Environmental Science and Policy 110

Winter 2006

I. Instructors


Peter Richerson, Professor. Office Hour  Th 4-6 2120J Wickson,


Kurt Vaughn, TA Office Hour Fr 2-4 2120J Wickson,


Muamar Al-Najjar, Reader Office Hour Wednesday 11-12 2120J Wickson,


Lecture: MWF 4:10-5:00 PM 119 Wellman


II. Text: Gilbert M. Masters. 1997. Introduction to Environmental Engineering and Science, Second Edition. Prentice Hall. See also course materials on the web


III. Objectives of the Course


            This course has two major functions: (i) to introduce you to the physical and chemical environmental sciences, and (ii) to improve your quantitative analytical skills.


            A decent knowledge of the physical/chemical environmental sciences is essential for environmental professionals. Organisms, including people, live in environments where they are buffeted by winds and currents, exposed to nutrient and toxic chemicals, and heated and cooled by radiative transfers of energy. The activities of organisms change the physical/chemical environment which in turn affects other organisms. Thus, virtually all of the environmental problems that you will spend your careers managing will have a physical/chemical dimension. Some of these problems are relatively simple, such as the management of the oxygen depleting, fish killing effects of sewage on receiving water bodies. Some of them are awesomely complex. Moving water from Northern to Southern California with minimal environmental damage is a formidable technical challenge even before we begin to consider the equally intimidating political complexities. Human effects on greenhouse gas concentrations impact a global system in which numerous intricately interlocked biogeochemical cycles and physical transport processes conspire to determine the Earth’s heat budget.


            Quantitative analysis is a fundamental part of understanding and managing ecological systems. Hardly ever do environmental problems yield to categorical analysis. The question is not whether to pollute or not to pollute, but how much of a pollutant do we need to remove to achieve some desirable outcome, such as reducing the mortality of fish (or humans) to a tolerable level. We need to estimate the performance of alternative protection or remediation plans in order to find a cost-effective solution to the problem. The main tactic for solving management problems consists of making a model of the physical, chemical, biological, and socio-economic processes involved and using it as a tool to solve the problem. We want you to come out of the course with the skills necessary to set up and solve basic models of environmental processes and problems.


IV. Approach


            This course is in the style of an engineering course in environmental science. The lectures will lay out the basic physical, chemical and biological processes that underlay problems in air pollution, water pollution, solid waste management, and the like. Major components of the course include weekly problem sets and a final project on a topic of your own choosing.


We want you to learn to think like an engineer. This isn’t the only way to think, but engineers have a very useful set of tools that you will want to have in your professional repertoire. Most fundamentally, engineers practice the art of building practical quantitative models to solve concrete problems. You might suppose that the models used in environmental science are normally complex because the environmental processes involved are almost always complex. It is true that complex models, such as General Circulation Models of the atmosphere, are essential tools in their place. However, such models are extra-ordinarily expensive to build and use. They often predict poorly because they are overfit. The data one has available is generally very limited in quantity and noisy. If a too-complex model is used, you will fit the noise in the data as well as the real patterns. The too-complex model will “predict” the noisy data all too well. But it will badly fit the next lot of data from the same system because the fit was bamboozled by the noise in the original data. As a practical matter, we always start out, and often finish, using simple models, even when we know the problem is complex in principle. Engineers call the most basic of such models back of the envelope calculations. The idea is to boil a complex problem to a simplicity that lies at its heart. You try to make some conservative simplifying assumptions, perhaps heroic simplifying assumptions. This often requires the killer instinct—a willingness to ignore all sorts of possible objections to get to some kind of model you can solve to get some kind of answer, any kind of answer. Then you turn the crank, and see what sort of answer pops out. Then you ask if the answer is reasonable given the science you know and whatever data you have at hand. If not, you take another cut. Very often, you soon work out an answer that practically has to be right to an order of magnitude (within factor of ten). Often, that is all you need to know, at least for the moment. A particular pollutant caused by a project may be well under a threshold of damage, for example—no action needed. Or the calculation may suggest that it will be well over the threshold—best available technology is obviously needed. Only if it is in between do you need to get fancy. Even if you do no more than discover that a more complex analysis is needed, you often gain a lot of insight into the problem working on the back of the envelope. If you do make a complex model, you’ll end up doing a lot of back of the envelope calculations to check that the complex model is working right, at least under simplifying assumptions.


