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Unit 8: Environmental Management Techniques
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Lecture 8
Environmental Management
Techniques
STRUCTURE
Overview Learning Objectives 8.1 Environmental Monitoring 8.2 Environmental Modelling 8.2.1 Forecasting modelling 8.2.2 Growth modelling 8.3 Sensitivity Analysis 8.4 Application of Remote Sensing and GIS in EM 8.5 Environmental Profile 8.6 Environmental Technology Assessment 8.7 Environmental Risk Assessment 8.7.1 Environmental risk management in industries 8.7.2 Ecosystem approach to risk assessment 8.8 Rapid Urban Environmental Assessment 8.9 Eco-mapping 8.10 Environmental Education Summary Suggested Readings Model Answers to Learning Activities
OVERVIEW
In Units 4 to 7, we discussed environmental management tools. In Unit 8, we will discuss some related environmental management techniques. We will begin the Unit by discussing environmental monitoring. We will then explain how modelling helps in applying the environmental tools in real-life situations for quantification of impacts, prediction of scenarios and simulation studies. We will also discuss other techniques such as sensitivity analysis, remote sensing, environmental profiling, environmental technology and risk assessments and eco-mapping. We will then
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introduce you to the techniques that can be adopted in the urban contexts. Finally, we will discuss the importance of environmental education, i.e., a learning process that increases people’s knowledge and awareness about the environment and associated challenges, develops the necessary skills and expertise to address the challenges and fosters attitudes, motivations and commitments to make informed decisions and take responsible actions.
LEARNING OBJECTIVES
After completing this Unit, you should be able to:
state the processes involved in monitoring, modelling, sensitivity analysis, remote sensing, environmental profiling, technology assessment, risk analysis and social impacts; use these techniques where relevant; carry out eco-mapping; discuss the need for and the role of environmental education in environmental management.
8.1 ENVIRONMENTAL MONITORING
Environmental studies often require information on physical, chemical, biological, economic or social aspects of particular environments. These can be obtained from a monitoring programme involving surveys, measurements and data collection activities. A monitoring programme, thus, helps establish baseline information and data for describing the present situation of an area likely to be impacted by a proposal.
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Predictive techniques audits, to assess the predictions made in the EIS and the methods of prediction used by comparing actual outcomes with forecasted ones. (This will aid future studies.)
Before you read further, note the distinction between monitoring, survey and surveillance programmes given below:
Monitoring: A long-term, standardised measurement programme involving observation, evaluation and reporting of part of the environment in order to define status and trends. Survey: A finite duration, intensive programme to measure, evaluate and report the quality of part of the environment for a specific purpose. Surveillance: A continuous, specific measurement programme involving observation and reporting for the purpose of environment management and operational activities.
8.2 ENVIRONMENTAL MODELLING
Before we discuss environmental modelling, let us first explain what modelling means. A model is a representation of real-life problems or situations. It copies significant attributes of a real prototype but is simpler and is easier to build, change or operate. Put differently, models are basic tools in science, engineering, business and various forms of planning. They can be applied to situations or systems, which are both existing and non-existent. For example, suppose that we are to assess the environmental impacts of releases of heated water from a proposed thermal power plant into a shallow lake. To assess the impact, do we have
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to build a power station and deliberately release varying amounts of heated water, as an experiment? Given the huge efforts, great costs involved, etc., this is unwise to do. Furthermore, this will cause the very environmental damage that the planning exercise is trying to prevent. To assess impacts, it is obviously better to use a model of the system.
Essentially, models allow us to extrapolate from the existing systems and knowledge to analyse potential situations. They are only useful to the extent that they accurately model the real world. Models can be constructed from logic or rational assumptions, scientific theories and information about similar situations or operations.
The model structure may be set up to search for an optimal answer (as in linear or dynamic programming models) or to generate possible solutions (e.g., stochastic models). The great majority of models, however, just stimulate the behaviour of a system. Variations can be explored by running the model several times, changing inputs or other features. There are many kinds of models such as statues, model aeroplanes, scientific theories, hydraulic laboratory models and computer programs. We can classify models into the following:
Iconic models, which have similar attributes to the prototype, e.g., a model-racing car. Analogue models, where some aspects of the model are analogous to the prototype, although they differ physically, e.g., flows of heat, electricity and ground water act similarly, and electric analogues have been used to model ground water flows.
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A cash flow analysis with receipts and payments as inputs, discount rates as a parameter, net present value as an output and compound interest equations as the model. The effects of development and environmental degradation on land values, with development and pollution levels as inputs, indices of social reactions as parameters and land values as outputs and relationships involving benefits of development, disadvantages of pollution, public perceptions and land values as the main model.
