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appendices:appendix5_additional_information [2022/09/15 13:09] – ↷ Page name changed from appendices:appendix5_addtional_information to appendices:appendix5_additional_information hausmann | appendices:appendix5_additional_information [2024/11/06 13:50] (current) – external edit 127.0.0.1 | ||
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===== 5.1 Introduction ===== | ===== 5.1 Introduction ===== | ||
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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)], | + | |
- | 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 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)], |
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+ | 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< | ||
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, | 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, | ||
The overall goal is to offer a semi-quantitative and qualitative comparison between the reported national totals of air pollution species and the independent datasets using time-series data. A direct sector-based comparison has not been considered, yet, as only EDGAR and PRTR data would offer a sectoral disaggregation of the national total data. | The overall goal is to offer a semi-quantitative and qualitative comparison between the reported national totals of air pollution species and the independent datasets using time-series data. A direct sector-based comparison has not been considered, yet, as only EDGAR and PRTR data would offer a sectoral disaggregation of the national total data. | ||
<figure CAMSEAC4> | <figure CAMSEAC4> | ||
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====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 NO2, PM10, PM2.5, SO2, CO, BC, NMVOC and NH3. 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., 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< |
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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 {{: | + | 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< | ||
====5.2.3 The Pollution Release and Transfer Register==== | ====5.2.3 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)]. {{: | + | 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. |
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+ | 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)]. {{: | ||
<figure PRTRDATA> | <figure PRTRDATA> | ||
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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, | + | EDGAR data was extracted from the national totals spread-sheets, |
+ | The EDGAR data for the eight AP species and a scatter plot are shown in {{: | ||
<figure EDGARDATA> | <figure EDGARDATA> | ||
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<figure CAMSDATA> | <figure CAMSDATA> | ||
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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)]. {{: | + | 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)]. {{: |
<figure PRTRAPDATA> | <figure PRTRAPDATA> | ||
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<figure PRTRHMDATA> | <figure PRTRHMDATA> | ||
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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 {{: | + | The EDGAR inventory usually is in good agreement with the national inventory data as shown in figure {{: |
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==== 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 {{: | The CAMS EAC4 data shows very high correlation values to the reported national totals as shown in {{: | ||
- | PM2.5 and PM10 values of the EAC4 data show a good agreement to the national totals of Germany as shown in figure {{: | + | |
+ | PM< | ||
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==== 5.4.3 The Pollution Release and Transfer Register ==== | ==== 5.4.3 The Pollution Release and Transfer Register ==== | ||
- | Data for PM10, NO2 and SO2 are well correlated with the reported emissions with correlation values above 0.9 as shown in {{: | + | Data for PM< |
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=====References===== | =====References===== | ||
- | [(CrippaMetal2019> | + | [(CrippaMetal2019> |
- | [(ECMWF2022> | + | [(ECMWF2022> |
- | [(HausmannKetal2021> | + | [(HausmannKetal2021> |
[(InnessAetal2019> | [(InnessAetal2019> | ||
[(PattersonTetal2022> | [(PattersonTetal2022> | ||
[(RomanoDetal2019> | [(RomanoDetal2019> | ||
- | [(ŠavričBetal2019> | + | [(SavricBetal2019> |
[(SchroederWetal2019> | [(SchroederWetal2019> | ||
[(SchroederWetal20199> | [(SchroederWetal20199> |