Discipline: Mathematics & Statistics
Subcategory: STEM Research
Eduardo Montoya - California State University, Bakersfield
Functional data analysis continues to attract attention because technological advances in many fields have permitted measurements to be made from continuous processes at finer discretizations. The effects of air pollutants continue to be a matter for concern. Particulate matter is among the most harmful air pollutants affecting public health and the environment, and levels of PM10 (particles less than 10 micrometers in diameter) for regions of California are among the highest in the US. The relatively high frequency of particulate matter sampling enables us to treat the data as functional data. In this work, we investigate the dominant modes of variation of PM10 using functional data analysis methodologies. Our analysis provides insight into the underlying data structure of PM10, and it captures the size and temporal variation of these underlying data structure potentially related to changes in climate.
Funder Acknowledgement(s): NSF grant HRD-1547784
Faculty Advisor: None Listed,