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Main Handbook of Environmental Engineering
Handbook of Environmental EngineeringMyer Kutz
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Handbook of Environmental Engineering Handbook of Environmental Engineering Edited by Myer Kutz Myer Kutz Associates, Delmar, NY, USA This edition first published 2018. © 2018 John Wiley & Sons, Inc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions. The right of Myer Kutz to be identified as the author of the editorial material in this work has been asserted in accordance with law. Registered Office John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA Editorial Office 111 River Street, Hoboken, NJ 07030, USA For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com. 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Description: First edition. | Hoboken, NJ : John Wiley & Sons, 2018. | Includes bibliographical references and index. | Identifiers: LCCN 2018015512 (print) | LCCN 2018028239 (ebook) | ISBN 9781119304401 (pdf ) | ISBN 9781119304432 (epub) | ISBN 9781118712948 (cloth) Subjects: LCSH: Environmental engineering–Handbooks, manuals, etc. Classification: LCC TA170 (ebook) | LCC TA170 .H359 2018 (print) | DDC 628–dc23 LC record available at https://lccn.loc.gov/2018015512 Cover design by Wiley Cover image: © Jonutis/Shutterstock Set in 10/12pt Warnock by SPi Global, Pondicherry, India Printed in the United States of America 10 9 8 7 6 5 4 3 2 1 To Rick Giardino and to all the other contributors to this handbook vii Contents List of Contributors xiii Preface xv 1 Environmental Systems Analysis 1 Adisa Azapagic 1.1 Introduction 1 1.2 Environmental Systems Analysis Methods 1 1.3 Summary 11 References 11 2 Measurements in Environmental Engineering 13 Daniel A. Vallero Summary 13 2.1 Introduction 13 2.2 Environmental Sampling Approaches 18 2.3 Laboratory Analysis 22 2.4 Sources of Uncertainty 25 2.5 Measurements and Models 27 2.6 Contaminants of Concern 27 2.7 Environmental Indicators 31 2.8 Emerging Trends in Measurement 33 2.9 Measurement Ethics 40 Note 41 References 41 3 Environmental Law for Engineers 45 Jana B. Milford 3.1 Introduction and General Principles 45 3.2 Common Law 48 3.3 The National Environmental Policy Act 50 3.4 Clean Air Act 52 3.5 Clean Water Act 55 3.6 Resource Conservation and Recovery Act 58 3.7 CERCLA 61 3.8 Enforcement and Liability 62 Notes 65 4 Climate Modeling 67 Huei‐Ping Huang 4.1 Introduction 67 4.2 Historical Development 67 viii Contents 4.3 Numerical Architecture of the Dynamical Core 68 4.4 Physical and Subgrid‐Scale Parameterization 71 4.5 Coupling among the Major Components of the Climate System 73 4.6 The Practice of Climate Prediction and Projection 73 4.7 Statistical Model 77 4.8 Outlook 77 References 78 5 Climate Change Impact Analysis for the Environmental Engineer 83 Panshu Zhao, John R. Giardino, and Kevin R. Gamache 5.1 Introduction 83 5.2 Earth System's Critical Zone 84 5.3 Perception, Risk, and Hazard 87 5.4 Climatology Methods 94 5.5 Geomorphometry: The Best Approach for Impact Analysis 99 References 114 6 Adaptation Design to Sea Level Rise 119 Mujde Erten‐Unal and Mason Andrews 6.1 Introduction: Sea Level Rise 119 6.2 Existing Structures and Adaptation Design to Sea Level Rise 120 6.3 Case Studies Reflecting Adaptation Design Solutions 124 Notes 135 References 135 7 Soil Physical Properties and Processes 137 Morteza Sadeghi, Ebrahim Babaeian, Emmanuel Arthur, Scott B. Jones, and Markus Tuller 7.1 Introduction 137 7.2 Basic Properties of Soils 137 7.3 Water Flow in Soils 158 7.4 Solute Transport 173 7.5 Soil Temperature, Thermal Properties, and Heat Flow 182 7.6 Summary 194 Acknowledgments 194 Abbreviations 194 Physical Constants and Variables 195 References 198 8 In Situ Soil and Sediment Remediation: Electrokinetic and Electrochemical Methods 209 Sibel Pamukcu 8.1 Introduction and Background 209 8.2 Overview and Theory of Direct Electric Current in Soil and Sediment Remediation 211 8.3 Electrokinetically and Electrochemically Aided Soil and Sediment Remediation 222 8.4 Summary and Conclusions 239 References 240 9 Remote Sensing of Environmental Variables and Fluxes 249 Morteza Sadeghi, Ebrahim Babaeian, Ardeshir M. Ebtehaj, Scott B. Jones, and Markus Tuller 9.1 Introduction 249 9.2 Radiative Transfer Theory 249 9.3 RS Technology 255 9.4 RS of Static Soil Properties 263 9.5 RS of State Variables 269 9.6 RS of Environmental Fluxes 282 Contents 9.7 Summary 287 Acknowledgments 288 Abbreviations 288 Physical Constants and Variables 289 References 290 10 Environmental Fluid Mechanics 303 Nigel B. Kaye, Abdul A. Khan, and Firat Y. Testik 10.1 Open‐Channel Flow 303 10.2 Surface Waves 308 10.3 Groundwater Flow 310 10.4 Advection and Diffusion 313 10.5 Turbulent Jets 318 10.6 Turbulent Buoyant Plumes 320 10.7 Gravity Currents 326 References 329 11 Water Quality 333 Steven C. Chapra 11.1 Introduction 333 11.2 Historical Background 334 11.3 Overview of Modern Water Quality 336 11.4 Natural or "Conventional" Water Quality Problems 11.5 Toxic Substances 345 11.6 Emerging Water Pollutants 348 11.7 Back to the Future 348 Note 349 References 349 12 339 Wastewater Engineering 351 Say Kee Ong 12.1 Introduction 351 12.2 Wastewater Characteristics and Treatment Requirements 351 12.3 Treatment Technologies 355 12.4 Summary 371 References 371 13 Wastewater Recycling 375 Judith L. Sims and Kirsten M. Sims 13.1 Introduction 375 13.2 Uses of Reclaimed Wastewater 376 13.3 Reliability Requirements for Wastewater Reclamation and Recycling Systems 414 13.4 Planning and Funding for Wastewater Reclamation and Reuse 416 13.5 Legal and Regulatory Issues 416 13.6 Public Involvement and Participation 418 13.7 Additional Considerations for Wastewater Recycling and Reclamation: Integrated Resource Recovery 418 13.8 Additional Sources of Information 423 References 423 14 Design of Porous Pavements for Improved Water Quality and Reduced Runoff 425 Will Martin, Milani Sumanasooriya, Nigel B. Kaye, and Brad Putman 14.1 Introduction 425 14.2 Benefits 428 ix x Contents 14.3 Hydraulic Characterization 430 14.4 Hydraulic and Hydrologic Behavior 435 14.5 Design, Construction, and Maintenance 442 References 448 15 Air Pollution Control Engineering 453 Kumar Ganesan and Louis Theodore 15.1 Overview of Air Quality 453 15.2 Emissions of Particulates 453 15.3 Control of Particulates 459 15.4 Control of Gaseous Compounds 476 Acknowledgment 491 References 491 Further Reading 491 16 Atmospheric Aerosols and Their Measurement 493 Christian M. Carrico 16.1 Overview of Particulate Matter in the Atmosphere 493 16.2 History and Regulation 493 16.3 Particle Concentration Measurements 494 16.4 Measuring Particle Sizing Characteristics 497 16.5 Ambient Aerosol Particle Size Distribution Measurements 498 16.6 Aerosol Measurements: Sampling Concerns 501 16.7 Aerosol Formation and Aging Processes 501 16.8 Aerosol Optical Properties: Impacts on Visibility and Climate 502 16.9 Measurements of Aerosol Optical Properties 505 16.10 Aerosol Chemical Composition 506 16.11 Aerosol Hygroscopicity 509 16.12 Aerosols, Meteorology, and Climate 511 16.13 Aerosol Emission Control Technology 513 16.14 Summary and Conclusion 515 References 515 17 Indoor Air Pollution 519 Shelly L. Miller 17.1 Introduction 519 17.2 Completely Mixed Flow Reactor Model 522 17.3 Deposition Velocity 524 17.4 Ultraviolet Germicidal Irradiation 526 17.5 Filtration of Particles and Gases 528 17.6 Ventilation and Infiltration 532 17.7 Ventilation Measurements 536 17.8 Thermal Comfort and Psychrometrics 539 17.9 Energy Efficiency Retrofits 541 17.10 Health Effects of Indoor Air Pollution 542 17.11 Radon Overview 546 17.12 Sources of Indoor Radon 548 17.13 Controlling Indoor Radon 550 17.14 Particles in Indoor Air 551 17.15 Bioaerosols 553 17.16 Volatile Organic Compounds 555 17.17 VOC Surface Interactions 556 17.18 Emissions Characterization 557 Contents 17.19 Odors 559 Acknowledgments 560 Note 560 References 560 18 Environmental Noise Pollution Sharad Gokhale 565 18.1 Introduction 565 18.2 Environmental Noise 565 18.3 Effects on Human Health and Environment 566 18.4 Sound Propagation in Environment 567 18.5 Characteristics of Sound 569 18.6 Relationship Between Characteristics 570 18.7 Environmental Noise Levels 573 18.8 Measurement and Analysis of Ambient Noise 574 18.9 Environmental Noise Management 579 Note 580 References 581 19 Hazardous Waste Management 583 Clayton J. Clark II and Stephanie Luster‐Teasley 19.1 Fundamentals 583 19.2 Legal Framework 585 19.3 Fate and Transport 591 19.4 Toxicology 593 19.5 Environmental Audits 594 19.6 General Overall Site Remediation Procedure 596 References 598 20 Waste Minimization and Reuse Technologies Bora Cetin and Lin Li 599 20.1 Introduction 599 20.2 Type of Recycled Waste Materials 599 20.3 Recycling Applications of Fly Ash and Recycled Concrete Aggregates 601 20.4 Benefit of Recycling Materials Usage 621 20.5 Conclusions 621 References 623 21 Solid Waste Separation and Processing: Principles and Equipment 627 Georgios N. Anastassakis 21.1 Introduction 627 21.2 Size (or Volume) Reduction of Solid Waste 629 21.3 Size Separation 636 21.4 Manual‐/Sensor‐Based Sorting 638 21.5 Density (or Gravity) Separation 649 21.6 Magnetic/Electrostatic Separation 653 21.7 Ballistic Separation 660 21.8 Froth Flotation 661 21.9 Products Agglomeration (Cubing and Pelletizing) 661 21.10 Compaction (Baling) 663 21.11 Benefits and Prospects of Recycling 666 References 669 xi xii Contents 22 Waste Reduction in Metals Manufacturing Carl C. Nesbitt 673 22.1 Wastes at the Mine Sites 674 22.2 Chemical Metallurgy Wastes 678 22.3 Conclusions 686 Reference 686 Further Reading 687 23 Waste Reduction and Pollution Prevention for the Chemicals Industry: Methodologies, Economics, and Multiscale Modeling Approaches 689 Cheng Seong Khor, Chandra Mouli R. Madhuranthakam, and Ali Elkamel 23.1 Introduction 689 23.2 Development of Pollution Prevention Programs 691 23.3 Economics of Pollution Prevention 698 23.4 Survey of Tools, Technologies, and Best Practices for Pollution Prevention 699 23.5 Concluding Remarks 707 References 707 24 Industrial Waste Auditing C. Visvanathan 709 24.1 Overview 709 24.2 Waste Minimization Programs 710 24.3 Waste Minimization Cycle 711 24.4 Waste Auditing 712 24.5 Phase I: Preparatory Works for Waste Audit 712 24.6 Phase II: Preassessment of Target Processes 717 24.7 Phase III: Assessment 719 24.8 Phase IV: Synthesis and Preliminary Analysis 722 24.9 Conclusion 724 Suggested Reading 729 Index 731 xiii List of Contributors Georgios N. Anastassakis Ali Elkamel School of Mining and Metallurgical Engineering, National Technical University of Athens, Athens, Greece Department of Chemical Engineering, University of Waterloo, Waterloo, Ontario, Canada Department of Chemical Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE Mason Andrews Department of Architecture, Hampton University, Hampton, VA, USA Emmanuel Arthur Department of Agroecology, Aarhus University, Tjele, Denmark Adisa Azapagic School of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK Ebrahim Babaeian Department of Soil, Water and Environmental Science, The University of Arizona, Tucson, AZ, USA Christian M. Carrico Department of Civil and Environmental Engineering, New Mexico Institute of Mining and Technology, Socorro, NM, USA Mujde Erten‐Unal Department of Civil and Environmental Engineering, Old Dominion University, Norfolk, VA, USA Kevin R. Gamache Water Management and Hydrological Science Program and High Alpine and Arctic Research Program (HAARP), The Bush School of Government and Public Service, Texas A&M University, College Station, TX, USA Kumar Ganesan Department of