Emissions and meteorological impacts on PM2.5 species concentrations in Southern California using generalized additive modeling

Ziqi Gao; Cesunica E Ivey; Charles L Blanchard; Khanh Do; Sang-Mi Lee; Armistead G Russell

doi:10.1016/j.scitotenv.2023.164464

Emissions and meteorological impacts on PM_2.5 species concentrations in Southern California using generalized additive modeling

Sci Total Environ. 2023 Sep 15:891:164464. doi: 10.1016/j.scitotenv.2023.164464. Epub 2023 May 27.

Authors

Ziqi Gao¹, Cesunica E Ivey², Charles L Blanchard³, Khanh Do⁴, Sang-Mi Lee⁵, Armistead G Russell⁶

Affiliations

¹ School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA. Electronic address: zgao71@gatech.edu.
² Department of Chemical and Environmental Engineering, University of California, Riverside, Riverside, CA, USA; Now at Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA.
³ Envair, Albany, CA, USA.
⁴ Department of Chemical and Environmental Engineering, University of California, Riverside, Riverside, CA, USA; Center for Environmental Research and Technology, Riverside, CA, USA.
⁵ South Coast Air Quality Management District, Diamond Bar, CA, USA.
⁶ School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA.

PMID: 37247741
DOI: 10.1016/j.scitotenv.2023.164464

Abstract

The chemical composition of PM_2.5 has a significant impact on human health and air quality, and its accurate knowledge can be used to identify contributing emission sources. Assessing and quantifying the impacts of various factors (e.g., emissions, meteorology, and large-scale climate patterns) on the main PM_2.5 chemical components can give guidance for implementing effective regulations to improve air quality in the future. In this study, we developed generalized additive models (GAMs) to assess how emissions, meteorological factors, and large-scale climate indices affected ammonium, sulfate, nitrate, elemental carbon, and organic carbon from 2002 to 2019 in the South Coast Air Basin (SoCAB). Concentration trends from three sites in the SoCAB are studied. The statistical results showed that GAMs can capture the variability of these species' daily concentrations (R² = 0.6 to 0.7) and annual concentrations (R² = 0.93 to 0.99). Precursor emissions most significantly affect PM_2.5 species production, though meteorological factors like maximum temperature, relative humidity, wind speed, and boundary layer height, also influence PM_2.5 composition. In the future, these meteorological factors will become more significant in affecting PM_2.5 speciation, although emissions will continue to strongly affect formation. Results show that the composition of most PM_2.5 species will decrease in the future except for OC, which will become the largest contributor to PM_2.5.

Keywords: Ammonium; Elemental carbon; GAM; Nitrate; Organic carbon; Sulfate.