Experiment Design for Environmental Engineering

Methods and Examples

Francis J. Hopcroft, Abigail Charest

• Publisher: CRC Press
• Year: 2022
• Print ISBN: 9781032026183
• eBook ISBN: 9781000527155
• Edition: 1
• Topic: Biological Sciences
• Subtopic: Civil Engineering
• Index: Biological Sciences

Experiment Design for Environmental Engineering provides a wide range of practical environmental engineering laboratory experiments for implementation by students in a university laboratory or by practicing professionals in the field, along with an extensive discussion on how to design an experiment that will provide meaningful and useful data, how to interpret the data generated from an experiment, and how to present those data to an audience of other students or professionals. The example experiments provide a way to evaluate a new design against an existing experiment to determine what information is most appropriate in each section and how to format the data for the most effective outcome.

Features

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• Fills in the gap in ABET requirements to teach students how to design experiments and includes key elements for a successful design

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• Covers experiments for a wide range of environmental engineering topics

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• Provides standardized approach that includes a basic background to the concepts and step-by-step procedure for conducting the experiment

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• Explains designs that are suitable for college laboratory and professional applications

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• Shows how to organize experimental data as it is collected to optimize usefulness

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• Provides templates for design of the experiment and for presenting the resulting data to technical and nontechnical audiences or clients

1 Introduction

DOI: 10.1201/9781003184249-1

The desire to change and shape the world is at the heart and soul of engineering. The art of effective experimentation is an invaluable function to learn if the engineer is going to excel at their profession, regardless of the specialty or the subspecialty an engineer chooses. This does not mean that every engineer needs to be proximate to a laboratory. Experimentation takes many forms and can involve intellectual experiments as well as physical ones. In fact, physical experimentation almost always requires some intellectual exercises before the experiment is designed and then again after the data are generated to properly evaluate and explain the outcomes.

How, then, should one go about designing an experiment?

Experimental design is as much an art as a practice. Elegant experiments exist, but not nearly as often as mundane exercises of practical judgment superimposed on some physical phenomenon. Indeed, most experiments do not work, at least not the first time. If they did, there would be limited need to do them. It is the failure of experiments and the iteration of design from which the most is often learned, and this new knowledge allows us to narrow the scope of subsequent experiments to better define, describe, or demonstrate the outcomes the initial experiment was designed to demonstrate in the first place. It is a noble goal, of course, to minimize the number of iterative experiments needed to resolve an issue or question, but it should not be considered a bad thing if more than one or two tries are needed to get something complex right. The more complex the issue, of course, the more difficult it will be to get the experiments right from the beginning.

This book is about designing experiments, and it is also a book of complete and detailed experiments. Experiments that have been designed to demonstrate specific environmental phenomena. The experiments in this book are for teaching various physical traits of common engineering basics so that the novice engineer can begin to see how the world works and begin to develop necessary skills for the effective design of new experiments.

Chapter 2 looks at how to design an experiment; Chapter 3 describes the most effective ways to sample media under different sample purity requirements; Chapter 4 examines how to establish the expected outcomes from an experiment, how to collect data for effective evaluation of actual results, how to interpret the data generated by an experiment, including what to do when the data do not coincide with the expected outcome, and considerations of uncertainty in the experimental outcomes, including a discussion of how probability plays into evaluating data; Chapter 5 evaluates some considerations for preparing the outcome data for effective presentation to others; Chapter 6 provides a Model Design Methodology and format; Chapter 7 provides a suggested template for a laboratory report that mirrors the data and information in the experiment outline with actual results, discussions, recommendations, and conclusions; Chapter 8 discusses the differences between the experiments described in this book and the type of experimentation needed in a research project, and discusses how to develop appropriate research projects; and Chapters 9–14 provide a series of successful experiments designed to show engineering students some of the basics of environmental engineering.

Each of the experiments in Chapters 9–14 provides an introductory section on the basic theory behind the experiment. Where more than one experiment relates to the same basic theory, the theoretical background is provided only once, and subsequent experiments that are based on the same theory refer the experimenter back to the appropriate section of a previous experiment. These are not intended to be detailed theses on the underlying theory, but rather a reminder of the engineering basics underlying the experiment. It is assumed that the student has a sufficient educational background to grasp the fundamentals outlined in the experiment introductions and to understand the context for the data to be developed during the experiment.