Environmental Engineering, Montana Tech, Butte, MT, USA John R. Giardino Department of Civil, Construction, and Environmental Engineering, Iowa State University, Ames, IA, USA Water Management and Hydrological Science Program and High Alpine and Arctic Research Program (HAARP), Department of Geology and Geophysics, Texas A&M University, College Station, TX, USA Steven C. Chapra Sharad Gokhale Bora Cetin Department of Civil & Environmental Engineering, Tufts University, Medford, MA, USA Clayton J. Clark II Department of Civil & Environmental Engineering, FAMU‐FSU College of Engineering, Florida A&M University, Tallahassee, FL, USA Ardeshir M. Ebtehaj Department of Civil, Environmental and Geo‐ Engineering, Saint Anthony Falls Laboratory, University of Minnesota, Minneapolis, MN, USA Civil Engineering Department, Indian Institute of Technology Guwahati, Guwahati, India Huei‐Ping Huang School for Engineering of Matter, Transport, and Energy, Arizona State University, Tempe, AZ, USA Scott B. Jones Department of Plants, Soils and Climate, Utah State University, Logan, UT, USA xiv List of Contributors Nigel B. Kaye Sibel Pamukcu Glenn Department of Civil Engineering, Clemson University, Clemson, SC, USA Department of Civil and Environmental Engineering, Lehigh University, Bethlehem, PA, USA Abdul A. Khan Brad Putman Glenn Department of Civil Engineering, Clemson University, Clemson, SC, USA Glenn Department of Civil Engineering, Clemson University, Clemson, SC, USA Cheng Seong Khor Morteza Sadeghi Chemical Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, Perak Darul Ridzuan, Malaysia Department of Plants, Soils and Climate, Utah State University, Logan, UT, USA Lin Li Utah Water Research Laboratory, Utah State University, Logan, UT, USA Department of Civil and Environmental Engineering, Jackson State University, Jackson, MS, USA Stephanie Luster‐Teasley Department of Civil, Architectural, & Environmental Engineering, College of Engineering, North Carolina A&T State University, Greensboro, NC, USA Chandra Mouli R. Madhuranthakam Chemical Engineering Department, Abu Dhabi University, Abu Dhabi, UAE Will Martin General Engineering Department, Clemson University, Clemson, SC, USA Jana B. Milford Department of Mechanical Engineering and Environmental Engineering Program, University of Colorado, Boulder, CO, USA Shelly L. Miller Department of Mechanical Engineering, University of Colorado, Boulder, CO, USA Carl C. Nesbitt Department of Chemical Engineering, Michigan Technological University, Houghton, MI, USA Say Kee Ong Department of Civil, Construction, and Environmental Engineering, Iowa State University, Ames, IA, USA Judith L. Sims Kirsten M. Sims WesTech Engineering, Inc., Salt Lake City, UT, USA Milani Sumanasooriya Department of Civil & Environmental Engineering, Clarkson University, Potsdam, NY, USA Firat Y. Testik Civil and Environmental Engineering Department, University of Texas at San Antonio, San Antonio, TX, USA Louis Theodore Professor Emeritus, Manhattan College, New York, NY, USA Markus Tuller Department of Soil, Water and Environmental Science, The University of Arizona, Tucson, AZ, USA Daniel A. Vallero Department of Civil and Environmental Engineering, Duke University, Durham, NC, USA C. Visvanathan Environmental Engineering and Management Program, Asian Institute of Technology, Khlong Luang, Thailand Panshu Zhao Water Management and Hydrological Science Graduate Program and High Alpine and Arctic Research Program (HAARP), Texas A&M University, College Station, TX, USA xv Preface The discipline of environmental engineering deals with solutions to problems whose neglect would be harmful to society's well‐being. The discipline plays a vital role in a world where human activity has affected the Earth's climate, the levels of the seas, the air we breathe, and the cleanliness of water and soil. It is hardly a stretch, in my view, to assert that the work of environmental engineers can contribute to mitigating problems caused by extreme weather events; protecting populations in coastal areas; reducing illnesses caused by polluted air, soil, and water from improperly regulated industrial and transportation activities; and promoting the safety of the food supply. Environmental engineers do not need to rely on political stands on climate change or pollution sources for motivation. As perceptive theoreticians and practitioners, they need to merely observe where problems exist. Then they can use their knowledge and experience to analyze elements of problems, recommend solutions, and enable effective action. This environmental engineering handbook provides sources of information for students and practitioners interested in both fundamentals and real‐world applications of environmental engineering. The handbook is organized around the assertions highlighted above. The first major section is composed of six wide‐ranging chapters that cover methods for analyzing environmental systems and making measurements within those systems, legal issues that environmental engineers have to know about, methods for modeling the Earth's climate and analyzing impacts of climate change, and lastly ways designed to respond to rise in sea levels. The next three major sections address, in order, pollution in soils, with three chapters focusing on the physics of soils, remediation methods for polluted soils and sediments, and remote sensing techniques; water quality issues, with five chapters dealing with fundamentals of environmental fluid mechanics, water quality assessment, wastewater treatment, and design of porous pavement systems (which can mitigate flooding); air pollution issues, with three chapters covering air pollution control methods, measuring disbursement of aerosols into the atmosphere, and mitigating indoor air pollution; and finally, there is a chapter on noise pollution, another serious environmental problem. The handbook's final section is devoted to confronting issues of contaminants and waste. The six chapters in this section provide information crucial for disposing of, and where possible, recycling solid and hazardous wastes and for assessing pollution created by metals manufacturing and chemical processes and plants. Crucial to success of these solutions is not only the active involvement of industry but also the participation of academia and government. The handbook is written at a level that allows upper‐level students and practitioners and researchers, including environmental scientists and engineers, urban planners, government administrators, and environmental lawyers, to understand major environmental issues. My heartfelt thanks to the contributors to this handbook, all of them recognized experts in their fields. It's a miracle that contributors, with their taxing professional lives, are able to produce well‐written, cogently presented, and useful chapters. Contributors write, as one of them told me recently, because it is a good way to organize one's thoughts and because it is part of my duty as a scientist to publish my work so that others can learn from it. I spend valuable time writing because it allows me the opportunity to access a wide audience. It is an investment. The time I spend writing today is the time I don't have to spend educating someone 1 : 1 in the future. Or as another contributor noted, for a handbook of this kind, the deciding factor [of whether to contribute a chapter] is the desire of the author to share his/her expertise with others who have a more general or superficial interest in the chapter topic. I use handbooks of this kind if I have (or are part of a team that has) to solve a complex multi‐facetted problem and need to quickly come up to speed on parts of the solution that I am not familiar with. xvi Preface In keeping with this idea about handbook usage, this volume is replete with illustrations throughout the text and extensive lists of references at the end of chapters. Guides to sources of information on the Internet and in library stacks are provided by experts, thereby improving research results. A final word of thanks, to my wife, Arlene, whose very presence in my life makes my work all that much easier. April 2018 Delmar, NY 1 1 Environmental Systems Analysis Adisa Azapagic School of Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK 1.1 Introduction ●● ●● Throughout history, engineers were always expected to provide innovative solutions to various societal challenges, and these expectations continue to the present day. However, nowadays, we are facing some unprecedented challenges, such as climate change, growing energy demand, resource scarcity, and inadequate access to food and water, to name but a few. With a fast‐growing population, it is increasingly clear that the lifestyles of modern society cannot be sustained indefinitely. Growing scientific evidence shows that we are exceeding the Earth's capacity to provide many of the resources we use and to accommodate our emissions to the environment (IPCC, 2013; UNEP, 2012). Engineers have a significant role to play in addressing these sustainability challenges by helping meet human needs through provision of technologies, products, and services that are economically viable, environmentally benign, and socially beneficial (Azapagic and Perdan, 2014). However, one of the challenges is determining what technologies, products, and services are sustainable and which metrics to use to ascertain that. Environmental systems analysis (ESA) can be used for these purposes. ESA takes a systems approach to describe and evaluate the impacts of various human activities on the environment. A systems approach is essential for this as it enables consideration of the complex interrelationships among different elements of the system, recognizing that the behavior of the whole system is quite different from its individual elements when considered in isolation from each other. The "system" in this context can be a product, process, project, organization, or a whole country. Many methods are used in ESA, including: ●● ●● Energy and exergy analysis Material and substance flow analysis (SFA) ●● ●● ●● ●● ●● Environmental risk assessment (ERA) Environmental management systems (EMS) Environmental input–output analysis (EIOA) Life cycle assessment (LCA) Life cycle costing (LCC) Social life cycle assessment (S‐LCA) Cost–benefit analysis (CBA). These methods are discussed in the rest of this chapter. 1.2 Environmental Systems Analysis Methods In addition to the methodologies that underpin them, ESA methods differ in many other respects, including the focus, scope, application, and sustainability aspects considered. This is summarized in Table 1.1 and discussed in the sections that follow. 