Interpreting a Murky Chlorophyll Extraction
A student is investigating the effect of light intensity on photosynthesis in pondweed. She places a 10 cm sprig of Elodea in a beaker of 1% sodium hydrogen carbonate solution at 25 °C and counts oxygen bubbles rising from the cut stem over 5 minutes at five distances from a lamp. At 10 cm she counts 82 bubbles; at 20 cm, 47; at 30 cm, 28; at 40 cm, 19; at 50 cm, 14. She then repeats the 10 cm trial but the lamp has been moved slightly and only 55 bubbles are counted. Her partner suggests discarding the 82 and using 55 as the 'true' result. A second student cannot get any bubbles at all, even at 10 cm, using the same setup but with tap water instead of sodium hydrogen carbonate solution.
1. Evaluate whether the partner is justified in discarding the 82-bubble result. What should the student do instead, and why?
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The partner is not justified. Discarding a result simply because it is higher than expected is data manipulation, not good practice - anomalous results should only be excluded on a defensible technical basis (e.g. a recognised procedural fault), and even then the exclusion must be reported. Here, the 82 count came from a valid trial under the stated conditions; the 55 count came after the lamp was moved, so the two trials are not directly comparable and the difference may reflect a real change in light intensity or geometry rather than error.
A better approach is to take repeated measurements at each distance (at least three), calculate a mean and identify outliers statistically or by inspection of spread, and report the range or standard deviation. The student should also standardise variables - fixed lamp distance measured from the beaker, same sprig, same temperature, same time interval - and record any deviation. If the 82 is genuinely anomalous, repeating the 10 cm condition will reveal it; if it is reproducible, it is real data.
The key reasoning is that reliability comes from replication and consistency, not from selecting the value that fits a preconception.
2. Explain why the second student obtained no bubbles, and predict what would happen to bubble counts if the first student used distilled water instead of 1% sodium hydrogen carbonate.
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Bubbles in this setup are oxygen produced by photosynthesis. The rate depends on the availability of dissolved inorganic carbon (mainly hydrogen carbonate and dissolved CO2) as a substrate for the Calvin cycle. Sodium hydrogen carbonate solution supplies a reservoir of dissolved carbon, so photosynthesis can proceed and oxygen is released. Tap water contains very little dissolved CO2 and essentially no hydrogen carbonate, so carbon fixation is severely limited; light and temperature are not limiting, so the light-dependent reactions cannot sustain oxygen evolution and no visible bubbles form. (Tap water may also contain chlorine, but the carbon limitation is the primary explanation.)
With distilled water, the same carbon limitation applies, so bubble counts would fall to near zero at all distances. The light-response curve would be flattened: increasing light intensity would produce little or no increase in oxygen evolution because carbon, not light, is the limiting factor. This illustrates the principle of limiting factors - the factor in shortest supply determines the rate, and relieving other factors has no effect until the limiting one is addressed.
3. The bubble-counting method is described as indirect. Explain what this means and suggest one improvement to make the measurement more reliable.
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Bubble counting is indirect because the observed variable (number of bubbles) is a proxy for the dependent variable of interest (rate of oxygen production). Each bubble may vary in volume, small bubbles may be missed, and bubbles may coalesce or dissolve before reaching the surface, so counts are only approximately proportional to oxygen evolved.
A more direct and reliable measure is to collect the gas in an inverted graduated tube (or a syringe) over a fixed time and record the volume, giving a quantitative measure of oxygen evolved per unit time. Alternatively, use a data logger with an oxygen sensor in a sealed chamber, or measure the volume of gas displaced. These reduce observer subjectivity and the error from variable bubble size.
Other improvements: count over a longer fixed interval to reduce percentage error, keep the same observer, use a controlled temperature water bath, and repeat each condition to calculate a mean.