Bayesian Hierarchical Modeling for Predicting Spatially Correlated Curves in Irregular Domains: A Case Study on PM10 Pollution

TL;DR

This paper introduces a Bayesian hierarchical model that effectively predicts spatially correlated pollution curves in irregular domains, demonstrated through PM10 data in Mexico City, improving environmental monitoring accuracy.

Contribution

It develops a novel Bayesian hierarchical framework combining Bernstein polynomial bases and autoregressive effects for spatially correlated functional data in irregular domains.

Findings

Accurately recovers spatially dependent curves

Predicts unmonitored site concentrations effectively

Provides realistic uncertainty estimates

Abstract

This study presents a Bayesian hierarchical model for analyzing spatially correlated functional data and handling irregularly spaced observations. The model uses Bernstein polynomial (BP) bases combined with autoregressive random effects, allowing for nuanced modeling of spatial correlations between sites and dependencies of observations within curves. Moreover, the proposed procedure introduces a distinct structure for the random effect component compared to previous works. Simulation studies conducted under various challenging scenarios verify the model's robustness, demonstrating its capacity to accurately recover spatially dependent curves and predict observations at unmonitored locations. The model's performance is further supported by its application to real-world data, specifically PM$_{10}$ particulate matter measurements from a monitoring network in Mexico City. This application is of practical importance, as particles can penetrate the respiratory system and aggravate various health conditions. The model effectively predicts concentrations at unmonitored sites, with uncertainty estimates that reflect spatial variability across the domain. This new methodology provides a flexible framework for the FDA in spatial contexts and addresses challenges in analyzing irregular domains with potential applications in environmental monitoring.