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Source: People's Republic of China in Russian – People's Republic of China in Russian –
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Source: People's Republic of China – State Council News
BEIJING, Sept. 15 (Xinhua) — Chinese researchers have developed a hybrid system to track the sources and changes of carbon dioxide emissions on roads in real time with a resolution of 30 meters, according to a research paper recently published in the journal Sustainable Cities and Society.
The technology has been applied in Shenzhen City, Guangdong Province, southern China, and is expected to be used in more cities in the future to assess and help reduce carbon emissions on urban roads.
Urban expansion and population mobility have led to ever-increasing carbon dioxide emissions from roads, posing serious challenges in terms of local climate regulation, public health and carbon neutrality.
A key limitation of previous carbon emission inventories is their coarse spatial resolution, said Wang Li, corresponding author of the paper and a research fellow at the Aerospace Information Research Institute of the Chinese Academy of Sciences (CAS).
The lack of granularity makes it difficult to account for small-scale changes in emissions across different road sections or over time, making it even more difficult to accurately track the sources of emissions or explain what causes them, Wang Li said.
Developing accurate monitoring methods to conduct multivariate analysis of CO2 levels on roads is essential for their effective reduction.
Wang Li and his team developed their system, which combines Panoptic-AI and a mobile surveillance system, to predict hourly CO2 concentration on roads with a resolution of 30 meters and provide dynamic prediction of the increase in CO2 concentration in urban transport networks during the daytime.
The development combines artificial intelligence with panoramic cameras, high-precision greenhouse gas analyzers and weather sensors to simultaneously obtain data from multiple sources on road carbon dioxide concentrations, traffic volumes, building layouts, land cover and weather conditions during mobile surveys.
The research team achieved an average accuracy of over 93 percent in identifying emission sources. At the same time, the system allows for the quantitative assessment of the influence of individual factors such as traffic conditions, surrounding land cover and meteorological variables, thereby clearly revealing the spatio-temporal dynamics and mechanisms that determine carbon emissions.
“This method is an innovative application of artificial intelligence in environmental monitoring, and can build a multi-dimensional carbon monitoring system with a full range of applications when combined with traditional emissions inventories and satellite greenhouse gas monitoring technologies,” said Wang Li. -0-
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