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 Information on the spatial distribution of emissions is important to answer a number of questions in the field of air quality monitoring. Emission data is used to model the dispersion of air pollutants or to visualize the structure of the spatial distribution of emissions. These models show if abatement strategies were successful. For this reason, an ESRI ArcGIS based software has been developed which allows the UBA, independently and on the basis of information generally available, to regularly generate regionalized emission datasets for the complete area of the Federal Republic of Germany. Information on the spatial distribution of emissions is important to answer a number of questions in the field of air quality monitoring. Emission data is used to model the dispersion of air pollutants or to visualize the structure of the spatial distribution of emissions. These models show if abatement strategies were successful. For this reason, an ESRI ArcGIS based software has been developed which allows the UBA, independently and on the basis of information generally available, to regularly generate regionalized emission datasets for the complete area of the Federal Republic of Germany.
  
-The following pollutants are currently considered: NO<sub>x</sub>, NH<sub>3</sub>, SO<sub>2</sub>, CO, NMVOC, particles (PM<sub>2.5</sub>, PM<sub>10</sub>,TSP, and BC) and  Heavy Metals (HM), POP (PAH, HCB, PCB, PCDD/PCDF - dioxins/ furans).+The following pollutants are currently considered: NO<sub>x</sub>, NH<sub>3</sub>, SO<sub>2</sub>, CO, NMVOC, particles (PM<sub>2.5</sub>, PM<sub>10</sub>,TSP, and BC) and  Heavy Metals (HM), POPs (PAHs, HCB, PCBs, PCDD/F - dioxins/furans).
  
-The next update of gridded emissions will be with the 2025 submission. 
  
 ===== Methodology ===== ===== Methodology =====
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 Distribution parameters are used for allocation of national emissions, spatially as accurately as possible, to individual point, line or area sources depending on the source group. The location of a point source is given clearly by coordinates; typical line sources are, for example, streets, which can consist of many sections. As surface sources such areas are defined in which from many small sources emissions are released,  for example, emissions from small combustion plants in built-up areas. A substantial database for distribution of national emissions in the sectors ‘energy supply’ and ‘industry’ are the emissions of individual sites or plants from the PRTR database. In addition, for example, also emissions of air traffic are allocated to point sources by location of the airports, whereas in the case of larger airports an additional local distribution is considered. Emissions from road traffic, rail traffic and inland water navigation are spatially assigned to line sources. The respective route networks consist of individual sections. To each of these network segments (line source) a share of the national emissions is assigned.  Distribution parameters are used for allocation of national emissions, spatially as accurately as possible, to individual point, line or area sources depending on the source group. The location of a point source is given clearly by coordinates; typical line sources are, for example, streets, which can consist of many sections. As surface sources such areas are defined in which from many small sources emissions are released,  for example, emissions from small combustion plants in built-up areas. A substantial database for distribution of national emissions in the sectors ‘energy supply’ and ‘industry’ are the emissions of individual sites or plants from the PRTR database. In addition, for example, also emissions of air traffic are allocated to point sources by location of the airports, whereas in the case of larger airports an additional local distribution is considered. Emissions from road traffic, rail traffic and inland water navigation are spatially assigned to line sources. The respective route networks consist of individual sections. To each of these network segments (line source) a share of the national emissions is assigned. 
  
-The spatial distribution of the emissions that are not distributed to point sources or line sources, is carried out in two steps on area sources. In the first step, these emissions are distributed by means of suitable distribution parameters to the district level.In the second step a more accurate spatial allocation of emissions using land cover data is carried out within the districts. Per NFR sector the areas of the relevant land use classes are chosen and only to these areas emissions are allocated. Here, emphasis can also be placed on different CLC groups, e. g. the land-cover class ‘residential areas’ could get a higher rating than land-cover class ‘residential areas loose’. As a result, the previously at district level distributed national emissions are now spatially localized to the relevant land-cover areas within the districts.Aim of the spatial distribution of emissions is the compilation of emissions in a defined grid. For this, the emissions, spatially distributed to individual point, line and area sources, are assigned to the grid cells of the selected grid in a further step. After determination of the coordinate reference system and grid size of the raster, the share of each emission source (point / line / area source) per grid cell is determined. The summation of the emissions of all source shares lying within a grid cell leads to the total of emissions of the grid cell. The spatial distribution of emissions is not only limited to horizontal distribution,  but also includes distribution to vertical height levels. Therefore, it was necessary to assign to each source category or to each NFR sector an average characteristic emission height above ground. +The spatial distribution of the emissions that are not distributed to point sources or line sources, is carried out in two steps on area sources. In the first step, these emissions are distributed by means of suitable distribution parameters to the district level.In the second step a more accurate spatial allocation of emissions using land cover data is carried out within the districts. Per NFR sector the areas of the relevant land use classes are chosen and only to these areas emissions are allocated. 
  
