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appendices:appendix5_additional_information [2023/03/15 16:00] – [5.3.3 The Pollution Release and Transfer Register] kotzullaappendices:appendix5_additional_information [2025/04/08 14:48] (current) – [5.4.3 The Pollution Release and Transfer Register] mielke
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 Four independent datasets were used for the verification work for the German Informative Inventory Report (IIR). The selection of these verification data were carried out on the basis of broadly accepted, independent data similar to the good guidelines for verification in the 2019 refinements of the IPCC guidelines for verification (Romano et al., 2019)[(RomanoDetal2019)]. Four independent datasets were used for the verification work for the German Informative Inventory Report (IIR). The selection of these verification data were carried out on the basis of broadly accepted, independent data similar to the good guidelines for verification in the 2019 refinements of the IPCC guidelines for verification (Romano et al., 2019)[(RomanoDetal2019)].
  
-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 NO2 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., 2023)[(CrippaMetal2023)], 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, 2025)[(Umweltbundesamt2025)]. 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, 2024)[(ECMWF2024)]. 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 Methods and Materials===== =====5.2 Methods and Materials=====
  
-Each of the four datasets require separate analysis using specialized python scripts, that have been developed for this verification work. The EDGAR spreadsheet data was used for the extraction of the EDGAR-AP national totals for Germany (Crippa et al., 2019)[(CrippaMetal2019)]. PRTR data were used in form of an sql-database file, which offers all the national reported data on large point sources (Umweltbundesamt, 2022)[(Umweltbundesamt2022)]. CAMS data from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used, which are available in monthly slices in netcdf-Format (Inness et al., 2019)[(InnessAetal2019)]. Data from the German part of the European moss survey (Schröder et al., 2019))[(SchroederWetal2019)]  was kindly provided by Gudrun Schütze from the Umweltbundesamt.+Each of the four datasets require separate analysis using specialized python scripts, that have been developed for this verification work. The EDGAR spreadsheet data was used for the extraction of the EDGAR-AP national totals for Germany (Crippa et al., 2023)[(CrippaMetal2023)]. PRTR data were used in form of an sql-database file, which offers all the national reported data on large point sources (Umweltbundesamt, 2025)[(Umweltbundesamt2025)]. CAMS data from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used, which are available in monthly slices in netcdf-Format (Inness et al., 2019)[(InnessAetal2019)]. Data from the German part of the European moss survey (Schröder et al., 2019))[(SchroederWetal2019)]  was kindly provided by Gudrun Schütze from the Umweltbundesamt.
  
  
 ====5.2.1 The EDGAR AP Inventory==== ====5.2.1 The EDGAR AP Inventory====
  
-EDGAR Inventory Data and its database (Crippa et al., 2019)[(CrippaMetal2019)] are available from the Joint Research Center (JRC) of the European Commission. We used the version 5.0 air-pollution database, which offers annual totals of major air pollutants, as well as gridded emissions, for air pollution modelling. National totals of air-pollutants (AP) were extracted from the EDGAR spreadsheets for verification. The air-pollutants offered in the database are NO<sub>2</sub>, PM<sub>10</sub>, PM<sub>2.5</sub>, SO<sub>2</sub>, CO, BC, NMVOC and NH<sub>3</sub>. The current EDGAR timeseries (Crippa et al., 2019)[(CrippaMetal2019)] covers the time period from 1970 until 2015. The EDGAR AP inventory is frequently update on a longer product cycle.+EDGAR Inventory Data and its database (Crippa et al., 2023)[(CrippaMetal2023)] are available from the Joint Research Center (JRC) of the European Commission. We used the version 8.1 air-pollution database, which offers annual totals of major air pollutants, as well as gridded emissions, for air pollution modelling. National totals of air-pollutants (AP) were extracted from the EDGAR spreadsheets for verification. The air-pollutants offered in the database are NO<sub>2</sub>, PM<sub>10</sub>, PM<sub>2.5</sub>, SO<sub>2</sub>, CO, NMVOC and NH<sub>3</sub>. The current EDGAR timeseries (Crippa et al., 2023)[(CrippaMetal2023)] covers the time period from 1970 until 2023. The EDGAR AP inventory is frequently update on a longer product cycle.
  
