A multi-scale urban atmospheric dispersion model for emergency management

To assist emergency management planning and prevention in case of hazardous chemical release into the atmosphere,especially in densely built-up regions with large populations,a multi-scale urban atmospheric dispersion model was established.Three numerical dispersion experiments,at horizontal resolutions of 10 m,50 m and 3000 m,were performed to estimate the adverse effects of toxic chemical release in densely built-up areas.The multi-scale atmospheric dispersion model is composed of the Weather Forecasting and Research （WRF） model,the Open Source Field Operation and Manipulation software package,and a Lagrangian dispersion model.Quantification of the adverse health effects of these chemical release events are given by referring to the U.S.Environmental Protection Agency＇s Acute Exposure Guideline Levels.The wind fields of the urban-scale case,with 3 km horizontal resolution,were simulated by the Beijing Rapid Update Cycle system,which were utilized by the WRF model.The sub-domain-scale cases took advantage of the computational fluid dynamics method to explicitly consider the effects of buildings.It was found that the multi-scale atmospheric dispersion model is capable of simulating the flow pattern and concentration distribution on different scales,ranging from several meters to kilometers,and can therefore be used to improve the planning of prevention and response programs.     

Simulating urban flow and dispersion in Beijing by coupling a CFD model with the WRF model

The airflow and dispersion of a pollutant in a complex urban area of Beijing, China, were numerically examined by coupling a Computational Fluid Dynamics （CFD） model with a mesoscale weather model. The models used were Open Source Field Operation and Manipulation （OpenFOAM） software package and Weather Research and Forecasting （WRF） model. OpenFOAM was firstly validated against wind-tunnel experiment data. Then, the WRF model was integrated for 42 h starting from 0800 LST 08 September 2009, and the coupled model was used to compute the flow fields at 1000 LST and 1400 LST 09 September 2009. During the WRF-simulated period, a high pressure system was dominant over the Beijing area. The WRF-simulated local circulations were characterized by mountain valley winds, which matched well with observations. Results from the coupled model simulation demonstrated that the airflows around actual buildings were quite different from the ambient wind on the boundary provided by the WRF model, and the pollutant dispersion pattern was complicated under the influence of buildings. A higher concentration level of the pollutant near the surface was found in both the step-down and step-up notches, but the reason for this higher level in each configurations was different： in the former, it was caused by weaker vertical flow, while in the latter it was caused by a downward-shifted vortex. Overall, the results of this study suggest that the coupled WRF-OpenFOAM model is an important tool that can be used for studying and predicting urban flow and dispersions in densely built-up areas.     

京津冀地区霾成因机制研究进展与展望

为满足当前对京津冀地区霾研究和控制的迫切要求,本文梳理了近年来京津冀地区霾的长期变化特征、天气学特征、污染物来源等相关研究成果,发现:从2000年以后,京津冀地区的霾日数呈现出了下降趋势;北京细颗粒物(PM2.5)质量浓度也在总体上呈现下降的趋势,但2013年年均质量浓度仍高达89.5μg m–3,约为我国空气质量标准的3倍(35μg m–3),京津冀空气污染的形势依然严峻;近年来京津冀地区的霾污染事件频发可以归因为不利天气条件与大量污染物人为排放的共同作用;大量的研究表明,区域输送对京津冀地区霾事件的形成和维持有不可忽视的影响;京津冀地区的大气污染不再局限于一时一地,针对重污染天气的预警以及应急控制应该以区域预报为基础实现区域联动;京津冀地区独特的地理环境条件加上城市群的快速发展,形成的局地大气环流也会对局地的污染过程产生重大的影响;大气边界层内气象要素的变化对重污染发生具有显著贡献。京津冀地区的污染控制需要城市群的联动应对治理。     

Strength of Stacking Technique of Ensemble Learning in Rockburst Prediction with Imbalanced Data: Comparison of Eight Single and Ensemble Models

Rockburst is a common dynamic geological hazard, severely restricting the development and utilization of underground space and resources. As the depth of excavation and mining increases, rockburst tends to occur frequently. Hence, it is necessary to carry out a study on rockburst prediction. Due to the nonlinear relationship between rockburst and its influencing factors, artificial intelligence was introduced. However, the collected data were typically imbalanced. Single algorithms trained by such data have low recognition for minority classes. In order to handle the problem, this paper employed stacking technique of ensemble learning to establish rockburst prediction models. In total, 246 sets of data were collected. In the preprocessing stage, three data mining techniques including principal component analysis, local outlier factor and expectation maximization algorithm were used for dimension reduction, outlier detection and outlier substitution, respectively. Then, the pre-processed data were split into a training set (75%) and a test set (25%) with stratified sampling. Based on the four classical single intelligent algorithms, namely k-nearest neighbors (KNN), support vector machine (SVM), deep neural network (DNN) and recurrent neural network (RNN), four ensemble models (KNN–RNN, SVM–RNN, DNN–RNN and KNN–SVM–DNN–RNN) were built by stacking technique of ensemble learning. The prediction performance of eight models was evaluated, and the differences between single models and ensemble models were analyzed. Additionally, a sensitivity analysis was conducted, revealing the importance of input variables on the models. Finally, the impact of class imbalance on the prediction accuracy and fitting effect of models was quantitatively discussed. The results showed that stacking technique of ensemble learning provides a new and promising way for rockburst prediction, which exhibits unique advantages especially when using imbalanced data.