-For further description of the distribution parameters, please refer to {{ :general:gridded_data:greta_nfr_verteilparameter_20240911.xlsx |}}+Here, emphasis can also be placed on different CLC groups, e. g. the land-cover class ‘residential areas’ could get a higher rating than land-cover class ‘residential areas loose’. As a result, the previously at district level distributed national emissions are now spatially localized to the relevant land-cover areas within the districts.Aim of the spatial distribution of emissions is the compilation of emissions in a defined grid. For this, the emissions, spatially distributed to individual point, line and area sources, are assigned to the grid cells of the selected grid in a further step.  
 +After determination of the coordinate reference system and grid size of the raster, the share of each emission source (point / line / area source) per grid cell is determined. The summation of the emissions of all source shares lying within a grid cell leads to the total of emissions of the grid cell. The spatial distribution of emissions is not only limited to horizontal distribution,  but also includes distribution to vertical height levels. Therefore, it was necessary to assign to each source category or to each NFR sector an average characteristic emission height above ground.  
 + 
 +<WRAP center round info 80%> 
 +For further description of the distribution parameters, please refer to {{ :general:gridded_data:greta_nfr_verteilparameter_20240911.xlsx | distribution parameters }} 
 + 
 +Further and more recent Greta improvements are described in additional reports (German only): Pelzer et al. (2018)[(PELZER2018)], Pelzer et al. (2021)[(PELZER2021)], and Pelzer et al. (2024)[(PELZER2024)]. 
 +</WRAP>
  
-Further and more recent Greta improvements are described in additional reports (German only): {{ :general:gridded_data:2019_01_14_doku_greta_erweiterung2018_190114.pdf | 
-Pelzer et al. (2018) [(PELZER2018)] [1]}}, {{ :general:gridded_data:2021_05_17_doku_greta_erweiterung_210517.pdf |Pelzer et al. (2021)[(PELZER2021)] [2]}} and {{ :general:gridded_data:2024_06_20_doku_greta_weiterentwicklung_2023_2024.pdf |Pelzer et al. (2024) [(PELZER2024)][3]}} 
  
 ===== Distribution Parameters ===== ===== Distribution Parameters =====
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 ==== Traffic or Transport ==== ==== Traffic or Transport ====
    
-For the traffic or transport sector (road, rail, shipping, aviation), at UBA emissions are being determined by means of the TREMOD model. These data are available in a more differentiated way than they are shown per NFR sector in national emissions. Therefore, suitable additional information from TREMOD for the spatial distribution is considered in the Gridding Tool. For the spatial distribution of national emissions of aviation, in addition to the national totals, additional TREMOD emission data for the 26 largest airports are available. These emissions are spatially allocated directly to their position. For the remaining smaller airports and landing sites in Germany, the national residual emissions from aviation,  which are not listed in the TREMOD data separately for each airport, are spatially distributed over the number of flight movements per airport. The location of airports is known as a point source. In addition, for the 15 largest (international) airports in Germany the landing and departure sectors were digitized as funnel-shaped three-dimensional sources. This allows a much differentiated spatial distribution of the emissions to local (three-dimensional) sources for these airports. Emissions of the source group Road Traffic are composed of exhaust emissions,  emissions from abrasions (tires, brakes, roads) and emissions due to fuel evaporation. The exhaust and abrasion emissions from road traffic are fully distributed over line sources,  since a digital geometric data basis exists for all roads. The distribution parameter for spatial distribution of emissions derived from data on mileage per route section. For this purpose, data was processed from different data sources. The evaporative emissions are spatially distributed over area sources to the built-up areas. For rail traffic,  emissions resulting from operation of diesel locomotives are reported. Abrasion-emissions caused by both, electrically and diesel-powered trains are currently not included in the reported emissions and are therefore not taken into account in the sector here under consideration. The distribution of emissions of rail transport is carried out entirely on line sources. The geometric base is the rail network and significant data base for the derivation of the distribution parameters are the section-related emissions of DB Umwelt AG. Also, the emissions of shipping traffic are completely spatially distributed on line sources. For this purpose, the digital routing network of watercourses as well as the distribution parameters derived from emission data from TREMOD are being applied. +For the traffic or transport sector (road, rail, shipping, aviation), at UBA emissions are being determined by means of the TREMOD model. These data are available in a more differentiated way than they are shown per NFR sector in national emissions. Therefore, suitable additional information from TREMOD for the spatial distribution is considered in the Gridding Tool.  
 + 
 +For the spatial distribution of national emissions of aviation, in addition to the national totals, additional TREMOD emission data for the 26 largest German airports are available. These emissions are spatially allocated directly to their position. For the remaining smaller airports, airfields and landing sites in Germany, the national residual emissions from aviation,  which are not listed in the TREMOD data separately for each airport, are spatially distributed over the number of flight movements per airport. The location of airports is known as a point source. In addition, for the 15 largest (international) airports in Germany the landing and departure sectors were digitized as funnel-shaped three-dimensional sources. This allows a much differentiated spatial distribution of the emissions to local (three-dimensional) sources for these airports.  
 + 
 +Emissions of the source group Road Traffic are composed of exhaust emissions, emissions from abrasion (tires, brakes, roads) and emissions due to fuel evaporation. The exhaust and abrasive emissions from road traffic are fully distributed over line sources, since a digital geometric data basis exists for all roads. The distribution parameter for spatial distribution of emissions is derived from data on mileage per route section. For this purpose, data was processed from different data sources. The evaporative emissions are spatially distributed over area sources to the built-up areas.  
 + 
 +For rail traffic, emissions resulting from both the operation of diesel engines as well as the abrasion caused by both electrically and diesel-powered trains are taken into account. The distribution of these emissions is carried out entirely on line sources based on the rail network with the section-related emissions of the DB Umwelt AGsignificant being an important data base for the derivation of the distribution parameters.  
 + 
 +As described for railways, emissions from national navigation (shipping) are completely spatially distributed on line sources, too. For this purpose, the digital routing network of watercourses as well as the distribution parameters derived from emission data from TREMOD are being applied. 
  
