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Source Region Identification Using Kernel Smoothing
Citation:
HENRY, R. C., G. A. NORRIS, R. VEDANTHAM, AND J. R. Turner. Source Region Identification Using Kernel Smoothing. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, 43(11):4090-4097, (2009).
Impact/Purpose:
The National Exposure Research Laboratory′s (NERL) 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′s 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:
As described in this paper, Nonparametric Wind Regression is a source-to-receptor source apportionment model that can be used to identify and quantify the impact of possible source regions of pollutants as defined by wind direction sectors. It is described in detail with an example of its application to SO2 data from East St. Louis, IL. The model uses nonparametric kernel smoothing methods to apportion the observed average concentration of a pollutant to sectors defined by ranges of wind direction and speed.
URLs/Downloads:
Source Region Identification Using Kernel Smoothing (PDF, NA pp, 4181 KB, about PDF)Environmental Science and Technology
