首页|Studies from Tsinghua University Provide New Data on Machine Learning (Dynamic Traffic Data In Machine-learning Air Quality Mapping Improves Environmental Justi ce Assessment)
Studies from Tsinghua University Provide New Data on Machine Learning (Dynamic Traffic Data In Machine-learning Air Quality Mapping Improves Environmental Justi ce Assessment)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published.According to news reporting originating in Beijing,Peo ple's Republic of China,by NewsRx journalists,research stated,"Air pollution poses a critical public health threat around many megacities but in an uneven ma nner.Conventional models are limited to depict the highly spatial- and time-var ying patterns of ambient pollutant exposures at the community scale for megaciti es." Funders for this research include National Key Research and Development Program of China,National Key Research and Development Program of China,National Natur al Science Foundation of China (NSFC),China National Postdoctoral Program for I nnovative Talents,Shuimu Tsinghua Scholar Program.The news reporters obtained a quote from the research from Tsinghua University,"Here,we developed a machine-learning approach that leverages the dynamic traff ic profiles to continuously estimate communitylevel year-long air pollutant con centrations in Los Angeles,U.S.We found the introduction of real-world dynamic traffic data significantly improved the spatial fidelity of nitrogen dioxide (N O2),maximum daily 8-h average ozone (MDA8 O-3),and fine particulate matter (PM 2.5) simulations by 47%,4%,and 15%,res pectively.We successfully captured PM2.5 levels exceeding limits due to heavy t raffic activities and providing an ‘out-of-limit map' tool to identify exposure disparities within highly polluted communities.In contrast,the model without r eal-world dynamic traffic data lacks the ability to capture the traffic-induced exposure disparities and significantly underestimate residents' exposure to PM2.5."
BeijingPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningTsinghua University
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