by Angela Guess
Will Knight recently wrote in the MIT Technology Review, “From the street, through Beijing’s heavy smog, it can sometimes be hard to make out IBM’s Chinese headquarters: a towering office building with a distinctive undulating architectural flourish and a large company logo at the top. But just a short distance away, on the northeast outskirts of the capital, IBM computer scientists are using artificial intelligence to develop what they think will be a way to manage China’s notorious and chronic pollution problem more successfully. The team is using complex computer models and machine learning to calculate how pollution will spread across the city. The researchers can now produce pollution forecasts, with a resolution of a kilometer square, up to 10 days in advance. These predictions can also tell the government how it might act to avoid the worst scenarios—for instance, by shutting certain factories, or by reducing the number of cars on the road.”
Knight continues, “When MIT Technology Review visited the offices of IBM Research–China last November, the air was particularly bad. Cold weather had increased demand for electricity, forcing nearby coal plants to boost output. This, combined with the usual traffic chaos, had produced some truly lung-scorching smog. Pollution is measured in terms of the amount of fine particulate matter per cubic meter. For a developed city, the World Health Organization recommends that this figure not exceed 25. During my visit, it reached almost 250. The modeling system, called Green Horizon, was being used to predict the spread of pollution; but it was not clear whether the government had decided to limit either factory output or the number of cars on the road. The need for heating seemed to be outweighing the ill effects.”
Photo credit: Flickr/ kevin dooley