IBM researchers are developing a system that can predict how 
bad pollution will be across the city of Beijing 72 hours in advance.

A woman wearing a face mask makes her way along a street in Beijing on January 16, 2014.

IBM is testing a new way to alleviate Beijing’s choking air pollution with the help of artificial
intelligence. The Chinese capital, like many other cities across the country, is surrounded
by factories, many fueled by coal, that emit harmful particulates. But pollution levels can vary
depending on factors such as industrial activity, traffic congestion, and weather conditions.

The IBM researchers are testing a computer system capable of learning to predict the
severity of air pollution in different parts of the city several days in advance by combining large
quantities of data from several different models—an extremely complex computational
challenge. The system could eventually offer specific recommendations on how to reduce
pollution to an acceptable level—for example, by closing certain factories or temporarily
restricting the number of drivers on the road. A comparable system is also being developed 
for a city in the Hebei province, a badly affected area in the north of the country.

“We have built a prototype system which is able to generate high-resolution air quality
forecasts, 72 hours ahead of time,” says Xiaowei Shen, director of IBM Research China.
“Our researchers are currently expanding the capability of the system to provide medium-
and long-term (up to 10 days ahead) as well as pollutant source tracking, ‘what-if’ scenario
analysis, and decision support on emission reduction actions.”

The project, dubbed Green Horizon, is an example of how broadly IBM hopes to apply its
research on using advanced machine learning to extract insights from huge amounts of
data—something the company calls “cognitive computing.” The project also highlights an
 application of the technology that IBM would like to export to other countries where pollution
 is a growing problem.

IBM is currently pushing artificial intelligence in many different industries, from health
care to consulting. The cognitive computing effort encompasses natural language processing
and statistical techniques originally developed for the Watson computer system, which
competed on the game show Jeopardy!, along with many other approaches to machine
learning (see “Why IBM Just Bought Millions of Medical Images” and “IBM Pushes Deep
Learning with a Watson Upgrade”).

Predicting pollution is challenging. IBM uses data supplied by the Beijing Environmental
Protection Bureau to refine its models, and Shen says the predictions have a resolution of a
kilometer and are 30 percent more precise than those derived through conventional
approaches. He says the system uses “adaptive machine learning” to determine the best
combination of models to use.

Pollution is a major public health issue in China, accounting for more than a million
deaths each year, according to a study conducted by researchers at the University of
California, Berkeley. It is also a major subject of public and political debate.

China has committed to improving air quality 10 percent by 2017 through the Airborne
Pollution Prevention and Control Action Plan. This past April, an analysis of 360 Chinese
cities by the charity Greenpeace East Asia, based in Beijing, showed that 351 of them
had pollution levels exceeding China’s own air quality standards, although levels had
improved since the period 12 months before. The average level of airborne particulates
measured was more than two and a half times the limit recommended by the World Health
Organization.

Thank You For Reading
For More Article
Click here