Abstract
Abstract. We present the Copernicus Atmosphere Monitoring Service TEMPOral profiles (CAMS-TEMPO), a dataset of global and European emission temporal profiles that provides gridded monthly, daily, weekly and hourly weight factors for atmospheric chemistry modelling. CAMS-TEMPO includes temporal profiles for the priority air pollutants (NOx; SOx; NMVOC, non-methane volatile organic compound; NH3; CO; PM10; and PM2.5) and the greenhouse gases (CO2 and CH4) for each of the following anthropogenic source categories: energy industry (power plants), residential combustion, manufacturing industry, transport (road traffic and air traffic in airports) and agricultural activities (fertilizer use and livestock). The profiles are computed on a global 0.1 × 0.1∘ and regional European 0.1 × 0.05∘ grid following the domain and sector classification descriptions of the global and regional emission inventories developed under the CAMS programme. The profiles account for the variability of the main emission drivers of each sector. Statistical information linked to emission variability (e.g. electricity production and traffic counts) at national and local levels were collected and combined with existing meteorology-dependent parametrizations to account for the influences of sociodemographic factors and climatological conditions. Depending on the sector and the temporal resolution (i.e. monthly, weekly, daily and hourly) the resulting profiles are pollutant-dependent, year-dependent (i.e. time series from 2010 to 2017) and/or spatially dependent (i.e. the temporal weights vary per country or region). We provide a complete description of the data and methods used to build the CAMS-TEMPO profiles, and whenever possible, we evaluate the representativeness of the proxies used to compute the temporal weights against existing observational data. We find important discrepancies when comparing the obtained temporal weights with other currently used datasets. The CAMS-TEMPO data product including the global (CAMS-GLOB-TEMPOv2.1, https://doi.org/10.24380/ks45-9147, Guevara et al., 2020a) and regional European (CAMS-REG-TEMPOv2.1, https://doi.org/10.24380/1cx4-zy68, Guevara et al., 2020b) temporal profiles are distributed from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/, last access: February 2021).
Highlights
And temporally resolved atmospheric emission inventories are key to investigate and predict the transport and chemical transformation of pollutants, as well as to develop effective mitigation strategies (e.g. Pouliot et al, 2015; Galmarini et al, 2017)
We provide a complete description of the data and methods used to build the Copernicus Atmosphere Monitoring Service (CAMS)-TEMPO profiles, and whenever possible, we evaluate the representativeness of the proxies used to compute the temporal weights against existing observational data
Several datasets even provide emission maps for selected pollutants or study regions with resolutions as fine as 1 × 1 km (e.g. ODIAC2016, Open-source Data Inventory for Anthropogenic CO2, version 2016, Oda et al, 2018; Hestia-LA, Hestia fossil fuel CO2 emissions data product for the Los Angeles megacity; Gurney et al, 2019; Super et al, 2020). This improvement is largely due to the emergence of new detailed, satellitebased and open-access spatial proxies such as the population maps at 1 × 1 km proposed by the Global Human Settlement Layer (GHSL) project (Florczyk et al, 2019), the global land cover maps at 300 × 300 m provided by the European Space Agency Climate Change Initiative (ESA CCI, https: //www.esa-landcover-cci.org/, last access: February 2021) or the georeferenced road traffic network distributed by OpenStreetMap (OSM, http://www.openstreetmap.org, last access: February 2021)
Summary
And temporally resolved atmospheric emission inventories are key to investigate and predict the transport and chemical transformation of pollutants, as well as to develop effective mitigation strategies (e.g. Pouliot et al, 2015; Galmarini et al, 2017). Several datasets even provide emission maps for selected pollutants or study regions with resolutions as fine as 1 × 1 km (e.g. ODIAC2016, Open-source Data Inventory for Anthropogenic CO2, version 2016, Oda et al, 2018; Hestia-LA, Hestia fossil fuel CO2 emissions data product for the Los Angeles megacity; Gurney et al, 2019; Super et al, 2020). This improvement is largely due to the emergence of new detailed, satellitebased and open-access spatial proxies such as the population maps at 1 × 1 km proposed by the Global Human Settlement Layer (GHSL) project (Florczyk et al, 2019), the global land cover maps at 300 × 300 m provided by the European Space Agency Climate Change Initiative (ESA CCI, https: //www.esa-landcover-cci.org/, last access: February 2021) or the georeferenced road traffic network distributed by OpenStreetMap (OSM, http://www.openstreetmap.org, last access: February 2021). Crippa et al (2020) developed a new set of high-resolution temporal profiles for the EDGAR inventory which allows for producing monthly and hourly emission time series and grid maps
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