Exploring spatiotemporal trends in air pollutants with Quantile Regression

Oregon Convention Center 

Concern regarding climate change and its influential impact on humanity is the talk of the hour. Air pollutant levels in air are constantly monitored, and we use the United States Environmental Protection Agency's available resources to access the distribution of particular pollutants for a given number of sites, over the years. Various spatial locations have their spatially dependent pollutant′s quantile functions which varies with time. Using an approach of simultaneously modelling the quantiles, our aim is to reduce the computational complexity than the existing methodologies. We use a quantile regression method that uses functional principal components to reduce the dimensions over space and quantile levels while testing for trends in air pollution data over the last 20 years. Extensive comparison among the existing methods in literature is demonstrated.

spatial

quantile

regression

functional

computation

pollutants 

Section on Statistics and the Environment