1.2.1 Energy and Exergy Analysis Energy analysis is used to quantify the total amount of energy used by a system and to determine its efficiency. It can also be used to identify energy "hot spots" and opportunities for improvements. Exergy analysis goes a step further, and, instead of focusing on the quantity, it measures the quality of energy or the maximum amount of work that can be theoretically obtained from a system as it comes into equilibrium with its environment. Exergy analysis can be used to determine the efficiency of resource utilization and how it can be improved. Although energy analysis has traditionally focused on production processes, it is also used in other applications, including energy analysis at the sectorial and national levels. However, the usefulness of exergy analysis is questionable for non‐energy systems. Furthermore, Handbook of Environmental Engineering, First Edition. Edited by Myer Kutz. © 2018 John Wiley & Sons, Inc. Published 2018 by John Wiley & Sons, Inc. 2 1 Environmental Systems Analysis Table 1.1 An overview of methods used in environmental systems analysis. Method Focus Scope/system boundary Sustainability aspects Application Energy/exergy analysis Production processes, supply chains, regions, countries Production process, sectorial, regional, national Energy Process or project analysis, energy efficiency, identification of energy "hot spots" Material flow analysis Materials Regional, national, global Natural resources Environmental accounting, preservation of resources, policy Substance flow analysis Chemical substances Regional, national, global Environmental pollution Environmental accounting and protection, strategic management of chemicals, policy Environmental risk assessment Products, installations Product or installation, local, regional, national Environmental, health and safety Risk analysis, evaluation of risk mitigation measures, financial planning, regulation Environmental management systems Organizations Organization Environmental Environmental management Environmental input–output analysis Product groups, sectors, Sectors, supply chains, national economy national economy Environmental and economic Environmental accounting, policy Life cycle assessment Products, processes, services, activities Life cycle/supply chain Environmental Benchmarking, identification of opportunities for improvements, eco‐design, policy Life cycle costing Products, processes, services, activities Life cycle/supply chain Economic Benchmarking, identification of opportunities for improvements Social life cycle assessment Products, processes, services, activities Life cycle/supply chain Social Benchmarking, identification of opportunities for improvements, policy Cost–benefit analysis Projects, activities Project, activity Socioeconomic and environmental Appraisal of costs and benefits of different projects or activities many users find it difficult to estimate and interpret the meaning of exergy (Jeswani et al., 2010). 1.2.2 Material Flow Analysis MFA enables systematic accounting of the flows and stocks of different materials over a certain time period in a certain region (Brunner and Rechberger, 2004). The term "materials" is defined quite broadly, spanning single chemical elements, compounds, and produced goods. Examples of materials often studied through MFA include aluminum, steel, copper, and uranium. MFA is based on the mass balance principle, derived from the M Imports Mining M Production M law of mass conservation. This means that inputs and outputs of materials must be balanced, including any losses or stocks (i.e. accumulation). As indicated in Figure 1.1, MFA can include the entire life cycle of a material, including its mining, production use, and waste management. In addition to the material flows, MFA also considers material stocks, making it suitable for analysis of resource scarcity. Material flows are typically tracked over a number of years enabling evaluation of long‐term trends in the use of materials. MFA can also serve as a basis for quantifying the resource productivity of an economy, but it is not suitable for consideration of single production systems (Jeswani et al., 2010). Use M Recycling M Disposal M Exports M M Stock Stock System boundary Figure 1.1 Material flow analysis tracks flows of materials through an economy from "cradle to grave." (M – flows of material under consideration). 1.2 Environmental Systems Analysis Method UF6 198.8 Extraction Import U3O8 891 1450 U3O8 Ore Stock Conversion 10.7 Loss Environment 2131.6 UF6 Enrichment 303.9 UF6 Depleted uranium 1827.7 Stock Fuel fabrication 499.7 Electricity generation UO2 487.8 12 Loss 3 Loss Interim storage SF 487.8 Spent fuel Stock Environment System boundary (China) SFR Figure 1.2 Material flow analysis of uranium flows and stocks in China in tonnes per year. Source: Adapted from Yue et al. (2016). An example of MFA applied to uranium in China is given in Figure 1.2. As can be seen, the annual flows and stocks of uranium, which is used as a fuel in nuclear power plants, are tracked within the country along the whole fuel life cycle. This includes extraction of the ore, conversion and enrichment of uranium, fuel fabrication, and electricity generation. Thus, MFA helps to quantify the total consumption of uranium over time and stocks of depleted uranium that could be used for fuel reprocessing. It can also help with the projections of future demand and estimates of how much uranium can be supplied from indigenous reserves and how much needs to be imported. 1.2.3 Substance Flow Analysis SFA is a specific type of MFA, focusing on chemical substances or compounds. The main aim of most SFA studies is to provide information for strategic management of chemical substances at a regional or national level (van der Voet, 2002). SFA can be also applied to track environmental pollution over time in a certain region. The latter is illustrated in Figure 1.3, which shows emissions of the pollutant of interest from ifferent sources to air, water, and land in a defined d region. However, the distinction between MFA and SFA is often blurred, and s ometimes the two terms are used interchangeably. 1.2.4 Environmental Risk Assessment ERA is used to assess environmental risks posed to ecosystems, animals, and humans by chemicals, industrial installations, or human activities. The risks can be physical, biological, or chemical (Fairman et al., 1998). Many types of ERA are used, including pollution, natural disaster, and chemical risk assessment. The assessment covers emissions and related environmental impacts in the whole life cycle of a chemical or an installation. For chemicals, this includes their production, formulation, use, and end‐of‐life management. For industrial installations, construction, operation, and decommissioning must be considered. ERA aims to protect the atmosphere, aquatic, and soil organisms as well as mammals and birds further up in the food chain. It is used by industry not only to comply with regulations but also to improve product safety, financial planning, and evaluation of risk mitigation measures. Figure 1.3 Substance flow analysis tracks the flows of pollutants into, within and out of a region (S – flows of substance under consideration). Source: Adapted from Azapagic et al. (2007). S Source 1 Air S S Source 2 Imports S S S S Source 3 S S S Source … Water Land S System boundary S Exports 3 4 1 Environmental Systems Analysis Figure 1.4 Environmental risk assessment steps according to the EUSES. Source: Based on Lijzen and Rikken (2004). 1. Data evaluation 3. Effects assessment: a. Hazard identification b. Dose–response assessment 2. Exposure assessment 4. Risk characterization There are many methods and tools for carrying out an ERA. One such tool used in Europe is the European Union System for the Evaluation of Substances (EUSES) that enables rapid assessments of risks posed by chemical substances (EC, 2016). As indicated in Figure 1.4, EUSES comprises the following steps (Lijzen and Rikken, 2004): 1) Data collection and evaluation 2) Exposure assessment: estimation of the concentrations/doses to which the humans and the environment are exposed 3) Effects assessment comprising: a) Hazard identification: identification of the adverse effects that a substance has an inherent capacity to cause b) Dose–response assessment: estimation of the relationship between the level of exposure to a substance (dose, concentration) and the incidence and severity of an effect 4) Risk characterization: estimation of the incidence and severity of the adverse effects likely to occur in a human population or the environment due to actual or predicted exposure to a substance. EUSES is intended mainly for initial rather than comprehensive risk assessments. The EUSES software is available freely and can be downloaded from the European Commission's website (EC, 2016). In the United States, ERA is regulated by the US Environmental Protection Agency (EPA); for various methods, consult the EPA guidelines (EPA, 2017). For a review of other ERA methods, see Manuilova (2003). 1.2.5 Environmental Management Systems An EMS represents an integrated program for managing environmental impacts of an organization, with the ultimate aim of helping it improve the environmental performance. The most widely used EMS standard is ISO 14001 (ISO, 2015). This EMS follows the concept of plan–do–check–act, an iterative process aimed at achieving continual improvement. The main steps of the ISO 14001 EMS outlined in Figure 1.5 are: 1) 2) 3) 4) Planning Support and operation Performance evaluation Implementation. The EMS is set up and driven by the organization's leadership who are responsible for its implementation. The EMS must be congruent with and follow the organization's environmental policy. 1) Planning: In the planning step, the organization must determine the environmental aspects that are relevant to its activities, products, and services. The aspects include both those the organization can control and those that it can influence, and their associated environmental impacts, considering a life cycle perspective (ISO, 2015). Significant environmental impacts must be addressed through appropriate action, also ensuring compliance with legislation. 2) Support and operation: This step involves providing adequate resources for the implementation of the EMS and appropriate internal and external communication. The organization must also establish and control the processes needed to meet EMS requirements. Consistent with a life cycle perspective, this must cover all relevant life cycle stages, including procurement of materials and energy, production of product(s) or provision of services, transportation, use, end‐of‐life treatment, and final disposal of its product(s) or services. 3) Performance evaluation: This step involves monitoring, measurement, analysis, and evaluation of the environmental performance. This is typically carried out over the period of one year. 4) Implementation: The information obtained in the previous step is then used to identify and implement improvement opportunities across the organization's 1.2 Environmental Systems Analysis Method Plan Planning Act Improvement Leadership Support and operation Do Performance evaluation Check Figure 1.5 Main steps in the ISO 14001 environmental management system. Source: Based on ISO (2015). activities (Figure 1.5). This whole process is repeated iteratively, typically on an annual basis, helping toward continuous improvement of environmental performance. An alternative to the ISO 14001 is the Eco‐Management and Audit Scheme (EMAS) developed by the European Commission. For details, see EC (2013). 1.2.6 Environmental Input–Output Analysis EIOA represents an expansion of conventional input– output analysis (IOA). While the latter considers monetary flows within an economic system, EIOA combines environmental impacts with the conventional economic analysis carried out in IOA. Environmental impacts are considered either by adding environmental indicators to IOA or by replacing the monetary input– output matrices with those based on physical flows (Jeswani et al., 2010). Different environmental indicators can be considered in EIOA, including material and energy inputs as well as emissions to air and water, and waste. Social aspects, such as employment, can also be integrated into EIOA (Finnveden et al., 2003). EIOA is suitable for determining the environmental impacts of product groups, sectors, or national economies. While this can be useful for environmental accounting and at a policy level, EIOA has many limitations. First, the data are too aggregated to be useful at the level of specific supply chains, products, or activities. It also often assumes an identical production technology for imported and domestic products, that each sector produces a single product, and that a single technology is used in the production process (Jeswani et al., 2010). Furthermore, allocation of environmental impacts between different sectors, products, and services is proportional to the economic flows. 