长三角典型区土壤重金属有效态的协同区域化分析、空间相关分析与空间主成分分析

以昆山市为典型区,采集了126个表层土壤样品,通过多元统计学、地统计学与GIS技术相结合,采用基于协同区域化理论的因子克立格法探讨了长三角多个土壤重金属有效态的区域分异,并在剖析不同空间尺度有效态重金属的空间结构特征基础上,应用空间相关分析和空间主成分分析来揭示引起这种分布格局的成因和污染来源,结果表明,昆山土壤有效态重金属服从正态或对数正态分布,变异系数较大,有效态Cd污染最重.重金属有效态在空间上可划分为块金尺度、小空间尺度（15 km左右）和大空间尺度（40 km左右）,它可用3个尺度的实验（交叉）变异函数的协同区域化模型线性拟合.空间相关分析中,Cd和Zn在3个尺度中的相关性均极显著,且元素在小尺度和大尺度的相关性比块金尺度更强,大尺度的负相关特征较其它尺度明显.空间主成分分析表明,不同尺度的空间污染来源不同.重金属有效态第一、二主成分的空间分布格局结果表明重金属有效态含量与工业活动、污水灌溉和土壤性质密切相关.     

Modelling Replicated Weed Growth Data using Spatially-varying Growth Curves

Weed growth in agricultural fields constitutes a major deterrent to the growth of crops, often resulting in low productivity and huge losses for the farmers. Therefore, proper understanding of patterns in weed growth is vital to agricultural research. Recent advances in Geographical Information Systems (GIS) now allow geocoding of agricultural data, which enable more sophisticated spatial analysis. Our current application concerns the development of statistical models for conducting spatial analysis of growth patterns in weeds. Our data comes from an experiment conducted in Waseca, Minnesota, that recorded growth of the weed Setariaspp. We capture the spatial variation in Setaria spp. growth using spatially-varying growth curves. An added challenge is that these designs are spatially replicated, with each plot being a lattice of sub-plots. Therefore, spatial variation may exist at different resolutions – a macro level variation between the plots and micro level variation between the sub-plots nested within each plot. We develop a Bayesian hierarchical framework for this setting. Flexible classes of models result which are fitted using simulation-based methods.