smog air pollution in china

AI system can predict air pollution before it happens

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An artificial intelligence system has been developed that can predict air pollution levels hours in advance and could help people with health problems to avoid going outside.

Air pollution kills an estimated seven million people every year and cities around the world are being forced to take action to do what they can to lower the risk to inhabitants.

A team of Loughborough University computer scientists believe their AI system has the potential to provide new insight into the environmental factors that have significant impacts on air pollution levels.

In particular it focuses on the amount of ‘PM2.5’ particulates in the air – that is particulate matter of less than 2.5 μm in diameter that is often characterised as causing reduced visibility in cities and hazy-looking air when levels are high.

In 2013, a study involving 312,944 people in nine European countries revealed that there was no safe level of particulates.

PM2.5 particulates were found to be particularly deadly, blamed for a 36 per cent increase in lung cancer per 10 μg/m3 as they can penetrate deep into the lungs.

Worldwide exposure to PM2.5 contributed to 4.1 million deaths from heart disease and stroke, lung cancer, chronic lung disease, and respiratory infections in 2016.

There are systems that already exist that can predict PM2.5 but Loughborough's research looks to take the technology to the next level.

The system the researchers have developed can predict PM2.5 levels in advance – one hour to several hours’ time, plus 1-2 days ahead.

It interprets the various factors and data used for prediction, which could lead to a better understanding of the weather, seasonal and environmental factors that can impact PM2.5

It even predicts the PM2.5 level plus a range of values the air pollution reading could fall within – known as ‘uncertainty analysis’.

It also has the capability to be used as an air pollution analysis tool for use in a carbon credit trading system.

The system’s uncertainty analysis and ability to understand factors that affect PM2.5 are particularly important as they will allow potential end-users, policymakers and scientists to better understand related causes of PM2.5 and how reliable the prediction is.

The Loughborough team created the system with machine learning and used public historical data on air pollution in Beijing to train and test the algorithms. China was selected as the focus as 145 of 161 Chinese cities have serious air pollution problems.

The developed system will now be tested on live data captured by sensors deployed in Shenzhen, China.

Project leader professor Qinggang Meng said: “Air pollution is a long-term accumulated challenge faced by the whole world, and especially in many developing countries.

“The project aims to measure and forecast air quality and pollution levels. We also explore the feasibility of linking the real-time information on carbon emission to end-to-end carbon credit trading, thus dedicating to carbon control and greenhouse gas emission reduction.

“We hope this research will help lead to cleaner air for the community and improve people’s health in the future.”

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