  
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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, 2024)[(ECMWF2024)] 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 2022. Therefore, we used the time-period from 2005 until 2022 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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 ====5.3.1 The EDGAR AP Inventory==== ====5.3.1 The EDGAR AP Inventory====
  
-EDGAR data was extracted from the national totals spread-sheets, offered as download from the JRC (Crippa et al., 2019)[(CrippaMetal2019)] using python scripts. The national totals for Germany were extracted as individual time series for the respective air pollutants (NO<sub>2</sub>, PM<sub>10</sub>, PM<sub>2.5</sub>, SO<sub>2</sub>, CO, BC, NMVOC, and NH<sub>3</sub>). +EDGAR data was extracted from the national totals spread-sheets, offered as download from the JRC (Crippa et al., 2023)[(CrippaMetal2023)] using python scripts. The national totals for Germany were extracted as individual time series for the respective air pollutants (NO<sub>2</sub>, PM<sub>10</sub>, PM<sub>2.5</sub>, SO<sub>2</sub>, CO, BC, NMVOC, and NH<sub>3</sub>). 
  
-The EDGAR data for the eight AP species and a scatter plot are shown in {{:appendices:edgar_figure53.jpg?linkonly| figure 3}}. The orange line depicts the time series from the EDGAR national total, whilst the blue line illustrates the national total from the German inventory, together with a scatterplot of all eight individual AP species.+The EDGAR data for the eight AP species and a scatter plot are shown in {{:appendices:iir_figure3.jpg?linkonly| figure 3}}. The orange line depicts the time series from the EDGAR national total, whilst the blue line illustrates the national total from the German inventory, together with a scatterplot of all eight individual AP species.
  
 <figure EDGARDATA> <figure EDGARDATA>
-{{ :appendices:edgar_figure53.jpg?direct&1000 | The upper images show EDGAR time series data plotted versus the reported inventory data of Germany. The lowermost image illustrates the correlation between each of the reported time-series with the respective EDGAR data}}+{{ :appendices:iir_figure3.jpg?direct&1000 | The upper images show EDGAR time series data plotted versus the reported inventory data of Germany. The lowermost image illustrates the correlation between each of the reported time-series with the respective EDGAR data}}
 <caption>The upper images show EDGAR time series data plotted versus the reported inventory data of Germany. The lowermost image illustrates the correlation between each of the reported time-series with the respective EDGAR data.  <caption>The upper images show EDGAR time series data plotted versus the reported inventory data of Germany. The lowermost image illustrates the correlation between each of the reported time-series with the respective EDGAR data. 
 </caption> </caption>
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 ====5.3.2 The CAMS EAC4 Data==== ====5.3.2 The CAMS EAC4 Data====
  
-The monthly averaged CAMS EAC4 data has been aggregated to the spatial scale of Germany with the help of a spatial vector data file, which symbolizes the country area of Germany (Patterson and Kelso, 2022)[(PattersonTetal2022)]. It has been intersected with the CAMS EAC4 data, enabling the cropping of the data pixels to the shape of Germany. The equal earth projection of (Šavrič, Patterson and Jenny, 2019)[(SavricBetal2019)] was used after this procedure to calculate the area of each cell in-order to convert the CAMS EAC4 field data to mass per pixel and month. These monthly time slices were summed up for all twelve months to retrieve the national total for the respective AP species of each year. The data is shown in {{:appendices:cams_figure_54.jpg?linkonly| figure 4}}.+The monthly averaged CAMS EAC4 data has been aggregated to the spatial scale of Germany with the help of a spatial vector data file, which symbolizes the country area of Germany (Patterson and Kelso, 2022)[(PattersonTetal2022)]. It has been intersected with the CAMS EAC4 data, enabling the cropping of the data pixels to the shape of Germany. The equal earth projection of (Šavrič, Patterson and Jenny, 2019)[(SavricBetal2019)] was used after this procedure to calculate the area of each cell in-order to convert the CAMS EAC4 field data to mass per pixel and month. These monthly time slices were summed up for all twelve months to retrieve the national total for the respective AP species of each year. The data is shown in {{:appendices:iir_figure_eac4.jpg?linkonly| figure 4}}.
  
