黄河下游堤防地质信息管理及安全评价系统（LHEGIS）开发

在对黄河下游堤防地质信息分析的基础上，根据系统需求，建立了黄河下游工程地质灾安评价系统。该系统将数据库管理和图形管理系统有机结合，不仅实现了数据库的输入，输出，统计分析，以及其与图形属性库之间的转换与管理，而且实现了信息的空间查询，空间分析，图形编辑，输出和图形管理，系统同时提供堤岸稳定性评价的方法和其它实有工具，为黄河下游堤防安全管理和规划提供了辅助决策工具。本文主要从系统功能需求分析，设计原则和开发方法，系统基本框架，及开发实施等五个方面，对系统开发进行了较详细地介绍。     

Using environmental stressor information to predict the ecological status of Maryland non-tidal streams as measured by biological indicators

In Maryland, U.S., an interim framework has recentlybeen developed for using biologically based thresholds, or `biocriteria', to assess the health of nontidal streams statewide at watershed scales. The evaluation of impairment is based on indices of biological integrity from the Maryland Biological Stream Survey (MBSS). We applied logistic regression to quantify how the biotic integrity of streams at a local scale is affected by cumulative effects resulting from catchment land uses, point sources, and nearby transmission line rights-of-way. Indicators for land use were developed from the remote sensing National Land Cover Data and applied at different scales. We determined that the risk of local impairment in nontidal streams rapidly increases with increased urban land use in the catchment area. The average likelihood of failing biocriteria doubled with every 10% points increment in urban land, thus an increase in urban land use from 0 to 20% quadruples the risk of impairment. For the basins evaluated in this study, catchments with more than 40–50% urban land use had greater than 80% probability of failing biocriteria, on average. Inclusion of rights-of-way and point sources in the model did not significantly improve the fit for this data set, most likely because of their low numbers. The overall results indicate that our predictive modeling approach can help pinpoint stream ecosystems experiencing or vulnerable to degradation.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Sharing the geo-Referenced Results of Climate Change Impact Research

The Prairie Adaptation Research Collaborative (PARC) has implemented an Internet Map Server (IMS) at the PARC web site (www.parc.ca) to 1) disseminate the geo-referenced results of PARC sponsored research on climate change impacts and adaptation, and 2) address data, information and knowledge management within the PARC network of researchers and partners. PARC facilitates interdisciplinary research on adaptation to the impacts of climate change in the Canadian Prairie Provinces. The web site is intended as a platform for sharing information and encouraging discussion of climate change impacts and adaptation. The IMS enables scientists and stakeholders to apply simple climate change scenarios to geo-referenced biophysical and social data, and dynamically create maps that display the geographic distribution of potential impacts of climate change. With a limited capacity for spatial analysis, most geo-processing and the climate impact modeling is done offline within a GIS environment. The IMS will serve the output from climate impact models, such that the model results can be customized by the web site user and be most readily applied to the planning and analysis of adaptation strategies.     

新疆生态环境质量综合评价研究

合适的评价指标体系对正确评价区域的生态环境质量起关键的决定性作用。本文综合前人的研究成果，应用RS和GIS技术，采用综合指数评价法，对全疆区域的生态环境质量进行评价、排序及分级，由此得出符合实际的全疆生态环境质量状况。结果表明，全疆生态环境质量总体较差，北疆生态环境质量优于南疆，北疆西部生态环境质量优于北疆东部地区。     

喻家湖水质时空分布特征和影响因子分析

通过设计合理的水质监测网,采用多元统计分析,并结合地理信息技术对武汉市喻家湖在2011年-2012年期间12个监测点、13个水质参数监测值进行水质时空分布特征研究.结果表明,喻家湖13个水质指标概括为4个主成分:第一主成分代表喻家湖的重金属污染,第二主成分代表其富营养化水平,第三主成分代表有机污染程度,第四主成分间接指示富营养化程度;在时间和空间变化上都可分为二组,显著性指标的时空差异较明显,水质污染程度从南至北逐渐减弱,湖溪河是喻家湖的最主要污染源.并对水质参数,监测点位和频次进行了优化.     

基于多种方法的长江上游小流域水质综合评价

选取大河和巴关河流域2018—2022年水质监测数据,运用单因子评价法、Spearman秩相关系数法、综合污染指数法和主成分分析法,对流域水质状况、变化趋势及主要污染物特征进行综合评价分析。结果表明:2018—2022年,大河流域水质未达到地表水Ⅲ类标准;流域整体综合污染指数超过了1,为重污染,下游污染程度较重;营养指标和有机污染物指标存在显著正相关。巴关河流域水质达到了地表水Ⅲ类标准;流域整体综合污染指数未超过1,为中污染,上游污染程度较重;营养指标、有机污染物指标和重金属指标等均呈现出不同程度的相关性。巴关河流域水质总体优于大河流域。TP、NH₃-N、COD_Mn、COD_Cr和BOD₅是影响大河流域和巴关河流域水质的主要因子,均属于有机型及富营养化污染指标,主要来自生活源和农业源。建议选择适宜的评价方法开展系统性小流域污染溯源,分河段有针对性地开展小流域水环境治理。     

A Simulation of Temporal and Spatial Variations in Carbon at Landscape Level: A Case Study for Lake Abitibi Model Forest in Ontario,Canada

Using a case study of the Lake Abitibi Model Forest (LAMF), this study aims to assess the temporal and spatial variability in carbon storage during 1990–2000, and to present a comprehensive estimation of the carbon budget for LAMF's ecosystems. As well, it provided the information needed by local forest managers to develop ecological and carbon-based indicators and monitor the sustainability of forest ecosystems. Temporal and spatial carbon dynamics were simulated at the landscape level using ecosystem model TRIPLEX1.0 and Geographical Information System (GIS). The simulated net primary productivity (NPP) and carbon storage in forest biomass and soil were compared with field data and results from other studies for Canada's boreal forests. The results show that simulated NPP ranged from 3.26 to 3.34 tC ha⁻¹ yr⁻¹ in the 1990s and was consistent with the range measured during the Boreal Ecosystem-Atmosphere Studies (BOREAS) in central Canada. Modeled NPP was also compared with the estimation from remote sensing data. The density of total above-and belowground biomass was 125.3, 111.8, and 106.5 tC ha⁻¹ for black spruce, trembling aspen, and jack pine in the LAMF ecosystem, respectively. The total carbon density of forested land was estimated at 154.4 tC ha⁻¹ with the proportion of 4:6 for total biomass and soil. The analysis of net carbon balance of ecosystem suggested that the LAMF forest ecosystem was acting as a carbon sink with an allowable harvest in the 1990s.     

中国省际资源节约指数的空间差异研究

论文首次利用改进的"节约指数"并结合图形数据,对中国各省区资源消耗现状进行定量分析。结果表明:中国资源节约指数空间差异明显,总体上东部比西部节约、中部过渡(除山西外);低于全国平均节约水平的省域约占2/3,其中大部分在西部地区;此外,经济发展水平越高的省域,资源环境绩效水平相对越好。研究结果揭示了中国不同省域建设节约型社会的节约水平和资源利用效率差异,同时也为促进区域协调发展以及经济社会的可持续发展提供依据。     

主成分和聚类分析应用于淮南矿区地下水水质评价

运用主成分方法分析淮南矿区23个地下水样的7个水样指标,提取出4个主成分,应用聚类分析对该4个主成分的得分进行聚类。在此基础上,将主成分得分与其对应的特征值相乘,得到水质综合评价指数。聚类分析后水样划分为5类,对每一类的水质综合评价指数取平均值,从而确定水质等级。