大气污染物监测数据不确定度评估方法体系建立及其对PMF源解析的影响分析

目前,正定矩阵因子法(PMF)在大气污染物来源解析中得到了广泛应用,而监测数据不确定度评估是PMF源解析的重要内容之一.当前绝大部分研究采用的数据不确定度仅通过借鉴前人的方法简单计算而得,缺乏对计算方法的合理性和适用性进行评估.本研究通过3种常用的不确定度算法交互应用建立了一套有效评估不确定度的方法体系.通过对香港荃湾站点1998—2008年PM_(10)组分监测数据进行源解析并与平行采样数据源解析结果进行对比,对该方法体系的合理性进行了验证.结果发现,应用该方法体系可以将某些常规方法无法分离的因子进一步分解,得到的源解析结果的残差值甚至小于平行采样方法解析结果的残差值,得到的因子贡献率均处于常规方法得出的因子贡献率之间.这些均表明了该方法体系所得源解析结果的可靠性和全面性.因此,本研究建立的评估不确定度的方法体系具有较强的可行性,对确保源解析结果的准确性有重要意义.     

基于替代模型的煤矸石堆地下水污染随机模拟

为分析参数的不确定性对地下水污染质运移数值模拟模型输出结果的影响,以抚顺西舍场煤矸石堆及周边地区为研究对象,以硫酸根离子为模拟因子建立污染质运移的数值模拟模型,运用灵敏度分析法筛选对模拟模型影响较大的参数作为随机变量;为减少反复调用模拟模型产生的计算负荷,运用克里格方法建立污染质运移模拟模型的替代模型,利用替代模型完成蒙特卡洛随机模拟;最后,对随机模拟的输出结果进行统计分析,利用污染质浓度分布函数估算单井遭受污染的风险.结果表明,井1,井2和井3中达到地下水质量标准的Ⅲ类标准的概率分别为0.6%,97.6%和0%,藉此为地下水污染防治提供更加科学,丰富的参考依据.     

A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions

Spatial information in the form of geographical information system coverages and remotely sensed imagery is increasingly used in ecological modeling. Examples include maps of land cover type from which ecologically relevant properties, such as biomass or leaf area index, are derived. Spatial information, however, is not error-free: acquisition and processing errors, as well as the complexity of the physical processes involved, make remotely sensed data imperfect measurements of ecological attributes. It is therefore important to first assess the accuracy of the spatial information being used and then evaluate the impact of such inaccurate information on ecological model predictions. In this paper, the role of geostatistics for mapping thematic classification accuracy through integration of abundant image-derived (soft) and sparse higher accuracy (hard) class labels is presented. Such assessment leads to local indices of map quality, which can be used for guiding additional ground surveys. Stochastic simulation is proposed for generating multiple alternative realizations (maps) of the spatial distribution of the higher accuracy class labels over the study area. All simulated realizations are consistent with the available pieces of information (hard and soft labels) up to their validated level of accuracy. The simulated alternative class label representations can be used for assessing joint spatial accuracy, i.e., classification accuracy regarding entire spatial features read from the thematic map. Such realizations can also serve as input parameters to spatially explicit ecological models; the resulting distribution of ecological responses provides a model of uncertainty regarding the ecological model prediction. A case study illustrates the generation of alternative land cover maps for a Landsat Thematic Mapper (TM) subscene, and the subsequent construction of local map quality indices. Simulated land cover maps are then input into a biogeochemical model for assessing uncertainty regarding net primary production (NPP).     

环境监测中仪器分析方法不确定度的评估(Ⅱ)——原子吸收光谱分析中的测量不确定度

通过对环境样品分析中常用的原子吸收光谱法测量不确定度分量的分析,探讨了原子吸收光谱法测量不确定度的评估方法,针对不同环境基体样品、不同测定组分以及不同前处理方法的扩展不确定度进行了评定。原子吸收光谱分析中的测量不确定度主要来源于样品称量、消化液定容、仪器漂移及由吸收值通过标准曲线拟合求浓度部分,其中最主要的分量是由吸收值通过标准曲线拟合求样品溶液浓度时引起的测量不确定度。     

环境监测中仪器分析方法不确定度的评估(Ⅲ)——色谱分析中的测量不确定度

通过对环境样品分析中常用的色谱分析法测量不确定度分量来源及影响的讨论,探讨了色谱分析法测量不确定度的评估方法,获得了针对不同环境基体样品、不同测定组分以及不同前处理方法中的扩展不确定度。色谱分析中的测量不确定度主要来源于流速稳定性、柱箱温度稳定性、基线噪声、程序升温重复性、定量重复性、标准物质进样量及由峰面积通过标准曲线拟合求浓度等部分。     

环境监测中仪器分析方法不确定度的评估(Ⅰ)

结合我国环境监测的实际情况,通过对环境监测中常用仪器分析方法中测量不确定度的评估,主要对环境监测中常用仪器分析方法不确定度的来源如称量、体积校准、温度波动、标准物质、化学试剂、摩尔质量、工作曲线、方法和仪器重复性、仪器显示和读数、数字修约等作一评述,并指出了环境监测中常用仪器分析方法测量不确定度的评估应注意解决的问题。     

