ORIGINAL PAPER Mass fraction spatiotemporal geostatistics and its application to map atmospheric polycyclic aromatic hydrocarbons after 9/11 William B. Allshouse Æ Joachim D. Pleil Æ Stephen M. Rappaport Æ Marc L. Serre Published online: 17 July 2009 Ó Springer-Verlag 2009 Abstract This work proposes a space/time estimation method for atmospheric PM 2.5 components by modelling the mass fraction at a selection of space/time locations where the component is measured and applying the model to the extensive PM 2.5 monitoring network. The method we developed utilizes the nonlinear Bayesian maximum entropy framework to perform the geostatistical estimation. We implemented this approach using data from nine car- cinogenic, particle-bound polycyclic aromatic hydrocar- bons (PAHs) measured from archived PM 2.5 samples collected at four locations around the World Trade Center (WTC) from September 22, 2001 to March 27, 2002. The mass fraction model developed at these four sites was used to estimate PAH concentrations at additional PM 2.5 moni- tors. Even with limited PAH data, a spatial validation showed the application of the mass fraction model reduced the mean squared error (MSE) by 7–22%, while in the temporal validation there was an exponential improvement in MSE positively associated with the number of days of PAH data removed. Our results include space/time maps of atmospheric PAH concentrations in the New York area after 9/11. Keywords Bayesian maximum entropy  Polycyclic aromatic hydrocarbons  Particulate matter  World Trade Center  Space/time modeling 1 Introduction Extensive research has been conducted on effects resulting from exposure to ambient particulate matter. Particulate matter has been linked to cardiovascular diseases, respi- ratory problems, and reproductive effects. A large body of work on particulate matter focuses on atmospheric particles less than 10 lm in size (PM 10 ); more recently, research has been extended to investigation of fine particulate matter (particles less than 2.5 lm in aerodynamic diameter, PM 2.5 ), which travel deeper into the lungs and increase the risks of health effects. The overwhelming evidence that high concentrations of atmospheric particulate matter (PM) are associated with adverse health effects led the United States Environmental Protection Agency (EPA) to create the aerometric information retrieval system (AIRS) in order to document ambient PM levels for purposes of data storage, retrieval, and interpretation. This is a nationwide system of stations that typically monitor daily concentra- tions of PM. Since the effects of exposure to this criteria Electronic supplementary material The online version of this article (doi:10.1007/s00477-009-0326-y) contains supplementary material, which is available to authorized users. W. B. Allshouse  J. D. Pleil  S. M. Rappaport Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Room 148, Rosenau Hall, CB #7431, Chapel Hill, NC 27599-7431, USA J. D. Pleil National Exposure Research Laboratory/Office of Research and Development, United States Environmental Protection Agency, Research Triangle Park, NC, USA S. M. Rappaport Department of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, USA M. L. Serre (&) Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Room 163B, Rosenau Hall, CB #7431, Chapel Hill, NC 27599-7431, USA e-mail: marc_serre@unc.edu 123 Stoch Environ Res Risk Assess (2009) 23:1213–1223 DOI 10.1007/s00477-009-0326-y