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3.2.8Exchange and Transport

Pollution and Smoking Data — AQA A Level Biology 7402 Revision Notes & Topical Questions

5 min read 9 parts Lesson 17 of 48 1 quiz

📚 AQA A Level Biology 7402 Hub / Topical Revision
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3.2.8 Pollution and Smoking Data — AQA A Level Biology 7402 notes: Interpret data on air pollution, smoking and lung disease, describing trends, doing calculations and drawing valid conclusions.The effect of smoking and pollution on lung health is mostly shown by epidemiological …

  • epidemiological data
  • carbon monoxide
  • Percentage change = (new − original) ÷ original × 100
  • confounding variables
  • Exam-style question 1
  • Exam-style question 2

Section 1 of 9Why data matter

The effect of smoking and pollution on lung health is mostly shown by epidemiological data: large studies comparing groups of people.

Why data matter
In examinations you will interpret tables and graphs and need to describe, calculate and evaluate.
Use the command words carefully: describe means state the pattern with figures; explain means give the biological reason; evaluate means judge strengths and weaknesses.

Section 2 of 9Smoking and the lungs

Tobacco smoke contains tar (carcinogens), carbon monoxide and nicotine. The main effects are:

1
Tar paralyses and destroys cilia and leads to goblet cell overproduction of mucus, causing chronic bronchitis and a smoker's cough.
2
Irritants trigger inflammation and release of elastase, causing emphysema.
3
Carcinogens cause mutations in tumour suppressor genes and proto-oncogenes, leading to lung cancer.
4
Carbon monoxide combines with haemoglobin, reducing oxygen transport.

Data usually show that risk of lung cancer increases with the number of cigarettes smoked daily and with years of smoking, and falls after quitting but does not reach the non-smoker level at once.

Section 3 of 9Pollution

Pollutants include particulates (PM2.5 and PM10), nitrogen dioxide, sulfur dioxide and ozone. Fine particles enter small airways and alveoli, causing inflammation, asthma attacks and increased risk of cancer. High pollution days are associated with increased hospital admissions for respiratory conditions, particularly in children and the elderly.

Section 4 of 9Describing and manipulating data

Quote values from the axes, including units, and describe the overall trend and any anomalies.
Percentage change
(new − original) ÷ original × 100
.
Rate of change from a graph: change in y ÷ change in x, using a tangent for curves.
Ratios or relative risk: risk in exposed group ÷ risk in unexposed group.
Use of mean, range and standard deviation to compare groups; overlapping error bars suggest the difference may not be significant.

Section 5 of 9Evaluating studies

A sound study uses a large, random sample, similar groups matched by age, sex and occupation, and is repeated.

Cohort studies follow people over years; case-control studies compare those with disease to healthy controls.
Confounding variables
Be aware of confounding variables: smokers may also differ in diet, exercise, alcohol and pollution exposure.
Self-reported smoking can be unreliable.
These ideas lead into 3.2.9 and 3.2.10 on risk and causation.

When a question asks for a conclusion, make it precise: name both variables, state the direction of the relationship, and add a qualification such as "in this sample". For example: "the more cigarettes smoked per day, the higher the death rate from lung cancer in this sample of men aged 50–60".

Section 6 of 9Worked interpretation of a data set

Imagine a table of deaths from lung cancer per 100 000 men per year: non-smokers 15, 1–14 cigarettes a day 120, 15–24 a day 250, 25 or more a day 400. A good description states that death rate rises with the number of cigarettes smoked, from 15 for non-smokers to 400 for the heaviest smokers, which is more than 26 times higher (400 ÷ 15 = 26.7). The data show a dose-response relationship, which supports (but does not by itself prove) a causal link.

For pollution, consider a graph of hospital admissions for asthma against the PM10 concentration measured on the same day.

Worked interpretation of a data set
A positive correlation might be seen, with admissions about 5% higher when PM10 rises by 10 µg m−3.
You should say that the particles may trigger the inflammation of the airways,
  • and also point out that temperature, pollen and viral infection can vary at the same time.

Data may also be displayed as bar charts with error bars, or as a line graph with two lines, such as smokers and ex-smokers. For a comparison, quote values at the same point on both lines. For an ex-smoker's graph, the lower line shows the benefit of stopping.

When asked about the reliability of the data, comment on sample size, how the numbers were collected (for example death certificates against self-reported symptoms), how many years the study lasted and whether the results agree with other independent studies. When asked about validity, ask whether the investigation controlled for age, sex, occupation and other factors affecting lung health.

Section 7 of 9Exam-style questions

  1. In a study, 12 of 1000 non-smokers and 96 of 1000 smokers develop lung cancer. Calculate the relative risk for smokers. [2]2 marks

  2. Cases of emphysema in a town rose from 240 to 330 over five years. Calculate the percentage increase. [2]2 marks

  3. Explain how smoking can lead to chronic bronchitis. [3]3 marks

Section 8 of 9Key terms

TermMeaning
CarcinogenSubstance that causes cancer.
TarMixture in tobacco smoke that damages cilia and causes cancer.
ParticulatesTiny solid particles in air that enter the lungs.
Relative riskRisk in exposed group divided by risk in unexposed group.
Cohort studyFollows a group over time to see who develops disease.
Confounding variableAnother factor that may affect the outcome.

Section 9 of 9Exam technique

1
Stating that a correlation proves smoking caused a disease without discussing other variables.
2
Describing a trend without quoting values from the data.
3
Using the wrong base value for percentage change.
4
Saying that carbon monoxide blocks the airways; it binds to haemoglobin.
5
Confusing relative risk with absolute risk.

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