Examination of the Mathematical Models Used in Atmospheric Source Apportionment Studies

点击翻译按钮获取中文摘要

9523731 Hopke The PIs have developed and tested a number of different mathematical techniques that can help extract information on source/receptor relationships in the atmospheric transport of airborne particulate matter from compositional data sets. These methods include techniques for projecting high dimensionality, multielemental composition data into two or three dimensions; and the integration of meteorological information in the form of back trajectory calculations into the analysis by creating a conditional probability field. For the interpretation of multivariate air quality data, the use of Neural Networks has been explored, as well as its combination with the minimal spanning tree technique. In this project, the development, testing, and application of new multivariate statistical methods to atmospheric sample composition data, primarily particles and precipitation, will be continued. Extensive collaborations have been established with atmospheric scientists from all parts of the world who have initiated field studies, including measurements in the Great Lake area, upstate New York, the Chesapeake Bay, as well as Australia, Finland and Sweden. The PI's analysis methods will provide new tools for examining data, and help extract maximum useful information from these data bases. Among the development and testing projects that will be continued are Positive Matrix Factorization, and multimodal factor analysis for looking at several different types of 3-way data tables.