松花江流域水生态环境质量评价研究

采用生物完整性指数(IBI)法评价松花江流域的水生态环境质量。对25个候选生物参数进行敏感度分析、Pearson相关性分析,最终筛选出由总分类单元数、EPT分类单元数、EPT密度、敏感种分类单元比例、敏感物种数量、Hilsenhoff生物指数(HBI)6个核心参数构成的IBI评价指标。采用95%分位数法建立了IBI评价标准,将IBI评价结果划分为5个等级:大于35.84为优,26.88~35.84为良好,17.92~26.88为一般,8.96~17.92为较差,小于8.96为很差。结果表明,建立的IBI评价方法适用于松花江流域水生态环境质量评价,松花江流域各位点30.0%生物状况为优和良好,23.3%为一般;46.7%为较差和很差,说明流域内近一半区域的水生态质量存在不同程度的受损。流域生境质量主要处于一般-良好的状态;水质处于轻度污染。     

生物配体模型预测太湖水体中Cu的形态分布和生物有效性

利用BLM（生物配体模型）预测了太湖水环境条件下Cu的不同形态分布、Cu对大型蚤（Daphnia magna）的生物有效性及Cu在太湖水体的水质基准值,并分析了环境因子与这些预测值的相互关系.结果显示,太湖水体中的Cu主要以有机结合态形式存在,有机结合态Cu占水体中总Cu的99.75%.而大型蚤体内Cu主要以离子态与生物体活性点位相结合,其质量分数为3.97 ng·mg-1.Cu对大型蚤的LC50（半致死浓度）的预测值平均为710.60 μg·L-1,其最大值出现在北部湖区的竺山湾,而最小值出现在东部湖区,并表现出显著的空间差异性.利用BLM推导出的太湖水体中Cu的CMC（基准最大浓度值）平均值为434.01 μg·L-1,CCC（基准连续浓度值）平均值为217.04 μg·L-1,分别为美国水质基准值的36和24倍.环境因子和各参数预测值的相关性分析显示,溶解性有机质中的腐殖酸含量是影响太湖中Cu的形态的主要因子,而pH是影响Cu对大型蚤的生物有效性和水质基准值的关键因子.因此,在制订太湖Cu的水质基准值时,要充分考虑水体溶解性有机质组成和pH的影响.     

我国流域水生态完整性评价方法构建

流域水生态完整性评价是指通过对水生态系统中不同水生态指标(生物和非生物)的监测以及由数学方法综合形成的综合评价指数,来反映水生态系统完整性状况。近年来,世界各国水环境管理政策发生了变化,开始强调生态保护,重视水体的生态质量。中国现行的常规理化监测指标(如COD、氨氮、BOD5)很难满足水环境管理的需求,难以全面准确地反映水环境质量变化的趋势。因此,在借鉴欧美发达国家流域水生态完整性评价方法的基础上,结合中国目前监测现状以及流域水环境管理需求,构建了包括物理生境指标、理化指标、水生生物指标在内的流域水生态完整性监测与评价方法,以期为中国流域水质目标管理技术体系的业务化运行提供可资借鉴的技术支撑,实现从单一的化学指标监测转向综合的水生态系统监测,实现流域水生态完整性的监测与评价。     

Integrity of Tailings Infrastructure and Information Management: A GIS-Based Conceptual Model

Managing the integrity of tailings infrastructure is an important aspect of handling mine tailings. Unfortunately, little research is directed towards its efficient modeling. This paper presents salient aspects of tailings management. It proposes an integrated geographic information systems (GIS)-based conceptual model for efficient handling of these aspects. The paper also discusses the various integrated modules and comments on the systematic implementation of the model to achieve desired results. The modules include: structural integrity management (SIM), release impact assessment (RIA), and hazardous risk assessment and control (HRAC).     

Application of the benthic index of biotic integrity to environmental monitoring in Chesapeake Bay

The Chesapeake Bay benthic index of biotic integrity (B-IBI) was developed to assess benthic community health and environmental quality in Chesapeake Bay. The B-IBI provides Chesapeake Bay monitoring programs with a uniform tool with which to characterize bay-wide benthic community condition and assess the health of the Bay. A probability-based design permits unbiased annual estimates of areal degradation within the Chesapeake Bay and its tributaries with quantifiable precision. However, of greatest interest to managers is the identification of problem areas most in need of restoration. Here we apply the B-IBI to benthic data collected in the Bay since 1994 to assess benthic community degradation by Chesapeake Bay Program segment and water depth. We used a new B-IBI classification system that improves the reliability of the estimates of degradation. Estimates were produced for 67 Chesapeake Bay Program segments. Greatest degradation was found in areas that are known to experience hypoxia or show toxic contamination, such as the mesohaline portion of the Potomac River, the Patapsco River, and the Maryland mainstem. Logistic regression models revealed increased probability of degraded benthos with depth for the lower Potomac River, Patapsco River, Nanticoke River, lower York River, and the Maryland mainstem. Our assessment of degradation by segment and water depth provided greater resolution of relative condition than previously available, and helped define the extent of degradation in Chesapeake Bay.     