大气污染物排放源清单不确定性定量分析方法及案例研究

大气污染物排放源清单由于在数据收集过程中存在的不可避免的监测误差、随机误差、关键数据缺乏以及数据代表性不足等因素而具有不确定性,而排放源清单的不确定性指的是人们对排放清单的真实值缺乏认识和了解.介绍了目前大气排放源清单定量不确定性方法框架,并使用电厂NO_x在线监测数据,通过实际案例量化排放源清单中的不确定性.结果表明:即使对被认为具有较高准确性的火电厂点源排放清单,案例中NO_x的排放源清单来自随机误差的不确定性在±15%左右.对排放源清单的不确定性量化有助于决策者确定污染物排放削减目标的可达性和科学制定大气污染物控制策略,指导排放源清单的改进和数据收集工作.同时,对我国排放源清单开发中不确定性分析提出建议.      

基于改进LAM模型的河流污染源解析方法与例证

污染源精准解析是河流水环境治理的基础和关键技术 .针对基于流量-浓度关系的 LAM 模型(负荷分摊模型)存在参数不确定性高的问题,本研究提出基于流量-通量关系的新型 LAM 源解析模型,并以广东省北江为案例,解析了 2018 年北江 COD_Mn的污染来源特征 . 结果表明：(1)改进模型与原始模型的计算结果存在显著差异,基于改进模型的解析结果显示 2018 年北江 COD_Mn主要来源于非点源,其负荷占比高达95.1%,点源仅占 4.9%,而原始模型的结果为非点源占比 100%;(2)改进后的 LAM 模型可实现拟合优度的提升,相对于原始模型,判定系数由0.89提升到 0.92,主要参数的标准误差显著下降;(3)原始模型无法识别负荷占比较小的污染源,改进后的模型识别能力显著提升,结论与研究区实际污染源结构更相符 .研究提出的改进模型与应用可为多源河流污染源精准解析提供方法和案例借鉴 .     

Exploring the use of computer models in participatory integrated assessment – experiences and recommendations for further steps

Integrated assessment (IA) can be defined as a structured process of dealing with complex issues, using knowledge from various scientific disciplines and/or stakeholders, such that integrated insights are made available to decision makers (J. Rotmans, Enviromental Modelling and Assessment 3 (1998) 155). There is a growing recognition that the participation of stakeholders is a vital element of IA. However, only little is known about methodological requirements for such participatory IA and possible insights to be gained from these approaches. This paper summarizes some of the experiences gathered in the ULYSSES project, which aims at developing procedures that are able to bridge the gap between environmental science and democratic policy making for the issue of climate change. The discussion is based on a total of 52 IA focus groups with citizens, run in six European and one US city. In these groups, different computer models were used, ranging from complex and dynamic global models to simple accounting tools. The analysis in this paper focuses on the role of the computer models. The findings suggest that the computer models were successful at conveying to participants the temporal and spatial scale of climate change, the complexity of the system and the uncertainties in our understanding of it. However, most participants felt that the computer models were less instrumental for the exploration of policy options. Furthermore, both research teams and participants agreed that despite considerable efforts, most models were not sufficiently user-friendly and transparent for being accessed in an IA focus group. With that background, some methodological conclusions are drawn about the inclusion of the computer models in the deliberation process. Furthermore, some suggestions are made about how given models should be adapted and new ones developed in order to be helpful for participatory IA. This revised version was published online in July 2006 with corrections to the Cover Date.     

Uncertainty assessment of mapping mercury contaminated soils of a rapidly industrializing city in the Yangtze River Delta of China using sequential indicator co-simulation

Accurate characterization of heavy-metal contaminated areas and quantification of the uncertainties inherent in spatial prediction are crucial for risk assessment, soil remediation, and effective management recommendations. Topsoil samples (0–15 cm) (n = 547) were collected from the Zhangjiagang suburbs of China. The sequential indicator co-simulation (SIcS) method was applied for incorporating the soft data derived from soil organic matter (SOM) to simulate Hg concentrations, map Hg contaminated areas, and evaluate the associated uncertainties. High variability of Hg concentrations was observed in the study area. Total Hg concentrations varied from 0.004 to 1.510 mg kg⁻¹ and the coefficient of variation (CV) accounts for 70%. Distribution patterns of Hg were identified as higher Hg concentrations occurred mainly at the southern part of the study area and relatively lower concentrations were found in north. The Hg contaminated areas, identified using the Chinese Environmental Quality Standard for Soils critical values through SIcS, were limited and distributed in the south where the SOM concentration is high, soil pH is low, and paddy soils are the dominant soil types. The spatial correlations between Hg and SOM can be preserved by co-simulation and the realizations generated by SIcS represent the possible spatial patterns of Hg concentrations without a smoothing effect. Once the Hg concentration critical limit is given, SIcS can be used to map Hg contaminated areas and quantitatively assess the uncertainties inherent in the spatial prediction by setting a given critical probability and calculating the joint probability of the obtained areas.