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Unmix Optimum analysis of PAH sediment sources
Citation:
Norris, G. AND R. Henry. Unmix Optimum analysis of PAH sediment sources. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, Netherlands, 673:831-838, (2019). https://doi.org/10.1016/j.scitotenv.2019.03.227
Impact/Purpose:
A Source Identification Tool called Unmix Optimum (UnmixO) was developed to quantify contaminant sources contributing to sediment samples. UnmixO determines the composition of sources contributing to the samples using a geometrical approach and the model was used to evaluate the contribution of coal tar (CT) asphalt parking lot sealcoats to PAH concentrations in urban pond and lake sediments. Recent studies have used the EPA Chemical Mass Balance (CMB) model to quantify sources contributing to the PAHs: CT sealcoats, motor vehicle exhaust, oil burning for home heating, and residential fireplace pine burning. A chi-square approach was used to determine which UnmixO source profile best matched measured profiles. Two USGS published data sets were evaluated with UnmixO: Lady Bird Lake (LBL) in Austin, TX; and 40 Lakes covering the entire U.S. UnmixO found two CT sealcoat sources contributed to the sediment PAHs. UnmixO was not able to apportion all the sources reported in the CMB analyses; instead, itidentified a mixed source consisting of motor vehicle exhaust, wood burning, and oil burningas possible sources of sediment PAHs. The UnmixO results were consistent with the CMB results for the LBL and 40 Lakes studies estimating that CT sealcoats contribute over 80% of the PAHs to urban lakes.
Description:
Unmix Optimum (UnmixO) was developed to analyze data, such as sediment PAH data, that were resistant to existing methods of multivariate analysis. Using a geometrical approach, UnmixO uses multiple advanced nonlinear optimization algorithms to find potential sources that obey non-negativity constraints while optimally fitting the data. UnmixO does not require specific knowledge of the uncertainties in the data and will work better for smaller data sets than other multivariate models. UnmixO was able to identify polycyclic aromatic hydrocarbon (PAH) contaminant sources contributing to sediment samples based on sample composition data with good diagnostic values. Results were compared to published EPA Chemical Mass Balance (CMB) sediment results from Lady Bird Lake (LBL) Austin, TX and 40 lakes (40LKS) across the U.S. A Chi-sum approach determined which UnmixO source profile best matched profiles used in CMB sediment studies; two coal tar (CT) sealcoat sources and a mixed combustion source contributed to the sediment PAHs. These results were consistent with CMB results for the LBL and 40LKS studies that estimated CT sealcoats contribute over 80% of PAHs to urban lakes. UnmixO results also showed that CT sealant's contribution to sediments decreased after the City of Austin ban in 2006.