Thus, paradoxically, simple models are one of the most important tools to analyze complex problems. Hence, we stress the art of the back-of-the-envelope calculation in this course. We say “art” because no surefire recipe exists to pick the right simple model to apply to a complex problem. You use all the knowledge you have about the system, add a bit of creative thinking, and hope for the best. Quite often, the uncertainty in complex environmental problems remains large after our analysis is complete. Hence, we often monitor systems, collect more data, and take new cuts at models as the new data accumulates. This is called adaptive management.


            The lecture part of the course introduces you to the kinds of general physical and chemical knowledge you need to set up simple models. Albert Einstein is supposed to have remarked “imagination is more important than knowledge.” At least as far as solving practical problems, he was only half right. Most of you also know about biological ecology from other courses, so by the end of this course, you’ll know a lot about environmental science. Of course, the details are endless, and when you come to solve real problems on the job you’ll have to read deeper. We hope that this course will give you a running start on most problems you’ll run into. Your project will give you experience at ferreting out the knowledge you need to solve a particular problem.


            The problem sets are designed to give you lots of hands-on experience solving problems. The text and the problems in it are quite reflective of real-world situations. Many back-of-the-envelope calculations are no more complex than these problems. The harder part, where Einstein’s imagination comes in, is to set the problem up in a way that lets you get away with a simple calculation—the art idea again. The project requirement is designed to give you experience reviewing how others think about a problem of your own choice, and trying you hand at setting up your own calculations.


            This course is designed to put marketable skills in your hands. Biological ecologists with good practical quantitative skills and some knowledge of the physical environmental sciences are not common, nor are engineers with a good knowledge of ecology. You will be able to solve many problems that would ordinarily take an engineer, saving your employer (or yourself, if self-employed) the cost of hiring an expensive piece of talent. When problems do get too complex for your physical/chemical expertise, you’ll be in a position to make a timely, persuasive, recommendation to hire an expert. Often environmental professionals work in multi-disciplinary teams. Successful teams have members that appreciate each other’s strengths and weaknesses and can communicate easily. Your level of knowledge of environmental science and engineering will impress the engineers and physical scientists you work with, allow you to talk fluently with them, and thus make you a valuable team member. As your career advances, you will likely acquire management responsibilities. The more you know about the disciplines of the members of your team the better team leader you can be. Part of the motivation for the project assignment is to give you a sample of work you can show to potential employers and graduate and professional school admissions committees. Some of you will find this course rather challenging. We hope that you will remember the expected future payoffs to learning this material when the going gets tough!


V. Course Requirements


A.     Exams. There will be a midterm and final exam, worth 15% and 30% of your grade, respectively. The exams will test your comprehension of the concepts and principles outlined in the reading and lectures. The exams will also have some quantitative problems to solve.

B.     Problem sets. The problem sets will be worth 25% of your grade. You are perfectly free to seminar with other students about how to do a problem, but you must do your own work. Problem sets turned in late will be docked 10% per day up to a maximum loss of 50%.

C.     Term project. This will be worth 25% of your grade. Choose a problem that interests you. It must have a physical/chemical environmental science component and be amenable to quantitative analysis. Your task is to write a technical briefing paper on your problem. Imagine you have been hired as a consultant or delegated by your employer to solve the problem, or at least recommend the next step in its solution. Describe the problem. Review the scientific principles involved. Illustrate the problem with a simple model implemented like a problem set. The idea here is not to make a fancy model but to think the problem down to something susceptible to a back-of-the-envelope calculation or small set of calculations. Be sure that your model is integrated into the text description of your project. Motivate the model, describe how it works and use the results to underpin your conclusions. In technical prose, about half your persuasive power rests on your words and about half on your numbers, and everything depends upon making the two work together. You may find Excel, or other more sophisticated programming tools, useful when problems get a bit more complex than those in the problem sets. If others have done a more complex quantitative analysis, critique it. Suggest what further data collection or modeling is required. You can pick a big problem like the effect of anthropogenic CO2 on global climate change or a small one, such as how to grow a good lawn without allowing excess nitrogen fertilizer to run off or leach downwards. You should submit a proposal for your project to Professor Richerson by February 1 and a first draft by March 8. Professor Richerson will make himself available to help with projects. Come to his office hour, shoot him an email, or make an appointment if you want help. If you are interested in a more ambitious project I can arrange for 198 credit, perhaps in Spring Quarter, to finish a project that gets out of hand.

