Statistical techniques for analyzing of soil vapor intrusion data: a case study of manufactured gas plant sites.

J Air Waste Manag Assoc

Department of Civil, Structural and Environmental Engineering, University at Buffalo, 204 Jarvis Hall, Buffalo, NY 14260, USA.

Published: February 2013

Unlabelled: As part of an ongoing study of soil vapor intrusion (SVI), concentration data for approximately 2000 air and vapor samples were assembled from remedial site investigations and stand-alone assessments conducted at New York State Manufactured Gas Plant (MGP) sites. Vapor samples were collected from ambient outdoor air indoor air, beneath building slabs, and from outside of buildings. Despite the large sample size, the considerable variability in compound and sample-specific censoring limits inhibited the use of conventional tools for statistical interpretation. This paper describes the development and application of improved statistical tools to address an unusually high degree of data censoring and possible artifacts related to uneven distributions of samples across sites and buildings. In addition to methods for calculating population percentiles and associated confidence intervals, methods for comparing the population of MGP-SVI data with a reference population were also developed and evaluated via illustrative comparisons with the published 2001 EPA Building Assessment Survey and Evaluation (BASE) study of industrial buildings. The focus of this work is on the development and evaluation of new statistical methods; a more complete summary and evaluation of the full NYS MGP-SVI data set will be presented in a companion paper.

Implications: Data from vapor intrusion and other environmental studies are often stratified and/or censored, which complicates comparisons with background data or reference populations. In some cases, statistical methods for censored data can be modified to support population-based inference and reduce biases associated with the presence of repeated measurements from multiple sources. Such modifications are particularly appropriate for retrospective data mining studies that are not guided by a formal experimental design.

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http://dx.doi.org/10.1080/10962247.2012.742473DOI Listing

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