AirExposure

AirExposure is an R package for estimating individual exposure to air pollution using both fixed-location and mobility-based approaches.

The package integrates information on daily mobility, activity schedules, travel routes, and hourly pollutant concentrations to estimate personal exposure in urban environments and compare traditional and dynamic exposure assessment methods.

Installation

The development version of AirExposure can be installed directly from GitHub:

# remotes::install_github("flortames/AirExposure")
install.packages("AirExposure", repos = c('https://flortames.r-universe.dev', 'https://cloud.r-project.org'))

After installation, load the package with:

library(AirExposure)

Main features

• Retrieve hourly pollutant concentration grids.
• Aggregate pollutant concentrations over user-defined time periods.
• Compare alternative travel routes using air pollution information.
• Estimate daily exposure using a Dynamic Exposure Method (DEM).
• Estimate daily exposure using a Fixed-Location-Based Method (FLBM).
• Compare mobility-based and fixed-location exposure estimates.
• Assess exposure along alternative travel routes.

Vignette

A complete workflow describing dynamic PM2.5 exposure assessment in the Mendoza Metropolitan Area is available through the package vignette:

• Dynamic PM2.5 Exposure Assessment in Mendoza Metropolitan Area

Mendoza case study

The package documentation and vignette include a case study based on hourly PM2.5 concentration grids from the Mendoza Metropolitan Area (Argentina). The grids used for the case study are publicly available through Zenodo:

DOI: Mendoza PM2.5 dataset

The dataset contains 24 hourly PM2.5 concentration grids corresponding to 1 August 2019.

Citation

If you use AirExposure in your work, please consider citing the related publications listed below.

Related publications

Tames, M.F., Puliafito, S.E., Urquiza, J. et al. Spatio-temporal analysis of bicyclists’ PM2.5 exposure levels in a medium sized urban agglomeration. Environ Monit Assess 196, 1194 (2024). https://doi.org/10.1007/s10661-024-13356-w

Tames, M.F., Urquiza, J., Berná-Peña, L.L. et al. Modeling Influence of Population Mobility to Airborne PM2.5 Exposure. Environ Model Assess 30, 1235–1251 (2025). https://doi.org/10.1007/s10666-025-10050-0