MISSION LOADINGS Greenhouse Gas Loading See Appendix 1 Acidification Loading See Appendix 1 Ground Level Ozone Loading See Appendix 1 Stratospheric Ozone Depletion Loading Defined under the Montreal Protocol and subsequent agreements Hazardous Chemical (Air) Loading Defined under various national government jurisdictions as “sufficient for (by specific sub-indicator) listing”; in the U.S., includes Hazardous Air Pollutants (HAPs) listed in Title III of the 1990 CAAA, plus criteria pollutants not covered under other indicators for which there are National Ambient Air Quality Standards in CAAA, Title I. Eutrophication Loading See Appendix 1 Total Oxidizing Chemical (TOC) Loading See Appendix 1 Total Suspended Solids See Appendix 1 Hazardous Aquatic Loading Defined under various national government jurisdictions as “sufficient for (by specific sub-indicator) listing”; in the U.S., includes Toxic Water Pollutants listed in the Clean Water Act. Thermal See Appendix 1

R ESIDUAL HAZARDOUS WASTES Heavy Metal and Ash Wastes Defined under national government jurisdiction as “sufficient for listing.” Includes both post-treatment and non-treatable wastes that are landfilled or held in storage. Radioactive Waste See Appendix 1

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2.3.1. Ecosystem Disruption from Direct Physical Disturbance

The indicators “disruption to terrestrial and aquatic habitats” and “disruption to key species” represent the non-elementary flow stressor-effects networks stemming from the direct physical alteration of the landscape (ecosystem) from mechanical or structural causes. A wide range of industrial activities can cause ecosystem disruption — from logging, road construction, dams and mining to the construction of manufacturing plants, the construction of stockpile facilities, and landfilling.

Ideally, ecosystem disruption would be measured in terms of the depletion of the affected ecosystems. However, such assessment is not yet supported by current ecological field assessment techniques. Instead, this disruption is currently measured in terms of the physical alteration of habitats and, as warranted, identified key species.

Disruption to Terrestrial and Aquatic Habitats Habitats are described in terms of the particular vegetative cover types/states across the defined landscape (either terrestrial or aquatic).

Disruption to Selected Key Species Key species include those species officially classified as rare, threatened or endangered, or other species of local or regional significance.

At the Lake Chelan Project, ecosystem disruption was assessed in terms of five major variables:

· Terrestrial habitat5 · Shoreline erosion · Wetlands · Aquatic habitat: Lake Chelan, Dewatered Chelan River Channel and Tailrace · Mudflats

5 Terrestrial habitat disruption includes site physical impacts as well as impacts due to roads, transmission lines and substation disruptions.

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No key species were identified for the region impacted by the Lake Chelan Project. The method for assessing the Ecosystem Disruption indicator is described in detail in Appendix 2.

2.3.2. Other Indicators

2.3.2.1. Water resource depletion

One potential effect from the impoundment of water behind a power station is the net depletion of water resulting from evaporation. Increased evaporation (as compared to baseline levels of evaporation from non-impounded rivers) is not necessarily a function of increased water column temperature, but is mainly a function of ambient air temperature, wind velocity, increased surface area, and the overall surface area of static water behind the impoundment.

For instance, in the southwestern United States, it has been estimated that an added 500,000 acre/ft of water evaporate each year behind the Glen Canyon Power station,6 even though this is snow melt water, with temperatures averaging no more than 8° Celsius over the entire column. This loss represents enough water to supply the city of Tucson, Arizona, with a population of 600,000 people.

By contrast, it should be noted that the Lake Chelan Project is sited on the location of a preexisting natural lake of only slightly smaller size; as such, added evaporation was not anticipated to be an issue for this system.

2.3.2.2. Fossil fuel and uranium resources depletion

Electricity and transportation fuels used both in the initial construction and the on-going operation and maintenance of hydroelectric plants are included in the assessment. The extent to which coal, natural gas, oil and uranium resources are depleted will therefore be largely dependent on the make-up of the regional power pool.

61 acre/ft = 325,000 gallons

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2.3.2.3. Greenhouse gas loading

The greenhouse gas loading for a hydroelectric facility stems from two potential sources: 1) the use of grid electricity and transportation fuels, and 2) release of methane and other greenhouse gases from the water impoundment area behind the power station and dam. These latter releases vary markedly, depending on the amount of accumulation of biomass in the impoundment area, the depth of the impoundment, the rate of stream flow, and the vegetative make-up of the surrounding environment.

2.3.2.4. Acidification and ground level ozone loadings

Acidification and ground level ozone loadings are primarily associated with the use of grid electricity and transportation fuels during on-going operations and maintenance. As discussed in Appendix 1, these loadings are dependent upon the dispersion of system releases and the specific characteristics of the receiving environment.

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S ECTION 3 — Goal and Scoping Definition

3.1. System Function and Functional Unit

For the purposes of this study, the system function was defined as the generation and delivery of electric power to the western interconnected grid, comprising most of the Western US and portions of Canada and northern Mexico.

The generation capacity of the Lake Chelan Project is 50 MW, with an average annual production of about 383 GWh over the past ten years. For 1998, the most recent year for which data were reported by the Chelan PUD, production was 375 GWh. 375 GWh per annum was identified as the functional unit for the study as a conservative figure.

The discrete industrial processes that comprise the system were grouped under two core unit operations for the purposes of LCA calculations: the construction of the Project, and the operation, maintenance and upgrades of the Project (Figure 3.1).7 It was important to deaggregate the data associated with the initial construction of the facility with those data associated with on-going power production operations. This speciation was important because of the long temporal separation between the construction of the dam (1926-1927) and current operations.

• Construction-related unit operations included capital equipment manufacturing (e.g., turbines, transmission lines); and production of ancillary chemicals such as sulfuric acid, nitric acid, ammonia used in the manufacturing of equipment.8

• Unit operations associated with the operation of the dam include maintenance (e.g., turbines, vehicles) and general administration. Energy resources, emission loadings and residual hazardous wastes associated with the demolition phase of the life-cycle were omitted from the overall environmental performance calculations because: 1) it is not anticipated that the facility will be dismantled after its assumed 100-year lifetime, and 2) the anticipated values are insignificant and would, therefore, not change the performance of the Lake Chelan Project. The

7 Specific numerical codes are assigned to these unit operations in the calculation model, as reflected in the emission loading tables in Section 9. 8 There may be human health and eco-toxic sub-indicators associated with these unit operations, but these potential sub-indicators were not analyzed due to the complexity of the analysis, the difficulty in obtaining data, and the potentially high costs of such an analysis.

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depletion of material resources associated with usage of steel and concrete during construction has been integrated into the overall depletion rates presented in the current calculations. The turbines are assumed to be recycled at the end of their useful lifetimes.9

• Production of replacement turbines and other operational capital equipment and ancillary structures were also included in the study boundary.

Manufacture of generator Extractionof turbines, overhead cranes, Ballast spillway gates, cables Steel Concrete reinforcing rods Manufacture Transport transmission lines Cement Manufacture Manufacture Trans. Manufacture Transport Copper ofgenerators Wood Manufacture andcables Production Transport Design and Construc- DisposaDisposal Trans. tion Manufacture Transport Lead l Extraction Transport ofbatteries Manufacture of Soil Transport Matter Use, operation Extraction Transport and Trans. Manufacture Transport Aluminium of maintenance ofcables Manufacture Moraine

Trans. Transport Extractionof Transport PVC Transport Manufacture filler ofcables Manufacture Transport

Extraction Transport of Transport Manufacture rock offuels Recycling

Disposal Transport Site Manufacture of lubricants

9 Irrespective of this assumption, sensitivity analysis shows that demolition-related impacts do not significantly alter the overall environmental performance of the hydroelectric project. Moreover, it should be noted that study calculations do account for that portion of the material in the dam that would normally be expected to go to the waste stream upon demolition (see Appendix 1, Resource Depletion).

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Figure 3.1. — Simplified flow diagram of hydroelectric power production system, including construction and operation of the powerhouse and dam.

The main location considered was that of the powerhouse itself, even though some minor part of emissions and other environment loadings occurred offsite where materials like cement and machinery parts were produced.

3.2. Study Scope

The scope of the assessment was “cradle-to-user” — that is, the LCI results and the corresponding impact indicator loadings were assessed for each unit operation, starting from the initial extraction of raw materials through the production and delivery of the electricity to the western interconnected grid. Comparable results were calculated or estimated based on the Boustead LCI model and Battelle Northwest Laboratory data to establish an average impact profile for the regional power production pool.

As discussed in Section 2, LCSEA studies seek the appropriate local, regional and global characterization data required to calculate the indicator results for all relevant stressor- effects networks for each industrial unit operation. The system configuration and boundaries for the production systems are described in greater detail below.

LCSEA quantifies indicators associated with environmental impacts only. These effects are defined by ISO 14040 as effects on resources, human health and the environment. No attempt was made in this study to consider social, economic or aesthetic effects.

3.3. System Description

3.3.1. General Description of the Lake Chelan Project

3.3.1.1. Ownership and Operation

The Chelan PUD owns and operates the Lake Chelan Project.10 The Chelan PUD is licensed by the Federal Energy Regulatory Commission (FERC) to operate the project. Its existing license, No. 637, was issued on May 12, 1981, retroactive to 1974. The current license expires on March 31, 2004.

10 Project Lands Management and Socioeconomic Study Plan, Final Draft, Lake Chelan Project FERC Project No. 637, Public Utility District No. 1 of Chelan County, Wenatchee, Washington, February 12, 1999.

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Chelan PUD intends to seek a new license to operate the Lake Chelan Project and has begun the preparation for the process referred to as "relicensing." The FERC relicensing process is based on laws and regulations that require years of extensive planning, including environmental studies, agency consensus, and public involvement.

3.3.1.2. Physical Description of the Project

The Lake Chelan Project consists of a 40-foot high concrete gravity dam located at the City of Chelan, a 2.2-mile long steel and concrete tunnel (penstock) that is 14 feet in diameter, and a powerhouse located at the confluence of the Chelan and Columbia rivers near the City of Chelan Falls. The vertical elevation drop between the dam and powerhouse is 401 ft.

Between the dam and 4.1 miles downstream to the powerhouse is the area known as the “bypass reach,” the channel of the Chelan River. It ranges from a low gradient (55 ft/ mile) stream at the upstream end characterized by a relatively wide flood plain to a very steep “gorge” at the downstream portion with a gradient of more than 400 ft./ mile.

The powerhouse contains two Francis turbine units, each rated at 34,000 hp at 1,100 cfs and 377 feet net head. The turbines produce approximately 50 MW of electricity annually.

The project reservoir, Lake Chelan, is operated between a maximum water surface elevation of 1,100 feet and 1,079 feet to ensure optimum utilization of the reservoir for power generation, fish and wildlife conservation, recreation, water supply, and flood control purposes. The average drawdown of the lake for the past 30 years has been to 1083.5 feet. The reservoir has 676,000 acre-feet of usable storage above 1,079 feet.

The annual drawdown of the lake begins in early October. The lowest lake elevation normally occurs in April. From May through June the lake refills from spring runoff. The reservoir is maintained at or above elevation 1,098 feet from June 30 through September 30 of each year. Since the project was originally licensed in 1926, the lake has never been drawn down to the minimum allowable elevation (1,079 feet). The lowest drawdown on record was 1,079.7 feet in 1970. That occurrence coincides with the lowest annual precipitation on record. The Chelan PUD has never failed to refill the reservoir to elevation 1,098 feet by June 30.

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3.3.1.3. Location and Geography

The Lake Chelan Project is located on the Chelan River in Chelan County, north central Washington, approximately 32 miles north of the city of Wenatchee. The 4.1-mile long Chelan River, the shortest river in Washington State, flows from the lower end of Lake Chelan into the Columbia River.

Lake Chelan is a natural lake that developed within a glacial trough. It is deep and narrow, extending northwesterly approximately fifty miles from the City of Chelan at its lower end to Stehekin at the head of the lake. The lake averages one mile in width, and has depths of over 1,480 feet.

Lake Chelan is bordered by more than two million acres of National Forest Lands, more than half of which are designated as wilderness. To the south are the Entiat and Chelan Mountains and Glacier Peak complex, and to the north is the Sawtooth Mountain Range. From Twenty Five Mile Creek uplake, the terrain is mountainous and rugged. Surrounding peaks reach elevations as high as 7,000 feet. In many cases, the steep slopes run directly into the lake with no flat beaches or shoreline. The terrain of the lower end of the lake is much less severe, mainly semiarid. Except where irrigation has taken place, the hills of the lower end of the lake are barren with grasses and a few scattered pines.11

The lake serves as a waterway approach to the Forest Service's Wenatchee National Forest above Twenty Five Mile Creek, and to the National Park Service's Lake Chelan National Recreation Area at Stehekin. The lower fifteen miles of the lake are mostly privately owned, the next 35 are within the Wenatchee National Forest, and the upper five miles are within the Lake Chelan National Recreation Area. The lake and its environs support multiple uses, including boating, fishing, hunting, swimming, and irrigation, as well as power generation.

The average surface area of the lake is 32,000 acres. The drainage area of the project at the dam is 924 square miles. The confluence of the Chelan River and Columbia River is approximately 1.5 miles southeast of the City of Chelan.

11 A more complete description of the grasses, pines, and other local plants is available in “Public Utility District of Chelan County, Wenatchee, Washington. February 12,1999. Lake Chelan Hydroelectric Project FERC No. 637 Botanical Study Plan”, in other local ecological investigations and in national classifications of plant communities (Küchler, 1964).

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3.3.2. General Description of the WSCC Power Production Pool

As a goal of the study (Section 1), the impact indicators of the Lake Chelan Project were to be compared to the average impact indicator results for the regional power production pool — in this case, the western interconnected grid.

In the United States, electricity is transmitted through a variety of transmission lines, interties, and substations. The North American Electric Reliability Council (NERC) coordinates and promotes policies and practices which are intended to maintain and improve reliability and adequacy of supply in North American bulk electric power systems. NERC consists of nine regional councils and one affiliate council, encompassing all of the U.S., the southern provinces of Canada, and part of northwestern Mexico. The nine regions, in turn, are interconnected via a number of interties.

The region encompassing the western interconnected grid is coordinated by the Western System Coordinating Council (WSCC), and is geographically the largest of these regions. The WSCC electric system consists of generating capacity and transmission lines. The transmission system provides for distribution of electricity within WSCC, and in addition, allows for some import and export capability among WSCC and neighboring states.

This region is connected to the eastern region by six back-to-back direct current (DC) interties.

3.3.2.1. Geographic Territory

The WSCC western interconnected grid encompasses approximately 1.8 million square miles and consists of a network of transmission lines throughout Washington, Oregon, Idaho, Wyoming, Utah, Nevada, California, Arizona, Colorado, Montana and New Mexico, part of northwestern Mexico, and the Canadian provinces of British Columbia and Alberta. These transmission lines connect to many large power generating facilities, such as the Hoover and Grand Coulee dams, the Diablo Canyon and Palo Verde nuclear power plants, and the Navajo Generating Station, as well as the many smaller generating facilities throughout the region.

3.3.2.2. Electricity Generation

The power plants long distance transmission lines and distribution power lines delivering power to customers are owned by a combination of public utility districts (PUDs),

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municipal utilities (munis), investor owned utilities (IOUs), and agencies of the federal government. As of 1998, the installed capacity of the western interconnected grid was 157,915 megawatts (MW).

Large utilities in the WSCC provide the largest share of the system power. The IOUs include PacificCorp, SCE Corp, Pacific Gas and Electric, and Portland General Electric. The federal agencies include Bonneville Power Administration (BPA), Western Area Power Administration (WAPA), the U.S. Army Corps of Engineers, municipal organizations including Los Angeles Department of Water and Power (LADWP), and public utility districts such as the Chelan County, Douglas County, and Grant County PUDs, as well as others in these classes.

In 1997 the generation in the WSCC totaled 745,382 gigawatt hours (GWh). WSCC electricity is generated by a combination of fossil fuel, nuclear and hydropower facilities, with minor contributions from additional renewable sources (Figure 3.2.). The energy mix depends, largely, upon how much water is available for hydroelectric generation. Hydroelectric is the largest source of electricity generation in WSCC and represented 39% of the energy in the WSCC in 1997.12 After hydro is coal, contributing 33% to the energy generation. Nuclear power plants are currently providing approximately 9% of the WSCC’s energy. Other fossil fuels, primarily natural gas, generates 7% of WSCC ’s electricity each year. Geothermal contributes approximately 3%. Other resources including cogeneration, wind, solar, and pumped storage contribute approximately 9%.

Other Geothermal

Natural gas & oil Hydro

Nuclear

Coal

12 Western System Coordinating Council (WSCC) Web Page http://www.wscc.com/

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Figure 3.2. WSCC Electricity Generation 1997

3.3.2.3. Electricity Transmission and Delivery

The transmission system connects the generators to the load center at substations where the voltages are lowered. Distribution lines then connect the substations to the end user. Transmission systems are generally considered the bulk transfer systems. Operation of the generation stations in conjunction with the transmission system requires careful coordination and control to accommodate multiple suppliers and customers at one time.

WSCC's transmission system consists of AC power lines operating at 115-161kV, 230kV, 287-360kV, and 500kV. The 500-kV lines are used for transmitting long distance bulk power and are generally referred to as extremely high voltage (EHV) transmission lines. The 115kV-161kV and 230kV lines are generally referred to as the transmission level. Systems below the 115kV level, such as 69kV, are generally referred to as the subtransmission level. In 1998, there were 112,811 circuit miles of transmission in the WSCC western interconnected grid, at voltages of 115kV and higher as well as 1545 miles of DC transmission lines at the 260kV to 500kV levels. The transmission lines are owned by the electric utility companies that are also responsible for maintaining their own transmission lines and substations, including right-of-way maintenance to ensure that trees near transmission and distribution lines do not make contact with the wires.

Locally, utilities own and maintain the electric lines that distribute electricity to end-use customers in their service territories. Utilities provide non-discriminatory distribution services to all customers, with distribution lines generally delivering power below 69 kV. Generally, distribution lines deliver power below 69 kV, for example, 13.8 kV, 240 V, and 120 V.

Reliability is fulfilled by having excess generating (reserve capacity) and transmission capability. The transmission system is continuously being upgraded to meet increasing demand. The system is operated regionally by the various load control centers such as the California Independent System Operator (ISO), the Bonneville Power Administration, and the mid-Columbia PUDs. These organizations dispatch power plants, operate transmission level switching, and ensure overall system reliability.

Wholesale power marketers buy power produced in the wholesale market and then sell it to either large consumers or power distributors. Power brokers arrange transactions between power producers and distributors or large consumers.

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3.4. Key Assumption for Calculating the WSCC Indicators

It was assumed that the indicator results for the Lake Chelan Project are at least an order of magnitude lower than thermal units for all impact categories except ecosystem disruption. As a result, it was possible to simplify the data required to characterize these indicator results for the system average by using "best case" modeling for the thermal units. The end effect was to reduce the quantitative advantages of the Lake Chelan Project for these indicators without increasing the uncertainty of the certified claim.

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SECTION 4 — LCSEA Inventory

4.1. Inventory Data Sources

The majority of inventory data used in this report, as well as the primary unit operations, were derived from material input data supplied by the Chelan PUD, and by deaggregating LCI data available in the Boustead LCI Model IV. In addition, some inventory data were obtained from the Swiss Institute of Technology (Zurich) databases.13 The bibliography contains a more complete listing of references.

4.2. Inventory Assumptions and Conventions

4.2.1. Raw Materials

Raw materials and fuels extracted from the earth were burdened with their inherent feedstock value and, in addition, were burdened with the resources, energy, emissions, and wastes associated with extraction, refining and transportation. Materials used in the construction of the power station as well as those used in continuing operations, maintenance and upgrades were included within the study boundary conditions, and thus included in the collection of inventory data.

4.2.2. Electricity Inputs (Dam Construction)

Within LCSEA, electricity is recognized as a product rather than as an input; the resources used in generating the electricity, and the emissions released, are the inputs and outputs of interest to the study. (See Appendix 1.) These inputs/outputs are directly proportional to the amount of electric energy used by a specific unit process or a group of unit processes from a specific electric grid.

The calculations for equivalent electricity used in the construction of the power station are based on the LCI data provided by the Chelan PUD and from available LCA databases. As no primary data were available to calculate these inputs for the construction phase of the dam in the mid-1920s, surrogate data were substituted from

13 “Environmental Inventory Data for Energy Systems (in German).” Swiss Federal Institute of Technology, Safety and Environmental Technology Group, Zurich 1997.

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modern-day LCI models. These data reflect the current-day energy grid mix for the region, almost half of which is derived from coal or other fossil-fuel sources. As such, these data represented a worst-case scenario, since actual energy production at the time would be assumed to have been based much more extensively on local hydropower sources.

4.2.3. Fuel Inputs

Fuel inputs include the total inputs from all sources in the industrial system, including fuels used for transportation and processing, as well as in production and delivery.

Transportation data were recorded in terms of the type of vehicle and the distance involved, including whether a trip was one-way or round-trip. These data were then converted into units of vehicle-kilometers, and converted into quantities of resources and fuel consumed and emissions and wastes generated.

4.2.4. Emissions to Air

Air emissions, speciated by chemical and recorded in units of concentration (mass per volume per unit of time), represent discharges into the atmosphere after passing through emission control devices. Air emissions were calculated for all subsystems, including operations associated with the generation of off-site electricity, process emissions, and emissions resulting from the production and combustion of fuels for process or transportation energy.

4.2.5. Discharges to Water

Water effluents, recorded in units of concentration flux (mass per volume per unit of time), represent discharges into a lake or river, or from relevant publicly owned treatment works, after on-site wastewater treatment. All such discharges were left unaggregated and unallocated. As in the case of air emissions, water effluents were calculated for all subsystems, including off-site electric power production, process effluents, and from the production and combustion of fuels for process or transportation energy.

4.2.6. Solid and Hazardous Wastes

Solid and hazardous wastes are inventoried by species on a mass basis.

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4.2.7. Processes Excluded from Inventory Calculations

Some items have been excluded from the inventory calculations, mainly because of their anticipated low significance. These items include:

- working and sanitation facilities for the process staff; and - chemicals used in small amounts in construction or operations, such as lubrication oil, boiler feedwater treatment chemicals.

4.3. Inventory Results

Summary results of the mass and energy (M/E) inventory calculation are based upon the Lake Chelan Project LCI data, which are normalized to 375 GWh/year of delivered production. The following tables summarize these results, including the inputs and outputs from two separate unit operations: 1) the initial power station construction (1926- 1927), and 2) on-going operations. All results have been rounded.

In calculating the annualized indicator results for the construction unit operation, the dam was assumed to have a 100-year lifetime. Thus, construction inputs and outputs were amortized over 100 years in order to arrive at tonnage per year. Resource and energy data related to construction inputs were modeled based on the Boustead database.

4.3.1. Raw Material Resource Requirements

The key raw material resource requirements of the Lake Chelan Project are summarized in Table 4.1.

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Table 4.1. — Aggregated Inventory Resource Calculation Input Values for the Lake Chelan Project (based on 375 GWh production)

Inputs Construction Plant System Operation total t/year t/year t/year Uranium 0.000 0.000 0.000 Coal 95 0 95 Crude Oil 9 32 41 Natural Gas 2 1 3

Limestone 422 422 Gypsum 16 16 Sand and Gravel 1,356 1,356 Shale 46 46 Iron Ore (as iron) 23 23 Water 4,514 52 4,565

4.3.2. Emissions and Wastes

The emissions and wastes of the Chelan PUD hydroelectric power production system are summarized in Table 4.2.

Table 4.2. — Aggregated Inventory Calculation Output Values for the Lake Chelan Project (based on 375 GWh production)

Emissions Construction Plant System total Operation total t/year t/year t/year CO2 493 104 597 SOx 3 0 4 NOx 1 1 2 Particulates 3 0 3 Hydrocarbons 0 0 0 CO 1 1 2 CH4 0 0 0 HCl 0 0 Susp.solids to water 139 139 Mineral 1,285 0 1,285 Slags and ash 12 0 12

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4.3.3. Comparing the Inventory Profiles of the Lake Chelan Project and the WSCC Average

For comparison, the inventory results for average western interconnected grid electricity have been calculated, based on the Boustead data base and recent EPA average emission coefficients (Table 4.3).

Table 4.3. —Lake Chelan Project LCI Data Compared to Average WSCC Inventory Data (based on 375 GWh annual production)

Chelan WSCC grid 375 GWh/year 375 GWh/year t/year t/year

Inputs Uranium ore (2.2%) 0 60 Coal 95 75,723 Crude Oil 41 1,675 Natural Gas 3 9,372

Limestone 422 * Gypsum 16 * Sand and Gravel 1,356 * Shale 46 * Iron Ore (as iron) 23 18 Water 4,565 172,737

Emissions CO2 597 253,085 SOx 4 2,214 NOx 2 1,010 Particulates 3 1,041 Hydrocarbons 0 42 CO 2 149 CH4 0 648 HCl 0 41 Susp.solids to water 139 23 Mineral 1,285 14,438 Slags and ash 12 4,483 * Amounts used not reported; insignificant relative to the virtually inexhaustible resource reserve bases.

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SECTION 5 — Classification

The purpose of the classification step is to organize and consolidate the inventory data in order to facilitate characterization. Consistent with other classification methods, LCSEA assigns inventory data to relevant impact indicator categories. In addition, the LCSEA framework also requires that the contribution of each unit process to the indicator be further delineated.

5.1. Identify Potential Stressor-Effect Networks

Under the LCSEA approach, a listing of potential stressor-effects networks is first compiled, then analyzed to determine the relevance of each potential network to the particular system being studied, based on measurable impacts or known indicators of impacts. (See Appendix 1)

For this study, a listing of environmental impacts known to be associated with hydroelectric power production was compiled, based on literature review, a review of the Scientific Certification Systems and Jaakko Pöyry / Soil and Water Ltd. databases, and interviews with the Chelan PUD and Lake Chelan Project personnel. Subsequent data analysis and collection revolved around confirming the activity of each stressor-effect network, or eliminating it from consideration.

Each stressor-effect network is represented by a category indicator. Each category indicator must be mechanistically related to the original inventory results through either chemical or physical transformations, and these mechanistic pathways must be generally accepted. For example, in the case of ground level ozone formation, a clear chemical reaction has been established between NOx and VOCs emissions and ground level ozone, in a NOx and VOC limited environment and in the presence of sunlight. For each category indicator, the indicator value must accurately reflect the severity of the effect for a given stressor-effect network.

5.2. Assign Inputs and Outputs to Identified Networks

Unaggregated inventory inputs and outputs were assigned to the appropriate stressor- effects networks as represented by the category indicators listed in Appendix 1, Table 1. The integrated classification and characterization spreadsheets for key indicators are provided in subsequent sections.

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SECTION 6 — Energy Resources Depletion

In calculating energy resource depletion, the resource inputs were first converted into tons of oil equivalents (toe), then multiplied by the environmental characterization factor (or “Resource Depletion Factor”) reflecting the relative reserve bases of each resource type (see Appendix 1).

On an annualized basis, crude oil was the most heavily depleted fossil fuel resource (42 toe/a), stemming largely from on-going annual hydroelectric project operations. Coal depletion, calculated at 11 toe/a, was associated exclusively with hydroelectric project construction, while natural gas depletion, calculated at 16 toe/a, reflected both initial construction and ongoing operations.

The final fossil fuel and uranium resource depletion indicator results are summarized in Table 6.1.

Table 6.1. — Fossil Fuel and Uranium Resource Depletion for the Lake Chelan Project and the WSCC Average Grid

Impact Indicator Potency Fossil fuel resources Characteri- Equivalent Providing Net Resource zation Stressor Environment Resource TOTAL Unit Operation Inventory Use Factor values Characterization Depletion Code Name Resource (t/a) (toe/t) (toe/a) Factor (toe/a) (toe/a) 7118 Dam construction Crude oil 9 1.000 9 1.03 9 Natural gas 2 1.203 3 4.00 11 Coal 95 0.622 59 0.18 11 Uranium ore 0.00 195.000 0 0.44 0 31

7119 Dam operation Crude oil 32 1.000 32 1.03 33 Natural gas 1 1.203 1 4.00 5 Coal 0 0.622 0 0.18 0 Uranium ore 0 195.000 0 0.44 0 38 Total Sum 69 7115 WSCC average Crude oil 1,675 1.000 1,675 1.03 1,726 grid electricity Natural gas 9,372 1.203 11,274 4.00 45,097 Coal 75,723 0.622 47,100 0.18 8,478 Uranium ore 60 195.000 11,608 0.44 5,108 Total Sum 60,408

For perspective, the fossil fuel indicator results are shown compared to the average indicator results for the WSCC Western Interconnected Grid in Table 6.2.

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Table 6.2. — Comparing the Fossil Fuel and Uranium Resource Depletion for the Lake Chelan Project and the WSCC Western Interconnected Grid Average by Indicator, based on 375 GWh production

Lake Chelan Western Inter- Project connected Grid toe/a Average toe/a Crude oil 42 1726 Natural gas 16 45,097 Coal 11 8,478 Uranium ore 0 5,108

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SECTION 7 — Renewable and Mineral Resource Depletion

7.1. Water Resource Depletion

Lake Chelan was a natural lake in existence prior to impoundment for hydroelectric power generation. Given the fact that the impoundment resulted in only a small increase in the lake’s size and surface area, and no measurable increase in water temperature, there is little likelihood (and there was no evidence to indicate) that the construction of the dam has resulted in any significant enhanced evaporation of water. 14

7.2. Net Mineral and Metal Depletion

Limestone and gypsum, sand and gravel, and iron ore were used in the initial construction of the power station and power station and maintenance of the power station.

Although the mass inventory values for these minerals and metals are quite high, it was possible to determine from the outset that, from a “net resource depletion” perspective, the significance of the depletion of these resources was minor, given their large reserve bases and the high recycling rates for iron ore.

In determining the RDF of iron ore, the waste percentage was assumed to be 0.1 % and the recycled content was assumed to be 0.5 %, based upon recent data obtained from the steel industry. The reserve base extension from recycled material also includes the metal “standing stock” that would accumulate during 50 years with the given recycling and waste percentages.

For the non-metal minerals the amounts were very small or the reserves were abundant, resulting in very low RDF values.

Over the last 25 years, it has become a convention in life-cycle studies to exclude capital equipment and facilities from the assessment of energy systems of all types, since the associated depletion and loadings are insignificant when compared to energy production operations. The findings of this study support that conclusion.

14 The authors refer readers to standard tables on potential water evaporation (Thornthwaite and Holzman).

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SECTION 8 — Ecosystem Disruption

8.1. Calculation of the Ecosystem Disruption Indicator

8.1.1. Measuring Deviation from Baseline

Ecosystem disruption from hydropower station construction is calculated by assessing the direct losses and gains in terrestrial habitats resulting from the initial construction activities and inundation, the subsequent indirect losses of habitats in riparian zones and adjacent areas, and the changes in aquatic habitats from baseline conditions. (See Appendix 1.)

These calculations constitute a measurement of the deviation from a defined baseline of terrestrial and aquatic habitat before regulation. In the process of modelling major land- use changes, a combination of methods, including literature study (scientific investigations, construction documentation, and maps), interpretation of aerial photographs and additional field surveys, were used.

8.1.2. Accounting for Temporal Nature of Disruptions

Hydroelectric power generation results in areas of temporary and permanent states of ecosystem disruption— temporary (e.g., auxiliary structures built to support the construction of the power station), and permanent (e.g., the impoundment area, permanent structures).

The ecosystem disruption indicator results incorporate temporary habitat depletion only insofar as the impacts have persisted to the present (assuming that the goal of the study, as in this case, is to reflect the current state of impacts on the environment). No attempt was made to develop an environmental characterization factor (ECF) to account for future planned or probable mitigation efforts.

8.1.3. Key Species

The methodology requires that any species that is federally listed as threatened, endangered, or as “species of concern” be identified. In the Lake Chelan Project region, the following key faunal species have been identified:

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· fish — burbot, westslope cutthroat trout, pygmy whitefish and bull trout · birds — bald eagle.

8.1.4. Calculating the Indicator for General Habitats

The U.S. Fish and Wildlife Service has established a relative ranking system intended to facilitate comparisons between habitat types. This system, the Habitat Evaluation Procedure, rates habitats by multiplying the acres of habitat impacted by a Habitat Suitability Index (HSI). 15 On the HSI index, HSI=0 is assigned to habitats of the lowest importance, while HSI=1 is assigned to habitats of the highest importance.

The LCSEA methodology applied in this study did not go so far as to attempt to rank habitat types. Under a slightly more conservative approach, habitats were separately classified into two groups:

· Degradation or Conversion of General Habitats on a Non-Equivalent Basis This category included habitat acreage that was significantly degraded by site disruptions associated with the Lake Chelan Project (e.g., erosion). It also included acreage of habitat conversion that resulted in the loss of a significant habitat type (e.g., mudflats). For acres of habitat loss, the environmental characterization factor was set at one (ECF=1).

· Conversion of General Habitats on an Equivalent Basis This category included acreage of habitat conversion that did not result in the loss of a habitat of high importance on the HSI index, and that resulted instead in the establishment of new habitat of equivalent or greater importance. Where such new habitat increased the overall acreage of existing habitat (e.g. deep-water lake habitat), this addition must have occurred without any measurable loss of integrity in the existing habitat. While there is no measurable loss, there could be up to 15% change in the habitat that is not observable. Based on this uncertainty, this ECF was set at 0.85.

