CINEI V1.1: Python code for creating an integrated inventory of anthropogenic emission for China

This Python code aims to create an integrated anthropogenic emissions inventory for China. The code integrates existing emission inventories for uniform spatial resolution and more comprehensive sectors of human activities and speciated non-methane volatile organic compounds (NMVOCs). To accomplish this, we follow this workflow, which is provided as files of Python code: (1) The 'regridding.py' file is used to create the grid proxy in 0.25 spatial degrees for all data in different resolutions. (2) 'inte_all.py' file is used to create the NETCDF files for all types with 8 sectors and combine the national inventory for China and global inventory for region outside of China. The power generation, transportation, industry, residential sectors are from national inventory (MEIC), as well as the national-omitted sectors are from global inventories, which include shipping (from Copernicus Atmosphere Monitoring Service emissions inventory, CAMs), aviation (from Hemispheric Transport of Air Pollution inventory, HTAP), agriculture (from HTAP), and waste (from CEDS). (3) The 'VOC_species_nc.py' file is used to speciate the NMVOC emission as 20 species, following the chemical speciation of the Model for Ozone and Related chemical Tracers mechanism (MOZART), and finally to create the NETCDF file for each speciated emission. Therefore, this code works as a tool to process the emissions and make more representative inventory for policy making and application in chemistry and transport models (CTMs).

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-10
DOI
https://doi.org/10.5281/zenodo.15000794
Primary Topic
Acute Lymphoblastic Leukemia research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

CINEI V1.1: Python code for creating an integrated inventory of anthropogenic emission for China

Zenodo (CERN European Organization for Nuclear Research)
Acute Lymphoblastic Leukemia research
article

CINEI V1.1: Python code for creating an integrated inventory of anthropogenic emission for China

article en

Abstract

This Python code aims to create an integrated anthropogenic emissions inventory for China. The code integrates existing emission inventories for uniform spatial resolution and more comprehensive sectors of human activities and speciated non-methane volatile organic compounds (NMVOCs). To accomplish this, we follow this workflow, which is provided as files of Python code: (1) The 'regridding.py' file is used to create the grid proxy in 0.25 spatial degrees for all data in different resolutions. (2) 'inte_all.py' file is used to create the NETCDF files for all types with 8 sectors and combine the national inventory for China and global inventory for region outside of China. The power generation, transportation, industry, residential sectors are from national inventory (MEIC), as well as the national-omitted sectors are from global inventories, which include shipping (from Copernicus Atmosphere Monitoring Service emissions inventory, CAMs), aviation (from Hemispheric Transport of Air Pollution inventory, HTAP), agriculture (from HTAP), and waste (from CEDS). (3) The 'VOC_species_nc.py' file is used to speciate the NMVOC emission as 20 species, following the chemical speciation of the Model for Ozone and Related chemical Tracers mechanism (MOZART), and finally to create the NETCDF file for each speciated emission. Therefore, this code works as a tool to process the emissions and make more representative inventory for policy making and application in chemistry and transport models (CTMs).

Zenodo (CERN European Organization for Nuclear Research)
University of Bremen (DE)
Openalex Percentile: Top 43%
Acute Lymphoblastic Leukemia research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.