Use of Passive PM Samples in Source Apportionment
Oral Presentation
Prepared by S. Raja
ENERCON Services, Inc., 15770 North Dallas Parkway, Suite 400, Dallas, TX, 75248, United States
Contact Information: sraja@enercon.com; 972-484-3854
ABSTRACT
Particulate Matter (PM) exposure has drawn considerable attention in recent years due to its link to human mortality and morbidity. Spatial heterogeneity of PM chemical constituents is an important factor in studies of both short- and long-term effects. This is because there is potential for exposure mis-classification in time-series epidemiological studies when regressing health outcomes against source contributions estimated at a single central monitoring site. The primary goal of the present study is to better understand the spatial variability of fine-particle exposure, in terms of particle size and composition, in a range of urban and populated rural areas. PM samples were collected using UNC Passive samplers in an array of sites. After collection, the PM samples were analyzed for chemical components, and particle size and morphology using a computer controlled scanning-electron microscope. Elemental composition of individual particles was then classified using an Adaptive Resonance Theory neural networks algorithm (Carpenter, 1991). Based on particle class memberships, inter-site and intra-urban variability in PM exposure was analyzed in this work. In addition to this, speciation data was used to assess differences in source profiles between each neighborhood/sites. Source profiles were developed using Positive Matrix Factorization (PMF). This work will focus on the PMF results and how Passive Sampling methodology could be useful for policy makers and epidemiologist, and in State Implementation Plan (SIP) planning documents.
Oral Presentation
Prepared by S. Raja
ENERCON Services, Inc., 15770 North Dallas Parkway, Suite 400, Dallas, TX, 75248, United States
Contact Information: sraja@enercon.com; 972-484-3854
ABSTRACT
Particulate Matter (PM) exposure has drawn considerable attention in recent years due to its link to human mortality and morbidity. Spatial heterogeneity of PM chemical constituents is an important factor in studies of both short- and long-term effects. This is because there is potential for exposure mis-classification in time-series epidemiological studies when regressing health outcomes against source contributions estimated at a single central monitoring site. The primary goal of the present study is to better understand the spatial variability of fine-particle exposure, in terms of particle size and composition, in a range of urban and populated rural areas. PM samples were collected using UNC Passive samplers in an array of sites. After collection, the PM samples were analyzed for chemical components, and particle size and morphology using a computer controlled scanning-electron microscope. Elemental composition of individual particles was then classified using an Adaptive Resonance Theory neural networks algorithm (Carpenter, 1991). Based on particle class memberships, inter-site and intra-urban variability in PM exposure was analyzed in this work. In addition to this, speciation data was used to assess differences in source profiles between each neighborhood/sites. Source profiles were developed using Positive Matrix Factorization (PMF). This work will focus on the PMF results and how Passive Sampling methodology could be useful for policy makers and epidemiologist, and in State Implementation Plan (SIP) planning documents.