1.2.7 Life Cycle Assessment LCA applies life cycle thinking to quantify environmental sustainability of products, processes, or human activities on a life cycle basis. As shown in Figure 1.6, the following stages in the life cycle of a product or an activity can be considered in LCA: ●● ●● ●● ●● ●● ●● Extraction and processing of raw materials Manufacture Use, including any maintenance Re‐use and recycling Final disposal Transportation and distribution. LCA is a well‐established tool used by industry, researchers, and policy makers. Some of the applications of LCA include (Azapagic, 2011): ●● ●● ●● ●● ●● Measuring environmental sustainability Comparison of alternatives to identify environmentally sustainable options Identification of hot spots and improvement opportunities Product design and process optimization Product labeling. The LCA methodology is standardized by the ISO 14040/44 standards (ISO, 2006a, b) that define LCA as 5 6 1 Environmental Systems Analysis Environment Primary resources System boundary from "cradle to grave" System boundary from "cradle to gate" T Extraction Manufacture T Use T Reuse and/or recycle T Disposal Emissions and wastes Figure 1.6 The life cycle of a product or an activity from "cradle to gate" and "cradle to grave." Source: Based on Azapagic (2011). 1. Goal and scope definition 4. Interpretation Figure 1.7 LCA methodology according to ISO 14040 (ISO, 2006a). - Purpose of the study - System boundaries - Functional unit 2. Inventory analysis - System definition - Data collection - Estimation of environmental burdens Identification of significant issues Evaluation of results Conclusions 3. Impact assessment - Selection of impact categories - Estimation of impacts "…a compilation and evaluation of the inputs, outputs and the potential environmental impacts of a product throughout its life cycle." According to these standards, LCA comprises four phases (Figure 1.7): 1) 2) 3) 4) Goal and scope definition Inventory analysis Impact assessment Interpretation. 1) Goal and scope definition: An LCA starts with a goal and scope definition that includes definition of the purpose of the study, system boundaries, and the functional unit (unit of analysis). As indicated in Figure 1.6, the system boundary can be from "cradle to grave" or "cradle to gate." The former considers all stages in the life cycle from extraction of primary resources to end‐of‐life waste management. The "cradle‐to‐gate" study stops at the factory "gate" where the product of interest is manufactured, excluding its use and end‐of‐life waste management. Definition of the system boundary depends on the goal and scope of the study. For example, the goal of the study may be to identify the hot spots in the life cycle of a product or to select environmentally the most sustainable option among alternative products delivering the same function. 1.2 Environmental Systems Analysis Method Defining the function of the system is one of the most important elements of an LCA study as that determines the functional unit, or unit of analysis, to be used in the study. The functional unit represents a quantitative measure of the outputs that the system delivers (Azapagic, 2011). In comparative LCA studies it is essential that systems are compared on the basis of an equivalent function, i.e. the functional unit. For example, comparison of different types of drinks packaging should be based on their equivalent function that is to contain a certain amount of drink. The functional unit is then defined as "the quantity of packaging material necessary to contain a specified volume of a drink." 2) Inventory analysis: This phase involves detailed specification of the system under study and collection of data. The latter includes quantities of materials and energy used in the system and emissions to air, water, and land throughout the life cycle. These are known as environmental burdens. If the system has several functional outputs, e.g. produces several products, the environmental burdens must be allocated among them. Different methods are used for this purpose, including allocation on a mass and economic basis (ISO, 2006b). 3) Impact assessment: In this phase, the environmental impacts are translated into different environmental impacts. Example impacts considered in LCA include global warming, acidification, eutrophication, ozone layer depletion, human toxicity, and ecotoxicity. A number of life cycle impact assessment methods are available but the most widely used are CML 2 (Guinee et al., 2001) and Eco‐indicator 99 (Goedkoop and Spriensma, 2001). The former is based on a "midpoint" approach, linking the environmental burdens somewhere in between the point of their occurrence (e.g. emissions of CO2) and the ultimate damage caused (e.g. global warming). Ecoinvent 99 follows a damage‐oriented approach that considers the "endpoint" damage caused by environmental burdens to human health, ecosystem, and natural resources. An overview of the CML 2 and Eco‐indicator 99 methods can be found in Boxes 1.1 and 1.2. The ReCiPe method (Goedkoop et al., 2009) is gradually superseding CML 2 as its updated and broadened version. In addition to the midpoint approach, ReCiPe also enables calculation of endpoint impacts, thus combining the approaches in CML 2 and Eco‐indicator 99. 4) Interpretation: The final LCA phase involves evaluation of LCA findings, including identification of significant environmental impacts and hot spots that can then be targeted for system improvements or innovation. Sensitivity analysis is also carried out in this phase to help identify the effects that data gaps and uncertainties have on the results of the study. Further details on the LCA methodology can be found in the ISO 14040 and 14044 standards (ISO, 2006a, b). Numerous LCA databases and software packages are available, including CCaLC (2016) and Gemis (Öko Institute, 2016), which are freely available, and Ecoinvent (Ecoinvent Centre, 2016), Gabi (Thinkstep, 2016), and SimaPro (PRé Consultants, 2016), which are available at a cost. 1.2.8 Life Cycle Costing Like LCA, LCC also applies life cycle thinking, but, instead of environmental impacts, it estimates total costs of a product, process, or an activity over its life cycle. Thus, as indicated in Figure 1.8, LCC follows the usual life cycle stages considered in LCA. LCC can be used for benchmarking, ranking of different investment alternatives, or identification of opportunities for cost improvements. However, unlike LCA, LCC is yet to become a mainstream tool – while microeconomic costing is used routinely as a basis for investment decisions, estimations of costs on a life cycle basis, including costs to consumers and society, are still rare. Although there is no standardized LCC methodology, the code of practice developed by Swarr et al. (2011) and largely followed by practitioners is congruent with the ISO 14040 LCA methodology, involving definition of the goal and scope of the study, inventory analysis, impact assessment, and interpretation of results. Inventory data are similar to those used in LCA, but in addition they include costs and revenues associated with the inputs into and outputs from different activities in the life cycle (Figure 1.8). The comparable structure, data, system boundaries, and life cycle models provide the possibility of integrating LCA and LCC to assess simultaneously the economic and environmental sustainability of the system of interest and to identify any trade‐offs. This also enables estimations of the eco‐efficiency of products or processes by expressing environmental impacts per unit of life cycle cost or vice versa (Udo de Haes et al., 2004). 1.2.9 Social Life Cycle Assessment S‐LCA can be used to assess social and sociological aspects of products and supply chains, considering both their positive and negative impacts (UNEP and SETAC, 2009). There is no standardized methodology for S‐ LCA. In an attempt to ease implementation of S-LCA and make it congruent with LCA, UNEP and SETAC 7 8 1 Environmental Systems Analysis Box 1.1 CML 2 method: Definition of environmental impact categories (Azapagic, 2011) Abiotic resource depletion potential represents depletion of fossil fuels, metals, and minerals. The total impact is calculated as: ADP J ADPj B j kg Sb eq . j 1 where Bj is the quantity of abiotic resource j used and ADPj represents the abiotic depletion potential of that resource. This impact category is expressed in kg of antimony used, which is taken as the reference substance. Alternatively, kg oil eq. can be used instead for fossil resources. Impacts of land use are calculated by multiplying the area of land used (A) by its occupation time (t): ILU A t m 2 .yr Climate change represents the total global warming potential (GWP) of different greenhouse gases (GHG), such as carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), etc. GWP is calculated as the sum of GHG emissions multiplied by their respective GWP factors, GWPj: J GWP GWPj B j kg CO2 eq. j 1 where Bj represents the emission of GHG j. GWP factors for different GHGs are expressed relative to the GWP of CO2, which is defined as unity. The values of GWP depend on the time horizon over which the global warming effect is assessed. GWP factors for shorter times (20 and 50 years) provide an indication of the short‐term effects of GHG on the climate, while GWP for longer periods (100 and 500 years) are used to predict the cumulative effects of these gases on the global climate. Stratospheric ozone depletion potential (ODP) indicates the potential of emissions of chlorofluorohydrocarbons (CFCs) and other halogenated hydrocarbons to deplete the ozone layer and is expressed as: ODP J ODPj B j kg CFC -11 eq. j 1 where Bj is the emission of ozone depleting gas j. The ODP factors are expressed relative to the ozone depletion potential of CFC‐11. Human toxicity potential (HTP) is calculated by taking into account releases toxic to humans to three different media, i.e. air, water, and soil: HTP J HTPjA B jA j 1 J HTPjW B jW j 1 J HTPjS B jS kg 1,4 - DB eq. j 1 where HTPjA, HTPjW, and HTPjS are toxicological classification factors for substances emitted to air, water, and soil, Source: Reproduced with permission of John Wiley & Sons. respectively, and BjA, BjW, and BjS represent the respective emissions of different toxic substances into the three environmental media. The reference substance for this impact category is 1,4‐dichlorobenzene. Ecotoxicity potential (ETP) is also calculated for all three environmental media and comprises five indicators ETPn: J ETPn I j i 1 ETPi , j Bi , j kg 1,4 - DB eq. where n (n = 1–5) represents freshwater and marine aquatic toxicity, freshwater and marine sediment toxicity, and terrestrial ecotoxicity, respectively. ETPi,j represents the ecotoxicity classification factor for toxic substance j in the compartment i (air, water, soil), and Bi,j is the emission of substance j to compartment i. ETP is based on the maximum tolerable concentrations of different toxic substances in the environment by different organisms. The reference substance for this impact category is also 1,4‐dichlorobenzene. Photochemical oxidants creation potential (POCP) is related to the potential of volatile organic compounds (VOCs) and nitrogen oxides (NOx) to generate photochemical or summer smog. It is usually expressed relative to the POCP of ethylene and can be calculated as: J POCP POCPj B j kg ethylene eq. j 1 where Bj is the emission of species j participating in the formation of summer smog and POCPj is its classification factor for photochemical oxidation formation. Acidification potential (AP) is based on the contribution of sulfur dioxide (SO2), NOx and ammonia (NH3) to the potential acid deposition. AP is calculated according to the equation: AP J APj B j kg SO2 eq. j 1 where APj represents the AP of gas j expressed relative to the AP of SO2 and Bj is its emission in kg. Eutrophication potential (EP) is defined as the potential of nutrients to cause over‐fertilization of water and soil, which can result in increased growth of biomass (algae). It is calculated as: EP J EPj B j kg PO 4 3 eq. j 1 where Bj is an emission of species such as N, NOx, NH4+, PO43−, P, and chemical oxygen demand (COD); EPj represent their respective EPs. EP is expressed relative to PO43−. See Guinée et al. (2001) for a full description of the methodology. 