Emissions, meteorological and climate impacts on PM2.5 levels in Southern California using a generalized additive model: Historic trends and future estimates

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

doi:10.1016/j.chemosphere.2023.138385

Emissions, meteorological and climate impacts on PM_2.5 levels in Southern California using a generalized additive model: Historic trends and future estimates

Chemosphere. 2023 Jun:325:138385. doi: 10.1016/j.chemosphere.2023.138385. Epub 2023 Mar 13.

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, 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, 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: 36921775
DOI: 10.1016/j.chemosphere.2023.138385

Abstract

Annual fine particulate matter (PM_2.5) mass concentrations in the South Coast Air Basin (SoCAB) of California decreased from around 30 μg/m³ to 11 μg/m³ between 2000 and 2013 but rose from 11 μg/m³ to 13 μg/m³ between 2014 and 2018, raising important questions about the effectiveness of ongoing emission control policies. A two-step generalized additive model (GAM)-least squares approach was developed to explore the effects of emissions, large-scale climate events and meteorological factors on daily PM_2.5 mass concentrations from 2000 to 2019 to quantitatively link impacts of emissions and meteorological on PM_2.5 and to assess factors leading to the increase. The GAM had an R² = 0.99 and root mean square error (RMSE) = 0.7 μg/m³ for the annual average PM_2.5 concentrations. The two-step method had an R² = 0.93 and RMSE = 4.07 μg/m³ for the 98th percentile 24-hr average PM_2.5 concentrations. Variations in both emissions and relative humidity were of high importance compared with other included factors. Interactions of NH₃ emissions with NOx and SO₂ emissions, which lead to ammonium nitrate and sulfate aerosol formation, were the most important factors. Meteorological effects on PM_2.5 explained the majority of the daily PM_2.5 fluctuations. Emission changes (increases in SO₂ and PM_2.5) led to increases in predicted PM_2.5 between 2014 and 2018. Predicted future PM_2.5, using projected emissions and meteorological data from model simulations of representative concentration pathway (RCP) scenarios, are around 12 μg/m³ (annual) and 30 μg/m³ (98th percentile daily), which are both close to the current National Ambient Air Quality Standards (NAAQS) for PM_2.5. Meteorological impacts on the predicted PM_2.5 in future years lead to variations of ±2 μg/m³ for the annual average and ±5 μg/m³ for the 98th percentile daily level. Future climate changes lead to a probable year-to-year variation that will let PM_2.5 levels in some years exceed the standard.

Keywords: Emission impact; Generalized additive model; Meteorological impact; PM(2.5); South coast air basin.

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
California
Environmental Monitoring / methods
Nitrates
Particulate Matter / analysis

Substances

Air Pollutants
Particulate Matter
Nitrates