Predicting spatio-temporal concentrations of PM<Subscript>2.5</Subscript> using land use and meteorological data in Yangtze River Delta,China

The prediction of PM_2.5 concentrations with high spatiotemporal resolution has been suggested as a potential method for data collection to assess the health effects of exposure. This work predicted the weekly average PM_2.5 concentrations in the Yangtze River Delta, China, by using a spatio-temporal model. Integrating land use data, including the areas of cultivated land, construction land, and forest land, and meteorological data, including precipitation, air pressure, relative humidity, temperature, and wind speed, we used the model to estimate the weekly average PM_2.5 concentrations. We validated the estimated effects by using the cross-validated R² and Root mean square error (RMSE); the results showed that the model performed well in capturing the spatiotemporal variability of PM_2.5 concentration, with a reasonably large R² of 0.86 and a small RMSE of 8.15 (μg/m³). In addition, the predicted values covered 94% of the observed data at the 95% confidence interval. This work provided a dataset of PM_2.5 concentration predictions with a spatiotemporal resolution of 3 km × week, which would contribute to accurately assessing the potential health effects of air pollution.     

几种自由边界实施方法在完全 匹配层条件下的对比研究

传统的完全匹配层技术是一种能够较为有效地消除边界反射的边界条件,但是当表层为泊松比较高的自由表面时,该技术可能会产生不稳定的现象.针对传统的完全匹配层技术固有的不稳定和掠射情况下吸收效果不佳等缺陷,发展了多轴完全匹配层、卷积完全匹配层以及将两者结合的多轴卷积完全匹配层等3种边界条件.本文介绍了水平自由表面的不同处理方法以及传统、多轴、卷积和多轴卷积等4种完全匹配层条件的原理,通过二维半无限空间模型的交错网格有限差分正演模拟对比,分析了几种自由边界实施方法在这几种完全匹配层条件下的稳定性,并通过提取单道波形与解析解进行对比,定性分析了水平自由表面几种不同处理方法的准确性以及各自的适用条件. 结果表明,泊松比和水平自由表面实施方法对波场模拟效果及其稳定性有重要影响.     

天水市某典型黄土边坡块石支护稳定性效果分析

以天水地磁013台黄土边坡为研究对象,通过现场勘测、室内强度试验确定了该边坡的土质条件及其相应的土层力学性质,根据相应规范的要求提出运用块石挡土墙对此边坡进行上下分层支护。采用强度折减理论验证了边坡施加支护后的稳定性评价。结果表明,相比未加块石支护的边坡模型,采用块石支护的边坡的安全系数达到1.41,属稳定状态;同时块石支护可以有效地减小土体的水平方向位移,并约束土体总位移向临空面滑动。     

一些地区的地声特征及其作为地震前兆的可能性

本文收集了河北卢龙、昌黎,辽宁营口,四川成都,山东莒县和山西介休等地的地声观测资料。对这些资料进行分析处理,得到了它们的频谱特性和其他特性。最后对声源的机制及其作为地震前兆的可能性作了扼要的讨论。     

Comparison of soil moisture products from microwave remote sensing,land model,and reanalysis using global ground observations

High-quality soil moisture (SM) datasets are in great demand for climate, hydrology, and other fields, but detailed evaluation of SM products from various sources is scarce. Thus, using 670 SM stations worldwide, we evaluated and compared SM products from microwave remote sensing [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) (C- and X-bands) and European Space Agency's Climate Change Initiative (ESA CCI)], land surface model [Global Land Data Assimilation System (GLDAS)], and reanalysis data [ECMWF Re-Analysis-Interim (ERA-Interim) and National Centers for Environmental Prediction (NCEP)] under different time scales and various climates and land covers. We find that: (a) ESA CCI and GLDAS have the closest values to the in situ SM on the annual scale, whereas others overestimate the SM; ERA-Interim (averaged R = 0.58) and ESA CCI (averaged R = 0.54) correlate best with the in situ data, while GLDAS performs worst. (b) Overall, the deviations of each product vary in seasons. ESA CCI and ERA-Interim products are closer to the in situ SM at seasonal scales, and AMSR-E and NCEP perform worst in December–February and June–August, respectively. (c) Except for NCEP and ERA-Interim, others can well reflect the intermonthly variation of the in situ SM. (d) Under various climates and land covers, AMSR-E products are less effective in cold climates, whereas GLDAS and NCEP products perform poorly in arid or temperate and dry climates. Moreover, the Bias and R of each SM product differ obviously under different forest types, especially the AMSR-E products. In summary, SM from ESA CCI is the best, followed by ERA-Interim product, and precipitation is an important auxiliary data for selecting high-quality SM stations and improving the accuracy of SM from GLDAS. These results can provide a reference for improving the accuracy of the above SM products.     

