Geochemical characterization of karst groundwater in the cradle of humankind world heritage site,South Africa

The karst of the Cradle of Humankind World Heritage Site plays a major role in the assimilation or carrying of acid mine drainage, sewage effluent return flow and agricultural run-off. Infiltration of contaminated water has altered the chemical composition of the natural waters of the karst system. A multivariate statistical method in combination with conventional geochemical and spatial analysis was applied on groundwater and surface water quality samples to determine the spatial extent of hydrochemical impacts from different anthropogenic sources. The application of hierarchical cluster analysis of the major ions (148 samples) recognised three distinct hydrochemical regimes. Cluster 1 is moderately mineralized, especially with regard to chloride, nitrate and sulphate, cluster 2 has a low mineralization with all elements well within the recommended drinking water limits of South Africa and cluster 3 represents highly mineralized samples taken in the vicinity of decanting mineshafts. The cluster solution is confirmed by a simple mixing model, indicating varying contributions of three identified end members (acid mine drainage, treated sewage effluents and pristine dolomitic groundwater) to the groundwater quality in the catchment. The combination of statistical, geochemical and spatial methods in conjunction with end-member mixing analysis provides a reliable method to understand the processes responsible for the groundwater quality variations and to assist in the identification of anthropogenic impacts.     

An Ensemble of Geostatistical Simulated Realizations Using a Clustering Algorithm to Reproduce Model-Based Statistics: Case Study of a Gravel Reserve at Kivalina,Alaska

This paper demonstrates the convergence of model-based statistics from multiple simulated realizations. Theoretically, the convergence of realization statistics is guaranteed over the number of realizations that are independent among themselves. The rate at which realization-based statistics converges with model-based statistics is important and must be assessed. However, due to poor selection of the random number generator, the generated realization might be far from mutual independence. We use the k-means clustering algorithm to select nearly independent realizations from a set of realization models. We apply the proposed algorithm to a coastal erosion problem in Alaska to estimate the amount of gravel.     

基于地统计学和GIS技术的呼伦湖溶解氧的空间变异特征分析

呼伦湖是内蒙古第一大湖,在草原生态环境保护中举足轻重。在气候变化和人类活动的影响下,呼伦湖的水环境状况不容乐观。为了掌握呼伦湖水体的水质状况,在实地采样的基础上,基于地统计学半方差函数理论和GIS克立格空间插值方法,定量分析了呼伦湖水体溶解氧含量的空间变异特征及分布格局。结果表明：溶解氧的半方差拟合最优模型为球面模型,该水质参数具有强烈的空间自相关性;空间分布上,结合地表水环境质量标准,呼伦湖大部分水域的水质为IV类;考虑到鱼类等水生生物的生存需要,呼伦湖整体的溶解氧状况基本适合鱼类的生长,其中比较适合的区域分布在湖中部。     

山西襄汾农田土壤磁化率和养分空间变异性

应用地统计学方法研究了山西省西南部襄汾县农田土壤的环境磁学和养分特征。结果表明:土壤低频磁化率、高频磁化率、频率磁化率、pH值、有机质、全氮和全钾平均含量分别为92.54×10-8 m3·kg-1、86.93×10-8 m3·kg-1、6.03%、8.26、1.94%、1.02 g·kg-1和2.39%;变异函数分析显示,土壤低频磁化率的空间相关距离最大为14.33 km,说明在一个较大的距离范围内存在空间相关性;块金系数比为23.41%,说明土壤磁化率主要取决于土壤母质、土壤性质、地形等结构因素。耕作和施肥等人为活动虽对土壤磁化率有一定的影响,但未改变其原有空间结构。本结果揭示土壤磁化率和养分的空间分布规律,为农田土壤环境质量评价和管理提供重要依据。     

Analysis of patterns of vertical and temporal distribution of phytoplankton using multifactorial analysis: Acidic Lake Caviahue, Patagonia, Argentina

Most existing studies on the algal communities of acid lakes are based on environments that have been caused by anthropogenic disturbances. Such lakes have a different origin compared to the natural acidic lakes and could be expected to differ also in the mechanisms controlling phytoplankton and trophic status. Planktonic community in Lake Caviahue is somewhat diverse in spite of the low pH of the water. Algae have a distinctive vertical distribution: the values of phytoplankton biomass remain constant throughout the water column and at times were highest in the upper end of the hypolimnion, forming a maximum or a layer of chlorophyll a at depth. The goal of this work was to investigate the factors influencing the seasonal and vertical distribution of phytoplankton. The lake was sampled between the years 2004 and 2006. Physical, chemical and biological parameters at different depths throughout the water column were determined. The interrelationships between environmental variables at different sampling dates were analyzed using an integration of multivariate matrices, multiple factor analysis, to analyze any joint partnerships in the samples. We found that phytoplankton biomass is dominated by Keratococcus rhaphidioides. With regard to zooplankton, we found a single species of rotifers (Philodina sp.). The two arms of the lake and the depths have different behaviours showing differences in the arms' conductivity, dissolved oxygen and pH. The more superficial layers were characterized by high values of phytoplankton and zooplankton biomass, organic and inorganic carbon, dissolved oxygen and pH. The deeper layers showed high values of chlorophyll a, ammonium and phosphorus (dissolved and particulate). From the multivariate analysis the relationships of the each algal species with pH, as a possible indicator of the degree of “acidophilia”, could be extracted.     

