Department of Environmental Sciences, Rutgers University, New Brunswick, NJ, USA.
J Air Waste Manag Assoc. 2011 Jun;61(6):696-710. doi: 10.3155/1047-3289.61.6.696.
Sampling and handling artifacts can bias filter-based measurements of particulate organic carbon (OC). Several measurement-based methods for OC artifact reduction and/or estimation are currently used in research-grade field studies. OC frequently is not artifact-corrected in large routine sampling networks (e.g., U.S. Environmental Protection Agency (EPA)'s Chemical Speciation Network). In some cases, the OC artifact has been corrected using a regression method (RM) for artifact estimation. In this method, the gamma-intercept of the regression of the OC concentration on the fine particle (PM2.5) mass concentration is taken to be an estimate of the average OC sampling artifact (net of positive and negative artifacts). This paper discusses options for artifact correction in large routine sampling networks. Specifically, the goals are to (1) articulate the assumptions and limitations inherent to the RM, (2) describe other artifact correction approaches, and (3) suggest a cost-effective method for artifact correction in large monitoring networks. The RM assumes a linear relationship between measured OC and PM mass: a constant slope (OC mass fraction) and a constant intercept (RM artifact estimate). These assumptions are not always valid. Additionally, outliers and other individual data points can have a large influence on the RM artifact estimates. The RM yields results within the range of measurement-based methods for some datasets and not for others. Given that the adsorption of organic gases increases with atmospheric concentrations of organics, subtraction of an average artifact from all samples (e.g., across multiple sites) will underestimate OC for lower-concentration samples (e.g., clean sites) and overestimate OC for higher-concentration samples (e.g., polluted sites). For relatively accurate, simple, and cost-effective artifact OC estimation in large networks, the authors suggest backup filter sampling on at least 10% of sampling days at all sites with artifact correction on a sample-by-sample basis as described herein.
采样和处理过程中的误差可能会影响基于滤膜的颗粒物有机碳（OC）测量。目前，在研究级野外研究中，有几种基于测量的方法可用于减少或估计 OC 误差。在大型常规采样网络（例如美国环境保护署（EPA）的化学物质形态网络）中，OC 通常未经误差校正。在某些情况下，已使用误差估计的回归方法（RM）来校正 OC 误差。在这种方法中，将 OC 浓度与细颗粒物（PM2.5）质量浓度之间的回归的伽马截距视为 OC 采样误差（扣除正负误差后的平均值）的估计值。本文讨论了在大型常规采样网络中进行误差校正的选项。具体而言，目标是：（1）阐述 RM 中固有的假设和局限性；（2）描述其他误差校正方法；（3）为大型监测网络中的误差校正提出一种经济有效的方法。RM 假设测量的 OC 与 PM 质量之间存在线性关系：斜率恒定（OC 质量分数），截距恒定（RM 误差估计值）。这些假设并不总是成立的。此外，异常值和其他个别数据点可能对 RM 误差估计值有很大影响。RM 在某些数据集上的测量值范围内产生结果，但在其他数据集上则没有。鉴于有机气体的吸附随有机物在大气中的浓度增加，从所有样品中减去平均误差（例如，在多个站点之间）将低估低浓度样品（例如，清洁站点）的 OC，并高估高浓度样品（例如，污染站点）的 OC。对于大型网络中相对准确，简单且经济有效的误差 OC 估计，作者建议在所有站点上至少在 10％的采样日进行备用滤膜采样，并按本文所述进行逐个样本的误差校正。
