A Knowledge-Based Approach to Environmental Biomonitoring

This paper presents the design, development and implementation of an integrated GIS-controlled knowledge-based system for environmental monitoring applications, utilizing indigenous flora for assessing quality. The system gathers and combines geographical, ecological, and physicochemical data of organisms' response to pollution within an intelligent computer program that (a) recognises groups of indigenous species suitable for long-term monitoring of a specific pollutant or a combination of pollutants, (b) estimates the ambient concentration of pollutant(s) from the population of the species comprising the bioindicator group and (c) provides biomonitoring capacity indices at national and international/transboundary levels. Significantly, a novel system in the form of a rational framework at the conceptual design level has been developed, that actually contributes towards achieving a cost-effective long-term biomonitoring program, with the flexibility to counter on-course any (anticipated or not) variations/modifications of the surveillance environment: the scheme assumes a robust dynamic cooperation between instrumental and biomonitoring systems, with a view to minimise uncertainty and monitoring costs and increase reliability of pollution control and abatement, aiming eventually at the shifting, partially or totally, from instrumental to natural monitoring. The proposed approach is presently implemented at pilot-scale for establishing a biomonitoring network at a large industrial area in Greece. The results obtained indicate that a cost-effective program can be only attained and maintained under a suitable financial/organizational scheme at the macro level, whereas the micro level viability strongly depends upon careful management of human resources and fixed assets.     

USE OF A HYDROLOGIC MODEL IN A BASIN-WIDE WATER ALLOCATION PROCEEDING1

ABSTRACT: The Montana Department of Natural Resources and Conservation developed a hydrologic model to help analyze the effects of allocating water for consumptive and instream uses in the upper Missouri River basin of Montana. The model, a PC-based FORTRAN program, uses a mass-balance approach to compute monthly streamflows, reservoir operations, hydropower production, and irrigation and municipal water uses throughout the 54,000 square mile basin for a 59-year base period. Simulation results are presented as monthly mean and percentile-exceedence values. The model was run for baseline conditions and six hypothetical water-allocation alternatives. Results were used by staff resource area specialists to assess potential impacts to water quantity and distribution, water rights, water quality, stream channel form, fisheries, wildlife, recreation, hydropower production, and economics. These analyses were presented to the public and the decision-making board in an environmental impact statement (EIS). Though, in many instances, the model did not allow for detailed, site-specific analyses, the model was an important tool and its simulation results formed the hydrologic basis for the EIS.     

决策树模型在水环境监测网络中选取代表性样点的应用

地表水体中的硝酸盐污染已经成为全球关注的热点环境问题之一。现今,国内外均建立了相关的监测网络对地表水体的水质实施长期监测,但是却导致大量的监测数据累积,给后续的科学研究工作带来了不便,尤其是在庞大的监测网络中如何选取有代表性样点的研究点则成为急需解决的问题之一。以比利时弗拉芒地区地表水的长期监测物理化学指标为例,利用决策树模型评估地表水样点的硝酸盐污染来源专家分类的有效性,为点位优化提供理论依据。原有监测点位的污染源专家分类和模型输出的可匹配率为80%,优化后监测点位从原有47个点降低到30个点,提高了监测工作效率。     

基于DEMATEL/ISM的地下商业街火灾风险影响因素研究

为研究某地下商业街火灾风险影响因素，针对该地下商业街火灾的复杂性和特殊性，建立了包括人员、设备、环境、管理等4个大类下的16个影响因素的火灾风险影响因素指标体系，运用DEMATEL方法分析影响火灾风险的关键因素，并结合ISM方法建立影响因素多层次递阶解释结构模型。结果表明：火灾风险由多种影响因素耦合而成，消防人员工作能力、地下建筑结构复杂度、消防资金投入大小和消防安全规程的执行力度是影响地下商业街火灾的关键因素。     

基于改进的多目标决策的水环境质量综合评价

在对水环境质量综合评价中,多目标决策-理想区间法解决了水环境质量评价标准是区间而非点的缺陷。但是在计算监测点到各理想区间向量的距离时,各水环境质量指标权重直接影响综合评价的结果,通常的确定方法是简单的假设各水环境质量指标的权重相等,这与实际情况相悖。为了解决这一问题,提出了将超标法用于多目标决策法中,利用超标法确定各水环境质量指标的权重,然后将其应用于多目标决策-理想区间法来分析水环境质量等级。并将改进后的多目标决策-理想区间法应用于珠江口及邻近海域的水环境中。基于超标法确定权重的多目标决策-理想区间法与聚类分析相比更有效,与等权重的多目标决策-理想区间法相比,更能体现水环境的污染状况,可应用于各种环境因子的综合评价中。     

Lessons from other disciplines for setting management thresholds for biodiversity conservation

Successful, state-dependent management, in which the goal of management is to maintain a system in a desired state, involves defining the boundaries between different states. Once these boundaries have been defined, managers require a strategic action plan with thresholds that initiate management interventions to either maintain or return the system to a desired state. This approach to management is widely used across diverse industries from agriculture, to medicine, to information technology, but it has only been adopted in conservation management relatively recently. Conservation practitioners have expressed a willingness to integrate this structured approach in their management systems, but they have also voiced concerns, including lack of a robust process for doing so. Given the widespread use of state-dependent management in other fields, we conducted an extensive review of the literature on threshold-based management to gain insight into how and where it is applied and identify potential lessons for conservation management. We identified 22 industries using 75 different methods for setting management thresholds in 843 studies. Methods spanned six broad approaches, including expert driven, statistical, predictive, optimization, experimental, and artificial intelligence methods. The objectives of each of these studies influenced the approaches used, including the methods for setting thresholds and selecting actions, and the number of thresholds set. The role of value judgments in setting thresholds was clear; studies across all industries frequently involved experts in setting thresholds, often accompanied by computational tools to simulate the consequences of proposed thresholds under different conditions. Of the 30 conservation studies examined, two-thirds used expert-driven methods, consistent with prior evidence that experience-based information often drives conservation management decisions. The methods we identified from other disciplines could help conservation decision makers set thresholds for management interventions in different contexts, linking monitoring to management actions and ensuring that conservation interventions are timely and effective.     

基于SDSS的水环境决策系统技术研究

空间决策支持系统(SDSS)作为地理信息系统与决策支持系统的结合体,兼有两者优点。文章将空间决策支持技术引入水环境计算中,讨论如何将水环境预测模型、决策分析方法与空间分析技术进行有机的结合,分析水环境空间决策模型的结构和建立过程,并结合应用实例——突发性水污染事故应急系统为例,阐明了其应用模型的特点,最后给出了相应的建设性结果。     

微塑料污染监测现状与研究进展

微塑料(粒径小于5 mm的塑料颗粒和碎片)普遍存在于水体、陆地表层土壤和空气中,严重影响着生态环境安全及人类健康。微塑料污染监测是环境污染治理与生态保护的重要数据基础。首先,在介绍国内外微塑料监测和研究进展的基础上,分析了微塑料污染的来源及其在陆地和水环境中的赋存状态、迁移转化路径。其次,分析了发达国家和地区在微塑料原位监测网布设、样品采集、分析测试和数据模型等方面的技术特征,对国内微塑料监测技术与方法进行了总结。最后,结合现阶段我国在微塑料监测和污染防治方面的不足,初步提出了针对我国微塑料污染监测技术发展和监测体系建设的对策建议,阐述了数据模型方法在微塑料污染监测中的重要作用。研究成果可为我国“十四五”期间微塑料监测网络和监测体系建设等提供参考和借鉴。