 ==== Offroad / Mobile Machinery ==== ==== Offroad / Mobile Machinery ====
  
-This source group includes emissions which are released by the off-road traffic (e.g. in the building and construction industry,  forestry and agriculture) and by the use of mobile devices and machines. The emissions from these source categories are allocated completely as area sources. The distribution parameters are mainly based on statistical data at district level.+This source group includes emissions released by off-road vehicles and mobile machinery (e.g. in the building and construction industry, forestry and agriculture) and by the use of mobile devices and machines in households. Emissions from these sources are allocated as area sources completely. The distribution parameters are mainly based on statistical data at district level.
  
 ==== Solvents and other Product Use ==== ==== Solvents and other Product Use ====
  
-Emissions that are released by application of solvent-based and other products in the private sector as well as in industrial and other sectors, are fully distributed as area sources. The distribution parameters are predominantly based on statistical data at district level,  e.g. employees in economic departments of G-U (Trade and Services) or inhabitants.+Emissions that are released by application of solvent-based and other products in the private sector as well as in industrial and other sectors, are fully distributed as area sources. The distribution parameters are predominantly based on statistical data at district level, e.g. employees in economic departments of G-U (Trade and Services) or inhabitants.
  
 ==== Agriculture ==== ==== Agriculture ====
  
-Emissions from agriculture consist of the emissions arising from animal husbandry (e.g. cows, pigsetc.), and the emissions that occur during agricultural activity on arable land and pastures. An important data source for spatial distribution is the data of the Thünen Institute, which annually determines the emissions from agriculture at district level for Germany. In addition, emissions from stables that underlay reporting obligations are reported in the PRTR database. They only cover a small proportion of national emissions in agriculture. Therefore, a synthetic stable point source dataset was derived from the German Land Cover dataset (LBM-DE) to distribute the stable (and storage) emissions directly to the stables as point source (PQ). In the past these emissions were distributed to agricultural areas (FQ). To derive the distribution parameters used in the Gridding Tool for the affected NFR sectors, the data of the Thünen Institute, from the PRTR database and synthetic stable point source dataset were considered.+Emissions from agriculture consist of the emissions arising from animal husbandry (e.g. cows, pigsetc.), and emissions that occur during agricultural activity on arable land and pastures. 
 + 
 +An important data source for spatial distribution is the data of the Thünen Institute, which annually determines the emissions from agriculture at district level for Germany. In addition, emissions from stables that underlay reporting obligations are reported in the PRTR database. They only cover a small proportion of national emissions in agriculture. Therefore, a synthetic stable point source dataset was derived from the German Land Cover dataset (LBM-DE) to distribute the stable (and storage) emissions directly to the stables as point source (PQ). In the past these emissions were distributed to agricultural areas (FQ). To derive the distribution parameters used in the Gridding Tool for the affected NFR sectors, the data of the Thünen Institute, from the PRTR database and synthetic stable point source dataset were considered.
  
