Data fusion modeling for groundwater systems

Engineering projects involving hydrogeology are faced with uncertainties because the earth is heterogeneous, and typical data sets are fragmented and disparate. In theory, predictions provided by computer simulations using calibrated models constrained by geological boundaries provide answers to support management decisions, and geostatistical methods quantify safety margins. In practice, current methods are limited by the data types and models that can be included, computational demands, or simplifying assumptions. Data Fusion Modeling (DFM) removes many of the limitations and is capable of providing data integration and model calibration with quantified uncertainty for a variety of hydrological, geological, and geophysical data types and models. The benefits of DFM for waste management, water supply, and geotechnical applications are savings in time and cost through the ability to produce visual models that fill in missing data and predictive numerical models to aid management optimization. DFM has the ability to update field-scale models in real time using PC or workstation systems and is ideally suited for parallel processing implementation. DFM is a spatial state estimation and system identification methodology that uses three sources of information: measured data, physical laws, and statistical models for uncertainty in spatial heterogeneities. What is new in DFM is the solution of the causality problem in the data assimilation Kalman filter methods to achieve computational practicality. The Kalman filter is generalized by introducing information filter methods due to Bierman coupled with a Markov random field representation for spatial variation. A Bayesian penalty function is implemented with Gauss–Newton methods. This leads to a computational problem similar to numerical simulation of the partial differential equations (PDEs) of groundwater. In fact, extensions of PDE solver ideas to break down computations over space form the computational heart of DFM. State estimates and uncertainties can be computed for heterogeneous hydraulic conductivity fields in multiple geological layers from the usually sparse hydraulic conductivity data and the often more plentiful head data. Further, a system identification theory has been derived based on statistical likelihood principles. A maximum likelihood theory is provided to estimate statistical parameters such as Markov model parameters that determine the geostatistical variogram. Field-scale application of DFM at the DOE Savannah River Site is presented and compared with manual calibration. DFM calibration runs converge in less than 1 h on a Pentium Pro PC for a 3D model with more than 15,000 nodes. Run time is approximately linear with the number of nodes. Furthermore, conditional simulation is used to quantify the statistical variability in model predictions such as contaminant breakthrough curves.     

Knowledge Production and Learning for Sustainable Landscapes: Seven Steps Using Social–Ecological Systems as Laboratories

There are multiple challenges regarding use and governance of landscapes’ goods, functions and intangible values for ecosystem health and human well-being. One group of challenges is to measure and assess principal sustainability dimensions through performance targets, so stakeholders have transparent information about states and trends. Another group is to develop adaptive governance at multiple levels, and management of larger geographical areas across scales. Addressing these challenges, we present a framework for transdisciplinary research using multiple landscapes as place-based case studies that integrates multiple research disciplines and non-academic actors: (1) identify a suite of landscapes, and for each (2) review landscape history, (3) map stakeholders, use and non-use values, products and land use, (4) analyze institutions, policies and the system of governance, (5) measure ecological, economic, social and cultural sustainability, (6) assess sustainability dimensions and governance, and finally (7) make comparisons and synthesize. Collaboration, communication and dissemination are additional core features. We discuss barriers bridges and bridges for applying this approach.     

安全科学技术也是第一生产力(第二部分)

提出安全科学技术也是第一生产力的观点。简析科学、技术和科学技术的内涵及其相互关系;研究科学技术与生产力的关系;阐述安全科学技术也是第一生产力;探讨发展高科技与安全科学技术的关系;安全科学技术发展中应注意的问题。     

安全科学技术也是第一生产力(第三部分)

提出安全科学技术也是第一生产力的观点。简析科学、技术和科学技术的内涵及其相互关系;研究科学技术与生产力的关系;阐述安全科学技术也是第一生产力;探讨发展高科技与安全科学技术的关系;指出安全科学技术发展中应注意的问题。     

基于生态环境监测机构省级监督抽查工作的分析与思考

基于2019年度11个省级监督管理部门对704家生态环境监测机构开展的监督抽查工作情况,总结了此次监督抽查的概况、发现的问题、处罚情况,重点分析了存在的主要问题.提出了应持续强化监测质量控制和管理、积极借鉴和创新监管方式、注重监督抽查结果运用、加强社会监督和行业自律的工作建议.为生态环境监测机构提高监测质量和管理水平提...     

Safety Ⅱ学术思想的科学传播研究*

为全面了解Safety Ⅱ学术思想的传播情况,采集Web of Science数据库中284篇引用Safety Ⅱ学术著作的论文,采用科学计量和知识网络的方法,分析Safety Ⅱ学术思想的传播时间趋势、传播主体分布、传播领域与期刊以及传播的热点主题分布。研究结果表明:Safety Ⅱ学术思想在传播时间上呈线性增长趋势;核心传播区域为英格兰、美国、澳大利亚、挪威和意大利;核心传播机构为挪威斯塔万格大学、荷兰代尔夫特理工大学以及罗马第一大学;核心传播者为Saurin Tarcisio Abreu、Hollnagel Erik以及Patriarca Riccardo;Safety Ⅱ传播的核心领域为工业工程、运筹管理科学、人机工程学、应用心理学以及健康护理科学与服务等领域;Safety Ⅱ传播的主题为“韧性与功能共振”、“病患安全”、“事故与人的失误”、“安全与风险管理”、“社会技术系统与人的因素”以及“安全风险的评估”6大方向。研究结果对我国安全科学理论与实践的发展有重要作用。     

我国安全软科学的发展历程、现状与未来趋势

为准确梳理我国安全软科学进展,以GB/T 13745—2009《学科分类与代码》安全科学技术(620)一级学科体系中的软科学分支学科为范畴,论证安全软科学的价值作用及其要素贡献率;从历史与历程、演进与现状、趋势与挑战3个层面以及学科发展、学科体系、学科范畴、体制法制、监管治理、研究热点、研究前沿等多维度和多视角进行归纳、评论和综述;分析探讨未来安全软科学在理论创新、智慧安全、安全治理和高质量发展模式等方面的发展趋势和面临的挑战。结果表明:安全软科学要素对系统安全具有较大比例的贡献作用,全社会还需进一步加强对发展安全软科学的重视;安全管理学、安全系统学、安全文化、安全法学等学科研究成熟度相对较高,安全史学、安全经济学、安全伦理学、安全哲学研究成熟度较低;智慧安全、智慧应急、本质安全、安全脆弱性、安全领导力、党政同责将成为未来研究热点。