15 Ecological Services Manuals 101-103, Division of Ecological Services, Fish and Wildlife Service, U.S. Department of the Interior

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8.2. Lake Chelan Project Ecosystem Disruption

8.2.1. Terrestrial and Aquatic Habitat

Appendix 2 describes the assessment conducted to calculate the “Ecosystem Disruption” terrestrial and aquatic habitat indicator. All habitat disruptions were measured in acres of depleted/accreted habitat.

Table 8.1 provides measurement results for general habitat acreage changes, including both areas of habitat loss and gain. The acreage of terrestrial and aquatic habitat lost was 1,489 acres, while the acreage of terrestrial and aquatic habitat gained was 1080.4 acres, resulting in a net loss of 408.6 acres.

A discussion of habitat depletion is presented in Appendix 2. In terms of habitat gains:

· Rising water levels in the lake have seasonally increased the acreage of open water from 31,499 to 32,754 acres for an increase in inundated acreage of 1,255 acres. While the original 31,499 acres have been deepened, the habitats and ecological setting of the lake itself are believed to have changed little. Thus, although the lake is deeper, aquatic resources in the shoreline areas (pre- and post- dam installation) are comprised of essentially comparable ecological features and habitats. This is currently characterized as a sheer deep-water zone with virtually no littoral zone, and in general, characterized the pre-dam shoreline environment.

· Recent (1998) use of the created tailrace and channel at the power plant by fall and summer chinook for spawning suggests a potential positive benefit of this created habitat of less than 16 acres. However, to maintain a conservative position, this acreage was not added back into the overall net habitat depletion calculation.

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Table 8.1. — Site Ecosystem Disruptions: Acreages of habitat changes, Lake Chelan Project Acreage Acreage LCSEA Habitat Affected Lost/Gained ECF Acreage Conversion of General Habitats on an Equivalent Basis

Reservoir Pre-dam lake habitat 31,499 Post-dam lake habitat 32,754 Forest, cliff, grassland habitat lost to inundation (1,255) 1 (1255) Lake habitat gain due to inundation +1,255 0.85 +1066.8

Bottom of Chelan River at Columbia River Pre-dam floodplain habitat 16 Post-dam floodplain habitat 0 Floodplain habitat lost (16) 1 (16)

Degradation or Conversion of General Habitats on a Non-Equivalent Basis

Mudflat habitat inundation (seasonal) 34 (34) 1 (34)

Wetlands (seasonal) Pre-dam wetlands 24 Post-dam wetlands 0 Wetland habitat lost (24) 1 (24)

Chelan River channel Pre-water surface 95 Post-dam water surface (seasonally) 0 River habitat lost (95) 1 (95)

Terrestrial habitats lost to roads 13 (13) 1 (13)

Terrestrial Habitat lost to facilities footprint 10 (10) 1 (10) — Dam, powerhouse, surge tank, dam staging area, switchyard, tunnel spoils

Shoreline erosion16 42 (42) 1 (42)

SUBTOTALS (1,489) (1,489) +1,255 +1,066.8

NET HABITAT DEPLETION (422.2)

16 Additional data on the subject of soil erosion was collected and analyzed in 1999 subsequent to this study by the relicensing team and the resource agencies under a separate focussed study. This additional research considered soil erosion along the entire shoreline of the Lake Chelan. Subsequent to the completion of the data collection conducted for this study, but prior to the final publication of this report, this new research was published. Based on the new research, the 42-acre estimate calculated for this study would appear to be reasonable, and perhaps somewhat overstated. [Correspondence, G. Yow, Chelan PUD, February 25, 2000] The new research is reflected in Figures 5a-5c added to Appendix 2.

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8.2.2. Key Species

The study considered potential impacts to anadromous and resident fish species. The steep gradient of the Chelan River canyon is believed to have prevented anadromous fish passage from the Columbia River into Lake Chelan. In terms of resident fish, while burbot, westslope cutthroat trout, pygmy whitefish and bull trout have been identified as key species, there was no evidence to suggest that the Lake Chelan Project has contributed in any measurable way to the decline in the population of such species.17

The region is also known to provide habitat for Bald Eagles. Again, the evidence assembled does not indicate that these birds are impacted by Lake Chelan Project, either in terms of foraging or nesting, given the range of this species.

17 The authors are aware that ongoing studies of dam impacts on anadromous species are being conducted as part of the FERC renewal process, as well as an on-going sedimentation study examining the impacts of maintaining the lake at maximum elevation on tributaries and their resident fish populations. As part of the certification renewal process, the current findings will be subject to revision if such field-based studies detect direct impacts on anadromous or resident fish populations.

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SECTION 9 — Emission Loadings

9.1. Greenhouse Gas Loadings

An estimate of the biomass in the flooded area behind the dam, based on surrounding vegetation and historic maps, was used to calculate the greenhouse gas loading attributable to the Lake Chelan Project. The remaining tons of greenhouse gases reported in this study primarily reflect the releases of carbon dioxide from on-going operations (e.g., vehicles used for transport, biomass)

18 The contributors to the greenhouse gas loading were CO2 and methane. In the LCSEA framework, the greenhouse gas assessment is based on actual temporal events and the resulting cumulative gas emissions, specific knowledge of the retention times and decay

functions of CO2 and methane in the atmosphere, and the relative radiation forcing properties of greenhouse gases, as reflected by their relative GWPs.

According to IPCC (1992), the decay of greenhouse gases can be represented by exponential functions

-t/T M = M0 e

where M is the remaining mass of the gas after t years, M0 is the mass at t=0, and T is the atmospheric lifetime in years, a specific parameter for each gas. The T values for three example gases are given below:

Atmospheric lifetime, years

CO2 (40 %) 5

CO2 (60 %) 150

CH4 10

N2O 150

The decay curves are shown in Figure 9.1.

18 Carbon monoxide is also a greenhouse gas. However, CO does not have as strong a GWP as do many other greenhouse gases, and sensitivity analysis in this study has shown that the relative contribution of CO to this indicator is insignificant when compared to the contribution of CO2 and methane.

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Figure 9.1 Greenhouse gas decay curves %

100 90 80 70 N2O 60 50 CO2 40 30 20 CH4 10 0 0 10 20 30 40 50 60 70 80 90 100 years

Specifically, over the time period of the hydroelectric power station, the retained level of greenhouse gas emissions from the first construction phase after 76 years is 0.0005 for

methane and 0.36 for CO2.

Hydroelectric power generation systems differ markedly from other electricity generating

systems in that virtually all of the CO2 and methane releases, as described above, are traced back to the initial construction and impoundment, and dissipate over time. Fossil

fuel based power plants, on the other hand, generate and release CO2 and methane as elementary flows from the system over time. A temporal characterization of the greenhouse gas loading is therefore critical to establish the relative loading on an accurate basis.

The calculated indicator loadings are shown in Table 9.1.

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Table 9.1. — Greenhouse Gas Loading for the Lake Chelan Project and Average WSCC Grid, based on 375 GWh/a

Impact Indicator Stressor Equivalent Receiving Greenhouse Gases Characteri- Stressor Environment Emission TOTALS Unit Operation Inventory Value zation values Characterization Loading (t CO2- (t CO2- (t CO2- Code Name Emission (t/a) Factor equiv./a) Factor equiv./a) equiv./a) 7118 Project construction CO2 493 1.0 493 0.38 187 CH4 0.11 24.5 3 0.001 0 187 7119 Project operation CO2 104 1.0 104 1 104 CH4 0.00 24.5 0 1 0 104 Total Sum 291

7115 WSCC average CO2 253,085 1.0 253,085 1 253,085 grid electricity CH4 648 24.5 15,876 1 15,876 Total Sum 268,961

9.2. Acidifying Chemical Loading

There was no significant acidification loading either from the construction or operations of the power facility.

The acidification stressors from the system are SOx and NOx. The indicator calculations are shown in Table 9.2. In the table, the acidification loading is expressed in SO2- equivalents. The potency characterization factor for SO2 is thus 1, while for NOx it is 0.7, according to molar ratios of the substances. The receiving environment endpoint characterization factor values have been estimated in accordance with the methodology outlined in Appendix 1. For the initial power station construction, the emissions happened long ago, and no effects would be measurable today; thus, the environmental characterization factor has been assessed to equal zero.

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Table 9.2. — Acidification Loading for the Lake Chelan Project and Average WSCC Grid, based on 375 GWh/a

Impact Indicator Receiving Acidification Stressor Equivalent Environment Characteri- Stressor Endpoint Emission TOTALS Unit Operation Inventory Value zation values Characterization Loading (t SO2- (t SO2- (t SO2- Code Name Emission (t/a) Factor equiv./a) Factor equiv./a) equiv./a) 7118 Project construction SOx 3.2 1.00 3.2 0 0.0 NOx 1.0 0.70 0.7 0 0.0 HCl 0.0 0.88 0.0 0 0.0 0.0 7119 Project operation SOx 0.4 1.00 0.4 0.35 0.1 NOx 1.1 0.70 0.8 0.35 0.3 HCl 0.88 0.0 0.35 0.0 0.4 Total Sum 0.4 7115 WSCC average SOx 2,214 1.00 2,214 0.35 775 grid electricity NOx 1,010 0.70 707 0.35 247 HCl 41 0.88 36 0.35 12 Total Sum 1,035

9.3. Ground Level Ozone Loading

There was no evidence of significant ground level ozone loading either from the construction or operations of the power facility.

Ground level ozone formation is calculated according to the method described in Appendix 1. The potency characterization factor for conversion of unspecified VOCs (which are emitted by diesel combustion engines used in transport and mining) into ethylene equivalents uses a POCP value of 0.42 (Heijungs et al. 1992). The amounts of harmful ozone with concentrations exceeding the critical concentration (40 ppb) are then

calculated for NOX and for ethylene equivalents using the receiving environment endpoint characterization factors shown (Table 9.3). In these results, the power station construction emissions again have an environmental characterization factor of zero, since the emissions do not give any effects today.

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Table 9.3. — Ground Level Ozone Loading for the Lake Chelan Project and Average WSCC Grid, based on 375 GWh/a

Impact Indicator Stressor Equivalent Receiving Ground Level Ozone Characteri- Stressor Environment Emission TOTALS Unit Operation Inventory Value zation values Characterization Loading Code Name Emission (t/a) Factor (t/a) Factor (t O3/a) (t O3/a) 7118 Project construction NOx 1.0 1.00 1.0 0 0.0 VOC, HC 0.1 0.42 0.0 0 0.0 CH4 0.1 0.01 0.0 0 0.0 0.0 7119 Project operation NOx 1.1 1.00 1.1 0.3 0.3 VOC, HC 0.3 0.42 0.1 2 0.3 CH4 0.0 0.01 0.0 2 0.0 0.6 Total Sum 0.6

7115 WSCC average NOx 1,010 1.00 1,010 0.3 303 grid electricity VOC, HC 42 0.42 18 2 35 CH4 648 0.01 5 2 9 Total Sum 347

9.4. Stratospheric Ozone Depletion Loadings

There was no source of stratospheric ozone depleting chemical emissions associated with the Lake Chelan Project; as such, no measured indicator result was recorded.

9.5. Hazardous Chemical (Air) Loadings

Emissions of PM-10 associated with the on-going operations of the Lake Chelan Project are extremely low. The only sources of such emissions from today’s hydropower system are maintenance vehicles and other infrequent transport related functions. Compared to the WSCC average, they are too small to measure on the same scale for on-going thermal unit emissions. Although about three tons of PM10 emissions were calculated to have been released in conjunction with the initial dam construction in the 1920s (Section 4.3.2, Table 4.2), these particulates are no longer relevant in today’s receiving environment, either in terms of environmental or human health impacts.

While no quantitative comparisons could be established for this study, the Lake Chelan Project clearly is far below the system power average.

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There were no measured indicator results for any other hazardous air emissions, either during the construction or on-going operations of the power facility. Again, any hazardous chemical loadings released during the construction phase in the 1920s would no longer have active measurement endpoints.

9.6. Eutrophication and TOC Loadings

Data provided by the Chelan PUD to SCS shows that the increase in potential eutrophication non-point source outputs caused no measured eutrophication or oxidizing chemical loadings to the Lake Chelan Project impoundment area or to its downstream river course water system.

9.7. Total Suspended Solids (TSS) Loadings for Turbidity or Sedimentation

Data provided by the Chelan PUD to SCS shows no measured turbidity or sedimentation loading from suspended solids either behind the Lake Chelan Project impoundment area or along its downstream river course water system. Indeed, the lake’s water quality, which has remained quite stable, is characterized as oligotrophic (i.e., low accumulation of dissolved nutrient salts, sparse algal growth, and high oxygen content due to low organic content).19

9.8. Hazardous Aquatic Loadings

There were no significant indicator results for any hazardous aquatic discharges, either during the construction or on-going operations of the power facility. The occasional oil that may come off of operating equipment is negligible, resulting in no measured indicator result when normalized to the functional unit.

9.9. Thermal Loading

There were no measured thermal loadings either behind the Lake Chelan Project impoundment area or along its downstream river course water system.

19 Results from an on-going Chelan PUD study of upstream tributary sedimentation resulting from maintaining lake levels at maximum elevation were not available for review at the time of this study. See comment, footnote 17.

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SECTION 10 — Residual Hazardous Wastes

10.1. Ash Wastes

Ash was generated as a waste product associated with the construction of the Lake Chelan Project. This waste amount serves as the impact indicator.

Outputs Construction, Plant System total, Operation total, ton/year ton/year ton/year

Slags and ash 12.4 -- 12.4

Although coal ash wastes are currently exempted under RCRA, the data are conclusive regarding its hazardous constituents and its fate and transport in the environment. As a result, the environmental characterization factor (ECF) is set equal to 1 at this time until additional about the constituents and regarding containment methods are available. As such, the amount of waste generated is equal to the reported as the impact indicator, and represents a worst-case for the indicator.

On the scale of impact indicator reporting (tonnage), no other measured residual hazardous wastes were generated during construction of the power station or during current operations of the power facility.

10.2. Radioactive Waste

Nuclear power was non-existent at the time of power plant construction, nor was there any measured contribution of nuclear power to any on-going operations.

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SECTION 11 — Study Results

11.1. Summary of LCSEA Results

The LCSEA impact indicator results for the Lake Chelan Project are summarized in Tables 11.1.a and 11.1.b. Relative eco-efficiencies compared to the system power average are presented in the right hand column.

Table 11.1.a — LCSEA Indicator Results Based on 375 GWh annual production basis – Resources and Ecosystem Disruption

CATEGORY INDICATORS LAKE WSCC UNITS ENVIRONMENTAL CHELAN AVERAGE SAVINGS AND ECO- EFFICIENCIES (expressed as “x” - times more efficient) ENERGY RESOURCE DEPLETION Coal Depletion 11 8,478 Tons of oil equiv. 8,467 toe Biomass Depletion NMIR20 Not Tons of oil equiv. Measured Oil Depletion 42 1,726 Tons of oil equiv. 1,684 toe Natural Gas Depletion 16 45,097 Tons of oil equiv. 45,081 toe Uranium Depletion NMIR 5,108 Tons of oil equiv. 5,108 toe Total Energy Resources 69 60,408 Tons of oil equiv. 60,339 toe = 875 x RENEWABLE RESOURCE DEPLETION Water Resource Depletion NMIR Not Equiv. Cubic meters Measured

MINERAL RESOURCE DEPLETION Mineral Resource Depletion NMIR Not Equivalent tons Measured

ECOSYSTEM DISRUPTION Terrestrial & Aquatic Habitats 422 Estimated Equivalent acres Below WSCC average range: 400- 1200 Key Species21 % increased mortality No measured - Bull Trout NMIR Not reductions in - Pygmy whitefish NMIR measured NMIR population due - Western Cutthroat Trout to Lake Chelan NMIR - Burbot Project operations - Bald Eagle NMIR

20 NMIR = No measured indicator result

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Table 11.1.b — LCSEA Indicator Results Based on 375 GWh production basis – Emissions and Wastes

CATEGORY INDICATORS LAKE WSCC UNITS ENVIRONMENTAL CHELAN AVERAGE SAVINGS AND ECO- EFFICIENCIES (expressed as “x” - times more efficient) EMISSION LOADINGS

Greenhouse Gas Loading 291 268,961 Equiv. tons CO2 268,670 = 924 x

Acidification Loading 0.4 1,035 Equiv. tons SO2 1034.6 = 2587 x Ground Level Ozone Loading 0.6 347 Equiv. tons ozone 346.4 = 578 x Stratospheric Ozone Depletion Loading NMIR Not Equiv. tons CFC-11 Below WSCC average measured PM-10 (inventory result) NMIR Not Equiv. tons PM-10 Below WSCC average measured Other Haz. Chemical (Air) Loading NMIR Not Equiv. kg. Below WSCC average measured Eutrophication Loading NMIR Not Equiv. tons P Below WSCC average measured Total Oxidizing Chem. (TOC) Loading NMIR Not Equiv. tons C Below WSCC average measured Total Suspended Solids (TSS) NMIR Not Equiv. tons C Below WSCC average measured Hazardous Aquatic Loading NMIR Not Equiv. Kg Below WSCC average measured Thermal Loading NMIR Not Degrees over ambient Below WSCC average measured RESIDUAL HAZARDOUS WASTES Ash Wastes 12.4 4483 Equiv. Tons 4,470.6 = 361 x Radioactive Waste NMIR Not Equiv. Tons Below WSCC average measured

11.2. Conclusions and Recommendations

11.2.1. Ecosystem Disruption Indicator

The Lake Chelan Project has been calculated to affect a total of 1,489 acres of terrestrial and aquatic habitat, with a net loss of 422.2 acres. On a 375 GWh annualized production

21 For these species, the baseline for comparison is the Lake Chelan catchment.

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basis, this translates into 1.13 acres of net loss per GWh. With respect to key species, none of the data reviewed for this study indicated that the Lake Chelan project adversely impacted the populations of such species.

Overall, the present impacts of human use and management on ecosystems are calculated to be insignificant; nor is the natural resource base of the watershed highly fragmented. With the exception of a modest number of erosion-related issues, and zones of deposition at the mouths of montane rivers, the system appears to be quite stable physically.

At this pilot study stage of assessment, it was not possible to precisely quantify the relative eco-efficiency of this indicator against the average ecosystem disruption for the WSCC interconnected grid. However, given the extremely low acreage of habitat loss per GWh production, and given SCS’s estimated range of ecosystem disruption associated with average power generation per GWh in the region, SCS is prepared to make a designation of the Lake Chelan Project as representing an “environmentally preferable” power production source in the region for this indicator.

11.2.2. Other Indicators

In all other impact indicator categories, the Lake Chelan Project represents an extremely low impact power generation source within the WSCC production pool.

As can be seen from the seven quantitative indicator comparisons provided in Table 11.1, and the relative bar lengths shown in the impact profile (Figure 11.1), the eco-efficiencies represented by the Lake Chelan Project are significant when compared to the WSCC average. For the remaining impact indicators, it was not necessary within the scope of this study to quantify the system power average to establish a comparison, since The Lake Chelan Project generates zero or negligible indicator results in these indicator categories. As the data system for the WSCC matures, it will be possible to establish the precise eco-efficiencies for the Lake Chelan Project under these indicators.

As mentioned in Section 4, no primary data were available to calculate the energy-related inputs during the construction phase of the dam in the mid-1920s. Instead, surrogate data were substituted from modern-day LCI models. These data reflect the current-day energy grid mix for the region, almost half of which is derived from coal or other fossil-fuel sources. As such, these data represented a worst-case scenario, since actual energy production at the time would be assumed to have been based much more extensively on local hydropower sources.

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ENVIRONMENTAL PERFORMANCE RATING based on results of a Life-Cycle Impact Assessment

Lake Chelan Hydropower — Chelan, Washington

Resources Depletion Results* Scale of Impacts Coal (toe) 11 Oil (toe) 42 Natural Gas (toe) 16 Uranium (toe) 0 Water Resources (eq cu. m3) negligible Mineral Resources (tons) negligible

Ecosystem Disruption Terrestrial and Aquatic Habitats (eq. acres) 422 Key Species (% increased mortality) negligible Emission Loadings and Hazardous Wastes

Greenhouse Gas (eq. tons CO 2) 291 Acidification (eq. tons SO 2) 0.4 Ground Level Ozone (eq. tons O 3) 0.6 Title III Hazardous Air Pollutants (eq. kg) negligible Eutrophication (eq. tons P) negligible Priority Water Pollutants (eq. kg) negligible Residual Hazardous Waste - RCRA (eq. tons) 12

toe = tons of oil equivalents Lower Higher eq. = equivalent

* Based on 375 GWh annual production Average WSCC Impacts (1998)

Figure 11.1 – Lake Chelan Project Impact Profile per 375 GWh production basis

11.2.3. Environmentally Preferable Energy Source

Based on the data, SCS is prepared to issue certification to the Lake Chelan Project as a “Certified Environmentally Preferable Energy Source” (Figure 11.2).

SCS Certified Environmentally Preferable Energy Source In every category of comparison, the Lake Chelan hydro power plant has lower environmental impacts than average energy production in the western U.S. power pool.

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Figure 11.2 – Lake Chelan Project Certified Environmentally Preferable Energy Source This certification is issued annually, and is subject to renewal based on continual demonstration that the hydro project continues to have an environmental performance rating better than the system power average.

11.2.4. Recommendations

This study has established the environmental footprint of the Lake Chelan Project on a life-cycle basis, based on data available at the time of the study. The data developed for the study could now be used, for instance, as the basis for calculating the comparative environmental impacts of displacing any portion of the Lake Chelan Project energy production with energy production from another power source.

The results of the study should be refined over time to reflect the findings of new research pertaining to any of the environmental impact indicators addressed in this report. The authors were apprised of several ongoing studies during the period of the current study; these have been noted in the report.

In addition, the results of the study could be further refined by developing additional environmental characterization data for the remainder of the WSCC grid generation sources. In addition, additional field studies to further characterize populations of listed key species in the study region would be beneficial.

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SECTION 12. — Practitioner Qualifications

Stanley Rhodes, Scientific Certification Systems

SCS is a leading U.S. practitioner of Life-Cycle Assessment (LCA) science. Using LCA, SCS has assisted companies and institutions in (1) determining the environmental burden profile of existing production operations, (2) evaluating the environmental advantages and trade-offs associated with various product and material choices, (3) assessing improvement strategies, (4) documenting environmental performance and achievements in the marketplace, and (5) enhancing corporate and institutional procurement programs to take environmental factors into consideration. SCS has also led an international effort to modernize the LCA technical framework through the development and implementation of Life-Cycle Stressor-Effects Assessment (LCSEA) methodology to solidify LCA's role as a leading environmental assessment tool.

Stanley P. Rhodes, Ph.D. is the founder, president, and chief executive officer of Scientific Certification Systems, Inc. (SCS). Upon earning his doctorate degree from Purdue University, Dr. Rhodes spent five years at Eastman Kodak in Rochester, New York, performing advanced exploratory confidential patent research as Senior Research Chemist and Research Group Head. Subsequently, he worked at Delta Labs in Rochester, performing extensive soil and water analysis. Dr. Rhodes has actively participated in the ISO 14000 process for over 5 years, and is presently a US expert for both the Type III labeling and Life-Cycle Impact Assessment Working Groups.

Dr. Rhodes is provides technical and management support on a variety of SCS Life-Cycle Stressor-Effects Assessment (LCSEA) projects. He is currently involved in LCSEA studies of consumer appliance manufacturing and electronic communications systems, forestry, pulp and paper operations and energy production systems.

James Wazlaw, CNEX

James Wazlaw is Director of Environmental Services at CNEX, a company specializing in consulting services for the power industry. Mr. Wazlaw has previously served as environmental manager at a major U.S. electric company. He has also served with Stone and Webster Engineering Corporation and the California Energy Commission where he

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was involved in siting and environmental evaluation of power plants. Mr. Wazlaw serves as project manager and technical manager for environmental management projects for CNEX’s projects in the U.S. and abroad. He has twenty-five years of experience in the field and holds a B.S. Geology Degree from the University of Massachusetts, Amherst and a Masters Degree in Business. For the past year, Mr. Wazlaw has worked with SCS to introduce the LCSEA concept to the electric power industry in the U.S.

Fjalar Kommonen, FFK Ltd.

Fjalar Kommonen holds a Master of Science degree in Process Engineering from the Åbo Akademi University in Finland. He has experience with process, energy and environmental tasks within the consulting company Ekono from 1959 to 1974, and was part of the Ekono management team from 1974 to 1985. When Ekono in 1993 merged with the Jaakko Pöyry Group he joined Soil and Water as Leading Consultant. At his retirement from Soil and Water Ltd. in 1999 he founded the independent company FFK Ltd, specializing in environmental consulting and software development. He is a member of the Finnish Paper Engineers' Association, SETAC Europe, and the European Association of Environmental and Resource Economists EAERE.

His professional experience, working with industry, government and finance institution clients, includes industrial process modeling, process modernization and optimization, energy strategy, environmental strategy, management consulting, profitability and feasibility studies, especially in the pulp and paper industry. A pioneer work, during 1994-1995, was the evaluation of environmental cost-benefit in a cement factory investment case, commissioned by IFC of the World Bank Group. Recently a study was completed for the Finnish Government regarding the cost efficiency and potential to reduce carbon dioxide emissions from the Finnish base industry, exploring the immediate and long term options for Finland to comply with the UN Rio declaration of greenhouse gas emission reduction. Life-cycle studies include, among others, steel-making, pulp and paper, packaging material, energy supply and food production processes. In a research work for the Kone Foundation in 1995 Fjalar Kommonen explored the possibilities to advance synergy between environment protection and business enterprises in Finland.

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Bibliography

Ackemann, W.C., White, G.F., and Worthington, E.B., 1973, Man-made lakes: Their problems and environmental effects. American Geophysical Union, Washington, D.C.

Boustead, I. Life-Cycle Inventory, Model 3, proprietary software.

Cassidy, R.A. and Dunn, P.E., 1985, Water temperature control and aerial oxygen consumption rates at a new reservoir, and the effects of the release waters. IN: Craig, J.F. and Kemper, J.B. (eds.), Regulated streams — advances in ecology, 3rd International Symposium on Regulated Streams, Plenum Press, NY, USA.

Frichknect, R., Hofstetter P., Knoepfel I., Swiss Federal Institute of Technology, Zürich; Dones R., Zollinger E., ed., Environmental Life-Cycle Inventories of Energy Systems: Methods and Selected Results, sponsored by the Swiss Federal Office of Energy, Zürich, August 1994.

Lindfors L.G., et. al., LCA-Nordic Technical Reports No. 1-10 and Special Reports 1-2, , Nordic Council of Ministers (TemaNord 1995:502), Copenhagen, 1995.

Lindfors L.G., et.al., Nordic Guidelines on Life-Cycle Assessment, Nordic Council of Ministers (Nord 1995:20), Copenhagen, 1995.

Lindroth, A. 1957, Abiogenic gas supersaturation of river water. Archiv für Hyrobiologie 53.

Morrison, K.A. and Thérien, N. 1995, Changes in mercury levels in lake whitefish (Coregonus clupeaformis) and northern pike (Esox lucius) in the LG-2 reservoir since flooding. Water, Air and Soil Pollution 80.

SCS 1997. Developing a Certified Eco-Profile for Safe Harbour Hydroelectric Power Generation based on Life-Cycle Stressor-Effects Assessment. Version July 14, 1997.

Strahler, A.N., Strahler, A.H. 1989. Elements of physical geography, 4th edition. John Wiley & Sons.

Scientific Certification Systems Page 51 The Lake Chelan Project LCSEA Study Report Final Report March 3, 2000

Swiss Federal Institute of Technology, Safety and Environmental Technology Group “Environmental Inventory Data for Energy Systems” (in German), Zürich 1997.

Webb, B.W. and Walling, D.E., 1993, Temporal Variability in the Impact of River Regulation on Thermal Regime and Some Biological Implications. Freshwater Biology 29.

In addition, see the bibliography in Appendix 2.

Scientific Certification Systems Page 52 Appendix 1

Life-Cycle Stressor-Effects Assessment (LCSEA) A Practitioner’s Manual

December 1999 Version 2.1

SCIENTIFIC CERTIFICATION SYSTEMS

© 1999. Scientific Certification Systems, Inc. and Soil and Water, Ltd. All Rights Reserved LCSEA Practitioner’s Manual, Version 2.0, Draft © 1999. Scientific Certification Systems, Inc. , Soil and Water Ltd.

LIFE-CYCLE STRESSOR-EFFECTS ASSESSMENT (LCSEA): A PRACTITIONER'S MANUAL

Principal Authors:

Stanley P. Rhodes, Ph.D., Scientific Certification Systems, Inc. Fjalar Kommonen, Soil and Water, Ltd. = Editorial Support:

Linda Brown — Scientific Certification Systems, Inc.

Acknowledgments:

The authors wish to acknowledge the many important contributions to the development of this methodology made by researchers in the field of life-cycle assessment. In addition, it has been crucial to test the methodology in actual industrial system case examples in order to validate its development. The authors wish to thank those individuals, corporations and industry associations who have made these case studies possible. LCSEA Practitioner’s Manual, Version 2.0, Draft © 1999. Scientific Certification Systems, Inc. , Soil and Water Ltd.

TABLE OF CONTENTS Page PREFACE i. PART I – INTRODUCTION TO LCSEA

CHAPTER 1 – KEY PRINCIPLES OF LCSEA 1-1 1.1. Historical Perspective 1-1 1.2. ISO DIS-14042 1-1 1.2.1. Mandatory Elements of DIS-14042 1-1 1.2.1. DIS-14042 Requirements for LCIA Used for Comparative Assertions 1-4 1.3. Overview of LCSEA Protocols 1-4 1.3.1. Functional Analysis for System LCA 1-5 1.3.2. Modeling Relevant Stressor-Effects Networks and Unit Operations 1-6 1.3.2.1. Stressor-Effects Networks 1-6 1.3.2.2. Unit Operations 1-6 1.3.3. Selection of Category Indicators and Measurement Endpoints 1-6 1.3.3.1. Uniform Set of Category Indicators 1-6 1.3.3.2. Grouping of Category Indicators 1-9 1.3.4. Collection of LCI and Environmental Data 1-10 1.3.5. Calculation of Category Indicator Results 1-11 1.3.5.1. Characterization Factor for Resource Depletion Indicators 1-12 1.3.5.2. Characterization Factors for Emission and Waste Loadings 1-12 1.3.5.3. Establishing Baselines 1-13 1.3.5.4. Calculating the Results 1-13 1.3.5.5. Units of Measure by Indicator 1-14 1.3.5.6. Selection of Core Unit Operations 1-15 1.3.5.7. Establishing Confidence Levels 1-15 1.3.6. Generation of LCSEA Impact Profiles 1-17

PART II – CALCULATION OF INDICATOR RESULTS

CHAPTER 2 – RESOURCE DEPLETION 2-1 2.1. Resource Stressor-Effects Networks 2-1 2.2. Calculating the Resource Depletion Factor (RDF) 2-2 2.3. Resource Depletion Calculations 2-3 2.4. Specific Resource Depletion Calculation Considerations 2-4 2.4.1. Energy Resources 2-4 2.4.1.1. Fossil Fuels 2-4 2.4.1.2. Uranium Resources 2-5 2.4.1.3. Energy Resource Reserves 2-7 2.4.2. Renewable Resources 2-8 2.4.2.1. Wood and peat 2-9 2.4.2.2. Water 2-11 2.4.2.2.1. Defining the Stressor-Effect Network 2-11 2.4.2.2.2. Calculating Water Resource Depletion 2-13 2.4.2.3. Marine Resources 2-13 2.4.2.4. Integrating Recycling into Resource Depletion Calculations 2-13 LCSEA Practitioner’s Manual, Version 2.0, Draft © 1999. Scientific Certification Systems, Inc. , Soil and Water Ltd.

