Environmental Studies: Systems, Pollution and Sustainability
What this domain tests
SEC Environmental Studies tests whether you can explain environmental systems rather than recite slogans. Expect definitions, processes, data interpretation and Maltese contexts: water scarcity and reverse osmosis, coastal erosion, construction dust, traffic emissions, waste separation and marine litter. Marks go to accurate terminology and clear cause–effect chains, often in structured answers that ask you to define, explain, give an example and suggest a management measure.
Core rules and concepts
- Systems thinking: name the input, pathway and output - for example fertiliser → runoff → watercourse.
- Ecosystem terms: habitat, population, community, biodiversity and carrying capacity must be used precisely; a population is one species, a community is all species.
- Pollution: a harmful change in air, water or soil caused by a substance or energy at a rate the environment cannot absorb.
- Resources: classify renewable and non-renewable; conservation means using less, while preservation means protecting from use.
- Sustainability: meeting present needs without reducing future options; mitigation reduces the cause, adaptation copes with the effect.
- Fieldwork and data: quadrats and transects estimate abundance; always state sample size, units and a limitation.
Worked example
Question: Nitrate fertiliser washes into a valley watercourse. Explain how this can kill fish. Reasoning: Nitrates are nutrients → algae grow rapidly in an algal bloom → algae die and decompose → bacteria consume dissolved oxygen → oxygen levels fall → fish suffocate. A full-mark answer names each link and adds a measure such as buffer strips or reduced fertiliser application.
Traps that cost marks
- Writing ‘pollution is bad’ without the mechanism.
- Confusing the greenhouse effect (essential; enhanced by CO₂) with ozone depletion (caused by CFCs).
- Mixing mitigation with adaptation.
- Suggesting a solution that does not match the cause.
- Ignoring units, sample size or scale in data questions.