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Based on PM2.5historical data, meteorological observational data, Subseasonal-to-Seasonal Prediction Project (S2S) forecasts and Madden-Julian oscillation (MJO) monitoring data, we developed an extended-range PM2.5prediction model in Shanghai using the Light-gradient boosting machine (LightGBM)algorithm. The analysis and prediction results demonstrated that the MJO improved the predictive skill of the extended-range PM2.5forecast. The MJO indices RMM1 and RMM2 ranked first and seventh, respectively, in terms of the predictive contribution of all meteorological predictors. Without the introduction of the MJO, the correlation coefficients for the forecasts on lead times of 11–40 days ranged from 0.27 to 0.55, and the root mean square errors (RMSEs) ranged from 23.4 to 31.8 µg/m3. After we introduced the MJO, the correlation coefficients for the 11–40 day forecast ranged from 0.31 to 0.56, among which those for the 16–40 day forecast substantially improved. The RMSEs ranged from 23.2 to 28.7 µg/m3. When comparing the prediction scores, such as percent correct (PC), critical success index (CSI) and equitable threat score (ETS), the forecast model was more accurate when it introduced the MJO. A novel aspect of our study is to investigate the effects of the MJO mechanism on the meteorological conditions of air pollution in eastern China through advanced regression analysis.These findings can guide forecasters regarding the utility of MJO and S2S for subseasonal air pollution outlooks.&nbsp, The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

A visibility forecast model called a boosting-based fusion model (BFM) was established in this study. The model uses a fusion machine learning model based on multisource data, including air pollutants, meteorological observations, moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data, and an operational regional atmospheric environmental modeling System for eastern China (RAEMS) outputs. Extreme gradient boosting (XGBoost), a light gradient boosting machine (LightGBM), and a numerical prediction method, i.e., RAEMS were fused to establish this prediction model. Three sets of prediction models, that is, BFM, LightGBM based on multisource data (LGBM), and RAEMS, were used to conduct visibility prediction tasks. The training set was from 1 January 2015 to 31 December 2018 and used several data pre-processing methods, including a synthetic minority over-sampling technique (SMOTE) data resampling, a loss function adjustment, and a 10-fold cross verification. Moreover, apart from the basic features (variables), more spatial and temporal gradient features were considered. The testing set was from 1 January to 31 December 2019 and was adopted to validate the feasibility of the BFM, LGBM, and RAEMS. Statistical indicators confirmed that the machine learning methods improved the RAEMS forecast significantly and consistently. The root mean square error and correlation coefficient of BFM for the next 24/48 h were 5.01/5.47 km and 0.80/0.77, respectively, which were much higher than those of RAEMS. The statistics and binary score analysis for different areas in Shanghai also proved the reliability and accuracy of using BFM, particularly in low-visibility forecasting. Overall, BFM is a suitable tool for predicting the visibility. It provides a more accurate visibility forecast for the next 24 and 48 h in Shanghai than LGBM and RAEMS. The results of this study provide support for real-time operational visibility forecasts.

贺兰山构造带及邻区的构造属性长期以来存在争议，确定该地区中新生代的构造事件及隆升过程是了解这一重要陆内变形带动力学机制的关键所在。本文采用不整合面分析法和低温热年代学方法，综合分析探讨了贺兰山构造带及邻区中新生代的构造事件及其构造演化过程。通过对该地区的野外地质调查，本次在中-新生代地层中由底到顶识别出6个不同类型的不整合面，它们分别是：（1）T2-3/P平行不整合面；（2）J/AnJ角度不整合面-微角度不整合面；（3）K1/AnK1高角度不整合面；（4）E3q/AnE3；（5）N1/AnN1；（6）Q/AnQ。在T3d3、J2y和K1变形前锋，可见与逆冲-褶皱造山带相关的同构造沉积生长地层，其在形态上表现为超覆、削截，在黄草滩等地局部与倒转背斜相伴生。这些不整合和生长地层是构造活动的直接证据。本次研究对采自该地区的12件样品分别进行了磷灰石、锆石裂变径迹测年及热史模拟分析。结果表明，裂变径迹年龄主要分布在4个区间，对应地质时代分别为中侏罗世-晚侏罗世（168~159Ma）、早白垩世末（139~91Ma）、晚白垩世末（79~66Ma）、始新世（59~50Ma），反映出该地区在这4个时期发生了明显的冷却抬升事件，且这4期构造事件与野外观察到的地质特征有很好的地质响应。同时，热史模拟表明该地区整体上经历了晚侏罗世、早白垩世、晚白垩世末-始新世3期快速隆升事件。综合研究表明，该地区主体逆冲褶皱的时间是从中侏罗世开始，早白垩世末构造运动最强烈，新生代又有所活动。

The dust case was analyzed by using aerosol extinction coefficient sequence chart retrieved by lidar observation data and meteorological observation data.The key factor of these two dust cases occurred was the dust transferred from North China,and the key reason of those two dust cases maintain so long was exogenous input and the vertical wind.The atmospheric profile affected the intensity of dust case.The dust case was holding when the temperature inversion occurred at the ground.The solar radiation affected the height of boundary layer.The main particulate matter is big size particle in the first dust case,and the aerosol scattering coefficient was higher than the aerosol absorption coefficient.The proportion of PM2.5 rose in the reflux dust case,and the aerosol scattering coefficient corresponded to the aerosol absorption coefficient.