海河下游水体中DO与NH_3-N、COD_(Mn)相关关系探讨

本文应用近年来海河下游监测数据对ＤＯ与ＮＨ３Ｎ和ＣＯＤＭｎ进行一元线性回归,得到两回归方程：ＮＨ３Ｎ＝－１２８ＤＯ＋１０５４;ＣＯＤＭｎ＝－１１３ＤＯ＋１６９２。并经相关系数和回归系数显著性检验,表明在９９９％的置信水平下ＤＯ与ＮＨ３Ｎ和ＣＯＤＭｎ线性相关均极其显著     

Towards a planning support system for environmental management and agri-environmental measures--the Colorfields study

The authors present the beginnings of a planning support system (PSS) for agri-environmental measures exemplified by a virtual implementation of Colorfields and blooming strips on model farms, based on real-world data. This paper starts with an introduction to the Colorfields, a concept for transdisciplinary and sustainable landscape design of set-aside land. Colorfields comprise of blooming strips of flowering annual or biennial plants, which are designed and drilled in pattern on fallow land creating Land Art. The temporary scenic arrangements of the Colorfields combine the advantages of ecological strips, e.g. providing habitats for insects (especially bees), improving soil fertility through the cultivation of intercrops, with improvements of the social recognition of farmers as producers of pleasant landscapes instead of monoculture fields.The prototype of the PSS uses two software tools of different scientific origin, the bio-economic modeling system MODAM and the landscape visualization system Lenné3D, which are linked based on geo-data. The resulting system helps to assess the economic effects and visualizes the effects of the specific landuse patterns under different scenarios.The economic assessment of blooming strips on arable land and of one Colorfield on fallow land shows that these measures prove to be profitable from an economic viewpoint assuming the current area payments for the obligatory European Union set-aside program. Furthermore, the visualizations enable the design to be tested virtually by exploring the resultant scenery. They provide artists, planners and stakeholders including farmers with a tool to virtually wander through landscape scenarios supporting a collaborative design and a shared vision for the community.The results of the two model farms and previous case studies for Colorfields demonstrate how current policy conditions could be used for the improvement of environmental and scenic qualities. Furthermore, the ability of the tools, MODAM and Lenné3D, suggests to support and promote these activities.     

室内甲醛污染影响因素的多元回归分析

本文在室内甲醛质量守恒定律基本公式合理推导的基础上,对影响甲醛浓度的主要因素做了正交实验。对实验数据进行多元回归分析,建立室内甲醛浓度预测模型。对模型做检验,检验结果表明,本文的预测模型能较好地预测室内空气中的甲醛浓度。     

Design and evaluation of a local analytic-deliberative process for climate adaptation planning

In the midst of rapidly proliferating engagement efforts around climate adaptation, attention to the design and evaluation of decision support processes and products is warranted. We report on the development and evaluation of a process framework called the Vulnerability, Consequences, and Adaptation Planning Scenarios (VCAPS) process. VCAPS is a systematic approach to integrate local knowledge with scientific understanding by providing opportunities for facilitated, deliberative learning-based activities with local decision makers about climate change vulnerability and adaptation. We introduce the conceptual basis of the process in analytic-deliberation, hazard management, and vulnerability. Our evaluations from eight coastal communities where the approach was applied point to four assets of VCAPS: it promotes synthesis of local and scientific knowledge; it stimulates systems thinking and learning; it facilitates governance by producing action plans with transparent justifications; and it accommodates participant time constraints and preferences.     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.     

A measurement of community disaster resilience in Korea

Building a community that is resilient to disasters has become one of the main goals of disaster management. Communities that are more disaster resilient often experience less impact from the disaster and reduced recovery periods afterwards. This study develops a methodology for constructing a set of indicators measuring Community Disaster Resilience Index (CDRI) in terms of human, social, economic, environmental, and institutional factors. In this study, the degree of community resilience to natural disasters was measured for 229 local municipalities in Korea, followed by an examination of the relationship between the aggregated CDRI and disaster losses, using an ordinary least squares (OLS) regression method and a geographically weighted regression (GWR) method. Identifying the extent of community resilience to natural disasters would provide emergency managers and decision-makers with strategic directions for improving local communities' resilience to natural disasters while reducing the negative impacts of disasters.     

