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Adaptive Decomposition of Highly Resolved Time Series into Local and Non‐local Components
Citation:
Vedantham, R., G. Hagler, Sue Kimbrough, R. Snow, AND K. Holm. Adaptive Decomposition of Highly Resolved Time Series into Local and Non‐local Components. AEROSOL AND AIR QUALITY RESEARCH . Chinese Association for Aerosol Research in Taiwan, , Taiwan, Province Of China, 15(4):1270-1280, (2015).
Impact/Purpose:
The National Exposure Research Laboratory's (NERL's) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA's mission to protect human health and the environment. HEASD's research program supports Goal 1 (Clean Air) and Goal 4 (Healthy People) of EPA' strategic plan. More specifically, our division conducts research to characterize the movement of pollutants from the source to contact with humans. Our multidisciplinary research program produces Methods, Measurements, and Models to identify relationships between and characterize processes that link source emissions, environmental concentrations, human exposures, and target-tissue dose. The impact of these tools is improved regulatory programs and policies for EPA.
Description:
Highly time-resolved air monitoring data are widely being collected over long time horizons in order to characterizeambient and near-source air quality trends. In many applications, it is desirable to split the time-resolved data into two ormore components (e.g., local and regional) for apportionment and mitigation purposes. While there may be increasedinformation 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 filteringmay 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 frequenciespresent in the time series. CADETS is also a flexible approach that allows the user to choose the bifurcation point withminimal negative impacts. Using this algorithm, we demonstrate that a time series signal may be decomposed into twouseful and interpretable signals that can help identify aspects that may otherwise be hidden or distorted. Using the outputfrom the CADETS algorithm, we show that ultrafine particles (30–100 nm) collected near a major highway may be splitinto a 64:36 ratio of highly varying (local) and slowly varying (regional) components, meanwhile identical measurementsat a background location were estimated to split into a 56:44 local versus regional ratio.
