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
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
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DOI：
10.5194/acp-2015-997
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发表时间：
2016
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通讯作者：
Cheng Wu;J. Yu
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文献类型：
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作者：
Cheng Wu;J. Yu
元素碳(EC)已被广泛用作示踪剂，用于示踪共排放的初级有机碳(OC)部分，进而从EC和OC的环境观测中估算二次有机碳(SOC)。这种EC示踪方法的关键是确定代表观察点主要燃烧排放源(即(OC/EC)Pri)的适当的OC/EC比率。传统的方法包括在最低(OC/EC)比率数据的固定百分位数(通常为5-20%)内将OC与EC进行回归，或者依赖于光化学活性较低且以本地排放为主的采样天数的子集。这些方法的缺点在于其经验性，即在为确定(OC/EC)PRI选择数据子集时缺乏明确的量化标准。我们在这里研究一种方法，该方法通过计算(OC/EC)PRI和SOC的假设集合，然后寻求SOC和EC之间的相关系数(R)的最小值来推导(OC/EC)PRI。如果EC和SOC的变化是独立的，并且(OC/EC)PRI在研究期间相对恒定，则产生最小R(SOC，EC)的假设(OC/EC)PRI表示实际的(OC/EC)PRI比率。这种最小R平方(MRS)方法为(OC/EC)PRI的计算提供了一个明确的定量标准。本文利用数值模拟数据对MRS方法估计SOC的精度进行了评估，并与两种常用的方法：最小OC/EC(OC/ECmin)和OC/EC百分位数(OC/EC10%)进行了比较。已知SOC比例的对数正态分布的EC和OC浓度是通过伪随机数发生器数值产生的。考虑了三种情况，包括单个主源、两个独立的主源和两个相关的主源。MRS方法始终如一地产生最准确的SOC估计。只有当OC/EC分布的左尾与(OC/EC)Pri分布的峰值对齐时，才会出现OC/ECmin和OC/EC10%的无偏SOC估计，这是偶然的，而不是正常的。相反，当测量不确定度小时，MRS提供无偏的SOC估计。MRS结果对测量不确定度的大小很敏感，但如果不确定度在20%以内，偏差不会超过23%。
Elemental carbon (EC) 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 (R) between SOC and EC. The hypothetical (OC /EC)pri that generates the minimum R(SOC,EC) then represents the actual (OC /EC)pri ratio if variations of EC and SOC are independent and (OC /EC)pri is relatively constant in the study period. This Minimum R Squared (MRS) method has a clear quantitative criterion for the (OC /EC)pri calculation. 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 %). Lognormally distributed EC and OC concentrations with known proportion of SOC are numerically produced through a pseudorandom number generator. Three scenarios are considered, including a single primary source, two independent primary sources, and two correlated primary sources. The MRS method consistently yields the most accurate SOC estimation. Unbiased SOC estimation by OC /ECmin and OC /EC10 % only occurs 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 when measurement uncertainty is small. MRS results are sensitive to the magnitude of measurement uncertainty but the bias would not exceed 23 % if the uncertainty is within 20 %.