A multi-agent simulation model for spatial optimisation of manure allocation

The EU Nitrate Directive has spurred many countries to regulate manure production and manure application. Farmers have three allocation options: spreading manure on their own land, transporting manure to other farmers' land or processing manure. The manure problem can be seen as an allocation problem. To better understand this allocation problem, we have developed the spatial mathematical programming multi-agent simulation (MP-MAS) model. This model has been applied in Flanders, Belgium, a region with a high concentration of livestock. The model evaluates the cost efficiency of policy intervention in the manure market through obliged processing. We propose to further optimise the policy using a regionally differentiated manure pressure indicator, which is directly derived from the dual outcome of the mathematical programme. This indicator increases transparency in the manure and processing market, leading to better decision support about location and type of manure processing.     

火电厂电除尘器电控系统的节能改造

电除尘器是火力发电厂的高能耗设备,从细节入手,挖掘电除尘器节能潜力,利用原有设备进行改造,采用简易脉冲供电方式和临界反电晕供电方式可以提高电除尘器电能的利用率,大幅度地降低电除尘器的能耗,实现节能。通过对电除尘器电控系统改造实例的前后电场参数进行比较,说明这种改造方法是可行的,实现了经济、节能与环保三者的兼顾与统一。     

Effects of Urban Spatial Structure,Sociodemographics, and Climate on Residential Water Consumption in Hillsboro,Oregon1

House-Peters, Lily, Bethany Pratt, and Heejun Chang, 2010. Effects of Urban Spatial Structure, Sociodemographics, and Climate on Residential Water Consumption in Hillsboro, Oregon. Journal of the American Water Resources Association (JAWRA) 46(3):461-472. DOI: 10.1111/j.1752-1688.2009.00415.x Abstract: In the Portland metropolitan area, suburban growth in cities such as Hillsboro is projected to increase as people seek affordable housing near a burgeoning metropolis. The most significant determinants for increases in water demand are population growth, climate change, and the type of urban development that occurs. This study analyzes the spatial patterns of single family residential (SFR) water consumption in Hillsboro, Oregon, at the census block scale. The following research questions are addressed: (1) What are the significant determinants of SFR water consumption in Hillsboro, Oregon? (2) Is SFR water demand sensitive to drought conditions and interannual climate variation? (3) To what magnitude do particular census blocks react to drought conditions and interannual climate variation? Using ordinary least squares multiple regression and spatial regression methods, we found that base use, representing indoor water use, is dependent on household size and that seasonal use, representing external water use is dependent on both education level and the size of the property’s outdoor space. Spatial analysis techniques determined that although the water demand of the study area as a whole is not sensitive to drought conditions, certain individual census blocks do respond with a higher magnitude of water use. The most climate-sensitive census blocks tend to contain newer and larger homes, and have higher property values and more affluent and well-educated residents.     

Phospholipid fatty acid biomarkers in a freshwater periphyton community exposed to uranium: discovery by non-linear statistical learning

Phospholipid fatty acids (PLFA) have been widely used to characterize environmental microbial communities, generating community profiles that can distinguish phylogenetic or functional groups within the community. The poor specificity of organism groups with fatty acid biomarkers in the classic PLFA-microorganism associations is a confounding factor in many of the statistical classification/clustering approaches traditionally used to interpret PLFA profiles. In this paper we demonstrate that non-linear statistical learning methods, such as a support vector machine (SVM), can more accurately find patterns related to uranyl nitrate exposure in a freshwater periphyton community than linear methods, such as partial least squares discriminant analysis. In addition, probabilistic models of exposure can be derived from the identified lipid biomarkers to demonstrate the potential model-based approach that could be used in remediation. The SVM probability model separates dose groups at accuracies of ∼87.0%, ∼71.4%, ∼87.5%, and 100% for the four groups; Control (non-amended system), low dose (amended at 10 μg U L⁻¹), medium dose (amended at 100 μg U L⁻¹), and high dose (500 μg U L⁻¹). The SVM model achieved an overall cross-validated classification accuracy of ∼87% in contrast to ∼59% for the best linear classifier.     

Does Including Soil Moisture Observations Improve Operational Streamflow Forecasts in Snow‐Dominated Watersheds?

