太湖流域上游平原河网污染物综合衰减系数的测定

改善太湖水质需要削减上游河流进入太湖的污染物总量.为了探求太湖流域上游平原河网的自净能力,开展原位实验测定了枯水期高锰酸盐指数、氨氮(NH_4~+-N)、总氮(TN)和总磷(TP)的综合衰减系数,根据河道的水力特征对综合衰减系数进行了修正,并利用一维稳态水质模型对修正前后综合衰减系数的可靠性进行了验证.结果表明,高锰酸盐指数、NH_4~+-N、TN和TP的综合衰减系数分别为:0.0296~0.4106、0.0224~0.3564、0.0137~0.3046和0.0555~0.5725 d~(-1).可靠性验证表明高锰酸盐指数、NH_4~+-N、TN和TP综合衰减系数修正前的平均相对误差分别为8.39%、14.40%、11.43%和19.22%,修正后的平均相对误差分别为10.65%、14.34%、11.37%和19.24%.修正前后高锰酸盐指数、NH_4~+-N、TN和TP的平均相对误差均小于20%且变化不显著,表明综合衰减系数的测定结果能够为太湖流域上游平原河网的污染物总量控制管理提供科学参数;也表明枯水期的水力条件对综合衰减系数的影响较小.     

基于ANN的土壤重金属分布和污染评价研究

农田土壤重金属污染与备受关注的农产品安全问题有密切联系,因此对其进行研究意义重大。以江苏省南通市为研究区,利用采样点实测数据,借助神经网络模型(ANN)并结合3S技术对问题进行研究,从而对土壤重金属的空间动态分布进行描述,并对各个空间位点重金属的污染状况进行评价。结果表明,神经网络模型能够智能地学习各个样点的空间位置与该点各重金属含量之间的映射关系和预先设计好的分类评价模式,并能够稳健地对各个空间插值点处的重金属含量和各个位点的重金属污染状况进行预测和评价。结论显示,南通市大部分农田土壤重金属污染较轻,但也存在局部地区的严重污染。结论与实际情况相符,表明神经网络模型可以为农田土壤重金属的研究提供一个新的思路和方法。     

基于BP神经网络的鄱阳湖水位模拟

考虑到鄱阳湖水位受流域五河与长江来水等多因素的共同作用而表现出高度非线性响应,采用典型的三层BPNN神经网络模型来模拟鄱阳湖水位与其主控因子之间的响应关系。分别将湖口、星子、都昌、棠荫和康山水位作为目标变量进行BPNN模型构建和适用性评估。结果显示：综合考虑流域五河及长江来水（汉口或九江）的BPNN水位模型,空间站点水位模拟精度（R2和Ens）可达090以上,各站点的均方根误差（RMSE）变化范围约050～10 m,若忽略长江来水的影响作用,仅将流域五河来水作为湖泊水位的主控影响因子,模型训练期与测试期的纳希效率系数（Ens）和确定性系数（R2）显著降低,且低于050,均方根误差（RMSE）也明显增大（124～288 m）,意味着综合考虑流域五河与长江来水是获取结构合理、精度保证的鄱阳湖水位模型的重要前提。同时建议针对鄱阳湖湖盆变化对水位的影响,尽可能选择一致性较好的长序列数据集来训练和测试BPNN模型。所构建的BPNN神经网络模型可进一步结合流域水文模型,用来预测气候变化与人类活动下流域径流变化对湖泊水位的潜在影响,也可作为一种有效的模型工具来回答当前鄱阳湖一些备受关注的热点问题,如定量区分流域五河与长江来水对湖泊洪枯水位的贡献分量,为湖泊洪涝灾害的防治和对策制定提供科学依据     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

Predicting land cover using GIS, Bayesian and evolutionary algorithm methods

Modelling land cover change from existing land cover maps is a vital requirement for anyone wishing to understand how the landscape may change in the future. In order to test any land cover change model, existing data must be used. However, often it is not known which data should be applied to the problem, or whether relationships exist within and between complex datasets. Here we have developed and tested a model that applied evolutionary processes to Bayesian networks. The model was developed and tested on a dataset containing land cover information and environmental data, in order to show that decisions about which datasets should be used could be made automatically. Bayesian networks are amenable to evolutionary methods as they can be easily described using a binary string to which crossover and mutation operations can be applied. The method, developed to allow comparison with standard Bayesian network development software, was proved capable of carrying out a rapid and effective search of the space of possible networks in order to find an optimal or near-optimal solution for the selection of datasets that have causal links with one another. Comparison of land cover mapping in the North-East of Scotland was made with a commercial Bayesian software package, with the evolutionary method being shown to provide greater flexibility in its ability to adapt to incorporate/utilise available evidence/knowledge and develop effective and accurate network structures, at the cost of requiring additional computer programming skills. The dataset used to develop the models included GIS-based data taken from the Land Cover for Scotland 1988 (LCS88), Land Capability for Forestry (LCF), Land Capability for Agriculture (LCA), the soil map of Scotland and additional climatic variables.     