2.4.3. Mineral Resources 2-15 2.5. Regional Considerations 2-16

CHAPTER 3 – PHYSICAL DISRUPTION 3-1 3.1. Defining the Stressor Effect Networks 3-1 3.1.1. The Two Ecosystem Disruption Indicator 3-1 3.1.2. Modeling the Ecosystem Disruption Stressor-Effects Networks 3-1 3.1.3. Selecting the Measurement Endpoint(s) 3-2 3.2. Calculating the Net Depletion/Gain of Individual Habitats 3-2 3.2.1. Classification and Characterization of Baseline Habitats 3-3 3.2.1.1. Initial Baseline 3-4 3.2.1.2. Natural Trajectory Baseline 3-5 3.2.2. Landscape Scale Assessment 3-5 3.2.3. Environmental Characterization Factors (ECFs) 3-6 3.2.3.1. Establishing ECFs for Individual Habitats 3-6 3.2.3.2. Establishing ECFs between Different Habitats 3-7 3.2.4. Calculation of Overall Habitat Depletion/Gain 3-8 3.3. Calculating the Depletion of Key Species 3-8

CHAPTER 4 – EMISSION LOADINGS 4-1 4.1. Greenhouse Gas Loading 4-2 4.1.1. Defining the Stressor-Effect Network 4-2 4.1.2. Characterizing the Stressors 4-2 4.1.2.1. The Greenhouse Gas Stressors 4-2 4.1.2.2. Other Stressors 4-3 4.1.3. Characterizing the Category Indicator 4-4 4.1.4. Characterization Factor – Global Warming Potential (GWP) 4-5 4.1.5. Calculating the Greenhouse Gas Loadings 4-7 4.1.6. Limitations 4-8 4.2. Acidification Loading 4-9 4.2.1. Defining the Stressor-Effect Network 4-9 4.2.2. Characterizing the Receiving Environment 4-9 4.2.2.1. Soil Acidification 4-9 4.2.2.2. Surface Waters 4-10 4.2.2.3. Threshold Characterization 4-10 4.2.2.4. Spatial Characterization 4-11 4.2.2.5. Example: Regional Environmental Characterization Factors 4-12 4.2.3. Calculating Acidification Loadings 4-12 4.2.4. Current Limitations 4-13 4.3. Ground Level Ozone Loading 4-14 4.3.1. Defining the Stressor-Effect Network 4-14 4.3.2. Characterizing the Receiving Environments 4-14 4.3.3. Calculating the Ground Level Ozone Loading 4-15 4.3.4. Calculating Indicator for Regions Outside of Europe 4-17 4.3.5. Limitations 4-18 4.4. Hazardous Chemical Loadings (Water) 4-18 4.4.1. Defining Stressor-Effects Networks 4-19 4.4.2. Threshold Level Assumptions 4-20 4.4.3. Accepted Threshold Levels (Water) 4-20 4.5. Hazardous Chemical Loadings (Air) 4-21 4.5.1. Particulates (PM10) – Human Health 4-22 4.5.1.1. Characterization of the Measurement Endpoint 4-22 4.5.1.2. Threshold and Non-Linearity Characterization 4-22 LCSEA Practitioner’s Manual, Version 2.0, Draft © 1999. Scientific Certification Systems, Inc. , Soil and Water Ltd.

4.5.1.3. Calculation of Indicator Results 4-23 4.5.1.4. Case Example 4-23 4.5.2. Other Hazardous Chemical Loadings (Air) 4-26 4.6. Stratospheric Ozone Depleting Chemical (ODC) Loadings 4-26 4.7. Total Oxidizing Chemical Loadings for Aquatic Systems 4-27 4.8. Eutrophication Loading (Aquatic) 4-28 4.8.1. Background 4-28 4.8.2. Defining the Stressor-Effects Network 4-28 4.8.3. Selection of Measurement Endpoints 4-29 4.8.4. Defining Threshold Levels 4-30 4.8.5. Characterizing the Receiving Environment in Finland 4-30 4.8.6. Calculating the Eutrophying Chemical Loading 4-32 4.9. Thermal Loadings 4-34 4.10. Noise 4-34 4.11. Radio Frequency Emission Loading 4-34

CHAPTER 5 – RESIDUAL HAZARDOUS WASTE 5-1 5.1. Nonhazardous Wastes 5-1 5.2. Hazardous Ash and Heavy Metal Wastes, Radioactive Wastes 5-1

PART III. LCSEA Impact Profiles … to be inserted in later version

PART IV. Future Developments … to be inserted in later version LCSEA Practitioner’s Manual, Version 2.0, Draft © 1999. Scientific Certification Systems, Inc. , Soil and Water Ltd.

FIGURES AND TABLES Page

FIGURES Figure 1.1. Diagram of the LCSEA Technical Framework 1-5 Figure 1.2. Linking Unit Operations under LCSEA 1-13 Figure 2.1. Stressor-Effect Network for Net Resources Depleted 2-2 Figure 2.2. The Energy Resource Depletion Case 2-4 Figure 2.3. Resource Depletion with Accretion but Without Recycling 2-8 Figure 2.4. Stressor-Effect Network for Water Resources 2-12 Figure 2.5. Resource Depletion with Accretion, Including Recycling 2-14 Figure 2.6. Mineral Resource Depletion Including Recycling 2-15 Figure 3.1. Simplified Schematic of the Ecosystem Disruption Stressor-Effects Network 3-2 Figure 3.2. The Initial and Natural Recovery Trajectory Baselines 3-3 Figure 3.3. Measuring the Current System Against the Initial Baseline 3-4 Figure 3.4. Measuring the Current System Against the Natural Recovery Trajectory 3-5 Baseline Figure 3.5. Example of Factors Influencing the Population of a Specific Anadromous 3-9 Fish Species Figure 4.1. Simplified Greenhouse Gas Stressor-Effects Network 4-2 Figure 4.2. Principle for Calculating Radiative Forcing from Emission Scenarios 4-5 Figure 4.3. Calculated Radiative Forcing as Function of Time (Peat) 4-8

TABLES Table 1.1.a. LCSEA Category Indicators — Resources and Physical Disruption 1-7 Table 1.1.b. LCSEA Category Indicators — Emission Loadings and Residual 1-8 Hazardous Wastes Table 1.2.a. Category (Impact) Indicators and Units of Measure: 1-14 Resources and Physical Disruption Table 1.2.a. Category (Impact) Indicators and Units of Measure: 1-15 Emission Loadings and Residual Hazardous Wastes Table 2.1. The Fossil Fuel and Uranium Primary Energy Demand (Gtoe/yr-1990) 2-6 Table 2.2. Deriving RDF(50) from Gas Resource Reserves and Production 2-6 Table 2.3. Fossil Fuels Depletion Calculations 2-7 Table 2.4. Fossil Fuels and Uranium Reserves at the End of 1993 2-8 Table 4.1. Most Important Greenhouse Gases 4-3 Table 4.2. GWPs Following Instantaneous Release of 1Kg of Each Trace Gas 4-7 Table 4.3. Typical Input and Output Values for Sulfur and Nitrogen to Ecosystems 4-12 In Various European Areas Table 4.4. SOx and NOx Deposition – Europe 4-12 Table 4.5. Calculating the Cumulative Acidification Loading for the System 4-13 Table 4.6. Arithmetical Example Performed for Eastern Part of Svealand, Sweden 4-16 Table 4.7. Calculation of the Receiving Environment Equivalency Factor 4-16 Table 4.8. POCP Values for Four Different Areas Within Europe 4-17 Table 4.9. Proportion of Year During Which Ozone Levels Exceed 60ppbv 4-18 Table 4.10. EPA Aquatic Toxicity Criteria 4-21 Table 4.11. Fuels Used in 1993 4-24 Table 4.12. Emissions Contributing to PM10 Concentration 4-24 Table 4.13. Health Effects from PM10 Emissions 4-25 Table 4.14. Characterization of Trophic Types According to Selected Characteristics 4-29 Table 4.15. Trophic Characterizations According to Chlorophyll-a 4-29 Table 4.16. Water Quality Characterization Developed for the Coastal Area 4-30 Near Kaskinen, Finland Table 4.17. Water Quality Characterization Developed for the Southwestern Finland 4-33 LCSEA Practitioner’s Manual, Version 2.0, Draft © 1999. Scientific Certification Systems, Inc. , Soil and Water Ltd.

Archipelago and Coast Table 4.18. Defining Concentration Threshold Values for this Study 4-30 Table 4.19. Mill Contribution to Loadings 4-32 Table 4.20. Water Quality Measurements and Comparison to Threshold 4-33 Table 4.21. LCSEA Eutrophication Loading for Fine Paper 4-34 LCSEA Practitioner’s Manual, Version 2.0, Draft © 1999. Scientific Certification Systems, Inc. , Soil and Water Ltd.

P REFACE

Life-Cycle Stressor-Effects Assessment (LCSEA) is a cradle-to-grave assessment technique used to quantify a set of indicators representing the local, regional and global impacts of a selected industrial system on the environment. Together, these indicators form an "impact profile" of the system.

The LCSEA technical framework was developed by SCS and Soil and Water, Ltd. in international collaboration with other LCA practitioners, environmental assessment professionals, and industry stakeholders. It builds extensively on the work of pioneers in the field of LCA, including many of the contributors to the Nordic Guidelines and SETAC monograms, and has been made publicly available for comment in initial draft form through ISO/TC 207(SC3/ WG1/ Type III Task Group/N22, 1997). For instance, the 1993 SETAC monogram identified important areas of additional research for future technical framework development: 1) setting appropriate boundaries at the outset of the project and iteratively reviewing these boundary conditions; 2) defining stressor-impact chains and their implications along the life-cycle; and 3) identifying additional characterization data needs, including the need to determine whether or not methods developed for human health and environmental risk assessments could be adapted to the practice of LCA. The LCSEA technical framework is intended to serve the needs of a wide range of industry users, in supporting both internal and external applications. Internal applications include environmental performance evaluation and internal environmental improvement strategies. External applications include environmentally preferable procurement (EPP) and environmental labeling programs. The LCSEA technique has also been designed to comply with the requirements of the ISO/DIS 14042 standard, including those for comparative assertions.

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C HAPTER 1. Key Principles of LCSEA

1.1. Historical Perspective Life-cycle assessment (LCA) was first developed as a systems-oriented tool for tracking energy flows in industrial systems. LCA is an extended input/output analysis of an industrial system across a “cradle-to-grave” scope.1 Under the assessment framework, individual unit processes involved in the extraction, production, distribution, use and disposal of a product or service performing a defined function are linked together. For many years, LCA was limited in practice to life-cycle inventory (LCI) — i.e., the identification and quantification of system inputs and outputs, resulting in an overall mass and energy balance for the system. Computer models were developed in Europe and the US to conduct the LCI calculations needed to complete these extended mass and energy balances. Over time, these models have been extended to include data pertaining to a range of emissions and wastes to air, water, and soils. An increasing number of LCA practitioners and commissioners have sought to use this assessment tool for environmental decision-making purposes. However, while LCI quantifies material and energy flows, it cannot be used to examine the significance of these flows on the environment. The need for a methodology capable of connecting system inputs and outputs to actual environmental effects thus became apparent, leading to the development of life-cycle impact assessment (LCIA) practice, and ultimately, to the drafting of an international standard (ISO/DIS-14042).2 1.2. ISO DIS -14042 1.2.1. Mandatory Elements of DIS-14042 The LCIA framework is based upon the establishment of links between the activities of an industrial system and any resulting local, regional or global impacts on the environment. As a practical matter, the assessment boundaries must therefore be extended into the affected environments, and there must be an ability to link system inputs and outputs to their associated environmental effects through defined environmental mechanisms. As defined in DIS-14042, Clause 5, LCIA consists of three mandatory elements: 1) the selection of impact categories, category indicators and models; 2) the assignment of LCI

2. 1 In common practice, LCA’s are frequently conducted on a partial basis, such as cradle-to-gate (i.e., from raw material extraction through product manufacturing) instead of cradle-to-grave (i.e. through disposal). 2 A number of collaborative publications contributed toward the development of the LCIA standard. Among the most prominent of these are the Nordic Guidelines on Life-Cycle Assessment (1995), including the LCA-Nordic Technical Reports 1-10, and the Society for Environmental Toxicology and Chemistry (SETAC) publications A Conceptual Framework for Life-Cycle Impact Assessment (1993) and Towards a Methodology for Life-Cycle Impact Assessment (1996).

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results to their appropriate impact categories; and 3) the calculation of category indicator results. Impact categories are intended to describe known environmental issues. The physical, chemical or biological environmental mechanisms that link a system input, output or activity (i.e., the “stressor”) to an observable environmental impact (i.e., the “effect”) =can be modeled as “stressor-effect” (or “cause-impact”) networks.3 These environmental mechanisms include effects on ecosystems, effects on human health, and the depletion or accretion of natural resources. LCI results are assigned to their respective impact categories. In some cases, inventory results are assigned exclusively to one impact category. For instance, the release of carbon dioxide gases is assigned to the global warming category. In other cases, inventory results may be assigned to one or more impact categories, when the environmental mechanisms being represented are either parallel or serial in nature. For example, NOX emissions might be assigned to both ground level ozone formation and to acidification depending upon the regional and local environmental status and dispersion of the emission. Using the same considerations, SOX emissions might be allocated between acidification and human health effects. Indicators are identified to represent each stressor-effect network, as described in Section 1.3.3.1 below. The draft international standard refers to these as “category indicators.” Indicator results are the effective amounts of resource depletion, physical disruption, emission loadings, and wastes associated with all unit operations included in the scope of the study. Indicator results are additive (as opposed to cumulative) across all unit operations within the life-cycle. The calculation of category indicator results involves the following procedure: 1) For each unit operation, LCI results are converted to common equivalent units using stressor characterization factors that are based upon relative potency and the degree to which such converted equivalent units affect the environment; 2) Environmental characterization factors, which integrate fate and transport data and the severity, duration and reversibility of the measurement endpoints, are then applied. 3) Once the results are fully characterized for each unit operation, indicator results are aggregated across all unit operations within the system. Indicators are only “environmentally relevant” if they have clear links to the identified impact endpoint and if they retain the necessary spatial, temporal, dose-response and

2. 3 In its 1993 publication, A Conceptual Framework for Life-Cycle Impact Assessment , SETAC provided a number of important contributions toward the development of a technical, scientifically defensible LCIA framework, including: • introduction of the stressor concept as the bridge between the inventory and impact components of LCA; • descriptions of the linkages between stressors and environmental effects through “stressor-impact chains;” • recognition of the need for spatial, temporal and dose-response characterization of input/output data ; • recognition of issues related to the selection of scientifically defensible impact indicators; and • recognition of issues not traditionally included in LCI, such as land use, noise, and thermal releases. Stressors were defined as environmental releases or other disturbances associated with industrial systems that can be linked to human health, ecological and resource depletion impacts.

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threshold resolutions. Environmental data are used to characterize the environmental relevance of category indicators. Such data is related to the intensity of projected impacts, spatial aspects, temporal aspects and the reversibility of a given impact. Clause 5.2 sets forth the criteria for environmental relevance as follows. The characterization of each indicator should include:

· The environmental condition and intensity of the category endpoint(s), · The spatial extent of projected impacts on category endpoints, · The temporal aspects, duration, resident time, persistence, timing, etc. of projected impacts on category endpoints, · The reversibility of projected impacts on category impact endpoints, and · The uncertainty of projected impacts with respect to category endpoints.

1.2.2. DIS-14042 Requirements for LCIA Used for Comparative Assertions As described in DIS-14042 Clause 9, LCIA that support comparative assertions must conform with the following additional requirements:

· A comprehensive set of indicators must be identified and results calculated. · Comparisons may only be made on an indicator-by-indicator basis. · Indicators must be scientifically and technically defensible, and if possible, internationally accepted. · Indicators must be environmentally relevant. · Sensitivity and uncertainty analysis must be included in the study · No weighting (i.e., the process of converting indicator values by using numerical values based on value choices) are allowed.

1.3. Overview of LCSEA Protocols This section provides an overview of the LCSEA framework, as depicted in Figure 1.1. Further discussion regarding the calculation of specific indicators is contained in Chapters 2-5 of this text.

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GoalFu ancntiodna Sl Acnaolypisisng

Modeling of Relevant Stressor-Effects Networks/Unit Operations

Selection of Category Indicators and Measurement Endpoints

Collection of Unaggregated LCI & Environmental Data

Calculation of Indicator Results

Generation of LCSEA Impact Profile

Iterative process

Sccieienntiftifiicc Ceertrtifiificcatatiionon Sysstetemss Figure 1.1. — Diagram of the LCSEA Technical Framework

1.3.1. Functional Analysis for System LCA LCA’s historic support from expert, industry and government circles derives from its foundation as a systems analysis tool. LCA techniques evolved specifically to link together the unit operations of a defined system that performs a well-defined function. In order to conduct a system LCA, a complete and accurate functional analysis must be conducted. The specific steps of this functional analysis are as follows • Define the function to be studied • Describe the related industrial system • Identify and describe the unit operations • Select the LCSEA functional unit • Establish functional equivalencies and functional enhancements There is a distinction between system LCA and product LCA. Oftentimes, products are operationally interconnected within a larger system of products and services, such that the choice of one product, and the efficiency of its use, affects the choice and use of other products. Assessing the life-cycle of a specific product without the context of the larger system function often leads to distortion in evaluating the environmental relevance of indicator results. System analysis ensures that all of the various products that together perform specific functions within a system context (i.e., the “system components”) are assessed in an integrated framework. The value of a system LCA becomes particularly apparent when developing data to support comparative assertions among competing systems. If the two systems to be compared are not performing the same function, then equivalence cannot be established. If products only perform their function in the context of a larger system of products and services, then this larger system should be defined.

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The LCSEA functional unit reflects both the function and the scale of the system. As such, the system must include the actual unit operations located at specific locations during a specified time period, and include the associated scale of production and product use.

1.3.2. Modeling Relevant Stressor-Effects Networks and Unit Operations 1.3.2.1. Stressor-Effects Networks A central objective of an LCSEA study is to establish an “impact profile” for an identified industrial system. This profile serves as a representation of the degree to which the system impacts the environment, based on observed environmental mechanisms. In order to develop the impact profile, it is first necessary to identify and model these environmental mechanisms. "Stressor-effect networks" are developed to model the relationship of a system input, output, or other activity (the "stressor") to an effect on the environment. Examples of specific stressor-effect networks are diagrammed in Chapters 2- 5 below.

1.3.2.2. Unit Operations A distinguishing characteristic of all LCA studies is the identification and linking of unit operations that together comprise a system and perform a defined function. Through this process, LCA models have been able to structure the data to support the necessary iterative calculations. Within the LCSEA framework, the identified environmental mechanisms are modeled with respect to individual unit operations that are characterized both spatially and temporally. The system unit operations are fully diagrammed and described. Once this diagramming is completed, major industrial processes and chemical flows can be identified.

1.3.3. Selection of Category Indicators and Measurement Endpoints 1.3.3.1. Uniform Set of Category Indicators System LCA requires the establishment of a uniform set of category indicators for all components and operations of the system. “Category indicators" are quantifiable links along the stressor-effect network that can reflect the relationship of a system stressor (input, output, or other system activity) to an effect on the environment (the "category endpoint"). While a given component or aspect of an operation might not always produce results for all of the indicators, when combined with other components and operations, the overall set of indicators should represent a uniform matrix. Without such a uniform set of indicators, the effects from various components and industry operations could not be integrated into an overall LCIA profile. Moreover, before comparisons can be made among systems performing the same function, the systems must be comparable on the basis of a uniform set of indicators. According to ISO/DIS 14042, for a given category, indicators can be chosen anywhere along the stressor-effect network from the initial inventory input or output to the measurement endpoint, depending on the certainty and the availability of data to support the calculation. Generally speaking, the closer the choice of the indicator is to the measurement endpoint, the greater the environmental relevance of the indicator results. Though indicator results are quantitatively linked to selected category endpoints through the application of characterization (equivalency) factors, they may be only qualitatively linked

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to associated environmental effects. For example, global warming potentials (GWPs) provide a measure of the radiative forcing potential of greenhouse gases, but do not provide a measure of the degree to which these gases cause endpoint effects such as local flooding, droughts, sea level rise, changes in growing seasons, etc. Traditionally in LCIA, category indicators have been limited to global impacts. This limitation has been largely overcome by the LCSEA technique described in Chapters 2-5. The indicators listed in Tables 1.1.a. and 1.1.b. represent a full set of indicators.

Table 1.1. a — LCSEA Category Indicators – Resources and Ecosystem Disruption4

INDICATORS ASSUMPTIONS AND PROTOCOLS

ENERGY RESOURCE DEPLETION5 Coal Depletion Coal is assumed to be a global resource. Biomass Depletion Assumed to be regionally-based resources. Oil Depletion Oil is assumed to be a global resource, and includes not only fuels for fleet but also petroleum products such as process oils, lubricants, etc. Natural Gas Depletion Natural gas resources are assumed to be regionally based resources. Uranium Depletion Uranium is assumed to a global resource. Hydraulic Energy Resource Depletion (Under Hydraulic resources are site-specific, referring to the potential electric review, 1999) power generation from falling water at a given site. RENEWABLE RESOURCE DEPLETION Water Resources (by specific indicator) Water may be divided into indicators such as potable water, gray water, and industrial grade water reserve bases. Wood and Paper Resources Marine Resources Commercial fish species MINERAL RESOURCE DEPLETION Tonnage Metal Resources (by specific metal) This indicator includes a wide assortment of metals utilized on a major tonnage basis. Precious Metal Resources (by specific metal) These metal resources have significant depletion rates on a 50-year timeframe. Other Minerals (by specific mineral) These minerals are consumed in production processes. Examples are limestone used in steel making. ECOSYSTEM DISRUPTION Terrestrial & Aquatic Habitat Direct alteration caused by physical or mechanical industrial activity. (due to physical disruption) Key Species Direct alteration caused by physical or mechanical industrial activity.

2. 4 The indicator groupings are discussed in Section 1.3.3.2 below. Additional indicators and sub-indicators may be identified. 5 There is no “net” fossil fuel depletion, since all fossil fuels burned for energy are irreversibly converted to carbon dioxide. For coal used in steel-making, the embodied energy portion is considered a non-fuel coal resource, while the thermal energy use is accounted for under “fossil fuel depletion.” For coal used in cement production, all coal is accounted for fossil fuel depletion.

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Table 1.1. b — LCSEA Category Indicators – Emission Loadings and Residual Hazardous Wastes

INDICATORS ASSUMPTIONS AND PROTOCOLS

EMISSION LOADINGS Greenhouse Gas Loading See Chapter 4 Acidification Loading See Chapter 4 Ground Level Ozone Loading See Chapter 4 Stratospheric Ozone Depletion Loading Defined under the Montreal Protocol and subsequent international agreements. Hazardous Chemical (Air) Loading Defined under various national government jurisdictions as “sufficient (by specific indicator) for listing”; in the U.S., includes Hazardous Air Pollutants (HAPs) listed in Title III of the 1990 CAAA, plus criteria pollutants not covered under other indicators for which there are National Ambient Air Quality Standards in CAAA, Title I. Eutrophication Loading See Chapter 4 Total Oxidizing Chemical (TOC) Loading See Chapter 4 Hazardous Aquatic Loading Defined under various national government jurisdictions as “sufficient (by specific indicator) for listing”; in the U.S., includes Toxic Water Pollutants listed in the U.S. Clean Water Act Thermal See Chapter 4 RESIDUAL HAZARDOUS WASTES Heavy Metal and Ash Wastes Defined under national government jurisdiction as “sufficient for listing.” Includes both post-treatment and non-treatable wastes that are landfilled or held in storage. High Level Radioactive Waste Defined under national government jurisdiction as “sufficient for listing.” Medium Level Radioactive Waste Defined under national government jurisdiction as “sufficient for listing.” Low Level Radioactive Waste Defined under national government jurisdiction as “sufficient for listing.”

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For hazardous air emission and hazardous aquatic loadings, there are three possible types of indicators: (1) speciated inventory6 indicators (2) equivalent inventory indicators, and (3) effects-based indicators. (See Chapter 4) Much of the required environmental and epidemiological data required to establish measurement endpoints for hazardous air emission and hazardous aquatic loadings is current lacking.7

1.3.3.2. Grouping of Category Indicators As discussed in DIS-14042, the grouping of related category indicators helps to clarify the interrelationships between indicators of a similar nature. Indicators grouped together have common units of measure based on established interrelationships. Grouping is also critical in summarizing the system's environmental performance in an impact profile, and in establishing environmental relevance of the indicators within a given group. Finally, grouping also helps prevent unintended comparisons between unrelated indicators. The category indicators listed in Tables 1.1a and 1.1.b have been assigned to six groupings.

· Energy Resource Depletion - This grouping comprises the indicators for all resources used to generate heat or electricity. (See Chapter 2.) In this case, the resource characterization factor equation is relatively simple, given that recycling, reuse and accretion do not apply. The indicators of this grouping generate a common product — thermal energy or electricity — and have a unified unit of measure —tons of oil equivalent, or “toe”.

· Renewable Resource Depletion - This grouping contains all non-energy resource indicators that may potentially be replenished, or accreted, through natural processes, as well as depleted through human intervention or through natural processes. (See Chapter 2.) A unified characterization factor has been established within the LCSEA technique to permit comparisons among the depletion or

2. 6 The speciation of LCI data from current practices must be adjusted in order to conform the requirements of the LCSEA technique. For instance, while LCI results may be gross aggregations of many compounds, these aggregations may have no environmental relevance. One example is AOX (absorbable organic halides) emissions, which are generally aggregated under one sum total, even though this class of chlorinated hydrocarbons consists of up to 10,000 different chemical compounds representing a spectrum of impacts and half-lives in the environment. As shown below, the initial discharge values of AOX are radically different than the bioaccumulative fraction.

Type of bleaching Typical AOX values for kraft EOX values for softwood pulp bleaching effluent kraft pulp bleaching effluent (bioaccumulative fraction) Conventional6 3.7 - 6.8 kg/t 0.008 - 0.07 kg/t Elemental Chorine Free (ECF) 0.9 - 1.7 kg/t 0.0009 - 0.01 kg/t

7 An exception is the particulate matter (PM10) emissions loading indicator, for which there is extensive environmental and epidemiological data available to support health-based indicators (e.g.,: Hospital Admissions, Emergency Room Visits, Symptoms, Restricted Activity Day, Bronchitis in Children, Chronic Bronchitis in Adults, Asthma Attacks, Increase in Mortality). However, while it is conceivable that the equivalent tons of PM10 emissions could be converted quantitatively into the number of people affected, the uncertainty surrounding this dose/response relationship generally precludes the inclusion of such effects-based indicator results.

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accretion of various resources. The equation by which this characterization factor is calculated takes into consideration the size of the reserve base, the amount of material used, the rates of recycling and reuse, and the rate of natural accretion.

· Mineral Resource Depletion - This grouping contains all metal and mineral resource indicators — those that cannot be replenished through natural processes. (See Chapter 2.) The characterization factor equation for this group of indicators is identical to that of the renewable material resources; however, in this case, the rate of natural accretion equals zero. Depletion for metal indicators can be mitigated through recycling and reuse.

· Physical Disruption - The indicators in this grouping reflect those impacts on habitats or key species that occur in the receiving environment as a result of physical or mechanical alterations of the terrestrial or aquatic landscape caused directly by the physical or mechanical activities of the system. (See Chapter 3.) Because of this impact on receiving environments, one possibility is to assign these indicators to the grouping of emission loadings. However, these indicators do not fit easily into such a grouping since their unit of measurement is not an emission loading, but rather, a measure of the actual depletion (or accretion) of habitats/species which has occurred. As a result, a separate Physical Disruption grouping, has been identified. For the greatest transparency, habitat classes and key species would remain deaggregated under this grouping.

· Emission Loadings - This grouping of indicators has a long history within environmental impact assessment and LCIA practice; nevertheless, there exist very limited interrelationships among the indicators of this group. (See Chapter 4.) Each is governed by its own unique characterization factor that has little or no linkage to other characterization factors within the group. Although often reliant on fate and transport models, each stressor-effect network relies upon different measurement endpoints and type of indicators. While the grouping does have similar units of measure (equivalent tons), these units invite comparisons of loadings between indicators that cannot be justified. Because of the differences in endpoints, comparisons should be conducted strictly on an indicator-by-indicator basis. LCIA profiles normalized against industry baselines may therefore offer a valid basis for comparative assessments by indicator (See Conclusions and Recommendations).

· Residual Hazardous Waste - The indicators in this grouping are frequently associated with those in the emission loadings grouping; however, this association is qualified because these indicators rarely represent actual releases to the environment. Instead, this grouping represents contingency risk of release to the environment. (See Chapter 5.) The characterization factors to be used in comparing different residual hazardous wastes stream are under development, and there does appear to be a potential to establish a unified characterization factor for this grouping.

1.3.4. Collection of LCI and Environmental Data Once the stressor-effects networks are modeled, and category indicators and measurement endpoints are identified, the actual data collection process can begin.

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Two types of data are collected: 1) LCI data (i.e., the inputs and outputs of each unit operation) and 2) environmental data (i.e., data pertaining to the "providing" and "receiving" environments from which inputs are obtained or into which outputs are released). Environmental data are used to characterize the LCI data in order to calculate indicator results. (See discussion in Section 1.3.5. below.) Examples of the environmental data include: • local/regional exposure data; • background concentration levels; • exceedance of threshold levels by GIS mapping for acidification and ground level ozone formation; • size of reserve base for a given resource; • recycling rates for a given material, and number of times recycled; and • composition and structure of floral and faunal types. Both LCI data and environmental data must be spatially and temporally appropriate for each unit operation. LCI data remain unaggregated to ensure that indicator calculations are environmentally relevant. To increase confidence in the final results, the following basic information should be collected for individual unit operations whenever possible:

· Characterization of local and regional receiving environments, ownership, scale of production, date of LCI and environmental data. · Description and age of technologies representing the unit processes of the unit operation. · Description of all known environmental control technologies and their associated efficiencies. · General information about conditions in local and regional receiving environments (e.g., lack of primary, secondary and tertiary water treatment infrastructure, lack of control technologies for air emissions, regional use of ozone depleting substances, etc.). This information makes it possible to develop preliminary estimates of selected indicator results if precise quantification is unavailable, allowing for inclusion of a broad range of issues that are normally excluded in LCI models or for which no data are available to establish environmentally relevant indicators. Using preliminary indicator results, it is often possible to pinpoint unit operations for which additional data should be collected. Using this technique helps to streamline LCI data collection while at the same time delving deeper into upstream unit operations.8

1.3.5. Calculation of Category Indicator Results

2. 8 For instance, unit operation specifications provide some accountability for localized issues associated with hazardous chemical releases, such as eco-toxicity or localized health impacts, and for global issues such as ozone- depleting substance (ODS) releases. LCSEA techniques allow a first iteration accounting of these emission loadings. In the case of electronic suppliers, for instance, components sourced from developing countries could be flagged as potentially having higher ODS loadings per unit produced than products manufactured in comparable North American production facilities. To calculate environmentally relevant indicator results, additional information would be collected by those flagged operations.

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Once the LCI data and environmental data are collected, it is possible to calculate the category indicator results. Specific calculation methods for each indicator are discussed in Chapters 2-7 below.

1.3.5.1. Characterization Factor for Resource Depletion Indicators For most net resource depletion calculations, there are no stressor characterization factors. An exception is the case of energy resources. These resources can be characterized in terms of their relative energy content (e.g., tons of oil equivalent).

The environment characterization factor, referred to as the “resource depletion factor,” is calculated for each resource as follows:

(å W - N) (DT) + (Rb - R) Resource Depletion Factor (RDF) = ______

R + (å Simisi ) (DT)

Where: å W = Cumulative amount of wastes of a specific resource from all uses (primary "P" and subsequent "S"). N = Rate of natural replenishment, or "gain," of the resource within the defined reserve base.

DT = A specified period of time. (T1 = initial date) R = The total amount of resources currently available in the defined reserve base from which the industrial system draws its resources.9 Rb = A baseline reserve base that represents the optimum resource capacity for a defined geographic area at T1, and that serves as a benchmark for R. (For non- accreting resources, Rb = R.) 10

å Si = Cumulative amount of recyclable stock, that is, processed resources recovered for subsequent sequential uses (i.e., secondary, tertiary, etc.).

mis = Performance equivalency factor reflecting the proportional loss in material integrity (mi) in each subsequent reuse of a processed resource.

1.3.5.2. Characterization Factors for Emission and Waste Loadings

2. 9The reserve base is defined as that part of an identified resource that meets the minimum chemical and physical characteristics required to satisfy current mining and production practices. This definition is consistent with definitions proposed by Heijungs et. al. (1992), and by the US Bureau of Mines (1993), which defines economic reserves as that part of a reserve base which can be economically extracted at the time of determination. 10 A discussion of the methods for establishing and calculating the baseline reserve base is contained in subsequent chapters related to specific resources where appropriate.

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Stressor characterization factors exist for each emission and waste indicator, based on the physical, chemical, or biological properties of the emissions/wastes. These factors have been established by government and intergovernmental agencies, and are generally accepted. The environmental characterization factors (“ecf”) for calculating emission loadings have three aspects: 1) Any emission released at a level that does not exceed the observable effects threshold for that receiving environment is assigned a value of zero (ecf = 0), while any emission that exceeds such a threshold is assigned a value of one (ecf = 1). 2) For those emissions that released at a level that do exceed the observable effects threshold in a given receiving environment, the percentage of emission that deposits in areas of exceedance is determined by fate and transport modeling. 3) Data permitting, the environmental characterization factor may be further adjusted to reflect the duration and/or severity of exceedance, as in the case of greenhouse gas loadings.

1.3.5.3. Establishing Baselines Two distinct types of baselines are used within the LCSEA framework. The first of these, the measurement endpoint baseline, is used to develop indicator calculations by unit operation. This type of baseline represents the pre-function status of either the providing environment from which resources are derived or the receiving environment into which emissions or wastes are released. The measurement endpoint is calculated as the difference between the pre-function baseline and the current status of the selected endpoint. The second type of baseline — the industrial baseline — is used in generating the LCSEA impact profile, and is described in Section 1.3.6.

