Source apportionment of polychlorinated biphenyls in the New York/New Jersey Harbor
Highlights
► A data set of 90 PCB congeners in 93 water samples was analyzed. ► PCB 11 is a tracer for wastewater, stormwater, and combined sewer overflows. ► Aroclor 1254 sources are widespread; Aroclor 1260 sources are localized. ► The Upper Hudson River contributes 41% of the external loads of PCBs to the Harbor. ► PCB reductions require remediation of contaminated sites and stormwater management.
Introduction
The New York/New Jersey Harbor runs through the New York City metropolitan area. Its watershed is home to approximately 20 million people. It contains several major ports and is heavily industrialized. As such, the harbor is contaminated with many anthropogenic chemicals, including polychlorinated biphenyls (PCBs). ΣPCB concentrations in the harbor near New York City are in the tens of ng L−1, several orders of magnitude higher than the Federal Water Quality Standard of 64 pg L−1, which is based on the protection of human health for the consumption of water and organisms (USEPA, 2009). In addition to the urban sources, PCBs have long been recognized as a problem in the harbor due to inputs from the Upper Hudson River. General Electric discharged hundreds of kg of PCBs to the Upper Hudson from its plants at Fort Edward from the 1930s to the 1970s (USEPA, 2002). Because PCBs are very hydrophobic and sorb strongly to sediments, this legacy contamination is still an important source of PCBs to the harbor (Farley et al., 1999, Totten, 2005, HydroQual, 2007).
In order to inform management strategies for PCBs in the harbor, models of their fate have been constructed (Farley et al., 1999, HydroQual, 2007). These models require information on the sources of PCBs to the system, but this information was severely limited until recently, when the Contamination Assessment and Reduction Project (CARP) was initiated in the late 1990s to measure contaminants in suspected sources such as wastewater effluents and storm water outfalls, as well as in the ambient water column and the sediments of the system (Contamination Assessment and Reduction Project (CARP), 2007a, Contamination Assessment and Reduction Project (CARP), 2007b). The CARP measured metals such as mercury and cadmium, as well as a wide range of organic pollutants, including PCBs, polycyclic aromatic hydrocarbons, organochlorine pesticides, and polychlorinated dibenzo-p-dioxins and -furans. This data has been used to construct loading estimates for input into a fate and transport model of pollutants that is being used to construct Total Maximum Daily Loads (TMDLs) for key species in the harbor (Contamination Assessment and Reduction Project (CARP), 2007b).
In a separate effort, the CARP data was used to construct a loading assessment for PCBs in the harbor (Totten, 2005) which indicated that although the Upper Hudson River is responsible for about half of the ΣPCBs entering the harbor, this load is dominated by lower molecular weight (MW) congeners due to two factors: the use of the low MW Aroclor 1242 by General Electric and the extensive dechlorination of PCBs that occurs in the sediments of the Upper Hudson. For high molecular weight PCBs (those the seven or more chlorines), Totten (2005) suggested that storm water runoff and combined sewer overflows (CSOs) are the primary sources to the harbor. These are important sources of high MW PCBs because of their relatively large flows and the use of a variety of Aroclors in the urban area, including many that contain more high MW congeners than Aroclor 1242.
Previous studies have attempted to identify PCB sources in this area (Huntley et al., 1997, Monosson et al., 2003). This study differs from previous efforts in that it utilizes a much larger data set of 90 congeners measured in 93 samples. The present study also investigates a larger geographic area (Fig. 1). In this work, the CARP database was examined using Positive Matrix Factorization to identify co-varying congener patterns that are thought to be associated with sources. This analysis was intended to test the validity of the various loading estimates as well as to attempt to identify previously unrecognized PCB sources.
Section snippets
PMF data matrix
Thirty-eight sampling sites were repeatedly sampled between September 1998 and October 2001 at sampling locations ranging from the Upper Hudson to the NY/NJ Bight Apex (Fig. 1). Latitude and longitude data for each sample site is presented in Table 1. Although 209 congeners were measured, only 90 were included in the PMF model. The excluded congeners were either below detection limit in most samples and/or were not present in significant amounts in the Aroclors (Rushneck et al., 2004). This
Determination of the number of factors
A critical step in PMF is determining the correct number of factors that provide clear, physically meaningful results and, at the same time, reduce matrix dimensionality. If errors are accurately estimated, the calculated Q value should be approximately equal to the theoretical Q value. In the present case, the plot of calculated Q most closely matched the theoretical Q when seven factors were requested (see Supporting information, Figure S-1). Determination of the number of factors can also be
Acknowledgements
This work was funded by the Hudson River Foundation. We thank Simon Litten at the New York State Department of Environmental Conservation for helpful discussions. We also thank the anonymous reviewers for their helpful comments.
References (31)
- et al.
Source identification of atmospheric PCBs in philadelphia/camden using positive matrix factorization followed by the potential source contribution function
Atmos. Environ.
(2007) - et al.
Combined sewer overflows (CSOs) as sources of sediment contamination in the lower Passaic River, New Jersey. II. Polychlorinated Dibenzo-P-Dioxins, Polychlorinated Dibenzofurans, and Polychlorinated biphenyls
Chemosphere
(1997) - et al.
Sorption of hydrophobic pollutants on natural sediments
Water Res.
(1979) - et al.
Identification of a novel PCB source through analysis of 209 PCB congeners by US EPA modified method 1668
Chemosphere
(2002) - et al.
PCB congener distributions in muscle, liver and gonad of Fundulus heteroclitus from the lower Hudson River Estuary and Newark Bay
Chemosphere
(2003) - et al.
Concentrations of dioxin-like PCB congeners in unweathered Aroclors by HRGC/HRMS using EPA Method 1668A
Chemosphere
(2004) - et al.
Factors influencing the rate of polychlorinated biphenyl dechlorination in Hudson River Sediments
Environ. Sci. Technol.
(1993) Determination of PCB metabolic, excretion, and accumulation rates for use as indicators of biological response and relative risk
Environ. Sci. Technol.
(1994)- et al.
PCB loading from sediment in the Hudson River: congener signature analysis of pathways
Environ. Sci. Technol.
(2004) - Contamination Assessment and Reduction Project (CARP), 2007a. Data Archive: Water, Sediment and Biota Data collected...
Statistics and Data Analysis in Geology
Source apportionment of PCBs in the Tidal Delaware River
Environ. Sci. Technol.
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Present address: Guizhou State Key Laboratory of Environmental Geochemistry, No. 46, Guanshui Rd., Guiyang, Guizhou Province 550002, China.