基于改进粒子群算法和元胞自动机的城市扩张模拟——以南京为例

为合理利用多智能体算法解决城市扩张动态模拟问题,基于地理学理论和社会学规律对粒子群算法进行有针对性的改进,提出分段式粒子群算法(SPSO),并结合元胞自动机模拟复杂时空过程的能力,构建出适用于城市扩张模拟的地理元胞自动机SPSO-CA。在SPSO-CA中我们利用多时像的土地利用数据、交通路网数据和地形数据,挖掘出1995~2000年南京城市扩张的土地转换规则。再由此规则实现1995~2008年的南京市城市扩张过程的动态模拟。最后对比SPSO-CA、PSOCA及NULL模型结果得:SPSO-CA总精度86.3%,Kappa系数为0.792,Moran’s I为0.078,PSO-CA总精度83.6%,Kappa系数为0.755,Moran’s I为0.054,NULL模型总精度81.9%,Kappa系数为0.741,真实的Moran’s I为0.072。这表明无论是总精度还是空间一致性,SPSO-CA都优于PSO-CA和NULL模型,即用SPSO-CA模拟城市扩张是可行的。     

基于加权叠加模型的高原湖泊流域重要生态用地识别——以星云湖流域为例

生态用地在维持区域生态平衡和保障区域生态安全具有重要意义。以云南星云湖流域为研究区,运用层次分析法和GIS技术,从水土保持、地质灾害规避与防护、生物多样性保护和水资源安全4个方面,构建了流域重要生态用地识别指标及其识别方法,并识别出流域重要生态用地空间分布。结果表明:(1)加权叠加模型更适用于高原湖泊流域重要生态用地识别;(2)根据生态用地重要性分为核心型、辅助型、过渡型和非重要生态用地,面积分别为75.98 km~2、105.05 km~2、89.47 km~2和65.11 km~2,分别占流域生态用地总面积的22.64%、31.30%、26.66%和19.40%。识别结果能较好地反映重要生态用地维护流域的生态安全。以星云湖流域作为高原湖泊流域的典型,为高原湖泊生态保护提供科学方向,以期协调流域经济发展与生态保护的矛盾,促进可持续发展。     

Environmental forensic investigation of the air pollution from a cement manufacturing unit

Abstract

Cement manufacturing is a process that results in the emission of significant quantities of suspended particulate matter (SPM) to the ambient air. An environmental forensic investigation was carried out in the surroundings of a major cement manufacturing unit at a place called Coimbatore in the southern Indian state of Tamil Nadu. The investigation was carried out to identify the contribution of the cement manufacturing unit to the SPM concentration of the surrounding air environment. The sampling points’ selection and sample collection were done following the principles outlined in the INTERPOL Manual for Pollution Crime Forensic Investigation. On-site monitoring of the air samples was carried out using Mini Laser Aerosol Spectrometer (GRIMM, Mini-LAS Model 11R). The instrument was capable of measuring particles ranging from 0.25 to 32 µm and classifying them into 31 size channels. The test results at majority of the monitoring locations were well above the limits specified in the National Ambient Air Quality Standards of India. Microscopic studies of the dust samples were carried out for surface texture and particle shape. The spatial distribution of particles was analysed using geographic information system (GIS) for the visual identification of the extent of the pollution by keeping the cement factory as the focal point. The results from the GIS and microscopic analysis established the role of the cement factory in the particulate matter pollution of its surroundings, specifically in the areas North-West of the factory. The successfully adopted procedure can serve as a guideline for the environmental forensic investigation of similar pollution incidences.     

