Igcse environmental management, Apuntes de Ciencias Ambientales

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Gary Skinner, Ken Crafer, Melissa Turner, Ann Skinner and John

Stacey

Cambridge IGCSE

and O Level

Environmental

Management

Coursebook

Contents

How to use this resource

Introduction

Key skills in Environmental Management

Planning investigations Identifying limitations of methods and suggesting possible improvements Presenting explanations for phenomena, patterns and relationships Making judgements and reaching conclusions

1 Rocks and minerals and their exploitation

1.1 Formation of rocks 1.2 Extraction of rocks and minerals from the Earth 1.3 Impact of rock and mineral extraction 1.4 Managing the impact of rock and mineral extraction 1.5 Sustainable use of rocks and minerals End-of-chapter questions EXTENDED CASE STUDY (The Island Copper Mine, Vancouver Island, British Colombia, Canada)

2 Energy and the environment

2.1 Fossil fuels 2.2 Energy resources 2.3 The demand for energy 2.4 Conservation and management of energy resources 2.5 Impact of oil pollution 2.6 Management of oil pollution End-of-chapter questions EXTENDED CASE STUDY ( Exxon Valdez: an environmental disaster)

3 Agriculture and the environment

3.1 The soil 3.2 Soils for plant growth 3.3 Agriculture 3.4 Increasing agricultural yields 3.5 The impact of agriculture on people and the environment 3.6 Causes and impacts of soil erosion 3.7 Methods to reduce soil erosion 3.8 An integrated approach for sustainable agriculture End-of-chapter questions EXTENDED CASE STUDY (Controlling pests naturally: a flawed decision)

4 Water and its management

4.1 The distribution of water on Earth 4.2 The water cycle 4.3 Why humans need water 4.4 The main sources of fresh water for human use 4.5 Availability of safe drinking water around the world 4.6 Multipurpose dam projects 4.7 Water-related diseases 4.8 Sources, impact and management of water pollution End-of-chapter questions

Acknowledgements

How to use this resource

This resource supports the full Cambridge IGCSE®^ and O Level Environmental Management syllabuses (0680/5014) for examination from 2019.

pumas (Figure 0.2); the new island, with an area of under 15 km^2 , was simply too small to support these large cats.

Figure 0.2 A female puma.

The cats would have died out in this isolated habitat and the coatis would escape being eaten. This would not have been predicted at the time: an unintended consequence of the construction of the Panama Canal was the extinction of the curassow in a 15 km^2 patch of tropical jungle, now an island (Figure 0.3).

Figure 0.3 Two great curassows.

An even more dramatic example comes from the story of rabbits in Australia. This European mammal was introduced into Australia in 1788 on the so-called First Fleet (the first fleet of ships that left Great Britain to found a penal colony in Australia). However, it was not until the deliberate release of just 24 individuals in October 1859 that the numbers began to soar. By 1869 hunters were killing 2 million rabbits every year with no effect on the population. The huge numbers of rabbits in Australia have caused massive species loss and even geological changes, such as extensive gully erosion.

The story of the cane toad, again from Australia (see Chapter 3's Extended case study), is another example of the important principle of unintended consequences in environmental management.

We all have an environment around us and we are all part of everybody else's environment. This simple fact makes managing the environment one of the most important challenges for humans in the future.

Key skills in Environmental Management

When thinking about an investigation in environmental management you need to find out about a problem and how it is affecting the environment. An investigation has a sequence of stages:

1. planning the investigation 2. identifying limitations of the methods that were used and suggesting possible improvements 3. presenting reasoned explanations for phenomena, patterns and relationships that you have observed in your data 4. making reasoned judgements and reaching conclusions based on qualitative and quantitative information. All these stages involve certain skills and techniques, all of which are explained below and in the following chapters.

Planning investigations

Planning an investigation involves formulating an aim and one or more hypotheses. An aim identifies the purpose of your investigation and you should have a suitable aim in mind when planning. 'To investigate the effects of coal mining waste on soil pH' is an example of an aim. From the aim, the hypothesis or hypotheses arise. A hypothesis is a statement on the topic that you are investigating. It is a testable prediction that proposes a relationship between two variables: the independent variable , which is not changed by other variables you are measuring e.g. the age of a person the dependent variable , which is what you are measuring. In research, the hypothesis is written in two forms: the null hypothesis and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment). The null hypothesis states that there is no relationship between the two variables being investigated (one variable does not affect the other). Results are due to chance and are not significant in terms of supporting the aim being investigated. The alternative hypothesis states that there is a relationship between the two variables being investigated (one variable has an effect on the other). The results are not due to chance and they are significant in terms of supporting the aim being investigated. A hypothesis can be accepted or rejected by testing. This is achieved through data collection and analysis. A good hypothesis should: be a statement not a question be a prediction with cause and effect state the independent and dependent variables being tested be short in length. An example of a good hypothesis is 'There will be a decrease in soil pH with increasing distance from a site of coal mining waste'.