D. Attendance and Participation.  Your level of attendance and participation at the weekly discussion sections will constitute 5% of your final grade.  Attendance at office hours will also be taken into account when the TA determines this portion of your grade.  By putting in time at discussion and office hours, your quantitative skills will improve more rapidly and with less effort as you benefit from the interactions with others.


VI. Discussion Section


            The discussion sections serve two purposes. First, they give you an opportunity to ask questions about the scientific concepts and principles reviewed in the text. Second, the TA will review the methods for handling the problems, particularly difficulties experienced by many students. Mr. Vaughn is especially committed to helping you get your problem solving skills honed to the highest level you can in one quarter. He will use the discussion session to introduce some analyses that are a little more imaginative than those in the text. These are intended to be a half-way house between the text problems and your project. If you need extra help with some of the problems please take advantage of our office hours. Our office hours are held in 2120J Wickson, the big teaching lab, in order to encourage students meet with each other as well as us to master the problem sets.


VII. Grading Policy


            In the past, students in this course have differed widely in background and skills. Our objective is to raise everyone’s skill level as far as possible, whatever level you enter with. We will put out extra effort to help those with weaker backgrounds achieve a decent level of proficiency, if you will put in effort in proportion. We are also keen to help skilled students who want to push the envelope of their skills, say to produce a really outstanding project. We will not abandon curve grading entirely, but will give a lot of consideration to students who show improvement relative to their entry skills. We’d like to think that we can help any student who tries hard at to get least a B in this course. We’d be happy to hand out any number of A+s to able students who do elegant projects. In the past, abler students have often acted as informal mentors for the less able. We will count this as a positive contribution to the class.


VII. Lecture, Reading, and Assignment Outline


I. Introduction to the Course (January 4, 6)

            Reading: Masters Chapter 1

            Due January 13, Problems 1.2 1.5 1.6, 1.12,1.22, 1.29


II. Environmental Chemistry (January 9, 11, 13)

            Reading: Masters, Chapter 2.

            Due January 20 Problems 2.1, 2.5 2.7, 2.10, 2.20, 2.26

            Also do the Fickian diffusion problem from the web page


III. Water Pollution: Surface Water (January 18, 20, 23)

            Reading: Masters: Chapter 5.1-5.7

            Due January 27 Problems 5.2, 5.5, 5.7, 5.17, 5.23, 5.34, 5.36


IV. Water Pollution: Ground Water (January 25, 27, 30)

            Reading: Masters 5.8-5.18

            Due February 1: Term Project Proposal

            Due February 3 Problems 5.38, 5.40, 5.42, 5.43, 5.47, 5.52, 5.53


   February 1 Clear Lake—A polluted aquatic ecosystem


V. Air Pollution I (February 3, 6, 8)

            Reading: Masters 7.1-7.6, 7.10

            Due February 10  Problems 7.1, 7.4, 7.5, 7.6, 7.11, 7.12, 7.13, 7.14


February 13 Midterm Exam covering sections I-IV


VI. Air Pollution II (February 15, 17, 22)

            Reading: Masters 7.7-7.12

            Due February 24 Problems 7.19, 7.20, 7.22, 7.28, 7.33, 7.38, 7.44, 7.47


VII. Risk Assessment (February 24, 27)

            Reading: Masters Chapter 4

            Due March 3 Problems 4.2, 4.6, 4.13, 4.20, 4.22


VIII. Global Atmospheric Change (March 1, 3, 6)        Reading: Masters Chapter 8

            Due March 10 Problems 8.1, 8.2, 8.4, 8.6, 8.7, 8.15, 8.20, 8.35

            Due March 8: First Draft of Term Project


IX. Water Quality Control (March 8, 10)

            Reading: Masters Chapter 6

            No Problems: Work on Term Project


X. Solid Waste (March 13)

            Reading: Masters 9

            No Problems: Work on Term Project


XI. Conclusions and Review (March 15): No Reading, No Problems!


March 16: Projects due


March 18 08:00-10:00 Final Exam