British research says that the ability of plants to absorb carbon dioxide is beyond the original understanding
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Last Update: 2012-03-30
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Source: Internet
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Author: User
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According to a new British study, plants are more capable of absorbing carbon dioxide and slowing down the effects of global warming than previously thought, so some models for predicting global warming may need to be revised accordingly The report, published in the British journal nature climate change, said that the researchers planted plants in an airtight container in the experimental facility of Imperial College of technology, and controlled the environment artificially, so that the concentration of carbon dioxide in the air gradually increased as predicted by existing climate change models, and the temperature in the container also gradually increased accordingly It was found that in this process, plants absorbed more carbon dioxide than originally thought The researchers estimate that this is equivalent to slowing the temperature rise of 2.3 degrees Celsius At present, researchers hold different views on how global warming and the earth's ecosystem will affect each other Some people think that rising temperature will cause some soil to release carbon dioxide, which will aggravate global warming; others think that plants will accelerate growth after rising temperature, thus slowing down global warming Alexandru milku, who conducted the study, said that the use of closed environment experiments can better simulate the earth's situation and thus help predict the prospects of global warming However, the researchers also said that for the whole earth, the ability of plants to absorb carbon dioxide may not be able to slow down the 2.3c temperature rise, because in this small experimental device, plants are provided with sufficient water and nutrients, but in the real ecological environment, plants will be limited by these factors Nevertheless, the researchers believe that the results of this study are enough to remind climate change researchers to modify the relevant prediction models.
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