cinei 2.1.0

Coupled and Integrated Emission Inventory (CINEI): integrating anthropogenic emission inventories toward complete sectoral coverage, finer spatial resolution, and consistent NMVOC speciation for atmospheric chemistry, climate and multi-disciplinary applications.

CINEI — Coupled and Integrated Emission Inventory

📖 Full documentation: https://luyikeka.github.io/cinei/

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Overview

CINEI is a Python package for integrating anthropogenic emission inventories, combining global (e.g. CEDS) and regional (e.g. MEIC for China) datasets into a unified temporal and spatial resolution NetCDF product.

CINEI is dedicated to coupling and integrating anthropogenic emission inventories toward:

• Complete sectoral coverage — energy, residential, industry, agriculture, transportation, waste, shipping, aviation
• Finer spatial resolution — from global 0.5° to regional 0.25° or finer
• Consistent NMVOC speciation — disaggregation into lumped model species (MOZART/SAPRC99 mechanisms)

The outputs are designed for use in atmospheric chemistry simulations, climate change impact studies, and multi-disciplinary environmental research.

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Installation

pip install cinei

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Quick Start

import cinei

# 1. Download input data
cinei.download_ceds(save_dir='/data/CEDS', species=['NMVOC'])
cinei.download_htap_monthly(
    save_dir='/data/HTAP', species=['NMVOC'], year=2017, month=1)
cinei.download_meic_sample(save_dir='/data/MEIC', months=['jan'])

# 2. Run emission integration
output = cinei.emis_union(
    species   = 'NMVOC',     # target species (auto-mapped across inventories)
    month     = 1,           # month as integer 1-12
    year      = 2017,        # target year
    outer_dir = '/data/CEDS',  # global background inventory (CEDS)
    inner_dir = '/data/MEIC',  # regional inventory (MEIC for China)
    save_dir  = '/data/output',
    agg_dir   = '/data/HTAP',  # aggregated sectors (waste/shipping/aviation)
    region    = 'China',     # region of interest
    output_res= 0.25,        # output resolution in degrees
)

# 3. Plot all sectors
cinei.cinei_plot(output, save_path='/data/output/sectors.png')

# 4. Optional: NMVOC speciation into lumped model species
cinei.nmvoc_speciation(nmvoc_nc_path=output, save_dir='/data/output/voc/')

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Key Functions

Function	Description
emis_union()	Core integration: merge outer + inner inventories
cinei_plot()	Plot all 8 sectors + sum in one figure
download_ceds()	Download CEDS v2021 gridded data
download_htap() / download_htap_monthly()	Download HTAP v3 data
download_edgar() / download_edgar_monthly()	Download EDGAR v8.1 data
download_meic_sample()	Download MEIC 2017 sample data
nmvoc_speciation()	Disaggregate NMVOC into lumped model species
check_user_data()	Diagnose user-provided emission files
standardize_netcdf()	Standardize NetCDF to CINEI format
list_regions()	Show supported region presets

emis_union() argument reference

Argument	Type	Description
species	str	Species name, case-insensitive. e.g. 'NMVOC', 'SO2', 'NOx'
month	int or str	Month as integer (1-12), '01', or 'Jan'
year	int or str	Target year, e.g. 2017
outer_dir	str	Directory of global background inventory (CEDS/EDGAR/HTAP/user)
inner_dir	str	Directory of regional inventory (MEIC/user/EDGAR/HTAP)
save_dir	str	Output directory
outer_source	str	Outer inventory type: 'CEDS', 'EDGAR', 'HTAP', 'user'
inner_source	str	Inner inventory type: 'MEIC', 'user', 'EDGAR', 'HTAP'
agg_dir	str	Directory for HTAP aggregated sectors (waste/shipping/aviation)
mapper_path	str	Path to species mapper CSV. Default: bundled
country_shp	str	Path to country shapefile. Default: bundled
province_shp	str	Path to province shapefile. Default: bundled
output_res	float	Output resolution: 0.05, 0.1, 0.25, 0.5
sectors	list or 'all'	Sectors to integrate. Default: all 8 sectors
region	str	Region name: 'China', 'Beijing', 'NCP', 'Germany', etc.
global_domain	bool	If True, use global extent
lon_min/max	float	Manual longitude bounds (when region=None)
lat_min/max	float	Manual latitude bounds (when region=None)
nmvoc_speciation	bool	If True, auto-run VOC speciation after integration

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Supported Inventories

Inventory	Version	Resolution	Coverage
CEDS	v_2021_04_21	0.5°	Global, 1750–2019
MEIC	v1.4	0.25°	China, monthly
HTAP	v3	0.1° / 0.5°	Global, 2000–2018
EDGAR	v8.1	0.1°	Global, 1970–2022

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Citation

If you use CINEI in your research, please cite:

Zhang, Y.: CINEI V1.1, https://doi.org/10.5281/zenodo.15000795, 2025.

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Author

Yijuan Zhang, PhD candidate Institute of Environmental Physics (IUP), University of Bremen and Max Planck Institute for Meteorology (MPI-M), Hamburg, Germany

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Acknowledgements

The development of CINEI was supported by computing resources provided by:

Deutsches Klimarechenzentrum (DKRZ) (German Climate Computing Centre) Project allocation: b123456

DKRZ provides high-performance computing infrastructure for climate and earth system research in Germany and is a key facility for the German climate research community.

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License

MIT License