Tower-Based Greenhouse Gas Measurement Network Design——The National Institute of Standards and Technology North East Corridor Testbed

The North–East Corridor(NEC) Testbed project is the 3rd of three NIST(National Institute of Standards and Technology) greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions(footprints). Unlike other network design algorithms, an iterative selection algorithm, based on a k-means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. Analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and,therefore, biasing the retrieved fluxes.     

气象因子对太原地区空气质量的影响研究

利用2017—2019年太原小店区气象与空气质量指数逐时数据,分析了气象因子对空气质量指数的影响,对易污染月份空气质量进行了深入分析,利用神经网络方法构建了气象因子与空气质量指数的关系模型,并与逐步回归模型进行了对比。结果表明:(1)2017—2019年太原地区空气质量6个等级均出现过,其中,良占比最大,为51.9%;严重污染等级最少,为1.9%。每年8月空气质量最好,1月空气质量最差,11月—次年2月为易污染月份。(2)气温、风速、气压对空气质量指数的影响主要表现为负相关,9月—次年2月呈显著负相关;降水主要表现为负相关;相对湿度对空气质量指数影响较大,8月—次年3月呈显著正相关,6—7月呈显著负相关。(3)易污染月份,太原地区空气质量以良为主,占40.7%;其次为轻度污染,占24.8%;严重污染占4.6%。相对湿度、风速对空气质量影响较大,表现为强相关。(4)神经网络构建的气象因子与空气质量指数的关系模型,与逐步回归分析模型相比,准确率由91%提高到94%。神经网络模型模拟效果更佳,为太原地区治理大气污染提供重要的参考价值。     

Integration of linear and areal hierarchies for continuous multi-scale representation of road networks

Spatial data can be represented at different scales, and this leads to the issue of multi-scale spatial representation. Multi-scale spatial representation has been widely applied to online mapping products (e.g., Google Maps and Yahoo Maps). However, in most current products, multi-scale representation can only be achieved through a series of maps at fixed scales, resulting in a discontinuity (i.e., with jumps) in the transformation between scales and a mismatch between the available scales and users' desired scales. Therefore, it is very desirable to achieve smoothly continuous multi-scale spatial representations. This article describes an integrated approach to build a hierarchical structure of a road network for continuous multi-scale representation purposes, especially continuous selective omission of roads in a network. In this hierarchical structure, the linear and areal hierarchies are constructed, respectively, using two existing approaches for the linear and areal patterns in a road network. Continuous multi-scale representation of a road network can be achieved by searching in these hierarchies. This approach is validated by applying it to two study areas, and the results are evaluated by both quantitative analysis with two measures (i.e., similarity and average connectivity) and visual inspection. Experimental results show that this integrated approach performs better than existing approaches, especially in terms of preservation of connectivity and patterns of a road network. With this approach, efficient and continuous multi-scale selective omission of road networks becomes feasible.     

滑坡定量预测的非线性理论方法

滑坡时空预测是当前滑坡研究中的难题，特别是滑坡时间预测工作，其进展缓慢。本文基于非线性科学理论，分析了滑坡活动的分形特征及时间分形预测方法，研究了滑坡空间预测的人工神经网络特征，系统介绍了滑坡时间预测的非线性动力学理论。在此基础上，讨论了滑坡定量预测的发展趋势。     

岩溶隧道涌水量综合预测——以朱家岩岩溶隧道为例

长期以来岩溶隧道涌水问题是国内外隧道施工中的重大难题,往往对工程造成极大危害。基于岩溶水文地质学的最新理论和国内外工程实践,结合多年的经验,提出"岩溶隧道涌水量综合预测四步建模法"。运用地球系统科学的理论与方法,辅之系统论原理和类比方法,从多种地质调查与监测手段入手,在分析确定岩溶地下水系统结构特征(特别是地下岩溶含水介质类型)与其内水流运动关系的基础上,采用流量衰减分析法、物理模拟和BP神经网络模型三者相结合的方法,对岩溶隧道涌水量进行综合预测研究。     

基于植被动态的黄土高原生态地理分区

生态地理区划作为自然区划的新分支,近年来受到国内外地理学者的广泛关注,其在认识地理分异规律及区域规划活动中发挥着重要作用。传统生态地理分区多依据自上而下的三级演绎途径,且对于多分区方案的对比与优选缺乏定量化准则。黄土高原作为中国典型的生态脆弱区,植被生长与恢复对缓解当地生态困境十分重要,因此以植被多年动态一致性特征作为分区合理性的评价指标,有助于准确揭示当地生境特点及分异规律。为此,选取热量类、水分类、地形类及地表覆被类共9个指标,采用自组织映射网络（SOFM）与GIS空间分析技术,基于黄土高原近30年来自然本底与覆被状况进行生态地理分区;并依循植被动态一致性准则,依据两步筛选法对多种方案进行优选,最终将黄土高原分为六大生态地理区。研究表明：黄土高原修正6分区方案在12个备选分区方案中效果最好;同时,修正6分区方案多年平均NPP离散系数最低,表明该分区内部离散程度最小。分区方案与既有分区方案相比具有较好的一致性,但由于区划尺度存在一定的差异,整体区域划分更为清晰。对生态地理分区方案优选定量方法的探索,有助于提升自下而上生态地理区划的客观性。     

煤层含气量测井解释方法探讨

用多元线性回归建立煤层气含量与煤质参数、测井曲线值之间的回归方程,经F检验回归方程有效,但回归方程估算的煤层含气量与煤样解吸测定的含气量之间仍然存在较大的误差,为此利用BP神经网络进一步探讨它们之间的关系,实例表明预测精度较高。     

基于粒子滤波的行车轨迹路网匹配方法

行车轨迹是一种时间序列的地理空间位置采样数据,而传统的轨迹—路网匹配方法主要以全局或局部寻优的方式建立轨迹—路网匹配关系,影响了时空场景中数据的匹配计算过程的相对独立性。针对这个问题,本文基于粒子滤波（Particle Filter,PF）原理建立行车轨迹与道路网络之间的匹配关系。首先,沿轨迹中车辆运动方向在道路网络中搜索邻近道路节点,在与道路节点拓扑邻接的道路弧段上初始化随机生成粒子,根据轨迹中车辆运动模型将粒子沿所在道路弧段移动;然后,基于PF原理计算各时刻粒子运动状态及与行车轨迹采样点之间的距离误差,根据高斯概率密度函数计算粒子权重并利用随机重采样方法进行粒子重采样,迭代更新粒子运动状态;最后,计算与搜索到的道路节点拓扑邻接的每条道路弧段中累计粒子权重,通过各道路弧段累计权重计算轨迹—路网匹配关系。以行车轨迹进行实验表明,利用本文方法可以通过粒子时空变化反映采样点的移动,行车轨迹—路网匹配结果的正确率大于85%,能够实现行车轨迹和路网的准确匹配。     

伊朗地震台网现状分析

通过中伊双方合作交流，详细考察了伊朗的地震台网。伊朗现建有4类台网:地震遥测台网、国家地震台网、强震台网及水库诱发地震台网。这些台网中高性能宽频带地震仪数量不足，使得地震定位精度不高;且4类台网分属不同机构，在一定程度上限制了伊朗的地震监测能力。     

宽带接入用户认证技术分析与比较

给出了一个宽带接入用户认证系统的基本模型，并基于此模型对目前被广泛应用的PPPoE、WEB、DHCP等用户认证技术进行了分析与比较。最后，简单介绍了用户认证技术的新成员-802．1x(Port Based Network Access Control Protoco1)协议。