A joint bayesian space-time model to integrate spatially misaligned air pollution data in R-INLA

TL;DR

This paper introduces a joint Bayesian space-time model in R-INLA that effectively integrates misaligned air pollution data from multiple sources, improving prediction accuracy of NO2 concentrations in Greater London.

Contribution

It develops a novel joint modeling approach that combines dispersion models and ground observations, accounting for spatial and temporal misalignments in air pollution data.

Findings

Joint model outperforms bilinear interpolation in predictions

Incorporating multiple data sources enhances accuracy

Model successfully reconstructs latent pollution fields

Abstract

In air pollution studies, dispersion models provide estimates of concentration at grid level covering the entire spatial domain, and are then calibrated against measurements from monitoring stations. However, these different data sources are misaligned in space and time. If misalignment is not considered, it can bias the predictions. We aim at demonstrating how the combination of multiple data sources, such as dispersion model outputs, ground observations and covariates, leads to more accurate predictions of air pollution at grid level. We consider nitrogen dioxide (NO2) concentration in Greater London and surroundings for the years 2007-2011, and combine two different dispersion models. Different sets of spatial and temporal effects are included in order to obtain the best predictive capability. Our proposed model is framed in between calibration and Bayesian melding techniques for data fusion red. Unlike other examples, we jointly model the response (concentration level at monitoring stations) and the dispersion model outputs on different scales, accounting for the different sources of uncertainty. Our spatio-temporal model allows us to reconstruct the latent fields of each model component, and to predict daily pollution concentrations. We compare the predictive capability of our proposed model with other established methods to account for misalignment (e.g. bilinear interpolation), showing that in our case study the joint model is a better alternative.