Mapping a way to avoid pollution A team of researchers in Hong Kong is working on an application that will draw on artificial intelligence and big data to guide people away from air pollution hot spots, as Sylvia Chang reports.

With more than 1,700 deaths blamed on air pollution in the past year, Hong Kong is badly in need of help. It is on its way in the form of an air pollution map produced with artificial intelligence.

However, it will take about five years for the application, designed by researchers at the University of Hong Kong, to emerge from the city’s miasma of nitrogen dioxide, sulfur dioxide and ozone.

The map, capable of producing real-time readings or predictive analysis, will reveal where concentrations of toxic emissions are, and where they are likely to go. The app will not solve the pollution problem but it will allow people to see what they are getting into, and let them know if it would be better to change their plans.

The readings will show the concentration of pollutants right down to the level of the street they are on.

Badly polluted air can make outdoor exercise a health hazard, because tiny particles 30 times smaller than a human hair, known as PM2.5, can find their way deep into the lungs. The app will allow someone training for a marathon, children with physical education classes scheduled and people suffering from asthma to check PM2.5 levels and stay away from high concentrations, or even postpone training or going out that day.

According to the Hedley Environmental Index designed by the School of Public Health at the university, known as HKU, air pollution was responsible for about 1,780 deaths in Hong Kong in the past year.

Like a smartwatch, the system will be able to track a person’s fitness and activity level. But, more important, it will also read the air quality and even forecast it for the next hour or next day. That means it will be able to advise individuals about suitable activities based on both air quality and their personal health and fitness.

Researchers say it will provide estimates of PM2.5 concentrations for any geographical area in Hong Kong, down to the hectare level.

“It’s like weather reporting, on a real-time basis, to the street level,” said Victor Li On-kwok, chair professor of information engineering at the university’s Department of Electrical and Electronic Engineering, who is leading the team studying the system.

Hong Kong’s Environmental Protection Department operates 16 air quality monitoring stations across the city, three of which are beside roads. They provide the public with the government-designed Air Quality Health Index, which converts air pollution concentrations into a scale ranging from one, at the low end, to 10-plus.

But Li challenges the accuracy of the air quality readings provided by the monitoring stations, arguing that the number of measurements is limited and the data is too scattered geographically.

“Air quality is not like temperature, the variation of which is very small within a day,” Li said. “You can have one part of the city badly polluted, but just a few blocks away the air quality may be much better.”

Li and his team are designing a data-driven air pollution estimation model, based on more than 10 types of data related to urban dynamics, including air pressure, humidity, temperature, wind speed, wind direction, traffic speed and more.

The project evolved from an interdisciplinary study, involving scholars from public health management, environmental policy and environmental management. The University of Cambridge in the United Kingdom also cooperated in the study, joining with the Hong Kong research team to create the HKU-Cambridge University Clean Energy and Environment Research Platform. The Hong Kong government contributed HK$50 million ($6.37 million).

Preliminary research by the Hong Kong team has shown a clear relationship between higher air pollution and social deprivation.

The findings show low-income households are more likely to be located in areas exposed to higher PM2.5 concentrations than highincome households, with relatively