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Ice Core Data Shows Why Air Pollution is Reducing Slower than Sulfur Emissions Reductions

Ice Core Data Shows Why Air Pollution is Reducing Slower than Sulfur Emissions Reductions Written by AZoCleantechJun 1 2021 Ice core data obtained from Greenland indicates why air pollution has been decreasing slower compared to reductions in sulfur emissions. The researchers in the drilling operation (left) and the drilled samples (right). Image Credit: Hokkaido University When cloud droplets turn out to be less acidic, the chemical reaction that converts sulfur dioxide into sulfate aerosol becomes more effective. These findings can enhance the models that predict climate change and air quality. In the United States and Western Europe, the air is much cleaner compared to how it was a decade ago. Low-sulfur gasoline standards and regulations on power plants have been successful in cutting sulfate concentrations in the air, thereby decreasing the fine particulate matter that tends to impact human health and cleaning up the environmental risk of acid rain.

Declining deer population likely due to natural regulation

The price is right: Modeling economic growth in a zero-emission society

With increasing public awareness of crises associated with degraded environments and mounting pressure to act, governments worldwide have begun to examine environmentally sustainable policies. However, there are many questions about whether enacting these policies will negatively affect economic growth. Now, a model created by researchers in Japan suggests that sustained GDP growth is possible even after spending to clean up pollution as it is created, providing hope that a zero-emission society is an achievable goal.

Less is more? New take on machine learning helps us

Researchers from Tokyo Metropolitan University have enhanced super-resolution machine learning techniques to study phase transitions. They identified key features of how large arrays of interacting particles behave at different temperatures by simulating tiny arrays before using a convolutional neural network to generate a good estimate of what a larger array would look like using correlation configurations. The massive saving in computational cost may realize unique ways of understanding how materials behave.

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