The technology has the potential to provide new insight into the environmental factors that have significant impacts on air pollution levels.
Professor Qinggang Meng and Dr Baihua Li are leading the project which is focussed on using AI to predict PM2.5, which is particulate matter of under 2.5 microns (10−6 m) in diameter and a pollutant with the strongest evidence for public health concern. This is because the particles easily get into the lungs and then the bloodstream, resulting in cardiovascular, cerebrovascular and respiratory impacts.
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According to the UK’s Department for Environment, Food and Rural Affairs, there is understood to be ‘no safe threshold below which no adverse effects would be anticipated’.
The new system is said to give predictions for the levels of PM2.5 in one hour to several hours’ time, plus 1-2 days ahead, and 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 is further claimed that the new system predicts the PM2.5 level plus a range of values the air pollution reading could fall within, known as ‘uncertainty analysis’; and can be used as an air pollution analysis tool in a carbon credit trading system.
The system’s uncertainty analysis and ability to understand factors that affect PM2.5 are particularly important as this will allow potential end-users, policymakers and scientists to better understand related causes of PM2.5 and how reliable the prediction is.
China was the focus of the study as 145 of 161 Chinese cities have serious air pollution problems. Public historical data on air pollution in Beijing was used by the researchers to train and test the machine learning algorithms. The developed system will now be tested on live data captured by sensors deployed in Shenzhen, China.
The system developed at Loughborough University is part of a wider research project funded by the Newton Fund, which includes Satoshi Systems Ltd, Shenzhen Institutes of Advanced Technology, and EEG Smart Intelligent Technology in China as partners along with the University.
The aim is to integrate Loughborough University’s PM2.5 prediction model onto an online platform that can be accessed by participants in a carbon trading scheme, allowing them to access real-time, meaningful information on pollution levels that will help them design a trading strategy.
In a statement, Professor 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.”
More information can be found at Triple-Network Air Quality Monitoring and Carbon Credit Trading Platform for Sustainable Urban Environments.
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