Relative congener scaling of Polychlorinated dibenzo-p-dioxins and dibenzofurans to estimate building fire contributions in air, surface wipes, and dust samples

Our plain-English summary of Relative congener scaling of Polychlorinated dibenzo-p-dioxins and dibenzofurans to estimate building fire contributions in air, surface wipes, and dust samples, by J.D. Pleil, M.N. Lorber (Environmental Science & Technology, 2007).

The study

• Authors: J.D. Pleil, M.N. Lorber
• Journal: Environmental Science & Technology
• Published: 2007;41(21):7286-93
• Cited by: 12 articles, as counted by NIH iCite
• Read it: PubMed · Publisher

Abstract

The United States Environmental Protection Agency collected ambient air samples in lower Manhattan for about 9 months following the September 11, 2001 World Trade Center (WTC) attacks. Measurements were made of a host of airborne contaminants including volatile organic compounds, polycyclic aromatic hydrocarbons, asbestos, lead, and other contaminants of concern. The present study focuses on the broad class of polychlorinated dibenzo-p-dioxins (CDDs) and dibenzofurans (CDFs) with specific emphasis on the 17 CDD/CDF congeners that exhibit mammalian toxicity. This work is a statistical study comparing the internal patterns of CDD/CDFs using data from an unambiguous fire event (WTC) and other data sets to help identify their sources. A subset of 29 samples all taken between September 16 and October 31, 2001 were treated as a basis set known to be heavily impacted by the WTC building fire source. A second basis set was created using data from Los Angeles and Oakland, CA as published by the California Air Resources Board (CARB) and treated as the archetypical background pattern for CDD/CDFs. The CARB data had a congener profile appearing similar to background air samples from different locations in America and around the world and in different matrices, such as background soils. Such disparate data would normally be interpreted with a qualitative pattern recognition based on congener bar graphs or other forms of factor or cluster analysis that group similar samples together graphically. The procedure developed here employs aspects of those statistical methods to develop a single continuous output variable per sample. Specifically, a form of variance structure-based cluster analysis is used to group congeners within samples to reduce collinearity in the basis sets, new variables are created based on these groups, and multivariate regression is applied to the reduced variable set to determine a predictive equation. This equation predicts a value for an output variable, OPT: the predicted value of OPT is near zero (0.00) for a background congener profile and near one (1.00) forthe profile characterized by the WTC air profile. Although this empirical method is calibrated with relatively small sets of airborne samples, it is shown to be generalizable to other WTC, fire source, and background air samples as well as other sample matrices including soils, window films and other dust wipes, and bulk dusts. However, given the limited data set examined, the method does not allow further discrimination between the WTC data and the other fire sources. This type of analysis is demonstrated to be useful for complex trace-level data sets with limited data and some below-detection entries.

In plain English

This statistical study examined dioxins and furans, not asbestos. Asbestos was mentioned only as one of the contaminants measured in lower Manhattan after the September 11, 2001 World Trade Center (WTC) attacks. The authors treated 29 air samples taken near the WTC between September 16 and the end of October 2001 as fire-impacted. They treated 29 urban air samples from Los Angeles and Oakland, California (2001 and 2002) as background. From 17 toxic congeners they built a score, called OPT, near 1 for a WTC-like pattern and near 0 for background. The main model rested on one congener, OCDD. It made up more than half the profile in every background set and about 0.20 or less in fire-related sets. Applied to other data, most fire-related sets scored near 1 and background sets near 0. Some WTC dust samples, including Deutsche Bank building dust, scored like background on the main model. The authors concluded the method separated fire from background profiles but could not tell fires apart. They noted it was calibrated on small sample sets.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Statistical method-development study using existing sample data (cluster analysis followed by multivariate regression); no new sampling, no health outcomes
• Who was studied: Environmental samples, not people. Basis sets: 29 air samples taken near the WTC from September 16 to the end of October 2001, and 29 urban background air samples from Los Angeles and Oakland, California (2001 and 2002, California Air Resources Board). The model was then applied to other sets: WTC window wipes, Deutsche Bank building dust, personal air samples from rescue personnel, WTC outdoor dust, office and building fire wipes in Binghamton, NY and Philadelphia, PA, a Columbus, OH incinerator, and rural and California background air.
• Exposure: Polychlorinated dibenzo-p-dioxins (CDDs) and dibenzofurans (CDFs): the 17 congeners with mammalian toxicity, analysed as each congener's fraction of the total. Asbestos was not studied.
• Main results:
  • Model 1 (including OCDD) used only the OCDD fraction and had an r2 of 0.9263. Mean OPT was 0.9788 (standard deviation 0.143) for the WTC basis samples and 0.0174 (standard deviation 0.138) for the California background basis samples.
  • Model 2 (OCDD removed) had an r2 of 0.5636. Mean OPT was 0.782 (standard deviation 0.328) for the WTC basis samples and 0.218 (standard deviation 0.141) for the California basis samples. The authors reported it still separated the two basis sets completely at the 95% confidence interval.
  • OCDD made up more than half of the profile in all background sets and about 0.20 or less in the fire-related sets.
  • Applied to other data sets, both models gave OPT values close to 1.00 for the WTC basis set, the Philadelphia and Binghamton building fires and the combustor emission source, and values near 0 for known background sets.
  • Deutsche Bank building wipe samples (taken in 2003) had high concentrations averaging 28 ng TEQ/m2 but an OPT of 0.14 on model 1, because OCDD averaged 0.52 of the profile. Without OCDD the OPT rose to 0.54.
  • For outdoor bulk dust collected near the WTC in September and October 2001, only one of the four OPT values (two dust sets, two models) was higher than 0.33. OCDD fractions in these profiles were near 0.70.
• The authors' conclusion: The authors concluded that the method distinguished fire-related dioxin/furan profiles from background profiles. Given the limited data, it could not distinguish the WTC fire from other fire sources, and it was not designed to.
• Limitations the authors state:
  • The method was calibrated on relatively small sets of air samples.
  • The method was not designed to tell different fire-related data sets apart.
  • Some of the data sets with unexpected results were small and had large internal variance.
  • Weathering of dust over time may shift profiles toward a background-looking pattern. The authors said this needs study.
  • Only part of the national rural background data was available.
  • Twelve values below the limit of quantitation in the WTC basis set were replaced with imputed values.

Related studies

• A probability model for evaluating building contamination from an environmental event — R.C. Spicer, H.J. Gangloff (2000)
• Contamination of the air with mineral fibers following the explosive destruction of buildings and fire — J.A. Hoskins, R.C. Brown (1994)
• Characterization of the dust/smoke aerosol that settled east of the World Trade Center (WTC) in lower Manhattan after the collapse of the WTC 11 September 2001 — P.J. Lioy, C.P. Weisel, et al. (2002)
• Physical characteristics and health effects of aerosols from collapsed buildings — S.H. Gavett (2006)
• Acute eosinophilic pneumonia in a New York City firefighter exposed to World Trade Center dust — W.N. Rom, M. Weiden, et al. (2002)
• The legacy of World Trade Center dust — J.M. Samet, A.S. Geyh, et al. (2007)

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