1.2 Environmental Systems Analysis Method Box 1.2 Eco‐indicator 99: Definition of the damage (endpoint) categories (Azapagic, 2011) 1. Damage to Human Health ●● This damage category comprises the following indicators: ●● ●● ●● ●● ●● Carcinogenesis Respiratory effects Ionizing radiation Ozone layer depletion Climate change. ●● They are all expressed in disability‐adjusted life years (DALYs) and calculated by carrying out: 1) Fate analysis, to link an emission (expressed in kg) to a temporary change in concentration 2) Exposure analysis, to link the temporary concentration change to a dose 3) Effect analysis, to link the dose to a number of health effects, such as occurrence and type of cancers 4) Damage analysis, to link health effects to DALYs, using the estimates of the number of years lived disabled (YLD) and years of life lost (YLL). For example, if a cancer causes a 10‐year premature death, this is counted as 10 YLL and expressed as 10 DALYs. Similarly, hospital treatment due to air pollution has a value of 0.392 DALYs/year; if the treatment lasted 3 days (or 0.008 years), then the health damage is equal to 0.003 DALYs. 2. Damage to Ecosystem Quality The indicators within this damage category are expressed in terms of potentially disappeared fraction (PDF) of plant species due to the environmental load in a certain area over certain time. Therefore, damage to ecosystem quality is expressed as PDF.m2.year. The following indicators are considered: ●● Ecotoxicity is expressed as the percentage of all species present in the environment living under toxic stress (potentially affected fraction [PAF]). As this is not an observable damage, a rather crude conversion factor is used to translate toxic stress into real observable damage, i.e. convert PAF into PDF. Acidification and eutrophication are treated as one single impact category. Damage to target species (vascular plants) in natural areas is modeled. The model used is for the Netherlands only, and it is not suitable to model phosphates. Land use and land transformation are based on empirical data of occurrence of vascular plants as a function of land use types and area size. Both local damages in the area occupied or transformed and regional damage to ecosystems are taken into account. For ecosystem quality, two different approaches are used: 1) Toxic, acid, and the emissions of nutrients go through the following three steps: a) Fate analysis, linking the emissions to concentrations. b) Effect analysis, linking concentrations to toxic stress or increased nutrient or acidity levels. c) Damage analysis, linking these effects with the PDF of plant species. 2) Land use and transformation are modeled on the basis of empirical data on the quality of ecosystems, as a function of the type of land use and area size. 3. Damage to Resources Two indicators are included here: depletion of minerals and fossil fuels. They are expressed as additional energy in MJ that will be needed for extraction in the future due to a decreasing amount of minerals and fuels. Geostatical models are used to relate availability of a mineral resource to its remaining amount or concentration. For fossil fuels, the additional energy is based on the future use of oil shale and tar sands. Resource extraction is modeled in two steps: 1) Resource analysis, which is similar to fate analysis, as it links an extraction of a resource to a decrease in its concentrations (through geostatical models) 2) Damage analysis, linking decreased concentrations of resources to the increased effort for their extraction in the future. More detail on Eco‐indicator 99 can be found in Goedkoop and Spriensma (2001). Source: Reproduced with permission of John Wiley & Sons. (2009) have developed an S-LCA method that follows the ISO 14040 structure. Therefore, according to this method, S‐LCA involves the same methodological steps as LCA: goal and scope definition, inventory, impact assessment, and interpretation. However, while the impacts in LCA represent quantitative indicators, S‐ LCA also includes qualitative indicators. In total, there are 194 social indicators, grouped around five groups of stakeholder: workers, consumers, local community, society, and value chain actors. The main impact categories applicable to different stakeholders are listed in Table 1.2, with each impact category comprising a number of social indicators; for the details of the latter, see UNEP and SETAC (2009). 9 10 1 Environmental Systems Analysis Primary resources Costs Extraction Revenue Costs Costs Revenue Manufacture Costs Costs Revenue Revenue Use Costs Costs Revenue Revenue Emissions and wastes Figure 1.8 Life cycle costing estimates total costs in the life cycle of a product or an activity. Table 1.2 The UNEP–SETAC framework for social impact categories (UNEP and SETAC, 2009). Stakeholder group Impact category Workers Freedom of association and collective bargaining Child labor Fair salary Working hours Forced labor Equal opportunities/discrimination Health and safety Social benefits/social security Feedback mechanism Consumers Consumer privacy Transparency End‐of‐life responsibility Access to material resources Access to immaterial resources Delocalization and migration Cultural heritage Local community Safe and healthy living conditions Respect of indigenous rights Community engagement Local employment Secure living conditions Society Public commitments to sustainability issues Contribution to economic development Prevention and mitigations of armed conflicts Technology development Corruption Value chain actors Fair competition Promoting social responsibility Supplier relationships Respect of intellectual property rights End of life Revenue Reference As can be inferred from Table 1.2, a significant proportion of the indicators are qualitative and could be highly subjective; hence, their assessment poses a challenge. Another challenge associated with S‐LCA is data availability and reliability, particularly for complex supply chains. Furthermore, geographic location of different parts of the supply chain of interest is fundamental for the assessment of social impacts, requiring specific data as generic data may be a poor substitute (Jeswani et al., 2010). However, collecting site‐specific data is resource demanding and may hinder a wider adoption of the method. 1.2.10 Cost–Benefit Analysis CBA is used widely for assessing costs and benefits of a project or an activity and to guide investment decisions. In ESA it is used for weighing environmental and socioeconomic costs and benefits of different alternatives (Jeswani et al., 2010). CBA is based on the idea of maximum net gain – it reduces aggregate social welfare to the monetary unit of net economic benefit. So, for example, given several alternatives, the CBA approach would favor the one in which the difference between benefits and costs is the greatest. CBA has some similarities with LCC when applied to products (Finnveden and Moberg, 2005). The most widely applied CBA technique in ESA is contingent valuation (CV). In CV, participants are asked to say how much they would be prepared to pay to protect an environmental asset. This is known as the "willingness to pay" approach. Alternatively, participants can be asked how much they would be willing to accept for loss of that asset, which is known as the "willingness to accept" method. One of the advantages of CBA is that it presents the results as a single criterion – money – that can be easily communicated (Jeswani et al., 2010). However, measuring the expected benefits, or placing monetary value on the benefits in a simplistic way is often problematic (Ness et al., 2007). In particular, the results of the analysis largely depend on the way the questions are asked and whether the participants are familiar with the environmental asset in question. It is more likely that people who know nothing about the asset will place a nil value on it, although the life of others may depend on it. Furthermore, the value that people place on the environment strongly depends on their individual preferences and self‐interest that does not serve as a firm foundation for environmental decision‐making. 1.3 Summary This chapter has presented and discussed various methods used in ESA. Broadly, they can be divided into those that take a life cycle approach and those that are more narrow in their perspective. They can also be distinguished by their focus and application, with some tools being applicable to individual products, technologies or organizations, and others to regional or national-level analyses. A further distinguishing feature is the sustainability aspect they consider: environmental, economic, and social, or their combination. Which method is used in the end will depend on the specific decision‐ making context and on the question(s) being asked by those carrying out the analysis. Nevertheless, the general trend in legislation and engineering practice is toward application of life cycle methods that integrate all three aspects of sustainability – the environment, economy, and s ociety – in an attempt to balance them and drive sustainable development. Different approaches can be used to help integrate environmental, economic, and social indicators used in different ESA methods. One of the probably most useful approaches is multi‐ criteria decision analysis (MCDA). In MCDA, relevant stakeholders are asked to state their preferences for different sustainability aspects that are then used to aggregate the considered sustainability indicators into an overall sustainability score, allowing easy comparisons of alternative products, technologies, etc. For further details on MCDA used in ESA, see Azapagic and Perdan (2005a, b). References Azapagic, A. (2011). Assessing environmental sustainability: Life cycle thinking and life cycle assessment. In: Sustainable Development in Practice: Case Studies for Engineers and Scientists (eds. A. Azapagic and S. Perdan), Chapter 3. Chichester: Wiley. Azapagic, A. and Perdan, S. (2005a). An integrated sustainability decision‐support framework: problem structuring, part I. International Journal of Sustainable Development & World Ecology 12 (2): 98–111. Azapagic, A. and Perdan, S. (2005b). An integrated sustainability decision‐support framework: methods and tools for problem analysis, part II. 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Substance flow analysis methodology. In: A Handbook of Industrial Ecology (ed. R. Ayres and L. Ayres). Northampton, MA: Edward Elgar Publishing. Yue, Q., He, J., Stamford, L., and Azapagic, A. (2016). Nuclear power in China: an analysis of the current and near‐future uranium flows. Energy Technology 5 (5): 681–691. 