Diffuse Water Emissions in E-PRTR

Transcription

1 Diffuse Water Emissions in E-PRTR Joost van den Roovaart (Deltares) Peter Coenen (TNO) Jochen Theloke/Melinda Uzbasich (IER)

2 Outline 1. Organisation of the project 2. Selected sources 3. Not selected sources 4. Spatial allocation 5. Selected sources Brief introduction Methodology Spatial allocation Discussion

3 Consortium partners TNO (Netherlands): Peter Coenen IER University of Stuttgart (Germany): Jochen Theloke Deltares (Netherlands): Joost van den Roovaart lot of eperience on PRTR systems & emission inventories international activities & EU-wide network E-PRTR project on diffuse air emissions Dutch PRTR system

4 Goal of the project Objectives: Gather available data for selected source-substance combinations. If emission data not available on European scale alternative estimation methods. Develop a methodology to derive disaggregated spatial data. Derive 40 gridded emission map layers covering all EU27 Member states and the EFTA countries (Switzerland, Liechtenstein, Norway and Iceland) for the selected sectors and pollutants. Result: emission maps on a RBD scale integrated into the E-PRTR note: the project results can be seen as a first step

5 Planning Start project: September 2011 October 2011-May 2012: Inventory of data Selection of sources Developing quantification methods First draft factsheets: April 2012 Questionnaire: May 2012 Workshop: June 2012 Emission maps: October 2012 Draft final report: December 2012 Final report: February 2013

6 Goal of the workshop Presenting the project Presenting the response on the Questionnaire Presenting and discussing the selection of sources and substances Presenting and discussing the proposed methods of quantification and spatial allocation of the selected sources

7 Definition of diffuse sources Definition within this project: Diffuse emissions are all emissions not reported according to article 5 of the E-PRTR Regulation So: -also UWWTP s below the thresholds (capacity and substance) -also industrial emissions below the thresholds -also industrial emissions not covered by the E-PRTR activities -also stormwater overflows and separate sewer discharges

8 Approach Focus on the key sources Focus on the priority (hazardous) substances Clear definitions Simple and transparant methods Based on (international) available data Use the general scheme of the: (Draft) Guidance document on the preparation of an inventory of emissions, discharges and losses of priority and priority hazardous substances

9 General scheme (Guidance document)

10 E-PRTR P8 + P10 above thresholds

11 Not selected sources Railroads: part of P6 Minor source Main part emissions to the soil

12 Not selected sources II Stormwater overflows: P7 On RBD-scale, minor source Necessary information not available on EU scale Dependent on local factors (e.g. sewer type, amount and location)

13 Not selected sources III Mining: P11 + part of P10 Hardly impossible to develop a solid quantification method Loads are dependent of local circumstances (e.g. hydrology/geology)

14 Not selected sources IV Natural Background: P13 Definitions and quantification methods are difficult No link with measures

15 Selected sources 1. Atmospheric deposition 2. Agriculture 3. Road transport 4. Inland navigation 5. UWWTPs not in E-PRTR 6. Not connected households 7. Industry not in E-PRTR (case study)

16 Selected sources substances I Substances Atmospheric deposition Agriculture Road transport Railways Stormwater overflows & separate sewers UWWTP not in E-PRTR Not connected households Industry not in E- PRTR Mining Inland navigation TOC Nutrient-P Nutrient-N Cadmium Lead Mercury Nickel Diuron Alachlor Atrazine other pesticides Anthracene DEHP Fluoranthene Octylphenols

17 Selected sources substances II Additional substances Atmospheric deposition Agriculture Road trans port Railways Storm water overflows & separate sewers UWWTP not in E- PRTR Not connected house holds Industry not in E- PRTR (case study) Mining Inland navigation Copper Zinc Benzo[a]pyrene Benzo[b]fluoranthene Benzo[ghi]perylene Benzo[k]fluoranthene Indeno(1,2,3- cd)pyrene a-hch g-hch Dinitroaniline Trifluralin

18 First quantification contribution of sources + <10% ++: between 10 and 25% +++ > 25% Atmospheric deposition Agriculture Road transport UWWTP not in E- PRTR Not connected households Inland navigation Substances TOC Nutrient-P Nutrient-N Cadmium Lead Mercury Nickel Diuron +++ Alachlor Atrazine +++ other pesticides Anthracene DEHP Octylphenols

19 Spatial allocation general methodology

20 Spatial allocation Different spatial scales of grids EMEP: IER Europe: IER Germany: 50km 50 km (black) 0,05 0,05 (green) 0,01 0,01 (yellow)

21 UWWTPs

22 UWWTPs I A wide range of relevant substances, including specific pesticides. Emissions to and from a sewer system:

23 UWWTPs II In E-PRTR: UWWTP s with capacity > population equivalents (p.e.) substance loads above the thresholds voluntary etra reports possible (not much) So only a small part of all the UWWTP loads are in E-PRTR now! MS reports of Urban Waste Water Directive are very valuable: capacity location treatment way etc.

