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Forensic Analysis and Genetic Source Partitioning Model for Portland Harbor Contaminated Sediments

T. A. T. Aboul-Kassim* and K. J. Williamson

Environmental Analysis and Impact Assessment (EAIA), Department of Civil, Construction and Environmental Engineering, College of Engineering, Oregon State University, 202 Apperson Hall, Corvallis, OR, 97331, USA

*Corresponding author. Email:


Point and non-point source pollution to aquatic systems pose many challenges in maintaining ecosystem integrity. Aquatic sediments in many rivers, lakes, and harbors have been directly or indirectly contaminated by various types of chemical compounds. The Portland Harbor, a representative example of a contaminated sediment study, has played a key role in economic development of the City of Portland and the State of Oregon (USA) for decades. In addition, it has been a valuable resource for recreation, fishing, and navigation. Because of its current contamination, the United States Environmental Protection Agency (EPA) has placed the harbor onto the National Priorities List by its designation as a federal superfund site. Therefore, this paper aims at demonstrating a unique forensic analysis and genetic source partitioning model that would help characterize the extent of organic contamination and build up an ultimate sampling scheme to be adopted by the Oregon Department of Environmental Quality (DEQ) and EPA to aid in the final phase of feasibility study and remedial investigation of the harbor. Several sediment samples, collected from the Portland Harbor, are analyzed, characterized, and fingerprinted in terms of their hydrocarbon (HC) organic molecular marker (MM) signatures in this 0study. The distributions, chemical structures, and applicability of such MMs in determining characteristic group(s) representative for the study area are discussed and evaluated in this paper using the HC multi-tracer environmental forensic MM approach. Homologous long chain n-alkanes (C16-C38), carbon preference index (CPI), unresolved complex mixtures (UCM), and MMs such as pristane, phytane, tricyclic (C19-C29) and tetracyclic (C24, C28 and C29) terpanes, 17α(H), 21β(H)-hopanes (C27-C35), 5α(H),14β(H),17β(H)-steranes (C27-C29) with a minor amounts of 5α(H),14α(H),17α(H)-steranes and 13α(H),17β(H)-diasteranes are found to be the most suitable indicators to differentiate between petroleum hydrocarbon- from non-petroleum hydrocarbon-containing sediment particles. In contrast, high temperature thermogenic/pyrolytic-derived compounds are indicated by a specific group of polycyclic aromatic hydrocarbons (PAHs). This group, ranging from phenanthrene to dibenzo(ae)pyrene with different alkyl-substituted PAH series, is considered to be combustion products from fossil fuel. Extended Q-mode factor analysis and linear programming technique are also performed to: (a) examine the variations in the hydrocarbon MM data set, (b) group the data into specific associations (i.e., end-members), and (c) find statistically significant clusters in the data set to help assess and identify the various hydrocarbon pollution sources and original compositions reflecting aquatic sediment impact to the Portland Harbor.

Keywords: Sediment, forensic analysis, genetic source partitioning model, chemometrics

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