一种优化的环境测点信息管理模型及其在环境质量数据管理系统中的应用

分析了环境质量数据管理系统中环境测点信息管理存在的问题。针对地理空间、时间、环境管理服务功能类别三类影响因素,提出了一种灵活、高适应性、结构和层次清晰的基础点位信息管理架构———三维矩阵单元式测点信息管理模型,在信息系统开发中有效地实现了通过通用化的环境测点子集机制来把特定的环境质量数据查询需求转化为数据提取的规范化、结构化流程,较好地解决了环境质量数据为环境管理服务的柔性适应性问题。     

ArcGIS技术在环境质量报告书编制中的应用

江苏省在"十五"环境质量报告书的编制中,采用了先进的ArcGIS平台,并通过ArcSDE空间数据引擎规范化组织、管理,以及集成环境质量报告书需要的环境地理信息,满足了环境质量报告书编制过程中涉及的GIS处理和制图需求,同时可以在网络上形成共享,以建立环境数据的空间关联.介绍了环境数据地图可视化处理流程和制图整饰要点,提出了制定环境地图图式规范和建立环境空间数据模型的建议.     

环境空气自动监测系统信息的开发应用

环境空气自动监测系统获得的大量环境质量信息,是了解大气环境现状,判断污染影响的先进手段。全国已有南京等十几个城市利用环境空气自动监测系统的数据开展大气质量周报、日报,并逐步向预报发展。这标志着环境自动监测系统的信息,已经成为环境资源的重要组成部分。现...     

对几种环境质量综合指数评价方法的探讨

从《全国环境质量报告书》的编写出发,探讨了多种环境质量综合指数的应用。利用具体数据对各种方法进行了比较,进而提出在目前报告书中宜采用最大值法和算术平均法相结合的方法来对全国环境质量进行评价,并结合国外环境评价方法的发展趋势对未来报告书的评价模式提出了建议。     

利用GIS实现江苏省地表水省控断面管理

江苏省环境监测中心开发的环境数据可视化地理信息系统是根据断面管理功能,在建立符合管理需求的各类断面图层基础上．采用通用软件开发平台和专业的GIS工具软件相集而成。该系统具有水环境信息的空间查询、表达、统计和绘图等功能．可使环境管理综合决策部门直观、有效地进行水环境质量管理。     

区域环境质量综合评价指标体系的构建及实证研究

以社会经济系统与环境系统的可持续发展为核心思想,运用层次分析法,建立以人类与环境之间压力—状态—响应关系为框架的,综合环境监测各要素的,反映区域可持续发展水平的区域环境质量综合评价体系。运用全国10年的时间序列数据和2010年31个省(市、区)的截面数据进行了实证检验,得出了全国环境质量2001—2010年的发展趋势与变化规律,同时将2010年31个省(市、区)的综合评价结果进行聚类分析,将其划分为6个大类、8个小类,并深入分析了各类地区的环境质量现状、环境压力和环境管理方面的区域差异和特征。实证表明区域环境质量综合评价体系的评价指标具备合理性、科学性和可比性,可为环境管理和决策提供科学依据,具有较好的推广性和实用性。     

网络数据库公文查询系统的设计与实现

简单介绍了WEB数据库的ASP开发技术，设计了网络数据库公文查询系统的查询方案，已建立的公文数据库采取HTTP传输协议，应用WWW信息发布方式，实现了甘肃省环保局避域网内公文的共享，从而为网络技术在未来环境信息网络数据库数据的发布提供了有益的借鉴。     

提升综合分析服务环境管理水平的要素分析

环境质量综合分析是环境监测为环境管理提供技术支持的最重要途径,综合分析应具备信息准确、报送及时、重点突出、透彻有力4个要素.其中,信息准确要素分成数据、评价手段和结论准确3个层次;报送及时要素包含主动服务、捕捉信息、突破创新3类意识;重点突出要素是要求分析报告突出重点和横向比较;透彻有力要素是强调环境质量应与经济社会外...     