1.3.5.4. Calculating the Results The indicator result is the product of the unaggregated LCI result multiplied by the appropriate characterization factors. The cumulative indicator result is the sum of indicator values aggregated for all unit operations. It is critical that the category indicator be additive across all unit operations (Figure 1.1) in order to retain the integrity of the system LCA. Net Resource Net Resource Net Resource Depletion Depletion Depletion

Emission Emission Emission Loadings Loadings Loadings

Figure 1.2. — Linking unit operations under LCSEA

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The calculation of the indicator values is expressed by the following equation: 1. For each unit operation: Indicator Stressor Stressor Environmental Result Value Char. Factor Char. Factor (IRi) = [SV] x [SCF] x [ECF] n 2. Cumulative Indicator Result = å IRi across unit operations (n) i =1

1.3.5.5. Units of Measure by Indicator The units of measure reported for the indicators under each of these categories are not mass units, as in the case of mass and energy LCI study findings. Rather, the units of measure express the unique equivalencies used to calculate each indicator. For renewable material resources, mineral resources, and energy resources, the units of measure express the effective equivalent depletion (or accretion) of the identified resource, based on the resource depletion factors (RDF) mentioned earlier. In the case of physical disruption of habitats, disruption is similarly accounted for in effective equivalent hectares. For emissions and waste related indicators, the units of measure express equivalent loadings, based on the application of appropriate stressor and environmental characterization factors. (See Tables 1.2.a and 1.2.b)

Table 1.2.a. — Category (Impact) Indicators and Units of Measure: Resources & Ecosystem Disruption

CATEGORY INDICATORS UNITS ENERGY RESOURCE DEPLETION Coal Depletion (50 years) equivalent tons oil Lignite Depletion (50 years) equivalent tons oil Oil Depletion (50 years) equivalent tons oil Natural Gas Depletion (50 years) equivalent tons oil Uranium Depletion (50 years) equivalent tons oil Hydraulic Energy Resource Depletion (proposed, 1999) to be determined

RENEWABLE RESOURCE DEPLETION Water Resource Depletion equivalent cubic meters Wood and Paper Resource Depletion equivalent cubic meters Marine Resource Depletion To be determined MINERAL RESOURCE DEPLETION - Tonnage Metal Resources (by specific metal) equivalent tons of metal Precious Metal Resources (by specific metal) equivalent tons of metal Other Minerals (by specific mineral) equivalent tons of mineral ECOSYSTEM DISRUPTION Terrestrial & Aquatic Habitats equivalent hectares

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Key Species 11 percentage depleted

2. 11 The percentage depleted for a given species is calculated as the percentage from an individual cause against an established baseline for a given species in a specific habitat(s).

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Table 1.2.b. — Category (Impact) Indicators and Units of Measure: Emissions and Wastes EMISSION LOADINGS

Greenhouse Gas Loading equivalent tons CO2 Ground Level Ozone Loading equivalent tons ozone Stratospheric Ozone Depletion Loading equivalent tons CFC-11

Acidification Loading equivalent tons SO2 Hazardous Chemical (Air) Loading 12 equivalent tons (specific) - PM-10 equivalent tons PM-10 Eutrophication Loading equivalent tons P Total Oxidizing Chemical (TOC) Loading equivalent tons oxygen Hazardous Aquatic Loading equivalent tons (specific) Thermal Loading To be determined

RESIDUAL HAZARDOUS WASTES Heavy Metal and Ash Wastes equivalent kilograms High Level Radioactive Waste kilograms Medium Level Radioactive Waste cubic meters Low Level Radioactive Waste cubic meters

1.3.5.6. Selection of Core Unit Operations During the first LCSEA iteration (that is, the first round of assessment), the core unit operations of the system are identified. Core unit operations are those operations that contribute most significantly to a specific indicator or set of indicators. As such, each indicator has its own set of core unit operations. In contrast with LCI prioritization techniques, the LCSEA technique does not rely solely on mass and energy considerations for identifying core unit operations, but rather, is concerned with the establishment of a quantifiable relationship between individual unit operation and their associated indicators. For example, under a strict mass and energy prioritization, small releases of hazardous chemicals into air or water might easily be omitted from study, even though they might result in significant local or regional effects. Once the first iteration is completed, secondary unit operation data can then be examined. Sensitivity analysis is used to measure the potential significance of an input or output from a secondary unit operation against the cumulative indicator results for a given indicator. This analysis determines the extent to which additional data collection and analysis are required. It also indicates when there is no justification for further data collection from a given unit operation — that is, when inclusion of the inputs and outputs of a given unit operation into the calculations would not significantly affect any of the indicator results.

1.3.5.7. Establishing Confidence Levels

2. 12 Includes Hazardous Air Pollutants (HAPs) listed in Title III of the 1990 CAAA, plus criteria pollutants not covered under other indicators for which there are National Ambient Air Quality Standards in CAAA, Title I.

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In LCI studies, uncertainty analysis is concerned with determining the degree of confidence in the input/output values reported. Under the LCSEA framework, uncertainty analysis is aimed at measuring the degree of confidence in the descriptions of the environmental mechanisms themselves, and in the relationships between inputs/outputs and relevant measurement endpoints. Determining this level of uncertainty is important, given that the indicator results themselves are based on measures of specific indicators along the stressor- effect networks, and generally not on measures of actual category endpoints. There are four specific areas of potential uncertainty. These relate to: 1) LCI results, 2) stressor characterization factors, 3) environmental characterization factors, and 4) the environmental relevance of indicator results. • Uncertainty of LCI Results The factors that contribute to the uncertainty of LCI results have been described in many different life-cycle assessment publications (e.g., the discussion of data quality indicators in ISO/DIS 14041). Fundamentally, LCI data have no established linkage to affected environments. An additional source of uncertainty regarding input/output data is the premature aggregation and allocation of such data at the inventory stage. For example, if aggregation and allocation occurs without the appropriate spatial and temporal characterization, it is one of the principal sources of uncertainty. • Stressor Characterization Factor Uncertainty The uncertainties surrounding stressor characterization factors arise from the lack of linkage of aggregated inventory results to measurable endpoints. For example, photochemical ozone creation potentials (POCPs) are dependent upon ambient conditions and the specific local background concentrations of both volatile organic compounds (VOCs) and nitrogen oxides (NOx). While it is possible to estimate such background concentrations in annual averages within a given range, the corresponding POCPs will reflect the uncertainty of the estimation process. Furthermore, the speciation of VOCs used to estimate the overall ground level ozone formation may be based on generic data, resulting in greater uncertainty. • Environmental Characterization Factor Uncertainty The uncertainties surrounding environmental characterization factors are inherent in the risk assessment data and environmental data that currently exist. Threshold level values can have considerable uncertainties. Government established thresholds often include built-in safety factors to compensate for uncertainties that may not be transparent or science-based. • Environmental Relevance of Indicator Results The environmental relevance of indicator results is related to the degree of characterization of the relevant measurement endpoints. ISO DIS-14042, Clause 5.2 e outlines the steps required to characterize the endpoints and in order to reduce this uncertainty Under traditional LCI studies, it has been estimated that 98% of the unit operation data must be collected in order to achieve a 95% level of confidence that all of the environmentally significant data have been included.13 In LCSEA studies, sensitivity and uncertainty analyses are applied on an iterative basis to structure data collection and

2. 13 Franklin Associates, 1997

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determine the significance of results. These techniques are used to streamline the data collection and analysis.

1.3.6. Generation of LCSEA Impact Profile Once calculated, indicator results are reported in an impact profile. In its simplest form, the impact profile simply summarizes the indicator results for the system. In addition, the impact profile may also include a means of comparing these indicator results to a baseline, a step referred to in ISO DIS-14042 as normalization. This baseline represents a defined industrial standard or other benchmark for a given product or system. For instance, it may represent an industry average for a particular indicator . In the case of indicators for which there are no measurable indicator results or for which results are negligible, the indicator results are reported as (“NMIR”). This occurs when the use of a resource results in no net depletion (e.g., the resource is fully recycled or essentially infinite in supply), or when the release of an emission does not result in an exceedance of a science-based observable effects threshold level.

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C HAPTER 2. Resource Depletion

2.1. Resource Stressor-Effects Networks Most efforts to define stressor-effects networks have focused on effects linked to environmental releases. (See Chapters 4-5 below.) Less well understood, but equally important, are the stressor-effects networks associated with resources. The confusion surrounding the resource-related stressor-effect network stems from the fact that its mechanistic pathway closely parallels the linkages between unit operations that combine to perform the system function. For example, the resources used for the production of virgin products are clearly linked to subsequent recycled materials, since without the original virgin process, no resources would be available for recycling. This same linkage represents an identifiable cause and effect relationship impacting the depletion of the resource reserve base. Historic discussions on the subject of resource depletion have tended to focus on the size of the reserve base (R), primary resource uses (U), and, for some resources, the rate of biological accretion (N). However, by diagramming this stressor-effect network in its entirety (Figure 2.1), two additional integral components of this network have become apparent: 1) the subsequent recycling uses of the material (S), which result in an effective extension of the reserve base (R) by displacing additional raw material extraction; and 2) the cumulative generation of wastes (W), which is the major effect on the rate of depletion of the reserve base. The degree to which the reserve base is effectively extended through subsequent reuse of the resource is related to the degree to which the resource retains its material integrity; by the same token, as the resource is increasingly contaminated or loses integrity, it becomes more waste-like, reflecting the continuum between virgin resource and final waste. Finally, as illustrated in Figure 2.1, the stressor-effect mechanism for resources follows both parallel (Path "a") and serial (Path "d") mechanistic pathways, similar to emissions- related stressor-effects networks for such stressors as NOx. In approaching the resource depletion issue, LCA studies have frequently resorted to a variety of simple classification schemes for segregating resources. The most commonly suggested classifications are as follows: renewable versus non-renewable; flow versus stock; and abiotic versus biotic. However, each of these lacks the necessary spatial or temporal characterizations to distinguish the actual environmental significance of a given resource use. For instance, the fact that a wood resource is “renewable” in theory does not mean that it is in fact. Different wood production systems show markedly different rates of renewal.

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N Resource Accretion R Reserve Base

U(a) U(b) U(c) U(d) Primary Resource Primary Resource Primary Resource Primary Resource Use #1 Use #2 Use #3 Use #4

S(a1) S(a2) S(d) Secondary Secondary Secondasry Use #1 Use #2 Use #1

S(d) t Tertiary Use #1

å W i Landfilling/ Incinerating Wastes

Figure 2.1. — Stressor Effect Network for Net Resources Depleted Aggregation of dissimilar resources into such broad classifications is no less inappropriate than aggregating together unlike effects, such as global warming and acidification loadings. There is no connection between steel resource depletion, aluminum depletion, water depletion, and wood depletion.; they each have their own unique reserve bases, their unique recycling attributes, and in some cases, their unique accretion dynamics. LCSEA is only concerned with the calculation of the depletion for elements, compounds, and forces that are inherently depletable.14 Elements such as oxygen, hydrogen, carbon and nitrogen, as such, are thus excluded. Solar radiation, ocean currents and wind are similarly non-depletable and therefore excluded from depletion calculations.15 2.2. Calculating the Resource Depletion Factor (RDF)

2. 14 The concept of "depletable" is temporally defined such that depletion cannot occur during a time scale relevant to human activities. 15 This chapter is concerned with the integrity of the resources only. Methods for calculating loadings related to air pollution, habitat depletion, etc. are discussed in other chapters.

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Generally speaking, the environment characterization factor, referred to as the “resource depletion factor,” is calculated for each resource as follows:

(SW - N) (DT) + (Rb - R) Resource Depletion Factor (RDF) = ______R + (SSimisi ) (DT)

Where: SW =Cumulative amount of wastes of a specific resource from all uses I (primary "P" and subsequent "S"). N = Rate of natural replenishment, or "accretion," of the resource within the defined reserve base.

DT = A specified period of time. (T1 = initial date)

R = The total amount of resources currently available in the defined reserve base from which the industrial system draws its resources.16

Rb = A baseline reserve base that represents the optimum resource capacity for a

defined geographic area at T1, and that serves as a benchmark for R. (For 17 non-accreting resources, Rb = R.)

SSi = Cumulative amount of recyclable stock, that is, processed resources recovered for subsequent sequential uses (i.e., secondary, tertiary). mi = Performance equivalency factor reflecting the proportional loss in material integrity (mi) in each subsequent reuse of a processed resource.

2.3. Resource Depletion Calculations The term “depletion” suggests a process wherein natural resources are used irreversibly in such a way that the essential quality of the material (or energy carrier) when used (e.g. physical, chemical or energy potential) is lost. If such quality is only partially degraded, it may be characterized by a quality factor. Quantitative depletion calculations incorporate the issue of scarcity by relating the resource use to available stocks or reserves. The degree to which resources are reused or recycled, and the degree to which resources are replenished naturally, are also included in the depletion calculations.

2. 16The reserve base is defined as that part of an identified resource that meets the minimum chemical and physical characteristics required to meet current mining and production practices. This definition is consistent with definitions proposed by Heijungs et. al. (1992), and by the US Bureau of Mines (1993), which defines economic reserves as that part of a reserve base which can be economically extracted at the time of determination. 17 A discussion of the methods for establishing and calculating the baseline reserve base is contained in subsequent chapters related to specific resources where appropriate.

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Resource depletion calculations are consistent with other indicator calculations, involve in that the inventory resource flow is modified by a characterization factor that reflects the variables discussed above. This equation is as follows:

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RDIR = IFV x RDF (1a)

Resource Inventory Resource depletion flow value depletion indicator equivalency result factor The indicator result is an expression of the total net depletion (accretion) of the resource studied. 2.4. Specific Resource Depletion Calculation Considerations

2.4.1. Energy Resources 2.4.1.1. Fossil Fuels In the case of energy resource depletion the mathematical expression is fairly straightforward. Energy resource depletion is expressed in equivalent energy flow units.

Resource balance Process balance

Natural reserves Process system Waste flow

RN V Use U W

Figure 2.2. — The energy resource depletion case. The process is described in Figure 2.2, and the depletion calculation with the following equations:

The absolute resource depletion:

AD = U = V= W (1b)

AD = absolute resource depletion flow U = resource use flow W = resources transformed into waste V = flow of the resource from natural reserves The unit of measure for energy resource depletion is expressed in tons of oil equivalents (toe). The most commonly used unit conversion factor is a calorific heat value (HHV) of 45 MJ/kg oil equivalents (oe). MegaJoules (MJ) and multiples can also be used, and for some purposes the mass units (kg and multiples) can be illustrative, as for example when pointing out transport operations in the case of fuels with low heat value. The depletion ratio, which relates the flow of used resource to the existing reserves:

DR = U/R = W/R (1c)

DR = depletion ratio (also called “use-to-reserve ratio”) R = economically usable reserve base of the resource

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The depletion ratio has the dimension of time-1. An often-used concept is the inverse value R/W= R/U, called the static reserve life of the resource. The resource depletion factor (RDF) to be used as a characterization factor for resource flows may simply be defined by relating the depletion ratio for any resource flow to the depletion ratio of some well known and defined resource. For resource depletion, it has been proposed (Kommonen 1997) to use crude oil depletion as the reference resource, and thus the indicator result would express the depletion as oil depletion equivalents. As the LCSEA method matured, the decision was made not to use oil as the reference resource, but to choose an arbitrary time period (50 years) instead of the about 48 year static reserve life of crude oil. Even if crude oil is replaced by a hypothetical resource having a static reserve life of 50 years, the meaning of the resource depletion factor is to describe the net depletion factor for any resource in relation to and as an equivalency to a hypothetical resource that is depleted in 50 years. The resource depletion factor is applied in much the same way as the characterization factors for emission calculation. For example, the global warming indicator is calculated as the inventory flow value multiplied by a characterization factor (the global warming potential GWP), that transforms the flow to a CO2 equivalent flow. In the same way, the depletion of any resource is calculated by transforming the inventory flow of resource use to an equivalent depletion (by definition equivalent to the depletion of a resource with a static reserve life of 50 years). Thus, in order to switch from the absolute depletion to an equivalent depletion, the resource flow is multiplied by the resource depletion factor (RDF), which is the depletion ratio for the examined resource “i” divided by the depletion ratio for the hypothetical equivalency resource with 50 years static reserve life:

Wi/Ri Wi/Ri RDF50 = ------= ------= 50 years * Wi/Ri (1d) We/Re 1/50 years The equivalency factor (RDF) is dimensionless.

2.4.1.2. Uranium Resources In several countries, such as the USA, Sweden and Finland, no recycling of spent nuclear fuel is taking place, and the method to treat nuclear waste is deposition in a geologically safe site. In these countries the resource depletion calculation method is the same as for fossil fuels. In other countries (e.g. United Kingdom, France, Germany, Russia and Japan) the spent nuclear fuel is recycled for recovery of the energy in the waste. The present recycling and reprocessing is the production of MOX fuel, which consists of plutonium and depleted uranium oxides. MOX fuel elements can be used as substitutes of uranium fuel elements in PWR (pressure water reactors). To calculate the uranium resource depletion for this type of nuclear electricity production, the method incorporates this recycling (as in the case of mineral resources discussed below). The static reserve lives for some fossil fuels and for uranium have been illustrated in Tables 2.1.

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Table 2.1. — The fossil fuel and uranium primary energy demand, Gtoe/year in 1990, and calculated Static Reserve Life and RDF values

Global demand Calculation base: Calculation base: Proven Reserves Resource Base

Gtoe/year Static reserve RDF50 Static reserve RDF50 life, years life, years Oil - Conventional 3.1 48 1.03 95 0.53 Natural gas 1.7 83 0.60 247 0.20 Coal and Lignite 2.2 275 0.18 1545 0.03 Uranium - In Thermal Reactors 0.5 114 0.44 520 0.10

Table 2.2 shows local variations in the depletion variables for natural gas.

Table 2.2. — Deriving RDF50 from Gas Resource Reserves and Production Source: 1994 BP Review of World Gas

RDF50 Total Total Total Total Reserves/ = 50 year reserves reserves production production production production/ 1012 m3 % 109 m3/a % years reserves

Russia/former USSR 56,0 39,7 719,3 33,1 78 0,64 North America 8,9 6,3 710,6 32,7 13 4,00 Europe 6,1 4,3 262,9 12,1 23 2,17 Asia & Australia 10,0 7,1 202,1 9,3 50 1,01 Middle East 45,1 32,0 132,6 6,1 340 0,15 Africa 9,6 6,8 76,1 3,5 126 0,40 South/Central America 5,4 3,8 69,5 3,2 77 0,65 Total 141,0 100,0 2173,0 100,0 65 0,77

In Table 2.2, the RDF50 values for the most important energy resources have been calculated in the last column. As can be seen from the values in Table 2.2 the static reserve life of natural gas shows great variation when comparing different parts of the world. Even if natural gas is also traded in liquefied form, in most cases the use of this resource is bound to the actual gas distribution pipe network; thus it is necessary to apply the local resource depletion value. Oil and coal are traded worldwide, and the global RDF can be used.

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When presenting energy resource equivalent depletion indicator results, it is recommended to show not only an aggregated sum in equivalent toe, but also its distribution between the different types of energy resources. 18 Example: Electricity is produced in three 500 MW power stations that have equal efficiency. The fuel use is 0.6 Mtoe per annum or 30 Mtoe/50 years. For three different fossil fuels, when applying the RDF50 from Table 2.2, the following resource depletions result (Table 2.3.):

Table 2.3. — Fossil Fuels Depletion Calculations

Fuel LCI flow x RDF50 Equivalent depletion Coal: 0.6 Mtoe x 0.18 = 108 000 toe equiv. per annum Natural gas: 0.6 Mtoe x 0.60 = 360 000 toe equiv. per annum Oil: 0.6 Mtoe x 1.03 = 620 000 toe equiv. per annum

The result shows that the equivalent depletion indicator result for oil firing is about 6 times higher than for coal firing. Observe that energy consumption as such is not a category indicator, as explained earlier. The category indicator closest to “energy consumption” for a power plant is the effective energy resource depletion. Because the energy consumption as such gives useful information, the LCI value (0.6 Mtoe per annum in the example) should always be reported.

2.4.1.3. Energy resource reserves As for most primary data used in life cycle assessment, and for data regarding resource depletion calculations, the level of uncertainty is important to establish. It is a well-known fact that the quantity of reserves and the resource base for many materials is changing with time. For instance, for oil, the static life time has remained about the same for over 20 years, which means that new discoveries and enhanced extraction has added to the reserves about the same amount as has been extracted.

2. 18 The following observations can be made:

- the ratio between Resource Base and Proven Reserves (RB/R) varies from 2 for oil to about 6 for coal - if the various energy sources in Table 4.1 are ranked according to scarcity, using static reserve life or RDF, the ranking order stays the same, with oil as most depleted, followed by natural gas, and with coal as least depleted, regardless of which reserve definition is used.

The conclusion can be drawn that the RDF50 can be based upon the present resource use and the proven reserves, as is suggested in this manual for any resource. The variations in reserve amounts depending on reserve definition – and the changes in reserve amount with time – is typical for most materials, and is no reason to assign big uncertainty to the RDF value. Important are the ratios between RDF values for different resources, which can be considered best represented using the present definitions for variables in the RDF formula:

The reserve value to be used is global Proven Reserves The use value is the present global use, and if local use-to-reserve ratios are more appropriate, the word “global” should be changed to “local” in both definitions.

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For energy resources the World Energy Council (WEC) defines reserves and resources as follows:

· Proven Reserves are those that can be produced with existing technologies under present market conditions · Resources are those which with technical progress could become economically attractive · Resource Base is Proven Reserves plus Resources In Table 2.4, the WEC reserve and resource data are compared to the 1990 demand value (Source http://www.wec.co.uk/energy.htm):

Table 2.4. — Fossil Fuel and Uranium Reserves at end of 1993, as primary energy (Gtoe)

Proven Reserves Resources Resource base RB/R ( R ) ( RB ) Oil - Conventional 150 145 295 2,0 - Unconventional 193 332 525 2,7 - Total oil 343 477 820 2,4 Natural gas 141 279 420 3,0 Coal and Lignite 606 2 794 3 400 5,6 Uranium - In Thermal Reactors 57 203 260 4,6 - In Fast Reactors 3 390 12 150 15 540 4,6

2.4.2. Renewable Resources The resource depletion calculation of renewing or partly renewing resources, if recycling is ignored, is accomplished by taking into account the natural accretion flow, which is subtracted from the use/waste flow of the non-recycled portion of the resource (Figure 2.3).

Accretion Flow N N Resource balance Process balance

Natural reserves Process system Waste flow W RN V Use U

Figure 2.3. Resource depletion with accretion but without recycling.

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The equations take the form

AD = U - N = V – N = W - N (2b)

N = accretion flow

DR = (U – N)/R = (W – N)/R (2c)

(Wi-Ni)/Ri (Wi-Ni)/Ri RDF50 = ------= ------= 50 years * (Wi-Ni)/Ri (2d) We/Re 1/50 years

2.4.2.1. Wood and peat Wood resource depletion studies show that in most industrialized countries the accretion (growth) of wood resources on average exceeds or is about equal to the use (drain), and thus the depletion rates are actually negative or zero value. A negative or zero result indicates that wood is a stable resource. For project specific purposes the spatial availability of a desired wood raw material needs to be evaluated. The depletion situation can vary considerably from a national average, since wood is infrequently transported over long distances, even if there exists a minor global market for wood. The industrial use of wood may also cause other environmental impacts, such as loss of biodiversity or other natural forest bio-types from the physical disruption of the logging operations. (See Chapter 3.) Regarding peat used as fuel, an example studied (in Finland) indicates that the rate of accretion for a certain area assigned to long-term peat fuel production may be around 10 % of the rate of peat extraction. This slow rate of renewal is the reason why this biomass fuel often is classified together with energy resources. In some accreting resource cases, the difference between resource waste and accretion (W – N) may not adequately describe the depletion situation. An example would be an overfished location or a severely depleted forest region, where the resource use in the past has been on an unsustainable level such that the reserve has diminished severely (which in most cases also reduces accretion), and thus the present R value is far from the natural optimum resource capacity (denoted as Rb). In such case the RDF value turns out to be higher (because of the low value of R in the denominator, and because the accretion may be lower than optimum) than if the same amount would be taken out of an average resource stock. It is also possible to indicate an additionally increased depletion – which could be perceived as a reduced degree of resource use sustainability - by adding to the RDF formula (2d) a correction term (Rb – R)/R, which takes the value zero for all non-accreting resources, and also for accreting resources where the present reserve R is at a level that yields an optimum accretion of the resource. As part of establishing the measurement endpoint for wood resource depletion, it is necessary to select the appropriate reserve baseline for wood resources. The wood industry has established a recognized procedure to determine the appropriate stocking levels for a given production district. Some of these current techniques are designed for specific

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purposes that focus on management plans and potential return on investment. However, not all of these techniques translate directly into establishing Rb. The current LCSEA method for calculating the reserve baselines and two additional approaches are described below.

• LCSEA method for Rb. - Normative Baseline

The LCSEA methodology currently approaches the establishment of Rb in contemporaneous terms. Under this approach, Rb is normatively derived from actual stocking levels representing the 80th percentile of current commercial (i.e., managed) forest lands of the same forest type within the same region. Rb- R is positive for operations that presently maintain stocking levels below the 80th percentile, and negative for those operations that maintain stocking above the 80th percentile. Recall that a negative R translates to a decrease in the RDF. This normative approach has been selected for current LCSEA practice both for pragmatic reasons (i.e., availability of current information) and as a means to recognize the potential for net resource accretion within forests that are managed toward optimal resource production levels (maximum productive capacity). In practice, this approach represents a middle ground method as compared to other possible approaches. One potential disadvantage of the approach is associated with a scenario in which the entire industry, including that portion of industry performing at the 80th percentile, have through past management actions reduced average stocking per hectare well below the productive capacity of their forestlands. Such a pattern of industry-wide reserve base depletion would not be observable.

• Alternative Method A - Historic Baseline

An alternative approach in defining Rb is to calculate the average stocking per hectare that existed on the defined land base at the commencement of operations for the industrial function being studied (i.e., commercial forestry). Any change in the average stocking per hectare since commencement of operation would be reflected in R. Depletion of the average stocking would be associated with a positive R, thus increasing the value of the RDF. Conversely, increases in average stocking over initial levels would be associated with a negative R, thus reducing the RDF. One attraction of this approach is that it provides accountability (and credit) for resource use patterns over the duration of the industrial function, as compared to the potentially misleading results of examining only recent or current use patterns. Operations that have restored stocking levels over time from the initial condition are rewarded in the RDF calculation. There are two primary disadvantages. The first is the difficulty in collecting the necessary data to establish this type of baseline, depending on the availability of historic records. The second disadvantage is that recent and/or current use patterns that constitute a reversal of historic patterns would likely be masked (hidden) when using the historic baseline reserve base. For instance, an operation that has been building average stocking levels for the past 25 years after 150 years of prior depletion would likely still be depicted as a net resource depleter under the historic baseline. The converse scenario is also possible: past positive trends in reserve base accretion could mask more recent depletionary patterns. If the apparent inequity that this situation represents were deemed unacceptable, one variant approach would be to define the historic reserve base as the stocking level per

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hectare at the commencement of operations under the present management organization. But of course, industrial use patterns that span numerous generations of managers may well be ignored with this shorter-term definition of historical baseline.

• Alternative Method B - Maximum Productive Capacity A third baseline reserve base option would be the productive capacity associated with the forest type and productivity class of the land base being evaluated. Under this approach, the standard of comparison would be the maximum potential stocking per hectare that could exist on the land area in question, if optimal temporal and spatial stocking patterns were to be allowed. In that this standard is truly the maximum in terms of periodic accretion (N) associated with the standing stock, the best that could be achieved under a managed forest regime would be attainment of the maximum. Stocking levels below or above the optimal level would lead to reserve base depletion. That is, R would either be positive or zero; there would be no possibility of "reserve base accretion", as there would be under the other formulations. This may be viewed by some as a disadvantage of this approach, though if applied consistently across all commercial forestry operations, it would nonetheless be equitable.

2.4.2.2. Water 2.4.2.2.1. Defining the Stressor-Effect Network When considering water resources, the rate of depletion can be defined in terms of the rate at which the resource is being “consumptively used” in relation to the quantity of water that is available in a region and the rate at which it is naturally replenished. In many cases, the water used for human purposes will have negligible environmental significance in terms of water depletion. In water-abundant regions of the world, human consumptive uses represent an insignificant percentage of the available water supply. However, in certain arid or densely populated regions of the world, water scarcity can be an issue of relative environmental import. The stressor-effects network for water resource use is illustrated in Figure 2.4.

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N Resource Accretion R Water Reserve Base

P(a) P(b) P(c) Primary Resource Primary Resource Primary Resource Use #1 Use #2 Use #3

S(a1) Subsequent Use Direct Reclamaation S(a) å W i Consumptive Water Use

Figure 2.4. — Stressor-Effect Network for Water Resources Note that in this generic schematic, water can return to the reserve base as well as end up as waste (being consumptively used). The primary difference between water and other material resources is that water has the unique potential to be returned to the reserve base from which it came. While other material resources may endure a chain of subsequent uses (reserve base extension), all other material resources, once extracted, ultimately end up in the form of waste, or are combusted for fuel. For example, harvested timber never returns to the reserve base (the forest) from which it came, although particular wood fiber may endure a number of subsequent uses. Conversely, it is quite common for water diverted from a stream to eventually return to that stream after being “used” for human purposes, albeit in many instances, this water will be returned in a degraded state (i.e., municipal wastewater return flow). Another dissimilarity between water and other resources is that water is a “depletable” abiotic flow resource. While the level and rate of replenishment for water reserves fluctuates dramatically from year to year as well as over shorter time periods within the year, water is unique in its ability to regenerate in short time scales. Water reserves replenish at rates that are not seen with any other “depletable” resource. In most cases, regional water reserves fully regenerate within the course of the annual hydrologic cycle. The unique temporal and spatial characteristics of water resources dictate that its depletion be considered differently than other resources. The fact that precipitation is highly variable, but naturally replenishing in short time scales also affects how water reserve bases are determined. For example, unlike other material resources, there are certain instances, such as long-term ground water overdraft, where human uses can exceed the defined reserve base, which is based upon the rate at which water reserves are naturally replenished on an annual basis.

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In this methodology, the average amount of water “naturally” available on a yearly basis within a drainage system or aquifer will be used as the region’s reserve base. Natural replenishment through precipitation will not be considered “accretion” (N) to a region’s reserve base. This is because the accretion rate, in this case, is equal to the reserve base — i.e., the reserve base is defined as the annual average water availability of a region. This is, in effect, the rate of replenishment. The only cases of accretion (N) in this methodology refer to water that enters a particular reserve base from another (i.e., trans-basin imported water or crossover between ground and surface reserves). Furthermore, all consumptive water use in a study area will be counted as “depletion” against the annual reserve base. Consumptive water use will be equivalent to what is termed “waste” for other resources. Due to this approach, there will almost always be some degree of “depletion” if there is consumptive water use in a region.19 The pertinent question in this model then becomes whether regional consumptive water use is environmentally significant given the annual average reserves of the basin. The objective is to make a distinction between a water-abundant region where 0.001 percent of the reserve base is collectively depleted by all water users annually, and a water-scarce region where 95 percent of the reserve base is depleted by all water users.

2.4.2.2.2. Calculating Water Resource Depletion Section to be inserted, with modifications, from the LCSEA Practitioner’s Manual, Working Draft 1.2, April, 1997).

2.4.2.3. Marine Resources Section to be inserted.

2.4.2.4. Integrating Recycling into Resource Depletion Calculations For wood resources, and for many mineral resources (see below), reuse and recycling enters the resource depletion equation. A simplified picture of a process involving recycling is shown in Figure 2.5.

2. 19 It is conceivable, although highly unlikely, to have net accretion (more accretion than water consumption) for a region. A hypothetical example of this scenario would be a case where more water is “imported” into a river basin than consumptively used in that basin.

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Accretion Flow N N Resource balance Process balance

Process system Waste flow W Natural reserves RN V Use U

Recyclable reserves R R Recycled flow Suse

Recycled flow Srec

Figure 2.5. — Resource depletion with accretion including recycling.

The main new elements are: 1) the recycled flow Srec from the process to a storage, which reduces the waste flow W; 2) the recycled flow Suse from a storage to the process, which reduces extraction of resources from the natural reserve RN; and 3) finally, the existence of a reserve of recyclable material RR, possibly built up during decades of material use and waste recovery. For a certain process under study the two recycled flows may be equal, but they can also be of quite different magnitude.

The absolute resource depletion, if defined as reduction of the reserves RN and RR is

AD = V + Suse – Srec (3a) From the process balance it is evident that the depletion also is

U = V + Suse + N = W + Srec and combining with (3a) results in

AD = W – N (3b) or exactly the same as (2b). The depletion ratio equation is also the same, if we consider that the total reserves are

R = RN + RR The equation for the resource depletion ratio then is

DR = (W – N)/R = (W – N)/(RN + RR) (3c) and the resource depletion factor equation is

RDF50 = 50 years * (W - N)/ (RN + RR) (3d)

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The equation (3d) is seen to be the general equation valid for any system, which reduces to (2d) for resources without recycling, and to (1d) for resources where both accretion and recycling are zero. In equation (3d) the resource flow W that is related to the sum of reserves is the difference between resource use and flow recycled, which is the reason for the use of the term “net resource depletion”.

If the formula (3d) is extended with the term (Rb – R)/R needed for some cases with accretion, as discussed earlier, we arrive at the same conceptual formula that was given in the introduction:

50 years * (W - N) + (Rb – R) RDF50 = ------(3e) (RN + RR) 2.4.3. Mineral Resources Mineral resources constitute the third grouping of resource indicators. Figure 2.6 illustrates the resource depletion case for minerals.