     

长江三峡库区秭归杨林桥镇震群成因

对三峡数字地震台网记录的2016年3—6月三峡工程库区秭归库段杨林桥镇小震群进行双差精定位,通过Snoke方法计算该震群中较大地震的震源机制解,并结合野外宏观调查等方法,分析震源参数的时空变化特征。结果表明:杨林桥镇小震群密集分布在2 km×3 km矩形范围内,有较明显的展布方向,但与邻近的仙女山断裂、九畹溪断裂和天阳坪断裂走向不一致;震群距长江干流超过10 km,与三峡水库无水利联系,无相关性;震群展布方向上有岩溶洞分布,且降雨丰富,初步判断成因类型为岩溶塌陷型。     

2013—2017年汕尾市大暴雨特征及影响天气系统归类研究

该文利用汕尾市53个自动气象站常规降水观测资料,采用统计分析法及Surfer软件对2013—2017年62个大暴雨(≥100 mm/24 h)以上降水特征进行了分析。结果表明：①汕尾大暴雨季节性明显,主要集中在5—8月,且大暴雨日数月变化呈单峰型特征。②前、后汛期大暴雨日变化特征有所区别,前汛期呈双峰型特征;后汛期日变化特征不明显。③汕尾大暴雨小时雨强与出现频率成反比,易出现日雨量300 mm以上极端强降水。④汕尾有2个大暴雨高发区域,分别位于海丰西南部和陆丰中部。⑤汕尾大暴雨影响系统大致分为台风型、季风低压型、冷空气切变线型、西南季风型、东南气流型和偏南气流型,其中5—6月以西南季风型为主;7月以台风型、季风低压型为主;8—10月多为台风型。     

青岛外海氦氖激光水下衰减特性研究

激光水下传输的衰减特性 光束在水中传播时,辐射能量受到水介质的影响而衰减.     

四川稻城地区温泉流体地球化学特征及地震活动性分析

稻城地区位于青藏高原东南缘的川滇地块北部,为揭示该区域温泉流体地球化学特征以及其和地震活动性之间的关系,本次研究采集了稻城地区6个温泉的水样以及逸出气体样品。对温泉水中离子组分和浓度,温泉逸出气体组分及气体同位素进行了测试,得到以下认识。研究区温泉水化学类型主要为HCO₃-Na和HCO₃-Na·Ca型,通过阳离子温标估算热储温度在74℃～159℃之间,循环深度在2.2 km～5.0 km之间。温泉气体中CO₂主要是由储层中的碳酸盐岩受热分解或溶解产生的,氦来自幔源组分的比例较低,在0.4%～2.4%之间,研究区的温泉是由沿断裂带渗入的大气降水经地壳深部的热源加热形成的。在稻城地区,循环深度以及幔源气体贡献率等不同的温泉流体地球化学特征与地震活动性之间有很好的对应关系,并且研究区地震活动性要弱于周缘鲜水河断裂地区等深部流体上涌地区。同时在区域尺度上,未来位于断裂交汇部位的稻城仲堆温泉一带的地震活动性最值得关注。     

Construction and Validation of an Urban Area Flow and Dispersion Model on Building Scales

This paper presents a numerical model that simulates the wind fields, turbulence fields, and dispersion of gaseous substances in urban areas on building to city block scales. A Computational Fluid Dynamics（CFD） approach using the steady-state, Reynolds-Averaged Navier-Stokes（RANS） equations with the standard k-ε turbulence model within control volumes of non-uniform cuboid shapes has been employed. Dispersion field is computed by solving an unsteady transport equation of passive scalar. Another approach based on Gaussian plume model is used to correct the turbulent Schmidt number of tracer, in order to improve the dispersion simulation. The experimental data from a wind tunnel under neutral conditions are used to validate the numerical results of velocity, turbulence, and dispersion fields. The numerical results show a reasonable agreement with the wind tunnel data. The deviation of concentration between the simulation with corrected turbulent Schmidt number and the wind tunnel experiments may arise from 1） imperfect point sources, 2） heterogeneous turbulent difusivity, and 3） the constant turbulent Schmidt assumption used in the model.     

The response of warm-season precipitation extremes in China to global warming: an observational perspective from radiosonde measurements

Consensus has been reached that precipitation extremes vary proportionally with global warming. Nevertheless, the underlying cause and magnitude of these factors affecting their relationships remain highly debated. To elucidate the complex relationship between precipitation extremes and temperature in China during the warm seasons (May through September), a 60-year (1958–2017) record of hourly rain gauge measurements, in combination with surface air temperature, RH, precipitable water (PW), and convective available potential energy (CAPE) collected from 120 radiosonde stations were examined. Spatially, the scaling relationship between precipitation extremes and temperature exhibits a large geographic difference across China. In particular, the Clausius–Clapeyron (CC) and sub-CC relationships tend to occur in northwest (ROI-N) and southeast China (ROI-S), whereas the super-CC relationship is found to mainly concentrates in central China (ROI-C). Additionally, the response of precipitation extremes to temperature becomes more sensitive as precipitation intensity increases, shifting from CC to super-CC at a certain point of inflection that varies by geographic regions. This shift occurs at approximately 15 °C in ROI-C and ROI-N, but at around 20 °C in ROI-S. Within the temperature range of the super-CC slope, the PW rises with the increases in temperature, whereas the CAPE decreases with rising temperature, which is contrary to the monotonic scaling of precipitation with temperature. From the perspective of interannual variation, the precipitation extremes correlate positively with temperature. This further confirms the notion that global warming, through jointly affecting PW and CAPE, is able to considerably regulate precipitation extremes.