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appendices:appendix5_additional_information [2023/03/15 15:01] – [5.1 Introduction] kotzullaappendices:appendix5_additional_information [2024/11/06 13:50] (current) – external edit 127.0.0.1
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 The first recommended dataset is the air-pollution (AP) dataset of the Emission Database for Global Atmospheric Research (EDGAR) of the JRC (Crippa et al., 2019)[(CrippaMetal2019)], which is a worldwide gold standard source of data for global and local air-quality modelling. It contains standard air-pollutants, such as NO<sub>2</sub> or NMVOC. Data for several heavy metals species are, however, missing in this dataset. The second source of data is the mass median data for the German data from the European moss survey (Schröder and Nickel, 2019)[(SchroederWetal20199)], which is also part of the monitoring carried out at the Umweltbundesamt in Germany. Here the median of the mass fraction in moss for each heavy metal species is compared to the reported inventory data. The third dataset used is the Pollution Release and Transfer Register (PRTR) (Umweltbundesamt, 2022)[(Umweltbundesamt2022)]. Details for the PRTR data, as well as the database may be found under: [[https://thru.de/thrude/]]. Here analysis has been split into two parts, first the heavy metal air-pollutants and secondly the ordinary air-pollutants, due to their different mass in the reporting tables. The first recommended dataset is the air-pollution (AP) dataset of the Emission Database for Global Atmospheric Research (EDGAR) of the JRC (Crippa et al., 2019)[(CrippaMetal2019)], which is a worldwide gold standard source of data for global and local air-quality modelling. It contains standard air-pollutants, such as NO<sub>2</sub> or NMVOC. Data for several heavy metals species are, however, missing in this dataset. The second source of data is the mass median data for the German data from the European moss survey (Schröder and Nickel, 2019)[(SchroederWetal20199)], which is also part of the monitoring carried out at the Umweltbundesamt in Germany. Here the median of the mass fraction in moss for each heavy metal species is compared to the reported inventory data. The third dataset used is the Pollution Release and Transfer Register (PRTR) (Umweltbundesamt, 2022)[(Umweltbundesamt2022)]. Details for the PRTR data, as well as the database may be found under: [[https://thru.de/thrude/]]. Here analysis has been split into two parts, first the heavy metal air-pollutants and secondly the ordinary air-pollutants, due to their different mass in the reporting tables.
  
-The most important, modern dataset used in the verification work is available via the Copernicus Atmospheric Monitoring Service, Atmospheric Datastore (CAMS-ADS), which are the CAMS global reanalysis (EAC4) monthly averaged field (ECMWF, 2022)[(ECMWF2022)]. Details of these dataset are detailed in (Inness et al., 2019)[(InnessAetal2019)]. These data provide monthly averaged fields for standard air-pollutants such as NO₂, or particulate matter. Data for more insight into the distribution of heavy metal species are, however, missing. +The most important, modern dataset used in the verification work is available via the Copernicus Atmospheric Monitoring Service, Atmospheric Datastore (CAMS-ADS), which are the CAMS global reanalysis (EAC4) monthly averaged field (ECMWF, 2022)[(ECMWF2022)]. Details of these dataset are detailed in (Inness et al., 2019)[(InnessAetal2019)]. These data provide monthly averaged fields for standard air-pollutants such as NO<sub>2</sub>, or particulate matter. Data for more insight into the distribution of heavy metal species are, however, missing. 
  
 Time series data from all the four datasets are compared to the reported national inventory data time series on the basis of the national totals for Germany. This is done in a visual-quantitative way using plots of the time series data of the national totals, as well as scatter plots between the reported national totals and each of the four sets of data. In addition a quantitative analysis in form of correlation is carried out using standard mathematical similarity operators such as the Pearson and Spearman-Rank correlations, which are widely used to compute similarity between two mathematical vectors.  Time series data from all the four datasets are compared to the reported national inventory data time series on the basis of the national totals for Germany. This is done in a visual-quantitative way using plots of the time series data of the national totals, as well as scatter plots between the reported national totals and each of the four sets of data. In addition a quantitative analysis in form of correlation is carried out using standard mathematical similarity operators such as the Pearson and Spearman-Rank correlations, which are widely used to compute similarity between two mathematical vectors. 
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 ====5.2.2 The CAMS EAC4 data==== ====5.2.2 The CAMS EAC4 data====
  
