Evaluating CMAQ Representation of Wildfire PM₂.₅ in California (2008–2018): Regime-Dependent Performance and Nonlinear Error Structure

Abstract We evaluated daily paired Community Multiscale Air Quality (CMAQ) estimates and observations during California fire seasons (June–October), 2008–2018, using conventional metrics, classifications at 12 and 35 µg m⁻³, two case studies, and a leakage-resistant Random Forest diagnostic. After quarantining 18 CMAQ total PM₂.₅ values above 557 µg m⁻³ without capping, the primary analysis contained 187,051 records. Observed and modeled means were 10.69 and 9.29 µg m⁻³; Pearson r was 0.502 (r² = 0.252), mean bias was − 1.40 µg m⁻³, and RMSE was 9.96 µg m⁻³. CMAQ detected 45.4% of observed events at the descriptive/former annual level of 12 µg m⁻³ and 40.8% at the current 35 µg m⁻³ 24-hour standard level. At 35 µg m⁻³, CMAQ produced more events overall but missed 1,273 of 2,151 observed events because modeled and observed events often did not coincide by site-day. The Random Forest excluded observed PM₂.₅, CMAQ total PM₂.₅, and the no-fire component from its primary features. In an untouched site-year test partition (n = 37,430), R² was 0.711, RMSE was 5.82 µg m⁻³, and MAE was 3.44 µg m⁻³; fire-only CMAQ PM₂.₅ was the dominant predictor. CMAQ remains useful for regional and multi-year analyses, but paired-event detection and localized extreme-smoke estimates require explicit uncertainty treatment.

Authors

Institutions

Publication Details

Journal
Research Square
Published
2026-11-01
DOI
https://doi.org/10.21203/rs.3.rs-11167409/v1
Primary Topic
Atmospheric chemistry and aerosols
Type
preprint

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Evaluating CMAQ Representation of Wildfire PM₂.₅ in California (2008–2018): Regime-Dependent Performance and Nonlinear Error Structure

Sambit Bhattacharya, John C. Flunker, Rachel E. Connolly, Joseph L. Wilkins et al.
Research Square
Atmospheric chemistry and aerosols
preprint

Evaluating CMAQ Representation of Wildfire PM₂.₅ in California (2008–2018): Regime-Dependent Performance and Nonlinear Error Structure

Sambit Bhattacharya, John C. Flunker, Rachel E. Connolly, Joseph L. Wilkins, Claire L. Schollaert, Miriam E. Marlier, Christopher S. Blaszczak-Boxe, Eimy Bonilla, Mohammad Z. Al‐Hamdan, Osinachi F. Ajoku, Samuel Kane, Daja Elum, Jose Perez-Chavez, Olutoyosi Oyelowo, Jihoon Jung, Huikyo Lee
preprint en

Abstract

Abstract We evaluated daily paired Community Multiscale Air Quality (CMAQ) estimates and observations during California fire seasons (June–October), 2008–2018, using conventional metrics, classifications at 12 and 35 µg m⁻³, two case studies, and a leakage-resistant Random Forest diagnostic. After quarantining 18 CMAQ total PM₂.₅ values above 557 µg m⁻³ without capping, the primary analysis contained 187,051 records. Observed and modeled means were 10.69 and 9.29 µg m⁻³; Pearson r was 0.502 (r² = 0.252), mean bias was − 1.40 µg m⁻³, and RMSE was 9.96 µg m⁻³. CMAQ detected 45.4% of observed events at the descriptive/former annual level of 12 µg m⁻³ and 40.8% at the current 35 µg m⁻³ 24-hour standard level. At 35 µg m⁻³, CMAQ produced more events overall but missed 1,273 of 2,151 observed events because modeled and observed events often did not coincide by site-day. The Random Forest excluded observed PM₂.₅, CMAQ total PM₂.₅, and the no-fire component from its primary features. In an untouched site-year test partition (n = 37,430), R² was 0.711, RMSE was 5.82 µg m⁻³, and MAE was 3.44 µg m⁻³; fire-only CMAQ PM₂.₅ was the dominant predictor. CMAQ remains useful for regional and multi-year analyses, but paired-event detection and localized extreme-smoke estimates require explicit uncertainty treatment.

Research Square
University of North Carolina at Chapel Hill (US), University of Iowa (US), Jet Propulsion Laboratory (US), Howard University (US), University of California, Los Angeles (US), Fayetteville State University (US), University of Mississippi (US)
National Science Foundation, U.S. Department of Energy, National Aeronautics and Space Administration, John D. and Catherine T. MacArthur Foundation, Gordon and Betty Moore Foundation, Science for Nature and People Partnership, National Institutes of Health, U.S. Forest Service, Office of Advanced Research Computing, Rutgers, The State University of New Jersey, Northern Research Station
Climate action
Atmospheric chemistry and aerosols
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon