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

2 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 ©

3 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

4 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 testing

5 Similarity Between and Variability Within Pyrogenic PAH Source Profiles can Limit Models Gasoline Residential Data from Li et al EST 2003 TrafficDiesel

6 PCA of lake sediment data with sources used

7 The Partial Least Squares (PLS) Approach X-Block (signature)Y-Block (environmental)

8 Overlap between RTS and other signatures

9 Hypothesis Testing Requires a Negative Control  Comparison of three CMB Model runs –Published Results: Model A from Van Metre et al 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 –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

10 Excellent Match Between Measured and Modelled Concentrations Without RTS as a Source Input R 2 >0.996 for each method

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

12 Polytopic Vector Analysis (PVA) – an unmixing approach

13 How many End Members?

14 Choosing the number of EMs

15 End Member Compositions Tyres From Van Metre & Mahler, 2010

16 Geographical Distribution of EM5 contribution

17 Geographical Distribution of Concentrations

18 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.

19 Key References  Mahler, B.J., P.C. Van Metre, T.J. Bashara, J.T. Wilson, and D.A. Johns Parking lot sealcoat: An unrecognized source of urban polycyclic aromatic hydrocarbons. Environ. Sci. Technol. 39:  O’Reilly K, Pietari J, Boehm P A forensic assessment of coal tar sealants as a source of polycyclic aromatic hydrocarbons in urban sediments. Environ Forensics 13:  O’Reilly K, Pietari J, Boehm P 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 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 Contribution of PAHs from coal–tar pavement sealcoat and other sources to 40 U.S. lakes. Sci Tot Environ 409: