06 Jul 2023
 | 06 Jul 2023

A model for rapid wildfire smoke exposure estimates using routinely-available data - rapidfire v0.1.3

Sean Raffuse, Susan O'Neill, and Rebecca Schmidt

Abstract. Urban smoke exposure events from large wildfires have become increasingly common in California and throughout the western United States. The ability to study the impacts of high smoke aerosol exposures from these events on the public is limited by the availability of high-quality, spatially-resolved estimates of aerosol concentrations. Methods for assigning aerosol exposure often employ multiple data sets that are time consuming and expensive to create and difficult to reproduce. As these events have gone from occasional to nearly annual in frequency, the need for rapid smoke exposure assessments has increased. The rapidfire R package (version 0.1.3) provides a suite of tools for developing exposure assignments using data sets that are routinely generated and publicly available within a month of the event. Specifically, rapidfire harvests official air quality monitoring, satellite observations, meteorological modeling, operational predictive smoke modeling, and low-cost sensor networks. A machine learning approach (random forests regression) is used to fuse the different data sets. Using rapidfire, we produced estimates of ground-level 24-hour average particulate matter for several large wildfire smoke events in California from 2017–2021. These estimates show excellent agreement with independent measures from filter-based networks.

Sean Raffuse et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1031', Anonymous Referee #1, 21 Aug 2023
  • RC2: 'Comment on egusphere-2023-1031', Anonymous Referee #2, 01 Sep 2023
  • AC1: 'Author response to Comments on egusphere-2023-1031', Sean Raffuse, 27 Sep 2023

Sean Raffuse et al.

Sean Raffuse et al.


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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.