 ==== Other NFR Sectors ==== ==== Other NFR Sectors ====
  
-There are some more NFR sectors, e.g.: 1.A.4.c iii (national fishing), 1B2av (distribution of oil products) and 1.A.3.e i (pipeline compressors), which do not belong to the source groups already described. The distribution parameters for these sectors are based on different data; emissions are predominantly spatially distributed as area sources.+There are some more NFR sectors, e.g. 1.A.4.c iii (national fishing), 1.B.2a v (distribution of oil products) and 1.A.3.e i (pipeline compressors), which do not belong to the source groups already described. The distribution parameters for these sectors are based on different data; emissions are predominantly spatially distributed as area sources.
  
  
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 The results are available via the Central Data Repository CDR maintained by the [[https://cdr.eionet.europa.eu/de/un/clrtap/gridded|EEA/EIONET]]. The results are available via the Central Data Repository CDR maintained by the [[https://cdr.eionet.europa.eu/de/un/clrtap/gridded|EEA/EIONET]].
-In 2019, the calculation tools for the gridding data were updated. In 2025, new data were stored on the CDR for the years 1990, 1995, 2000, 2005, 2010, 2015, 2019 and 2023(see the data). +In 2019, the calculation tools for the gridding data were updated. In 2025, new data were stored on the CDR for the years 1990, 1995, 2000, 2005, 2010, 2015, 2019 and 2023. 
  
 The spatial resolution of reported emissions changed from a 50  x 50 km<sup>2</sup> EMEP to a 0.1° × 0.1° long-lat grid in a geographic coordinate system (WGS84). The change should improve the monitoring quality. The new EMEP domain covers the geographic area between 30°N-82°N latitude and 30°W-90°E longitude. More information about the grid development is available under EMEP grid. The spatial resolution of reported emissions changed from a 50  x 50 km<sup>2</sup> EMEP to a 0.1° × 0.1° long-lat grid in a geographic coordinate system (WGS84). The change should improve the monitoring quality. The new EMEP domain covers the geographic area between 30°N-82°N latitude and 30°W-90°E longitude. More information about the grid development is available under EMEP grid.
  
-In 2025, the EMEP grid, covering only the landmass of Germany, was extended to cover the domain of the baltic and north sea where emissions of the sector national navigation (1.A.3.d ii) are reported (see the data).+In 2025, the EMEP grid, covering only the landmass of Germany, was extended to cover the domain of the baltic and north sea where emissions of the sector national navigation (1.A.3.d ii) are reported.
  
 ==== Maps ==== ==== Maps ====
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 From 2000 onwards, information about point sources is available from the German PRTR or the EPER. For the earlier years 1990 and 1995, information from point sources were transferred from EPER data of the year 2001 and their emissions were scaled accordingly. From 2000 onwards, information about point sources is available from the German PRTR or the EPER. For the earlier years 1990 and 1995, information from point sources were transferred from EPER data of the year 2001 and their emissions were scaled accordingly.
 By presenting the spatial distribution of emissions, the emission hotspots can be precisely identified for all pollutants. In general, these are located in the German cities (e.g. Berlin, Munich or Hamburg) or the conurbations (district of the Rhine-Ruhr area). By presenting the spatial distribution of emissions, the emission hotspots can be precisely identified for all pollutants. In general, these are located in the German cities (e.g. Berlin, Munich or Hamburg) or the conurbations (district of the Rhine-Ruhr area).
 +
 The reduction measures of SO<sub>2</sub> emissions are a success story in itself. In the early 1970s, the use of flue gas desulphurization plants in coal-fired power plants and later brown coal power plants led to a significant SO<sub>2</sub> decrease in the air. Since the 1990s, this reduction process has been further advanced by the use of low-sulfur fuels, so that today only a few areas are contaminated with SO<sub>2</sub>. The reduction measures of SO<sub>2</sub> emissions are a success story in itself. In the early 1970s, the use of flue gas desulphurization plants in coal-fired power plants and later brown coal power plants led to a significant SO<sub>2</sub> decrease in the air. Since the 1990s, this reduction process has been further advanced by the use of low-sulfur fuels, so that today only a few areas are contaminated with SO<sub>2</sub>.
  
-{{ :general:gridded_data:so2_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:so2_emissions_1990_2023_sub2025.png?nolink800 | Sulphur dioxide emissions 1990-2023 }}
  
 NO<sub>x</sub> and CO emissions are not only generated in the energy but also in the transport sector. This is easily recognisable from the motorway structure. NO<sub>x</sub> and CO emissions are not only generated in the energy but also in the transport sector. This is easily recognisable from the motorway structure.
  
