Literature DB >> 17332255

Spatiotemporal nonattainment assessment of surface water tetrachloroethylene in New Jersey.

Yasuyuki Akita1, Gail Carter, Marc L Serre.   

Abstract

Tetrachloroethylene (PCE) is one of the most frequently detected volatile organic compounds (VOCs) in water systems across the USA. In New Jersey, the Department of Environmental Protection (NJDEP) monitors surface water quality at several sites throughout the state. However due to budget and scientific limitations, the sampling data is insufficient to assess all river streams in New Jersey. To address this problem, the objective of this study is to utilize a framework for the space/time estimation of PCE throughout all river reaches in New Jersey over the 1999 through 2003 time period and to track how this concentration evolves over time. We use the Bayesian maximum entropy (BME) mapping method to take into account the composite spatiotemporal variability of PCE, and we produce maps providing a stochastic description of the distribution of PCE at all times throughout the river network. In addition, we conduct a nonattainment assessment analysis by applying a criterion based on the estimated probability distribution function that allows us to identify the river miles that are highly likely in nonattainment of the standard, those that are highly likely in attainment of the standard, and the remaining labeled as nonassessed. Using this criterion we investigate how the river miles contaminated by PCE vary over space and time, and we identify watershed management areas (WMAs) with contamination problems. Finally, a cross validation comparison with a purely spatial analysis demonstrates that the space/time framework leads to a better estimation and a reduction of the number of nonassessed miles.

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Year:  2007        PMID: 17332255     DOI: 10.2134/jeq2005.0426

Source DB:  PubMed          Journal:  J Environ Qual        ISSN: 0047-2425            Impact factor:   2.751


  11 in total

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3.  Bayesian maximum entropy integration of ozone observations and model predictions: an application for attainment demonstration in North Carolina.

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Authors:  Gangduo Wang; Jianling Wang; G A Shakeel Ansari; M Firoze Khan
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5.  Using river distances in the space/time estimation of dissolved oxygen along two impaired river networks in New Jersey.

Authors:  Eric Money; Gail P Carter; Marc L Serre
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6.  Bayesian Maximum Entropy space/time estimation of surface water chloride in Maryland using river distances.

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Journal:  Environ Pollut       Date:  2016-09-09       Impact factor: 8.071

7.  Space/time analysis of fecal pollution and rainfall in an eastern North Carolina estuary.

Authors:  Angela D Coulliette; Eric S Money; Marc L Serre; Rachel T Noble
Journal:  Environ Sci Technol       Date:  2009-05-15       Impact factor: 9.028

8.  Modern space/time geostatistics using river distances: data integration of turbidity and E. coli measurements to assess fecal contamination along the Raritan River in New Jersey.

Authors:  Eric S Money; Gail P Carter; Marc L Serre
Journal:  Environ Sci Technol       Date:  2009-05-15       Impact factor: 9.028

9.  Flexible regression models over river networks.

Authors:  David O'Donnell; Alastair Rushworth; Adrian W Bowman; E Marian Scott; Mark Hallard
Journal:  J R Stat Soc Ser C Appl Stat       Date:  2014-01       Impact factor: 1.864

10.  Spatially weighted functional clustering of river network data.

Authors:  R A Haggarty; C A Miller; E M Scott
Journal:  J R Stat Soc Ser C Appl Stat       Date:  2014-10-14       Impact factor: 1.864

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