Accounting for Source Location and Transport Direction into Geostatistical Prediction of Contaminants

Saito, Hirotaka; Goovaerts, Pierre

doi:10.1021/es010580f

Cited by 31 publications

(21 citation statements)

References 13 publications

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“…This spatial pattern translates into an unbounded experimental semivariogram that never reaches a sill ( Figure S1c, Supporting Information). Similar behaviors were observed for other sites with point source of pollution (10,26,27). Once the spatial trend is subtracted, the semivariogram of regression residuals ( Figure S1d, Supporting Information) reaches a sill at an estimated distance of 776 meters, which is known as the distance of autocorrelation.…”

Section: Geostatistical Model For Soil Teq Distributionssupporting

confidence: 72%

Geostatistical Modeling of the Spatial Distribution of Soil Dioxins in the Vicinity of an Incinerator. 1. Theory and Application to Midland, Michigan

Goovaerts¹,

Trinh²,

Demond³

et al. 2008

Environ. Sci. Technol.

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Deposition of pollutants around point sources of contamination, such as incinerators, can display complex spatial patterns depending on prevailing weather conditions, the local topography and the characteristics of the source. Deterministic dispersion models often fail to capture the complexity observed in the field, resulting in uncertain predictions that might hamper subsequent decisionmaking, such as delineation of areas targeted for additional sampling or remediation. This paper describes a geostatistical simulation-based methodology that combines the detailed process-based modeling of atmospheric deposition from an incinerator with the probabilistic modeling of residual variability of field samples. The approach is used to delineate areas with high level of dioxin TEQ DF -WHO 98 (Toxic Equivalents) around an incinerator, accounting for 53 field data and the output of the EPA Industrial Source Complex (ISC3) dispersion model. The dispersion model explains 43.7% of the variance in soil TEQ data, while the regression residuals are spatially correlated with a range of 776 meters. One hundred realizations of soil TEQ values are simulated on a grid with a 50 meter spacing. The benefit of stochastic simulation over spatial interpolation is twofold: 1) maps of simulated point TEQ values can easily be aggregated to the geography that is the most relevant for decision making (e.g. census block, ZIP codes); and 2) the uncertainty at the larger scale is simply modeled by the empirical distribution of block-averaged simulated values. Incorporating the output of the atmospheric deposition model as spatial trend yields a more realistic prediction of the spatial distribution of TEQ value than lognormal kriging using only the field data, in particular in sparsely sampled areas away from the incinerator. The geostatistical model provided guidance for the study design (census block-based population sampling) of the University of Michigan Dioxin Exposure Study (UMDES), focused on quantifying exposure pathways to dioxins from industrial sources, relative to background exposures. *Corresponding author phone: 734-913-1098; fax: 734-913-2201; e-mail: goovaerts@biomedware.com. Corresponding author address: BioMedware Inc, 516 North State Street Ann Arbor, MI 48104 (USA). Brief This paper describes a geostatistical simulation-based methodology that combines the detailed process-based modeling of atmospheric deposition from an incinerator with the probabilistic modeling of residual field variability.

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Section: Geostatistical Model For Soil Teq Distributionssupporting

confidence: 72%

Geostatistical Modeling of the Spatial Distribution of Soil Dioxins in the Vicinity of an Incinerator. 1. Theory and Application to Midland, Michigan

Goovaerts¹,

Trinh²,

Demond³

et al. 2008

Environ. Sci. Technol.

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“…The probabil- ity map produced based on kriging interpolation and kriging standard deviation integrates information about the location of the pollutant source and transport process into the spatial mapping of contaminants [71,72]. There are a lot of studies of the performance of the spatial interpolation methods, but the results are not clear-cut [73].…”

Section: Spatial Distribution Of Heavy Metals In the Sediments Of Jinmentioning

confidence: 99%

Spatially Explicit Analysis of Metal Transfer to Biota: Infl uence of Soil Contamination and Landscape

Asrari¹

2014

Heavy Metal Contamination of Water and Soil

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The purpose of this study is to investigate the current status of metal pollution of the sediments from urban-stream, estuary and Jinzhou Bay of the coastal industrial city, NE China. Forty surface sediment samples from river, estuary and bay and one sediment core from Jinzhou bay were collected and analyzed for heavy metal concentrations of Cu, Zn, Pb, Cd, Ni and Mn. The data reveals that there was a remarkable change in the contents of heavy metals among the sampling sediments, and all the mean values of heavy metal concentration were higher than the national guideline values of marine sediment quality of China (GB 18668-2002). This is one of the most polluted of the world's impacted coastal systems. Both the correlation analyses and geostatistical analyses showed that Cu, Zn, Pb and Cd have a very similar spatial pattern and come from the industrial activities, and the concentration of Mn mainly caused by natural factors. The estuary is the most polluted area with extremely high potential ecological risk; however the contamination decreased with distance seaward of the river estuary. This study clearly highlights the urgent need to make great efforts to control the industrial emission and the exceptionally severe heavy metal pollution in the coastal area, and the immediate measures should be carried out to minimize the rate of contamination, and extent of future pollution problems.

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“…These works have relied on known source locations, such as discharge stacks from smelters, and prevailing wind directions over long periods of time to develop trends that are relatively simple functions of distance and direction (azimuth) from the known source (see Saito and Goovaerts, 2001;Mohammadi et al, 1997).…”

Section: Mapping Approachmentioning

confidence: 99%

Joint Sandia/NIOSH exercise on aerosol contamination using the BROOM tool.

Ramsey¹,

Melton²,

Finley³

et al. 2006

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In February of 2005, a joint exercise involving Sandia National Laboratories (SNL) and the National Institute for Occupational Safety and Health (NIOSH) was conducted in Albuquerque, NM. The SNL participants included the team developing the Building Restoration Operations and Optimization Model (BROOM), a software product developed to expedite sampling and data management activities applicable to facility restoration following a biological contamination event. Integrated data-collection, data-management, and visualization software improve the efficiency of cleanup, minimize facility downtime, and provide a transparent basis for reopening. The exercise was held at an SNL facility, the Coronado Club, a now-closed social club for Sandia employees located on Kirtland Air Force Base. Both NIOSH and SNL had specific objectives for the exercise, and all objectives were met. 4

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Accounting for Source Location and Transport Direction into Geostatistical Prediction of Contaminants

Cited by 31 publications

References 13 publications

Geostatistical Modeling of the Spatial Distribution of Soil Dioxins in the Vicinity of an Incinerator. 1. Theory and Application to Midland, Michigan

Geostatistical Modeling of the Spatial Distribution of Soil Dioxins in the Vicinity of an Incinerator. 1. Theory and Application to Midland, Michigan

Spatially Explicit Analysis of Metal Transfer to Biota: Infl uence of Soil Contamination and Landscape

Joint Sandia/NIOSH exercise on aerosol contamination using the BROOM tool.

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