  
-{{ :general:gridded_data:nox_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:nox_emissions_1990_2023_sub2025.png?nolink800  |  Nitrogen oxides emissions 1990-2023 }}
  
-{{ :general:gridded_data:co_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:co_emissions_1990_2023_sub2025.png?nolink800 | Carbon monoxide emissions 1990-2023 }}
  
 The main emitters of NMVOC are the industrial process sector and agriculture. The latter is mainly assigned to area and not to point sources. The main emitters of NMVOC are the industrial process sector and agriculture. The latter is mainly assigned to area and not to point sources.
  
-{{ :general:gridded_data:nmvoc_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:nmvoc_emissions_1990_2023_sub2025.png?nolink800 | NMVOC emissions 1990-2023 }}
  
 Compared to the above mentioned air pollutants, drastic reduction of ammonia emissions did not occur in the last decades and abatement measures are still a political issue. The highest ammonia emissions occur in rural areas, especially in the north-west of Germany. The emissions from intensive livestock farming (point sources) are clearly visible in the graphics. Compared to the above mentioned air pollutants, drastic reduction of ammonia emissions did not occur in the last decades and abatement measures are still a political issue. The highest ammonia emissions occur in rural areas, especially in the north-west of Germany. The emissions from intensive livestock farming (point sources) are clearly visible in the graphics.
  
-{{ :general:gridded_data:nh3_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:nh3_emissions_1990_2023_sub2025.png?nolink800 | Ammonia emissions 1990-2023 }}
  
 === Particle and Fine Particle Emissions === === Particle and Fine Particle Emissions ===
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 Corresponding to the SO<sub>2</sub> emissions, total suspended particles (TSP) in general could be reduced by additional built-in filters in power plants as well as in vehicles.  Corresponding to the SO<sub>2</sub> emissions, total suspended particles (TSP) in general could be reduced by additional built-in filters in power plants as well as in vehicles. 
  
-{{ :general:gridded_data:tsp_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:tsp_emissions_1990_2023_sub2025.png?nolink800 | emissions of Total Suspened Particles (TSP) 1990-2023 }}
  
 With a decision of the Member States in 2006, PM<sub>10</sub>, PM<sub>2.5</sub> and Black Carbon emissions for the years before 2000 are not mandatory in the reporting. In the 1990s, the sampling and analysis of particulate matter differed widely and a comparability was therefore not given. For this reason, the data was only scaled back to 1995. With a decision of the Member States in 2006, PM<sub>10</sub>, PM<sub>2.5</sub> and Black Carbon emissions for the years before 2000 are not mandatory in the reporting. In the 1990s, the sampling and analysis of particulate matter differed widely and a comparability was therefore not given. For this reason, the data was only scaled back to 1995.
  
-{{ :general:gridded_data:pm10_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:pm10_emissions_1990_2023_sub2025.png?nolink800 | PM10 emissions 1990-2023 }}
  
 For PM<sub>2.5</sub> and TSP, the point source emissions are calculated using the emission ratio between PM<sub>10</sub> and TSP / PM<sub>2.5</sub> sector by sector. For PM<sub>2.5</sub> and TSP, the point source emissions are calculated using the emission ratio between PM<sub>10</sub> and TSP / PM<sub>2.5</sub> sector by sector.
  
-{{ :general:gridded_data:pm2_5_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:pm2_5_emissions_1990_2023_sub2025.png?nolink800 | PM2.5 emissions 1990-2023 }}
  
 In addition to the other particle emissions, the black carbon emissions were also spatially distributed. In addition to the other particle emissions, the black carbon emissions were also spatially distributed.
  
-{{ :general:gridded_data:bc_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:bc_emissions_1990_2023_sub2025.png?nolink800 | Black Carbon (BC) emissions 1990-2023 }}
  
 === Emissions of Heavy Metals (HM)  === === Emissions of Heavy Metals (HM)  ===
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 Another source of emissions is the metal processing industry - iron and steel. The distribution parameter is partially covered by the PRTR point sources; rest: by number of employees per district (metal production and processing). Another source of emissions is the metal processing industry - iron and steel. The distribution parameter is partially covered by the PRTR point sources; rest: by number of employees per district (metal production and processing).
  
