Spatiotemporal large-scale networks shaped by air mass movements

TL;DR

This paper introduces a mathematical framework for constructing spatiotemporal networks based on air mass trajectories, revealing seasonal patterns and enabling applications in aerobiology and epidemiology.

Contribution

It presents a novel method to build and analyze large-scale spatiotemporal networks from atmospheric trajectory data, with adaptable estimators and real-world applications.

Findings

Identified seasonal patterns in air mass movements.

Demonstrated network construction in French watersheds and Mediterranean coast.

Potential applications in airborne pathogen studies.

Abstract

The movement of atmospheric air masses can be seen as a continuous and complex flow of particles hovering over our planet. It can however be locally simplified by considering three-dimensional trajectories of air masses connecting distant areas of the globe during a given period of time. In this paper, we present a mathematical framework to construct spatial and spatiotemporal networks where the nodes are the subsets of a partition of a geographical area and the links between these nodes are inferred from sampled trajectories of air masses passing over and across the nodes. We propose different estimators of link intensities relying on different bio-physical hypotheses and covering adjustable time periods. This approach leads to a new class of spatiotemporal networks characterized by adjacency matrices. We applied the approach in two real geographical contexts: the watersheds of the French region Provence-Alpes-C\^ote d'Azur and the coastline of the Mediterranean Sea. The analysis of the constructed networks allowed identifying a marked seasonal pattern in air mass movements in the study areas. These constructed networks can be used to investigate issues, e.g., in aerobiology and epidemiology of airborne plant pathogens. Similar networks could be estimated from other types of trajectories, such as animal trajectories.