A spatially explicit backcasting approach for sustainable land-use planning

General backcasting as a decision support and planning method starts from desired future states and simulates developments backwards until reaching the present state. Development pathways that reveal steps to be taken to reach a certain future state, and milestones that serve as interim goals, are created during the process. Backcasting has hitherto only been applied in workshops or as a theoretical framework and no spatially explicit backcasting model has previously been established. This paper presents the development of a spatially explicit backcasting model. The proposed model first creates a future scenario utilizing an agent-based model and then simulates backwards. It is implemented using the programming language Python. The model has been applied to a case study for sustainable land-use planning in Salzburg, Austria. The results of the model run show a successful backcasting of land-use classes from a future state back to the present, in 10 year time steps.     

Boosted Regression Tree Models to Explain Watershed Nutrient Concentrations and Biological Condition

Boosted regression tree (BRT) models were developed to quantify the nonlinear relationships between landscape variables and nutrient concentrations in a mesoscale mixed land cover watershed during base‐flow conditions. Factors that affect instream biological components, based on the Index of Biotic Integrity (IBI), were also analyzed. Seasonal BRT models at two spatial scales (watershed and riparian buffered area [RBA]) for nitrite‐nitrate (NO₂‐NO₃), total Kjeldahl nitrogen, and total phosphorus (TP) and annual models for the IBI score were developed. Two primary factors — location within the watershed (i.e., geographic position, stream order, and distance to a downstream confluence) and percentage of urban land cover (both scales) — emerged as important predictor variables. Latitude and longitude interacted with other factors to explain the variability in summer NO₂‐NO₃ concentrations and IBI scores. BRT results also suggested that location might be associated with indicators of sources (e.g., land cover), runoff potential (e.g., soil and topographic factors), and processes not easily represented by spatial data indicators. Runoff indicators (e.g., Hydrological Soil Group D and Topographic Wetness Indices) explained a substantial portion of the variability in nutrient concentrations as did point sources for TP in the summer months. The results from our BRT approach can help prioritize areas for nutrient management in mixed‐use and heavily impacted watersheds.     

Outlier identification and visualization for Pb concentrations in urban soils and its implications for identification of potential contaminated land

Outliers in urban soil geochemical databases may imply potential contaminated land. Different methodologies which can be easily implemented for the identification of global and spatial outliers were applied for Pb concentrations in urban soils of Galway City in Ireland. Due to its strongly skewed probability feature, a Box–Cox transformation was performed prior to further analyses. The graphic methods of histogram and box-and-whisker plot were effective in identification of global outliers at the original scale of the dataset. Spatial outliers could be identified by a local indicator of spatial association of local Moran's I, cross-validation of kriging, and a geographically weighted regression. The spatial locations of outliers were visualised using a geographical information system. Different methods showed generally consistent results, but differences existed. It is suggested that outliers identified by statistical methods should be confirmed and justified using scientific knowledge before they are properly dealt with.     

化学事故危害后果评估与应急救援的辅助决策系统设计

根据重大城市的化学事故后果评估与应急救援的实际需要出发,提出了研制辅助决策系统任务需求和设计构想,阐述了该系统的系统结构、系统实现的流程和系统功能,并做了可行性分析。系统的模块包括：化学品基础信息模块、化学事故类型划分模块、地理信息系统模块、化学源周围道路交通模块、实时的天气预报模块、数学模型模块、危害后果评估模块、救援方案生成模块、人员疏散撤离方案生成模块、人机交互模块。系统与地理信息系统（G IS）、卫星导航系统（GPS）、遥感成像技术结合后能够实现精确定位、快速评估。将物联网技术与系统结合,还可实现对大型固定化学源远程时时监控,确保在事故发生后第一时间掌握信息,为后果评估和应急救援赢得时间,将化学突发事故的危害降到最低。该系统在信息化条件下,较好地为决策者进行化学事故后果评估,为应急救援决策提供了依据,可大幅度提高应急救援效能。