Cryogenic Drilling: A New Drilling Method for Environmental Remediation

Cryogenic drilling is a technique developed at the University of California (UC), Berkeley, for drilling in unstable sediments of environmental monitoring, for characterizing, and for remediation wells, The method uses standard air rotary drilling techniques, but with cold nitrogen rather than ambient air as the circulating fluid in order to freeze and stabilize the borehole wall. Several laboratory and full-scale field tests have been performed. A.52-foot-deep (16 m) soil boring and 24 foot (7 m) monitoring well have been drilled as part of the Lawrence Berkeley Laboratory Site Characterization Project. Continued testing and refinement of the equipment and operational method are in progress. The method has been proposed for use as part of the Department of Energy (DOE) weapons site cleanup at locations with unstable sediments such as Hanford, Sandia, and Idaho National Engineering Laboratory (INEL).     

Allocation of Environmental Remediation Funds Using Economic Risk-Cost-Benefit Analysis: A Case Study

Efficient allocation of remediation resources is a critical need throughout the nation. Economic risk-cost-benefit analysis is an important tool for meeting this need. This paper provides site engineers, geologists, and managers with a conceptual understanding of economic risk-cost-benefit analysis and shows how it can be applied, even in situations where existing data are sparse or poor in quality. An example analysis is applied to the remediation of radioactive waste at Oak Ridge National Laboratory, in which the cost-effectiveness is compared for two remediation alternatives: containment of the waste or monitoring only. A data-worth analysis is also carried out to estimate the maximum justifiable exploration budget and the cost-effectiveness of two proposed data collection programs. Results indicate that the methodology has potential in making robust remediation decisions regarding certain types of questions.     

大数据环境下地震观测数据存储方案研究

大数据技术为处理海量地震观测数据提供了一种新的数据存储与计算模式。为了解决现有基于关系数据库存储方案的读写速度低、用户并发度低和可扩展性差等问题,以地震前兆观测数据为例,在详细分析业务需求的基础上,提出了基于HBase和Open TSDB的地震大数据存储方案,搭建了大数据测试平台,完成了不同存储方案下查询、插入性能实验和并发性实验。实验结果表明,与关系数据库存储方案相比,基于HBase和Open TSDB的存储方案具有很好的可扩展性和并发性,经过优化后的HBase存储方案具有更高的读取和存储性能。     

基于大数据架构的地球物理观测数据管理系统

中国地震地球物理观测网络的核心业务软件“十五”地震前兆数据管理系统已运行十多年,随着观测数据量的增长,数据库的读取和统计查询的性能逐渐降低。此外,管理系统存在SQL注入和跨站脚本攻击等漏洞的安全问题,系统架构难以满足业务发展的需求。中国地震台网中心基于分布式组件的大数据架构,使用消息中间件和时序数据库等技术对管理系统进行重构,将原来存储于Oracle数据库中的地球物理观测数据、基础信息、产品数据等按照各自特点在时序数据库中建表,并将历史数据和增量数据都迁移到新的时序数据库,显著提升了系统读写和统计性能、数据的安全性和可用性(多副本技术)。分布式和容器化等技术的引入,增强了系统的高可用性,并可通过横向扩展进一步增加系统的处理能力。     

Quantifying Data Worth Toward Reducing Predictive Uncertainty

The present study demonstrates a methodology for optimization of environmental data acquisition. Based on the premise that the worth of data increases in proportion to its ability to reduce the uncertainty of key model predictions, the methodology can be used to compare the worth of different data types, gathered at different locations within study areas of arbitrary complexity. The method is applied to a hypothetical nonlinear, variable density numerical model of salt and heat transport. The relative utilities of temperature and concentration measurements at different locations within the model domain are assessed in terms of their ability to reduce the uncertainty associated with predictions of movement of the salt water interface in response to a decrease in fresh water recharge. In order to test the sensitivity of the method to nonlinear model behavior, analyses were repeated for multiple realizations of system properties. Rankings of observation worth were similar for all realizations, indicating robust performance of the methodology when employed in conjunction with a highly nonlinear model. The analysis showed that while concentration and temperature measurements can both aid in the prediction of interface movement, concentration measurements, especially when taken in proximity to the interface at locations where the interface is expected to move, are of greater worth than temperature measurements. Nevertheless, it was also demonstrated that pairs of temperature measurements, taken in strategic locations with respect to the interface, can also lead to more precise predictions of interface movement.     

Internet Listservices as a Valuable Resource for Environmental Professionals in Soil and Ground Water Remediation

In 1957, four computers across the United States were linked to form the first version of what we now know as the Internet. The Internet has grown beyond what anyone working in the U.S. Department of Defense Advanced Research Projects Agency (DARPA) more than 40 years ago could have imagined. Yet despite the phenomenal growth of Internet users, and access to information in all fields of endeavor, finding genuinely useful and helpful resources is still a challenge. This article distils information on the most useful Internet listservers for environmental professionals working the site remediation.     

