Quantifying the drivers of PM_2.5 variation in Shenyang, China: A factor decomposition model

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Air Quality Management

Background:

• Particulate matter (PM2.5) variations are influenced by multiple factors beyond local emissions, complicating air quality management assessments.
• Relying solely on PM2.5 trends can lead to misjudgments of governmental performance in pollution control.

Purpose of the Study:

• To develop and validate a decomposition method for attributing PM2.5 variations to specific sources.
• To accurately assess the contributions of meteorological conditions, cross-regional pollutant transport, secondary aerosols, and local emissions to PM2.5 levels.

Main Methods:

• Utilized a semi-parametric generalized additive model to quantify the relationship between PM2.5 concentration and influencing factors.
• Conducted a case study in Shenyang, China, using extensive hourly data from 2014 to 2017.
• Decomposed PM2.5 variations into contributions from meteorological conditions, transboundary transport, secondary aerosols, and local emissions.

Main Results:

• PM2.5 levels in Shenyang decreased by 39.80% from 2014 to 2017, surpassing the government target.
• Only 11.79% of this reduction was attributable to the control of local emissions.
• A severe pollution event in November 2015 resulted from combined heating emissions and unfavorable meteorological conditions.

Conclusions:

• The proposed decomposition method provides a more accurate assessment of air quality management effectiveness.
• Local emission controls played a smaller role in PM2.5 reduction than overall trends suggested.
• The methodology is adaptable for application in diverse geographical locations with available air quality and meteorological data.