Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data

Area of Science:

• Environmental Science
• Atmospheric Science
• Public Health

Background:

• Particulate matter (PM2.5) poses significant environmental and health risks, with Xi'an experiencing severe pollution.
• Understanding PM2.5 sources is crucial for developing effective mitigation strategies.

Purpose of the Study:

• To investigate the primary sources and contributing factors of high PM2.5 concentrations in Xi'an City.
• To apply advanced statistical modeling for a more accurate estimation of PM2.5 relationships.

Main Methods:

• Utilized a generalized additive model (GAM) to analyze complex, non-linear relationships between PM2.5 and various pollutants (CO, NO2, SO2, O3).
• Incorporated remote sensing data (MODIS aerosol optical thickness - AOT) and meteorological variables (temperature, location, wind).
• Compared GAM performance against stepwise linear regression to quantify improvements in explanatory power.

Main Results:

• The GAM explained 69.50% of PM2.5 variability (R2 = 0.691), significantly outperforming linear regression (R2 = 0.582).
• Carbon monoxide (CO) concentration and aerosol optical thickness (AOT) were the most significant predictors, accounting for over 40% of the explained deviance.
• Other gaseous pollutants (SO2, NO2, O3) and environmental factors (temperature, location, wind) also showed significant non-linear associations with PM2.5.

Conclusions:

• Traffic and industrial emissions are identified as the predominant sources of PM2.5 pollution in Xi'an.
• The study highlights the effectiveness of GAMs in capturing complex atmospheric pollution dynamics.
• Findings provide critical insights for targeted air quality management policies in urban environments.