Choosing a Food Future: Differentiating Among Alternative Food Options

This article examines the diversity of food networks that fit within the alternative food system of the United States. While farmers’ markets, community supported agriculture schemes, and corporate organic food markets all fit within the alternative food system, they differ greatly in the conventions and beliefs that they represent. The alternative food system has divided into two movements: corporate, weak alternative food networks; and local, strong alternative food networks. The weak corporate version focuses on protecting the environment; however, it neglects issues concerning labor standards, animal welfare, rural communities, small-scale farmers, and human health. Local, strong alternative food networks not only assure environmental protection, but they also address the issues that weak alternatives neglect. Using three case studies from the Washington, D.C. metro area, the author explains that strong alternative food networks are better suited to create social and political change because they challenge the foundations of the conventional food system: standardized and generic products, price-based competition, consolidated power, and global scale. To affect true social and political change in the United States, the author recommends supporting strong alternative food networks by creating the requisite cultural and political space for them to succeed.     

Reflections on the use of Bayesian belief networks for adaptive management

A broad range of tools are available for integrated water resource management (IWRM). In the EU research project NeWater, a hypothesis exists that IWRM cannot be realised unless current management regimes undergo a transition toward adaptive management (AM). This includes a structured process of learning, dealing with complexity, uncertainty etc. We assume that it is no longer enough for managers and tool researchers to understand the complexity and uncertainty of the outer natural system-the environment. It is just as important, to understand what goes on in the complex and uncertain participatory processes between the water managers, different stakeholders, authorities and researchers when a specific tool and process is used for environmental management. The paper revisits a case study carried out 2001-2004 where the tool Bayesian networks (BNs) was tested for groundwater management with full stakeholder involvement. With the participation of two researchers (the authors) and two water managers previously involved in the case study, a qualitative interview was prepared and carried out in June 2006. The aim of this ex-post evaluation was to capture and explore the water managers' experience with Bayesian belief networks when used for integrated and adaptive water management and provide a narrative approach for tool enhancement.     

基于BP模型对城市交通噪声的数据处理和预测

城市交通噪声的预测和评价技术是城市交通可持续发展的重要研究内容,直接为城市交通规划中环境容量分析和环境影响评价服务。本文通过实测的大量数据,运用神经网络中的BP模型及其算法建立车辆数、道路宽度和交通噪声之间的关系,对城市道路交通噪声数据进行处理和预测。     

A comparison of the low frequency electrical signatures of iron oxide versus calcite precipitation in granular zero valent iron columns

Geophysical methods have been proposed as technologies for non-invasively monitoring geochemical alteration in permeable reactive barriers (PRBs). We conducted column experiments to investigate the effect of mineralogy on the electrical signatures resulting from iron corrosion and mineral precipitation in Fe0 columns using (a) Na2SO4, and (b) NaHCO3 plus CaCl2 mixture, solutions. At the influent interface where the reactions were most severe, a contrasting time-lapse electrical response was observed between the two columns. Solid phase analysis confirmed the formation of corrosion halos and increased mineralogical complexity in the corroded sections of the columns compared to the minimal/non-corroded sections. We attribute the contrasting time-lapse signatures to the differences in the electrical properties of the mineral phases formed within the two columns. While newly precipitated/transformed polarizable and semi-conductive iron oxides (mostly magnetite and green rust) increase the polarization and conductivity of the sulfate column, the decrease of both parameters in the bicarbonate column is attributed to the precipitation of non-polarizable and non-conductive calcite. Our results show that precipitate mineralogy is an important factor influencing the electrical properties of the corroded iron cores and must be considered if electrical geophysical methods are to be developed to monitor PRB barrier corrosion processes in situ.     

基于MSPA与MCR模型的余江县生态网络构建基于MSPA与MCR模型的余江县生态网络构建

在我国社会经济快速发展的背景下,城市化进程的不断推进导致城市生态斑块日益减少,生态环境问题正在逐年增加,构建生态网络是改善城市生态环境与服务功能的有效方法之一。以余江县为研究区,采用形态学空间格局分析(MSPA)方法和景观指数法,提取对生态网络具有重要意义的生态源地,并基于MCR模型构建综合阻力面,采用最小成本路径方法生成潜在廊道,再基于重力模型、中介中心度等对关键廊道及踏脚石斑块进行识别并提取,从而构建研究区潜在生态网络。结果表明：MSPA方法能够与MCR模型有机的结合,通过定量的分析识别出研究区潜在生态廊道,并根据斑块的中介作用选定踏脚石斑块,明确研究区景观要素的保护优先度,将景观中的潜在生态源地及廊道作为生态网络构建的主要依据,能够更加科学地为余江县生态网络的构建提供指导,同时也可为其他区域提供参考。