  
 <figure CAMSDATA> <figure CAMSDATA>
-{{ :appendices:cams_figure_54.jpg?direct&1000 | The upper images show yearly aggregated CAMS time series data plotted versus the reported inventory data of Germany. The lowermost image illustrates the correlation between each of the reported time-series with the respective CAMS data. }}+{{ :appendices:iir_figure_eac4.jpg?direct&1000 | The upper images show yearly aggregated CAMS time series data plotted versus the reported inventory data of Germany. The lowermost image illustrates the correlation between each of the reported time-series with the respective CAMS data. }}
 <caption>The upper images show yearly aggregated CAMS time series data plotted versus the reported inventory data of Germany. The lowermost image illustrates the correlation between each of the reported time-series with the respective CAMS data.  <caption>The upper images show yearly aggregated CAMS time series data plotted versus the reported inventory data of Germany. The lowermost image illustrates the correlation between each of the reported time-series with the respective CAMS data. 
 </caption> </caption>
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 ====5.3.3 The Pollution Release and Transfer Register==== ====5.3.3 The Pollution Release and Transfer Register====
  
-The PRTR database is an SQL-Database file, which is available for download at the domain thru.de. The data is compiled and curated by the Umweltbundesamt in Germany. It compiles data, which are reported for the large emission sources in Germany, which are e.g.: power plants, smelters, or plants from the chemical industry. The European Union Regulation No 166/2006 on the establishment of a PRTR register governs the process of PRTR data compilation. A modified Python script was used, to extract data from the PRTR database. The tool is based on the PRTR reporting tool of (Hausmann, Zagorski and Mielke, 2021)[(HausmannKetal2021)]. {{:appendices:prtr_figure52.jpg?linkonly| Figure 2}} shows the data of PRTR point sources extracted from the PRTR database file for visualization. We used the PRTR data from 2007 till 2018 for our verification work for the air pollutants: CO, NO<sub>2</sub>, SO<sub>2</sub>, NMVOC, PM<sub>10</sub> and NH<sub>3</sub>. Data from the heavy-metals species: Cd, Ni, Zn, As, Hg, Cr, Cu and Pb were also used for the here presented verification work of the national totals of Germany. +The PRTR database is an SQL-Database file, which is available for download at the domain thru.de. The data is compiled and curated by the Umweltbundesamt in Germany. It compiles data, which are reported for the large emission sources in Germany, which are e.g.: power plants, smelters, or plants from the chemical industry. The European Union Regulation No 166/2006 on the establishment of a PRTR register governs the process of PRTR data compilation. A modified Python script was used, to extract data from the PRTR database. The tool is based on the PRTR reporting tool of (Hausmann, Zagorski and Mielke, 2021)[(HausmannKetal2021)]. {{:appendices:prtr_figure52.jpg?linkonly| Figure 2}} shows the data of PRTR point sources extracted from the PRTR database file for visualization. We used the PRTR data from 2007 till 2022 for our verification work for the air pollutants: CO, NO<sub>2</sub>, SO<sub>2</sub>, NMVOC, PM<sub>10</sub> and NH<sub>3</sub>. Data from the heavy-metals species: Cd, Ni, Zn, As, Hg, Cr, Cu and Pb were also used for the here presented verification work of the national totals of Germany. 
  