Resource Process

Process system Natural reserves N Waste flow V Use U

Recyclable R R Recycled flow use

Recycled flow rec

Figure 2.6 — Mineral resource depletion including recycling. A long term global view of recycling will reveal that most metal resource uses are characterized by extensive material recycling, and the wasted amount is a small percentage of the total metal turnover. Also typical is the fact that the recyclable reserves consist of a spectrum of different types of reserves with varying degree of availability for subsequent use, much in the same way as natural mineral reserves are classified according to their availability. There is little quantitative information on the size of recyclable reserves, or of how the total recyclable reserves fall into various availability categories, but such information would be most useful for the LCI assessment work. The method for calculating mineral resource depletion is consistent with that described in Section 2.4.2.4 above, except that accretion (N) always equals zero.

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2.5. Regional Considerations The above-described definitions and calculation rules serve to define in general, the methods to calculate resource depletion equivalency factors (RDFs). When assessing the indicator results, the inventory flow value is multiplied by the RDF according to Eq. (1a), which yields the depletion rate of the resource. It must be decided for each case whether the general or global value for RDF is applicable. In many cases the local resource base or the recycling or accreting situation is quite different from the assumptions made for the global/general RDF calculation. In such cases, the RDF must be adapted. If, for example, the material recovery rate for a certain case is lower than average, the value for waste flow W increases (and vice versa), and RDF should be corrected for this situation. If the resource input is defined so that while the rate of material recovery is on normal level, the process can only use virgin resource because of quality requirements, and recycled quality is not usable, then the normal W value is used in the RDF calculation, but the reserve R does not include recycled material. The W value can be applied because it may be assumed that other uses of the recycled material compensate for the natural reserve depletion effects of this particular system. The average data upon which the RDF calculation is based should be seen as the cumulative sum of a great number of different processes, all with their specific recycling and accretion characteristics. In many cases the details of the recycling are not known or are not included in the assessment (cradle-to-gate). In such cases the global average RDF of the resource in question can be used, if properly reported.

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C hapter 3. Ecosystem Disruption

3.1. Defining the Stressor-Effects Networks 3.1.1. The Two Ecosystem Disruption Indicators The two ecosystem disruption indicators — “disruption to terrestrial and aquatic habitats” and “disruption to key species” — represent the non-elementary flow stressor-effects networks stemming from the direct physical alteration of the landscape (ecosystem) from mechanical or structural causes. A wide range of industrial activities can cause ecosystem disruption — from logging, road construction, dams and mining to the construction of manufacturing plants, the construction of stockpile facilities, and landfilling. Ideally, ecosystem disruption would be measured in terms of the quantifiable depletion of the actual ecosystems. However, such assessment is not yet supported by current ecological field assessment techniques. Instead, this disruption is currently measured in terms of the physical alteration of habitats and, as warranted, identified key species.

· Indicator — Physical Disruption to Terrestrial and Aquatic Habitats Habitats are described in terms of the particular vegetative cover types/states across the defined landscape (either terrestrial or aquatic).

· Indicator — Physical Disruption to Selected Key Species Key species include those species officially classified as rare, threatened or endangered, or other species of local or regional significance.

3.1.2. Modeling the Ecosystem Disruption Stressor-Effects Networks

Modeling the Ecosystem Disruption stressor-effects networks helps to clarify the potential serial and parallel environmental mechanisms at play, along with their respective measurement endpoints.

As shown in Figure 3.1, physical alterations caused by the industrial system may have parallel environmental mechanisms. On the one hand, these alterations may lead to the direct disruption of habitats, which may in turn affect key species and impede habitat integrity. At the same time, these alterations may create direct impediments to key species (e.g., migrating bird species), whether or not habitats are significantly disrupted.

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Physical alteration (mechanical and/or structural disturbance)

Physical impediment of key Direct physical disruption species of habitats

Delayed disruption of Depletion of key species habitats

Figure 3.1. — Simplified Schematic of the Ecosystem Disruption Stressor-Effects Networks

3.1.3. Selecting the Measurement Endpoint(s)

The measurement endpoint for the “physical disruption to habitat” indicator is the difference between the disturbed habitat and an undisturbed reference baseline case (see 3.2.1). Similarly, the measurement endpoint for the indicator “physical disruption to a key species” is the difference between the depleted species population and an undisturbed reference baseline case.

The measurement endpoint used in the calculation of disruption to habitats is the actual physical area upon which structures have been built (e.g., dams, roads, physical plants) and affected areas adjacent to such structures (e.g., land used for agriculture and mining).

For instance, in the case of a hydroelectric power production system, the calculations take into account: 1) the direct habitat area altered by the dam structure and its resulting impoundment, and 2) the direct impact of this structure on migratory fish populations over time. As such, there are two distinct environmental mechanisms, with two measurement endpoints. The impacts of the dam structure and impoundment are measured in hectares of habitat converted, while impacts on migratory fish populations are measured in terms of species population changes over a period of time, including appropriate allocations to other contributing factors, such as the upstream habitat degradation or downstream migration impediments.

The assumptions made in the selection of baselines for specific measurement endpoint calculations and the classification of key species as unique indicators are the greatest sources of uncertainty for these stressor-effect networks.

3.2. Calculating the Net Depletion/Gain of Individual Habitats

Habitats may be entirely removed as, for example, when areas are impounded behind a dam or when forests are converted to agricultural production. Alternatively, habitats may be only partially depleted, as in the case of riparian areas adjacent to the actual impounded

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areas behind a dam. It is also possible that these physical alterations of ecosystems may result in the creation of selected new habitats (e.g., from impoundment) or actually increase the size of existing ones. Such increases would represent a net accretion, or “gain”, of these habitats.

The calculation of net habitat depletion/gain involves two steps. First, the area and degree of depletion (or gain) of individual habitats (i.e., deviation from an established baseline) is determined. Next, the individual habitat depletion rates are aggregated together into an overall net depletion result.

3.2.1. Classification and Characterization of Baseline Habitats

In order to determine the area and degree of depletion/gain, both the “baseline habitat” and the current state of the affected habitats must be classified and characterized. The baseline habitat represents the integrity (i.e., the composition, structure and richness) of the habitat, assuming no disruption by the system being studied. The difference between the current state and the baseline state represents the degree of depletion or gain. This depletion/gain, in turn, is used to establish the environmental characterization factor (ecf), needed to calculate the net depletion/gain for each individual habitat.

The state of habitat integrity in a defined baseline habitat represents conditions at a particular point in time. For a given study, at least two types of baselines can be considered:

• The “initial” baseline. • The “natural recovery trajectory” or “target” baseline.

Each of these baselines provides an important benchmark against which to measure the impacts of the current industrial activity. (Figure 3.2)

100%

Habitat Integrity

Time Period of prior human Period of current intervention and/or industrial activity industrial activity

Figure 3.2 — The Initial and Natural Recovery Trajectory Baselines

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3.2.1.1. Initial Baseline

The initial baseline represents habitat conditions at the time of the initiation of the current industrial activity. These conditions are determined based on available data, and represent the cumulative impacts from prior human intervention in a representative geographic area.

If habitat conditions at the time of initiation were nearly pristine, the industrial system being studied may either have had no net impacts, or may have caused a net loss in habitat. If habitat conditions at the time of initiation were significantly degraded, the industrial system being studied may have had no net impacts, may have caused a further loss in habitat, or may have led to a net gain in habitat. For example, a well-managed forestry or mining operation in a degraded area might demonstrate an increase in habitat when compared to an initial degraded baseline.

Because the calculation of net habitat depletion/gain is intended to reflect only the measurable habitat deviations that can be directly linked to the operations of the particular industrial system, the influence of other system dynamics on habitat integrity in the same geographic region must be discounted. For instance, a tract of land currently managed for forestry may also be subject to acid depositions and ground level ozone originating in nearby urban areas. Any dieback linked to these emissions would not be allocated to the forestry system operations. (Figure 3.3)

100% Case 1 Loss due to current } industrial activity

Loss due to external factors Habitat Initial baseline } Integrity

Gain due to current industrial activity Case 2 }} Loss due to external factors

Prior activity Today

Figure 3.3. — Measuring the current system against the initial baseline. In Case Example 1, the initial baseline conditions are represented by a high level of habitat integrity. The current industrial system is assigned only the amount of habitat deviation directly associated with its activities. In Case Example 2, the initial baseline conditions are represented by a significantly degraded habitat. The gains attributed to the current industrial system take into the mitigation of any habitat losses that might otherwise be expected due to external factors.

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3.2.1.2. Natural Trajectory Baseline

The natural recovery trajectory (or target) baseline represents the habitat conditions in a defined geographic area that would exist in the present or at some future specified time if no industrial activity had taken place since the onset of the current industrial activity.

Again, it is important to separate out the influence of other system dynamics (e.g., natural disease vectors, climate changes, and urbanization) on habitat integrity in order to measure accurately any habitat deviations attributable to the system itself. For those areas that were essentially pristine prior to the current industrial activity, similar areas that have remained unindustrialized can serve as the baseline. However, in most areas, purposely-designated set-asides will generally be the most reliable baselines, provided that no industrial activity has occurred in the set-aside area since the time of initiation of the current activity.

The natural recovery trajectory, and thus the associated baseline, will vary widely, depending on two factors: 1) whether or not the areas studied were severely degraded prior to the onset of the current industrial activity, and 2) the period of time over which the current industrial activity has taken place. Industrial systems may result either in a slowing down or speeding up of the rate of recovery (Figure 3.4).

100%

Case 1: Recovery trajectory based on current Net gain due to industrial }industrial activity Habitat activity Integrity Natural recovery Net depletion due to trajectory }industrial activity

Case 2: Recovery trajectory based on current industrial activity

Prior activity Today

Figure 3.4. — Measuring the current system against the natural recovery trajectory baseline.

In Case 1, the current industrial system shows a net gain over the natural recovery trajectory. In Case 2, the current industrial system shows a net loss when compared to the natural recovery trajectory.

The factors involved in the establishment of specific baselines for a particular study must be transparent. The practitioner is responsible for obtaining the necessary regional expertise to classify and characterize both the initial and natural recovery trajectory baselines. If either of these baselines cannot be established, the reasons must likewise be transparent.

3.2.2. Landscape Scale Assessment

Physical disruption to habitats is calculated across the affected terrestrial or aquatic habitats at a landscape scale, relative to the baseline habitat conditions. Depletion can occur in the

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array of cover types represented, as well as in the pattern and distribution of these cover types.

For example, in the case of forestry operations, baseline habitat conditions are defined by a particular spatial pattern and distribution of an array of cover type conditions, including open meadow areas, riparian zones, early seral forest cover, mid-seral forest cover and late-seral forest cover. This full array of cover types will also exist in modern industrial forest operations, though the pattern and distribution of cover types may vary from baseline conditions. As such, an area recently cleared of trees does not necessarily represent a net habitat depletion when considered at the landscape scale, but may simply represent a change in the spatial distribution of open area cover type.

3.2.3. Environmental Characterization Factors (ECFs)

As discussed in Chapter 1, environmental characterization factors (ecf) are applied to ensure that indicator results reflect the relative severity of a given environmental effect in the relevant “providing” or “receiving” environments, and to ensure the equitable aggregation of different habitats under a single overall indicator result. Both the characterization factors and the corresponding algorithms are derived empirically from environmental data and assumptions.

3.2.3.1. Establishing ECFs for Individual Habitats

The environmental characterization factors established for individual habitats (ecf-1) address spatial, temporal, linear/non-linear, and threshold dynamics specific to that habitat.

Spatial characterization of habitats involves an assessment of the size of the geographic area in which 100% depletion/accretion occurs, and the size of the geographic area in which a partial (less than 100%) depletion/accretion occurs.

• 100% depleted can also be referred to as "maximum deviation from baseline," and refers to areas in which, as a result of physical disruption, there is no remaining semblance to the habitat identified as representative baseline conditions for the area. 100% loss occurs in connection with a wide range of activities, such as mine pits, roads, buildings, stockpiles, water impoundment’s from dams, landfills, etc. Within the affected area, the size of various defined ecological strata or variables is characterized.

• Partial depletion of (less than 100%), or "partial deviation from baseline," occurs in conjunction with industrial operations in a defined geographic area. Partial terrestrial habitat depletion can occur, for example, in connection with forestry operations, in riparian zones around dam impoundment areas, abandoned mines with incomplete reclamation, etc.

Temporally, the area of loss is ideally characterized in the following terms: 1) the duration of the effect; and 2) the rate at which the effect diminishes or increases over time. For example, a dirt road constructed for logging will represent 100% habitat depletion, but could return in time to its original state once abandoned. The construction of a building, on the other hand, will more permanently alter the habitat over a time scale relevant to the human activity being studied.

Natural accretion or active reclamation usually occurs only after the industrial activity ceases. Indirect gains likewise tend to occur in a timeframe characterized by a sharp spike at the onset of the industrial activity followed by a steady state period. For example, the

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damming of a river may result in the reduction of native riverine habitats such as waterfalls and rapids, but may also result in the creation of new aquatic habitats in impounded area.

Beyond spatial and temporal factors, the linearity or non-linearity of effects, and the thresholds at which effects occur, must be considered. While areas of 100% depletion can be measured directly, the effective area of depletion associated with partial deviation must involve some expertise to establish the appropriate relationship between loss of specific vegetative types and the loss of key indicator species. For those habitats that are only partially depleted, the non-linearity of the relationship between the percentage of depletion and the effective depletion must be determined. For instance, removal of 30% of the critical flora or fauna in a given region may represent an effective 90% terrestrial habitat depletion. This could occur from a selected removal of certain key flora, which in turn has disproportionate effects on fauna in a given habitat.

The percentage of depletion/gain of the various vegetative types generally can be determined by GIS or other landscape assessment techniques commonly in use. Appropriate field data is used to establish the significance of various species depleted compared to specific depletion of vegetative types.

The following types of environmental characterization data are required to establish the baseline and complete the spatial, temporal and non-linear characterization of habitat depletion:

• General size and identification of ecological strata of the landscape being studied and the baseline habitat.

• General composition and densities of the vegetation groups for areas of partial depletion and corresponding baseline.

• Specific population data for key indicator fauna groups (e.g., breeding birds, small mammals, fishes).

Many forestry industrial systems have maps showing general vegetative types for the landscapes under their management. Such maps are sufficient for the LCSEA assessment. In addition, modern remotely sensed imaging tools, such as satellite mapping, are capable of producing detailed maps distinguishing various habitat types by GIS grid. Such measurements can then be confirmed through ground truthing, with sampling sufficiency and variability characterized statistically.

3.2.3.2. Establishing ECFs Between Different Habitats

The selected measurement endpoint for this indicator must not only quantitatively represent the net depletion or accretion of the specific habitats, but must also be able to equate the various habitats to each other. A second environmental characterization factor (ecf-2) is applied for this purpose.

Each habitat is recognized to be uniquely suited to its own particular geographic setting. Habitat Equivalency Protocols (HEPs) used by the U.S. Department of Interior to calculate the relative importance of one habitat versus another are employed, where data exist. If no such data exist, habitat depletion data remain unaggregated.

The disruption impacts in each habitat are measured separately before any aggregation is attempted among habitats. The practitioner should make all assumptions on aggregation transparent.

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3.2.4. Calculation of Overall Habitat Depletion/Gain

Physical disruption is reported in terms of the effective area of 100% depletion/accretion of habitat in hectares. The areas of complete removal of habitats automatically result in the 100% loss in hectares. The areas of partial deviation from baseline use the following equation.

Effective 100% depletion of individual habitat = [total habitat area] [ecf-1]

At this point, it is possible to conduct the aggregations needed to calculate the cumulative indicator result for the system.

Cumulative Habitat = å [Effective 100% depl./gain of individual habitat] [ecf 2] depletion /gain

3.3. Calculating the Depletion of Key Species

The net depletion calculation for key identified species is based exclusively on that species in its local habitat, consistent with the scope and goals of the study. Furthermore, this calculation involves allocation to ensure that impacts attributable to the system are put into proper perspective as compared to other factors contributing to the same impact.

Overall rates of depletion and allocation protocols for calculating a key species indicator are determined through the use of established population dynamic models capable of integrating multiple variables affecting the changes in a species population.

In the example illustrated in Figure 3.5, an anadromous fish species is identified as a key species in the study of the impacts of a hydroelectric power station (Dam 1). Factors affecting this species include upstream degradation of spawning habitats, caused by industrial and urban pollution, and downstream impediments, including a series of dams along the river and habitat degradation in the ocean. In such an example, population dynamic models integrate specific environmental characterization data — the rates of fish passage efficiency at each dam site, the breeding rates per adult fish (200-500,000 fry), standard juvenile survival rates (1%), the percentage degradation of upstream habitats and ocean habitats, etc. — in order to model the life-cycle impacts of these factors on the species.

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River

50% depleted spawning habitat

Ocean Dam 1 Upstr. Eff = 75% Dam 2 Downst Eff. = 75% Upstr. Eff = 30% Dam 3 Downst Eff. = 30% Upstr. Eff = 30% Downst Eff. = 40%

Figure 3.5.— Example of factors influencing the population of a specific anadromous fish species

This indicator is calculated as the percentage of depletion attributable to a specific cause of physical disruption. If it is not possible to characterize all causal relationships, then worst case assumptions can be made.

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C hapter 4. Emission Loadings

The following chapter presents operational requirements and calculation methods for global, regional and local emission loading indicators..

This approach uses stressor-effect networks to model environmental mechanisms, the relationship between indicator results, the indicator, and endpoints. For emission loadings, there are two types of endpoints: measurable and projected. Measurable endpoints are those endpoints for which actual environmental information can be obtained today. Projected endpoints are those that represent modeled effects that may occur over an extended temporal period.

The LCSEA framework requires that each indicator be directly linked to a measurable endpoint (incorporating spatial, temporal and threshold differentiation as relevant) and remain additive between various unit operations. Furthermore, aggregation of the inventory data from different unrelated unit operations pertaining to regional or local effects should be avoided whenever possible. The following general approach is chosen in order to meet these requirements.

Emission loadings (ELx) are the sum of emissions (IVi) multiplied by the relevant equivalency factors (EFi) at each unit operation:

ELx = IV1*EF1 + IV2*EF2 + IV3*EF3 . . .

Characterization factors are mathematical expressions that characterize both the relative potency (by stressor-effect network) of each emission per unit operation as well as the characterization of the receiving environment (i.e., the total area of influence), including fate and transport, background concentrations and the degree of severity to the defined measurement endpoint.

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4.1. Greenhouse Gas Loading

4.1.1. Defining the Stressor-Effect Network

The temperature on earth is determined by the balance of the incoming solar radiation and the outgoing infrared radiation from the earth. Without an atmosphere, the average temperature at the surface of the earth would be about -19oC (Ramanathan, 1987). However, there are trace gases in the atmosphere that absorb the outgoing radiation and re- emit infrared heat radiation towards the earth. This so-called “greenhouse effect” is natural and holds the temperature at the earth's surface at an average of about +15oC.

Through various activities, humans have increased the atmospheric concentrations of trace gases. It is now generally believed that these gases are enhancing the natural greenhouse effect, which might in turn lead to changes in climate patterns and a higher global average temperature within the next century. Such a climate change could cause serious consequences on natural ecosystems and human settlements.

The relationship between emissions and climate change can be illustrated by the following network of processes (Figure 4.1):

Increased Increased Parallel Atmospheric Climate Emissions Radiative Effects Concentrations Forcing Change

Figure 4.1. — Simplified Greenhouse Gas Stressor-Effects Network

4.1.2. Characterizing the Stressors

The radiative budget is affected by natural processes such as volcanoes and variations in the sun’s radiative intensity, and also directly or indirectly by humans activities such as emissions of greenhouse gases, ozone precursors and atmospheric aerosols.

A change in average net radiation at the top of the troposphere, because of either solar or infrared radiation, is defined as a radiative forcing (measured in W/m2). Radiative forcing, averaged globally, has been used to compare the potential climatic effect of different climate change mechanisms. For a range of mechanisms, there appears to be a similar relationship between global mean radiative forcing and global mean surface temperature change. A positive radiative forcing tends to warm the surface, while a negative radiative forcing tends to cool it.

4.1.2.1. The Greenhouse Gas Stressors

Table 4.1 presents the mixing ratios of the most important greenhouse gases before the industrial revolution and at present. Also shown are the present rates of increase in these gases and their average atmospheric lifetimes. Two important gases, water vapor and ozone, are not presented in the table. Water vapor is the dominating greenhouse gas, but its mixing ratio in the troposphere is determined implicitly by the climate system and is not affected on a global scale by anthropogenic emissions and sinks. The mixing ratio of ozone is affected, both in the troposphere and in the stratosphere, by anthropogenic activities, but it is difficult to quantify these changes based on present observations.

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Table 4.1. The most important greenhouse gases. The table presents the mixing ratios before the industrial revolution and at present, the rates of increase and the average atmospheric lifetimes (IPCC, 1990).

Mixing ratio Mixing ratio Rate of Atmospheric 1750 - 1800 1990 increase lifetime [ppbv] [ppbv] [% year-1] [years] Carbon dioxide, CO2 280 000 353 000 0,4% 50 -200 Methane, CH4 700 1 714 0,8% 12 - 17 Nitrous oxide, N2O 275 311 0,25% 120 CFC-12 0 0,503 4% 102 HCFC-22 0 0,105 7% 13.3

Calculations have shown that a doubling of CO2 concentrations would increase the radiative forcing by ca 4 W/m2. This is estimated to increase the global mean surface temperature by between 1.5 and 4.5 degrees Celsius. Human activities have caused increases in CO2, CH4, N2O and CFCs, which correspond to a change in radiative forcing by ca 2.5 W/m2.

4.1.2.2. Other Stressors

Ozone

Ozone is an important greenhouse gas present in both the stratosphere and the troposphere. Changes in ozone can cause radiative forcing by influencing both solar and infrared radiation. The net radiative forcing is strongly dependent on the vertical distribution of ozone change. Estimation of the radiative forcing due to changes in ozone is more complex than for well-mixed greenhouse gases.

In the troposphere, ozone is produced from various short-lived precursor gases such as nitrogen oxides (NOx), carbon monoxide (CO) and non-methane hydrocarbons (NMVOC). This effect is difficult to quantify but is estimated at ca 0.2 - 0.6 W/m2.

Emissions of CFCs lead to depletion of stratospheric ozone. So besides their warming effect, the CFCs are responsible for an indirect cooling effect. This cooling is estimated to be ca -0.1 W/m2.

Aerosols

Aerosols are suspensions of particles in the range 0.001 - 10 mm in the atmosphere. They affect the radiative budget in two ways: 1) through absorption and scattering of incoming solar radiation, known as the direct effect; and 2) by acting as nuclei on which cloud droplets form, which influence the formation, lifetime and radiative properties of clouds (i.e., the indirect effect). The main sources for aerosols are emissions of sulfur, which lead to the formation of sulfate particles, organic and inorganic carbon.

The aerosols tend to reduce the radiative forcing and have a cooling effect. The size of this effect is difficult to quantify, but estimates show that it might lie between 0 and -1 W/m2. This means that the cooling effect from aerosols can be of the same order of magnitude as the warming effect from all greenhouse gases.

The total solar irradiance varies with an 11-year cycle. Measurements show that these

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changes are the equivalent of a radiative forcing of about 0.2 watt per square meter. However, these changes are cyclical in nature and it is believed that, due to the inertia in the climate system, only a small amount of the possible temperature changes resulting from such transient changes in irradiance is realized.

Volcanic eruptions can increase the amount of aerosol particles in the stratosphere. The dominant radiative effect is an increase in scattering of solar radiation that reduces the net radiation available to the surface/troposphere system, thereby leading to a cooling. Volcanoes have the potential to produce large radiative forcing, but the effects are transitory. The eruption of Mt. Pinatubo in the Philippines in 1991 reduced the radiative forcing by about -4 W/m2. Two years after the eruption, this value had decayed to about -1 W/m2.

4.1.3. Characterizing the Category Indicator

In assessing the effects from emissions, it would be desirable to express these effects in terms of climatic parameters, such as temperature rise, humidity, etc. General circulation models (GCMs) have won recognition for making predictions of future climatic patterns. For a given concentration or emission scenario, such models can calculate the climatic effects, such as the expected temperature rise. However, because of the complexity of the climate system, the GCMs require long execution times along with very powerful computer systems. It should also be noted that the results from GCMs involve serious uncertainties (Houghton et. al., 1990).

Given the uncertainties and costs associated with the use of climatic parameters, another approach is to compare the relative effects from the different gases, based on their relative radiative forcing.

The calculation of radiative forcing works in two steps:

1) Concentrations are calculated given emissions and original concentrations. 2) Radiative forcing is calculated given atmospheric concentrations.

The relationships between emissions and concentrations and between concentrations and radiative forcing, as calculated in the models, can be parameterized into simple functional expressions, making it possible to do simplified calculations of the model scheme shown in Figure 4.2.

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Emission scenario

Concentration Chemistry Transport expressions Model

Future concentrations

Radiative forcing Radiative Transfer expressions Model

Future

radiative forcing

Figure 4.2— Principle for calculating radiative forcing from emission scenarios

4.1.4. Characterization Factor - Global Warming Potential (GWP)

Policymakers have sought some measure of possible future contribution to global warming resulting from current anthropogenic emissions. The global warming potential (GWP) is an attempt to provide such a measure.

GWP is defined as the cumulative radiative forcing between the present and some chosen later time ”horizon" caused by a unit mass of gas emitted now, expressed relative to some reference gas (here CO2 is used). The future global warming effect of a greenhouse gas over the reference time horizon is the specific GWP multiplied by the amount of gas emitted. For example, GWPs could be used to calculate the effect of reducing CO2 emissions by a certain amount compared with reducing CFC-emissions, for a specified time horizon.

Derivation of GWPs requires knowledge of the fate of the emitted gas, which is typically not well understood, and the radiative forcing due to the amount remaining in the atmosphere, which is reasonably well understood.

The latest estimates of GWPs are given in Table 4.2. Although the GWPs are quoted as single values, the typical uncertainty is ±35% relative to the carbon dioxide reference. The majority of GWPs are larger than those reported in IPCC (1992), typically by 10-30%. These increases are largely due to: 1) an improved carbon dioxide reference; and 2) improved estimates of atmospheric lifetime. Because GWPs are based on the radiative forcing concept, they are difficult to apply to radiatively important constituents that are

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unevenly distributed in the atmosphere (e.g. aerosols). No attempt is made to define a GWP for aerosols.

The ozone precursors NOx, CO and NMVOC have an indirect greenhouse effect due to the production of ozone, carbon dioxide and stratospheric water vapor. However, no GWP values have been assigned to these gases due to difficulties in quantifying these effects, even if their contribution to the greenhouse effect can be considerable. These indirect effects are highly model-dependent and will need further revision and evaluation.

GWPs need to take account of any indirect effects of the emitted greenhouse gas (e.g., the formation of another greenhouse gas) if they are to correctly reflect future warming potential. The calculation of many indirect GWP components is not currently possible because of inadequate characterization of many of the atmospheric processes involved.

The GWP value for methane in Table 4.2 includes both the direct and indirect components (e.g. the formation of tropospheric ozone). In the 1992 IPCC report, only the direct GWP for methane was quoted; no account was taken of the effect of methane on its own lifetime and indirect effects were not quantified. For a 100-year time horizon, the indirect contributions to the total GWP value are tropospheric ozone change 19±12% and stratospheric water vapor change about 4%. As the range indicates, there is substantial current uncertainty in the methane GWP.

The GWPs presented in Table 4.2 were calculated on the assumption that present background atmospheric composition remains constant indefinitely. An assumption of increasing CO2 concentrations, which lowers the additional forcing of incremental CO2 emissions, would increase the GWP of other gases relative to CO2 because the absorption bands of CO2 are becoming increasingly more saturated. In the peat scenario, the atmospheric concentrations of all greenhouse gases were kept constant over the 100-year study period. In an alternative model run, the same emission scenario was studied while allowing the background concentrations of CO2, CH4 and N2O to change according to a global business-as-usual scenario. In this later model run, the calculated radiative forcing was ca 20 % lower than in the original run, as an average over the 100-year study period.

The indirect effect of CFCs and halons through stratospheric ozone depletion, which tends to reduce the GWPs for these gases, is not included in the values in Table 6.2. Some substitutes for CFCs have lower GWPs than the compounds they replace (e.g. short lived gases like HFC-152a). On the other hand, other potential substitutes like perfluorocarbons (such as CF4 and C2F6) have very long lifetimes and hence extremely large GWPs over long time horizons.

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Table 4.2 — GWPs following the instantaneous release of 1 kg of each trace gas, relative to carbon dioxide. The index should be interpreted: The instant release of 1 kg of nitrous oxide is expected to give the same contribution to global warming as 320 kg of carbon dioxide on a 100 year time scale (IPCC, 1990)

Trace gas GWP 20 years GWP 100 years GWP 500 years Carbon dioxide, CO2 1 1 1 Methane, CH4 62 24.5 7.5 Nitrous oxide, N2O 290 320 180 CFC-11 5000 4000 1400 CFC-12 7900 8500 4200 HCFC-22 4300 1700 520 Carbon tetrachloride, CCl4 1700 530 170 Methyl chloroform, CH3CCl3 360 110 35 Halon 1301, CF3Br 6200 5600 2200

4.1.5. Calculating the Greenhouse Gas Loadings

The following methods are to be used for quantifying the contribution from emissions of greenhouse gases to the greenhouse gas indicator:

1. If time resolution is necessary If the emissions are spread out over a time period of several decades, the contribution to the greenhouse indicator should be described in terms of radiative forcing over an integrated time period. Such radiative forcing can be calculated either by using models or by using parameterized functional relationships, as described in Figure 4.3.

2. If time resolution is not necessary When all the emissions occur in or around the same period and it is not necessary to illustrate when in time the impacts occur, the IPCC concept of global warming potentials is used for a time perspective of 100 years.

3. Temporal characterization of background concentrations and background saturation The loading should reflect both the increase in background concentrations and the potential saturation of the infrared absorption band for a given gas or aerosol.

As an example of #1, the method of using parameterized relationships for describing the contribution to the greenhouse indicator has been used in various applications (see for example IPCC, 1990, Zetterberg and Klemedtsson, 1996, Savolainen et al 1994). Figure 4.3 shows the resulting radiative forcing due to the use of peat for energy production in Sweden.

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200

All 150 CO2

100

aW/m2/MJ 50 N2O

0 0 20 10 70 90 30 40 50 60 80 CH4 100 -50

Figure 4.3. — Calculated radiative forcing as a function of time (years) for the production and combustion of 1 MJ peat as it is done today in Bergslagen, Sweden. Emissions and uptake of greenhouse gases are considered from mining, combustion and forestation of the mined areas. Emissions from virgin peat land have been subtracted. Mining and combustion are assumed to take place during the first 20 years. The contribution from each gas is shown. Values in aW/m2/MJ. From Zetterberg and Klemedtsson, 1996.

4.1.6. Limitations

The main issues to be resolved for reducing uncertainty include:

• Quantifying the contribution to the greenhouse effect from the ozone precursors NOx, CO and NMVOC; • If possible, aggregating NOx, CO and NMVOC into the overall global warming loading; • Quantifying the cooling effect of aerosols; • Calculating GWP values assuming changing atmospheric background concentrations; • Accounting for saturation of infra-red spectra; and • Including indirect effects in GWP values such as increasing cloud cover from specific point sources. Airplane sulfate emissions can dramatically increase cloud cover for specific climatic conditions. There are no models which can current quantifies the increase in W/m2.

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4.2. Acidification Loading

4.2.1. Defining the Stressor-Effect Network

Several pollutants, mainly the final oxidation products of emitted sulfur dioxide, nitrogen oxides and ammonia, act as acidifying substances in the environment. Acidification effects occur in different compartments of terrestrial and aquatic ecosystems, primarily in soils, groundwater and surface waters. The mechanisms for effects may differ substantially between systems and causes, and are briefly described in the following sections. Other valuable references include Posch et al., (1996), Grennfelt et. al., (1994) and Staaf and Tyler (1995).

4.2.2. Characterizing the Receiving Environment

4.2.2.1. Soil acidification

Effects in soils are complex and depend on the status of the soils. Soil acidification is often defined as the loss in base saturation. A soil with a high base saturation will have a high capacity of neutralizing a percolating acidified water, while in a soil with a low base saturation, the percolating water will only be partly neutralized. Instead, aluminum may be released, causing toxic effects in the soils as well as in surface waters downstream. Acidification of non-calcareous soils formed during the last glaciation may be considered as a process involving several steps:

• During the first phase, the acid will be totally neutralized during the percolation but the soil will loosen base cations;

• When the base saturation decreases, the neutralizing capacity of the soil will decrease and so will the pH of the soil water;

• When pH reaches levels below 5, aluminum will be dissolved from the soil particles and aluminum concentrations will increase with decreasing pH; and

• Finally, the soil will reach a “steady state” situation, at which base ion concentrations are determined by the actual weathering and where the system will be fully buffered by aluminum. The pH level in the mineral soil at this final stage is often between 4.0-4.5.

The effects and consequences will vary depending on the stage of the acidification process. For the assessment of soil acidification, the most important parameters are base saturation, cation exchange capacity (CEC), weathering rate, the thickness of the soil and the amount of percolating water. In soils affected by long-term acid deposition, sulfur is almost entirely transported through the soils as sulfate and will have a large acidification effect. For nitrogen, the picture is more complex. Most terrestrial ecosystems, which are vulnerable to acidification, are limited in their production by nitrogen availability. In these ecosystems, the primary effect from nitrogen deposition is fertilization, increased production and later eutrophication. (The eutrophication effect is discussed separately below).

