我国西南地区地面和低层大气臭氧的观测分析

本文介绍了1987—1989年在以重庆、成都和贵阳三地区为代表的西南地区进行了地面和低层大气臭氧的观测结果,给出了该地区大气臭氧时空分布的一些特征.分析表明,成都、重庆和贵阳等地区O_3浓度的时空分布及变化规律不尽相同,但都与前体污染物特征以及日照强度、大气层结稳定度.天气过程等密切相关.     

Stability of waterfront retaining wall subjected to pseudo-static earthquake forces

Waterfront retaining walls supporting dry backfill are subjected to hydrostatic pressure on upstream face and earth pressure on the downstream face. Under seismic conditions, if such a wall retains a submerged backfill, additional hydrodynamic pressures are generated. This paper pertains to a study in which the effect of earthquakes along with the hydrodynamic pressure including inertial forces on such a retaining wall is observed. The hydrodynamic pressure is calculated using Westergaard's approach, while the earth pressure is calculated using Mononobe-Okabe's pseudo-static analysis. It is observed that when the horizontal seismic acceleration coefficient is increased from 0 to 0.2, there is a 57% decrease in the factor of safety of the retaining wall in sliding mode. For investigating the effect of different parameters, a parametric study is also done. It is observed that if φ is increased from 30° to 35°, there is an increase in the factor of safety in the sliding mode by 20.4%. Similar observations were made for other parameters as well. Comparison of results obtained from the present approach with [Ebeling, R.M., Morrison Jr, E.E., 1992. The seismic design of waterfront retaining structures. US Army Technical Report ITL-92-11. Washington DC] reveal that the factor of safety for static condition (k_h=0), calculated by both the approaches, is 1.60 while for an earthquake with k_h=0.2, they differ by 22.5% due to the consideration of wall inertia in the present study.     

The use of digital technologies for landslide disaster risk research and disaster risk management: progress and prospects

Landslide is a serious natural disaster next only to earthquake and flood, which will cause a great threat to people’s lives and property safety. The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective , difficult to quantify, and no pertinence. As a new research method for landslide susceptibility assessment, machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models. Taking Western Henan for example, the study selected 16 landslide influencing factors such as topography, geological environment, hydrological conditions, and human activities, and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination (RFE) method. Five machine learning methods [Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)] were used to construct the spatial distribution model of landslide susceptibility. The models were evaluated by the receiver operating characteristic curve and statistical index. After analysis and comparison, the XGBoost model (AUC 0.8759) performed the best and was suitable for dealing with regression problems. The model had a high adaptability to landslide data. According to the landslide susceptibility map of the five models, the overall distribution can be observed. The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest, the Xiaoshan Mountain range in the west, and the Yellow River Basin in the north. These areas have large terrain fluctuations, complicated geological structural environments and frequent human engineering activities. The extremely high and highly prone areas were 12043.3 km² and 3087.45 km², accounting for 47.61% and 12.20% of the total area of the study area, respectively. Our study reflects the distribution of landslide susceptibility in western Henan Province, which provides a scientific basis for regional disaster warning, prediction, and resource protection. The study has important practical significance for subsequent landslide disaster management.     

气象服务在促进内蒙古政策性农业保险事业发展中的作用

文章在分析国内外农业保险发展现状的基础上,结合内蒙古农业气象观测站网建设和气象为农服务业务开展现状,阐述了气象服务在农业保险业务发展中的作用和优势,为我区各级气象部门开展政策性农业保险服务业务提供了思路。     

宜春地区三类典型天气的风廓线雷达产品特征分析

选取江西省宜春市晴空、弱降水、强降水三类天气过程个例,结合天气形势、雷达回波、气象要素等资料,对比分析了风廓线雷达产品特征。结果表明：（1）在晴空天气背景下,风廓线雷达探测高度低,水平风速小,垂直风速正负值交替出现,大气折射率结构常数（Cn2）值最小。（2）在稳定性弱降水天气背景下,大气呈稳定状态,风廓线雷达探测高度随降水的产生而逐渐抬升,高低层有明显的风速切变,850 hPa赣南至赣东北有西南急流穿过,赣北有切变线存在,利于降水产生,垂直风速因降水影响出现朝向雷达正速度,Cn2值比晴空时大。（3）在具有产生强对流天气背景下,大气中对流强烈,风廓线雷达的水平风速增大,西南急流深厚且不断下沉,850-700 hPa有强烈的垂直切变,动力条件和水汽条件利于强降水产生,垂直风速表现为更大的朝向雷达正速度,Cn2值比弱降水时的大。     

