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.     

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。     

Formalising informal solid waste recycling at the Pomona dumpsite in Harare,Zimbabwe

The role of informal recycling in poverty alleviation and solid waste management in cities in developing countries has been receiving increased attention. This study explores the integration of the informal recycling sector with the Harare City Council's solid waste management system, focusing on the Pomona dumpsite. The extent of this integration was compared with interventions proposed in InteRa, a new way of evaluating the integration of informal recyclers with the waste management systems of cities in developing countries. Our results suggest that the Harare City Council, which had the vision of transforming itself into a world‐class city, failed to fully integrate the informal recycling sector. We suggest to policymakers that complete integration of the informal sector will not necessarily prevent cities from achieving such visions. Rather, addressing the neglected interventions may help in achieving their visions.     

MFC-MBR耦合系统运行效果

膜生物反应器(MBR)是一种高效的污水处理工艺,而微生物燃料电池(MFC)能有效降解污泥中的胞外生物有机质(EBOM)并回收电能.将MFC与MBR联用,建立了一套能够有效抑制膜污染同时回收电能的新系统——MFC-MBR耦合系统,MBR的剩余污泥经MFC处理后回流.以传统MBR为对照,对耦合系统中污水处理效果、膜污染情况和污泥混合液的性质进行研究.研究表明,耦合系统的污水处理效果没有明显恶化,COD去除率为94％,NH4+-N的去除率为92％.耦合系统能够有效减缓膜污染的发生,清洗周期延长了28％.污泥混合液的MLVSS/MLSS稳定在80％ ～ 88％,系统内几乎没有无机颗粒积累.松散结合态胞外聚合物(LB-EPS)降低了48％,使污泥混合液性质得到改善.较低的污泥比阻(2.69×1012m/kg)和标准化毛细吸水时间(1.67 s·L/g MLSS),证明耦合系统污泥混合液脱水性能提高了.     

再生有色金属产业西部转移问题探讨

电解铝西部转移是大势所趋,再生有色金属的西部转移是目前行业内探讨的热门话题。但西部并不具备大规模发展再生有色金属产业的条件和基础。西部再生有色金属产业更多的是依赖内生性的发展。     

Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande1

Abudu, S., J.P. King, Z. Sheng, 2011. Comparison of the Performance of Statistical Models in Forecasting Monthly Total Dissolved Solids in the Rio Grande. Journal of the American Water Resources Association (JAWRA) 48(1): 10‐23. DOI: 10.1111/j.1752‐1688.2011.00587.x Abstract: This paper presents the application of autoregressive integrated moving average (ARIMA), transfer function‐noise (TFN), and artificial neural networks (ANNs) modeling approaches in forecasting monthly total dissolved solids (TDS) of water in the Rio Grande at El Paso, Texas. Predictability analysis was performed between the precipitation, temperature, streamflow rates at the site, releases from upstream reservoirs, and monthly TDS using cross‐correlation statistical tests. The chi‐square test results indicated that the average monthly temperature and precipitation did not show significant predictability on monthly TDS series. The performances of one‐ to three‐month‐ahead model forecasts for the testing period of 1984‐1994 showed that the TFN model that incorporated the streamflow rates at the site and Caballo Reservoir release improved monthly TDS forecasts slightly better than the ARIMA models. Except for one‐month‐ahead forecasts, the ANN models using the streamflow rates at the site as inputs resulted in no significant improvements over the TFN models at two‐month‐ahead and three‐month‐ahead forecasts. For three‐month‐ahead forecasts, the simple ARIMA showed similar performance compared to all other models. The results of this study suggested that simple deseasonalized ARIMA models could be used in one‐ to three‐month‐ahead TDS forecasting at the study site with a simple, explicit model structure and similar model performance as the TFN and ANN models for better water management in the Basin.     

Laboratory studies on formation and minimisation of polychlorinated dibenzodioxins and -furans (PCDD/F) in secondary aluminium process

The objectives of this work were to study the formation mechanisms of polychlorinated dibenzo-p-dioxins/polychlorinated dibenzofurans (PCDD/F) in thermal aluminium recycling processes by use of laboratory experiments. The pattern of isomers of PCDD/F indicates that de novo synthesis is important in aluminium smeltery. The mechanisms of PCDD/F formation in aluminium smelting are similar to that of various incineration processes of waste material. The results of bioanalysis (EROD-test) confirms the existence of de novo synthesis of PCDD/F, but points out to the existence to some additional, toxic compounds of unknown structure. To reduce the amount of PCDD/F the input of carbon at the metal should be reduced; in addition the metal smeltery plants should be cleaned from fly ash particles. It is suggested to use good primary methods in the technical plants like constant feeding of the metal into the oven will minimise PCDD/F concentration. The biological EROD-bioassay is a good tool to estimate PCDD/F-TEQ values also for this technical process simulated in the laboratory.     

Recycling of EDTA solution after soil washing of Pb, Zn, Cd and As contaminated soil

Soil washing with EDTA is known to be an effective means of removing toxic metals from contaminated soil. A practical way of recycling of used soil washing solution remains, however, an unsolved technical problem. We demonstrate here, in a laboratory scale experiment, the feasibility of using acid precipitation to recover up to 50% of EDTA from used soil washing solution obtained after extraction of Pb (5330 mg kg⁻¹), Zn (3400 mg kg⁻¹), Cd (35 mg kg⁻¹) and As (279 mg kg⁻¹) contaminated soil. Up to 100% of EDTA residual in the washing solution and 100%, 97%, 98% and 100% of initial Pb, Zn, Cd and As concentration in the solution, respectively, were removed in an electrolytic cell using a graphite anode. We employed the recovered EDTA and treated washing solution to prepare recycled soil washing solution with the same potential for extracting toxic metals from soil as the original. The efficiency of soil washing depends on the EDTA concentration. Using twice recycled 30 mmol EDTA kg⁻¹ soil, we removed 44%, 20%, 53% and 61% of Pb, Zn, Cd and As, respectively, from contaminated soil.