Determination of Primary combustion source organic carbon-to-elemental carbon (OC/EC) ratio using ambient OC and EC measurements: Secondary OC-EC correlation minimization method

Abstract. Elemental carbon (EC), due to its exclusive origin in primary combustion sources, has been widely used as a tracer to track the portion of co-emitted primary organic carbon (OC) and, by extension, to estimate secondary OC (SOC) from ambient observations of EC and OC. Key to this EC tracer method is to determine an appropriate OC/EC ratio that represents primary combustion emission sources (i.e., (OC/EC)pri) at the observation site. The conventional approaches include regressing OC against EC within a fixed percentile of the lowest (OC/EC) ratio data (usually 5-20%) or relying on a subset of sampling days with low photochemical activity and dominated by local emissions. The drawback of these approaches is rooted in its empirical nature, i.e., a lack of clear quantitative criteria in the selection of data subsets for the (OC/EC)pri determination. We examine here a method that derives (OC/EC)pri through calculating a hypothetical set of (OC/EC)pri and SOC followed by seeking the minimum of the coefficient of correlation (R2) between SOC and EC. The hypothetical (OC/EC)pri that generates the minimum R2(SOC,EC) then represents the actual (OC/EC)pri ratio if variations of EC and SOC are independent. This Minimum R Squared (MRS) method has a clear quantitative criterion for the (OC/EC)pri calculation. The general concept embodied in the MRS method was initially proposed by Miller et al (2005), but has not been evaluated for accuracy or utility since its debut. This work uses numerically simulated data to evaluate the accuracy of SOC estimation by the MRS method and to compare with two commonly used methods: minimum OC/EC (OC/ECmin) and OC/EC percentile (OC/EC10%). Log-normally distributed EC and OC concentrations with known proportion of SOC are numerically produced through a Mersenne twister pseudorandom number generator. Three scenarios are considered, including a single primary source, two independent primary sources, and two correlated primary sources. Among the three SOC estimation methods, the MRS method consistently yields the most accurate SOC estimation. Unbiased SOC estimation by OC/ECmin and OC/EC10% only occur when the left tail of OC/EC distribution is aligned with the peak of the (OC/EC)pri distribution, which is fortuitous rather than norm. In contrast, MRS provides an unbiased SOC estimation since it is insensitive to the position of (OC/EC)pri relative to the (OC/EC) distribution. Sensitivity tests of OC and EC measurement uncertainty on SOC estimation demonstrate the superior accuracy of MRS over the other two approaches.