In addition, many soils have a large capacity to immobilize nitrogen in very chemically stable forms. The capacity of immobilization is, however, limited. If the actual deposition is too high or remains above a threshold level over a long period, nitrogen leaching may occur, primarily as nitrate. Nitrate leaching then acts in the same way as sulfate, i.e., it

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causes a decrease in base saturation and a release of aluminum. Ammonia, being a neutralizing agent in the atmosphere and precipitation, is in this context considered as an acidifying compound since nitrification in soils may convert ammonium to nitrate. The nitrification itself is also an acidifying process.

Ecosystem studies indicate that nitrate leaching due to high nitrogen deposition is very common in natural ecosystems on the European continent, where deposition often exceeds 15-20 kg N ha-1 yr-1 (approximately 1 - 1.5 keq ha-1 yr-1). (See Table 4.3.) If the acidification effect of sulfur and nitrogen deposition is to be considered, it is reasonable to assume that:

a) All sulfur deposited may have the potential to contribute to an acidification effect. b) Nitrogen deposition may contribute directly to acidification when nitrate is leached. Since nitrate leaching mostly is less than 50% of the nitrogen deposition, nitrogen deposition has a lower acidification effect than sulfur. c) The time factor is important. In many areas, present nitrogen deposition may be considered as potentially acidifying, since although no effects show at present, deposition of this level cannot be withstood in the long run.

4.2.2.2. Surface Waters

Acidification of surface waters is mainly an effect of a very low buffering capacity of the surrounding soils. The main ecosystem effects are those caused by a low pH, which leads to an increased production of Sphagnum, low Ca/Al ratios , which are toxic to fish, and extremely low phosphorus values, which result in oligotrophication.20 The low phosphorus values are caused by precipitation with aluminum in the catchments as well as in the lakes.

4.2.2.3. Threshold Characterization

About 10 years ago, the concept of critical loads was launched in order to describe the highest deposition of acidifying agents that can occur without any adverse effects. The concept was presented as a tool for determining the necessary reductions in emissions but has been widely used for assessment of the actual acidification problem.

”Critical load” was defined as the “quantitative estimate of an exposure to one or more pollutants below which significant harmful effects on specified sensitive elements of the environment do not occur according to present knowledge.” (Nilsson and Grennfelt 1988)

The critical load concept was developed as a tool for the UN ECE protocols on transboundary air pollution. Actual deposition in a certain area can be measured as either above or below the critical load. The magnitude of exceedance is a measure of the need for deposition reduction to fall below the critical load level, i.e., to reach an ecosystem status without any adverse long term acidification effects. However, the exceedance value does not reveal anything about the severity of the actual deposition effects. In other words, the determination of exceedance of critical load serves as a measure of a late indicator along the stressor-effect network rather than an actual measurement endpoint.

The critical load concept for soils is based on the assumption that ecosystems are sensitive to the ratio of base cations to inorganic aluminum (BC/Alinorg) in soil water. For low ratios

2. 20 Definition to be inserted

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(<1) in soil solutions, it is assumed that tree roots will be damaged. A large number of laboratory investigations support this hypothesis, in spite of difficulties in verifying the laboratory results by field experiments and observations. Recent studies, however, support this hypothesis. (Westling & Röttorp 1996 and Frogner et al, 1995)

Given the uncertainties and possible shortcomings in the critical load concept, it is assumed that the amount of sulfur or nitrogen deposited in excess of the critical load causes the effects. The exceedance of critical loads can therefore be used as a measure of the deposition reduction need. Today, critical loads and actual deposition are mapped in a 150 x 150-km grid system over Europe. Within each grid square, the variation in ecosystem sensitivity is available as a frequency distribution of the critical loads.

Critical loads have been thoroughly established in Europe, and also to a large extent in southeast Asia, parts of Africa and South America, as well as in some parts of North America. Such mapped critical load data can be used to characterize the receiving environment and establish emission-specific (site and composition) effect relationships.

In the current method, local acidification effects (shorter distances than 150 km) are not considered. Since the European approach on regional distribution and effects from acidifying substances are based on a 150 km grid system, effects on a smaller scale will cause a within-grid square variability in the acidification effects from a single source. In many countries, models are available on a national scale (grid sizes typically 1-20 km) to take such local effect contributions into account. It is also necessary, however, that ecosystem sensitivity data be available on the same scale. This is often not the case.

Under the LCSEA method, the stressor is partitioned to a specific grid square (site) in relation to large-scale acidification effects. The indicator loading is the amount of the emission that is deposited where critical loads are exceeded.

4.2.2.4. Spatial Characterization

The receiving environment for emissions of sulfur dioxide, nitrogen oxides and ammonia may be more than 1000 km and links between emissions and receptors can only be established by advanced atmospheric transport and chemistry modeling. The transboundary nature of the problem has suggested such an approach and models are today applied establishing source-receptor matrices on continental (Europe, North America) and national scales. The most important work is being carried out by EMEP under the Convention on Long-Range Transboundary Air Pollution (LRTAP).21 Within the EMEP program, stressor-receiving environment matrices are established between emitting countries and receptor areas.

The stressor-effect relationship is measured using grid-to-grid calculations by the EMEP model that will be obtained via EMEP Synthesizing Centre West. The model will be used to calculate the fraction of emissions from a source that is deposited in areas where critical loads are exceeded. The part of the emission deposited elsewhere is not considered to contribute to acidification.

Such calculations are not routine within the LRTAP convention, but can be made on demand. A previous calculation of this was made for sulfur loadings based on 1980

2. 21 Citation to be inserted

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emission data and critical loads mapped within the LRTAP Coordinating Centre for Effects at RIVM, as presented in the example below.

The current procedure is to partition effect contributions in a proportional manner as employed in the application of the critical load concept for the sulfur protocol under the LRTAP Convention.

Table 4.3 — Typical input and output values for sulfur and nitrogen to ecosystems in various European areas

Region Input S Input N Output S Output N N fraction keq/ha·yr keq/ha·yr keq/ha·yr keq/ha yr in output (%) North and Central Scandinavia 0.3 0.2 0.3 <0.05 <15 South Scandinavia 1-1.5 0.7-1.5 1-1.5 0.1 7-10 Central Europe 2-4 1-3 2-4 0.3-1 10-30 The Netherlands 2-4 3-6 2-4 2-6 30-80

4.2.2.5. Example: Regional Environmental Characterization Factors

To present the idea behind the suggested calculation method, an example is provided. The calculations were made for the situation in 1980, the base year for the sulfur protocols under the LRTAP Convention. (This can easily be updated to the actual situation in the mid-1990's.) Due to SOx emission controls, large reductions have been made and are under implementation. As a result, the calculations will give quite different results if present emissions are used, and if the expected situation in the year 2000 or 2005 is considered. The countries in Europe will have to take considerable action to reduce emissions even further as a result of the protocol on sulfur signed in 1994. This protocol will lead to an approximate 60 percent control in sulfur emissions by 2010 in relation to 1980. The results of the calculation are presented in Table 4.4.

Table 4.4. — SOx and NOx Deposition

Percent emissions deposited in areas where critical loads are exceeded. Place of emission Sulfur dioxide Nitrogen oxides Northeastern Svealand (Sweden) 10 5 Northern Lappland 5 3 Eastern England 10 10 Central Germany 35 30 Poland 50 30

The table shows that approximately 50% of the emissions in Poland are deposited in areas where critical loads are exceeded, while less than 10% of the emissions in England are deposited in such areas. Such an approach leads to a much higher loading for Polish emissions than for those of England. The percentage of disposition by regions serves as the ECF for NOx and SOx emissions for specific unit operations within the Europe.

4.2.3. Calculating Acidification Loadings

The calculation of indicator results is illustrated for the acidification stressor-effect network in Table 4.5 below. In this example, the stressor characterization factor reflects the relative

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molar acid equivalence of each of the contributing releases. For each unit operation, the environmental characterization factor reflects the fate and transport fraction of each environmental release that is associated with an actual exceedance of a critical load (threshold level) for the receiving environment.22

Table 4.5. — Calculating the cumulative acidification loading for the system

Unit Inventory LCI Output Stressor Total Environ- LCSEA Operation Emission (ton/30a) Character- Molar Acid mental Emission ization Equiv. Character- Loading Factor ization Factor (ton/30a)

Coal SOx 31,620 1.00 31,620 0.5 15,810 mining/ NOx 9,660 0.70 6,762 0.3 2,029 transport HCl 270 0.88 238 0.5 119

CaO SOx 240 1.00 240 0.15 36 product/ NOx 1,260 0.70 882 0.075 66 transport

Coal SOx 50,190 1.00 50,190 0.15 7,529 use NOx 36,480 0.70 25,536 0.075 1,915 HCl 15,210 0.88 13,385 0.15 2,008

Total 128,853 29,512

As illustrated in Table 4.5, the use of stressor characterization factors (i.e., total molar acid equivalencies) would lead to a worst-case loading of 128,853 tons. By incorporating the environmental characterization factors, the reported loading is reduced by more than four- fold to 29,512 tons, reflecting only that fraction which actually contributes to acidification.

4.2.4. Current Limitations

• Data on critical loads and their variability within grid squares are available, mainly on a European scale, but not yet developed in other regions of the world. For example, grid mapping of critical loadings does not exist in the United States.

• Grid to grid matrices to link emissions with critical load exceedances must be calculated. The model and data necessary are available, but the model must be modified to give the required output.

• The current models do not factor in regional trends. The environmental characterization factor should include both the relative severity between different grids within the same region and longer-term trends between regions. For example, total exceedences in Europe is projected to be substantially reduced over the next decade while in Asia the total exceedences are expected to dramatically increase

2. 22 In this example, the simplifying assumption has been made that all NOx and SOx are converted to strong acids.

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4.3. Ground-Level Ozone Loading

4.3.1. Defining the Stressor-Effect Network

Tropospheric (ground level) ozone is produced from emissions of nitrogen oxides (NOx) and volatile organic compounds (VOCs) in the presence of sunlight. At high concentration levels, ozone presents a threat to human health, while at lower concentrations, it causes damage to vegetation.

The production of ozone is strongly dependent on the emission densities of VOCs and NOx as well as on the VOC/NOx ratios.

UV(sunlight) HC + NOx + O2 —> HC-1 + CO2 + O3 + NOx

Ozone formation due to an emission of NOx or VOCs varies depending upon the receiving environment into which they are emitted (Lin et. al., 1988; Sillman et. al., 1990; Bowman and Seinfeld, 1994; Altshuler et. al., 1995). Knowledge of the chemistry and climatological conditions of the receiving environment (i.e., the area into which the emissions are released as well as the area into which they are transported) is thus essential in order to estimate the amount of ozone produced.

4.3.2. Characterizing the Receiving Environments

To assess the environmental effects from the ozone formed due to an emission, the receiving environment (i.e., the area affected by the ozone) must first be defined. Once defined, the potential ozone formation affecting this area should be quantified, using the "photochemical ozone creation potentials" (POCPs) stressor characterization approach. Since POCPs are sensitive to the chemical background, both at the emission source as well as along the trajectory, background emissions of NOx and VOCs in the receiving environment need to be defined.

Finally, in order to assess the extent to which the ozone produced is likely to cause damage to the environment or human health, the exceedance of established thresholds are considered.

The receiving environment can be defined in different ways. It could be defined, for instance, as the area within which 90% of the emissions has been consumed or where 90% of the effects from the ozone produced has occurred. The area could also be limited by the time during which the contribution from the increase in ozone is less than a certain fraction of the maximum increase in ozone. In the example below, it is operationally defined as the area within a radius of 1000 km from the studied emission source, since most of the effects caused by the ozone produced from the emission of the source are likely to have occurred within this distance from the source.

The emission source and the receiving environment for production of ground level ozone are indicated on a map. The receiving environment is then divided into sectors. In the example below, the receiving environment is segmented into eight sectors, based on wind directions. When available, statistics of the trajectories in the atmospheric boundary layer in the area should be used since the wind directions often vary depending on the time of the year. If no wind data are available, the frequencies of the wind directions are divided equally between the sectors. Sectors that cover only sea or desert are considered not to experience the same magnitude of environmental effects from ground level ozone.

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At the emission source and in each sector, the average environmental concentrations of NOx and VOCs are read from inventory maps. In the example, emissions data from 1994 (Mylona, 1996) are used, with only one value of NOx and VOC representing each sector. In practice, each sector is represented by several background emission points, depending on the distance from the emission source.

Natural background concentrations of ozone are about 25 ppbv, and this concentration is still observed in oceanic air masses. However, it is thought that average background levels in post-industrial times are about twice that high (IPCC 1995). There are several locations where the average ozone concentrations are high due to natural sources of non-methane hydrocarbons (e.g. the Smoky Mountains and the Blue Ridge Mountains in the southeastern U.S.).

4.3.3. Calculating the Ground-Level Ozone Loading

In the example below, the POCP values for NOx and individual VOCs are calculated using the IVL photochemical trajectory model, a chemically detailed model that describes the chemical development in an air parcel as it follows a trajectory.23 It describes the decomposition of approximately 80 different VOCs and includes more than 700 species participating in approximately 2000 reactions (Andersson-Sköld, 1995).

The POCP values for the emission site and each representative sector are multiplied by each receiving environment equivalency factor (REEF) to yield an overall ozone loading.

Ground-Level Ozone = (POCP1)(REEF1) + (POCP2)(REEF2) + (POCP3)(REEF3) . . . Loading

The arithmetical example below has been performed for the eastern part of Svealand in Sweden. The receiving environment at the emission source has not been considered in the example below, but in practice, the resultant impact will vary depending upon conditions in the receiving environment.

The areas into which the emissions are transported have been divided into eight sectors within a radius of 1000 km of the emission source; emission densities may vary within these large sectors. In some sense, this variation is corrected when the fraction of the sector, which covers sea or desert, is omitted from the total equivalency factor. The emissions given in the example below are average values over land within each sector.

The exceedance of the effect thresholds can be used for the introduction of a receiving environment equivalency factor.

In this example, the emission is assumed to take place during the growing season and only damage to vegetation has been considered. AOT40 thresholds for damage to both forest and crops have been used to determine incidents of exceedance of threshold. In areas where

2. 23 The model was originally developed at Harwell (Derwent and Hov, 1979; Derwent and Hough, 1988) but has been revised and chemically expanded at IVL (Pleijel et. al., 1992; Andersson-Sköld et. al., 1992).

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both of the critical levels are exceeded, the equivalency factor is set to 1. If only one critical level is exceeded, the weighting factor for the area is set to 0.5. If none of the critical levels are exceeded, the factor is set to 0 POCP. Values for four different areas within Europe are given in Table 4.6 below, with and without consideration of the ozone critical levels for damage to vegetation.

Table 4.6. — An arithmetical example performed for the eastern part of Svealand in Sweden. The emissions are average emissions over land within each sector and have been used to work out the corresponding POCP values. The resulting total POCP values have been calculated as the sum of the products between the POCP and the weighting factor for all sector and are given in kg harmful ozone / kg emitted species . The calculation of equivalency factors is shown in Table 4.7.

Sector Emissions POCP REEF NOx VOC Ethene n-Butane o-Xylene NOx (tonnes/10 ×10km2×year) (kg ozone / kg emitted species) Fraction 1 22.2 22.2 2.0 0.5 1.4 2.7 0.043 2 88.9 40.0 2.4 1.2 2.3 2.0 0.000 3 88.9 111.1 2.5 1.4 2.4 0.9 0.038 4 177.8 155.6 2.3 1.4 2.1 1.1 0.028 5 266.7 222.2 2.3 1.4 2.1 1.1 0.071 6 444.4 222.2 3.2 2.0 2.9 0.7 0.094 7 111.1 177.8 2.4 1.4 2.2 0.7 0.064 8 35.6 44.4 2.4 1.0 2.6 2.0 0.020 Total 0.918 0.508 0.828 0.412

Table 4.7. — Calculation of the receiving environment equivalency factors (REEFs) for the different sectors in the example of eastern Svealand in Sweden

Sector Land AOT40 Wind frequency REEFs (%) Fraction (%) Fraction 1 80 0.5 10.8 0.043 2 80 0 20.2 0.000 3 70 0.5 10.9 0.038 4 60 0.5 9.5 0.028 5 50 1 14.2 0.071 6 70 1 13.4 0.094 7 50 1 12.9 0.064 8 50 0.5 8.0 0.020

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Table 4.8. — POCP values for four different areas within Europe, with and without including the aspects of critical levels in the receiving environment equivalency factors.

Area POCP POCP without critical levels with critical levels

(kg O3 / kg emitted species) (kg harmful O3 / kg emitted species) Ethene NOx Ethene NOx N Lappland 0.893 1.232 0.225 0.142 E Svealand 1.593 0.957 0.918 0.412 SE England 2.307 0.228 1.993 0.228 C Germany 2.727 0.276 2.727 0.276

4.3.4. Calculating Indicator for Regions Outside of Europe

Although a great deal of work has been done to model the generation and transport in Northern Europe, such characterization has not been conducted in the U.S. The models from Northern Europe are not necessarily transferable to U.S. locations, particularly those with high sunlight locations such as Sacramento and Tampa.

To model the excess ozone formation as the emission loading, the MIR model from the carbon monoxide and hydrocarbon emissions can be used, which amounts to one gram of ozone for every gram of non-methane hydrocarbon. Ozone generation can then be adjusted by the local annual ozone days exceeding a threshold.

Several possible thresholds could be employed, including: a minimum concentration of ozone at which crop effects are seen (40 ppbv); the post industrial average ozone concentration (50 ppbv); the USEPA 8-hour average standard (80 ppbv); the US EPA 1- hour maximum standard (120 ppbv). The tables below show the number of exceedance days in Stockholm and Sacramento as a function of the chosen threshold. It is desirable, though not necessary, to have local characterization factors that range from zero to one, covering the full range of environmental conditions. For this reason, the threshold of 60 ppbv is used to base the characterization factor. This threshold corresponds to the European target concentration that should not be exceeded.

Using days of exceedance of a threshold (rather than hours of exceedance) permits better worldwide coverage, since those countries that do not monitor their ozone levels tend to monitor during the seasons (summer) and the time of day (afternoon) during which elevated ozone concentrations are likely to occur.

This model for evaluating ozone generation differs from the European approach, which requires detailed modeling of ozone generation and hourly estimates of exceedance of 40 and 60 ppbv levels (See Table 4.9). This detailed ozone data is only available in Europe.

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Table 4.9. Proportion of Year During Which Ozone Levels Exceed 60ppbv

Stockholm 0.040 Sacramento 0.380 Malaysia 0.410 Sudbury 0.016 Mexico City 0.940 Lynchburg 0.362 Canada (unspecified) 0.030 Tampa 0.408

4.3.5. Limitations

• The resolution of the emissions within the receiving environment (including the emission source) needs to be defined for all regions.

• The temporal questions should be answered involving the years from which the input data for trajectories, wind directions, NOx and VOC emissions, and the effect thresholds should be taken. Remaining issues regarding averaging periods should be resolved, including whether averages over several years should be used, or whether the input data from the worst year, within the last five years for the largest part of receiving environment should be used.

• The time of year for the emission will be of importance regarding which effect thresholds to consider. The question of whether effects on vegetation should be completely neglected outside the growing season needs to be addressed.

• It is not yet resolved whether to include and assess production of ozone, within an area where the effect thresholds for ozone are not exceeded, or whether it should be neglected or weighted by a scaling factor?

• Measurement endpoint data should be considered in the receiving environment equivalency factors regarding effect thresholds for damage to vegetation (Posch et al., 1995).

• The POCP values which are used have been calculated for different receiving environments but only for cases in which the conditions have been kept constant throughout the entire calculation over 4 days. This is not fully realistic, nor is it entirely the case for the weather situation, which has been set to conditions favorable for ozone formation throughout all simulations. The time of day for the emission is another parameter, which has been shown to have an effect on the calculated POCP values and will thus also need to be considered in some way.

• The inclusion of N0x loadings in the total ozone loadings has been developed by the practitioners but is not included within the current section.

4.4. Hazardous Chemical Loadings (Water)

The assessment of hazardous chemical loadings in water is complicated by the complexity of the environmental mechanisms that describe the relationship between different chemical

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loadings and effects on human health and the environment. Among the issues that can be attributed to this complexity, are:

· The toxicity of chemicals varies even among chemical classes.

· Similar chemicals demonstrate varied fate and transport characteristics, even within the same receiving environment. For example, methylene chloride is more hydrophilic than trichloroethylene and migrates much more rapidly in groundwater.

· The cause-effect relationships of chemical exposures on human health and the environment are not fully understood and are the subject of continued toxicological research. This research is complicated often by the latent nature of effects (carcinogenicity), changes of studied populations, synergistic and additive responses with other chemicals and lifestyles considerations, and the changing nature of receiving environments.

· In addition, the effects of different chemicals often manifest themselves in effects to different species and in human health, to targeted organs.

· Even the same chemical when released to different receiving environments and populations will have varying half-lives and eco-toxic effects.

Despite the complexity of these environmental mechanisms, several practitioners have attempted to characterize eco-toxic effects. Among these practitioners, chemical loadings are allocated according to exposure ranking based on relative toxicity, given certain assumptions. Indices are then developed by aggregating the ranked chemical loadings based on weighting factors (derived from relative toxicity). Variations of these techniques may include some fate analysis, such as the inclusion of biodegradability and bioaccumulation factors [Nordic Guidelines on LCA; 98 – provisional CML combined method includes Cj = HCAjEja+ HCWjEjw +HCSjEjs a,w,s (air, water, soil, respectively); E is the emission HCA, HCW and HCS are weighting factors – human toxicological classification factors for air water and soil, respectively).

Other approaches involve comparing chemical loadings against published threshold values, below which there are no known measurable impacts to human health and the environment. The use of threshold values is considered controversial, particularly if the Precautionary Principle is considered. When the Precautionary Principle is assumed, then there is no threshold for chemical exposure; therefore, all chemical exposures should be prevented.

The LCSEA approach involves the deaggregation of chemical loadings at the inventory level, with the understanding of the complexity of the varying environmental mechanisms. At the inventory level (emission level), chemical loadings are then compared with published thresholds, for the purpose of sensitivity analysis. Future enhancements to the technique (currently used only for select chemicals would involve calculating indicator results using localized fate and transport modeling. After modeling, the same inventory indicator result from different unit operations could be aggregated together, if they share similar environmental mechanism and fate.

4.4.1. Defining Stressor-Effects Networks

Hazardous chemicals released to receiving waters can have a deleterious effect on aquatic plant and animal life. There are many components of water discharges that are potentially

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eco-toxic, either directly to aquatic organisms and humans or indirectly as toxic materials passed up the food chain. Every element and compound released has its own geochemistry and mode of action, and each organism has its own susceptibilities. As a result, there are numerous environmental mechanisms, and associated stressor-effects networks.

4.4.2. Threshold Level Assumptions

To address this variability, the U.S. EPA has developed ambient water quality standards based on the susceptibility of a wide range of organisms. At least eight families and at least three phyla are required for the development of numerical criteria. Salmonids (i.e. trout and salmon) are one of the groups that are required to be studied. EPA collects relevant aquatic toxicity studies, and calculates the concentration at which there is a five-percent chance of acute effects (usually death to the organisms) from the compound of interest. This number becomes the acute toxicity standard; the requirement is that this standard not be exceeded as a one-hour average more than once every three years. Chronic water standards are derived in a similar fashion. These standards should not be exceeded as a four-day average more than once in three years. For both acute and chronic standards, if a commercially important species (e.g., trout) is more sensitive than the calculated result, then the data for that species becomes the standard. Many of the standards are derived as a function of water quality, such as hardness, temperature or pH, where these factors have been shown to control the toxicity of the compound in question. Some aquatic toxicity standards are derived from risk assessments for people or wildlife eating the aquatic species contaminated with the compound of interest. These studies employ a reference dose, based on assumptions of consumption.

4.4.3. Accepted Threshold Levels (Water)

Table 4.10 below shows the substances for which aquatic toxicity criteria have been derived by the US EPA.

One approach is to calculate the concentration of pollutants in water discharged from the wastewater treatment plants at the appropriate cities, assuming no removal of the toxic material in the treatment plant (a very conservative assumption). Where the calculated concentration of pollutants at the outfall exceeds the US EPA standards, their concentrations in natural water bodies are then modeled to identify areas of the natural environment potentially affected by toxics in the water.

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Table 4.10. EPA Aquatic Toxicity Criteria

Pollutant Chronic Aquatic Human Health Toxicity mg/L Toxicity mg/L Biological Oxygen Demand 8 dissolved O2 Chemical Oxygen Demand 8 dissolved O2 Total Suspended Solids 390 1 Total Dissolved Solids 1000 Ammonia-Nitrogen 0.0009 Oil and Grease 0.01* Total Residual Chlorine .011 Color 15 Coliform Bacteria (per 100 mL) 235 Nitrate & nitrite-Nitrogen 0.06 10 Total-Phosphorus 0.025 Chromium 0.12 Hexavalent Chromium 0.011 Copper 0.065 Mercury 0.000012 Nickel 0.088 Selenium 0.035 Silver 0.0012 0.05 Thallium 1.4 0.013 Zinc 0.059 Cyanide 0.0052 0.2 Phenols 2.56 3.5 Aluminum 0.087 Barium 50 2 Boron 18000 Iron 1 0.3 Manganese 0.05 0.1 Ethyl Benzene 430 3.28 Toluene 17.5 424 Naphthalene 2.3

4.5. Hazardous Chemical Loadings (Air)

Hazardous air emissions cannot be aggregated together as a single indicator, even though most LCIA models attempt this type of simplification. The central reason why such aggregation is inappropriate for LCSEA is that most hazardous chemical releases represent unique environmental mechanisms and/or local measurement endpoints unique to a specific region. As a result, the current indicators of choice are inventory, equivalent inventory or effects-based depending upon the availability of environmental data. Furthermore, as has been already mentioned, the inventory data that is required to support such indicators must be carefully speciated.

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For the purpose of this methodology, the term “hazardous” is intended to cover a broad list of chemical emissions to air including but not limited to those listed by the USEPA as hazardous air pollutants (Title III, CAAA 1990) and criteria pollutants (Title I, CAAA 1990).

The following example illustrates how the LCSEA technique approaches this set of indicators.

4.5.1. Particulates (PM10 ) – Human Health

Current levels of fine particulate pollution are associated with a wide range of adverse health outcomes, including accelerated mortality, increased hospital admissions, aggravation of asthma and respiratory symptoms in children. The primary interest in particulate matter centers on respirable fraction, which is known as PM10. PM10 is the fraction of particulate matter with an aerodynamic diameter less than 10 µm. Inhalable particulates with aerodynamic diameters of 10 µm or less (PM10) are small enough to penetrate beyond the upper airways of human beings. Recent epidemiological studies have reported consistent associations between inhalable particles (PM10) and increased health effects, for example increased daily mortality. People with chronic obstructive pulmonary disease (COPD) are the most at risk, as well as people with pneumonia or cardiovascular disease.

The assessment of PM10 environmental impacts follows the impact-pathway method developed and applied in the ExternE and US Department of Energy joint research project to assess the external cost of fuel cycles (European Commission 1995).

The different stressor-effects networks assessed included:

- human health effects - effects on crops - effects on forests - effects on materials (corrosion) - climate changes (the greenhouse effect) - effects on stratospheric ozone

4.5.1.1. Characterization of the measurement endpoint

The resulting health effects in the described case have not been measured, but have been estimated from published exposure-response equations from epidemiological al studies in the US. These publications were also used in the joint ExternE/USDOE project mentioned above.

4.5.1.2. Threshold and non-linearity characterization

The PM10 exposure-response relations given are linear and show no threshold, which is rather unusual for health effects. The temporal aspects of the health effects are inherent in the exposure-response functions, which are given for acute effects. No information on differentiation of long-term exposure and short-term exposure is available.

Establishment of characterization factors

The human health effect loading is calculated as follows:

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Health effect loading = Inventory emission x cF

In this case the environmental characterization factor for PM10 emissions consists of the following factors:

EF = ?N [(number of persons exposed) x (ambient air concentration factor)]i x x (exposure-response coefficient)

For SO2 emissions a sulfate conversion factor must be included:

EF = ?N [(number of persons exposed) x (ambient air concentration factor)]i x (sulfate/SO2 conversion factor) x (exposure-response coefficient)

where i represents each of the N=256 subregions, and the ambient air concentration factor is the result of the dispersion calculation for each subregion.

4.5.1.3. Calculation of Indicator Results

First, the amount of emissions should be calculated. Then, the annual ground-level PM10 concentrations should be calculated using an atmospheric dispersion model. After that, the information of PM10 concentration levels should be combined with the population density information. Then, the exposure-response functions should be used to calculate the different health effects loadings.

4.5.1.4. Case Example

This stressor-effect network has be modeled in accordance recent study performed in Finland, the aim of which was to assess the environmental effects of energy production in the metropolitan area of Helsinki, Finland (Otterström 1995). The environmental effects were estimated over a time period from 1987, including 1993, and up to 2000. Calculations for all significant unit operations for this study should be approached in the same manner as described by this example.

The study concentrates on the emissions from the energy production plants in the region. The industrial system consisted in 1993 of 10 CHP (combined heat and power) plants with a total heat energy production capacity of 2200 MW and an electricity production capacity of 1130 MW, and additionally several heating plants totaling 2100 MW. The function of the system is to provide the region with district heat and electricity (in 1993 about 9200 GWh of heat and about 4600 GWh of electricity). The main emissions studied were emissions to air of NOx, N2O, SO2, C, CO2, CO, CH4, particulate matters, PAH and heavy metals.

The results of this study indicated that one of the main areas of effects were the health effects from PM10 particles on the population in the region and nearby areas. These health effects of PM10are attributed in large part to the emissions of SO2 (a part of which forms sulfate PM10), and to a minor extent from PM10 particle emission.

The stressor-effect network involved in assessment of PM10 health effects in this case contains the following parts:

• Stressors: Emission of SO2 and particulate PM10

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• Partial transformation of SO2 to sulfate PM10 and dispersion of sulfate PM10 and particle PM10 into ambient air in the region

• Exposure to PM10 concentrations in ambient air in each population grid unit

• Measurement endpoint: Health effects connected to PM10 concentration exposure

In this case the only unit operations involved are the heat and power production plants in the region, described above. The fuels used are shown in Table 4.11:

Table 4.11. — Fuels used in 1993

Fuel Use 1993, TJ/a

Coal 41855 Natural gas 12542 Heavy fuel oil 1295 Light fuel oil 22 Other 8

The location of the different plants in the region was mapped for the subsequent dispersion calculations for obtaining PM10 concentrations. Thus, the air emissions were not aggregated. The emission amounts in the two categories of interest for PM10 assessment are shown in Table 4.12.

Table 4.12 — Emissions contributing to PM10 concentration

Pollutant Emission 1993, t/a

SO2 9300 PM10 780

The ground-level pollutant concentrations of PM10 that could be expected to occur as a result of operation of a plant of interest are predicted using atmospheric dispersion modeling. The computer modeling predicts ambient annual concentration of PM10 from the plant as micrograms per cubic meter; µg/m3. Numerous epidemiological studies have reported relations between concentration of ambient particulate matter and health effects among the general population. Exposure-response functions have been determined for particulates and changes in mortality and morbidity. These exposure-response functions are connected with the information of estimated population (number of people exposed to different levels of PM10 concentrations) to determine the health effects of the population living near the plant. Exposure-response functions for particulate matter have been identified (e.g. hospital admissions, emergency room visits, restricted activity days and symptoms in adults, lower respiratory illness and asthma attacks).

The receiving environment in this case is represented by the population of the Helsinki metropolitan region (840000), and of a smaller nearby population outside the region

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(170000) in 1993. For the purpose of health effect determination, the area concerned was divided into 256 subregions. For each subregion, the yearly average PM10 concentration attributable to the energy production emissions was established using a modified Gaussian Plume dispersion model developed by the Meteorological Institute of Finland. The population was also characterized by age (children and adults) and by the percentage of asthmatic persons.

The spatial characterization is limited to the single unit operation of the coal-fired plant itself. This simplification was needed to attain some measure of certainty for the dispersion modeling calculation. Once this single unit operation has an established indicator value, it is possible to use sensitivity analysis to determine any loadings from other unit operations.

The temporal characterization of stressors is represented by the monthly fluctuation of flue gas emissions that are combined with the monthly fluctuations of the meteorological conditions of the investigated area.

A linear relationship is assumed for the formation of sulfate PM10 from a constant part of the SO2 emitted. The PM10 particles in Helsinki have been analyzed, and found to contain about 50 % sulfate, 8 % nitrate and 4 % chloride. The remaining 38% have not been characterized, but presumably contain the particulate matter. The conversion factor was estimated from these analysis results and from the relation between background concentrations of particles, SO2 and sulfate.

Without presenting the details of each calculation, the final result is given below in Table 6.13 for eight different health effects, for which exposure-response coefficients were available.

The population-weighed annual average PM10 concentration caused by the energy production emissions of particulate matter and sulfate from SO2 emissions was calculated to 3 2.1 µg/m . This is a significant part of the total measured PM10 concentration in the 3 Helsinki region, which varies from 6 to 10 µg/m . The PM10 background concentration outside the region is about 3 4 µg/m .

