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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 7
Atmos. Meas. Tech., 9, 3337–3354, 2016
https://doi.org/10.5194/amt-9-3337-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 9, 3337–3354, 2016
https://doi.org/10.5194/amt-9-3337-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 28 Jul 2016

Research article | 28 Jul 2016

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

Rob L. Modini and Satoshi Takahama Rob L. Modini and Satoshi Takahama
  • ENAC/IIE Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland

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.

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Short summary
Aerosol measurement techniques with high detection limits often result in poorly time-resolved measurements. We investigated sampling strategies and post-processing methods for constructing hourly resolved aerosol concentration time series from samples collected for 4 to 8 h. We show that this is an effective way to increase measurement time resolution, and that under realistic experimental conditions, simple methods can perform as well as more sophisticated methods.
Aerosol measurement techniques with high detection limits often result in poorly time-resolved...
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