-The CAMS global reanalysis data products are available from the ECMWF Atmospheric Composition Reanalysis (EAC4) process. They are available as either daily or monthly (ECMWF, 2022)[(ECMWF2022)] data products in either single, or multi-level variants. More detail on the data products and their generation can be found in (Inness et al., 2019)[(InnessAetal2019)]. For the verification work presented here the 0.75°x0.75° monthly averaged fields data product was used, which is available for the time-period from 2003 till 06/2021. Therefore, we used the time-period from 2003 until 2020 for comparison to the German inventory data. The update frequency of this monthly dataset is every six months, carried out by the ECMWF. The CAMS monthly dataset offers total column values for the following major air-pollutants (PM10, PM2.5, NO2, SO2 and several species of NMVOC such as HCHO), which were used in the following for a comparison to the national total values for Germany. An example of the monthly data aggregated to the respective year can be seen in {{:appendices:figure_51.jpg?linkonly| figure 1}}.+The CAMS global reanalysis data products are available from the ECMWF Atmospheric Composition Reanalysis (EAC4) process. They are available as either daily or monthly (ECMWF, 2022)[(ECMWF2022)] data products in either single, or multi-level variants. More detail on the data products and their generation can be found in (Inness et al., 2019)[(InnessAetal2019)]. 
  
 +For the verification work presented here the 0.75°x0.75° monthly averaged fields data product was used, which is available for the time-period from 2003 till 06/2021. Therefore, we used the time-period from 2003 until 2020 for comparison to the German inventory data. The update frequency of this monthly dataset is every six months, carried out by the ECMWF. The CAMS monthly dataset offers total column values for the following major air-pollutants (NO<sub>2</sub>, PM<sub>10</sub>, PM<sub>2.5</sub>, SO<sub>2</sub> and several species of NMVOC such as HCHO), which were used in the following for a comparison to the national total values for Germany. An example of the monthly data aggregated to the respective year can be seen in {{:appendices:figure_51.jpg?linkonly| figure 1}}.
  
 ====5.2.3 The Pollution Release and Transfer Register==== ====5.2.3 The Pollution Release and Transfer Register====
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 [(CrippaMetal2019>Crippa, M. et al. (2019) ‘EDGAR v5.0 Global Air Pollutant Emissions’. Available at: https://data.jrc.ec.europa.eu/dataset/377801af-b094-4943-8fdc-f79a7c0c2d19 (Accessed: 27 February 2022).)] [(CrippaMetal2019>Crippa, M. et al. (2019) ‘EDGAR v5.0 Global Air Pollutant Emissions’. Available at: https://data.jrc.ec.europa.eu/dataset/377801af-b094-4943-8fdc-f79a7c0c2d19 (Accessed: 27 February 2022).)]
 [(ECMWF2022>CAMS global reanalysis (EAC4) monthly averaged fields, Copernicus ADS. Available at: https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4-monthly?tab=overview)] (Accessed: 27 February 2022).)] [(ECMWF2022>CAMS global reanalysis (EAC4) monthly averaged fields, Copernicus ADS. Available at: https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4-monthly?tab=overview)] (Accessed: 27 February 2022).)]
-[(HausmannKetal2021>Hausmann, K., Zagorski, A. and Mielke, C. (2021) Convert ePRTR data to CLRTAP LPS submission. Umweltbundesamt (EPA Germany), V1.6 - Emission situation. Available at: https://github.com/UBA-DE-Emissionsituation/eprtr2lps (Accessed: 8 January 2022).)]+[(HausmannKetal2021>Hausmann, K., Zagorski, A. and Mielke, C. (2021) Convert ePRTR data to CLRTAP LPS submission. Umweltbundesamt (EPA Germany), V1.6 - Emission situation. Available at: https://gitlab.opencode.de/uba-emsit/reporting/eprtr2lps (Accessed: 24 September 2024).)]
 [(InnessAetal2019>Inness, A. et al. (2019) ‘The CAMS reanalysis of atmospheric composition’, Atmospheric Chemistry and Physics, 19(6), pp. 3515–3556. doi:10.5194/acp-19-3515-2019.)] [(InnessAetal2019>Inness, A. et al. (2019) ‘The CAMS reanalysis of atmospheric composition’, Atmospheric Chemistry and Physics, 19(6), pp. 3515–3556. doi:10.5194/acp-19-3515-2019.)]
 [(PattersonTetal2022>Patterson, T. and Kelso, V. (2022) Natural Earth - Free vector and raster map data at 1:10m, 1:50m, and 1:110m scales. Available at: https://www.naturalearthdata.com/ (Accessed: 8 January 2022).)] [(PattersonTetal2022>Patterson, T. and Kelso, V. (2022) Natural Earth - Free vector and raster map data at 1:10m, 1:50m, and 1:110m scales. Available at: https://www.naturalearthdata.com/ (Accessed: 8 January 2022).)]