Soil moisture temporal stability and spatio-temporal variability about a typical subalpine ecosystem in northwestern China

Knowledge of the spatial–temporal variability of soil water content is critical for water management and restoration of vegetation in semi-arid areas. Using the temporal stability method, we investigated soil water relations and spatial–temporal variability of volumetric soil water content (VSWC) in the grassland–shrubland–forest transect at a typical semi-arid subalpine ecosystem in the Qilian Mountains, northwestern China. The VSWC was measured on 48 occasions to a depth of 70 cm at 50 locations along a 240-m transect during the 2016–2017 growing seasons. Results revealed that temporal variability in VSWC in the same soil layer in the three vegetation types and averaged across vegetation types tended to exhibit similar patterns of a decrease with increasing soil depth. Temporal stability in each vegetation type was stronger with an increase in soil depth. However, the results of temporal stability determined with standard deviation of relative difference (SDRD) disagreed with those based on the Spearman's rank correlation coefficient; the forest site had the highest Spearman rank correlation coefficient while the shrubland—the smallest SDRD in the 0–20 cm soil layer. Correlation analyses of VSWCs between two vegetation types indicated that soil water was related among all three vegetation types at the 0–20, and 0–70 cm soil layer, but in the 20–40 and 40–70 cm soil layers, significant correlation (p < .01) occurred only between adjacent vegetation types. In the upper soil layer (0–20 cm), soil water relations were mainly affected by surface runoff. In the lower soil layer (20–40 and 40–70 cm), soil water relations among the three vegetation types were highly complex, and probably resulting from a combination of root distribution and activity, interflow, and the impact of deep soil freeze–thaw dynamics. These results suggest that the factors affecting soil water are complex, and further research should address the relative importance of and interactions among different determining factors.     

Cross double‐difference inversion for simultaneous velocity model update and microseismic event location

Microseismic monitoring is an approach for mapping hydraulic fracturing. Detecting the accurate locations of microseismic events relies on an accurate velocity model. The one‐dimensional layered velocity model is generally obtained by model calibration from inverting perforation data. However, perforation shots may only illuminate the layers between the perforation shots and the recording receivers with limited raypath coverage in a downhole monitoring problem. Some of the microseismic events may occur outside of the depth range of these layers. To derive an accurate velocity model covering all of the microseismic events and locating events at the same time, we apply the cross double‐difference method for the simultaneous inversion of a velocity model and event locations using both perforation shots and microseismic data. The cross double‐difference method could provide accurate locations in both the relative and absolute sense, utilizing cross traveltime differences between P and S phases over different events. At the downhole monitoring scale, the number of cross traveltime differences is sufficiently large to constrain events locations and velocity model as well. In this study, we assume that the layer thickness is known, and velocities of P‐ and S‐wave are inverted. Different simultaneous inversion methods based on the Geiger's, double‐difference, and cross double‐difference algorithms have been compared with the same input data. Synthetic and field data experiments suggest that combining both perforation shots and microseismic data for the simultaneous cross double‐difference inversion of the velocity model and event locations is available for overcoming the trade‐offs in solutions and producing reliable results.     

基于模糊聚类神经网络的煤炭资源等级划分方法——以内蒙古煤炭资源预测区为例

依据第三次煤田预测评价指标体系,选取影响煤炭资源自然禀赋的9个指标,针对内蒙古煤炭资源预测区的数据特点,设定指标标准值和加权值。应用模糊聚类神经网络模型,对标准值和10个内蒙古煤炭资源预测区样本进行规格化处理,找出聚类中心,通过计算样本与聚类中心的欧式距离,得出样本所属等级。划分结果:呼伦湖预测区煤炭资源为一等资源,鼎辉、东胜煤田4预测区等为二等资源,得尔布尔外围、福山、炭井沟预测区虽为长焰煤和无烟煤,但资源规模太小,丰度低,煤层薄,为三等资源。     

2009年秋末山西大暴雪天气过程分析

利用高空、地面和卫星云图资料,对2009年11月9-12日山西出现的有气象记录以来最强的一场大暴雪天气过程进行综合分析.结果表明:高空强盛的西南气流和低层东北气流以及地面回流为暴雪过程提供了有利的流场配置,500hPa阶梯槽和700hPa切变线是主要影响系统,低空急流的持续及水汽的辐合为这次暴雪提供了充足的水汽,卫星云...