Multidecadal Analysis of Meteorological and Emissions Regimes for PM_2.5 Across California

Abstract

Much of California's population lives in regions that do not meet the annual National Ambient Air Quality Standards (NAAQS) for PM 2.5 . Generalized additive modeling (GAM) can characterize PM 2.5 response to covariates, while sensitivity analysis enables variable importance ranking for covariates. These methods can improve understanding of chemical and meteorological drivers of PM 2.5 in California. We construct GAMs for PM 2.5 and particulate NO 3 − , SO 4 2−, NH 4 + , elemental carbon, and organic carbon in Bakersfield, Fresno, Los Angeles, Riverside, Sacramento, and San Jose. We compute performance metrics for all models and further analyze our Bakersfield, Riverside, and San Jose models for PM 2.5 and selected speciated components by performing sensitivity analysis and calculating marginal effects. We find chemistry drives PM 2.5 in Bakersfield (71% of model variance) and San Jose (66%), while meteorology balances chemistry's influence in Riverside (48% and 52%, respectively). In Bakersfield, NO X is the primary driver, whereas HCHO dominates in San Jose. Riverside is influenced nearly equally by NO X , HCHO, and relative humidity. Each of these covariates displays a nonlinear, monotonic, positive association with PM 2.5 . As analysis sites are in California Assembly Bill 617 communities, our results may inform local control strategies to alleviate exposure concerns in environmental justice communities.

An overview of outdoor low-cost gas-phase air quality sensor deployments: current efforts, trends, and limitations

“…However, even in regions where PM is the primary air quality concern, gas-phase chemistry has been shown to drive PM emissions (Ren et. al., 2017;Quevedo et. al., 2024), necessitating data from both to understand the bigger picture.…”

Comment on egusphere-2024-1004

“…However, even in regions where PM is the primary air quality concern, gas-phase chemistry has been shown to drive PM emissions (Ren et. al., 2017;Quevedo et. al., 2024), necessitating data from both to understand the bigger picture.…”