Processing methodology of global anthropogenic emissions for air quality modeling

Area of Science:

• Atmospheric Science
• Environmental Science
• Data Science

Background:

• Global emission inventories are crucial for air quality modeling.
• The Copernicus Atmosphere Monitoring Service (CAMS) provides updated global anthropogenic emission datasets.
• Preprocessing these datasets is necessary for compatibility with air quality models like the Sparse Matrix Operator Kerner Emissions (SMOKE) model.

Purpose of the Study:

• To preprocess Copernicus Atmosphere Monitoring Service (CAMS) emission data using NetCDF Command Operator (NCO) software.
• To convert CAMS data into a format suitable for the Sparse Matrix Operator Kerner Emissions (SMOKE) model.
• To compare global emission data with official reports for the on-road transport sector in central Chile.

Main Methods:

• Application of NetCDF Command Operator (NCO) software for data preprocessing.
• Conversion of CAMS dataset files into a format compatible with the Sparse Matrix Operator Kerner Emissions (SMOKE) model.
• Comparative analysis of CAMS data against official reports for the on-road transport sector in Chile.

Main Results:

• Six preprocessing steps were successfully applied to obtain the required file format for the SMOKE model.
• Discrepancies were identified between CAMS global datasets and official reports in highly populated areas of central Chile's on-road transport sector.
• Similar emission values were observed in less populated zones within the study domain.

Conclusions:

• The developed methodology using NCO for CAMS data preprocessing is transferable to other regions globally for air quality modeling.
• Comparing CAMS emission data and temporal profiles with official transport sector reports is essential for data validation.
• Global datasets like CAMS offer valuable insights for hemispheric analysis and mesoscale air quality estimations, especially in data-scarce regions.