基于GIS的造纸企业生态风险评价初探

造纸企业的生态危害问题是环保工作的热点,生态风险评价成为辅助决策过程的重要工具之一。本文应用GIS技术与层次分析法,以江苏省为研究区,对生态风险进行了定量综合评价和空间格局分析。结果为反映生态风险状况,优化防御措施以及进行预警决策提供了依据。     

Probabilistic Modelling of Cattle Farm Distribution in Australia

A probabilistic Bayesian method called weights of evidence (WofE) was used to develop a synthetic dataset of cattle farm locations at a national scale across Australia. The synthetic dataset was required for the modelling of livestock movements with a view to assessing biosecurity implications. The WofE method is based on the analysis of spatial relationships between evidential patterns with respect to an event, such as the actual location of a farm. The evidential patterns of cattle farms were derived from maps of land use, land tenure, drainage systems, roads, settlements and long-term averaged rainfall. These evidential patterns were used for delineating and ranking land areas suitable for cattle farming. For each evidential pattern statistics such as a positive weight, a negative weight and a contrast were calculated for estimating the degree of correlation between the evidential patterns and known farm locations. The integrated evidential patterns of known farms were then used for estimating posterior probabilities and splitting land into five different classes according to its suitability for farming.

Assessing alpine plant vulnerability to climate change: a modeling perspective

The potential ecological impact of ongoing climate change has been much discussed. High mountain ecosystems were identified early on as potentially very sensitive areas. Scenarios of upward species movement and vegetation shift are commonly discussed in the literature. Mountains being characteristically conic in shape, impact scenarios usually assume that a smaller surface area will be available as species move up. However, as the frequency distribution of additional physiographic factors (e.g., slope angle) changes with increasing elevation (e.g., with few gentle slopes available at higher elevation), species migrating upslope may encounter increasingly unsuitable conditions. As a result, many species could suffer severe reduction of their habitat surface, which could in turn affect patterns of biodiversity. In this paper, results from static plant distribution modeling are used to derive climate change impact scenarios in a high mountain environment. Models are adjusted with presence/absence of species. Environmental predictors used are: annual mean air temperature, slope, indices of topographic position, geology, rock cover, modeled permafrost and several indices of solar radiation and snow cover duration. Potential Habitat Distribution maps were drawn for 62 higher plant species, from which three separate climate change impact scenarios were derived. These scenarios show a great range of response, depending on the species and the degree of warming. Alpine species would be at greatest risk of local extinction, whereas species with a large elevation range would run the lowest risk. Limitations of the models and scenarios are further discussed. This revised version was published online in July 2006 with corrections to the Cover Date.     

基于RS和GIS技术的贵州省植被生态环境监测分析

为阐明贵州省植被生态环境变化的整体状况,基于RS和GIS技术,应用美国国家航空航天局最新的全球植被指数变化研究数据（GIMMS）,通过计算月归一化植被指数（NDVI）变化率,并对研究区一元线性回归模拟,分析了贵州省1982年-2003年的地表植被覆盖。结果表明：22年来,研究区植被覆盖呈增加趋势,表明贵州省植被生态环境向好的方向发展;贵州省平均植被覆盖在春季和秋季呈上升趋势,夏季和冬季呈下降趋势,其中春季对植被覆盖总变化量的贡献最大;植被覆盖程度增减因区域不同而异,变化程度呈增加的区域主要位于贵,ki-I省的中部地区;变化程度呈减小的区域分布在贵州省的四周边缘。     

基于ＧＩＳ的乌鲁木齐市ＮＯ２ 时空分布特征

以乌鲁木齐市2008-2012年7个空气自动监测点位小时浓度数据为基础数据,利用ArcGIS 技术,分析了其NO2年变化、月变化、日变化、空间分布等污染特征。结果表明,NO2年均值为0．065～0．068 mg/m3,基本保持稳定;NO2呈明显的季节变化,冬季污染较重,春节、秋季次之,夏季空气质量相对较好;NO2呈现“单峰型”的日变化特征,夜间NO2明显高于白天;不同季节 NO2的空间分布特征不同,与交通、供暖、人口密度、地理位置密切相关;NO2分布与风速相关关系明显,而与气温、湿度的关系为非线性。     

