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 mu g 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 mu g 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 mu g 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.