A high frequency kriging approach for non-stationary environmental processes
A high frequency kriging approach for non-stationary environmental processes
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DOI：
10.1002/env.473
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发表时间：
2001-08-01
期刊：
影响因子：
1.7
通讯作者：
Fuentes, M
中科院分区：
文献类型：
--
作者：
Fuentes, M
1990年的《清洁空气法》修正案规定了减排，期望这些修正案将导致大气中污染物浓度的大幅降低。减排旨在降低公共健康风险，保护敏感的生态系统。为了确定减排是否对大气浓度产生了预期的影响，必须考虑到数据所显示的空间结构，对监测数据进行分析。污染物浓度和通量地图在不同的地缘政治边界上很有用，可以发现美国空气质量在何时、何地以及在多大程度上正在改善或下降。由于数据所表示的空间协方差结构随位置而变化，因此标准的克立格法不能用于空间内插，因为它假设过程是平稳的。在这种方法中，场被局部地表示为平稳的各向同性随机场，但允许平稳随机场的参数在空间中变化。给出了一种利用高频谱密度表达式进行内插的方法。利用谱方法开发了新的拟合算法。在数据精确或近似分布在一个格子上的情况下，与最大似然法相比，谱方法具有潜在的巨大计算优势。使用近似贝叶斯方法将该方法推广到插补问题，以考虑参数不确定性。我们开发应用程序来获得不同地缘政治边界上的污染物浓度和通量的总负载量。版权所有(C)2001 John Wiley&Sons，Ltd.
Emission reductions were mandated in the Clean Air Act Amendments of 1990 with the expectation that they would result in major reductions in the concentrations of atmospherically transported pollutants. The emission reductions are intended to reduce public health risks and to protect sensitive ecosystems. To determine whether the emission reductions are having the intended effect on atmospheric concentrations, monitoring data must be analyzed taking into consideration the spatial structure shown by the data. Maps of pollutant concentrations and fluxes are useful over different geopolitical boundaries, to discover when, where, and to what extent the U.S. Nation's air quality is improving or declining. Since the spatial covariance structure shown by the data changes with location, the standard kriging methodology for spatial interpolation cannot be used because it assumes stationarity of the process.We present a new methodology for spatial interpolation of non-stationary processes. In this method the field is represented locally as a stationary isotropic random field, but the parameters of the stationary random field are allowed to vary across space. A procedure for interpolation is presented that uses an expression for the spectral density at high frequencies. New fitting algorithms are developed using spectral approaches. In cases where the data are distributed exactly or approximately on a lattice, it is argued that spectral approaches have potentially enormous computational benefits compared with maximum likelihood. The methods are extended to interpolation questions using approximate Bayesian approaches to account for parameter uncertainty. We develop applications to obtain the total loading of pollutant concentrations and fluxes over different geo-political boundaries. Copyright (C) 2001 John Wiley & Sons, Ltd.