Other examples of the above are oxygen sag in a river, operation of an industrial plant, econometric models describing consumer responses to changes in energy prices and ecological changes in an altered environment.
You must note that:
Models can be much more complex than the simple structure shown in Figure 8.1, with iterations and feedbacks. A steady- state model can describe static situations or snapshots of a system at a particular time. Situations changing with time can be modelled by unsteady or dynamic models, which work with a series of time steps. These usually employ the calculated outputs at the end of one time step as inputs at the beginning of the next step. Probably, the most famous models are those used by the Club of Rome (Meadows et al., 1972, Meadows et al., 1992), which stimulated the future of the Earth, exploring global scenarios of population growth, industrial production and environmental degradation. Models may be purpose-built for a particular application, or general models which often use proprietary computer programs. Examples of general models are those describing storm water pollution transports, such as the Storm Water
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Management Model (SWMM) from the U.S. Environmental Protection Agency and the Storage, Treatment, Overflow Runoff Model (STORM) from the U. S. Army Corps of Engineers. When general programs or packages are used, you should realise that two models are involved – the standard computer model which provides a shell into which a model for a particular situation (expressed in the input data file) can be fitted. While most computer models were written in languages such as FORTRAN in the past, many modellers are now likely to use spreadsheet programs such as Lotus 1-2-3 and Excel. These offer facilities for basic stimulation, special functions such as random number generation for Monte Carlo Analysis, regression and other statistical tests and easy presentation of results as charts and graphs. Microcomputers provide adequate computer power for most modelling applications. Models are ineffective without data and calibration. Model results are sometimes accepted without adequate scrutiny because they are generated through a computer. The axiom rubbish in – rubbish out applies to all computer programs. Computer generated results must therefore analyse critically and should be checked for consistency and logic, and if possible, validated against additional data.
The accuracy of a model depends upon the following, in order of importance:
amount of data used to build and operate the model; experience or skill of the analyst; quality of the model.
Modelling exercises involve prediction of uncertain system behaviour, usually during some future period. Forecasting is,
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economic projection the timeframe is, at the most, 12 months. Long-term forecasts by expert think tanks are usually wide off the mark. In addition, all forecasts can be invalidated by some drastic event, such as a war or a natural disaster. Thus, we should have a healthy skepticism about forecast results, and apply checks on them.
The factors/data needed, among others, to make a forecast are:
Information on the situation being forecast, preferably past records of the main factor of interest and related factor. A model of the system or situation, which could be a simple concept, such as the input-output system in Figure 8.1, or a full scientific model based on physical description and theory, such as the computer models used in weather and climate prediction.
Forecasting can be done on the basis of experience of judgement, but it is better to have some explicit model or basis. These can be causal or explanatory, seeking to describe the processes producing the phenomenon being predicted, or non-causal, considering results numerically or statistically, but not trying to define underlying processes.
Figure 8.2 shows the forecasting problem for a particular variable using past record of values for extrapolation:
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Figure 8. Extrapolation of Past Values: An Illustration
Note that the forecasting model illustrated in Figure 8.2 is used in population forecasting based on past census data.
A systematic forecasting may use such mathematical models as linear, multiple and non-linear regression relating future values to the past record or time series of values, smoothing using simple and double moving averages, exponential smoothing, etc., filtering and Box-Jenkins.
The literature is replete with mathematical forecasting models. However, to use these models properly, it is necessary to set up databases of past records.
Note that simple regression techniques are useful to establish a relationship between the value and time, which can be extrapolated to make predictions. Smoothing techniques help reduce the variability of data. Moving averages, for example, replace each value with the mean of this value and the values occurring immediately before or after it, and this allows us to work
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Population in year t + 1 = Population in year t + growth rate x population in year t.
Alternatively, we can represent this in terms of logistic S-curves as illustrated in Figure 8.3 below:
Figure 8. Logistic S-Curve as a Limitation on Populations
Biologists successfully use logistic curve models to model populations of many organisms including protozoa, yeast cells, etc. Such models describe the rate of change in population as a function of present population, natural growth rate and carrying capacity of the ecosystem. Mathematically, this is represented as:
dN dt rN(^
N K )
where N is population size; r is growth rate and K is carrying capacity of the environment.
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Notice that when N is less than K , the rate of change of population is proportional to population size. That is to say, the population grows exponentially with growth rate r. As N increases, the rate of growth slows down, and eventually, as N approaches K , growth stops altogether and the population stabilises at a level equal to the carrying capacity. The factor ( 1- NK ) is often called the environmental resistance, i.e., as the population grows, the resistance to further population growth continuously increases.
The solution to the above equation is:
N = K
(1 e r(t^ t* )^ )
Note that t* corresponds to the time at which N = K/2. Substituting t = 0 , lets us solve for t* :
t* =^1 r ln( NK 0 1)
Where N 0 is the population at a time t = 0.
