2010
DOI: 10.1007/s13253-009-0006-x
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Spatial Inference of Nitrate Concentrations in Groundwater

Abstract: We develop a method for multiscale estimation of pollutant concentrations, based on a nonparametric spatial statistical model. We apply this method to estimate nitrate concentrations in groundwater over the mid-Atlantic states, using measurements gathered during a period of 10 years. A map of the fine-scale estimated nitrate concentration is obtained, as well as maps of the estimated county-level average nitrate concentration and similar maps at the level of watersheds and other geographic regions. The fine-sc… Show more

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Cited by 7 publications
(2 citation statements)
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“…The PGRF model is a Cox process [Cox (1955)] in which the intensity function is modeled nonparametrically as the convolution of a spatial kernel and a gamma random field. This model has found widespread use due to its robustness in intensity function estimation and its computational efficiency [Ickstadt and Wolpert (1999), Best, Ickstadt and Wolpert (2000), Best et al (2002), Stoyan and Penttinen (2000), Niemi and Fernández (2010), Woodard, Wolpert and O'Connell (2010)]. Our generalization from the PGRF model to the HPGRF model is analogous to the extension of the mixture of Dirichlet process priors model to the hierarchical mixture of Dirichlet process priors model [Teh et al (2006)].…”
mentioning
confidence: 99%
“…The PGRF model is a Cox process [Cox (1955)] in which the intensity function is modeled nonparametrically as the convolution of a spatial kernel and a gamma random field. This model has found widespread use due to its robustness in intensity function estimation and its computational efficiency [Ickstadt and Wolpert (1999), Best, Ickstadt and Wolpert (2000), Best et al (2002), Stoyan and Penttinen (2000), Niemi and Fernández (2010), Woodard, Wolpert and O'Connell (2010)]. Our generalization from the PGRF model to the HPGRF model is analogous to the extension of the mixture of Dirichlet process priors model to the hierarchical mixture of Dirichlet process priors model [Teh et al (2006)].…”
mentioning
confidence: 99%
“…The geostatistical estimation method used to carry out the spatial interpolation of the obtained data was the Kriging method. Kriging is explained by Woodard et al (2010) and Lin et al (2012). This interpolation method provides unbiased information about values at non-sampled sites with a minimum estimated variance (Baume et al 2011).…”
Section: Methodsmentioning
confidence: 99%