一种基于大数据云计算的地震房屋损失评估模型

针对地震房屋损失评估问题,提出一种基于大数据云计算的评估模型,该模型采用HBase数据库存储采集到的地震房屋损失数据,运用Hadoop Mapreduce分布式计算模型对数据库进行计算操作,最后得到地震房屋损失的评估值。     

Optimizing the Environmental Performance of In Situ Thermal Remediation Technologies Using Life Cycle Assessment

In situ thermal remediation technologies provide efficient and reliable cleanup of contaminated soil and groundwater, but at a high cost of environmental impacts and resource depletion due to the large amounts of energy and materials consumed. This study provides a detailed investigation of four in situ thermal remediation technologies (steam enhanced extraction, thermal conduction heating, electrical resistance heating, and radio frequency heating) in order to (1) compare the life‐cycle environmental impacts and resource consumption associated with each thermal technology, and (2) identify options to reduce these adverse effects. The study identifies a number of options for environmental optimization of in situ thermal remediation. In general, environmental optimization can be achieved by increasing the percentage of heating supplied in off peak electricity demand periods as this reduces the pressure on coal‐based electricity and thereby reduces the environmental impacts due to electricity production by up to 10%. Furthermore, reducing the amount of concrete in the vapor cap by using a concrete sandwich construction can potentially reduce the environmental impacts due to the vapor cap by up to 75%. Moreover, a number of technology‐specific improvements were identified, for instance by the substitution of stainless steel types in wells, heaters, and liners used in thermal conduction heating, thus reducing the nickel consumption by 45%. The combined effect of introducing all the suggested improvements is a 10 to 21% decrease in environmental impacts and an 8 to 20% decrease in resource depletion depending on the thermal remediation technology considered. The energy consumption was found to be the main contributor to most types of environmental impacts; this will, however, depend on the electricity production mix in the studied region. The combined improvement potential is therefore to a large extent controlled by the reduction/improvement of energy consumption.     

Mapping Groundwater Potential Through an Ensemble of Big Data Methods

Groundwater resources are crucial to safe drinking supplies in sub-Saharan Africa, and will be increasingly relied upon in a context of climate change. The need to better understand groundwater calls for innovative approaches to make the best out of the existing information. A methodology to map groundwater potential based on an ensemble of machine learning classifiers is presented. A large borehole database (n = 1848) was integrated into a Geographic Information Systems (GIS) environment and used to train, validate and test 12 machine learning algorithms. Each classifier predicts a binary target (positive or negative borehole) based on the minimum flow rate required for communal domestic supplies. Classification is based on a number of explanatory variables, including landforms, lineaments, soil, vegetation, geology and slope, among others. Correlations between the target and explanatory variables were then generalized to develop groundwater potential maps. Most algorithms attained success rates between 80% and 96% in terms of test score, which suggests that the outcomes provide an accurate picture of field conditions. Statistical learners were observed to perform better than most other algorithms, excepting random forests and support vector machines. Furthermore, it is concluded that the ensemble approach provides added value by incorporating a measure of uncertainty to the results. This technique may be used to rapidly map groundwater potential for rural supply or humanitarian emergencies in areas where there is sufficient historical data but where comprehensive field work is unfeasible.     

Investigation and Remediation of a 1,2–Dichloroethane Spill Part I: Short and Long-Term Remediation Strategies

Release of an estimated 150,000 gallons (568,000 L).of 1.2–dichloroethane (EDC) from a buried pipeline into a ditch and surrounding soil resulted in shallow subsurface contamination of a Gulf Coast site. Short-term remediation included removal of EDC DNAPI. (dense nonaqueous phase liquid) by dredging and vacuuming the ditch, and by dredging the river where the ditch discharged. EDC saturation in shallow impacted sediments located beneath the ditch was at or below residual saturation and these sediments were therefore left in place. The ditch was lined, backfilled, and capped. Long-term remediation includes EDC DNAPL recovery and hydraulic containment from the shallow zone with long-term monitoring of the shallow, intermediate, and deep (200 foot) aquifers. Ground water, DNAPL., and dissolved phase models were used to guide field investigations and the selection of an effective remedial action strategy. The DNAPL. modeling was conducted for a two-dimensional vertical cross section of the site, and included the three aquifers separated by two aquitards with microfractures. These aquitards were modeled using a dual porosity approach. Matrix and fracture properties of the aquitards used for DNAPL modeling were determined from small-scale laboratory properties. These properties were consistent with effective hydraulic conductivity determined from ground water flow modeling. A sensitivity analysis demonstrated that the vertical migration of EDC was attenuated by dissolution of EDC into the matrix of the upper aquitard. When the organic/water entry pressure of the aquitard matrix, or the solubility of EDC were decreased to unrealislically low values. EDC DNAPL. accumulated in the aquifer below the upper aquitard.
EDC DNALM, did not reach the regional (deepest) aquifer in any of the cases modeled. The limited extent of vertical EDC migration predicted is supported by ground water monitoring conducted over the four years since the spill.     

地震前兆观测中的大数据挖掘应用探讨

大数据挖掘研究与应用是当今的热点问题。本文简要概括了大数据研究的基本思想,分析了地震监测中前兆观测数据大数据研究的必要性,并设计了几种前兆观测大数据研究在地震行业内部的可能应用方向和具体应用问题,以期能够推动前兆观测数据利用大数据研究思想的创新应用和研究工作。     

The El Salvador and Philippines Tsunamis of August 2012: Insights from Sea Level Data Analysis and Numerical Modeling

We studied two tsunamis from 2012, one generated by the El Salvador earthquake of 27 August (Mw 7.3) and the other generated by the Philippines earthquake of 31 August (Mw 7.6), using sea level data analysis and numerical modeling. For the El Salvador tsunami, the largest wave height was observed in Baltra, Galapagos Islands (71.1 cm) located about 1,400 km away from the source. The tsunami governing periods were around 9 and 19 min. Numerical modeling indicated that most of the tsunami energy was directed towards the Galapagos Islands, explaining the relatively large wave height there. For the Philippines tsunami, the maximum wave height of 30.5 cm was observed at Kushimoto in Japan located about 2,700 km away from the source. The tsunami governing periods were around 8, 12 and 29 min. Numerical modeling showed that a significant part of the far-field tsunami energy was directed towards the southern coast of Japan. Fourier and wavelet analyses as well as numerical modeling suggested that the dominant period of the first wave at stations normal to the fault strike is related to the fault width, while the period of the first wave at stations in the direction of fault strike is representative of the fault length.