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Updated: Apr 17, 2026

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Hourly PM2.5 Estimates across California from 2018 to 2023.

Sienna R White1, Rebecca A Sugrue2, Léo Guillotte1

  • 1Civil and Environmental Engineering, University of California, Berkeley, O'Brien Hall, Berkeley, California 94720, United States.

ACS ES&T Air
|April 16, 2026
PubMed
Summary
This summary is machine-generated.

A new dataset provides hourly PM2.5 air quality data for California from 2018-2023. This high-resolution data captures wildfire smoke impacts, offering valuable insights into air pollution exposure.

Keywords:
PM2.5air pollutiondata assimilationmachine learningwildfire smoke

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Area of Science:

  • Environmental Science
  • Atmospheric Science
  • Air Quality Monitoring

Background:

  • Wildfire smoke significantly impacts air quality, particularly fine particulate matter (PM2.5).
  • Accurate, high-resolution, and temporally resolved PM2.5 data are crucial for assessing public health risks.
  • Existing datasets often lack the hourly resolution and spatial detail needed to capture dynamic smoke events.

Purpose of the Study:

  • To develop a novel, high-resolution, hourly PM2.5 dataset for California covering 2018-2023.
  • To integrate satellite and ground-based observations with advanced modeling techniques for improved air quality estimation.
  • To provide a resource for analyzing subdaily PM2.5 exposure patterns and their health implications.

Main Methods:

  • Assimilation of PurpleAir and EPA Air Quality System monitor data into High-Resolution Rapid Refresh Smoke (HRRR-Smoke) model forecasts.
  • Utilized the Gridpoint Statistical Interpolation (GSI) three-dimensional variational data assimilation framework.
  • Developed a six-year (2018-2023) reanalysis dataset at a 3-kilometer resolution with hourly temporal frequency.

Main Results:

  • The dataset demonstrates strong validation performance, with monthly R-squared values between 0.73 and 0.91.
  • Case studies of major fires (Camp, Kincade, Lightning Complex) show the dataset's ability to resolve plume dynamics and local exposures.
  • Root-mean-squared error generally scales with PM2.5 concentrations, indicating higher accuracy under typical conditions.

Conclusions:

  • This is the first long-term, hourly PM2.5 dataset for California, offering unprecedented detail on air quality.
  • The data enables the calculation of subdaily exposure metrics, crucial for health impact studies.
  • The validated dataset is a valuable resource for researchers investigating the effects of short-term PM2.5 exposure.