Table 4.13. — Health effects from PM10 emissions

Health Effect Incidents/year Hospital Admissions 200 Emergency Room Visits 500 Symptoms 700 000 Restricted Activity Day 95 000 Bronchitis in Children 600 Chronic Bronchitis in Adults 14 Asthma Attacks 8 800 Increase in mortality 9

The same type of calculations were made in the study for also for SO2 and NOx, but they showed considerably lower health effect results. The uncertainty inherent in the used exposure-response functions is in the order of ±30 to ±50 %.

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Normalization

The results presented could be normalized against the function defined in part 1 — i.e., against the total energy amount of 9200 GWh/a of heat and 4600 GWh/a of electricity. More interesting would be to separate out that part of the production that contains the major SO2 and PM10 emissions.

Limitations

To be useful in LCSEA, the dispersion modeling must be as simple as is possible without loosing accuracy. The present view among scientists is that the exposure-response functions showing the relationship between PM10 concentration and health effects are linear and without a threshold value, which is rather unusual when dealing with health effects. However it facilitates the application of rather simple typical exposure patterns around an emission point source.

In this light a simple atmospheric dispersion model for calculating ground-level pollutant concentrations could be developed or modified from the present models. The model should be easy to use and the input data should be limited only to the most important factors, for example emission rate, stack-height, atmospheric data etc. An alternative for this would be a collection of calculated “maps” of ground level pollutant concentrations with different emission rates, stack heights and meteorological data. The population density could be combined with these maps using GIS technique.

A better knowledge of the mechanisms for the atmospheric conversion of SO2 into sulfate PM10 would help to reduce the uncertainty of the results.

The exposure-response functions for health effects of PM10 and PM2.5 are presently subject of intensified research, the results of which should be included as they emerge. The functions used in the described study probably represent the major uncertainty factor, and need to be better known.

4.5.2. Other Hazardous Chemical Loadings (Air)

In general, thresholds for hazardous air pollutants have not been as widely established as for water pollutants. For the US Clean Air Act, only 7 compounds have thresholds under NESHAPs. The 1990 Clean Air Act Amendments listed 189 hazardous air pollutants for which health-based thresholds will not be derived; instead, specific technologies are mandated for major sources (over 50 tons per year) and for area-wide sources such as dry cleaners.

As such, any hazardous air pollutant (HAPS) emissions without an established threshold level should reported as an emission loading. Furthermore, any environmental data should be reported but not processed into a characterization factor.

4.6. Stratospheric Ozone Depleting Chemical (ODC) Loadings

The action of intense solar radiation in the upper atmosphere (the stratosphere) causes the dissociation of oxygen to free atoms and the formation of ozone (O3). Ozone is a very effective barrier to the sun’s UV radiation, in effect, acting as a sunscreen for the earth. The

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loss of this sunscreen leads to increased mutations, leading to genetic damage, cancer and cataracts.

Many compounds act to dissociate ozone to the normal form of oxygen (O2), and so the ozone in the stratosphere is in a dynamic equilibrium balanced by the concentration of those compounds and the solar radiation. Halocarbons (compounds of carbon with bromine, chlorine and fluorine) are especially effective at dissociating ozone. They act as catalysts and many have a very long atmospheric half-life. Halocarbons have been produced in mass quantities over the past 50 years, and used as solvents, refrigerants and fire suppressants. The outcome has been the loss of ozone over the poles, and increased incidences of skin cancer at high latitudes. The political response has been the Montreal Protocols, international treaties designed to phase out the production of these halocarbons worldwide.

Currently, the model employed in the Montreal protocols to develop equivalent stratospheric loadings is used for the LCSEA methodology. The protocols are based on the integrated ozone destruction potential over the entire atmospheric lifetime of the chemicals in question.

Calculation of the ozone depleting chemicals category indicator loadings are conducted in accordance with the LCA-NORDIC Technical Reports No 10 and Special Reports No 1-2, section 3.9.

No correction has been made to the current model of projected endpoints, which has underestimated the current actual depletion of the Antarctic ozone hole by 20% for 1997.

4.7. Total Oxidizing Chemical Loadings for Aquatic Systems

This stressor-effect network has usually by represented by either BOD or COD indicators. It is curious that typical LCI studies have reported such indicator results as inventory results. While it is recognized that there are justifications for both BOD/COD, TOC represents a pure indicator of the portion of the organic and mineral emissions that result in the direct depletion of aquatic oxygen. The measurement endpoint (aquatic oxygen depletion) has been well characterized in a wide variety of literature and governmental regulations. LCSEA studies will typically rely on current government threshold levels for unit operation within the United States, Canada, Europe and Japan. Unit operations outside these areas should provide site-specific environmental data before a threshold level can be accepted.

Furthermore, aggregation of TOC loadings exceeding threshold levels from different unit operations must first establish the relevant environmental characterization factor. To date, no such ECF has been established. As a result, TOC loadings from different unit operations should not be aggregated.

Proposed Environmental Characterization Factor

ECF* = (1- Aquatic O2 Concentration Current/Aquatic O2 conc. Baseline) (AREAAFFECTED)(time period of measurable endpoint)

*by specific receiving waters

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4.8. Eutrophication Loading (Aquatic)

4.8.1. Background

This section is presented as a case example of how to calculate aquatic eutrophication loadings by unit operation. The focus during the first iteration is on calculating only core unit operations in order to keep the entire process practical. Sensitivity analysis is recommended to exclude unit operations that are minor contributors to the overall indicator loading. The following example is from Northern Finland, on the Baltic Sea, and was chosen because of the richness and variety of data available to characterize the receiving environment.

4.8.2. Defining the Stressor-Effects Network

Eutrophication of a water body is defined as increased production of biomass. Increased nutrient input is the main source of eutrophication caused by human activities. Input of oxygen demanding chemicals can cause secondary eutrophication by depleting oxygen from the water, which leads to the release of nutrients, mainly phosphorus from the sediment.

The role of nitrogen and phosphorus as the main nutrients controlling primary varies in different water bodies and also in the same water body with time.

The minimum nutrient can be determined with N/P calculations (Salonen et al. 1992, referred from several authors):

• total nutrients: total N/total P + - - 3- • mineral nutrients: (NH4 -N + NO3 -N + NO2 -N) / (PO4 -P) • nutrient balance: total-N / total-P + - - 3- (NH4 -N + NO3 -N + NO2 -N) / (PO4 -P)

N/P N/P N/P minimum total mineral nutrient nutrient nutrients nutrients balance <10 <5 >1 N 10-17 5-12 N or P >17 >12 < 1 P

The nutrient balance is considered to be the most reliable calculation.

In case the oxygen concentration of a water body is at a critical level, the oxygen- demanding load (measured as BOD or COD) can also cause secondary eutrophication. Depletion of oxygen close to the bottom of a receiving water leads to the release of sedimented nutrients, mainly phosphorus from the bottom. In brackish waters, the phosphorus is not as strongly bound to the sediment as in fresh waters, where oxygen concentration of approximately 20 % of saturation can lead to the release of phosphorus.

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4.8.3. Selection of Measurement Endpoints

Chlorophyll-a

The potential measurement endpoint of algal biomass can be indirectly measured by the amount of chlorophyll-a. Less frequently used, but more accurate, characteristics are primary production (in vivo), primary productivity (in vitro) or algal biomass. Chlorophyll-a is the primary photosynthetic pigment of all photosynthetic organisms (Wetzel 1975). The advantage of chlorophyll-a is that it is frequently used in routine monitoring and it has been used previously in the characterization of water quality. The disadvantage is that it is rather sensitive to short term variations, such as wind conditions, and it can only be measured during the growing period. Thus the average of several measurements during one summer is used in characterization. Also some algae groups have other photosynthesis pigments besides chlorophyll-a.

In flowing waters the primary production of plankton is less important than that of periphyton and macrophytae, and thus chlorophyll-a should be measured from periphyton rather than from the water. Periphytic chlorophyll-a is measured from artificial surfaces, which are incubated in water.

Trophic type characterizations based on chemical characteristics, chlorophyll-a and algal production are presented in Tables 4.14 and 4.15. For chlorophyll-a, the trophic type changes when the chlorophyll concentration increases approximately 7-50 times. Thus the relationship between chlorophyll-a and eutrophication is logarithmic rather than linear. In the hypereutrophic type the chlorophyll-a concentration range is very wide because it fluctuates largely with time.

Table 4.14. — Characterization of trophic types according to selected characteristics. Modified from Likens (1975) after many authors and sources.

Trophic type Mean Phyto- Phyto- Chloro- Total Total Total N productivity plankton plankton phyll-a organic P density biomass carbon -3 (mg C m-2d-1) (cm 3m-3) (mg C m-3) (mg m ) (mg l-1) (µg/l) (µg/l) Ultraoligotrophic <50 <1 <50 0.01-0.5 <1-5 <1-250 Oligotrophic 50-300 20-100 0.3-3 <1-3 Oligomesotrophic 1-3 5-10 250-600 Mesotrophic 250-1000 100-300 2-15 <1-5 Mesoeutrophic 3-5 10-30 500-1100 Eutrophic >1000 >300 10-500 5-30 Hypereutrophic >10 30- 500- >5000 >15000

Table 4.15. — Trophic characterizations according to chlorophyll-a.

Chlorophyll-a, µg/l Trophic class Welch 1980 Forsberg & OECD 1982 Ryding 1980 Oligotrophic 0-4 0-3 <2.5 Mesotrophic 4-10 3-7 2.5-8.0 Eutrophic 10-100 7-40 >8 Hypertrophic >100 >40 -

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A number of characterizations have been developed for certain Finnish coastal areas. Selected characterizations based on nutrient concentrations and/or chlorophyll-a are presented in Table 4.16 and 4.17.

Table 4.16. Water quality characterization developed for the coastal area near Kaskinen, Finland (Langi 1982).

Class O 2 % Suspended Color COD Mn Total N Total P matter µg/l µg/l mg/l 1 >90<105 <5 <30 <5 <250 <15 2 70-90 5-10 30-70 5-10 250-400 3 50-70 10-15 70-100 10-20 400-700 25-50 4 30-50 15-25 100-200 20-40 700-1500 50-150 5 <30>125 >25 >200 >40 >1500 >150

Table 4.17. Water quality characterization developed for the Southwestern Finland Archipelago and Coast (Water and Environment District of Turku).

Class Total P Chlorophyll-a Primary µg/l µg/l productivity mg C m-3 d-1 oligotrophic <12 <2 <100 slightly eutrophic 12-23 2-5 100-300 eutrophic 23-80 5-25 300-1500 hyper eutrophic >80 >25 >1500

4.8.4. Defining Threshold Levels

The basis for the characterization used in this example is presented in Table 4.18.

In the absence of official threshold values, this study assumed that the concentration values according to “oligomesotrophic water quality” are below threshold and that the concentration values indicated for the “mesotrophic quality” are above threshold.

Table 4.18. Defining concentration threshold values for this study.

Trophic type Chlorophyll-a, Phosphorus, Nitrogen, µg/l Threshold µg/l µg/l condition Oligotrophic < 4 < 12 < 250 Below threshold Oligomesotrophic 4-5 12-15 250-310 Below threshold Mesotrophic 5-6 15-25 310-600 Above threshold Eutrophic >6 >25 >600 Above threshold

4.8.5. Characterizing the Receiving Environment in Finland

The Baltic Sea is the world’s largest body of brackish (low-salinity) water. It is also distinguished by its division into a series of basins of varying depths, separated by shallow areas or sills. The many rivers flowing into the Sea are the reason for its brackish character. The link with the North Sea is very narrow and shallow, and inflows of salt water must be

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extremely forceful to renew the deepest waters of the Baltic. Such inflows are primarily determined by complex meteorological processes over Northern Europe and the North Sea.

The Northernmost parts of the Baltic Sea are the Bothnian Sea north of the Åland Islands, and the Bothnian Bay north of the sill at Vasa/Sundsvall latitude. The mill studied is situated in the Bothnian Bay area.

The Finnish National Board of Waters and the Environment (currently Finnish Environment Institute) has issued a characterization approach24 for the quality of surface waters. The quality of water is characterized for different purposes:

• recreational use • fisheries • raw water source • general characterization

The recreational use is focused on aesthetic parameters and hygienic quality. Fisheries characterization is focused on oxygen concentration, pH and trophic type. The raw water characterization is focused on toxic substances and certain aesthetic parameters. The general characterization is calculated from the other ones. The optimum quality of water differs slightly for different uses. For example, a slightly declined oxygen concentration does not affect recreational use, but limits the existence of Salmonid fishes. The characterization is primarily focused on inland waters and does not take into account the role of nitrogen as a limiting nutrient.

The LCSEA methods for characterization and quantification of emission load indicators require the definition of water pollutant thresholds; this is attempted in the following.

State of the receiving waters

The Bothnian Bay is a brackish water body, but the salinity is rather low. In the open sea outside the Hailuoto Island, the salinity in the surface water in summer is approximately 3, and in the deep profundal water, 4-5 ‰. Closer to the coast, the salinity in the surface water in summer is 2-3 and in the winter <1 ‰. The Hailuoto Island divides the sea area into the inner part clearly affected by local river waters and the open sea area.

The water quality of the Bothnian Bay is monitored over an area up to 20-30 km from the mouth of the river, where the main dischargers of wastewater are located. The monitored parameters include nutrients, oxygen concentration and saturation value, fecal coliform bacteria and during the summer period chlorophyll-a concentrations. There are three intensive monitoring points, where samples are taken 17 times annually. In the other stations samples are taken three times annually.

According to the monitoring results of 1993-1996, the nutrient indicator concentrations at point Ouvy 10 in the open Baltic Sea are as follows:

· annual average of tot. N = 251- 272 µg/l · annual average of tot. P = 7-8 µg/l in surface water

2. 24 The word used by the authorities is water quality classification system, but for consistency in this report we will use the term characterization, as long as the receiving environment is concerned.

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· chlorophyll-a concentration annual average of the growing period = 3.5-4.3 µg/l

According to recent monitoring results, phosphorus is the minimum nutrient in the open Bothnian Bay. In the area affected by the river waters, the role of nitrogen as a minimum nutrient is periodically important. The role of the nutrients as minimum factors varies clearly during the growing period and also between years.

4.8.6. Calculating the Eutrophying Chemical Loading

The discharge point of the plant is located at the mouth of a river in southern Finland that 3 has an average flow of MQ1961-1990=259 m /s. The flow of the river is intensively regulated for hydropower purposes, which increases the wintertime flow and decreases the flood period and summer flows when compared to the natural situation. In the winter the wastewater is mixed with the river water, which flows as a separate layer between the ice cover and the deeper, more saline water up to over 10 kilometers to the north-eastern side of the Hailuoto Island before mixing. Due to this fact, there is a clear difference in the vertical water quality during the winter. The river water has a higher color value and iron concentration and also contains more phosphorus than the deeper, clean brackish water.

The compounds causing eutrophication of water are phosphorus and nitrogen. Approximately 35 % of the discharged phosphorus of the plant is in biologically active phosphate-phosphorus form and the rest is bound to particles. Part of this phosphorus is released to biologically active form in the recipient water body and part is sedimented. Out of the nitrogen load approximately 40 % is in biologically active ammonium-nitrogen form and 60 % is in complexed form, which is unusable for neither bacteria nor algae. Thus they not decomposed by bacteria.

The wastewater discharges are noticed as slightly increased nutrient concentrations, elevated chlorophyll-a concentrations and especially during the winter as smell of the water. In this case the oxygen concentration is not critical, and thus the evaluation is concentrated on nutrients and the trophic status. Declined oxygen concentration affects the existence of bottom fauna, especially the relict species Pontoporeia affinis in a limited area close to the discharge point. The thermal load does not cause essential impacts on the water body.

The main contributors to the emission load to the sea area near the mill are the wastewater of the industries and the city. Additionally the fish farms and some minor polluters load the sea area. The local river is the main discharger of nutrients. Partly the load can be attributed to natural run-off and partly to increased human activities, such as agricultural load, forestry and especially in the region ditching of mires to drain them. The share of the mill is as follows:

Table 4.19. - Mill Contribution to Loadings

Load parameter Total load t/a Paper mill share, % Including Local River BOD 12303 8 % total P 160 6 % total N 3057 3 % Excluding Local River BOD 2003 51 % total P 19 47 %

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total N 657 16 %

Since in the area affected by the wastewater load both nutrients can limit the primary production, the share of the mill is the average of the above nutrient loads:

· 5 % of the total load · 32 % of the total waste water load. The chemical form of the nutrients was not taken into account. It is different for all of the discharges and affects the usability for algae. The discharged form depends on the wastewater treatment process and the source in the river water. The area directly impacted by the river discharge was approximately defined according to the declined conductivity of the water during the winter period and the discharge areas of other rivers to the north and to the south. In the open Bothnian Bay the impacts of the river are mixed with the impacts of other rivers to the north and south. The wastewater of the paper mill is mixed into the river water. It was assumed that the effects of the paper mill and other wastewater are stronger in the close recipient area and that the river is the main component causing eutrophication. Further out, the wastewater is diluted by the river water and cannot be separated from the considerably larger nutrient load of the river. The dilution is more effective in the summer than during the winter, when the river water and the wastewater flow as a layer between the heavier Baltic water and the ice. Based upon the measurements from the monitoring activities outside the city, the recipient area can be divided in different areas according to water quality. The recipient total area has been divided into zones, and has been quantified planimetrically from regional maps.

Table 4.20. — Water quality measurements and comparison to threshold

Zone on map, Area, Chlorophyll- Phosphorus, µg/l Nitrogen, Threshold boundary hectares a, µg/l µg/l characterization color Outer sea < 4 < 8 < 260 Below threshold Blue 30900 4-5 12-15 260-310 Below threshold Green 6650 5-5.5 > 15 > 310 Over threshold Red 3330 5.5-6 > 15 > 310 Over threshold Not 370 direct Over indicated discharge threshold areas Total area: 41250

Characterization factor calculation The receiving environment characterization factor (“ecf”) for the eutrophication loading calculation is approximately determined in the following way: The total area affected by the various wastewater discharges at the location is considered to be the sum area indicated in Table 4.20. Of this area, the three most affected areas, in total

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10350 hectares, are considered to be polluted over the thresholds set in recreational and other characterizations. This determines the background pollution. As the specific waste waters from the paper mill also spread over the total affected area, the part of the emission that causes an effect on areas over the threshold can be assumed as the above-threshold area percentage of the whole affected area 10 350/41 250 = 25% This percentage is taken as the ecf value. The assumption is conservative, and a more exact value could be determined with recipient modeling calculations.. The eutrophication loading from the mill is the product of the emission flows of phosphorus and nitrogen, converted to PO4 equivalents, and the characterization factor, as calculated in Table 4.21. Stressor characterization factors can be found in the Nordic Guidelines 1995.

Table 4.21. LCSEA Eutrophication Loading for Fine Paper

Category indicator: Eutrophication Inventory Value Stressor Equivalent Receiving Emission TOTALS loading Emission Equivalency Stressor Environment Loading Unit Operation (t/a) Factor values Characteriz.

Code Name (t PO4/a) Factor (t PO4/a) (t PO4/a) Pulp and paper P 9 3.06 28 0.25 7 production N 102 0.42 43 0.25 11 Total 18

4.9. Thermal Loadings

This indicator is still undergoing discussion and development.

4.10. Noise

This indicator is still undergoing discussion and development.

4.10. Radio Frequency Emission Loading While there has been considerable discussion concerning potential human health effects from the radio frequencies emitted from various radio transmitters such cellular phones, no measurable effects have yet to be reported in the literature. While it is important to list this issue as a potential category indicator of human health at the time of study, no quantifiable relationship exists between the amount of Rf and any measurement endpoint.

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C HAPTER 5. Residual Hazardous Waste

The distinction between hazardous and nonhazardous waste is often defined by country regulation and, therefore, tends to vary depending upon the location of the source unit operation. 5.1. Nonhazardous Wastes The management of nonhazardous waste may involve recycling and disposal practices, depending upon regulatory requirements and/or economic viability of recycled materials. Within the LCSEA framework, the disposal of nonhazardous waste is associated with several stressor-effects networks, including (potentially) physical disruption to terrestrial or aquatic habitats (landfills), greenhouse gas loadings (methane production from landfills) and leachate into receiving environments associated with various emission loadings, and specific resource depletion calculations. At this time, these effects cannot easily be included in indicator calculations, due to the lack of information regarding effects attributed to these management practices for the unit operations under study. 5.2. Hazardous Ash and Heavy Metal Wastes, Radioactive Wastes In general, hazardous wastes tend to exhibit characteristics of inherent toxicity, and/or when left untreated, demonstrate hazardous characteristics. In the U.S., hazardous wastes are either listed as hazardous according to the source of the waste or are considered characteristic wastes if they are ignitable, corrosive, reactive or toxic, according to the Toxicity Characteristic Leachate Procedure (TCLP) The stressor-effect models for hazardous wastes are often dependent upon the treatment, storage, and disposal practices of the waste stream. Most of these practices are considered separate unit operations. Treatment efficiencies are often dependent upon the nature of the waste stream and the effectiveness of the control/treatment technology. The use of the technology may be dependent upon the location of the source unit operation, particularly if a regional waste infrastructure is considered. As such, in order to characterize the waste management practices, information regarding the local waste infrastructure is needed. Even when hazardous wastes are treated, stored or disposed, there remain residual wastes. For the purpose of an LCSEA study, residual hazardous wastes are post-treatment wastes that do not undergo further treatment. For example, when hazardous wastes are incinerated, fly and bottom ash may exhibit hazardous characteristics sufficient for TCLP listing. This ash would then be considered residual hazardous waste if not subsequently treated, even though such wastes are exempted from Federal hazardous wastes listings. There are certain wastes that cannot be rendered nonhazardous. These wastes are often stored or treated via encapsulation or vitrification. As such, such wastes carry a certain

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“contingency risk” of being released into the environment at some future date. In general terms, therefore, residual hazardous waste is calculated based on the contingency risk of post-treated hazardous waste. The factors that control this type of exposure are 1) the expected life of the encapsulation or vitrification, 2) expected toxicity lifetimes or half-lives (radioactive) of the waste, 3) the effects from potential releases (human health and eco- toxicity), and 4) the probability of those wastes being released from a lack of infrastructure, misuse, or mismanagement. The following ecf is the current mathematical expression used for this indicator: ecf = (1-treatment efficiency-TE)(permeability of containment-POC)(Probability of breach of containment-POBOC)(leaching potential-LP)(length of storage-LS)(relative half-life of toxicity-RHOT) ECF = (1-TE)(POC)(FOBOC)(LP)(LS)(RHOT) Where LS is 100 to 1000 years The residual hazardous waste indicator equation then becomes:

RHW = S(Amount of Untreated Hazardous Wastes)(ecf)

While potentially reducing the risk of release, disposal and storage practices may not permanently or completely eliminate risk from the breach of containment. For such hazardous wastes, an indicator may be derived that considers the probability of the breach of containment. Ideally, the hazardous waste indicator is be calculated as the product of the residual hazardous waste amount multiplied by an environmental characterization factor (“ecf”) value. The ecf value is a contingency risk factor that considers such risk issues as the probability of breach of containment, the toxicity of the residual waste, the expected toxicity life time, and the fate and transport mechanism for a projected release. At present, for practical purposes in most LCSEA studies, there will not be enough information during the first iteration of core unit operations to derive an ecf for respective residual wastestreams. In the absence of site and chemical specific information, the hazardous waste indicator will be reported as the quantity of residual waste. The indicator is thus chosen close to the inventory and corrected with a probability for breach of containment. The rationale for selecting this indictor at this point in the stressor-effect network is that this indicator represents a potential emission for which a projected endpoint is yet to be defined. If site-specific disposal/treatment information is available, then the hazardous waste would be considered as an eco-toxic loading using the present specification. An example of a possible use of contingency risk may be the comparison of risks between the nuclear waste from U.S. and Southeast Asian facilities during energy generation. Nuclear waste from a given southeast Asian nuclear plant may require cross-boundary shipment for reprocessing, while a similar amount of nuclear waste from a U.S facility may not require transport at all. With everything else being equal, a relative contingency risk can be assigned to these waste streams based on the risk during transportation.

Page 5-2 Appendix 2

LAKE CHELAN ECOLOGICAL REVIEW

Applied Ecological Services

By

Steven I. Apfelbaum James P. Ludwig Chet Chaffee, Subcontractor Applied Ecological Services, Inc. 17921 Smith Road Brodhead, Wisconsin 53520 608/897-8641 Phone 608/897-8486 Fax [email protected] E-mail

Neil Thomas Glen Locascio Resource Data, Inc. 34 Wall Street Suite 400 Asheville, North Carolina 28801 828/251-1658 Phone 828/251-1662 Fax [email protected] E-mail

June 1999 Contents

I. Introduction ...... 1 II. General Description of the Region and Lake Chelan Project ...... 1 III. Methods ...... 4 IV. Summary of Results...... 8 V. Discussion ...... 12 Bibliography ...... 14-15 Figures...... 16-27

Fig. 1. Lake Chelan Watershed Land Use Fig. 2. Road Impacts Fig. 3. Powerhouse & Associated Features Fig. 4. Dam Footprint & Associated Features Fig. 5. Shoreline Erosion Fig. 5.A. Lake Chelan Erosion Sites, May 1999 – Lower Lake Fig. 5.B. Lake Chelan Erosion Sites, May 1999 — Middle Lake Fig. 5.C. Lake Chelan Erosion Sites, May 1999 — Upper Lake Fig. 6. Wetlands Fig. 7. Dewatered Channel and Created Bay Fig. 8. Mudflats Fig. 9A. Terrestrial Impacts Fig. 9B. Terrestrial Impacts Example Area Fig. 9C. Terrestrial Impacts Mid Lake Chelan Fig. 9D. Terrestrial Impacts Upper Lake Chelan LAKE CHELAN ECOLOGICAL REVIEW

I. Introduction

Applied Ecological Services, Inc. (AES) was retained to assist Scientific Certification Systems (SCS) in conducting a Life Cycle Impact Assessment using SCS’s Life Cycle Stressor Effects Assessment technique. The project objective is to determine the environmental aspects of hydropower generation by the Chelan County Public Utility District (PUD) #1 (“Chelan PUD”) at their Lake Chelan Hydroelectric Facility (“Lake Chelan Project”). This report addresses one aspect of the overall life cycle analysis — namely, the impact indicators associated with ecosystem disruption resulting from the direct physical disturbances caused by the hydroelectric facility throughout its life-cycle. This report thereby focuses on specific variables that have potentially contributed to changes in species and habitat in and around the hydropower project.

The purposes of this report are to:

1. Identify documented key ecological habitats influenced by the Lake Chelan Project and all its operations.

2. Identify large-scale changes (depletion or accretion) of terrestrial and aquatic habitat associated with the facility and its operations, and characterize any new habitats created.

3. Characterize the habitat changes as a metric (i.e., acres of depleted habitat) that can be used to compare these hydropower facilities with others.

In this report, terrestrial habitat is referenced as including several habitat types such as rocky shorelines, cliff features, grasslands, coniferous forests, and shrub lands. Terrestrial habitats are also referenced in general. In this context, they are upland, non-wetland and non- aquatic habitats. Aquatic habitat is referenced as including shallow wetland, riverine, littoral and deep water habitats.

II. General Description of the Region and Lake Chelan Project

Lake Chelan is located in Chelan County in north central Washington. Lake Chelan is bordered to the south of the Entiat and Chelan Mountains and Glacier Peak complex. To the north it is bordered by the Sawtooth Mountain Range. From Twenty-Five Mile Creek uplake, the

H:99062:060899 1 Lake Chelan Ecological Review terrain is mountainous and rugged. In most locations the steep slopes run directly into the lake with no flat beaches or shoreline. Along large reaches of the lake shoreline, terrestrial habitat is bedrock outcrops largely devoid of vegetation. The terrain of the lower end of the lake is much less severe, mainly arid or semi-arid vegetation cover along bordering uplands.

Lake Chelan is deep and narrow, extending northwesterly approximately 50 miles from the City of Chelan at its lower end to Stehekin at the head of the lake. Lake Chelan is a natural lake that developed within a glacial trough. The lake averages one mile in width, and has depths of over 1,480 feet. Lake Chelan is bordered by more than two million acres of National Forest Lands, more than half of which are designated as wilderness. Surrounding peaks reach elevations as high as 7,000 feet. The lake serves as a waterway approach to the Forest Service’s Wenatchee National Forest above Twenty-Five Mile Creek, and to the National Park Service’s Lake Chelan National Recreation Area at Stehekin. The lower 15 miles of the lake are mostly privately owned, the next 35 are within the Wenatchee National Forest, and the upper five miles are within the Lake Chelan National Recreation Area.

The average surface area of the lake is 32,000 acres. The drainage area of the project at the dam is 924 square miles. The confluence of the Chelan River and Columbia River is approximately 1.5 miles southeast of the City of Chelan.

The Lake Chelan Project is located approximately 32 miles north of the City of Wenatchee on the Chelan River. The current Dam was licensed in 1926, and first became operational in 1927; prior to that time, a series of wooden structures of limited lifetime were constructed on Lake Chelan starting in 1892.

The 4.1-mile long bypass reach (Chelan River or gorge), the shortest river in Washington State flows from the southern end of Lake Chelan to the Columbia River. The Lake Chelan Project consists of a 40-foot high concrete gravity dam located at the City of Chelan, a 2.2-mile long steel and concrete tunnel (penstock) that is 14 feet in diameter, and a powerhouse located at the confluence of the Chelan and Columbia Rivers near the city of Chelan Falls. The vertical elevation drop between the dam and powerhouse is 401 feet. The powerhouse contains two Francis turbine units; each rated at 34,000 hp at 1,100-cfs and 377 feet net head that produces approximately 50 MW of electricity.

The project reservoir, Lake Chelan, is operated between a maximum water surface elevation of 1,100 feet (MSL) and 1,079 feet to ensure optimum utilization of the reservoir for power generation, fish and wildlife conservation, recreation, water supply, and flood control

H:99062:060899 2 Lake Chelan Ecological Review purposes. The average drawdown of the lake for the past 30 years has been to 1083.5 feet. The reservoir has 676,000 acre-feet of usable storage above 1,079 feet.

The annual drawdown of the lake begins in early October. The lowest lake elevation normally occurs in April. From May through June the lake refills from spring runoff. The reservoir is maintained at or above elevation 1,098 feet from June 30 through September 30 each year. Since the project was originally licensed in 1926, the lake has never been drawn down to the minimum allowable elevation (1,079 feet). The lowest drawdown on record was 1,079.7 feet in 1970. That occurrence coincides with the lowest annual precipitation on record. The Chelan PUD has never failed to refill the reservoir to elevation 1,098 feet by June 30.

The 4.1-mile long bypass reach is comprised of four distinct sections. The 2.29-mile long upper section, Section 1, of the bypass reach below the dam is characterized by a relatively wide flood plain, low gradient, 55 ft./mile, and substrate comprised mainly of large cobble and boulders.

Section 2 of the bypass reach, 0.75-mile long and located in the upper end of the gorge, is characterized by a narrow channel, steep canyon walls, low gradient, 57 ft./mile, and cobble and boulder substrate that is much larger than the upstream section.

Section 3 of the bypass reach, referred to as the gorge area, is 0.38-mile long. The canyon walls are very steep and narrow. The gradient of the channel is very steep, 480 ft./mile. The stream channel is characterized by waterfalls, from 5 to 20 feet high, numerous cascades, bedrock chutes, and large, very deep pools. The substrate is very large, with some boulders exceeding 20 feet in diameter.

Section 4 of the bypass reach is 0.49-mile long and located below the gorge area. It is characterized by a wide flood plain, having gravel/cobble/boulder substrate, and low gradient, 22 ft./mile. Section 4 extends from the bottom of the gorge section downstream approximately 2,600 feet to the confluence of the powerhouse tailrace.

A very large part of the watershed land (85%) and shorelines (>65%) on Lake Chelan are in public ownership.

H:99062:060899 3 Lake Chelan Ecological Review III. Methods

A. Selection of Variables for Analysis The available information for Lake Chelan was reviewed in the context of a more global hydroelectric impact matrix developed previously from published and grey literatures (Apfelbaum and Ludwig 1998). Cross-references with information available on Lake Chelan suggests several key areas where ecological affects have been identified as a part of regulatory reviews, normal operational evaluations, and as a part of watershed initiatives. For instance, general watershed related issues and data needs are currently being addressed for Lake Chelan as a part of a FERC relicensing process. As part of that process, several key issues have been identified. Some of these issues have been the subject of detailed study to date, and the relicensing process is expected to invest additional focus on other key issues. Based on this review of available written records (see cited references), the following were identified as key variables likely affected by the project.

1. Terrestrial habitat 2. Shoreline erosion 3. Wetlands 4. Aquatic habitat: Lake Chelan, Dewatered Chelan River Channel, and Tailrace 5. Mudflats

A discussion follows on the methods and results of measurements of effects on key variables that have been identified at Lake Chelan.

B. Site Physical Impacts Habitat change as acreage of habitat types was measured by comparison of recent digital ortho-aerial photography and pre-dam topographic surveys of the dam and reservoir footprint, and by review of historic photographs. Use of ArcView/ArcInfo GIS made it possible to superimpose historic and recent data on a recent aerial photograph base. In turn, it was possible to generate a report of the changes found in habitat types based on the analysis. The report includes tabulations showing acreages of change by land-cover type and mapped locations of changes. Habitat loss, or addition, measured as changes in acreages, was measured using historic topographic mapping. Two shoreline boundaries were created -- pre-dam and post-dam shorelines. The acreage values presented below were calculated from these boundaries.