KEY TERMS Aim: identifies the purpose of your investigation Hypothesis: a statement on the topic that you are investigating Independent variable: the variable that is deliberately changed in an experiment Dependent variable: the variable that is measured in an experiment Null hypothesis: a hypothesis stating that there is no

taking a stratified sample ensures that each group is asked in the correct proportion.

KEY TERMS Bias: encouraging one outcome over another

Figure 0.5 summarises the different types of sampling methods.

Figure 0.5 Different types of sampling methods.

For example, imagine you wanted to give a questionnaire to a sample of 50 people from a farming area. The area has three villages. How many people should be given a questionnaire from each village?

Village 1 Village 2^ Village 3 356 people 233 people 426 people

Table 0.1 Population of the three villages.

a. The total number of people is 356 + 233 + 426 = 1015

b. To find the number of people from each village to be given the questionnaire, we multiply each village population by sample sizetotal population which in this case is 501015

Village 1 Village 2 Village 3 Sample size 365×501015=17.5 233×501015=11.4 426×501015=20. Rounded numbers of people to be questioned

Table 0.2 Sample size calculations.

A further example of stratified sampling is when soil types are being investigated in an area where 70% of the area consists of rock type A and the remaining 30% consists of rock type B. 70% of the soil samples should be taken on rock type A and 30% of the soil samples on rock type B.

Questionnaires and interviews This data collection method is used when trying to obtain people's opinions. Stratified sampling is used for questionnaires and careful consideration should be given to the day, time and location when the data are collected to avoid bias. Questionnaires can be carried out by approaching people in the street, knocking on people's doors, posting questionnaires or, if applicable, placing them on the internet. Each method has its own advantages and disadvantages. Can you think what they are? Questions should be pre-planned and it is always important to do a pilot survey to ensure that the people interviewed understand the questions (five respondents would be sufficient), and the answers provide the information you want to analyse. Always explain the aim of the questionnaire and be polite when asking people to complete them. Stress that the answers will be anonymous and although you should record the age and gender of the respondent, remember these are sensitive questions and should not be asked directly.

A good questionnaire should:

be carefully worded so people understand the questions and questions are not ambiguous be quick to complete and therefore have a limited number of questions in a logical order have closed questions at the beginning. Closed questions are those which can be answered by a 'yes' or 'no', or by a definite answer to the question being asked. Open questions are those which require more thought and require more than a simple one-word answer. They take longer to record but are useful if more information is required. However the answers might be difficult to record and analysis.

Always thank the respondent once the questionnaire has been completed.

An interview involves talking to a small group of people or an individual. You should have pre- planned questions and the answers are usually longer than those from a questionnaire.

Risk assessment

To collect data safely, you must be aware of potential health and safety issues relating to the equipment you are using (e.g. sulfuric acid or a Bunsen burner) or to the location of the investigation. You need to decide what equipment you might use and then ensure that the equipment is tested and, if necessary, calibrated before the investigation starts. You should always carry out a pilot survey.

KEY TERMS

Pilot survey: a trial run of a survey, which aims to discover any problems with the survey Calibrated: to check and make any necessary adjustments to a piece of equipment to ensure its accuracy

Recording data

It is useful to record data in a table format. The table should be created before data collection. When drawing up a table, remember the following guidelines:

When two or more columns are used, the first column should be the independent variable (i.e. the variable chosen by you, the experimenter) and the second and other columns should contain the dependent variable(s) (i.e. the readings taken for each change in the independent variable). Columns should be headed with the name of the variable and the appropriate unit. Numerical values inserted in the table should just be numbers, without units.

Practical activity 9.1 in Chapter 9 involves estimating plant coverage using quadrats. Table 0.3 is an example of a results table for this activity.

Independent variable/ units

Dependent variable 1/units

Dependent variable 2/units

Dependent variable 3/units

Dependent variable 4/units Distance/m Species 1/% cover Species 2/% cover Species 3/% cover Species 4/% cover 1 53 0 3 0 2 45 3 4 2 3 23 17 4 11 4 12 25 5 25 5 0 37 3 12

Table 0.3 Distribution of plant species along a transect line, following a standard layout for tables.

The first column shows the distance along the transect line (in metres), the second column shows the percentage coverage for a named species of plant, the third column shows the percentage coverage for a second species of plant, and so on.

One option in Practical activity 9.1 is to compare estimated plant coverage in two areas. Figure 0. shows a results table suitable for recording data for four species of plants, from two areas (A and B) using five quadrats in each area.

Identifying limitations of methods and suggesting possible

improvements

The chosen methods for collecting data for an investigation should be achievable and realistic, but you may still encounter limitations to your methods. The quantity and quality of data collected will be determined by available resources such as time, money, equipment, ICT, possible transport requirements and the number of people needed to collect the data.

Relying on other people to gather data can add a random element to the investigation so it is better if the same individual does the measuring. Your choice of sampling, in terms of type and size, is important and you should select a suitable sampling method at the planning stage. For example, you might think of choosing a systematic sampling method for an investigation into vegetation change along a transect but then realise that some of the sampling sites might be inaccessible.