2 How to Design an Engineering Experiment

DOI: 10.1201/9781003184249-2

The fundamental design of an experiment contains several distinct design elements. Those include the question to be answered; the variables involved; how the variables will be adjusted; the potential interferences that can occur; how the investigator intends to minimize or avoid the effects of those interferences, or to account for them in the experimental data; and what theoretical outcomes are expected. Once the data are generated, how those data are interpreted and how the results are presented will go a long way to validating the outcomes.

Note that not all experiments succeed. If they did, there would be no need to do an experiment because the outcome could be accurately predicted. It is important, therefore, to recognize that failure is an acceptable component of investigation. That recognition will minimize the tendency to interpret data in a way that supports the expected outcome and to reject data that do not support that outcome. If the data are not what is expected, it should be assumed that the data are correct and that the theory is wrong or that there was an error in the experiment design or conduct. The investigator then needs to try to figure out why the theory or the experiment was wrong and how to redo the experiment to account for the new thinking.

Certainly, equipment will occasionally fail, people will do things in a manner inconsistent with the planned protocol of the experiment, reagents will become contaminated, and all sorts of other things will go wrong with experiments. The data that are generated are always correct for the experiment that was done. If those data do not reflect the expected outcome, either the theory is wrong or the experiment incorporated some unknown flaw. Either way, it is imperative to accept the data as real and to adjust the experiment or the theory, or both, to incorporate what was learned from the unexpected outcome.

2.1 Defining the Question to Be Answered

It is axiomatic in engineering that in order to solve a problem, it is first necessary to define the problem accurately and completely. Much of what is done in experimentation is aimed at defining the basic problem. That implies that the initial question posed may not be the intended question the investigator is attempting to answer. Sometimes, an intermediary question is required to define the underlying details of the ultimate question.

In either case, the question posed needs to be as clear and concise as possible. Ambiguous questions lead to ambiguous results. For example, “How does temperature affect water quality?” might be an interesting question, but there are too many variables in the question that are not defined. For example, is the question intended to look at ambient air temperature above the water or the temperature of the water itself, or both? Does it intend to examine water quality from a reservoir, a pond, a lake, a stream, or the ocean? At what depth? How is water quality defined in the question? How much time variation and temperature variation are allowed? Multiple other questions will also quickly arise as the experiment is designed and any attempt is made to run it.

A clearer, more concise question might be “What is the effect of water temperature on the concentration of oxygen in a shallow (less than the average depth of 1 m (3 feet)) natural stream with a flow velocity less than 2 m (6 feet) per second?” By identifying a suitable water body for study, using continuous reading temperature, dissolved oxygen, flow rate, and water depth probes, all the pertinent data can be simultaneously measured over an extended period of time. Note that, as usual, there are some uncontrolled variables that may be at play here that must be considered, discussed, and assessed.

2.2 Defining the Variables Involved in a Question

Very few things in engineering are simple. Engineering questions are driven by physics, chemistry, biology, mathematics, mechanics, geology, and a host of other “ologies”. Those subjects all form engineering basics to which engineers routinely turn to find answers. How those basics interact with each other in any specific instance or circumstance is not always clear, but can be significant. Defining the variables at work in any experiment, whether those variables are to be controlled, and how to account for those not being controlled is important.

There are generally three kinds of variables involved in experiments and knowing which kind is being discussed is important. Independent variables are those that the experimenter controls. Dependent variables are those that respond to the changes made to the independent variables. Controlled variables are those that are kept constant throughout the experiment. The ideal experiment will be designed so that only one independent variable is changed at a time and the results in all the dependent variables can be observed. Occasionally, more than one variable may need to be changed at the same time, but that should be avoided to the maximum degree possible.

Note, too, that an important characteristic of a variable is that it can be reliably measured. Time, temperature, velocity, mass, and similar characteristics are measurable and are good quality variables. Emotions, feelings, opinions, judgments, and similar characteristics are not measurable and therefore are not suitable experimental variables.

In the question posed regarding the effects of water temperature on the concentration of dissolved oxygen in a natural shallow stream, there are many potential variables. Four independent variables have been identified as key to the selected question: water temperature, flow velocity, dissolved oxygen concentration, and stream depth. All four have been defined as measurable variables in the statement of the question. Stream depth is defined for the experiment within the experimental statement, and the stream velocity is restricted. The question statement would require removal of data generated when the stream velocity or depth exceeded the stated parameters, although data from outside the stated parameters might well suggest further areas for additional study.

