Use of Scale Invariance in Evaluating Judgement Indicators

Indicators are used to draw conclusions about ecological endpoints when these endpoints cannot be measured directly. In many cases, inferences about an endpoint are only possible because assumptions have been made about the relationship between indicator and endpoint; we refer to such indicators as judgement indicators. The validity of inferences made using a judgement indicator can be gauged by examining the known or assumed form of the general relationship between indicator and endpoint. The rules for this kind of inference are a consequence of scale invariance, which originates from measurement theory. For simple indicators comprised of a single indicator measurement, the inferences allowed – equivalence, rank, equality of intervals, and equality of ratios – depend on whether the data are nominal, ordinal, interval, or ratio scaled. For composite indicators containing two or more simple indicators, inferences are also affected by the mathematical form of combination; e.g., whether the terms are summed or multiplied. Standardizing simple or composite indicators can allow inferences about the relative importance of observations, based on the natural range of occurrence. Scale invariance is a particularly important consideration in landscape assessments, since these often make use of judgement indicators.     

土壤中酞酸酯类化合物监测方法的质量控制研究

从监测结果控制的角度对近10年的土壤中PAEs监测方法进行了探讨,重点比较了索氏萃取柱净化GC/MS、微波萃取柱净化GC、加速溶剂萃取柱净化GC/MS和加速溶剂萃取GPCGC/MS方法的检出限、精密度和准确度。旨在为中国土壤监测方法的建立提供基础支撑,为开展质量控制工作提供评价依据。     

不同生境苔类和角苔类植物对生态环境的指示作用

利用植物对生态环境进行监测能综合地判断生态环境变化情况。通过对黔北丹霞地貌区、香纸沟喀斯特地貌区及烂泥沟卡林型金矿区苔类、角苔类植物的比较研究,统计分析种群的分布情况。结果表明,角苔类植物主要生长于生态环境保护较好的区域,扁萼苔科、角苔科为生态环境保护较好区域监测指示科植物,蛇苔属为生态环境保护较好区域监测指示属植物,小蛇苔、蛇苔为生态环境保护较好区域监测指示种植物。     

全二维气相色谱-飞行时间质谱法分离复杂体系中7种指示剂 多氯联苯

采用全二维气相色谱-飞行时间质谱法(GC×GC TOF-MS)分析了混标样品Aroclor 1260、Aroclor 1254、Aroclor 1242中的多氯联苯(PCBs)单体,考察了在复杂体系下对7种PCBs指示剂的分离能力。结果表明,与优化柱系统的分离效果相比,136种单体的混标样品中共分离出121种单体,7种指示剂经一维和二维的保留时间及质谱定性,在121种单体的2D斑点图中清晰可辨。全二维对于复杂体系的PCBs和指示剂的分离表明其强大的分离能力和对于检测复杂体系中指示剂的分辨能力,对该类物质的定性和定量检测具有重要意义。     

Artificial neural network based carbon monoxide persistence models for episodic urban air quality management

This paper describes the development of artificial neural network (ANN) based carbon monoxide (CO) persistence (ANNCOP) models to forecast 8-h average CO concentration using 1-h maximum predicted CO data for the critical (winter) period (November–March). The models have been developed for three 8-h groupings of 10 p.m. to 6 a.m., 6 a.m. to 2 p.m. and 2–10 p.m., at two air quality control regions (AQCRs) in Delhi city, representing an urban intersection and an arterial road consisting heterogeneous traffic flows. The result indicates that time grouping of 2–10 pm is dominantly affected by inversion conditions and peak traffic flow. The ANNCOP model corresponding to this grouping predicts the 8-h average CO concentrations within the accuracy range of 68–71%. The CO persistence values derived from ANNCOP model are comparable with the persistence values as suggested by the Environmental Protection Agency (EPA), USA. This work demonstrates that ANN based model is capable of describing winter period CO persistence phenomena.     

克拉玛依市机动车污染现状调查与防治对策研究

在调查克拉玛依市机动车尾气污染的基础上,分析存在的问题,并针对性地提出城市交通污染的控制对策.     

水环境指示生物筛选及水质评价方法研究

以齐齐哈尔市近30年地表水生物监测数据、理化监测数据为研究依据,提出一套基于指示生物的地表水体评价方法。该方法将地表水体划分为5个级别,并从中筛选出每个级别对应的指示生物,最终建立一套基于指示生物描述水体污染程度的评价体系。该方法以理化指标为基础,筛选多种优势种作为指示生物,对水体进行综合评价。相比单一生物监测评价指标,该评价方法更能反映整个生态系统状况,从而更客观地反映水体真实情况,有望为地表水体污染评价新方法的建立提供参考。     

济南市空气污染现状及控制对策

利用2013年1—12月济南市大气监测数据,分析该市空气质量现状及时空分布特征,结果表明:2013年济南市空气主要污染物质量浓度均超过《环境空气质量标准》(GB 3095—2012)二级标准限值,空气污染严重时段为冬季采暖期,颗粒物污染严重区域为该市西部和西北部地区,SO2、NO2污染严重区为中心城区,并从点源、面源和移动源3个方面提出相应的污染防治对策。在SO2、NOx和工业烟粉尘分别减排22.5%、18.2%和31.4%的条件下,运用Models-3/CMAQ模型模拟计算得出:2015年1月和7月,济南市空气中SO2、NO2、PM10和PM2.5的质量浓度与2010年同期相比将分别降低23.9%和29.7%、11.4%和15.9%、21.9%和32.6%、13.5%和26.9%,空气质量得到明显改善。     

塔山水库蓝藻暴发的成因及控制

简述了塔山水库蓝藻暴发的过程及成因。指出 ,由于水库的营养物质增多、水质恶化 ,在不利的水文、气象 (高温、光照充足、微风 )等条件下 ,导致蓝藻暴发。提出了相应的控制对策     

利用空气自动监测车仪器对空气自动监测站仪器进行质量保证及质量控制的可行性分析

通过对辖区内空气自动监测车仪器与4家公司空气自动监测站仪器的比对分析,得出了空气自动监测车仪器与空气自动监测站仪器的PM10、SO2、NO2相对误差分别为:-32.8%、4.4%、-8.7%,仪器之间显现出良好的线性关系,从比对结果来看,利用空气自动监测车对空气自动监测站进行质量保证和质量控制是可行的.