Analyzing Ozone Concentration by Bayesian Spatio-temporal Quantile Regression
Priyam Das, Subhashis Ghosal

TL;DR
This paper introduces a Bayesian spatio-temporal quantile regression method to analyze ozone concentration trends across the US, effectively borrowing information across locations and capturing temporal and spatial variations.
Contribution
The proposed method incorporates smoothing across sites within a Bayesian framework, allowing for more accurate estimation of ozone quantiles with low sample sizes at each location.
Findings
Overall decreasing trend in ozone levels over the US in the last decade.
California shows a decrease in 1-hour maximum ozone levels, but no clear trend for 8-hour levels.
Method effectively captures spatio-temporal ozone concentration patterns.
Abstract
Ground level Ozone is one of the six common air-pollutants on which the EPA has set national air quality standards. In order to capture the spatio-temporal trend of 1-hour and 8-hour average ozone concentration in the US, we develop a method for spatio-temporal simultaneous quantile regression. Unlike existing procedures, in the proposed method, smoothing across the sites is incorporated within modeling assumptions thus allowing borrowing of information across locations, an essential step when the number of samples in each location is low. The quantile function has been assumed to be linear in time and smooth over space and at any given site is given by a convex combination of two monotone increasing functions and not depending on time. A B-spline basis expansion with increasing coefficients varying smoothly over the space is used to put a prior and a Bayesian analysis…
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