Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 1
Articles | Volume 17, issue 1
https://doi.org/10.5194/gmd-17-381-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-381-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 1
Model description paper
 | 
16 Jan 2024
Model description paper |  | 16 Jan 2024

A model for rapid PM2.5 exposure estimates in wildfire conditions using routinely available data: rapidfire v0.1.3

Sean Raffuse, Susan O'Neill, and Rebecca Schmidt

Viewed

Total article views: 2,755 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,144 531 80 2,755 71 144
  • HTML: 2,144
  • PDF: 531
  • XML: 80
  • Total: 2,755
  • BibTeX: 71
  • EndNote: 144
Views and downloads (calculated since 06 Jul 2023)
Cumulative views and downloads (calculated since 06 Jul 2023)

Viewed (geographical distribution)

Total article views: 2,755 (including HTML, PDF, and XML) Thereof 2,681 with geography defined and 74 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 18 Dec 2025
Download
Short summary
Large wildfires are increasing throughout the western United States, and wildfire smoke is hazardous to public health. We developed a suite of tools called rapidfire for estimating particle pollution during wildfires using routinely available data sets. rapidfire uses official air monitoring, satellite data, meteorology, smoke modeling, and low-cost sensors. Estimates from rapidfire compare well with ground monitors and are being used in public health studies across California.
Read more
Share