13 2 Measurements in Environmental Engineering Daniel A. Vallero Department of Civil and Environmental Engineering, Duke University, Durham, NC, USA Summary The environment consists of very complex systems ranging in scale from the cell to the planet. These systems are comprised of matrices of nonliving (i.e. abiotic) and living (biotic) components. To determine the condition of such systems calls for various means of measurement. Many of these measurements direct physical and chemical measurements, e.g. temperature, density, and pH of soil and water. Others are indirect, such as light scattering as indication of the number of aerosols in the atmosphere. This chapter provides an overview of some of the most important measurement methods in use today. In addition, the chapter introduces some of the techniques available for sampling, analysis, and extrapolation and interpolation of measured results, using various types of models. 2.1 Introduction According to their codes of ethics and practice, engineers must hold paramount the public's safety, health, and welfare (National Society of Professional Engineers, 2016). Engineers apply the sciences to address societal needs. Environmental engineers are particularly interested in protecting public health and ecosystem conditions. Myriad human activities, such as energy generation and transmission, transportation, food production, and housing, generate wastes and pollute environmental media, i.e. air, water, soil, sediment, and biota. Environmental engineers must find ways to reduce or eliminate risks posed by these wastes. The first step in assessing and managing risks is to determine the condition of the environment, which includes estimates of the amount of contaminants in each environmental medium (Vallero, 2015). Such estimates must be based on with reliable data and information, beginning by characterizing the release of a substance, e.g. an emission from a stack, the substance's movement and transformation in the environment, and its concentrations near or within an organism, i.e. the receptor (see Figure 2.1). Environmental measurement is an encompassing term, which includes developing methods, applying those methods, deploying monitoring technologies, and interpreting the results from these technologies. An environmental assessment can address chemical, physical, and biological factors (U.S. Environmental Protection Agency, 2015c). This article addresses measurements of concentrations of substances in the environment. Such measurements are one part of health, exposure, and risk assessments, but not everything that needs to be measured for such assessments. For example, exposure assessments require information about the pollutant concentrations in the locations where specific human activities occur. A measurement of a pollutant at a central monitoring site, therefore, is not an exposure measurement, since it does not reflect the concentration where the activity takes place. A personal monitor worn during a day would be a more precise and accurate measurement of exposure for that particular day if it were matched with the person's activities, e.g. using a diary. Measurements may be direct or indirect. Direct measurements are those in which the substance of concern is what is actually collected and analyzed. For example, a measurement of particulate matter (PM) would be directly measured by pumping air through a PM monitor and collecting particles on a filter. The particles would then be measured, e.g. weighed, sized, and chemically analyzed. Direct measurements can be in situ, i.e. taken in the environment, or ex situ, collected and taken elsewhere for measurement.1 An indirect PM measurement is one where the substance itself is not collected, but would be characterized by an indicator, e.g. light scattering in a nephelometer Handbook of Environmental Engineering, First Edition. Edited by Myer Kutz. © 2018 John Wiley & Sons, Inc. Published 2018 by John Wiley & Sons, Inc. 14 2 Measurements in Environmental Engineering Figure 2.1 Sites of environmental measurements. Source: Letcher and Vallero (2011). Reproduced with permission of Elsevier. Transport Transport Source Ecosystem receptors Response Release Transport Transport Response Human receptors (Vallero, 2014; Whitby and Willeke, 1979). The amount and type of scattering would indicate the quantity and size of particles. Remote sensing of pollutants relies on indirect measurements, e.g. using a laser to backscatter specific electromagnetic wavelengths is used to characterize aerosols in the atmosphere, including particles in the stratosphere. The principal method for aerosol profiling is light detection and ranging, i.e. LIDAR, which uses a pulsed laser with a system to detect the backscattered radiation (De Tomasi and Perrone, 2014). The monitoring underpinning the assessment is dependent upon the quality of sample collection, preparation, and analysis. Sampling is a statistical term, and usually a geostatistical term. An environmental sample is a fraction of air, water, soil, biota, or other environmental media (e.g. paint chips, food, etc. for indoor monitoring) that represents a larger population or body. For example, a sample of air may consist of a canister or bag that holds a defined quantity or air that will be subsequently analyzed. The sample is representative of a portion of an air mass. The number of samples must be collected and results aggregated to ascertain with defined certainty the quality of an air mass. More samples will be needed for a large urban air shed than for that of a small town. Intensive sampling is often needed for highly toxic contaminants and for sites that may be particularly critical, e.g. near a hazardous waste site or in an "at risk" neighborhood (such as one near a manufacturing facility that uses large quantities of potentially toxic materials). Similar to other statistical measures, environmental samples allow for statistical inference. In case, inferences are made regarding the condition of an ecosystem and the extent and severity of exposure of a human population. For example, to estimate the amount of a chemical compound in a lake near a chemical plant, an engineer gathers a 500 ml sample in the middle of the lake that contains 1 million liters of water. Thus, the sample represents only 5 × 10−7 of the lake's water. This is known as a "grab" sample, i.e. a single sample taken to represent an entire system. Such a sample is limited in location vertically and horizontally, so there is much uncertainty. However, if 10 samples are taken at 10 spatially distributed sites, the inferences are improved. Furthermore, if the samples were taken in each season, then there would be some improvement to understanding of intra‐annual variability. If the sampling is continued for several years, the inter‐annual variability is better characterized. Indeed, this approach can be used in media other than water, e.g. soil, sediment, and air. 2.1.1 Data Quality Objectives A monitoring plan must be in place before samples are collected and arrive at the laboratory. The plan includes quality assurance (QA) provisions and describes the procedures to be employed. These procedures must be strictly followed to investigate environmental conditions. The plan describes in detail the sampling apparatus (e.g. real‐time probes, sample bags, bottles, and soil cores), the number of samples needed, and the sample handling and transportation. The quality and quantity of samples are determined by data quality objectives (DQOs), which are defined by the objectives of the overall contaminant assessment plan. DQOs are qualitative 2.1 Introductio and quantitative statements that translate nontechnical project goals into scientific and engineering outputs need to answer technical questions (U.S. Environmental Protection Agency, 2006). Quantitative DQOs specify a required level of scientific and data certainty, while qualitative DQOs express decisions goals without specifying those goals in a quantitative manner. Even when expressed in technical terms, DQOs must specify the decision that the data will ultimately support, but not the manner that the data will be collected. DQOs guide the determination of the data quality that is needed in both the sampling and analytical efforts. The U.S. Environmental Protection Agency has listed three examples of the range of detail of quantitative and qualitative DQOs (Crumbling, 2001; U.S. Environmental Protection Agency, 1994): Thus, if the condition in question is tightly defined, e.g. the seasonal change in pH near a fish hatchery, a small number of samples using simple pH probes would be defined as the DQO. Conversely, if the environmental assessment is more complex and larger in scale, e.g. the characterization of year‐round water quality for trout in the stream, the sampling plan's DQO may dictate that numerous samples at various points be continuously sampled for inorganic and organic contaminants, turbidity, nutrients, and ionic strength. This is even more complicated for biotic systems, which may also require microbiological monitoring. The sampling plan must include all environmental media, e.g. soil, air, water, and biota, which are needed to characterize the exposure and risk of any biotechnological operation. The sampling and analysis plan should explicitly point out which methods will be used. For example, if toxic chemicals are being monitored, the US EPA specifies specific sampling and analysis methods (U.S. Environmental Protection Agency, 1999, 2007). The geographic area where data are to be collected is defined by distinctive physical features such as volume or area, e.g. metropolitan city limits, the soil within the property boundaries down to a depth of 6 cm, a specific water body, length along a shoreline, or the natural habitat range of a particular animal species. Care should be taken to define boundaries. For example, Figure 2.2 shows a sampling grid, with a sample taken from each cell in the grid (U.S. Environmental Protection Agency, 1) Example of a less detailed, quantitative DQO: Determine with greater than 95% confidence that contaminated surface soil will not pose a human exposure hazard. 2) Example of a more detailed, quantitative DQO: Determine to a 90% degree of statistical certainty whether or not the concentration of mercury in each bin of soil is less than 96 ppm 3) Example of a detailed, qualitative DQO: Determine the proper disposition of each bin of soil in real‐time using a dynamic work plan and a field method able to turnaround lead (Pb) results on the soil samples within 2 h of sample collection. Bldg F Bldg H Bldg G Bldg A Bldg B Road D Road C rA Rive Road A Bldg E Bldg D Road B Bldg C Figure 2.2 Environmental assessment area delineated by map boundaries. Source: U.S. Environmental Protection Agency (2002). 