  
 <figure PRTRAPDATA> <figure PRTRAPDATA>
-{{ :appendices:prtr_ap_plots_figure55.jpg?direct&1000 | The upper images show yearly time series data for air-pollutants extracted from the PRTR database plotted versus the reported inventory data of Germany. The lowermost image illustrates the correlation between each of the reported time-series with the respective AP species from the PRTR data.  }}+{{ :appendices:figure_aps_prtr.jpg?direct&1000 | The upper images show yearly time series data for air-pollutants extracted from the PRTR database plotted versus the reported inventory data of Germany. The lowermost image illustrates the correlation between each of the reported time-series with the respective AP species from the PRTR data.  }}
 <caption>The upper images show yearly time series data for air-pollutants extracted from the PRTR database plotted versus the reported inventory data of Germany. The lowermost image illustrates the correlation between each of the reported time-series with the respective AP species from the PRTR data.  <caption>The upper images show yearly time series data for air-pollutants extracted from the PRTR database plotted versus the reported inventory data of Germany. The lowermost image illustrates the correlation between each of the reported time-series with the respective AP species from the PRTR data. 
 </caption> </caption>
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 <figure PRTRHMDATA> <figure PRTRHMDATA>
-{{ :appendices:prtr_hm_plots_figure56.jpg?direct&1000 |  }}+{{ :appendices:figure_hm_prtr.jpg?direct&1000 |  }}
 <caption>The upper images show yearly time series data for heavy metals extracted from the PRTR database plotted versus the reported inventory data of Germany. The lowermost image illustrates the correlation between each of the reported time-series with the respective HM species from the PRTR data.  <caption>The upper images show yearly time series data for heavy metals extracted from the PRTR database plotted versus the reported inventory data of Germany. The lowermost image illustrates the correlation between each of the reported time-series with the respective HM species from the PRTR data. 
 </caption> </caption>
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 <figure CORRDATA> <figure CORRDATA>
-{{:appendices:correlations_figure58.jpg?direct&800|Alt-Text}}+{{:appendices:corcoeff.png?direct&800|Alt-Text}}
 <caption>Here the tabulated results for the correlation analysis between the individual datasets and the respective reported emissions time series are shown as individual blocks.  <caption>Here the tabulated results for the correlation analysis between the individual datasets and the respective reported emissions time series are shown as individual blocks. 
 </caption> </caption>
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 ==== 5.4.1 The EDGAR Inventory ==== ==== 5.4.1 The EDGAR Inventory ====
  
-The EDGAR inventory usually is in good agreement with the national inventory data as shown in figure {{:appendices:edgar_figure53.jpg?linkonly| figure 3}}. Close, almost perfect matches of the EDGAR national totals with the reported inventory data can be found in case of SO<sub>2</sub>, NMVOC, NO<sub>2</sub>, PM<sub>2.5</sub> and PM<sub>10</sub>. Slight deviations with a convergence around 2015 exist for CO and black carbon. Ammonia data from EDGAR are considerably higher than the reported national totals (~200 kt difference). This needs to be further investigated in future disaggregated, detailed analysis, which is not offered in this report, yet. The scatterplot shows similar exceptional correlations between the EDGAR data and the reported national totals of Germany as shown in the lowermost figure of  +The EDGAR inventory usually is in good agreement with the national inventory data as shown in figure {{:appendices:iir_figure3.jpg?linkonly| figure 3}}. Close, almost perfect matches of the EDGAR national totals with the reported inventory data can be found in case of SO<sub>2</sub>, NMVOC, NO<sub>2</sub>, PM<sub>2.5</sub> and PM<sub>10</sub>. Slight deviations with a convergence around 2015 exist for CO. Ammonia data from EDGAR are considerably higher than the reported national totals (~200 kt difference). This needs to be further investigated in future disaggregated, detailed analysis, which is not offered in this report, yet. The scatterplot shows similar exceptional correlations between the EDGAR data and the reported national totals of Germany as shown in the lowermost figure of  
-{{:appendices:edgar_figure53.jpg?linkonly| figure 3}}. The correlation values for the individual time series of the EDGAR data towards the inventory data can be found in {{:appendices:correlations_figure58.jpg?linkonly| figure 8}}. Here correlation values are relatively high above 0.8-0.9 with the already discussed exception of the ammonia data, as shown in {{:appendices:edgar_figure53.jpg?linkonly| figure 3}}.+{{:appendices:iir_figure3.jpg?linkonly| figure 3}}. The correlation values for the individual time series of the EDGAR data towards the inventory data can be found in {{:appendices:corcoeff.png?linkonly| figure 8}}. Here correlation values are relatively high above 0.8-0.9 with the already discussed exception of the ammonia data, as shown in {{:appendices:iir_figure3.jpg?linkonly| figure 3}}.
  