对环境质量综合分析工作的思考与建议

概述了环境质量综合分析对环境管理与综合决策的重要性,分析了环境质量综合分析工作现状与存在问题,阐述了新时期对综合分析工作的新要求,从创新理念、主动服务、收集信息、强化手段、健全机制、人才培养等方面提出做好环境质量综合分析,服务环境管理与决策的建议。     

水环境监测信息集成、共享与决策支持平台构建

以水环境监测数据可靠传输交换与集成、信息规范化处理分析、高效共享与决策支持为应用主线,对水环境监测信息集成、共享与决策支持平台的建设目标、总体架构、主要功能进行了研究,设计了平台的总体架构及主要功能,以实现涉水部门水环境信息的采集、传输、交换、存储、分析、发布、共享、展现等,为环境管理与决策人员、为公众、为各级(国家级流域级省级地市级县级)环境监测业务人员提供信息支撑。     

Managing troubled data: coastal data partnerships smooth data integration

Understanding the ecology, condition, and changes of coastal areas requires data from many sources. Broad-scale and long-term ecological questions, such as global climate change, biodiversity, and cumulative impacts of human activities, must be addressed with databases that integrate data from several different research and monitoring programs. Various barriers, including widely differing data formats, codes, directories, systems, and metadata used by individual programs, make such integration troublesome. Coastal data partnerships, by helping overcome technical, social, and organizational barriers, can lead to a better understanding of environmental issues, and may enable better management decisions. Characteristics of successful data partnerships include a common need for shared data, strong collaborative leadership, committed partners willing to invest in the partnership, and clear agreements on data standards and data policy. Emerging data and metadata standards that become widely accepted are crucial. New information technology is making it easier to exchange and integrate data. Data partnerships allow us to create broader databases than would be possible for any one organization to create by itself.     

The role of metadata and strategies to detect and control temporal data bias in environmental monitoring of soil contamination

It is crucial for environmental monitoring to fully control temporal bias, which is the distortion of real data evolution by varying bias through time. Temporal bias cannot be fully controlled by statistics alone but requires appropriate and sufficient metadata, which should be under rigorous and continuous quality assurance and control (QA/QC) to reliably document the degree of consistency of the monitoring system. All presented strategies to detect and control temporal data bias (QA/QC, harmonisation/homogenisation/standardisation, mass balance approach, use of tracers and analogues and control of changing boundary conditions) rely on metadata. The Will Rogers phenomenon, due to subsequent reclassification, is a particular source of temporal data bias introduced to environmental monitoring here. Sources and effects of temporal data bias are illustrated by examples from the Swiss soil monitoring network. The attempt to make a comprehensive compilation and assessment of required metadata for soil contamination monitoring reveals that most metadata are still far from being reliable. This leads to the conclusion that progress in environmental monitoring means further development of the concept of environmental metadata for the sake of temporal data bias control as a prerequisite for reliable interpretations and decisions.     

The countryside information system: A strategic-level decision support system

The Institute of Terrestrial Ecology (ITE) has monitored ecological change in Great Britain (GB) since 1978. The task has been undertaken using a stratified sampling scheme working with a 1 km square as the sample unit. In more recent years, scientific researchers at ITE have been working closely with the policy-makers of the United Kingdom Department of the Environment. The presentation of information to policy advisors and planners was a component within a large project investigating the ecological consequences of land-use change. A simple PC-based decision support system was developed during the project and subsequently has been expanded to produce a marketable product. The system, called the Countryside Information System (CIS), presents and links information at national, regional and thematic levels along with qualifying data describing accuracy and appropriateness of use (i.e., metadata). An integral part of the CIS is the ITE Land Classification, which divides GB into 32 environmental land classes; all 250 000 squares have been classified. The classification allows sampled data to be presented and, as the co-ordinate system is widely used in GB, it allows census datasets to be linked and compared. CIS has been described as a Geographical Information System, but the classification, data held within the system, and the use of metadata to assist in interpretation of results make the system much more decision-support oriented. Indeed, government departments have been involved in directing the development and are now starting to use the system to answer parliamentary questions and formulate, assess and monitor environmental policy. The CIS is an open system, running on a standard PC in Microsoft Windows. Tools for loading and editing new datasets (both sample and census) are incorporated in the suite of programs. The Windows environment and users comments during development have produced a system with an intuitive feel, removing some of the overhead of acquiring specialised technical skills before being able to operate a system. This paper describes the CIS and presents examples of its applications.     