基于生物导向的水生态健康监测及评估概述

环境监测是水生态健康监测与评估的重要环节,基于物理、化学监测的传统水质监测通常仅能提供独立的数据信息,不能全面、直观地反映水环境状况。基于生物等生命体导向的水生态监测通过生物对环境的响应,能够直接反应复杂水体状况,在水环境健康监测与评估中占据重要地位。基于病原微生物、指示生物介绍了生物监测中的常规生物指标,总结了包括藻类、无脊椎动物和鱼类在内的常见指示生物在不同类型污染水体中的环境指示作用。从生物毒性效应出发介绍了常用的毒性效应测试方法、分析了污染物在不同生物学水平的响应,从而指明生物毒性效应在水环境健康评估中的发展优势。再从生态完整性角度阐述了生态完整性评价的一般方法和新兴分子生物学技术在水生态健康评估中的应用。重点指出环境毒理学和分子生物学在水环境监测的优势,以期为更加科学精确地进行水生态健康监测预警提供支撑。     

基于环境DNA宏条形码的太湖流域底栖动物监测与生态健康评价

大型底栖动物是生态环境监测和评估的主要目标生物类群之一。环境DNA宏条形码技术的发展为提高底栖动物多样性监测的通量、精准性、标准化程度提供了新的机遇,但该方法在我国尚未有流域尺度的应用先例,其结果的可靠性和对生态环境健康状况的指示性有待检验。率先将环境DNA宏条形码技术用于太湖流域65个点位的底栖动物监测和流域生态健康评价,并与同步进行的形态学监测结果进行了比较。结果表明:①环境DNA方法能检出更多的底栖动物类群,在科、属、种水平上检出的分类单元数分别是形态学监测结果的106%、132%、155%;②基于环境DNA技术的检测方法能够很好地识别形态学监测结果中的优势物种,检出的科级、属级分类阶元能够覆盖形态学监测结果中90%以上的生物量和个体数,同时包含60%以上的物种数;③两种方法对同一物种的检出频次显著相关(R²>0.7,P<0.0001),总体检出一致率达72.3%;④在底栖动物完整性指数(B-IBI)方面,环境DNA方法与形态学方法的计算结果显著相关(R²=0.235,P<0.0001),94%的点位的B-IBI等级划分误差在1级以内,且两种方法的计算结果在底栖动物完整性的流域空间格局描绘上高度重合。综上所述,环境DNA宏条形码技术在太湖流域底栖动物群落监测和评价中的整体应用结果表明,环境DNA监测方法结果可靠,将其进一步规模化应用有望显著提高我国水生态系统生物监测结果的准确性和生态健康评价的技术水平。     

水生态环境质量评价方法及在黄河流域的应用进展

开展水生态环境质量评价方法研究,建立合适的河流健康评价体系是解决河流健康问题的前提和实现河流健康管理的重要手段。黄河自上而下生态环境现状迥异,水生态环境脆弱,寻求一种或多种适合黄河流域水生态环境质量的评价方法具有重要意义。该文介绍了国内外水生态环境质量评价方法及在黄河流域的应用进展,总结了鱼类生物完整性指数(F-IBI)、底栖无脊椎动物完整指数(B-IBI)、着生藻类生物完整性指数(P-IBI)及水生态环境质量综合评价法的主要指标,以及黄河流域不同区域生物种类的评价指标,分析了各评价方法优缺点及应用范围,并根据目前研究基础评述了黄河流域水生态环境质量评价存在的问题,对黄河流域水生态环境质量评价方法、指示生物的选择及水生态环境质量综合评价法的研究等提出了具体建议。     

西北江下游河流底栖动物群落健康评价

2020年12月在西北江下游河流设置11个采样点,调查底栖动物群落结构,应用底栖动物完整性指数(B-IBI)评价河流健康状况,并分析B-IBI值与环境因子的关系.结果表明,参照点位的B-IBI值在0.59～4.00之间,处于中等—优状态;受损点位的B-IBI值在0～0.82之间,处于很差—良好状态,其中较差和很差状态占比为87.5％.氨氮浓度与B-IBI值呈现极显著的负相关性,降低水污染负荷是促进底栖动物群落恢复健康的有效途径.在水质已经实现好转的基础上,可通过营造河底卵石生境来促进底栖动物群落恢复,同时也应加强河岸带的修复.     

Using environmental stressor information to predict the ecological status of Maryland non-tidal streams as measured by biological indicators

In Maryland, U.S., an interim framework has recentlybeen developed for using biologically based thresholds, or `biocriteria', to assess the health of nontidal streams statewide at watershed scales. The evaluation of impairment is based on indices of biological integrity from the Maryland Biological Stream Survey (MBSS). We applied logistic regression to quantify how the biotic integrity of streams at a local scale is affected by cumulative effects resulting from catchment land uses, point sources, and nearby transmission line rights-of-way. Indicators for land use were developed from the remote sensing National Land Cover Data and applied at different scales. We determined that the risk of local impairment in nontidal streams rapidly increases with increased urban land use in the catchment area. The average likelihood of failing biocriteria doubled with every 10% points increment in urban land, thus an increase in urban land use from 0 to 20% quadruples the risk of impairment. For the basins evaluated in this study, catchments with more than 40–50% urban land use had greater than 80% probability of failing biocriteria, on average. Inclusion of rights-of-way and point sources in the model did not significantly improve the fit for this data set, most likely because of their low numbers. The overall results indicate that our predictive modeling approach can help pinpoint stream ecosystems experiencing or vulnerable to degradation.