PM_(1.0),particulate matter with an aerodynamic diameter smaller than 1.0μm,can adversely affect human health.However,fewer stations are capable of measuring PM_(1.0) concentrations than PM2.5 and PM10 concentrations...PM_(1.0),particulate matter with an aerodynamic diameter smaller than 1.0μm,can adversely affect human health.However,fewer stations are capable of measuring PM_(1.0) concentrations than PM2.5 and PM10 concentrations in real time(i.e.,only 9 locations for PM_(1.0) vs.623 locations for PM2.5 or PM10)in South Korea,making it impossible to conduct a nationwide health risk analysis of PM_(1.0).Thus,this study aimed to develop a PM_(1.0) prediction model using a random forest algorithm based on PM_(1.0) data from the nine measurement stations and various environmental input factors.Cross validation,in which the model was trained in eight stations and tested in the remaining station,achieved an average R^(2) of 0.913.The high R^(2) value achieved undermutually exclusive training and test locations in the cross validation can be ascribed to the fact that all the locations had similar relationships between PM_(1.0) and the input factors,which were captured by our model.Moreover,results of feature importance analysis showed that PM2.5 and PM10 concentrations were the two most important input features in predicting PM_(1.0) concentration.Finally,the model was used to estimate the PM_(1.0) concentrations in 623 locations,where input factors such as PM2.5 and PM10 can be obtained.Based on the augmented profile,we identified Seoul and Ansan to be PM_(1.0) concentration hotspots.These regions are large cities or the center of anthropogenic and industrial activities.The proposed model and the augmented PM_(1.0) profiles can be used for large epidemiological studies to understand the health impacts of PM_(1.0).展开更多
Using monthly observations and ensemble hindcasts of the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS1.0) for the period 1983–2020, this study investigates the forecast s...Using monthly observations and ensemble hindcasts of the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS1.0) for the period 1983–2020, this study investigates the forecast skill of marine heatwaves(MHWs) over the globe and the predictability sources of the MHWs over the tropical oceans. The MHW forecasts are demonstrated to be skillful on seasonal-annual time scales, particularly in tropical oceans. The forecast skill of the MHWs over the tropical Pacific Ocean(TPO) remains high at lead times of 1–24 months, indicating a forecast better than random chance for up to two years. The forecast skill is subject to the spring predictability barrier of El Nino-Southern Oscillation(ENSO). The forecast skills for the MHWs over the tropical Indian Ocean(TIO), tropical Atlantic Ocean(TAO), and tropical Northwest Pacific(NWP) are lower than that in the TPO. A reliable forecast at lead times of up to two years is shown over the TIO, while a shorter reliable forecast window(less than 17 months) occurs for the TAO and NWP.Additionally, the forecast skills for the TIO, TAO, and NWP are seasonally dependent. Higher skills for the TIO and TAO appear in boreal spring, while a greater skill for the NWP emerges in late summer-early autumn. Further analyses suggest that ENSO serves as a critical source of predictability for MHWs over the TIO and TAO in spring and MHWs over the NWP in summer.展开更多
基金supported by the Fine Particle Research Initiative in East Asia Considering National Differences Project through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(No.NRF-2023M3G1A1090660)supported by a grant from the National Institute of Environmental Research(NIER),funded by the Ministry of Environment of the Republic of Korea(No.NIER-2023-04-02-056).
文摘PM_(1.0),particulate matter with an aerodynamic diameter smaller than 1.0μm,can adversely affect human health.However,fewer stations are capable of measuring PM_(1.0) concentrations than PM2.5 and PM10 concentrations in real time(i.e.,only 9 locations for PM_(1.0) vs.623 locations for PM2.5 or PM10)in South Korea,making it impossible to conduct a nationwide health risk analysis of PM_(1.0).Thus,this study aimed to develop a PM_(1.0) prediction model using a random forest algorithm based on PM_(1.0) data from the nine measurement stations and various environmental input factors.Cross validation,in which the model was trained in eight stations and tested in the remaining station,achieved an average R^(2) of 0.913.The high R^(2) value achieved undermutually exclusive training and test locations in the cross validation can be ascribed to the fact that all the locations had similar relationships between PM_(1.0) and the input factors,which were captured by our model.Moreover,results of feature importance analysis showed that PM2.5 and PM10 concentrations were the two most important input features in predicting PM_(1.0) concentration.Finally,the model was used to estimate the PM_(1.0) concentrations in 623 locations,where input factors such as PM2.5 and PM10 can be obtained.Based on the augmented profile,we identified Seoul and Ansan to be PM_(1.0) concentration hotspots.These regions are large cities or the center of anthropogenic and industrial activities.The proposed model and the augmented PM_(1.0) profiles can be used for large epidemiological studies to understand the health impacts of PM_(1.0).
基金jointly supported by the National Natural Science Foundation of China (Grant Nos.42192562 and 42030605)。
文摘Using monthly observations and ensemble hindcasts of the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS1.0) for the period 1983–2020, this study investigates the forecast skill of marine heatwaves(MHWs) over the globe and the predictability sources of the MHWs over the tropical oceans. The MHW forecasts are demonstrated to be skillful on seasonal-annual time scales, particularly in tropical oceans. The forecast skill of the MHWs over the tropical Pacific Ocean(TPO) remains high at lead times of 1–24 months, indicating a forecast better than random chance for up to two years. The forecast skill is subject to the spring predictability barrier of El Nino-Southern Oscillation(ENSO). The forecast skills for the MHWs over the tropical Indian Ocean(TIO), tropical Atlantic Ocean(TAO), and tropical Northwest Pacific(NWP) are lower than that in the TPO. A reliable forecast at lead times of up to two years is shown over the TIO, while a shorter reliable forecast window(less than 17 months) occurs for the TAO and NWP.Additionally, the forecast skills for the TIO, TAO, and NWP are seasonally dependent. Higher skills for the TIO and TAO appear in boreal spring, while a greater skill for the NWP emerges in late summer-early autumn. Further analyses suggest that ENSO serves as a critical source of predictability for MHWs over the TIO and TAO in spring and MHWs over the NWP in summer.
