Receptor Models for PAH Source Characterisation: Opportunities and Limitations

Development of this presentation was supported by the Pavement Coatings Technology Council

Stephen Mudge, Kirk O’Reilly, Sungwoo Ahn, Jaana Pietari and Paul Boehm

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Receptor Models

Receptor models are mathematical or statistical procedures for characterising potential sources and quantifying their contribution to the chemicals found at receptor location.

Larson ©

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Receptor Models

Mixing Models– Example: EPA Chemical Mass Balance (CMB)

Modeller pre-identifies sources and inputs profiles Model calculates best fit to the receptor (sediment)

data to estimate relative contributions Unmixing Models– Example: PVA

Model uses receptor data to identify potential source profiles

Model calculates best fit to the receptor data to estimate relative contributions

Modeller compares identified source profiles to potential sources

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Case Study: Use of CMB to Evaluate of the Role of Refined Tar Pavement Sealers (RTS) in Urban Lake Sediments

USGS researchers used CMB to test the hypothesis that RTS is a significant PAH source (Van Metre and Mahler, 2010)

Results being used to advocate product bans

Lack of negative controls limited value for hypothesis testinghttp://water.usgs.gov/nawqa/home_maps/sealcoat.html

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Similarity Between and Variability Within Pyrogenic PAH Source Profiles can Limit Models

Gasoline Residential

Data from Li et al EST 2003

PhA An FlA Py BaA Chy BbF BkF BeP BaP IP BgP0.00

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Traffic Diesel

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PCA of lake sediment data with sources used

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The Partial Least Squares (PLS) Approach

X-Block (signature) Y-Block (environmental)

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Overlap between RTS and other signatures

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Hypothesis Testing Requires a Negative Control

Comparison of three CMB Model runs

– Published Results: Model A from Van Metre et al. 2010. RTS source profile is the mean of dust from six sealed lots.

– Negative Control: Did not include RTS as a source profile. Remaining sources from Van Metre et al. 2010.

– Different RTS Source Profile: Replaced Van Metre RTS with Selbig (2009) mean of 9 RTS lot runoff samples. Remaining sources from Van Metre et al. 2010.

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Excellent Match Between Measured and Modelled Concentrations Without RTS as a Source Input

R2>0.996 for each method

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The PAH Profile of Lake Sediments can be Modelled Without Any RTS Contribution

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Polytopic Vector Analysis (PVA) – an unmixing approach

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How many End Members?

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Choosing the number of EMs

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End Member Compositions

PhA An FlA Py BaA Chy BbF BkF BeP BaP IP BgP0

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PhA An FlA Py BaA Chy BbF BkF BeP BaP IP BgP0

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Tyres

From Van Metre & Mahler, 2010

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Geographical Distribution of EM5 contribution

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Geographical Distribution of Concentrations

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Conclusions

Receptor models can be helpful in determining the sources to a system.

However…– CMB pre-defines the sources in advance.

Changing these changes the outcome.– Need sufficient compounds to distinguish

between source (in all models). Overlap.– Unmixing type models may identify sources that

have yet to be characterised.

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Key References

Mahler, B.J., P.C. Van Metre, T.J. Bashara, J.T. Wilson, and D.A. Johns. 2005. Parking lot sealcoat: An unrecognized source of urban polycyclic aromatic hydrocarbons. Environ. Sci. Technol. 39: 5560-5566.

O’Reilly K, Pietari J, Boehm P. 2012. A forensic assessment of coal tar sealants as a source of polycyclic aromatic hydrocarbons in urban sediments. Environ Forensics 13:185-196.

O’Reilly K, Pietari J, Boehm P. 2013. Parsing Pyrogenic PAHs: Forensic Chemistry, Receptor Models, and Source Control Policy. Integrated Environ Assessment and Management, in press

O’Reilly, K, Ahn, S. in prep. Polycyclic Aromatic Hydrocarbon Source Contribution Modeling: Negative Controls and Source Profile Collinearity.

Van Metre PC, Mahler BJ, Wilson JT. 2009. PAHs underfoot: Contaminated dust from coal-tar seal coated pavement is widespread in the United States. Environ Sci Technol 43(1):20‑25.

Van Metre PC, Mahler BJ. 2010. Contribution of PAHs from coal–tar pavement sealcoat and other sources to 40 U.S. lakes. Sci Tot Environ 409:334-344.