首页|Reports on Machine Learning from Chinese Academy of Sciences Provide New Insights (Machine Learning Revealing Key Factors Influencing Hono Chemistry In Beijing During Heating and Nonheating Periods)
Reports on Machine Learning from Chinese Academy of Sciences Provide New Insights (Machine Learning Revealing Key Factors Influencing Hono Chemistry In Beijing During Heating and Nonheating Periods)
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NETL
NSTL
Elsevier
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learning have been published. According to news originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “Nitrous acid (HONO) is of great interest due to its contribution to hydroxyl (OH) radicals by self-photolysis. Nowadays, machine learning (ML) algorithms are good at capturing complicated non-linear relationships be-tween predictors and dependent variables.” Funders for this research include National Natural Science Foundation of China (NSFC), Beijing Na- tional Laboratory for Molecular Sciences, Youth Innovation Promotion Association of Chinese Academy of Sciences, China Postdoctoral Science Foundation. Our news journalists obtained a quote from the research from the Chinese Academy of Sciences, “Here, using the whole year of 2018 of observed HONO and related pollutant data at an urban site in Beijing, an ML-RF (random forest) model is carried out to predict HONO concentrations and explore the main factors influencing HONO formation mechanisms. ML-RF models show satisfactory performance during the heating, non-heating and whole year periods with R values of 0.95, 0.96 and 0.95, respectively. Primary emissions and diffusion have an obvious influence on ambient HONO during the heating period, while chemical formation processes such as NO2 heterogeneous reaction and photolysis of nitrate are important for HONO during the non-heating period with higher RH and stronger solar intensity. O3 and NH3 are the most important variables for HONO in both periods, indicating the close relationship of HONO with atmospheric oxidation and the important role of NH3 in HONO formation processes. Although there are de-viations due to some variability in HONO formation mechanisms between years, ML-RF models based on 2018 data are able to roughly predict HONO for three periods in 2017 and 2021.”
BeijingPeople’s Republic of ChinaAsiaChemistryCyborgsEmerging TechnologiesMachine LearningChinese Academy of Sciences
NETL
NSTL
Elsevier
