An iteratively optimized downscaling method for city-scale air quality forecast emission inventory establishment

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Computational Modeling

Background:

• Air quality models (AQMs) are crucial for forecasting and pollution control but are limited by inaccurate local emission inventories.
• Accurate emission data is often unavailable for many urban areas, hindering effective air quality management.
• This limitation is particularly pronounced in regions with complex geographical and meteorological conditions.

Purpose of the Study:

• To develop and validate a novel technique for generating AQM-ready emission inventories with iterative optimization capabilities for Chinese cities.
• To address the challenge of missing local emission data by enhancing AQM performance using available environmental observations.
• To provide a transferable methodology for generating reliable emission files for air quality forecasting.

Main Methods:

• Developed an efficient emission processing tool utilizing the High-Resolution Multi-resolution Emission Inventory for China (HR-MEIC) as input.
• Implemented an iterative optimization method that adjusts regional emissions using scale factors derived from model results and environmental observations.
• Applied the methodology to the Eight Cities in the Chengdu Plain (CP8C) using the WRF-CMAQ model for monthly simulations throughout 2023.

Main Results:

• The iterative optimization significantly improved model performance for PM2.5 and NO2 after five cycles, increasing correlation coefficients (R) from 0.62 to 0.77 and 0.37 to 0.73, respectively.
• Normalized Mean Bias (NMB) for PM2.5 and NO2 substantially decreased from 22.8% to 3.6% and 100.4% to 3.3%, respectively.
• Ozone (O3) concentration underestimation was reduced, though O3 modeling enhancements were modest.

Conclusions:

• The developed technique effectively generates reasonable AQM-ready emission files using open data sources, improving air quality forecasts.
• Iterative optimization enhances AQM performance even in data-scarce environments, offering a practical solution for cities lacking local emission inventories.
• This method provides a valuable, easily replicable approach for improving urban air quality management and forecasting.