15 16 2 Measurements in Environmental Engineering 2002). The target population may be divided into relatively homogeneous subpopulations within each area or subunit. This can reduce the number of samples needed to meet the tolerable limits on decision errors and to improve efficiency. Time is another essential parameter that determines the type and extent of monitoring needed. Conditions vary over the course of a study due to changes in weather conditions, seasons, operation of equipment, and human activities. These include seasonal changes in groundwater levels, seasonal differences in farming practices, daily or hourly changes in airborne contaminant levels, and intermittent pollutant discharges from industrial sources. Such variations must be considered during data collection and in the interpretation of results. Some examples of environmental time sensitivity are: ●● ●● ●● ●● ●● Concentrations of lead in dust on windowsills may show higher concentrations during the summer when windows are raised and paint/dust accumulates on the windowsill. Terrestrial background radiation levels may change due to shielding effects related to soil dampness. Amount of pesticides on surfaces may show greater variations in the summer because of higher temperatures and volatilization. Instruments that may not give accurate measurements when temperatures are colder. Airborne PM measurements that may not be accurate if the sampling is conducted in the wetter winter months rather than the drier summer months. Feasibility should also be considered. This includes gaining legal and physical access to the properties, equipment acquisition and operation, environmental conditions, and times and conditions when sampling is prohibited (e.g. freezing temperatures, high humidity, and noise). 2.1.2 Monitoring Plan Example Consider a plan to measure mobile source air toxic (MSAT) concentrations and variations in concentrations as a function of distance from the highway and to establish relationships between MSAT concentrations as related to highway traffic flows including traffic count, vehicle types and speeds, and meteorological conditions such as wind speed and wind direction. Specifically, the monitoring plan has the following goals (Kimbrough et al., 2008): 1) Identify the existence and extent of elevated air pollutants near roads. 2) Determine how vehicle operations and local meteorology influence near road air quality for criteria and toxic air pollutants. 3) Collect data that will be useful in ground truthing, evaluating, and refining models to determine the emissions and dispersion of motor vehicle‐related pollutants near roadways. A complex monitoring effort requires management and technical staff with a diversity of skills that can be brought to bear on the implementation of this project. This diverse skill set includes program management, contracts administration, field monitoring experience, laboratory expertise, and QA oversight. The purpose of any site selection process is to gather and analyze sufficient data that would lead one to draw informed conclusions regarding the selection of the most appropriate site for the monitoring at a specific location. Moreover, the site selection process needs to include programmatic issues to ensure an informed decision is reached. 2.1.3 Selection of a Monitoring Site Selecting a monitoring site must be based on scientific and feasibility factors, as shown in Table 2.1 and Figure 2.3. Each step has varying degrees of complexity due to "real‐world" issues. The first step was to determine site selection criteria (see Table 2.2). The follow‐on steps include (ii) develop list of candidate sites and supporting information, (iii) apply site selection filter ("coarse" and "fine"), (iv) site visit, (v) select candidate site(s) via team discussion, (vi) obtain site access permission(s), and (vii) implement site logistics. A list of candidate sites based on these criteria can then be developed. Geographic information system (GIS) data, tools and techniques, and on‐site visits would be used to compare various sites that meet these criteria. Quite commonly, even a well‐designed environmental monitoring plan will need to be adjusted during the implementation phase. For example, investigators may discover barriers or differing conditions from what was observed in the planning phase (e.g. different daily traffic counts or new road construction). After applying site selection criteria as a set of "filters," candidate sites are incrementally eliminated. For example, the first filter would be sites with low traffic counts; the next filter, the presence of extensive sound barriers, eliminates additional sites; and other filters, e.g. complex geometric design or lack of available traffic volume data, eliminates additional sites. Next, feasibility considerations would eliminate additional candidate sites. An important component of "ground truthing" or site visit is to obtain information from local sources. Local businesses and residents can provide important information needed in a decision process, such as types of 2.1 Introductio Table 2.1 Example of steps in selecting an air quality monitoring site. Step Site selection steps Method Comment 1 Determine site selection criteria Monitoring protocol 2 Develop list of candidate sites Geographic information system (GIS) data; on‐site visit(s) Additional sites added as information is developed 3 Apply coarse site selection filter Team discussions, management input Eliminate sites below acceptable minimums 4 Site visit Field trip 5 Select candidate site(s) Team discussions, management input Application of fine site selection filter 6 Obtain site access permissions Contact property owners 7 Site logistics (i.e. physical access, utilities – electrical and communications) Site visit(s), contact utility companies If property owners do not grant permission, then the site is dropped from further consideration Source: Kimbrough et al. (2008). Reproduced with permission of Elsevier. FHWA technical staff Develop information about sites No EPA technical staff Develop table of candidate sites Does site fit criteria? (Y or N) No further discussions needed No Yes Advantages/ limitations of sites that meet criteria FHWA management and technical staff input Select most suitable site(s) Obtain site access permissions Will property owners grant site access permissions? (Y or N) Yes Finalize site selection Implement project EPA management and technical staff input Figure 2.3 Monitoring location selection decision flowchart. Source: Kimbrough et al. (2008). Reproduced with permission of Elsevier. chemicals stored previously at a site, changes in vegetation, or even ownership histories. Spatial tools are an important part of the environmental engineer's daily work. They are very useful in making and explaining environmental decisions (Malczewski, 1999; Sumathi et al., 2008). Until recently, the use of GIS and other spatial tools in decision processes have required the acquisition of large amounts of the data. In addition, the software has not been user‐friendly. GIS data have now become more readily available in both quantity and quality, and GIS exists in common operating system environments. Indeed, environmental regulatory agencies increasingly use data layers to assess and describe environmental conditions. For example, the US EPA has developed the EnviroAtlas, a system of interactive tools to support and to document "ecosystem goods and 17 18 2 Measurements in Environmental Engineering Table 2.2 Example selection considerations and criteria. Selection considerations Monitoring protocol criteria Essential criteria for this monitoring study AADT (>150 000) Only sites with more than 150 000 annual average daily traffic (AADT) are considered as candidates Geometric design The geometric design of the facility, including the layout of ramps, interchanges, and similar facilities, will be taken into account. Where geometric design impedes effective data collection on MSATs and PM2.5, those sites will be excluded from further consideration. All sites have a "clean" geometric design Topology (i.e. sound barriers, road elevation) Sites located in terrain making measurement of MSAT concentrations difficult or that raise questions of interpretation of any results will not be considered. For example, sharply sloping terrain away from a roadway could result in under representation of MSAT and PM2.5 concentration levels on monitors in close proximity to the roadway simply because the plume misses the monitor as it disperses Geographic location Criteria applicable to representing geographic diversity within the United States as opposed to within any given city Availability of data (traffic volume data) Any location where data, including automated traffic monitoring data, meteorological, or MSAT concentration data, is not readily available or instrumentation cannot be brought in to collect such data will not be considered for inclusion in the study Meteorology Sites will be selected based on their local climates to assess the impact of climate on dispersion of emissions and atmospheric processes that affect chemical reactions and phase changes in the ambient air Desirable, but not essential criteria Downwind sampling Any location where proper siting of downwind sampling sites is restricted due to topology, existing structures, meteorology, etc. may exclude otherwise suitable sites for consideration and inclusion in this study Potentially confounding air pollutant sources The presence of confounding emission sources may exclude otherwise suitable sites for consideration and inclusion in this study Site access (admin/ physical) Any location where site access is restricted or prohibited either due to administrative or physical issues, will not be considered for inclusion in the study Source: Kimbrough et al. (2008). Reproduced with permission of Elsevier. s ervices," i.e. ecological benefits to humans from nature, including food supply, water supply, flood c ontrol, security, public health, and economy (U.S. Environmental Protection Agency, 2015a). Table 2.3 shows some of the map layers that underpin the EnviroAtlas (U.S. Environmental Protection Agency, 2015b). The GIS data layers that are commonly needed in environmental engineering include the location of suitable soils, wells, surface water sources, residential areas, schools, airports, roads, etc. From these data, layers queries are formulated to provide the most suitable sites (e.g. depth to water table may help identify sources of pollution). Typically, quantitative weighting criteria are associated with the siting criteria as well as elements of the data layers, e.g. certain types of soils would be more suitable than others and thus would have applicable quantitative values (Environmental Systems Research Institute, 1995). 