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Sampling strategies and post-processing methods for increasing the time resolution of organic aerosol measurements requiring long sample-collection times

机译:提高需要长时间采样时间的有机气溶胶测量时间分辨率的采样策略和后处理方法

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The composition and properties of atmospheric organic aerosols (OAs) change on timescales of minutes to hours. However, some important OA characterization techniques typically require greater than a few hours of sample-collection time (e.g., Fourier transform infrared (FTIR) spectroscopy). In this study we have performed numerical modeling to investigate and compare sample-collection strategies and post-processing methods for increasing the time resolution of OA measurements requiring long sample-collection times. Specifically, we modeled the measurement of hydrocarbon-like OA (HOA) and oxygenated OA (OOA) concentrations at a polluted urban site in Mexico City, and investigated how to construct hourly resolved time series from samples collected for 4, 6, and 8?h. We modeled two sampling strategies – sequential and staggered sampling – and a range of post-processing methods including interpolation and deconvolution. The results indicated that relative to the more sophisticated and costly staggered sampling methods, linear interpolation between sequential measurements is a surprisingly effective method for increasing time resolution. Additional error can be added to a time series constructed in this manner if a suboptimal sequential sampling schedule is chosen. Staggering measurements is one way to avoid this effect. There is little to be gained from deconvolving staggered measurements, except at very low values of random measurement error (?5?%). Assuming 20?% random measurement error, one can expect average recovery errors of 1.33–2.81?μg?m?3 when using 4–8?h-long sequential and staggered samples to measure time series of concentration values ranging from 0.13–29.16?μg?m?3. For 4?h samples, 19–47?% of this total error can be attributed to the process of increasing time resolution alone, depending on the method used, meaning that measurement precision would only be improved by 0.30–0.75?μg?m?3 if samples could be collected over 1?h instead of 4?h. Devising a suitable sampling strategy and post-processing method is a good approach for increasing the time resolution of measurements requiring long sample-collection times.
机译:大气有机气溶胶(OAS)的组成和性质在分钟时间的时间内变化。然而,一些重要的OA表征技术通常需要大于几个小时的样品收集时间(例如,傅里叶变换红外(FTIR)光谱)。在这项研究中,我们已经进行了数值模型,以调查和比较采样策略和后处理方法,以增加需要长时间采样时间的OA测量的时间分辨率。具体地,我们在墨西哥城污染的城市现场模拟了烃类OA(HOA)和含氧OA(OOA)浓度的测量,并调查了如何从收集4,6和8的样本构建每小时分辨的时间序列? H。我们建模了两种采样策略 - 顺序和交错的采样 - 以及一系列后处理方法,包括插值和解卷积。结果表明,相对于更复杂和昂贵的交错的采样方法,顺序测量之间的线性插值是令人惊讶的有效方法,用于增加时间分辨率。如果选择了子优化顺序采样计划,则可以将附加错误添加到以这种方式构造的时间序列。惊人的测量是避免这种效果的一种方法。除了在随机测量误差的非常低的值(?5?%)之外,除了解构的交错测量几乎没有才能获得。假设60?%随机测量误差,一个人可以预期使用4-8〜长的顺序和交错样品时预期1.33-2.81ΩΩ3Ω·3的平均恢复误差为0.13-29.16的时间序列测量时间序列值。 μg?m?3。对于4?H样本,该总误差的19-47个?%误差可以归因于单独增加时间分辨率的过程,这取决于所用的方法,这意味着测量精度只会提高0.30-0.75?m? 3如果样品可以超过1?H而不是4?H。设计合适的采样策略和后处理方法是增加需要长采样时间的测量时间的良好方法。

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