Air pollution models in epidemiologic studies with geostatistics and machine learning

TL;DR

This paper reviews recent advances in air pollution modeling for epidemiologic studies, emphasizing the integration of geostatistics and machine learning to improve spatial accuracy and exposure assessment.

Contribution

It highlights recent developments and proposes future extensions combining geostatistics and machine learning for more precise air pollution models.

Findings

Machine learning enhances spatial trend modeling.

Extensions will incorporate spatial covariance in learning algorithms.

Refinements will improve exposure assessment accuracy.

Abstract

Development of air pollution models for large regions is a priority for population-based epidemiologic studies. The rapid development of big data information systems and machine learning algorithms have opened new grounds for refinements of current model frameworks. This commentary overviews recent contributions and outlines extensions from geostatistics and machine learning perspectives. For the coming years, expected advances will expand the use of learning algorithms to model spatial trends and incorporate spatial covariance models in the learning processes. These extensions will refine existing modelling frameworks contributing to improve accuracy of air pollution models for exposure assessment.