Multi-perspective analysis and spatiotemporal mapping of air pollution monitoring data.
Space-time data analysis and assimilation techniques in atmospheric sciences typically consider input from monitoring measurements. The input is often processed in a manner that acknowledges characteristics of the measurements (e.g., underlying patterns, fluctuation features) under conditions of uncertainty; it also leads to the derivation of secondary information that serves study-oriented goals, and provides input to space-time prediction techniques. We present a novel approach that blends a rigorous space-time prediction model (Bayesian maximum entropy, BME) with a cognitively informed visualization of high-dimensional data (spatialization). The combined BME and spatialization approach (BME-S) is used to study monthly averaged NO2 and mean annual SO4 measurements in California over the 15-year period 1988-2002. Using the original scattered measurements of these two pollutants BME generates spatiotemporal predictions on a regular grid across the state. Subsequently, the prediction network undergoes the spatialization transformation into a lower-dimensional geometric representation, aimed at revealing patterns and relationships that exist within the input data. The proposed BME-S provides a powerful spatiotemporal framework to study a variety of air pollution data sources.
Kolovos
Alexander
A
SAS Institute, Inc., 100 SAS Campus Dr. S3042, Cary, North Carolina 27513, USA. kolovos@email.unc.edu
Skupin
André
A
Jerrett
Michael
M
Christakos
George
G
D000397:Air Pollution / Q000032:analysis*; D001499:Bayes Theorem; D019277:Entropy; D004784:Environmental Monitoring / Q000379:methods*; D008956:Models, Chemical; D009585:Nitrogen Dioxide / Q000032:analysis; D013431:Sulfates / Q000032:analysis; D013997:Time Factors
D013431:Sulfates; D009585:Nitrogen Dioxide