Gridded mobile source emissions with multiple processes and pollutants from 2011 to 2020 in China

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Data Science

Background:

• Accurate mobile source (MS) emissions data are crucial for air quality management and atmospheric research.
• Existing emission inventories have limitations in coverage of MS categories, emerging pollutants, and model parameterizations.

Purpose of the Study:

• To introduce the Gridded Mobile-source Emission Dataset (GMED), a novel, model-ready emission inventory.
• To provide detailed source classification and multi-pollutant coverage for mobile sources.

Main Methods:

• Developed GMED with monthly emissions from 2011-2020 at 36km x 36km resolution.
• Included eight MS categories, tailpipe and non-exhaust emissions, and integrated localized measurements.
• Incorporated emission factors, organic compound speciation, spatiotemporal proxies, and activity data.

Main Results:

• GMED offers detailed source classification and multi-pollutant coverage.
• Validation confirmed good agreement with established inventories and big traffic data.
• The dataset provides a validated, long-term, and differentiated view of MS emissions.

Conclusions:

• GMED addresses critical gaps in existing mobile source emission inventories.
• The dataset supports emission assessments, air quality modeling, and policy evaluation.
• GMED is a valuable resource for advancing atmospheric research and air quality management.