Conditions in which data is collected, such as the weather when conducting a questionnaire, must be considered. Timing and location of data collection can also affect results, as can the sample size, so these factors should be considered too. Questionnaires should ideally be done at regular intervals as well as being timed to maximise respondents. The use of questionnaires can have other limitations, for instance some age groups can be reluctant to answer and not all age groups will be available if the questionnaire is conducted during work time.

Some data collection methods are subjective, for example visually estimating sediment shape and size. You can often use digital equipment in order to collect more objective information and limit human error. If the method involves the use of measuring equipment, ensure that it is calibrated before the investigation starts. By repeating the measurements, or using another piece of equipment, you can increase the reliability of your results.

If you use the internet for data collection, you must consider whether the websites you use are biased or if the information is inaccurate or outdated. Government websites usually provide reliable data.

Presenting explanations for phenomena, patterns and

relationships

There are a wide variety of presentation techniques that can be used to display collected data. The skill is choosing the technique that is most suitable for the data. It is important that all techniques used should have a title and that axes are labelled. If you use a key, it should also be labelled. Tables of data are simple to design but when data are presented visually it is often easier to see patterns and trends, so graphs and diagrams can be a better choice.

There are many different types of graph, such as line graphs, bar graphs, histograms, pie graphs and scattergraphs. More specialised types of graph include climate graphs and population pyramids.

Line graphs

A line graph is used when there is a continuous change in the data, often over time (Figure 0.7). The points are plotted as crosses or encircled dots and are connected with a clear straight line. The axes of a line graph begin at zero and the independent variable is put on the horizontal or x axis (e.g. time) and the dependent variable on the vertical or y axis. A suitable scale is important as it will influence the appearance of the line graph.

Figure 0.7 A line graph to showing the growth of a bean plant over time.

Bar graphs

Bar graphs are used to show data that fit into categories, e.g. the total number of plant species at different sites.

A bar graph has two axes. The bars should be drawn with equal width and with equal spaces between them (Figure 0.8).

Figure 0.8 A bar graph showing the average precipitation in Agra, India.

A divided bar graph can be used to show a set of data that is represented by percentages and is an alternative technique to a pie graph. A single bar representing 100% is subdivided into the different data categories.

Histograms

A histogram may look like a bar graph without the spaces between the bars, but it is different. Histograms are used to show frequencies of data in different categories (Figure 0.9) or change over

countries.

Scattergraphs are plotted in a similar way to line graphs but the points are not joined with a line. The data set that is likely to cause the change is called the independent variable and is plotted on the x axis. The dependent variable is plotted on the y axis. If the scattergraph shows a likely linear relationship, it is then appropriate to plot the line of best fit (trend line) by eye with an equal number of points above and below the line. The best-fit line does not have to pass through the origin of the graph and should be a single, thin, smooth straight line. There is more information on scattergraphs in Chapter 6.

Analysing your data

Once the data have been presented in a table, graph or diagram, it should be possible to analyse it by describing and explaining what you can see. For example, can you see any trends or associations and explain them? Simple statistical techniques can also be helpful in analysis. These include working out the range (the difference between the largest and smallest values) and the average. There are three kinds of average: mean, median and mode:

The mean is the total of all values divided by the total number of values. It is used when there are no extremes of values, which would distort the mean. The mode is the value with the highest frequency. The median is the value in the middle after the data has been sorted into ascending order. It is not affected by extreme values.

KEY TERMS Mean: the total of all values divided by the total number of values Mode: the value with the highest frequency Median: the value in the middle after the data has been sorted into ascending order

The median, range and interquartile range (the range of the middle 50% of the data) can be shown on a box-and-whisker plot. In Figure 0.12 the range of the data is 17–100 and the median is 68.

Figure 0.12 A box-and-whisker plot to show range and median values.

Making judgements and reaching conclusions

The conclusion is a summary of the investigation. Using evidence from your data analysis you should now be able to state whether you can accept or reject your hypothesis, giving reasons for your decision. For example the results for the hypothesis from the start of this chapter: 'There will be a decrease in soil pH with increasing distance from a site of coal mining waste', may show exactly that: soil pH does decrease as the distance from a coal mining waste site increases. If so, your conclusion should state this and your hypothesis can be accepted. As well as accepting or rejecting your hypothesis, the conclusion should set out the main findings and discuss if the aim has been achieved. You should also suggest reasons for your findings. For example, if you found that in some cases the soil pH remained the same at different distances, you would need to explain these findings further. Think about whether your findings relate to any previous studies you read about before you carried out the investigation or any theories you have studied.

However interesting your results, your investigation could probably be improved by identifying any areas of weakness. This is called evaluating your investigation. Was the sample, size too small? Was your investigation affected by bias? Did you have difficulty collecting your data? For example, if you were investigating how plant biomass changes in a saltmarsh you may have had an incomplete set of results if the tide came in and prevented you reaching some of the sampling sites. Which variables led to inaccuracy or unreliability of the data you collected? Also, think about how your investigation could be extended: could you test further hypotheses?