In the usual application of logistics growth equation, the growth rate is known at t = 0 , but this is not the same as the growth rate r. To find r , let us introduce another factor R0. Let R0 = instantaneous rate of growth at t = 0. If we characterise the growth at t = 0 as exponential, then:
(dNdt )t 0 R 0 N 0 But (dNdt )t 0 rN 0 (1 N K^0 )
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This means if the population growth is logistic, then the maximum sustainable yield will be obtained when the population is half the carrying capacity.
In any analysis modelling, certain degrees of uncertainty are unavoidable. However, by carrying out sensitivity analyses, we can determine the degree of uncertainty. Let us discuss it next. But, first, work out Learning Activity 8.2.
8.3 SENSITIVITY ANALYSIS
Any analysis, be it an environmental model, a cost-benefit study or other investigation, will involve a number of input factors having
LEARNING ACTIVITY 8.
Suppose the human population follows a logistic curve until it stabilises at 15.0 billion. In 1986, the world’s population was 5.0 billion and its growth rate was 1.7%. Calculate when the population will reach 7.5 billion > 14 billion. Note : a) Write your answer in the space given below. b) Check your answer with the one given at the end of this Unit.
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different degrees of uncertainty. These will influence the outcomes of the study to varying extents. In an input-output process (see Figure 8. 1), we usually select the inputs as the most-likely values. The relative responses of outputs to changes in inputs are termed by their sensitivity. That is to say, if large changes in an input produce insignificant changes in an output, the output is insensitive to the input.
A basic test of sensitivity is whether a percentage change in an input factor produces a higher or lower percentage change in an output. For example, consider now the model shown in Figure 8.
- The factors in the analysis can be seen as inputs (A, B), and the outcomes as outputs (D, E). If the parameters were set at X = 1.1 and Y=1.9 , and the most likely inputs were A = 75 and B = 102 , the outputs will be D = 27.1 and E = 12..
Now, suppose that input A is considered to be accurate to ±40%, and input B to be accurate to ±25%. The limits for A may, therefore, be from 45 to 105, and for B from 76.5 to 127.5. Taking the highest and lowest sets of values, we can repeat the calculations. The low values of A and B lead to outputs of D = 22.3 (-18%) and E = 11. 4 (-7%). The high values of inputs (with A truncated from 105 to 100) will give D = 30.8 (+44%) and E = 12.7 (+4%). The percentage figures show that the outputs are relatively insensitive to the 25% changes in inputs. This is due to the square roots and log functions in the equations for D and E. Other relationships may amplify the changes in inputs and accordingly give sensitive responses.
The extent of the change in an outcome, such as a level of CO 2 emissions or a net present value, NPV, relative to the change in the input, can be used as a mathematical indicator of sensitivity, similar to the concept of elasticity in microeconomics. Some
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varying a factor sufficiently to cause a reversal of the outcome given with its most likely value. (For example, if a transportation project produces pollution in excess of some regulatory standard, the analyst could determine how far the process must be modified to meet the standard. This technique requires an iterative search, and is probably only practical if an analysis has been computerised.)
It is useful to carry out sensitivity analyses at a preliminary stage of a large study to identify which of the factors involved have the greatest bearing on results. Particular attention can then be paid to data collection and estimation, so that they can be estimated as accurately as possible. The less important factors need only be estimated approximately.
LEARNING ACTIVITY 8.
In the prediction of impacts of combustion of coal in thermal power plant on surrounding air quality, describe three parameters which you would consider in sensitivity analysis. Note : a) Write your answer in the space given below. b) Check your answer with the one given at the end of this Unit.
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Following our discussion of sensitivity analysis, which is important in any kind of terrain study, we will next discuss the application of remote sensing and GIS in environmental management. Modelling and sensitivity analysis find immense use in such applications.
8.4 APPLICATION OF REMOTE SENSING AND
GIS IN EM
Benefits of harnessing the new developments in high technology areas like space technology and information technology for sustainable development have been well recognised and many developing countries are looking towards assimilating these technologies as part of their developmental plans. Satellite remote sensing integrated with Geographical Information System (GIS) technology provides a tool for addressing the issues of spatial reference in enhancing the quality of life and sustainable development.
Geographical Information Systems are computer aided decision support and planning tools, which integrate data from maps (spatial data) and other auxiliary data (attribute data) for a geographical area of interest. They can be used to create and maintain geographic databases and are eminently suited for what- if-analysis in any planning related activity. GIS applications are developing rapidly. A GIS application is able to provide many simulated results, which help in making informed decisions. These simulated results also help decision-makers in addressing such questions as the following:
Where is the most polluted area in the industrial area and how much is the total?
