Abstract
Abstract. 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.
Highlights
Organic aerosols (OAs) comprise 20–90 % of total, dry, submicrometer atmospheric aerosol mass, and have important influences on air quality and aerosol-climate effects (Jimenez et al, 2009; Fuzzi et al, 2015)
The ideal conditions we have modeled in this study could represent, for example, the measurement of organic functional groups that represent hydrocarbon-like OA (HOA) and oxygenated OA (OOA)
Recovery error (RE) is the combination of two types of errors: the error due to the measurement noise simulated by the linear error model described by Eq (7), and the error resulting from increasing the measurement time resolution from 4, 6, or 8 h to 1 h via one of the post-processing methods
Summary
Organic aerosols (OAs) comprise 20–90 % of total, dry, submicrometer atmospheric aerosol mass, and have important influences on air quality and aerosol-climate effects (Jimenez et al, 2009; Fuzzi et al, 2015). Takahama: Sampling strategies and post-processing methods for OA measurement scopicity, which in turn determine OA concentrations and the ability of OA to take up water These effects combined are relevant for assessing aerosol impacts on health and climate. As an alternative or complement to hardware design, it is possible to devise sampling strategies and post-processing methods for constructing higher time resolution measurements from a set of low resolution samples. This is the approach that we investigate in this work. We performed numerical modeling to compare the effectiveness of sampling strategies and post-processing methods for achieving 1 h time resolution with measurements requiring 4, 6, and 8 h of sample-collection time.
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