Chinese reearchers have developed and pioneered an innovative hybrid framework to trace sources and changes the origins and fluctuations of CO2, carbon dioxide emissions from roads in real - time with 30 - metre resolution according to a research article published in the Journal Sustainable Cities and Society and also reported by CGTN, news channel of state - run China Global Television Network, based in the capital of the country, Beijing.
Urban expansion and population mobility have driven a continuous rise in road carbon dioxide expansion, poising significant challenges in terms of local climate regulation, public health and carbon neutrality.
In China, the development of precise monitoring methods for performing multi - factor analysis of on - carbon dioxide levels is considered of great importance for their effect reduction.
Chinese experts consider that this novel approach has the potential to be rolled out across other cities such as Shenzhen, south China’s Province, and is expected to be used as a crucial tool in more Chinese centres to assess and promote carbon dioxide reduction on urban road networks.
According to a researcher at the Aerospace Information Research Institute of Sciences and corresponding author of the study, Wang Li, a significant drawback of existing carbon emission inventories is their limited spatial resolution.
The development of precise monitoring techniques for conducting multifaceted analyses of on - road carbon dioxide levels is therefore considered vital for implementing effective reduction measures.
The researcher and his team’s framework combines Panoptic - Artificial Intelligence (Panoptic AI) with a mobile observation framework able to predict hourly on - road carbon dioxide at the 30 - metre resolution.
This advanced technology offers a dynamic, daytime prediction of carbon dioxide increments within urban traffic networks, an innovative development integrating AI with panoramic cameras, high - precision greenhouse gas analysers, and meteorological sensors to simultaneously collect a range of data.
This data include road carbon dioxide concentrations, traffic volumes, building layouts, vegetation cover, and meteorological conditions, all gathered during mobile surveys.
The research team achieved an average identification accuracy of over 93% for emission sources and in addition this framework can quantify the influence of specific factors, such as meteorological variables, surrounding land cover and traffic conditions, clearly revealing the spatio - temporal dynamics and underlying mechanism driving carbon emissions.
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