Processing methodology of global anthropogenic emissions for air quality modeling

Ernesto Pino-Cortés

doi:10.1016/j.mex.2021.101505

Abstract

The Global Emissions Initiative (GEIA) stores and offers global datasets of emission inventories developed in the last 30 years. One of the most recently updated global datasets covering anthropogenic source emissions is the Copernicus Atmosphere Monitoring Service (CAMS). This study applied NetCDF Command Operator (NCO) software to preprocess the anthropogenic sources included in the CAMS datasets and converted those files as an input in the Sparse Matrix Operator Kerner Emissions (SMOKE) model for future air quality modeling. As a result, six steps were applied to obtain the required file format. The case of the central coast in Chile was analyzed to compare the global database and official reports for the on-road transport sector. As a result, some differences were shown in the most populated locations of the domain of analysis. The rest of the zones registered similar values. The methodology exposed in this report could be applied in any other region of the planet for air quality modeling studies. The development of global datasets such as CAMS is useful for hemispheric analysis and could bring an estimation on the mesoscale. It represents an opportunity for those locations without official reports of non-updated data.•This study applied NCO commands available for the preprocessing of the CAMS dataset files.•The emissions and temporal profile registered in CAMS datasets must be compared to official reports of transport sectors.•The development of global datasets such as CAMS is useful for hemispheric analysis and could bring an estimation on the mesoscale.

Highlights

The most accurate and absolute air emission inventory estimation is crucial to achieving an air quality simulation [1]
This study applied Network Common Data Form (NetCDF) Command Operator (NCO) software to preprocess the anthropogenic sources included in the Copernicus Atmosphere Monitoring Service (CAMS) datasets and converted those files as an input in the Sparse Matrix Operator Kerner Emissions (SMOKE) model for future air quality modeling
The development of global datasets such as CAMS is useful for hemispheric analysis and could bring an estimation on the mesoscale scale

Summary

Introduction

The most accurate and absolute air emission inventory estimation is crucial to achieving an air quality simulation [1]. Some countries have established datasets with this information at a high level of detail, improving and enhancing studies for better environmental policy at local, regional and national levels. There are many regions with unclear or undefined emission inventories, avoiding air quality models in those locations. In the last 30 years, various global datasets of emission inventories have been developed for different sources and covering specific periods of analysis. The Global Emissions Initiative (GEIA) stores and offers those datasets. It represents an opportunity for those locations without official reports of nonupdated data. One of the most recently updated global datasets covering anthropogenic source emissions is the Copernicus Atmosphere Monitoring Service (CAMS) developed by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Commission

Methods

Results

Conclusion