Department of Mechanical Engineering, College of Engineering and Applied Science, University of Colorado at Boulder Boulder, Colorado 80309, USA.
Department of Applied Mathematics and Statistics, Colorado School of Mines, Golden, Colorado, USA.
J Air Waste Manag Assoc. 2013 Dec;63(12):1386-98. doi: 10.1080/10962247.2013.816643.
In studies of coarse particulate matter (PM10-2.5), mass concentrations are often estimated through the subtraction of PM2.5 from collocated PM10 tapered element oscillating microbalance (TEOM) measurements. Though all field instruments have yet to be updated, the Filter Dynamic Measurement System (FDMS) was introduced to account for the loss of semivolatile material from heated TEOM filters. To assess errors in PM10-2.5 estimation when using the possible combinations of PM10 and PM2.5 TEOM units with and without FDMS, data from three monitoring sites of the Colorado Coarse Rural-Urban Sources and Health (CCRUSH) study were used to simulate four possible subtraction methods for estimating PM10-2.5 mass concentrations. Assuming all mass is accounted for using collocated TEOMs with FDMS, the three other subtraction methods were assessed for biases in absolute mass concentration, temporal variability, spatial correlation, and homogeneity. Results show collocated units without FDMS closely estimate actual PM10-2.5 mass and spatial characteristics due to the very low semivolatile PM10-2.5 concentrations in Colorado. Estimation using either a PM2.5 or PM10 monitor without FDMS introduced absolute biases of 2.4 microg/m3 (25%) to -2.3 microg/m3 (-24%), respectively. Such errors are directly related to the unmeasured semivolatile mass and alter measures of spatiotemporal variability and homogeneity, all of which have implications for the regulatory and epidemiology communities concerned about PM10-2.5. Two monitoring sites operated by the state of Colorado were considered for inclusion in the CCRUSH acute health effects study, but concentrations were biased due to sampling with an FDMS-equipped PM2.5 TEOM and PM10 TEOM not corrected for semivolatile mass loss. A regression-based model was developed for removing the error in these measurements by estimating the semivolatile concentration of PM2.5 from total PM2.5 concentrations. By estimating nonvolatile PM2.5 concentrations from this relationship, PM10-2.5 was calculated as the difference between nonvolatile PM10 and PM2.5 concentrations.
在对粗颗粒物（PM10-2.5）的研究中，通常通过从共置的 PM10 渐缩元素振荡微天平（TEOM）测量值中减去 PM2.5 来估算质量浓度。尽管所有现场仪器尚未更新，但引入了过滤动态测量系统（FDMS）来考虑加热 TEOM 过滤器中半挥发性物质的损失。为了评估在使用带有和不带有 FDMS 的 PM10 和 PM2.5 TEOM 单元的可能组合进行 PM10-2.5 估算时的误差，使用科罗拉多粗农村-城市源与健康（CCRUSH）研究的三个监测站点的数据模拟了估计 PM10-2.5 质量浓度的四种可能的减法方法。假设使用带有 FDMS 的共置 TEOM 可以完全估算出质量，则评估了其他三种减法方法在绝对质量浓度、时间变化、空间相关性和均一性方面的偏差。结果表明，由于科罗拉多州 PM10-2.5 的半挥发性浓度非常低，因此没有 FDMS 的共置单元可以很好地估计实际的 PM10-2.5 质量和空间特征。使用没有 FDMS 的 PM2.5 或 PM10 监测器进行估算会分别引入 2.4 微克/立方米（25%）至-2.3 微克/立方米（-24%）的绝对偏差。这些误差与未测量的半挥发性质量直接相关，并改变了时空变异性和均一性的度量，这两者都对关注 PM10-2.5 的监管和流行病学界具有重要意义。科罗拉多州运营的两个监测站被认为应纳入 CCRUSH 急性健康影响研究，但由于使用配备 FDMS 的 PM2.5 TEOM 和未校正半挥发性质量损失的 PM10 TEOM 进行采样，浓度存在偏差。通过开发基于回归的模型，通过从总 PM2.5 浓度估算 PM2.5 的半挥发性浓度，来消除这些测量中的误差。通过从这种关系估计非挥发性 PM2.5 浓度，将 PM10-2.5 计算为非挥发性 PM10 和 PM2.5 浓度之间的差异。
