首页|Predicting PM2.5 Concentration in the Yangtze River Delta Region Using Climate System Monitoring Indices and Machine Learning
Predicting PM2.5 Concentration in the Yangtze River Delta Region Using Climate System Monitoring Indices and Machine Learning
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国家科技期刊平台
NETL
NSTL
万方数据
Changing meteorological conditions during autumn and winter have considerable impact on air quality in the Yangtze River Delta(YRD)region.External climatic factors,such as sea surface temperature and sea ice,together with the atmospheric circulation,directly affect meteorological conditions in the YRD region,thereby modulating the variation in atmospheric PM2.5 concentration.This study used the evolutionary modeling machine learning technique to investigate the lag relationship between 144 climate system monitoring indices and autumn/winter PM2.5 concen-tration over 0-12 months in the YRD region.After calculating the contribution ratios and lagged correlation coeffi-cients of all indices over the previous 12 months,the top 36 indices were selected for model training.Then,the nine indices that contributed most to the PM2.5 concentration in the YRD region,including the decadal oscillation index of the Atlantic Ocean and the consistent warm ocean temperature index of the entire tropical Indian Ocean,were selec-ted for physical mechanism analysis.An evolutionary model was developed to forecast the average PM2.5 concentra-tion in major cities of the YRD in autumn and winter,with a correlation coefficient of 0.91.In model testing,the cor-relation coefficient between the predicted and observed PM2.5 concentrations was in the range of 0.73-0.83 and the root-mean-square error was in the range of 9.5-11.6 pg m-3,indicating high predictive accuracy.The model per-formed exceptionally well in capturing abnormal changes in PM2.5 concentration in the YRD region up to 50 days in advance.
Key Laboratory of Polar Atmosphere-Ocean-Iice System for Weather and Climate,Ministry of Education,Department of Atmospheric and Oceanic Sciences,Fudan University,Shanghai 200438
Shanghai Key Laboratory of Meteorology and Health,Shanghai Meteorological Service,Shanghai 200030
Yangzhou Meteorological Office,Yangzhou 225001
Department of Physics,Yangzhou University,Yangzhou 225003
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National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of China
国家科技期刊平台
NSTL
万方数据
