AI and Big Data vs. Air Pollution

Physics simulations and AI combine to give pollution forecasts to city dwellers in Beijing and beyond

3 min read
Photo: Imaginechina/AP Photo
Could’ve Seen It Coming: A heavy smog day in Beijing in November 2016 might have been predicted by IBM and Microsoft.
Photo: Imaginechina/AP Photo

Beijing and other Chinese cities are choking under a blanket of smog. It’s so thick in Tianjin that planes can’t land. Authorities have issued the first “red alert” of 2016, and 1,200 Beijing-area factories were ordered to shut down or to reduce production, according to press reports.

This winter, officials will be equipped with forecasting tools from IBM and Microsoft that they tested last year. IBM’s tool, used by the city government, is designed to incorporate data from traditional sources, such as the 35 official multipollutant air-quality monitoring stations in Beijing, and lower-cost but more widespread sources, such as environmental monitoring stations, traffic systems, weather satellites, topographic maps, economic data, and even social media. Microsoft’s system incorporates data from over 3,000 stations around the country. Both IBM’s and Microsoft’s tools blend traditional physical models of atmospheric chemistry with data-hungry statistical tools such as machine learning to try to make better forecasts in less time.

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Greg Mably


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