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Signal Decomposition of High Resolution Time Series River Data to Separate Local and Regional Components of Conductivity
Citation:
Holm, K., A. Kamal, AND G. Norris. Signal Decomposition of High Resolution Time Series River Data to Separate Local and Regional Components of Conductivity. Society of Industrial and Applied Math (SIAM) Conference, Chicago, IL, July 07 - 11, 2014.
Impact/Purpose:
The National Exposure Research Laboratory (NERL) Human Exposure and Atmospheric Sciences Division (HEASD) conducts research in support of EPA mission to protect human health and the environment. HEASD 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:
Signal processing techniques were applied to high-resolution time series data obtained from conductivity loggers placed upstream and downstream of an oil and gas wastewater treatment facility along a river. Data was collected over 14-60 days. The power spectral density was used to determine a cut-off frequency. A low-pass, zero-phase digital filter was applied to decouple the local and background (e.g. regional) sources of halides (e.g. chloride, bromide) impacting the conductivity signal.