Changing climate and growing water demand are increasing the need for robust streamflow forecasts. Historically, operational streamflow forecasts made by the Natural Resources Conservation Service have relied on precipitation and snow water equivalent observations from Snow Telemetry (SNOTEL) sites. We investigate whether also including SNOTEL soil moisture observations improve April‐July streamflow volume forecast accuracy at 0, 1, 2, and 3‐month lead times at 12 watersheds in Utah and California. We found statistically significant improvement in 0 and 3‐month lead time accuracy in 8 of 12 watersheds and 10 of 12 watersheds for 1 and 2‐month lead times. Surprisingly, these improvements were insensitive to soil moisture metrics derived from soil physical properties. Forecasts were made with volumetric water content (VWC) averaged from October 1 to the forecast date. By including VWC at the 0‐month lead time the forecasts explained 7.3% more variability and increased the streamflow volume accuracy by 8.4% on average compared to standard forecasts that already explained an average 77% of the variability. At 1 to 3‐month lead times, the inclusion of soil moisture explained 12.3‐26.3% more variability than the standard forecast on average. Our findings indicate including soil moisture observations increased statistical streamflow forecast accuracy and thus, could potentially improve water supply reliability in regions affected by changing snowpacks.     

湖岸缓冲带反硝化作用的研究进展

反硝化作用是湖岸缓冲带去除硝酸盐的重要途径。湖岸缓冲带是联系陆地与湖泊生态系的纽带,不仅为许多动植物提供适宜生境,而且通过反硝化作用能去除地下水中的硝酸盐,提高湖泊水质。概述不同检测反硝化速率的方法,并对比各种方法的优点与缺点。阐述反硝化作用的影响因素：厌氧环境、有机碳、湖岸坡度、缓冲带坡度、pH与温度、硝酸盐浓度。介绍反硝化速率模型的研究开发状况。最后,提出了目前国内外反硝化研究中存在的不足及发展方向。     

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.     

Sensitivity and Uncertainty of Ground‐Water Discharge Estimates for Semiarid Shrublands1

Abstract: We proposed a step‐by‐step approach to quantify the sensitivity of ground‐water discharge by evapotranspiration (ET) to three categories of independent input variables. To illustrate the approach, we adopt a basic ground‐water discharge estimation model, in which the volume of ground water lost to ET was computed as the product of the ground‐water discharge rate and the associated area. The ground‐water discharge rate was assumed to equal the ET rate minus local precipitation. The objective of this study is to outline a step‐by‐step procedure to quantify the contributions from individual independent variable uncertainties to the uncertainty of total ground‐water discharge estimates; the independent variables include ET rates of individual ET units, areas associated with the ET units, and precipitation in each subbasin. The specific goal is to guide future characterization efforts by better targeting data collection for those variables most responsible for uncertainty in ground‐water discharge estimates. The influential independent variables to be included in the sensitivity analysis are first selected based on the physical characteristics and model structure. Both regression coefficients and standardized regression coefficients for the selected independent variables are calculated using the results from sampling‐based Monte Carlo simulations. Results illustrate that, while as many as 630 independent variables potentially contribute to the calculation of the total annual ground‐water discharge for the case study area, a selection of seven independent variables could be used to develop an accurate regression model, accounting for more than 96% of the total variance in ground‐water discharge. Results indicate that the variability of ET rate for moderately dense desert shrubland contributes to about 75% of the variance in the total ground‐water discharge estimates. These results point to a need to better quantify ET rates for moderately dense shrubland to reduce overall uncertainty in estimates of ground‐water discharge. While the approach proposed here uses a basic ground‐water discharge model taken from an earlier study, the procedure of quantifying uncertainty and sensitivity can be generalized to handle other types of environmental models involving large numbers of independent variables.     

Chlorinated solvents in a petrochemical wastewater treatment plant: an assessment of their removal using self-organising maps

The self-organising map approach was used to assess the efficiency of chlorinated solvent removal from petrochemical wastewater in a refinery wastewater treatment plant. Chlorinated solvents and inorganic anions (11 variables) were determined in 72 wastewater samples, collected from three different purification streams. The classification of variables identified technical solvents, brine from oil desalting and runoff sulphates as pollution sources in the refinery, affecting the quality of wastewater treatment plant influent. The classification of samples revealed the formation of five clusters: the first three clusters contained samples collected from the drainage water, process water and oiled rainwater treatment streams. The fourth cluster consisted mainly of samples collected after biological treatment, and the fifth one of samples collected after an unusual event. SOM analysis showed that the biological treatment step significantly reduced concentrations of chlorinated solvents in wastewater.     

三种综合评价数学模型优选旅游区发展影响因素比较

以优选旅游区发展影响因素为例,阐明3种综合评价数学模型的特点及选择数学模型的原则,同时介绍了如何加强综合评价的科学性和合理性。