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Solution Manual Fundamentals of Environmental Sampling and Analysis Second Edition
Chunlong Zhang
University of Houston-Clear Lake Texas, USA
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This edition first published 2024 © 2024 John Wiley & Sons, Inc. Edition History First edition © 2007 John Wiley & Sons, Inc. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies. 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 Chunlong Zhang to be identified as the author of this 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 For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com. Wiley also publishes its books in a variety of electronic formats and by print-on-demand. Some content that appears in standard print versions of this book may not be available in other formats. Trademarks: Wiley and the Wiley logo are trademarks or registered trademarks of John Wiley & Sons, Inc. and/ or its affiliates in the United States and other countries and may not be used without written permission. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc. is not associated with any product or vendor mentioned in this book. Limit of Liability/Disclaimer of Warranty In view of ongoing research, equipment modifications, changes in governmental regulations, and the constant flow of information relating to the use of experimental reagents, equipment, and devices, the reader is urged to review and evaluate the information provided in the package insert or instructions for each chemical, piece of equipment, reagent, or device for, among other things, any changes in the instructions or indication of usage and for added warnings and precautions. While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/ or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Library of Congress Cataloging-in-Publication Data Names: Zhang, Chunlong, 1964- author. | John Wiley & Sons, publisher. Title: Fundamentals of environmental sampling and analysis / Chunlong Zhang. Description: Second edition. | Hoboken, NJ : JW-Wiley, 2024. | Includes bibliographical references and index. Identifiers: LCCN 2023057815 (print) | Set ISBN 9781394244621 | ISBN 9781394241651 (epdf) | ISBN 9781394241668 (epub) | ISBN 9781394241644 (SM) Subjects: LCSH: Environmental sampling. | Environmental sciences--Statistical methods. Classification: LCC GE45.S75 Z43 2024 (print) | LCC GE45.S75 (ebook) | DDC 628--dc23/eng/20240131 LC record available at https://lccn.loc.gov/2023057815 LC ebook record available at https://lccn.loc.gov/2023057816 Cover Design: Wiley Cover Image: © Iana Kunitsa/Getty Images Set in 9.5/12.5pt STIXTwoText by Integra Software Services Pvt. Ltd, Pondicherry, India
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Chapter 1 Questions 1) Give an example for each of the objectives of environmental sampling and analysis listed in the text. Students’ answers may vary. Corresponding to each listed objective, examples are: a) Sampling and analysis of contaminant concentrations in wastewater effluent from an industrial source to comply with effluent standards (e.g., NPDES). b) Monitor atmospheric ozone to determine air quality change over time against the NAAQ standard. c) Collections of samples and analysis of contaminant concentrations in the air surrounding an oil spill site to ensure health standards are met. d) Collections of soil and groundwater samples for the analysis of pollutants in a former gasoline station where a bioremediation project is underway. The information on how much contaminant we currently have, and how quickly is the progress of this ongoing project is important for us to make decision and take action in environmental remediation. 2) Give examples of practice that will cause data to be scientifically defective or legally nondefensible. Some of these examples are: improperly trained sampler and analyst, lack of good laboratory practice, knowingly falsifying test results, using nonvalidated equipment, unsecured chain of custody, nontraceable standards for instruments, misconduct, conflict of interest (e.g., contract labs oftentimes work for the company that hired them), ineffective ethics programs, etc. 3) Define and give examples of systematic errors and random errors. Determinate errors (systemic errors) produce a known bias in the data. These errors can be traced and corrected. They are avoidable mistakes that are known to have occurred or were found later. Measurements that result from these types of errors can be discarded. Random errors are indeterminate errors. They cannot be identified or compensated for, and statistics must be applied to deal with the data. 4) Why are sampling and analysis integral parts of data quality? Between sampling and analysis, which one often generates more errors? Why? If a sample isn’t collected properly, then all subsequent careful lab work is useless. On the other hand, if an analyst is unable to define data quality (precision, accuracy), then such data are also useless and the money and time spent in collecting these samples are wasted. Most people
Fundamentals of Environmental Sampling and Analysis, Second Edition. Chunlong Zhang. © 2024 John Wiley & Sons, Inc. Published 2024 by John Wiley & Sons, Inc. Companion Website: www.wiley.com/go/EnvironmentalSamplingandAnalysis2e
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would think that data quality is supported by a state-of-the-art instrument in the lab analysis, rather than sampling stage. However, this is typically not true. On the opposite, most error comes from sampling rather than lab analysis. For example, a sophisticated analytical instrument cannot justify the variations of chemicals in a very heterogeneous matrix. 5) Describe how errors in environmental data acquisition can be minimized and quantified. Errors can be quantified and minimized through a quality assurance/quality control (QA/QC) program. QA is more of a management system ensuring QC is working properly, and QC is a system of technical activities to meet certain data specifications. The use of a QA/QC program and adequate protocols will reduce errors associated with sampling, sample preservation, sample transportation, sample preparation, and sample analysis. 6) Why can’t standard errors be added, but variances can? We cannot just add the standard errors or standard deviations, but we can add the variances as long as the variables are assumed to be independent such as errors from the stages of sampling and analysis. This is because during the variance calculations, the differences between every individual value from the mean are squared to get the positive values. 7) How does environmental analysis differ from traditional analytical chemistry? Analytical chemists trained in traditional chemistry curriculum may not be immediately adapted to environmental analysis. Environmental analysis differs from traditional analytical chemistry in many ways, for example: Traditional analytical chemists stay in the lab, whereas environmental analytical chemists deal with measurements both in situ and in the lab. ● The analytical costs of environmental chemicals are typically high. ● There are always a large number of samples that require instrument automation. ● Sample matrices are often complex and unknown (water, air, soil, waste, and living organisms). ● The concentrations are typically very low. They are measured at ppm, ppb, ppt, or even lower levels. ● The markets and analytical protocols are driven by regulations. An environmental analyst needs a working knowledge of the regulations for the purposes of both regulatory compliance and regulatory enforcement. 8) Describe the difference between “classical” and “modern” analysis. Modern analysis typically uses more or less sophisticated instrumental methods that make it possible to detect small quantities of almost anything. Examples of modern methods are spectrometric, electrometric, and chromatographic methods. The differences between “classical” and “modern” analysis are very arbitrary and ever-changing as technology advances. However, it is generally accepted that any “wet chemistry based” methods are termed “classical,” while anything employing relatively sophisticated instrumentation is considered “modern” methods. Classical methods use wet chemicals analysis, mostly volumetric and gravimetric methods. 9) Why should a QA/QC program be used in an environmental lab or environmental analysis consulting lab to ensure they meet guidelines? QA/QC programs are implemented not only to minimize errors from both sampling and analysis, but also to quantify the errors in the measurement. Knowing how much the error and the means to minimize the errors will ensure guidelines are met. ●
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