meta data for this page
Differences
This shows you the differences between two versions of the page.
Both sides previous revisionPrevious revisionNext revision | Previous revisionNext revisionBoth sides next revision | ||
appendices:appendix5_addtional_information [2022/03/05 08:56] – [5.4 Results and Discussion] mielke | appendices:appendix5_additional_information [2022/09/15 13:18] – [References] Fix link hausmann | ||
---|---|---|---|
Line 1: | Line 1: | ||
====== Appendix 5 - Verification with Independent Data ====== | ====== Appendix 5 - Verification with Independent Data ====== | ||
- | ==== 5.1 Introduction ==== | + | ===== 5.1 Introduction |
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 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)], | ||
Line 13: | Line 13: | ||
</ | </ | ||
- | ====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, | 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, | ||
- | ==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 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. | ||
Line 24: | Line 24: | ||
- | ==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 (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 {{: | ||
- | ==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. 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)]. {{: | ||
Line 39: | Line 39: | ||
</ | </ | ||
- | ==5.2.4 The German Moss Survey Data== | + | ====5.2.4 The German Moss Survey Data==== |
Data from the German Moss survey, which is carried out within the larger context of the European moss survey (Frontasyeva et al. 2020)[(Frontasyevaetal2020)]; | Data from the German Moss survey, which is carried out within the larger context of the European moss survey (Frontasyeva et al. 2020)[(Frontasyevaetal2020)]; | ||
- | + | =====5.3 Analysis===== | |
- | + | ||
- | + | ||
- | + | ||
- | ===5.3 Analysis=== | + | |
The analysis of the data for the national totals is carried out on the available time-series/ | The analysis of the data for the national totals is carried out on the available time-series/ | ||
Line 53: | Line 49: | ||
Pearson and the spearman-rank correlation were computed for each, individual time-series pair (verification data and reported national total). These standard similarity measures were computed, to quantify the similarity between the temporal trends of the individual air-pollutant dataset. | Pearson and the spearman-rank correlation were computed for each, individual time-series pair (verification data and reported national total). These standard similarity measures were computed, to quantify the similarity between the temporal trends of the individual air-pollutant dataset. | ||
- | ==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, | ||
Line 66: | Line 62: | ||
- | + | ====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 {{: | 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 {{: | ||
Line 78: | Line 73: | ||
</ | </ | ||
- | ==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)]. {{: | ||
Line 96: | Line 91: | ||
- | ==5.3.4 The German Moss Survey Data== | + | ====5.3.4 The German Moss Survey Data==== |
Data from the German Moss survey, which is carried out within the larger context of the European moss survey (Frontasyeva et al. 2020; Schröder et al., 2019) is also used for verification of the temporal trend of heavy metals in the German inventory. This survey is carried out every 5 years. It has been conducted since 1990. The time series for Germany currently covers 1990, 1995, 2000, 2005 and 2015. The data is sampled in a specified grid following a specific methodology detailed in (Schröder and Nickel, 2019)[(SchroederWetal20199)] and (Schröder et al., 2019)[(SchroederWetal2019)]. Here the median for each heavy metal species per year was calcluated and compared to the reported heavy metal emission value of that year as shown below in {{: | Data from the German Moss survey, which is carried out within the larger context of the European moss survey (Frontasyeva et al. 2020; Schröder et al., 2019) is also used for verification of the temporal trend of heavy metals in the German inventory. This survey is carried out every 5 years. It has been conducted since 1990. The time series for Germany currently covers 1990, 1995, 2000, 2005 and 2015. The data is sampled in a specified grid following a specific methodology detailed in (Schröder and Nickel, 2019)[(SchroederWetal20199)] and (Schröder et al., 2019)[(SchroederWetal2019)]. Here the median for each heavy metal species per year was calcluated and compared to the reported heavy metal emission value of that year as shown below in {{: | ||
Line 107: | Line 102: | ||
</ | </ | ||
- | ====5.4 Results and Discussion==== | + | =====5.4 Results and Discussion===== |
The trend data in figures 3-7 show an overall good agreement of the national totals from the verification data with the national inventory data in the time series, as well as scatter plots. Individual differences in the four datasets for specific air-pollutants are discussed below. | The trend data in figures 3-7 show an overall good agreement of the national totals from the verification data with the national inventory data in the time series, as well as scatter plots. Individual differences in the four datasets for specific air-pollutants are discussed below. | ||
Line 118: | Line 113: | ||
- | 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 {{: | ||
- | 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 {{: | ||
Line 129: | Line 124: | ||
- | 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 PM10, NO2 and SO2 are well correlated with the reported emissions with correlation values above 0.9 as shown in {{: | ||
- | 5.4.4 The German Moss Survey Data | + | ==== 5.4.4 The German Moss Survey Data ==== |
The moss survey data shows exceptional high correlation | The moss survey data shows exceptional high correlation | ||
- | ====References==== | + | =====References===== |
- | [(CrippaMetal2019> | + | [(CrippaMetal2019> |
[(ECMWF2022> | [(ECMWF2022> | ||
[(HausmannKetal2021> | [(HausmannKetal2021> | ||
Line 147: | Line 142: | ||
[(PattersonTetal2022> | [(PattersonTetal2022> | ||
[(RomanoDetal2019> | [(RomanoDetal2019> | ||
- | [(ŠavričBetal2019> | + | [(SavricBetal2019> |
[(SchroederWetal2019> | [(SchroederWetal2019> | ||
[(SchroederWetal20199> | [(SchroederWetal20199> |