-{{ :general:gridded_data:pb_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:pb_emissions_1990_2023_sub2025.png?nolink800 | Lead (Pb) emissions 1990-2023 }}
  
 == Cadmium Emissions == == Cadmium Emissions ==
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 In the chemical industry, cadmium is a by-product of the extraction of zinc, lead or copper. The most important emission sources in Germany are the sectors of the metal processing industry - iron and steel, and copper production. It is also emitted by petroleum refineries and coal and oil combustion. For the latter, it can be captured via appropriate particle filters and thus reduced. For the dominant emission sources, the distribution is partially covered by PRTR point sources; the rest: by number of employees per district (metal production and processing). In the chemical industry, cadmium is a by-product of the extraction of zinc, lead or copper. The most important emission sources in Germany are the sectors of the metal processing industry - iron and steel, and copper production. It is also emitted by petroleum refineries and coal and oil combustion. For the latter, it can be captured via appropriate particle filters and thus reduced. For the dominant emission sources, the distribution is partially covered by PRTR point sources; the rest: by number of employees per district (metal production and processing).
  
-{{ :general:gridded_data:cd_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:cd_emissions_1990_2023_sub2025.png?nolink800 | Cadmium (Cd) emissions 1990-2023 }}
  
 == Mercury Emissions == == Mercury Emissions ==
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 Mercury (Hg) belongs to the group of heavy metals that occur both naturally (e.g. volcanic eruptions) and through industrial processes (e.g. mining, burning coal or heating oil) in the environment. A distinction is made between elemental (metallic) mercury (Hg0), inorganic mercury (iHg) and organic mercury compounds such as methylmercury (MeHg). It is mostly emitted bound to fine particles. It is mainly released during energy production from fossil fuels such as coal, oil and natural gas, and during metal-producing processes such as iron and steel production.The spatial distribution is made for energy supply via PRTR point sources (the rest: number of other power plants (<25 MW electric) for public supply per district). Mercury (Hg) belongs to the group of heavy metals that occur both naturally (e.g. volcanic eruptions) and through industrial processes (e.g. mining, burning coal or heating oil) in the environment. A distinction is made between elemental (metallic) mercury (Hg0), inorganic mercury (iHg) and organic mercury compounds such as methylmercury (MeHg). It is mostly emitted bound to fine particles. It is mainly released during energy production from fossil fuels such as coal, oil and natural gas, and during metal-producing processes such as iron and steel production.The spatial distribution is made for energy supply via PRTR point sources (the rest: number of other power plants (<25 MW electric) for public supply per district).
    
-{{ :general:gridded_data:hg_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:hg_emissions_1990_2023_sub2025.png?nolink800 | Mercury (Hg) emissions 1990-2023 }}
  
 == Nickel Emissions == == Nickel Emissions ==
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 Nickel (Ni) is a carcinogen and allergen. The main sources for Ni emissions in Germany are combustion processes of fossil fuels and the production of Ni containing alloys. The spatial distribution of Ni emissions from energy production and industrial activity is conducted as described above. Nickel (Ni) is a carcinogen and allergen. The main sources for Ni emissions in Germany are combustion processes of fossil fuels and the production of Ni containing alloys. The spatial distribution of Ni emissions from energy production and industrial activity is conducted as described above.
  
-{{ :general:gridded_data:ni_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:ni_emissions_1990_2023_sub2025.png?nolink800 | Nickel (Ni) emissions 1990-2023 }}
  
 == Arsenic Emissions == == Arsenic Emissions ==
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 Arsenic (As) is a highly toxic and carcinogenic metalliod. In the environment, two oxidation states are most common, +3 and +5, were the first one has the highest toxic potential. Analogous to other anthropogenic metal emissions in Germany, As is released mostly during energy production from fossil fuels, especially coal, and during metal-producing processes. The spatial distribution of As emissions from energy production and industrial activity is conducted as described above. Arsenic (As) is a highly toxic and carcinogenic metalliod. In the environment, two oxidation states are most common, +3 and +5, were the first one has the highest toxic potential. Analogous to other anthropogenic metal emissions in Germany, As is released mostly during energy production from fossil fuels, especially coal, and during metal-producing processes. The spatial distribution of As emissions from energy production and industrial activity is conducted as described above.
  
-{{ :general:gridded_data:as_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:as_emissions_1990_2023_sub2025.png?nolink800 | Arsenic (As) emissions 1990-2023 }}
  
 === Emissions of persistent organic pollutants (POP) === === Emissions of persistent organic pollutants (POP) ===
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 PAHs are formed during the incomplete combustion of organic material (e.g. coal, heating oil, fuel, wood, tobacco, forest fires). The dominant sources of PAHs in the environment are thus from human activity: wood-burning and combustion of other biofuels. The dominating source is the residential sector - Stationary. The spatial distribution is therefore mainly covered by distribution according to energy carriers (national). PAHs are formed during the incomplete combustion of organic material (e.g. coal, heating oil, fuel, wood, tobacco, forest fires). The dominant sources of PAHs in the environment are thus from human activity: wood-burning and combustion of other biofuels. The dominating source is the residential sector - Stationary. The spatial distribution is therefore mainly covered by distribution according to energy carriers (national).
  