 ==== 5.4.2 The CAMS EAC4 Data ==== ==== 5.4.2 The CAMS EAC4 Data ====
  
-The CAMS EAC4 data shows very high correlation values to the reported national totals as shown in {{:appendices:correlations_figure58.jpg?linkonly| figure 8}}., all correlation values are above 0.9 with the exception of formaldehyde (HCHO). The low values here for formaldehyde are compared to the much higher national total values for NMVOC. This shows that formaldehyde on its own is not a sufficient proxy for NMVOC in case of Germany. +The CAMS EAC4 data shows very high correlation values to the reported national totals as shown in {{:appendices:corcoeff.png?linkonly| figure 8}}., all correlation values are above 0.9 with the exception of formaldehyde (HCHO). The low values here for formaldehyde are compared to the much higher national total values for NMVOC. This shows that formaldehyde is anticorelated with NMVOC in case of Germany.
- +
-PM<sub>2.5</sub> and PM<sub>10</sub> values of the EAC4 data show a good agreement to the national totals of Germany as shown in figure {{:appendices:cams_figure_54.jpg?linkonly| figure 4}}. The higher values of the CAMS NO<sub>2</sub>  data and the reported NO<sub>2</sub> data in the inventory are due to the fact that the here shown CAMS NO<sub>2</sub> (ECMWF, 2022)[(ECMWF2022)] data has been compiled as the sum of the total columns of the CAMS EAC4 NO, NO<sub>2</sub> and HNO<sub>3</sub> data product. The NO2 data alone or even the sum of all NO<sub>x</sub> total column products would yield significantly lower values as reported in the national totals of Germany. Only the sum of all NO<sub>x</sub> related chemical species yields a value, which is close to the reported national totals of the inventory.+
  
 +PM<sub>2.5</sub> and PM<sub>10</sub> values of the EAC4 data show a good agreement to the trend of the national totals of Germany as shown in figure {{:appendices:iir_figure_eac4.jpg?linkonly| figure 4}}, however, both species show an offset. 
  
  
 ==== 5.4.3 The Pollution Release and Transfer Register ==== ==== 5.4.3 The Pollution Release and Transfer Register ====
  
-Data for PM<sub>10</sub>, NO<sub>2</sub> and SO<sub>10</sub> are well correlated with the reported emissions with correlation values above 0.9 as shown in {{:appendices:correlations_figure58.jpg?linkonly| figure 8}}. This is also shown in the scatterplots and trend diagrams of {{:appendices:prtr_ap_plots_figure55.jpg?linkonly| figure 5}}. NMVOC and CO show moderate correlation values above 0.7, whilst ammonia data shows almost no correlation. For the heavy metals As and Hg correlation values above 0.8 are shown in {{:appendices:correlations_figure58.jpg?linkonly| figure 8}}, whilst only moderate correlation values exist for Pb and Ni (around 0.5), whilst Cu, Cr, Zn and Ni show almost no correlation, which is also visible in the scatter plot of {{:appendices:prtr_hm_plots_figure56.jpg?linkonly| figure 6}}. +Data for PM<sub>10</sub>, NO<sub>2</sub> and SO<sub>2</sub> are well correlated with the reported emissions with correlation values above 0.9 as shown in {{:appendices:correlations_figure58.jpg?linkonly| figure 8}}. This is also shown in the scatterplots and trend diagrams of {{:appendices:prtr_ap_plots_figure55.jpg?linkonly| figure 5}}. NMVOC and CO show moderate correlation values above 0.5. For the heavy metals As and Hg correlation values above 0.8 are shown in {{:appendices:corcoeff.png?linkonly| figure 8}}, whilst only moderate correlation values exist for Pb and Ni (around 0.5), whilst Cu, Cr, Zn show almost no correlation, which is also visible in the scatter plot of {{:appendices:prtr_hm_plots_figure56.jpg?linkonly| figure 6}}. 
  
  
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 =====References===== =====References=====
  
-[(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).)] +[(CrippaMetal2023>Crippa, M. et al. (2023) ‘EDGAR v8.1 Global Air Pollutant Emissions’. Available at: https://edgar.jrc.ec.europa.eu/index.php/dataset_ap81 (Accessed: 27 February 2025).)] 
-[(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).)] +[(ECMWF2024>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).)]
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