时间轮盘表征图技术设计及其在环境监测数据展示中的应用

针对环境质量管理需求,提出了一种新的环境监测数据表征方法——时间轮盘表征图技术,研究了该方法的设计及功能实现,并给出了时间轮盘表征图在环境质量数据展示中的具体应用实例。实例表明该方法具有强有力的时态数据展示能力,可以更加有效地展示环境监测数据的周期性、时态性变化特征和趋势,使环境质量的变化对比更加直观、清楚。     

Research on deep integration of application of artificial intelligence in environmental monitoring system and real economy

Environmental monitoring, modeling, and managing allow a better understanding of major processing and techniques for managing environmental changes. The pollution level has risen over time due to many factors such as a rise in population and the use of the vehicle, industrialization, and urbanization that have a direct impact on people ‘s health. Hence, in this paper, Artificial intelligence assisted Semantic Internet of Things (AI-SIoT) has been proposed using a wireless sensor network (WSN) for the environmental monitoring system and the real economy. The Artificial Intelligence technique can very effectively analyze data and make precise decisions on the provision of services in different types. This study provides a mathematical framework for the analysis of interdependent aspects of the WSN protocol for communication and design of signal processing. The Internet of Things (IoT) based framework comprises the complete information system from the sensor level to data management about the environment. The experimental results show that the proposed method provides an effective way to analyze the long-term monitoring of environmental data. The proposed AI-SIoT method using the WSN method enhances accuracy(95.6%), performance(98.7%) increase efficiency (93.7%) with reliability (97.4%) when compared to other existing methods.     

Barriers to the use of remote sensing in providing environmental information

Remote sensing from aircraft and earth-observing satellites is an essential source of environmental information and, at regional and global scales, remote sensing from satellites is often the only way in which some information can be collected. Naturally there are technical limitations, such as low resolution and the inability of optical sensors to see through clouds that restrict the use of satellite data, but technology is moving rapidly and major advances can be expected during the current decade, especially from radar satellites.The main barriers to the use of environmental information provided by remote sensing are not technological, but include cost and a need for training and transfer of technology, and a requirement for users to depart from traditional methods where new technology offers distinct advantages. Perhaps the most important contributions that users of remote sensing data can make to breaking down the barriers to the use of environmental data is to provide very clear statements of their information requirements so that technology can develop to meet these requirements.     

Factors influencing acquisition of ecological and exposure information about hazards and risks from contaminated sites

Considerable research indicates that a wide range of socio-economic factors influence attitudes and perceptions about environmental hazards and risks, and that social trust in those who manage a hazard is strongly correlated to judgements about risks and benefits. We suggest that there are three steps that lead to environmental risk perceptions: acquisition of information, interpretation and synthesis of different pieces of information, and understanding of that information in light of previous knowledge, perceptions, or attitudes. In this study we presented 211 college students in the sciences and non-sciences with ecological and exposure information using text, tables and maps to examine the factors that affect information acquisition and interpretation concerning ecological issues at a fictitious hazardous waste site. Students were allowed about an hour to read the materials and answer questions. The percent of students answering each question correctly varied from 4 to 82%, indicating that some questions were extremely difficult to answer. We attributed these differences to variations in the number of places information was presented (in text, tables, maps, or a combination) and the complexity of the information (how many pieces of information were required to answer the question correctly). The correlation between the number of students answering each question correctly and these combined measures (frequency, complexity) was −0.72. Thus, although there were differences in accuracy concerning ecological information as presented in this study, the major differences were accounted for by how the information was presented, and how much information was required. This suggests that risk communicators should carefully determine which ecological information is critical for the target audience, and ensure that it is presented several times (in different forms). That a lower percentage of people correctly answered questions that required synthesizing several pieces of information suggests that this complexity should be reduced where possible, or that the pieces of information should be tied clearly to the conclusion. Self-declaration of effort and perceptions of safety of the site did not markedly influence the relationship between accuracy, difficulty of finding information, and complexity of information. Other possible confounding variables (i.e., science vs non-science major) only accounted for about 27% of the variation in student’s overall score on ecological questions; age, ethnicity, and gender did not enter as significant variables. We cannot manage environmental hazards with appropriate stakeholder input unless we understand how to present environmental information to achieve a full understanding. Protection of human health and the environment requires that people understand ecological and exposure information, particularly on buffer lands.