贵州省沿河县耕地土壤元素地球化学特征及其特色耕地利用建议

本文基于沿河土家族自治县耕地质量地球化学调查评价项目野外调查数据资料,分析了耕地土壤元素在不同成土母岩及不同土壤类型的地球化学特征。在摸清耕地土壤地球化学特征的基础上,利用SPSS统计分析软件,对全县表层土壤样品22个元素(指标)进行因子分析,得到有代表性的7个因子成分,分析不同元素组合的区域分布基于何种因素,并对各主因子进行了地质解释:F1主因子代表的变量与地质背景密切相关;F2主因子代表的变量与土壤中的As、Mo、Se与有机质易形成络合物有关;F3主因子代表的变量与区域上常形成与矿产有关的地球化学异常有关;F4主因子代表的变量可能与人工施肥及植被有关;F5主因子代表的变量与铜矿床有关;F6主因子代表的变量与分布的地层岩性有关;F7主因子代表的变量与萤石矿有关。根据全县富硒、富锗特色耕地分布特征及农产品优势,建议优先开发塘坝镇、新景镇等4个区域,为沿河县调整农业种植结构、发展现代山地特色优质农产品提供地球化学依据。     

南疆一次强沙尘暴前后塔中近地面各气象要素的变化特征

利用塔克拉玛干沙漠腹地塔中站2006年4月10日一次强沙尘暴前后连续5天地面、土壤中、以及80 m高塔站梯度观测系统的各气象要素资料,详细地分析了该沙尘暴前后不同沙尘天气日各要素的变化特征.结果表明:这次沙尘暴是南疆气温持续回暖、热低压强烈发展、沙尘暴和浮尘天气持续时间长的一次南疆东灌型强沙尘暴过程.温、湿廓线在沙尘暴过境时,因强湍流交换,整个近地层大气基本上处于中性层结,沙尘暴日为降温增湿的过程;沙尘暴日前后大气层结发生了转变,破坏了晴天正常的逆温、逆湿结构.风速廓线在沙尘暴日满足对数律关系,而在沙尘暴日前后的天气里,风速较小,规律性差.沙尘暴过境时0O cm和5 cm地温变化趋势与1.5 m气温相似,过境前1.5 m气温比5 cm地温达到峰值的时间约提前1～2 h,过境后反而又滞后1～2 h.     

北京地区“环流－降水－谷物”系统的短期振动研究

文章用因子分析方法提取北京地区小麦气象产量主要特征及代表序列,并对该序列和北京地面气象要素的关系用交叉谱进行研究。发现它们在前期秋冬季中的４－６年的周期振荡（中心周期为４．５年）中有显著的相关,当前期秋季和冬季的降水量和气温在振荡中处于正距平时,当年的气象产量为丰年,反之为欠年;进一步对该序列与大气环流的静力和动力物理量之间关系研究发现它们在同样的周期振荡中有十分强烈的表现,尤其表现在与动力物理量上。研究结果进一步证实“环流－降水－谷物”系统的存在,并揭示在短期气候振动（４－６年）上的规律性。     

一种裂缝流体因子的提出及应用

为了解决各向异性下的流体识别问题,将纵波各向异性裂缝预测以及Russell的流体因子融合到直角坐标系中,提出了一种能够同时检测裂缝发育情况以及流体性质的新的裂缝流体因子(Factor of Fluid-filled Fracture,FFF),并通过一组岩性参数检验了裂缝流体因子在裂缝预测及流体识别中的有效性.在理论研究的基础上,选取松辽盆地某地区的火成岩裂缝及流体识别研究为应用实例.通过与测井流体及裂缝信息的对比验证,裂缝流体因子能够较为准确地预测研究区裂缝和流体的分布情况,且裂缝流体因子在单井上的计算结果与单井含气饱和度吻合度较高.此外,根据实际应用效果,指出裂缝流体因子在应用中的局限性:裂缝流体因子在平面成图时受地层厚度影响较大,且无法预测裂缝方向.     

2015—2018年海南省臭氧污染特征及其天气类型

根据2015—2018年海南省18个市县32个空气质量监测站O₃浓度资料,分析了区域性O₃污染（O₃-8h浓度超标市县≥3个）时空变化特征,并对造成O₃污染的天气系统进行主观分型。结果表明：2015—2018年海南省共有40 d发生了区域性O₃污染,发生概率为2.73%。其中2015年和2017年达到了13 d,发生概率为3.56%,2018年为11 d（3.01%）,2016年仅为3 d（0.82%）。发生区域性O₃污染主要有4种天气类型：冷空气偏西下型、冷空气偏东下型、变暖高压脊型和热带系统型。其中冷空气偏西下型是最主要的天气类型,共出现了14 d,占所有天数的35%,且污染较重。不同天气类型下海南省O₃污染表现出不同的分布特征。500 hPa有下沉气流、低层受东北风控制,有相对湿度低值区从中国东部向海南省延伸,地面位于冷高压底部或热带气旋西北侧,温度露点差在5 ℃以上等条件均有利于海南省区域性O₃污染天气出现。