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dc.contributor.authorLi, M. -Y.en_US
dc.contributor.authorMichalak, A.  m.en_US
dc.date.accessioned2007-07-11T18:18:32Z
dc.date.available2007-07-11T18:18:32Z
dc.date.issued2006-06en_US
dc.identifier.citationLi, M.-Y.; Michalak, A. M. (2006). "Scaling Methods of Sediment Bioremediation Processes and Applications." Engineering in Life Science 6(3): 217-227. <http://hdl.handle.net/2027.42/55251>en_US
dc.identifier.issn1618-0240en_US
dc.identifier.issn1618-2863en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/55251
dc.description.abstractBioremediation has been argued to be one of the most cost-effective remediation technologies available to reduce soil, sediment, or groundwater contamination, particularly because this approach may allow for the implementation of in-place strategies. Recent trends have advocated the application of innovative sediment stabilization strategies through placement of (reactive) capping material to allow long-term biodegradation of contaminants in these complex biogeochemical environments. The potential long-term risk reduction associated with this approach requires a demonstration of causal relationships between sediment or contaminant stability on the one hand, and microbial reactivity on the other. The spatial analysis needed to fully understand and quantify these correlations requires sensitive probabilistic techniques. Geostatistics has been used for the characterization of multi-scale spatial patterns for the last few decades, and the analysis of microbial attributes has shown significant spatial structures on microbial abundance and activity. However, there is a dearth of information on the applicability of geostatistics to quantitatively describe the interaction between the microorganisms and their environment. Using the Passaic River (NJ) dioxin data as a model dataset, multiple scaling models were applied to scale and interpolate sampled dioxin data and derive dechlorination signatures in sediments. Unlike conventional geostatistic tools that are based on the point-to-point spatial structures, the new multi-scale model (M-Scale) introduces a new framework for spatial analysis in which regional values at different scales are anchored by the correlations to each other. Spatial dioxin distributions and microbial dechlorination signatures were used as benchmarks for comparison of M-Scale to ordinary kriging. The results from cross-validation and jackknifing approaches applied to these datasets were analyzed and compared using Quantile-Quantile (Q–Q) plots and reproduction coefficients. These plots indicated that the M-Scale better preserves the local features of hotspots during data interpolation to a basin-wide scale. Current efforts focus on mapping microbial abundance and respiratory competence in the Anacostia River, based on measurements at three different scales. The outcomes of this work will be used to develop an uncertainty-based spatial decision tool for site remediation in this watershed using various capping strategies.en_US
dc.format.extent1067449 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherWILEY-VCH Verlagen_US
dc.subject.otherChemistryen_US
dc.subject.otherIndustrial Chemistry and Chemical Engineeringen_US
dc.titleScaling Methods of Sediment Bioremediation Processes and Applicationsen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelBiomedical Engineeringen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumThe University of Michigan, Department of Civil and Environmental Engineering, 1351 Beal Ave, 175 EWRE Bldg, Ann Arbor, MI 48109-2125, USAen_US
dc.contributor.affiliationumThe University of Michigan, Department of Civil and Environmental Engineering, 1351 Beal Ave, 175 EWRE Bldg, Ann Arbor, MI 48109-2125, USAen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/55251/1/217_ftp.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1002/elsc.200520127en_US
dc.identifier.sourceEngineering in Life Scienceen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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