BIG DATA VS. BAD AIR

BIG DATA VS. BAD AIR

Physics simulations and AI combine to give pollution

forecasts to city dwellers in Beijing and beyond

In mid-October 2016, officials

from China’s Ministry

of Environmental Protection

counted five illegal trashburning

sites and hundreds of thousands

of vehicles exceeding emission standards

in Beijing alone. For the first time since

last winter’s pollution high season, city

officials issued a yellow air-quality alert,

which required shutting down power

plants and reining in Beijing’s frenetic

factories and road traffic. If this winter is

anything like past winters, the city will

have to pull out the yellow card again—and

may even have to reach for its red card.

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 datahungry

statistical tools such as machine

learning to try to make better forecasts

in less time.

“Our advantage or differentiation is to

combine all those together,” says environmental

engineer Jin Huang, who is

project manager for the Green Horizon

Initiative at IBM Research–China,

in Beijing. IBM reports an accuracy of

over 80 percent for 3-day forecasts and

around 75 percent for its 7- to 10-day forecasts.

Microsoft now provides China’s

Ministry of Environmental Protection

NEWS

with a 48-hour forecast that as of 2015

reached 75 percent accuracy for 6 hours

and 60 percent for 12 hours in Beijing.

How best to combine physics models

and machine learning for air-quality forecasts

is “an active research area,” says

atmosphere scientist Vincent-Henri Peuch,

the head of the European Copernicus

Atmosphere Monitoring Service in Reading,

England. He adds that blending is the

right choice: Both types of models have

something to offer and do not need to

preclude each other. The market seems

to agree so far. IBM now offers its combined

model in New Delhi and Johannesburg,

and the Beijing startup AirVisual

also offers machine-learning-enhanced

forecasts for private commercial use.

Beijing officials have been able to claim

some success beating down their fineparticle

pollution levels: They reported

that 2015 levels were 6 percent below

2014 levels. And while governments are

under pressure to reduce air pollution,

they are also under pressure not to let economic

growth slip. IBM’s forecasting tool

includes a simulator for measures such as

shutting down factories upwind of the city

or reducing road traffic for a day or two.

“The tool estimates both emissions outcomes

and the economic consequences of

each proposed intervention,” Huang says.

AirVisual, IBM, and Microsoft are all

generalizing their software to work in

different locations, which requires integrating

different local physical models

on the one hand but also tuning for

differing types of input data and their

changing parameters. Johannesburg, for

example, has just 8 monitoring stations

to Beijing’s 35. Still, “there’s an opportunity

to reuse some of the assets they

developed here in South Africa,” says

computer engineer Tapiwa M. Chiwewe,

at the newly opened IBM Research lab

in Johannesburg.

Each setting may require its own

type of machine learning, a University

of British Columbia team reported

in 2016. In their study, they found that

the computational expense of several

types of learning depended on how

much data they included up front versus

how much data they fed into the

program during its operation. The best

solution for a place such as Beijing,

with just a couple of years of historic

air-quality data, may differ from what’s

best for a city with many more years of

historical data, and that poses a challenge

for officials trying to choose the

right system for their city. It is difficult

to compare different models without

using the exact same data set at the

same location, Peuch warns.

And cities around the world have a

long way to go before they bring air

quality down to levels recommended

by the World Health Organization. In

2015, ambient particulate matter—which

does not include tobacco smoke—cost

103.1 million disability-adjusted life years

(a measure of the quality and length of

human life), according to the 2015 Global

Burden of Disease Study in The Lancet,

making it the sixth most harmful disease

risk factor. That makes it an important

target for governments and companies.

By one estimate, the market for monitoring

air quality will grow 8.5 percent

per year for the next five years, reaching

US $5.64 billion. It seems safe to forecast

that the market for air-quality forecasting

will grow, too.