-{{ :general:gridded_data:pahtotal_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:pahtotal_emissions_1990_2023_sub2025.png?nolink800 | PAH 1-4 emissions 1990-2023 }}
  
 Benzo(a)pyrene is considered an indicator substance of polycyclic aromatic hydrocarbons. For this reason, more measurement data and further information are available than for other POPs. In the determination of environmental pollution by this group of substances, benzo[a]pyrene is usually used as a reference.   Benzo(a)pyrene is considered an indicator substance of polycyclic aromatic hydrocarbons. For this reason, more measurement data and further information are available than for other POPs. In the determination of environmental pollution by this group of substances, benzo[a]pyrene is usually used as a reference.  
      
-{{ :general:gridded_data:bap_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:bap_emissions_1990_2023_sub2025.png?nolink800 | Benzo[a]pyrene emissions 1990-2023 }}
  
 == Polychlorinated biphenyls (PCB) == == Polychlorinated biphenyls (PCB) ==
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 PCBs are classified as industrial chemicals and were used in various applications in pure form or as additives until the restrictions and bans came into force. Due to their properties (thermal stability, low water absorption and non-flammability), they were used in insulating oils in transformers, capacitors, additive to joint sealants and anti-corrosion coatings.The maps show the magnitude of HCB emissions in kilograms. The key sources are the sectors Public Power, Iron & Steel, and Residential - Stationary. The spatial distribution is therefore mainly covered by PRTR Point Sources (PS). PCBs are classified as industrial chemicals and were used in various applications in pure form or as additives until the restrictions and bans came into force. Due to their properties (thermal stability, low water absorption and non-flammability), they were used in insulating oils in transformers, capacitors, additive to joint sealants and anti-corrosion coatings.The maps show the magnitude of HCB emissions in kilograms. The key sources are the sectors Public Power, Iron & Steel, and Residential - Stationary. The spatial distribution is therefore mainly covered by PRTR Point Sources (PS).
  
-{{ :general:gridded_data:pcb_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:pcb_emissions_1990_2023_sub2025.png?nolink800 | emissions of PCBs 1990-2023 }}
  
 == PCDD/PCDF == == PCDD/PCDF ==
  
-PCDD/PCDF emissions are formed as by-products in a variety of thermal processes, in the production of organochlorine chemicals, or in any oxidation reactions of hydrocarbon compounds in the presence of chlorine compounds. The magnitude of dioxins emissions is presented in the g I-TEQ range (toxic equivalence, TEQ) in the maps.+Emissions of dioxins and furans are formed as by-products in a variety of thermal processes, in the production of organochlorine chemicals, or in any oxidation reactions of hydrocarbon compounds in the presence of chlorine compounds. The magnitude of dioxins emissions is presented in the g I-TEQ range (toxic equivalence, TEQ) in the maps.
 The major key sources are the Residential - stationary combustion (1.A.4.b i), Other Waste: Building and Car Fires (5.E.2), and Metal Industrie - Iron and Steel (2.C.1). The spatial distribution is partially covered by information of PRTR point sources and for the residential sector according to energy carriers. The major key sources are the Residential - stationary combustion (1.A.4.b i), Other Waste: Building and Car Fires (5.E.2), and Metal Industrie - Iron and Steel (2.C.1). The spatial distribution is partially covered by information of PRTR point sources and for the residential sector according to energy carriers.
  
-{{ :general:gridded_data:pcddpcdf_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:pcddpcdf_emissions_1990_2023_sub2025.png?nolink800 | emissions of dioxins and furans 1990-2023 }}
  
 == Hexachlorobenzene Emissions == == Hexachlorobenzene Emissions ==
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 Emissions from this sector are distributed over the agricultural area. However, this distribution is subject to great uncertainties, as the application is carried out depending on the mould infestation and the need for action. More information is described under [[sector:agriculture:agricultural_soils:3df_agriculture_other | 3.D.f - Agriculture other including use of pesticides]]. Emissions from this sector are distributed over the agricultural area. However, this distribution is subject to great uncertainties, as the application is carried out depending on the mould infestation and the need for action. More information is described under [[sector:agriculture:agricultural_soils:3df_agriculture_other | 3.D.f - Agriculture other including use of pesticides]].
  