郑州市PM2.5浓度时空分布特征及预测模型研究

利用统计学原理和GIS技术,对郑州市2013年8月17—12月31日期间PM2.5浓度时空分布特征进行分析,同时结合气象资料与前一日污染数据,建立人工神经网络反向传播算法模型(BP-ANN)和多元线性回归模型用于该市细颗粒物污染的短期预测。结果表明,郑州市PM2.5浓度日变化呈单峰模式,随逆温现象的发生和交通的密集于上午11:00达到峰值,午后逐步下降。在工作日、周末与国庆节的对比中,国庆节期间颗粒物污染浓度高出平日32.8%,表明人为活动的加剧影响PM2.5的排放;周末与工作日期间无显著差异。在空间分布上,金水区、管城回族区污染最为严重,工业燃煤、地铁施工等源排放是造成污染的主要原因;位于远郊的岗里水库,受秸秆焚烧和市区污染输送等影响,PM2.5浓度亦维持较高水平。最后,研究将所构建的BP-ANN预测模型和多元线性回归模型对比,结果发现两模型在建模阶段预测值与真实值的拟合一致性指标分别为0.944、0.918,均方根误差分别为59.788、70.611;验证阶段拟合一致性指标分别为0.854、0.794,平均绝对误差分别为25.298、32.775,表明BP-ANN模型在预测郑州市PM2.5污染过程中更具优势。     

Directional Connectivity of Wolf (Canis lupus) Populations in Northwest Spain and Anthropogenic Effects on Dispersal Patterns

In view of the extensive information available on wolf ecology and habitat suitability, and on the fragmentation of wolf populations by motorways and similar infrastructures, a key factor in their conservation, the aim of the present study was to model the directional connectivity of wolf populations in the region of Galicia in northwest Spain, and to quantify anthropogenic effects on wolf dispersal patterns. To this end, we map the probability of wolf movement by means of known relationships between wolf movement and anthropogenic, vegetation and topographic factors. The relative importance of each factor was quantified by sensitivity analyses. Three types of cost surface were constructed: (a) isotropic surfaces, (b) anisotropic cost surfaces taking into account terrain slope effects in the movement, and (c) surfaces obtained by combining the isotropic and anisotropic surfaces. The results obtained by approaches (a) and (c) indicate that one of the region’s motorways (the AP-9) probably acts as a significant barrier to wolf movement, possibly isolating two subpopulations, while the remaining motorways probably do not have major effects on dispersal. Estimation of lowest-cost routes for wolf displacement allowed identification of areas critical for connectivity, in which it would be of interest to perform detailed studies with more precise input data on motorway course and the location of drainage channels and underpasses, etc. (these being the factors identified by sensitivity analysis to be those with the most marked effects on the cost surfaces). The visualization of connectivity enabled by this approach will allow wolf management and conservation efforts to be focused on critical areas: such efforts might include measures aimed to encourage wolf dispersal through areas in which conflict with human activity is minimized, thus contributing positively to the management of a socially conflictive species. Finally, evaluation of the different cost surfaces suggests that it would be of interest to introduce two modifications to the anisotropic algorithm, to allow the user to weigh the importance of the different input factors, and to allow the inclusion of more than one anisotropic factor in the model.     

Effects of Traffic Management and Transport Mode on the Exposure of Schoolchildren to Carbon Monoxide

The exposure to CO of schoolchildren was assessed in the town of Northampton, UK, both by direct measurement and by GIS-based activity modelling. Personal measurement of CO showed that exposures when travelling by car were significantly greater than those when walking, although journey times by car were shorter. However, journey exposures had little effect on maximum 8h mean CO exposures. CO concentration fields in the study area were modelled from current traffic flows, and those expected under different traffic management scenarios. These fields were then used, in combination with children's home and school location, and their activity profiles, to simulate frequency distributions of exposure for different transport modes and traffic management scenarios. The results show a large variability in the effect of traffic management interventions, depending on the child's home and school location.     

灵宝市金矿选冶区土壤汞的地统计分析及污染评价

利用数理统计、地统计方法和地理信息系统(GIS)技术研究了灵宝市的金矿选冶区土壤汞的空间分布特性和污染程度评价。Hg和Cu、Zn、Pb、Cd重金属元素存在较大的偏度和峰度,为了使这些元素的数据服从正态分布并降低其偏度和峰度,采用了Box-Cox变换。地统计分析认为研究区内土壤Hg元素具有空间结构特性。用普通克里格插值绘制的研究区Hg含量地球化学图体现了其空间分布特征。结合地累积指数法评价指标得出的研究区Hg污染等级分布图为土壤恢复治理提供了一定科学依据。     

Forest cover change and fragmentation using Landsat data in Maçka State Forest Enterprise in Turkey

In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids (SSeff) and chemical oxygen demand (CODeff) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the influent quality or online monitoring parameters were adopted as the input variables. ANN was also adopted for comparison. The results indicated that the minimum MAPEs of 16.13 and 9.85% for SSeff and CODeff could be achieved using GMs when online monitoring parameters were taken as the input variables. Although a good fitness could be achieved using ANN, they required a large quantity of data. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. Therefore, GM could be applied successfully in predicting effluent when the information was not sufficient. The results also indicated that these simple online monitoring parameters could be applied on prediction of effluent quality well.