2.2 Environmental Sampling Approaches Engineers use various methods of collecting environmental samples. As mentioned, the grab sample is simply a measurement at a site at a single point in time. Composite sampling physically combines and mixes multiple grab samples (from different locations or times) to allow for physical, instead of mathematical, averaging. The acceptable composite provides a single value of contaminant concentration measurement that can be used in statistical calculations. Multiple composite samples can provide improved sampling precision and reduce the total number of analyses required compared to non‐composite sampling (U.S. Environmental Protection Agency, 2015d), e.g. "grab" or integrated soil sample of x mass or y volume, the number of samples needed (e.g. for statistical significance) the minimum acceptable quality as defined by the QA plan and sampling standard operating procedures (SOPs), and sample handling after collection. A weakness of composite sampling is the false negative effect. Consider, for example, samples collected from an evenly distributed grid of homes to represent a neighbor exposure to a contaminant, as shown in Figure 2.4. The assessment found the values of 3, 1, 2, 12, and 2 mg l−1, and the mean contamination concentration is only 4 mg l−1. If cleanup is needed above the threshold of 5 mg l−1, the mean concentration would indicate the area does not need remediation and would be reported below the threshold level. However, the fourth home is well Table 2.3 National scale map layers used in the EnviroAtlas (page 1 of 20 pages). Metadata link Biodiversity conservation Clean air Clean and plentiful water Climate stabilization Food, fuel, and materials Map layer title Description Acres of crops that have no nearby pollinator habitat This map layer depicts the total acres of agricultural crops within a subwatershed (12‐digit HUC) that require or would benefit from the presence of pollinators, but are without any nearby supporting habitat Meta data Agricultural water use (million gal d−1) This map estimates the millions of gallons of water used daily for agricultural irrigation for each subwatershed (HUC‐12) in the contiguous United States. Estimates include self‐supplied surface and groundwater, as well as water supplied by irrigation water providers, which may include governments, companies, or other organizations Meta data Area of solar energy (km2) This map estimates the square kilometers of area within each subwatershed (12‐digit HUC) that offers the potential for harvesting solar energy. This map does not take into account land use or ownership Meta data ♦ Average annual daily potential (kWh m−2 d−1) This map estimates the average daily potential kilowatt hours of solar energy that could be harvested per square meter within each subwatershed (12‐digit HUC). This calculation is based on environmental factors and does not take into account land ownership or viability of installing solar harvesting systems Meta data ♦ Average annual precipitation (in. yr−1) This map estimates the average number of inches of precipitation that fall within a subwatershed (12‐digit HUC) each year. Precipitation includes snow and rain accumulation Meta data Carbon storage by tree biomass (kg m−2) This map estimates the kilograms of dry carbon stored per square meter of above ground biomass of trees and forests in each subwatershed (12‐digit HUC) Meta data ♦ Carbon storage by tree root biomass (kg m−2) This map estimates the kilograms of dry carbon stored per square meter in below ground biomass in each subwatershed (12‐digit HUC). Biomass below ground includes tree root biomass and soils Meta data ♦ Cotton yields (thousand tons yr−1) This map depicts the thousands of tons of cotton that are grown annually within each subwatershed (12‐digit HUC) Meta data Cultivated biological nitrogen fixation (kg N ha−1 yr−1) This map depicts the mean rate of biological nitrogen fixation from the cultivation of crops within each subwatershed (12‐digit HUC) in kg N ha−1 yr−1 Meta data Domestic water use (million gal d−1) This map estimates the millions of gallons of water used daily for domestic purposes in each subwatershed (HUC‐12). For the purposes of this map, domestic or residential water use includes all indoor and outdoor uses, such as for drinking, bathing, cleaning, landscaping, and pools for primary residences Meta data Natural hazard mitigation Recreation, culture, and aesthetics ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ 20 2 Measurements in Environmental Engineering Home 2 Home 1 Home 3 Home 4 Home 5 Soil sampling location Figure 2.4 Hypothetical composite sampling grid for a neighborhood. Source: Vallero (2015). Reproduced with permission of Elsevier. above the safety level. This could also have a false positive effect. For example if the mean concentration were 6 mg l−1 in the example, the whole neighborhood may not need cleanup if the source is isolated to a confined area in the yard of home 5. Another example of where geographic composites may not be representative is in cleaning up and monitoring the success of cleanup actions. For example, if a grid is laid out over a contaminated groundwater plume (Figure 2.5), it may not take into account horizontal and vertical impervious layers, unknown sources (e.g. tanks), and flow differences among strata, so that some of the plume is eliminated but pockets are left (as shown in Figure 2.5b). Soil is highly heterogeneous in its texture, chemical composition, moisture content, organic matter content, sorption coefficients, and other physical and biological characteristics (Vallero, 2000; Vallero and Peirce, 2002; Wang et al., 2015). Thus, it is often good practice to assume that a contaminated site will have a heterogeneous (a) Extraction well 2D extent of contamination of vadose zone Radius of influence of well (b) Areas missed due to impervious zones Figure 2.5 Extraction well locations on a geometric grid, showing hypothetical cleanup after 6 months (a) Before treatment. (b) After treatment. Source: Vallero (2015). Reproduced with permission of Elsevier. 2.2 Environmental Sampling Approache distribution of contamination. Selecting appropriate sampling methods and considerations on their use are key parts of any study design and environmental assessment, since the results will be the basis for exposure models, risk assessments, feasibility studies, land use and zoning maps, and other information used by fellow engineers, clients, and regulators (Environmental Health Australia, 2012b). As indicated in Table 2.4, measurement errors and uncertainties will accompany the results and even be compounded as the data are translated into information (U.S. Environmental Protection Agency, 2004), so it is important to include all necessary metadata to ensure others may deconstruct, quality assure, and ensure appropriate applications. Table 2.4 Types of uncertainty and contributing errors in environmental engineering. Type of uncertainty Type of error causing uncertainty Emissions Misclassification and miscalculation Reliance on third party and other sources of information with little or no metadata regarding quality. Confusing actual emission measurements with reported estimates Transport and transformation Incorrect model application Applying a model for the wrong chemistry, atmospheric, terrain, and other conditions, e.g. using a simple dispersion model in a complex terrain. Applying a quantitative structure–activity relationship (QSAR) to inappropriate compounds, e.g. for metals when the QSAR is only for organic compounds, or for semi‐volatile compounds when the QSAR is only for volatile organic species Exposure scenario Misclassification Failure to adequately identify exposure routes, exposure media, and exposed population Sampling or measurement (parameter uncertainty) Measurement: random Random errors in analytical devices (e.g. imprecision of continuous monitors that measure stack emissions) Measurement: systemic Systemic bias (e.g. estimating inhalation from indoor ambient air without considering the effect of volatilization of contaminants from hot water during showers) Surrogate data Use of alternate data for a parameter instead of direct analysis of exposure (e.g. use of population figures as a surrogate for population exposure) Misclassification Incorrect assignment of exposures of subjects in historical epidemiologic studies resulting from faulty or ambiguous information Random sampling error Use of a small sample of individuals to estimate risk to exposed workers Nonrepresentativeness Developing exposure estimates for a population in a rural area based on exposure estimates for a population in a city Relationship errors Incorrectly inferring the basis of correlations between environmental concentrations and urinary output Oversimplification Misrepresentations of reality (e.g. representing a three‐dimensional aquifer with a two‐dimensional mathematical model) Incompleteness Exclusion of one or more relevant variables (e.g. relating a biomarker of exposure measured in a biological matrix without considering the presence of the metabolite in the environment) Surrogate variables Use of alternate variables for ones that cannot be measured (e.g. using wind speed at the nearest airport as a proxy for wind speed at the facility site) Failure to account for correlations Not accounting for correlations that cause seemingly unrelated events to occur more frequently than expected by chance (e.g. two separate components of a nuclear plant are missing a particular washer because the same newly hired assembler put them together) Model disaggregation Extent of (dis)aggregation used in the model (e.g. separately considering subcutaneous and abdominal fat in the fat compartment of a physiologically based pharmacokinetic [PBPK] model) Biological plausibility Applying a PBPK model to chemicals for which it has not been coded, e.g. a one‐ compartment model for a compound with known metabolites Observational or modeling Description or example Source: U.S. Environmental Protection Agency (2004) and Vallero (2014). 21 22 2 Measurements in Environmental Engineering 2.2.1 Sampling Approaches Random sampling: While it has the value of statistical representativeness, with a sufficient number of samples for the defined confidence levels (e.g. x samples needed for 95% confidence, random sampling may lead to large areas of the site being missed for sampling because due to chance distribution of results. It also neglects prior knowledge of the site. For example, if maps show an old tank that may have stored contaminants, a purely random sample will not give any preference to samples near the tank. Stratified random sampling: By dividing the site into areas and randomly sampling within each area, avoiding the omission problems of random sampling alone. Stratified sampling: Contaminants or other parameters are targeted. The site is subdivided and sampling patterns and densities varied in different areas. Stratified sampling can be used for complex and large sites, such as mining. Grid or systematic sampling: The whole site is covered. Sampling locations are readily identifiable, which is valuable for follow‐on sampling, if necessary. The grid does not have to be rectilinear. In fact, rectangles are not the best polygon to use in the value to be a representative of a cell. Circles provide equidistant representation, but overlap. Hexagons are sometimes used as a close approximation to the circle. The US Environmental Monitoring and Assessment Program (EMAP) has used a hexagonal grid pattern, for example. Judgmental sampling: Samples are collected base upon knowledge of the site. This overcomes the problem of ignoring sources or sensitive areas but is vulnerable to bias of both inclusion and exclusion. Obviously, this would not be used for spatial representation, but for pollutant transport, plume characterization, or monitoring near a sensitive site (e.g. a day care center). At every stage of monitoring from sample collection through analysis and archiving, only qualified and authorized persons should be in possession of the samples. This is usually assured by requiring chain‐of‐ custody manifests. Sample handling includes specifications on the temperature range needed to preserve the sample, the maximum amount of time the sample can be held before analysis, special storage provisions (e.g. some samples need to be stored in certain solvents), and chain‐of‐custody provisions (only certain, authorized persons should be in possession of samples after collection). Each person in possession of the samples must require that recipient sign and date the chain‐of‐custody form before transferring the samples. This is because samples have evidentiary and forensic content, so any compromising of the sample integrity must be avoided. 2.3 Laboratory Analysis Although real‐time analysis of air and other media is becoming more commonplace, most environmental samples must be brought to a laboratory analyzed after collection. The steps that must be taken to interpret the concentration of a chemical in the sample are known as "wet chemistry." 2.3.1 Extraction When an environmental sample arrives at the laboratory, the next step may be "extraction." Extraction is needed for two reasons. First, the environmental sample may be in sediment or soil, where the c