基于多元统计分析的渭河西咸段水质评价

基于2012年旱季和雨季的水质监测数据,采用主成分分析(PCA)和绝对主成分多元线性回归分析(APCS-MLR)方法对渭河西咸段水体的污染特征进行了综合评价。结果表明,2012年渭河西咸段水体主要以有机污染和富营养化污染为主,同时存在一定的As污染。总体上,研究水体旱季比雨季污染严重,旱季和雨季提取的主成分及大部分断面综合排名不同,说明降雨对水质产生一定的影响。渭河西咸段水体在皂河入渭之前的上游污染相对较轻,污染贡献以干流排污为主;对应的下游水质较差,皂河是其最大的污染源。     

Decline of traditional rice farming constrains the recovery of the endangered Asian crested ibis (Nipponia nippon)

Traditional agriculture benefits a rich diversity of plants and animals. The winter-flooded rice fields in the Qinling Mountains, China, are the last refuge for the endangered Asian crested ibis (Nipponia nippon), and intensive efforts have been made to protect this anthropogenic habitat. Analyses of multi-temporal satellite data indicate that winter-flooded rice fields have been continuously reduced across the current range of crested ibis during the past two decades. The rate of loss of these fields in the core-protected areas has unexpectedly increased to a higher level than that in non-protected areas in the past decade. The best fit (R² = 0.87) numerical response model of the crested ibis population shows that a reduction of winter-flooded rice fields decreases population growth and predicts that the population growth will be constrained by the decline of traditional winter-flooded rice fields in the coming decades. Our findings suggest that the decline of traditional rice farming is likely to continue to pose a threat to the long-term survival and recovery of the crested ibis population in China.

Electronic supplementary material

The online version of this article (doi:10.1007/s13280-015-0649-5) contains supplementary material, which is available to authorized users.     

Synergistic Techniques for Better Understanding and Classifying the Environmental Structure of Landscapes

The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the best ELC at the required scale of analysis.     

GIS技术在区域土壤有机碳储量估算方面的应用--以柴达木盆地为例

目前在估算土壤有机碳储量方面的方法较多,并且都有其各自的特点所在。地理信息系统(GIS)技术作为当今一种在地学统计分析方面具有强大空间分析功能的计算机应用技术,在地学及相关研究领域得到了广泛的应用。文章首次尝试了利用该技术估算柴达木盆地土壤有机碳储量,该方法与其它计算方法相比,有简单、易操作、可视性好等优点。经计算,柴达木盆地的土壤有机碳储量约为62754.442 t,土壤平均碳面密度为6.105 kgm-2。     

GIS在城市给水排水中的应用

阐述了GIS在城市给水排水领域的应用和发展趋势，包括水资源管理与分析、给水排水管网设计与管理、城市污水处理厂设计与管理、管网应急预警系统分析、水质污染状况和趋势分析等，并以大庆市给水排水管网信息管理系统实例，证明GIS可以及时准确完整的提供所需信息，更好的为市政管理者辅助决策提供依据。     

Quantitative Pollution Spill Risk Assessment: Using A Gis-Based System

Federal and State agencies have recently advocated risk-based analysis as a mechanism for advancing regulatory reform and safety determination in marine systems. the present investigation promotes this objective through the development of risk-based environmental planning strategies for oil spill contingency plans. This alternative approach to contingency planning departs from conventional methodology by employing quantitative risk assessment methods to identify hazardous oil spill zones and sensitive environmental areas, R_o and R_e respectively. the product of this conversion is referenced on a single “Risk” layer within a Geographic Information System (GIS) framework allowing coastal managers to evaluate natural resource data with associated elements of oil spill risk. As a new tool for coastal pollution management, risk-based environmental planning strategies have shown potential for evolving more efficient oil spill contingency plans.     

Characterization of airborne particulate matter in Thessaloniki,Greece

A receptor modeling approach has been applied to identify and apportion sources of airborne particulate matter in Thessaloniki, Greece. The absolute principal component analysis source apportionment technique used, provided quantitative information regarding both source particle characteristics and impacts. The analysis identified four major sources of heavy metals within total suspended particles (TSP) in the centre of the city: oil burning, pyrometallurgical non‐ferrous metal processes, motor vehicles and soil resuspension. Their contributions to TSP estimated by regression on absolute principal component scores (APCS) were 12%, 8%, 5% and 4%, respectively. A similar analysis conducted for a sampling site close to the industrial area identified five major sources: oil burning, industrial Cr source, soil resuspension, pyrometallurgical non‐ferrous metal processes and motor vehicles with contributions 20%, 15%, 9%, 8% and 4%, respectively.