It is noted, however, that a lot of variables exist in a natural stream that have not been identified or considered in the question statement. For example, natural water bodies typically contain a wide variety of dissolved minerals and organics that can vary in concentration with water temperature and velocity, along with the underlying soil and rock strata, and animals discharge organic waste products into the environment at random locations and random times, without concern for any effects this might have on the engineer’s experiments. Silt or soil particles may be suddenly mixed into the streamflow by a land animal entering the water or an aquatic animal swimming near the probes. Sunlight hitting a probe might inadvertently elevate the temperature readings above the actual water temperature at any time during the experiment, or aquatic debris could temporarily clog or block a probe receptor, generating false readings that are not inherently obvious. Rain could occur during the experiment that could change the dissolved oxygen concentrations sharply over a short time interval. None of these variables are proposed to be measured or controlled by the experimenter, but each could, alone or in conjunction with one or more of the others, significantly impact the data.

2.3 Measuring and Controlling the Variables

Clearly, there are lots of things that could go wrong with this experiment. Minimizing those negative effects takes some planning. In the stream example experiment, the experimenter is not actually controlling anything; not even the four independent variables that are the basis for the question. The question presupposes a natural stream and a measurement of four variables over time. Those data would then be correlated to generate an analysis of the effects of water temperature on dissolved oxygen in a natural stream defined by the four stated parameters. Note that because only those four variables are being measured and there are so many other variables that are not being measured, the data generated would apply only to the specific stream measured at the specific location of the measurements. This experiment would not generate universally applicable data (but may identify applicable trends).

There is usually more than one way to do everything in engineering, and there are three general ways to conduct this experiment. The first is to find a suitable natural stream meeting the stated flow and depth parameters, inserting the necessary probes, and collecting data. All the uncontrolled and unmeasured parameters cited earlier would be potential problems for data analysis. The measurements would then be taken by the probes and the data analyzed, noting the potential impacts of the unmeasured variables.

A second way to conduct this experiment is to create a streamflow in a suitably long manufactured trough in the lab. The trough would need to be designed to recreate natural stream bed conditions of soil, rock, depth variations, roughness, stream width variations, and other factors to replicate the natural stream conditions to which the data are intended to be applied. In this case, the water temperature could be artificially adjusted by the experimenter and the dissolved oxygen concentration measured directly. The stream depth and flow could be held constant and most of the external parameters could be eliminated. This would eliminate many of the other unmeasured variables, but would not account for their impact, alone or in conjunction with each other, on the outcome data from a natural environment. The resulting data would directly provide the answer to the stated question, but only for the limited controlled conditions under which the experiment was conducted; not in regard to an actual, natural stream.

A third possibility is to construct some form of dam across a suitable stream with an automated variable depth weir controlled by a downstream depth gauge and flow rate monitor such that the flow rate and water depth are maintained within a very narrow range during the experimental period. Additional real-time monitoring probes could be installed for a variety of organic constituents, suspended solids concentrations, and other parameters of concern to determine whether there were significant changes in those parameters sufficient to warrant further experiment for their effects. The water temperature and dissolved oxygen concentrations would then be measured continuously for some appropriate period of time and the data analyzed, noting the potential interferences from all the other measured and unmeasured parameters.

Regardless of the experiment design employed, there are several factors that would need to be measured or controlled during this experiment, including the water temperature, dissolved oxygen concentration, flow rate, and w...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• List of Figures
• List of Tables
• Preface
• Acknowledgments
• Authors
• Chapter 1 Introduction
• Chapter 2 How to Design an Engineering Experiment
• Chapter 3 Sampling Source Media
• Chapter 4 Expected Outcomes and Interpretation of Data
• Chapter 5 Model Design Methodology
• Chapter 6 Laboratory Report
• Chapter 7 Effective Presentation of the Data in Outcome Reports
• Chapter 8 Designing Research Experiment Projects
• Chapter 9 General Experiments
• Chapter 10 Oil and Petroleum-Based Experiments
• Chapter 11 Oxygen and BOD Experiments
• Chapter 12 Environmental Microbiology Experiments
• Chapter 13 Water Quality Experiments
• Chapter 14 Contaminant Removal Experiments
• Appendix
• Bibliography
• Index