H:99062:060899 4 Lake Chelan Ecological Review The following basic statistics derived from the Chelan PUD (http://www.chelanpud.org/) were useful in some analyses in this report:

Minimum lake elevation before dam 1077 feet Maximum lake elevation before dam 1084.5 feet Minimum lake elevation after dam 1079.7 feet Maximum lake elevation with dam 1100 feet Drainage area 608,000 acres (950 square miles)

C. Key Variables

1. Terrestrial Habitat (1277.86 acres)

a. Habitat Change from Dam and Related Infrastructure (10.07 acres) The following areas were digitized from an aerial orthophoto quadrangle base in Arcview 3.1 (see Figures 3 and 4, “Powerhouse & Associated Features” and “Dam Footprint & Associated Features”).

Dam footprint 0.35 acres Powerhouse 0.29 acres Surge tank 0.12 acres Switchyard 1.93 acres Tunnel (penstock) spoils 0.78 acres Dam staging area 6.60 acres

b. Habitat Impacts Along Lake’s Historic Upland Slopes By Inundation (1255 acres) Terrestrial impacts were measured to be approximately 1255 acres (see Figures 9A-9D, “Terrestrial Impacts”). The affected land is mainly comprised of evergreen forestland (see Figure 1, “Lake Chelan Watershed Land Use”). These data were obtained from the EPA (http://www.epa.gov/ost/basins).

H:99062:060899 5 Lake Chelan Ecological Review Terrestrial habitat effects were derived from the “Map of Lake Chelan” (April 1917, Great Northern Railway-Western District, Chelan Electric Co.). This map was scanned and digitized in AutoCAD by Chelan PUD. Over twenty separate AutoCAD files were mosaiced together using: 1) the original 1917 coordinates; 2) section markers placed by Chelan PUD; and 3) best fit. Initial processing was done in AutoCAD 13. Coverages were created using AutoCAD R13 to determine areas (acreage) of interest. Terrestrial impacts were calculated as the difference between the pre-dam lake level (1077 foot contour — 31,499 acres) and the maximum current lake level (1100 foot contour — 32,754 acres). These calculations are approximate. There may be several sources of error involved in the digital compilation of paper maps that are over eighty years old. However, it is important to note that the terrestrial impact figure of 1255 acres is derived solely from the 1077 and 1100 foot contours on the 1917 map. In other words, no calculations were made from disparate sources of data. Therefore, the main source of potential error is within the 1917 map itself.

c. Road Impacts (12.79 acres) Gorge Road and Powerhouse Access Roads were created as a result of the Lake Chelan dam (see Figure 2 “Road Impacts”). Calculations incorporate a road width of 20 feet and are approximations. Road data is at a 1:24,000 scale. The road length was 5.28 miles, and the corresponding road area was calculated at 12.79 acres.

d. Transmission Lines (0 acres) At the Lake Chelan site, the switchyard is located directly next to the powerhouse (see Figure 3, “Powerhouse and Associated Features”). The Chelan PUD does not own any transmission lines associated with the Lake Chelan Project. From the switchyard, the power enters the general grid. Therefore, there are no transmission line impacts.

2. Shoreline Erosion (42 acres) Shoreline erosion was estimated in areas where glacial till substrates occurred within a distance of 25 horizontal feet from the controlled lake edge. Glacial till areas

H:99062:060899 6 Lake Chelan Ecological Review were identified from digital soils data. Glacial till along the lake edge’s total length is 13.78 miles. Based on available data, this area was estimated to be 42 acres (see Figure 5, “Shoreline Erosion”). Soils information was obtained from the U.S. Forest Service.1

3. Wetlands (24 acres) Wetlands located along the southernmost shoreline of the Lake were inundated and lost as a result of raising water levels behind the Lake Chelan Dam. This wetland acreage was calculated from ‘Map of Lake Chelan’ (April 1917, Great Northern Railway - Western District, Chelan Electric Co.) — see Figure 6, “Wetlands”. This study reviewed changes in wetland and littoral edge dimensions from pre-dam to present day.

4. Aquatic Habitat: Lake Chelan (1255 acres), Dewatered Channel (94.61 acres) and Tailrace (16.29 acres) As described above, rising water levels in the lake have seasonally resulted in an increase in inundated acreage of open water of 1,255 acres. Between Lake Chelan Dam and the Columbia River there is a channel (the Chelan River) that remains dewatered most of the time as a result of water diversions into the penstock at the dam. This area was measured to be approximately 94.61 acres. Between the powerhouse and the Columbia River there is also a tailrace created by the Rocky Reach facility downstream. This area was measured to be approximately 16.29 acres. These features were digitized from aerial orthophoto quadrangle base maps (see Figure 7, “Dewatered Channel and Tailrace”).

5. Mudflats (33.86 acres) At the northern tip of Lake Chelan near Stehekin, there were mudflats that were inundated as a result of rising water behind the Lake Chelan Dam. This area of seasonal inundation was estimated to be 33.86 acres. This area was calculated from

1 Additional data on the subject of soil erosion was collected and analyzed in 1999 subsequent to this study by the relicensing team and the resource agencies under a separate focussed study. According to the Chelan PUD, this additional research considered soil erosion along the entire shoreline of the Lake Chelan. Subsequent to the completion of the data collection conducted for this study, but prior to the final publication of this report, this new research was published. (See Figures 5a, 5b, 5c.) Based on the new research, the 42-acre estimate calculated for this study would appear to be reasonable, and perhaps somewhat overstated. [Correspondence, G. Yow, Chelan PUD, February 25, 2000].

H:99062:060899 7 Lake Chelan Ecological Review “Map of Lake Chelan” (April 1917, Great Northern Railway-Western District, Chelan Electric Co.) — see Figure 8, “Mudflats”).

D. Key Species

1. Anadromous Fish The steep gradient of the Chelan River canyon is believed to have prevented anadromous fish passage into Lake Chelan. Therefore, anadromous fish have not been considered during this investigation. They have also not been considered because no historic records occur for even the most efficient of the passing fishes (e.g., red or sockeye salmon or steelhead) in Lake Chelan. All individuals of these species in Lake Chelan, and many other species of fishes, have been stocked. Recent (Giorgi 1991, 1998) use of the tailrace and channel at the power plant by summer and fall chinook for spawning suggests a potential positive benefit of this created habitat of less than 16 acres.

2. Resident Fish Brown (1984) has identified several resident fish species, some of which may be characterized as key species by virtue of being listed as threatened, endangered, or as a “species of concern.” These include burbot, westslope cutthroat trout, pygmy whitefish and bull trout.

3. Birds Bald eagles are known to winter in the area of the Lake Chelan Project.

IV. Summary of Results

This report section identifies the habitat effects of the Lake Chelan Project. All habitat effects were measured in acres of depleted (or accreted) habitat. Basic assumptions regarding fish passage and several other measurements made during this investigation are also described and provided. Table 1 (following page) provides an overview of habitat acreage affected, including both areas of habitat loss and gain. A short discussion of each category of site physical disruption follows.

H:99062:060899 8 Lake Chelan Ecological Review Table 1. Site Physical Disruptions: Acreages of habitat changes, Lake Chelan Project

Habitat Acres Affected Acres Lost/Gained Reservoir Pre-dam lake habitat 31,499 Post-dam lake habitat 32,754 Forest, cliff, grassland habitat lost to inundation (1,255) Lake habitat gain due to inundation 1,255 Bottom of Chelan River at Columbia River Pre-dam floodplain habitat 16 Post-dam floodplain habitat 0 Floodplain habitat lost (16) Mudflat habitat inundation (seasonal) 34 (34) Wetlands (seasonal) Pre-dam wetlands 24 Post-dam wetlands 0 Wetland habitat lost (24) Chelan River channel Pre-water surface 95 Post-dam water surface (seasonally) 0 River habitat lost (95) Terrestrial habitats lost to roads 13 (13) Terrestrial Habitat lost to facilities footprint 10 (10) — Dam, powerhouse, surge tank, dam staging area, switchyard, tunnel spoils Shoreline erosion 42 (42) TOTAL HABITAT LOSS (1,489) TOTAL HABITAT GAINED 1,255

A. Reservoir dimension and inundation of historic shoreline environment. Physical changes to aquatic and terrestrial habitats were calculated as total acres of change in general habitat categories, including floodplains, wetlands, Chelan River channel, upland forests and cliffs, and grassland habitats.

1. Inundation of the reservoir.

Inundation behind the dam has resulted in the loss of 1,255 acres of historic steep upland and 24 acres of perimeter wetland around the lake, and has contributed to

H:99062:060899 9 Lake Chelan Ecological Review the erosion of 42 acres of upland terrestrial habitat in narrow steep rocky shoreline environments.

Some areas of the historic shallow lake margin of the otherwise deep Lake Chelan had wetlands and shallow aquatic habitats, which have been converted to “lake- like” habitat. In addition, forested and other upland systems, including cliff vegetation and high palousse grasslands systems along the historic lake, have been submerged or are being impacted by erosion. Erosion is prevalent in locations where colluvial, alluvial, or till soils have been intercepted by new water levels in the lake. (See Figures 5 and the updated Figures 5a-c.)

Rising water levels in the lake have seasonally increased the acreage of open water from 31,499 to 32,754 acres for an increase in inundated acreage of 1,255 acres. The original 31,499 acres have been deepened, although on this lake the habitats and ecological setting of the lake itself are believed to have changed little. Thus, although the lake is deeper, aquatic resources in the shoreline areas (pre- and post-dam installation) are comprised of essentially comparable ecological features and habitats. This is currently characterized as a sheer deep-water zone with very little littoral zone; and in general, characterized the pre-dam shoreline environment.

2. Terrestrial forest disruption.

On this project, the terrestrial forest disruptions are primarily associated with shoreline environments. No transmission line related forest fragmentation has been associated with the Lake Chelan installation and maintenance.

3. Mudflat deposits.

Inundation of mudflat sediment deposits near Stehekin at the upstream end of the lake is believed to be minimal. Little change in sediment deposition and its retention in Lake Chelan is believed to have occurred from historic pre-dam times to present. However, seasonal inundation of 33.86 acres of mudflat habitat is considered disrupted habitat.

H:99062:060899 10 Lake Chelan Ecological Review B. Linear feature changes.

Measurements of changes in linear features directly associated with the Lake Chelan Project included the measurement of the following categories.

1. Dam footprint.

The footprint of the dam, gates, and access to the dam (historically over the river channel) were measured. This measurement is 0.35 acres.

2. Roads.

Roads specifically installed to service the Lake Chelan Dam and power plant were identified and measured. The acreage of roads for this purpose was 12.79 acres.

3. Transmission lines.

Measurement of transmission line disruptions to habitat suggests there are no Lake Chelan line impacts from the dam to larger regional and network line corridors that transmit electricity from other grid network facilities.

4. Facilities.

Powerhouse (0.29 acres), surge tank (0.12 acres), switchyard (1.93 acres), penstock excavation spoil areas (0.78 acres) and historic dam staging areas (6.60 acres) were also identified for a total of 10.07 affected habitat.

C. Wetlands.

Review of historic documentation, historic and recent surveys, and photographs suggested that wetlands were present before the installation of the Dam. The measured acreage of wetlands present in the reservoir on the pre-dam topographic survey was 24 acres.

H:99062:060899 11 Lake Chelan Ecological Review D. Downstream Changes.

As a part of this investigation, both field examination and studies using recent aerial photographs have suggested strongly that the entire channel reach for the Chelan River has been impacted by dewatering and diversion. Thus, the entire reach has been impacted for a total of 94.61 acres.

E. Summary of Key Habitat or Species Disruptions

No habitats or species identified as rare, threatened, or endangered, or of other key significance, appeared to have been affected by the operations of the Lake Chelan Project. Passage of anadromous species did not historically occur, due to the steep gradient. Apparent declines in certain resident fish species do not appear related to hydroelectric project operations, based on evidence available to reviewers at the time of this study. Nor does the evidence assembled indicate that bald eagle populations are in any way impacted by Lake Chelan Project, either in terms of foraging or nesting, given the range of this species. As such, no key species or habitat subindicator calculations were made.

The authors are aware that ongoing studies of dam impacts on anadromous and resident species are being conducted as part of the FERC renewal process. As part of any future certification renewal process to be conducted by SCS for the Chelan PUD, the current findings will be subject to revision if ongoing field-based studies uncover new evidence of potential impacts of the Lake Chelan Project on anadromous or resident fish populations.

V. Discussion

This analysis provides a conservative estimate of the effects to large-scale natural resource issues without assignment of habitat specific values associated with impacts or effects, based on a review of available data.

Overall, the present impacts of human use and management have been minimal, and the natural resource base of the watershed has remained largely unfragmented. Key findings supporting this conclusion include the following:

H:99062:060899 12 Lake Chelan Ecological Review · Lake Chelan existed as a deep-water lake prior to impoundment, and its general ecological characteristics have not changed substantially as a result of impoundment.

· There were no power transmission line impacts directly attributable to the Lake Chelan project.

· Anadromous fish passage into Lake Chelan from the Columbia River was historically impeded by the steep gradient of the Chelan River canyon.

· The littoral zone in the lake was quite limited, localized and small prior to impoundment, given the particular morphological characteristics of the shoreline, and steep gradient rock. As such, lake level fluctuations have not resulted in significant changes to littoral zones, and therefore would not be assumed to result in significant reductions in productivity.

· Based on evidence available to the reviewers, it would appear that noted reductions in resident fish populations are due to the stocking of non-native species rather than to the operations of the Lake Chelan Project.

Among the most prominent effects observed were the following:

· Impoundment resulted in the inundation of 1255 acres of terrestrial habitat, with the corresponding creation of 1255 (surface) acres of aquatic lake habitat.

· The change of the Chelan River from the pre-dam condition is the total cut-off of its discharge for most of the year, affecting 95 acres of habitat.

· 24 acres of wetlands located along the southernmost shoreline of the Lake were inundated and lost.

· 34 acres of mudflats were likewise inundated and lost.

· Erosion is conservatively calculated to have occurred on 42 acres of land around the lake shoreline.

· Road impacts were measured at just under 13 acres.

· Zones of deposition have been created at the mouths of tributary montane rivers.

H:99062:060899 13 Lake Chelan Ecological Review Bibliography

Apfelbaum and Ludwig, 1998, Cause-Effects Linkages on Regulated River Systems. Appendix B. in Scientific Certification Systems April 1998. A study of Vattenfall Hydroelestric Power Generation based on Life Cycle Stressor-Effects Assessment. Pilot Project: Harspranget Power Station, Lule River. 68 pps. + appendices.

Brown, L.G. 1984 Lake Chelan Fishery Investigations. Cooperative Project of P.U.D. No. 1 of Chelan County and Washington Department of Game. 238 pp.

Küchler, A.W., 1964, “Potential Natural Vegetation of the Conterminous United States,” Am. Geog. Society, Special Publication 36, 166 pp.

Public Utility District No. 1 of Chelan County, Wenatchee, Washington. February 12, 1999. Lake Chelan Hydroelectric Project FERC NO. 637, Botanical Study Plan. 11 pps.

Public Utility District No. 1 of Chelan County, Wenatchee, Washington. February 12, 1999. Lake Chelan Hydroelectric Project FERC NO. 637, Bypass Reach (Gorge) flow Releases Study Plan. 13 pps.

Public Utility District No. 1 of Chelan County, Wenatchee, Washington. February 12, 1999. Lake Chelan Hydroelectric Project FERC NO. 637, Columbia River Flow Augmentation Study Plan. 7 pps.

Public Utility District No. 1 of Chelan County, Wenatchee, Washington. February 12, 1999. Lake Chelan Hydroelectric Project FERC NO. 637, Fishery Investigation Study Plan. 14 pps.

Public Utility District No. 1 of Chelan County, Wenatchee, Washington. February 12, 1999. Lake Chelan Hydroelectric Project FERC NO. 637, Fishery Stranding Investigation Study Plan. 9 pps.

Public Utility District No. 1 of Chelan County, Wenatchee, Washington. February 12, 1999. Lake Chelan Hydroelectric Project FERC NO. 637, Lake Chelan Recreation/Aesthetics Study Plan. 15 pps.

Public Utility District No. 1 of Chelan County, Wenatchee, Washington. February 12, 1999. Lake Chelan Hydroelectric Project FERC NO. 637, Paddling Feasibility Study Plan. 25 pps.

Public Utility District No. 1 of Chelan County, Wenatchee, Washington. February 12, 1999. Lake Chelan Hydroelectric Project FERC NO. 637, Project Lands Management and Socioeconomic Study Plan. 17 pps.

Public Utility District No. 1 of Chelan County, Wenatchee, Washington. February 12, 1999. Lake Chelan Hydroelectric Project FERC NO. 637, Riparian Zone Investigation Study Plan. 10 pps.

H:99062:060899 14 Lake Chelan Ecological Review Public Utility District No. 1 of Chelan County, Wenatchee, Washington. February 12, 1999. Lake Chelan Hydroelectric Project FERC NO. 637, Shoreline & Bypass Reach Erosion Control Study Plan. 15 pps.

Public Utility District No. 1 of Chelan County, Wenatchee, Washington. February 12, 1999. Lake Chelan Hydroelectric Project FERC NO. 637, Water Quality Monitoring Study Plan. 17 pps. Public Utility District No. 1 of Chelan County, Wenatchee, Washington. February 12, 1999. Lake Chelan Hydroelectric Project FERC NO. 637, Wildlife Investigation Study Plan. 10 pps.

Public Utility District No. 1 of Chelan County, Washington. October 5, 1998. Lake Chelan Hydroelectric Project FERC NO. 637, Scoping Document No. 1 and Initial Consultation Document, Compact Disc Digital Audio.

Soil and Water, Ltd. and Scientific Certification Systems, Inc. January, 1999. LCSEA Operational Manual.

Thornthwaite, C.W., 1944, Report of the Committee on Transpiration and Evaporation,” Transactions of the Am. Geophys. Union, Volume 25, Part V, p. 687.

Thornthwaite, C.W. and B. Holzman, 1942, “Measurement of Evaporation from Land Water Surfaces,” U.s. Department of Agriculture, Technical Bulletin No. 817.

H:99062:060899 15 Lake Chelan Ecological Review Figure 1. Lake Chelan Watershed (Source: US EPA Office of Water/OST, 1998, “1-250,000 Scale Quadrangles of Land Use/Land Cover GIRAS,” Spatial Data of CONUS in BASINS, 1970 - early 1980s)

H:99062:060899 16 Lake Chelan Ecological Review Figure 2. Road Impact

H:99062:060899 17 Lake Chelan Ecological Review Figure 3. Powerhouse

H:99062:060899 18 Lake Chelan Ecological Review Figure 4. Dam Footprint

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H:99062:060899 22 Lake Chelan Ecological Review Figure 8. Mudflats

H:99062:060899 23 Lake Chelan Ecological Review Figure 9A. Terrestrial Impacts

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H:99062:060899 24 Lake Chelan Ecological Review Figure 9B. Terrestrial Impacts Example Area

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H:99062:060899 25 Lake Chelan Ecological Review Figure 9C. Terrestrial Impacts Mid Lake Chelan

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H:99062:060899 26 Lake Chelan Ecological Review Figure 9D. Terrestrial Impacts Upper Lake Chelan

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H:99062:060899 27 Lake Chelan Ecological Review

Appendix 3.b.

PEER REVIEW COMMENTS OF PAMELA SPATH, NATIONAL RENEWABLE ENERGY LABORATORY

COMPILED BY MARY ANN CURRAN U.S. ENVIRONMENTAL PROTECTION AGENCY SYSTEMS ANALYSIS BRANCH

February 11, 2000

Page 1: Reviewer noted “Good statement” next to the first paragraph –“These impacts can vary significantly not only between energy sources, but also among power production systems using the same energy source, depending on difference in the technologies in place, as well as differences in the surrounding environments.”

Page 19: Reviewer noted “Sounds fine. I believe this is true.” next to the text under section 4.2.7 Operations Excluded from Inventory Calculations.

Page 20: Referring to Table 4.1 Aggregated Inventory Resource Calculation input Values…, the reviewer noted “Perhaps I missed this, but how many years does construction take place?” followed by “These can’t be added like this. The number of construction years will be much shorter than the number of operating years.”

Page 21: Referring to Table 4.3 Lake Chelan Project LCI Data … the reviewer noted the following:

“These (pointing to the WSCC grid column) are probably averages. Therefore, these numbers (pointing to the first, Chelan column) should be averaged for comparison. (i.e. ( CO2 ( # of + ( CO2 ( # of construction) construction yrs) operating) operating years) CO2 Chelan should = ------(# of construction yrs + # of operating years)

Page 23: Referring to Table 6.1 Fossil Fuel and Uranium Resource Depletion “Again I don’t think these should be added like this. (Pointing to the Table next to Total Sum 69) The Dam construction is a much shorter timeframe than the years of plant operation. Therefore, the construction will have much less of an environmental impact than the plant operation.”

Page 25: Last paragraph, “For fossil fuel systems, most of the energy is in the feedstock. Where does most of the energy consumed come from for a hydro plant?” Page 27: Second paragraph, “How was the ranking of the habitats determined? (I.e. how was one habitat determined to be more important than another?)”

Page 39: Figure 11.1, “I like this Figure!” Appendix 4

SCS RESPONSE TO PEER REVIEWER COMMENTS

February, 2000

Richard Whitney, a fisheries expert in the Pacific Northwest, and Pamela Spath, an expert on energy production systems at the National Renewable Energy Laboratory, were each asked to provide independent peer review of the Lake Chelan LCSEA Study. Dr. Whitney’s review and comments focussed on the issues relevant to the ecosystem disruption impact indicator, while Ms. Spath’s review focussed on the other indicators pertaining to electricity production reviewed in this study, and the quality of the overall report. Ms. Spath’s comments, in turn, were compiled by Ms. Mary Ann Curran, U.S. Environmental Protection Agency, Systems Analysis Branch, who current chairs the independent peer review panel of SCS LCSEA projects in the energy sector.

The reviewers have provided useful questions and insightful comments in their independent reviews of this report. This response document addresses both the general and the specific comments raised. As warranted, changes have been made to the final report text consistent with these responses. We have combined those comments that updated or improved the draft, along with additional information received since the draft was released, to produce the final report. Thus some sections have been rewritten, and page numbering (and even, in some cases, section numbering) has been altered in the final report.

Response to Richard Whitney, General Comments

1. Focus of the analysis — The reviewer identifies two areas in which he believes the study could be strengthened in order to improve its usefulness as a tool for comparing the relative environmental effects of the Lake Chelan Hydroelectric Project to other power generation in the regional grid.

a. The adequacy of the model — The reviewer suggests that the “analysis appears to spring from the model, rather than from the basic question at hand.” He suggest that study should be directed to the question of relicensing – i.e., with the construction of the project as a given rather than as included in the impact assessment. He suggests that the analysis as currently conducted would be most helpful in assessing alternative power generation at the planning stages, but argues that the environmental effects from the construction phase should be considered outside the current question of relicensing. Response: This study seeks to answer a broader question than that posed by the reviewer, or by the relicensing process. This is not simply a question of whether or not the project should be relicensed, but rather, an attempt to quantify to cradle-to-grave impacts of the use of this hydro system. The impacts from construction cannot, and should not, be ignored in addressing the larger issue of which generation sources represent the greatest (or fewest) impacts when compared to the WSCC average. Indeed, operational impacts are broken out separately from construction impacts throughout the report — the model already accommodates this need. b. Environmental Footprint — The reviewer has some concern that the concept of establishing an "environmental footprint" may be too static. He is concerned that there "may be a tendency to overlook or minimize some continuing operational effects."

Response: Life-cycle impact assessment is by its very nature a snapshot of impacts at the time of analysis; as such, such studies are indeed concerned with the status of impacts as they can be quantified today. The method does not hamper the study of effects. To the contrary, it seeks to integrate all available data on known effects, and develop quantified indicators to reflect these effects. The results, therefore, are only as good as the data that are made available for inclusion, but the method itself does not limit the assessment.

The reviewer should note that this study is part of an ongoing certification process. Thus, as new information become available, these data can be incorporated in order to revise the results and corresponding environmental performance rating. In this way, the certification is able to address the “living organism” aspects of the Lake Chelan Project, much as Dr. Whitney suggests. Once a certification is issued, this certification remains in effect only if relevant data are updated to ensure that operations are stable, to incorporate any technological changes or enhancements that may be made in plant operations, and to reflect the findings of new research.

· On the issue of impacts to westslope cutthroat trout, the best evidence available to the authors suggested that reductions in their population were largely attributable to the introduction of rainbow troat and kokanee, rather than to lake level fluctuations. Further study on this question would be useful, however, in light of Brown (1984).

· The authors agree with Dr. Whitney’s comments that lake level fluctuations affect littoral zones, which can in turn affect the overall productivity level of a lake. It is useful for readers to consider these issues carefully when evaluating the impacts of hydroelectric projects, and therefore very much appreciated that these points have been raised. However, in the specific case of Lake Chelan, the littoral zone in the lake was quite limited, localized and small prior to impoundment, given the particular morphological characteristics of the shoreline, and steep gradient rock. As such, in this case, lake level fluctuations have not resulted in significant changes to littoral zones, and therefore would not be assumed to result in significant reductions in productivity. Thus, no discussion of this issue has been added to the text. Likewise, the 85% environmental characterization factor to account for non-observed effects has been left intact.

2. Biological Basis Provided in Appendix 2

The reviewer is concerned that Appendix 2 may "not adequately address fish or other biota of the lake or biota affected by plant discharges."

Response: LCIA does not attempt to provide a detailed ecological analysis; rather, it is a data integration model that draws from existing analyses, field research, mapping techniques and other available documents and tools to develop a first approximation of the scale of impacts on a per-hectare basis.

· With respect to resident fish species, such species were considered by the authors for this study. Consistent with Dr. Whitney’s recommendation, a short discussion of resident species has been added to Appendix 2, Methods Section III, and corresponding information has been added to the main text of the report, Section 8. Based on the data available to the authors at the time of this study, there was no evidence to indicate that the Project operations have negatively impacted resident fish species.

The authors were made aware of an on-going sedimentation study being conducted by the Chelan PUD, in which the impacts of maintaining the lake at maximum elevation on eight tributaries and their resident fish populations were examined. The study was conducted using different methods and different stratified sampling techniques than the prior reference study (1984), and in addition, was conducted during an unusually cold runoff season. As such, direct comparisons of measured fish populations are not possible. The preliminary findings of this 1999 study have not yet been fully analyzed in light of these differences.

As part of the certification renewal process, the LCSEA study findings will be subject to revision if any ongoing or future field-based studies of resident fish populations indicate reductions in such fish populations due to Lake Chelan Project operations. A note to this effect has been added to Appendix 2, and to the main text in Section 8.

· The reference to forebay was an error by the editor. The error has been corrected.

· With respect to sockeye and chinook salmon, the authors did review several Chelan PUD documents prepared for FERC, and found no evidence that such species had navigated up the steep gradient channel of the Chelan River Canyon prior to impoundment. For the area below the outfall, the discussion in Section III. D.1. on anadromous fish states: “Recent (Giorgi 1991, 1998) use of the tailrace and channel at the power plant by summer and fall chinook for spawning suggests a potential positive benefit of this created habitat of less than 16 acres.” The authors conclusions were based in part on discussions with Chelan PUD staff as well as direct visual observation. The authors appreciate Dr. Whitney’s citation of additional data on chinook spawning in the area. The authors are aware that ongoing studies of dam impacts on anadromous species are being conducted as part of the FERC renewal process. As part of the certification renewal process, the current findings will be subject to revision if such field-based studies detect direct impacts on anadromous fish populations below the dam. A note to this effect has been added to Appendix 2 and to the main text in Section 8.

· In addition to these references cited in the bibliography, the authors did review limited available data, including: 1) creel census data; and 2) population assessment data for selected fish species. These were internal Chelan PUD documents.

· The authors have subsequently consulted with the Brown reference suggested by Dr. Whitney. The reference to Brown (1984) has been added to Appendix 2 accordingly.

Response to Richard Whitney, Specific Comments

Specific Comment 1. Pg. 8 – American Shad

Response: The reference to American Shad was an error by the editor. The error has been corrected.

Specific Comment 2. Pg. 10 – Evaporation

Response: On the subject of evaporaton, the authors referred to standard references on potential water evaporation (Thornthwaite and Holzman, 1942). The report text has been clarified as follows: “Given the fact that the impoundment resulted in only a small increase in the lake’s size and surface area, and resulted in no measurable increase in water temperature, there is little likelihood (and there was no evidence to indicate) that the construction of the dam has resulted in any significant enhanced evaporation of water.”

Specific Comment 3. Pg. 14 – Broad glacial trough

Response: The description of Lake Chelan as a natural lake that developed within a “broad glacial trough” was taken from documents provided by the Public Utility District of Chelan County as part of the FERC licensing process (e.g., Public Utility District of Chelan County, Wenatchee, Washington. February 12,1999. Lake Chelan Hydroelectric Project FERC No. 637 Fishery Stranding Investigation Study Plan.” ). Consistent with Dr. Whitney’s comments, the word “broad” has been deleted from the text.

Specific Comment 4. Pg. 14 — Botanical descriptions.

Response: This study was not intended to provide detailed repetition of information contained in prior ecological investigations of the region, but rather to survey such information to derive conclusions relevant to quantifying the habitat disruption impact indicator.

To guide the reader, the following note has been added to the text: A more complete description of the grasses, pines, and other local plants is available in “Public Utility District of Chelan County, Wenatchee, Washington. February 12,1999. Lake Chelan Hydroelectric Project FERC No. 637 Botanical Study Plan”, in other local ecological investigations and in national classifications of plant communities (Kuchler, 1964).

Specific Comment 5. Pg. 29 — Comparable ecological features and habitats.

Response: Dr. Whitney has raised important issues that should be considered generally in the study of hydroelectric projects, based on the experience of other hydro projects. However, for the specific case studied here, the Lake Chelan Project, the authors stand by their conclusion that the ecological features and habitats of aquatic resources in the shoreline areas have not been significantly altered by the hydroelectric project, due to: 1) the particular morphology of the Lake, including the steep water entry angles, and its naturally limited, localized and small littoral zone, 2) the relatively small increase in depth of the lake as a result of impoundment, and 3) the fact that lake temperatures have not measurably changed. (See response to general comments #2 above, second bullet pertaining to littoral zones.) Changes in resident fish species populations that have been observed in the Lake do not appear to be attributable to the Project, but rather to fish stocking programs.

Response to Pamela Spath, General Comments

Comment 1 (pg. 1), Comment 2 (pg. 19), Comment 8 (pg. 39, Table 11.1)

Response: Reviewer’s comments noted and appreciated.

Comment 3 (pg. 20, Table 4.1) Number ofyears construction; handling of construction loads in table.

Response: Answer: The explanation is found in text section 4.3. The Lake Chelan Project was constructed between 1926 and 1927. The construction loads (resources and emissions) are totals for the construction period. Because the power plant and dam are assumed to have a 100-year lifetime, these total construction loads are then divided by 100 years, which gives an annual amortized value that can be added to yearly operating loads in order to determine total annual loads for the Lake Chelan Project. In order to assess the full cradle-to-grave impacts of any electric power generation system, it is crucial that construction and demolition related impacts be considered, as well as on-going operations.

Comment 4 (pg. 21, Table 4.3) Calculation of values listed the Lake Chelan Project column.

Response: The study is reporting annualized loads, rather than cumulative loads from the time of construction. Normalization of loads on an annual basis is necessary in order to compare the relative impacts of the Lake Chelan Project to those of the WSCC power pool, given that the latter is comprised of electric generation systems of widely varying ages.

As described in the response to Comment 3 above, construction loads are amortized over the whole lifetime of the hydro project. Similarly, the loads reported for the WSCC include loads from initial construction as well as annual operating loads. Thus, the table provides an “apples to apples” comparison in the two columns.

Comment 5 (pg. 23, Table 6.1)

Response: See responses to Comments 3 and 4 above.

Comment 6 (pg. 25) Energy consumption of a hydro plant

Response: The energy consumption of a hydro plant largely comes from the energy used during the construction phase (e.g., concrete/cement manufacturing and vehicle fuel consumption), as well as from the vehicle fleet associated with ongoing hydro plant operations. For clarification, "feedstock" in this report refers to such fossil (or bio-) fuel consumption that is not combusted for energy, but is used for material outputs (e.g., plastics).

Comment 7 (pg. 27) Ranking of habitats.

Response: The methodology does not not curently rank terrestrial and aquatic habitats, but assumes that all habitats are of equal value. This reflects that fact that every habitat is uniquely adapted to its particular locality. Generally speaking, the weighting of habitats based on relative biodiversity involves value judgements.

At the same time, it is important to recognize certain habitats, such as wetlands, that are limited in size, rare within the bioregion, and may be lost or converted as a result of the industrial activity. SCS does not recognize as a gain the conversion of these habitats into other habitats. (It should be noted that wetland habitats restored by hydropower operators can be counted as a gain, provided that they are of equal value to the wetland habitats converted or removed.)

The extent to which identified key species are affected is captured under separate “key species” indicators. See Appendix 1, Chapter 3, “Ecosystem Disruption” and Appendix 2, Section III.