塔里木河下游表层土壤盐分空间变异和格局分析

土壤盐化是造成塔里木河下游天然植被退化的主要因素之一,应用地统计学方法,分析了塔里木河下游表层土壤盐分的空间异质性特征。结果表明,研究区土壤含盐量普遍较高,表层土壤盐分呈现强变异性和具有较强的空间自相关格局,主要受结构性因子影响。空间插值结果表明,表层土壤盐分整体上呈现出条带状的分布格局,局部地区表现出斑块状的空间变异特征。从河流纵向看,表层土壤盐分“两头高,中间低”;从河流横向看,离河道越远,盐分越低,地下水埋深逐渐加深,物种数和植被盖度分别表现出与之相应的递减趋势。     

Optimization of mobile radioactivity monitoring networks

In case of a nuclear accident, decision makers rely on high-resolution and accurate information about the spatial distribution of radioactive contamination surrounding the accident site. However, the static nuclear monitoring networks of many European countries are generally too coarse to provide the desired level of spatial accuracy. In the Netherlands, authorities are considering a strategy in which measurement density is increased during an emergency using complementary mobile measuring devices. This raises the question, where should these mobile devices be placed? This article proposes a geostatistical methodology to optimize the allocation of mobile measurement devices, such that the expected weighted sum of false-positive and false-negative areas (i.e. false classification into safe and unsafe zones) is minimized. Radioactivity concentration is modelled as the sum of a deterministic trend and a zero-mean spatially correlated stochastic residual. The trend is defined as the outcome of a physical atmospheric dispersion model, NPK-PUFF. The residual is characterized by a semivariogram of differences between the outputs of various NPK-PUFF model runs, designed to reflect the effect of uncertainty in NPK-PUFF meteorological inputs (e.g. wind speed, wind direction). Spatial simulated annealing is used to obtain the optimal monitoring design, in which accessibility of sampling sites (e.g. distance to roads) is also considered. Although the methodology is computationally demanding, results are promising and the computational load may be considerably reduced to compute optimal mobile monitoring designs in nearly real time.     

Spatio-temporal analysis of tree height in a young cork oak plantation

Cork oak is one of the most valuable natural forest genera in the Mediterranean basin. Modelling cork oak growth has been a challenge for foresters in recent years because of strong site and genetic influences, below-ground competition, management regimes and age effects. Because cork productivity is related to forest height, which is, in turn, related directly with site characteristics, an increase in the accuracy of height prediction implies improved productivity estimation. A Bayesian maximum entropy (BME) geostatistical model was applied to characterize the space–time pattern of height of young cork oak in a forest stand from central Sardinia in the years 2000, 2002, 2003, 2006 and 2008. Cork oak height maps were produced for each of the 5 years. The main goals were to analyse and interpret through time (i) the changes in spatial correlation and (ii) the changes in spatial distribution of cork oak height. The plantation was characterized by an increasing spatial dependence through time, whereas the temporal range was 2 years. Cork oak height was significantly correlated with wind speed (reduced by a neighbouring forest) in all the years implying a single trend. The correlations were larger for 2006 and 2008 than for previous years. Three other environmental variables (shade, elevation and slope) were less significant and their influence restricted to 2 years only. This research has several implications for the management of cork oak in the young phase.     

Statistics-based outlier detection for wireless sensor networks

Wireless sensor network (WSN) applications require efficient, accurate and timely data analysis in order to facilitate (near) real-time critical decision-making and situation awareness. Accurate analysis and decision-making relies on the quality of WSN data as well as on the additional information and context. Raw observations collected from sensor nodes, however, may have low data quality and reliability due to limited WSN resources and harsh deployment environments. This article addresses the quality of WSN data focusing on outlier detection. These are defined as observations that do not conform to the expected behaviour of the data. The developed methodology is based on time-series analysis and geostatistics. Experiments with a real data set from the Swiss Alps showed that the developed methodology accurately detected outliers in WSN data taking advantage of their spatial and temporal correlations. It is concluded that the incorporation of tools for outlier detection in WSNs can be based on current statistical methodology. This provides a usable and important tool in a novel scientific field.