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. 2020 Apr 17;7(1):121.
doi: 10.1038/s41597-020-0462-2.

High resolution temporal profiles in the Emissions Database for Global Atmospheric Research

Monica Crippa  1 , Efisio Solazzo  2 , Ganlin Huang  3 , Diego Guizzardi  2 , Ernest Koffi  2 , Marilena Muntean  2 , Christian Schieberle  3 , Rainer Friedrich  3 , Greet Janssens-Maenhout  2
Affiliations

High resolution temporal profiles in the Emissions Database for Global Atmospheric Research

Monica Crippa et al. Sci Data. .

Abstract

Emissions into the atmosphere from human activities show marked temporal variations, from inter-annual to hourly levels. The consolidated practice of calculating yearly emissions follows the same temporal allocation of the underlying annual statistics. However, yearly emissions might not reflect heavy pollution episodes, seasonal trends, or any time-dependant atmospheric process. This study develops high-time resolution profiles for air pollutants and greenhouse gases co- emitted by anthropogenic sources in support of atmospheric modelling, Earth observation communities and decision makers. The key novelties of the Emissions Database for Global Atmospheric Research (EDGAR) temporal profiles are the development of (i) country/region- and sector- specific yearly profiles for all sources, (ii) time dependent yearly profiles for sources with inter-annual variability of their seasonal pattern, (iii) country- specific weekly and daily profiles to represent hourly emissions, (iv) a flexible system to compute hourly emissions including input from different users. This work creates a harmonized emission temporal distribution to be applied to any emission database as input for atmospheric models, thus promoting homogeneity in inter-comparison exercises.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Regional aggregation of world countries for yearly profiles mapping.
Fig. 2
Fig. 2
Inter-annual variability of monthly scaling factors over the 2000–2017 time series for the power generation sector for Asian countries (red), North America (light blue), Oceania (dark green) and Latin America (blue). Mean values ± one standard deviation are represented.
Fig. 3
Fig. 3
Inter-annual variability of monthly scaling factors over the 2000–2017 time series for the power generation sector for Central European countries (light green) and Turkey (grey). Mean values ± one standard deviation are represented.
Fig. 4
Fig. 4
Inter-annual variability of monthly scaling factors over the 2000–2017 time series for the power generation sector for Northern European countries (pink). Mean values ± one standard deviation are represented.
Fig. 5
Fig. 5
Inter-annual variability of monthly scaling factors over the 2000–2017 time series for the power generation sector for Southern European countries (yellow) and Western European countries (grey). Mean values ± one standard deviation are represented.
Fig. 6
Fig. 6
Monthly weights for rice-area and for the 23 world regions.
Fig. 7
Fig. 7
Time series (2000–2018) of monthly fossil CO2 emissions by sector in the world.
Fig. 8
Fig. 8
Seasonality of regional fossil CO2 emissions in 2015 (expressed in Mt/month).
Fig. 9
Fig. 9
Seasonality of regional CH4 emissions in 2015 (expressed in Mt/month).
See this image and copyright information in PMC

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