-{{ :general:gridded_data:hcb_emissions_1990_2023_sub2025.png?nolink |}}+{{ :general:gridded_data:hcb_emissions_1990_2023_sub2025.png?nolink800 | Hexachlorobenzene (HCB) emissions 1990-2023 }}
  
  
-[(SCHNEIDER2016> Schneider et al. (2016): Schneider, Chr., Pelzer, M., Toenges-Schuller, N., Nacken, M., and Niederau, A.: ArcGIS basierte Lösung zur detaillierten, deutschlandweiten Verteilung (Gridding) nationaler Emissionsjahreswerte auf Basis des Inventars zur Emissionsberichterstattung. UBA Texte 71/2016, p.224; https://www.umweltbundesamt.de/sites/default/files/medien/1968/publikationen/2016-11-09_griddingtool_greta_langfassung_final.pdf; ISSN 1862-4804; AVISO GmbH on order of the UmweltbundesamtDessau-Roßlau, November 2016)]+[(SCHNEIDER2016> Schneider et al. (2016): Schneider, Chr., Pelzer, M., Toenges-Schuller, N., Nacken, M., and Niederau, A.: ArcGIS basierte Lösung zur detaillierten, deutschlandweiten Verteilung (Gridding) nationaler Emissionsjahreswerte auf Basis des Inventars zur Emissionsberichterstattung. UBA Texte 71/2016, p.224; https://www.umweltbundesamt.de/sites/default/files/medien/1968/publikationen/2016-11-09_griddingtool_greta_langfassung_final.pdf; ISSN 1862-4804; AVISO GmbH on order of the Umweltbundesamt; Dessau-Roßlau, November 2016)] 
 [(PELZER2018> Pelzer et al. (2018): Michael Pelzer, M., Schneider, Chr., Gallus, E. and Toenges-Schuller, N.: Gridding Emission Tool for ArcGIS (GRETA): Weiterentwicklungen und Erweiterung des Stoffspektrums - documentation regarding GRETA-AddIn version 1.1.2.2: https://iir.umweltbundesamt.de/2025/_media/general/gridded_data/2019_01_14_doku_greta_erweiterung2018_190114.pdf, AVISO GmbH on order of the Umweltbundesamt, October 2018, Aachen)] [(PELZER2018> Pelzer et al. (2018): Michael Pelzer, M., Schneider, Chr., Gallus, E. and Toenges-Schuller, N.: Gridding Emission Tool for ArcGIS (GRETA): Weiterentwicklungen und Erweiterung des Stoffspektrums - documentation regarding GRETA-AddIn version 1.1.2.2: https://iir.umweltbundesamt.de/2025/_media/general/gridded_data/2019_01_14_doku_greta_erweiterung2018_190114.pdf, AVISO GmbH on order of the Umweltbundesamt, October 2018, Aachen)]
 +
 [(PELZER2021> Pelzer et al. (2021): Michael Pelzer, M., Schneider, Chr., Gallus, E. and Toenges-Schuller, N.: Gridding Emission Tool for ArcGIS (GRETA): WeiterentwicklungeAVn - GRETA-AddIn Version: 1.1.4.2; ArcGIS 10.8: https://iir.umweltbundesamt.de/2025/_media/general/gridded_data/2021_05_17_doku_greta_erweiterung_210517.pdf; AVISO GmbH on order of the Umweltbundesamt, May 2021, Aachen)] [(PELZER2021> Pelzer et al. (2021): Michael Pelzer, M., Schneider, Chr., Gallus, E. and Toenges-Schuller, N.: Gridding Emission Tool for ArcGIS (GRETA): WeiterentwicklungeAVn - GRETA-AddIn Version: 1.1.4.2; ArcGIS 10.8: https://iir.umweltbundesamt.de/2025/_media/general/gridded_data/2021_05_17_doku_greta_erweiterung_210517.pdf; AVISO GmbH on order of the Umweltbundesamt, May 2021, Aachen)]
 +
 [(PELZER2024> Pelzer et al. (2024): Michael Pelzer, M., Toenges-Schuller, N. and Schneider, Chr.: Weiterentwicklungen 2023/2024: documentation as of 20.6.2024, GRETA-AddIn Version: 1.5.4.3; ArcGIS 10.8.2; https://iir.umweltbundesamt.de/2025/_media/general/gridded_data/2024_06_20_doku_greta_weiterentwicklung_2023_2024.pdf; AVISO GmbH on order of the Umweltbundesamt, June 2024, Aachen)] [(PELZER2024> Pelzer et al. (2024): Michael Pelzer, M., Toenges-Schuller, N. and Schneider, Chr.: Weiterentwicklungen 2023/2024: documentation as of 20.6.2024, GRETA-AddIn Version: 1.5.4.3; ArcGIS 10.8.2; https://iir.umweltbundesamt.de/2025/_media/general/gridded_data/2024_06_20_doku_greta_weiterentwicklung_2023_2024.pdf; AVISO GmbH on order of the Umweltbundesamt, June 2024, Aachen)]