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Taking a Smart, Software-Based Approach to Air Pollution

In many cases, the data needed to drive smart pollution analytics already is readily available.

Data is going to fix everything, from managing global computer networks to predicting Aunt Millie’s next online knitting-needle purchase, or so we’ve been told. Now add to the growing roster of data-driven insights: the very air we breathe.

That’s what San Francisco-based startup BreezoMeter is pitching, and it’s an idea that's gaining traction. The U.S. Environmental Protection Agency says it is poised to award two big grants to encourage data analytics in the realm of air quality, while IBM is helping China use data to clean up that nation’s famously foul air.

“Eighty percent of people living cities around the world are exposed to air quality level that the World Health Organization calls unsafe,” said Ziv Lautman, a co-founder and chief marketing officer of BreezoMeter. “The solution starts with understanding the patterns. It starts with knowledge and intelligence. It starts with data.”

Founded in 2014, with operations in the U.S. and Israel, the company demonstrated its Smart City technology at the Smart City Expo in Barcelona, Spain, in mid-November. As a software-based approach to air pollution, BreezoMeter aims to apply sophisticated analytics to data drawn from a range of systems. It looks at weather, traffic and other existing metrics, and combines these to produce maps of pollution intensity and patterns.

In a demonstration project with Cisco in Place de la Nation, Paris, the company has shown that it can use sensor data to identify areas where air pollution may be headed over the course of a few hours. This may enable city officials to strategically reroute traffic or take other corrective action.

The company is not alone in parsing data as a smart-city solution on the air quality front. IBM, for example, has been working with the Beijing Environmental Protection Bureau through a project it has dubbed Green Horizons.

That project combines cognitive computing with the Internet of Things to generate high-resolution 1km-by-1km pollution forecasts as much as 72 hours in advance, and may even predict pollution trends 10 days out, according to IBM researchers. Data can be used to model and predict the impact of weather on the dispersal of pollutants.

Better information leads to better management. Guided by IBM data, the Beijing government was able to achieve a 20 percent reduction in ultra-fine particulate matter in 2015, according to the company. Spurred by this success, IBM has sought to extend the air quality data effort. It has struck a deal with the Delhi Dialogue Commission to explore the correlation between traffic patterns and air pollution in India’s capital. The company also says it is working with the city of Johannesburg and South Africa’s Council of Scientific and Industrial Research to model air pollution trends.

Even as the private sector pursues these early forays, the U.S. government also is taking a hard look at the potential for data analytics to impact air quality. The EPA is expected to announce in December the winners of its Smart Cities Air Challenge, a contest launched in August by CIO Ann Dunkin.

Two communities will receive up to $40,000 each to deploy air sensors, share data with the public and develop data management best practices from these sensors.

“Air quality sensors are becoming less expensive and people are beginning to use them to measure pollution levels in their neighborhoods and homes,” Dunkin said in a press release announcing the contest. With the EPA’s help in the deployment phase, “communities will learn how to use data analytics, which can be applied to other aspects of community life.”

In many cases, the data needed to drive smart pollution analytics already is readily available, Lautman said.

Sixty percent of pollution comes from cars, and civic leaders have plenty of traffic data. In addition, the government operates some 2,500 EPA monitoring stations, gathering information on a range of pollutants. Add to this publicly available weather information, and you can start to get a pretty solid picture of where the bad air accumulates.

“We know where the pollution is coming from, where it is going, what times and what days are more sensitive to pollution. We can alert you in real time when there is a problem,” Lautman said, adding that governments can then take corrective measures. “If we see a traffic jam on the north side of the city — that is knowledge, that is intelligence that the city can take action on.”

Data can be used not only to inform action, but also to mold public understanding. With the facts of environmental degradation still dismissed even by national leaders, smart city advocates need tools that help to tell the story.

“How can you fight an enemy that you can’t see? Everyone speaks about climate change, but you can’t see it. Likewise, in most places in the world you can’t see air pollution, and that makes it hard to address,” said Lautman. “With data analytics you can see this street is polluted. You can see that you are downwind from a factory. You can see where the traffic is. The data is everywhere already. Now we want the pollution information to be everywhere too.”

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