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SUGGESTIONS FOR OPTIMIZED PLANNING OF MULTIVARIATE MONITORING OF ATMOSPHERIC POLLUTION
Citation:
PAATERO, P., S. I. EBERLY, AND P. K. HOPKE. SUGGESTIONS FOR OPTIMIZED PLANNING OF MULTIVARIATE MONITORING OF ATMOSPHERIC POLLUTION. Presented at International Environmental Modeling and Software Society, Burlington, VT, July 09 - 13, 2006.
Impact/Purpose:
The goal of this task is to develop methods and models to reduce the uncertainty in quantifying local and regional air pollutant source impacts on ambient samples collected in speciated PM, air toxic, and semi-continuous measurement networks. A combination of high resolution sampling, organic and inorganic analytical methods, and models will be developed and evaluated to reduce the uncertainty in source apportionment:
(1) semi-continuous inorganic species sampling
(2) inorganic analysis
(3) organic analysis for medium flow samples
(4) multivariate receptor models for ambient samples
(5) regional and local models
In addition, this task contributes to two additional tasks that have research focused on reducing the uncertainty in source apportionment: Identify Sources of Human Exposure (21176), and NAAQS implementation (21179).
Description:
Recent work in factor analysis of multivariate data sets has shown that variables with little signal should not be included in the factor analysis. Work also shows that rotational ambiguity is reduced if sources impacting a receptor have both large and small contributions. These two aspects are important for designing field studies involving receptor modeling.