Ozone Exposure and Population Density in Harris County, Texas

Carroll, R. J.; Chen, R.; George, Edward I.; Li, T. H.; Newton, H. Joseph; Schmiediche, Henrik; Wang, N.

doi:10.2307/2965684

Cited by 40 publications

(30 citation statements)

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“…Another review is that of Dimitrakopoulos and Luo (1997), who propose three alternative types of trend: traditional polynomial functions, Fourier expressions and combinations of the two. When covariates are available, a regression-type estimator may effectively represent the trend component (Carroll et al 1997). When there is no specific focus on trend detection, and no covariate is available, a trend component may be expressed as a simple function of spatial coordinates.…”

Section: A General Spatiotemporal Modelmentioning

confidence: 99%

“…Such approaches have been driven by both the desire for more accurate predictive models, and the need to take confounding effects into account when investigating ozone trends and the health effects of ozone. An example is Carroll et al (1997), who proposed a model designed to evaluate the risk assessment of ozone from emissions, by introducing meteorology into the model.…”

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confidence: 99%

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A simple non-separable, non-stationary spatiotemporal model for ozone

2008

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The past two decades have witnessed an increasing interest in the use of space-time models for a wide range of environmental problems. The fundamental tool used to embody both the temporal and spatial components of the phenomenon in question is the covariance model. The empirical estimation of space-time covariance models can prove highly complex if simplifying assumptions are not employed. For this reason, many studies assume both spatiotemporal stationarity, and the separability of spatial and temporal components. This second assumption is often unrealistic from the empirical point of view. This paper proposes the use of a model in which non-separability arises from temporal non-stationarity. The model is used to analyze tropospheric ozone data from the Emilia-Romagna Region of Italy.

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Section: A General Spatiotemporal Modelmentioning

confidence: 99%

mentioning

confidence: 99%

A simple non-separable, non-stationary spatiotemporal model for ozone

2008

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“…Haslett and Raftery (1989) and Handcock and Wallis (1994) considered models of spatio-temporal data that concentrated more on the temporal aspects and de-emphasized prediction at locations not at monitoring sites. Early spatio-temporal models assumed separable spatio-temporal covariance functions and stationarity (Guttorp et al 1992;Carroll et al 1997). Recently, various spatio-temporal models with nonseparable, stationary spatio-temporal covariance functions have been developed (Jones and Zhang 1997;Cressie and Huang 1999;Brown et al 2000;de Iaco et al 2001;Gneiting 2002;Stein 2005).…”

Section: Introductionmentioning

confidence: 99%

Dynamic multi-resolution spatial models

2007

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Data from remote-sensing platforms play an important role in monitoring environmental processes, such as the distribution of stratospheric ozone. Remotesense data are typically spatial, temporal, and massive. Existing prediction methods such as kriging are computationally infeasible. The multi-resolution spatial model (MRSM) captures nonstationary spatial dependence and produces fast optimal estimates using a change-of-resolution Kalman filter. However, past data can provide valuable information about the current status of the process being investigated. In this article, we incorporate the temporal dependence into the process by developing a dynamic MRSM. An application of the dynamic MRSM to a month of daily total column ozone data is presented, and on a given day the results of posterior inference are compared to those for the spatial-only MRSM. It is apparent that there are advantages to using the dynamic MRSM in regions where data are missing, such as when a whole swath of satellite data is missing.

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“…Geostatistics offer a variety of methods to model such processes as realizations of random functions and there are examples of such geostatistical applications in studies on atmospheric pollution (Rouhani et al 1992;Vyas and Christakos 1997), earth geophysics (Handcock and Wallis 1994;Bogaert and Christakos 1997), soil moisture content (Goovaerts and Sonnet 1993;Heuvelink et al 1997;Cao 2008), rainfall or piezometric head fields (Rouhani and Wackernagel 1990;Armstrong et al 1993), risk for exposure to pollutants (Carroll et al 1997;Christakos and Hristopulos 1998), soil impedance (Castrignanò et al 2002) and ecology (Hohn et al 1993). An exhaustive review of geostatistical space-time models was given by Kyriakidis and Journel (1999).…”

Section: Introductionmentioning

confidence: 99%

An approach for assessing soil health: a practical guide for optimal ecological management

et al. 2011

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How to integrate environmental geographic information and biodiversity data combined with management measures to effectively assess soil health is still an unresolved problem. This paper suggests an approach for systematically estimating soil quality and guiding ecological management. First, canonical correspondence analysis is used to predict the distributions of plant species or microorganism communities, principle pollutants and environmental variables from which spatial and environmental data are extracted by the geographic information system (GIS). Secondly, geostatistical methodologies are then used to estimate and quantify the spatial distribution characteristic of the species and pollutants and to create maps of spatial uncertainty and hazard assessment through ArcGis technology. Finally, redundancy analysis provides a suggestion about better management strategy and environmental factor for improving soil health and biodiversity. The combination of these methods with ''3S'' techniques as an assessment approach effectively meets the challenges for estimation and management in different soil environments.

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Ozone Exposure and Population Density in Harris County, Texas

Cited by 40 publications

References 0 publications

A simple non-separable, non-stationary spatiotemporal model for ozone

A simple non-separable, non-stationary spatiotemporal model for ozone

Dynamic multi-resolution spatial models

An approach for assessing soil health: a practical guide for optimal ecological management

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