Work

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

Year: 2026

Type: preprint

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.

Source: Research Square

DOI: 10.21203/rs.3.rs-11167409/v1