Distribution and fluxes of metals in the St. Lawrence River from the outflow of Lake Ontario to Québec City

Six anchor stations in the St. Lawrence River from the outflow of Lake Ontario to Quebec City, were occupied for ca. 24 hours in June 1987 during low flow conditions. Samples of water and suspended particulate matter were separated by continuous-flow centrifugation, and were collected every two hours.During this sampling period, fluxes of dissolved forms of zinc, copper and nickel increased in Lac St. François and downstream relative to the sum of the fluxes for the two upstream stations at the outflow of Lake Ontario. Increases in the flux of dissolved zinc and copper were pronounced below Montreal and above Lac St. Pierre. For particulate forms of metals, all five metals show that there are significant inputs in the section of the St. Lawrence River between Lac St. François and the station just above the entrance to Lac St. Pierre.The average concentrations of dissolved cadmium, lead, zinc, copper and nickel ranged from 7–23 ng/l; 9–35 ng/l; 0.434–0.939 g/l; 0.15–0.89 g/l and 0.58–1.12 g/l respectively.Regression analysis of the dissolved and particulate metal concentrations suggests that the concentration of dissolved cadmium, lead, zinc and nickel can be predicted from the regression equation and the determination of particulate metal concentration. This prediction appears to be independent of the suspended particulate matter concentration which varies from ca. 1 to 10 mg/l from the outflow of Lake Ontario to Québec City.     

Doline probability map using logistic regression and GIS technology in the central Ebro Basin (Spain)

In the surroundings of Zaragoza, karstification processes are especially intense in covered karst areas where fluvial terraces lie directly on Tertiary evaporites. Since the beginning of Quaternary, these processes have lead to the development of collapse and subsidence dolines with a wide range of sizes, which have significant economic impacts. To reduce economic impact and increase safety, a regional analysis of this phenomenon is needed for spatial management. Therefore, a probability map of dolines was developed using logistic regression and geographic information system (GIS) techniques. This paper covers the selection of input data, manipulation of data using the GIS technology, and the use of logistic regression to generate a doline probability map. The primary variable in the doline development in this area is geomorphology, represented by the location of endorheic areas and different terrace levels. Secondary variables are the presence of irrigation and the water table gradient.     

青岛港风暴潮经验统计预报

本文利用青岛港多年实测资料,分析了该港风暴潮概况。而后通过多元回归技术,求取了该港极值增减水的预报公式。经非独立和独立检验,结果令人满意。     

成都城市发展对热岛强度影响的主成份回归分析

本文对成都市总人口、建成区面积等11个因子、作了主成份回归L-S估计和M-估计,讨论了成都城市发展对“热岛”强度的主要影响因子。结果表明,城区房屋建筑面积及总人口数是影响城市气候(气温)的主要因子,其次为城市人口总户数、建成面积等。 文中,对回归方程进行了拟合计算,回归效果比较满意(尤其是稳健回归)。     

Comparison of structural and least-squares lines for estimating geologic relations

Two different goals in fitting straight lines to data are to estimate a true linear relation (physical law) and to predict values of the dependent variable with the smallest possible error. Regarding the first goal, a Monte Carlo study indicated that the structural-analysis (SA) method of fitting straight lines to data is superior to the ordinary least-squares (OLS) method for estimating true straight-line relations. Number of data points, slope and intercept of the true relation, and variances of the errors associated with the independent (X) and dependent (Y) variables influence the degree of agreement. For example, differences between the two line-fitting methods decrease as error in X becomes small relative to error in Y. Regarding the second goal—predicting the dependent variable—OLS is better than SA. Again, the difference diminishes as X takes on less error relative to Y. With respect to estimation of slope and intercept and prediction of Y, agreement between Monte Carlo results and large-sample theory was very good for sample sizes of 100, and fair to good for sample sizes of 20. The procedures and error measures are illustrated with two geologic examples.     

基于GIS的滑坡CF多元回归模型及其应用

目前基于GIS滑坡变形失稳危险性评价方法均有各自的局限性。本文在基于数据的数学统计模型的基础上提出CF多元回归模型。通过将滑坡确定性稳定系数与回归模型的融合，在一定程度以解决了滑坡评价过程中影响因子的选择和量化的问题，有利于建立准确的滑坡分析模型。最后，将模型应用于云南小江流域，进行了该区的滑坡空间分布及稳定性分析。     

合肥市耕地和粮食可持续发展研究

收集1980～1999年合肥市耕地、人口、粮食等资料，定量分析在城市化快速发展下耕地的数量、质量变化和耕地减少的形式以及人口、粮食的变化，并用回归法分析它们的变化趋势。研究认为，在2005，2010年合肥市食物保障能力分别为97％和95％，对粮食生产与其影响因素进行了灰色关联分析，提出提高粮食产量的对策和实现途径。     

Surface sediment Chironomidae (Insecta: Diptera) distributions along an ecotonal transect in subarctic Fennoscandia: developing a tool for palaeotemperature reconstructions

We used multivariate statistical techniques to analyse the distributions of surface sediment chironomid assemblages with respect to surface-water temperature, and an additional set of 27 environmental variables, in 30 freshwater lakes of northern Fennoscandia. Our study transect spans boreal coniferous forest to subarctic tundra and includes a steep temperature gradient. Canonical correspondence analysis (CCA) with forward selection and associated Monte Carlo permutation tests revealed that there were statistically significant (P<0.05) relationships between chironomid distributions and two environmental variables, namely lakewater temperature and maximum lake depth. A constrained CCA with temperature as the only predictor variable suggested that the relationship between lakewater temperature and chironomid composition was sufficiently robust for developing a weighted-averaging (WA) based quantitative inference model that will allow palaeotemperature reconstructions using subfossil chironomid remains preserved in lake sediments.     

The separation of the influence of nutrients and climate on the varve time-series of baldeggersee, Switzerland

The varve data-set from a freeze-core taken in the deepest part of Baldeggersee was subjected to different multivariate statistical analyses in order to estimate the amount of variance in the varve thickness measures explicable by past climate and by the trophic state of the lake. A comparison of two different time-periods (1902 to1992 versus 1920 to 1980) revealed that the lake restoration programme since 1982 has had a significant impact on the formation of the seasonal layers. Results of the partitioning of the variance in the varve thickness measures showed that about two thirds of the variance are unexplained by a climate and trophic state model and that trophic state explains 6%, whereas climate accounts for about 28% of the variance before the effect of lake restoration had a strong impact on the varves. Among the climate parameters the amount of annual precipitation is a strong predictor for explaining the thickness of both dark layer and total couplet thickness, whereas summer precipitation is important for the thickness of the light layer.     

Spatially weighted supervised classification for remote sensing

A simple approach for incorporating a spatial weighting into a supervised classifier for remote sensing applications is presented. The classifier modifies the feature-space distance-based metric with a spatial weighting. This is facilitated by the use of a non-parametric (k-nearest neighbour, k-NN) classifier in which the spatial location of each pixel in the training data set is known and available for analysis. A remotely sensed image was simulated using a combined Boolean and geostatistical unconditional simulation approach. This simulated image comprised four wavebands and represented three classes: Managed Grassland, Woodland and Rough Grassland. This image was then used to evaluate the spatially weighted classifier. The latter resulted in modest increase in the accuracy of classification over the original k-NN approach. Two spatial distance metrics were evaluated: the non-centred covariance and a simple inverse distance weighting. The inverse distance weighting resulted in the greatest increase in accuracy in this case.