Adaptive Decomposition of Highly Resolved Time Series into Local and Non-Local Components

ABSTRACT

Highly time-resolved air monitoring data are widely being collected over long time horizons in order to characterize ambient and near-source air quality trends. In many applications, it is desirable to split the time-resolved data into two or more components (e.g., local and regional) for apportionment and mitigation purposes. While there may be increased information content in highly time-resolved data, the temporal resolution may also increase entropic effects on the data, thereby dramatically clouding the very information sought in time-resolved data. Specialized methods such as filtering may be required to extract the underlying information content. Constrained and Adaptive Decomposition of Time Series (CADETS) is a new method that can help carve out components of time series based on the content of the frequencies present in the time series. CADETS is also a flexible approach that allows the user to choose the bifurcation point with minimal negative impacts. Using this algorithm, we demonstrate that a time series signal may be decomposed into two useful and interpretable signals that can help identify aspects that may otherwise be hidden or distorted. Using the output from the CADETS algorithm, we show that ultrafine particles (30–100 nm) collected near a major highway may be split into a 64:36 ratio of highly varying (local) and slowly varying (regional) components, meanwhile identical measurements at a background location were estimated to split into a 56:44 local versus regional ratio.