24 UWWTPs III Activity Rate: UWWTP database: load entering UWWTP If not available -> organic design capacity Emission Factors: 1. GIS: linking UWWTPs E-PRTR database to UWWTPdatabase (not easy: different codes!) 2. EF per substance and per UWWTP = Emission E-PRTR/load UWWTP-database (kg/p.e.) 3. Calculation of the EF EF per treatment-way (Primary, Secondary, Other treatment) EF per treatment Class

25 UWWTPs IV Total number of UWWTP s in the UWWTP database: Eample of total UWWTP s with a load for total-n in the E-PRTR substance number of UWWTPs loads in E-PRTR total Organic Carbon 1778 total Phosphorus 1619 total Nitrogen 1834 cadmium 364 lead 782 mercury 537 nickel 1332 anthracene 18 floranthene 21 This means two things: 1 For a lot of substances we do miss a lot of loads 2 For some substances we do have an international average effluentfactor

26 UWWTPs V Eample effluent factors Nutrient-N for treatment ways towards treatment class Emission Factor for total-nitrogen in kg/p.e. for three different treatment classes UWWTP Treatment-class Effluent Factor (kg/p.e.) 1256 OT 2,2 560 ST 15,2 17 PT 25,9 4. Check with results MS Calculation method: Emission per TC = AR (load entering UWWTP) EF (kg per substance) that means we can calculate the effluents of all UWWTP s

27 UWWTPs VI Methodology of the spatial allocation 1. Regionalization Emission on national level to Emission on NUTS 3 level Population data (EUROSTAT, FAO) 2. Gridding into target high resolution grids (5km 5km) using Corine Land Cover UWWTPs (Point source data) CORINE Land Cover (Water bodies) (EEA) 3. Allocation to the RBDSU level Amount of CORINE Land Cover Water bodies classes

28 UWWTPs VII

29 UWWTPs VIII

30 UWWTPs IX Discussion Advantages: presumably high emissions from this source Improvements: EF can be updated easily Weak: link between location of UWWTP s in E-PRTR and in UWWTP database Data canbecheckedbyms Development similar methods: Flanders, Austria, France, EEA

31 Not connected households

32 Not connected households II Source contains a wide range of relevant substances, Population not connected to sewer system can be quite large.

33 Unconnected households III Calculation method: UWWTP database Treated: - A Collecting system - B IAS - C Transport by trucks Without treatment: - D No collecting/ias Two important loads per NUTS area: Septic tanks (%) = IAS (B) trucks (C) Unconnected households = D

34 Unconnected households IV Activity Rates (ER) Inhabitants per NUTS area Emission Factor (EF) Mg per substance per inhabitant per year Removal Efficiency (RE) septic tanks Pathways (PW) Distribution % to compartment water or soil Formula: Septic tanks = AR EF %sept.tanks RE PW Unconnected households = AR EF %with. treatment PW

35 Unconnected households V Methodology of the spatial allocation 1. Regionalization Obtained emission Emission on NUTS3 level Population data (EUROSTAT) 2. Gridding into target high resolution grids (5km 5km) using Corine Land Cover 3. Allocation to the RBDSU level CORINE Land Cover (Amount of Surface Water) Urban-Rural population data (EEA Data service, SEDAC) Amount of CORINE Land Cover Water bodies classes

36 Unconnected households VI Population data proy data EUROSTAT population data on NUTS3 level are available without gaps for all countries across EU27 and EFTA4 Information on NUTS level are important for the emission regionalization. The reasons for using population data are: a strong spatial relationship between emissions from not connected households and population density that it is readily available across EU and EFTA countries

37 Unconnected households VII proy data SEDAC Socioeconomic Data and Application Center SEDAC s Global Rural-Urban Mapping Project (GRUMP) carried out by CIESIN depicts the spatial distribution of urban etent across the entire globe. the data are in a longitude-latitude grid format of 30 arc sec resolution available. The reasons for using population data distinguish in urban vs. rural area: The number of not connected households in rural areas is higher than in urban areas

38 Unconnected households VIII Discussion Data of MS in UWWTP database not always realistic High contribution of this source to total loads to surface water Data canbeimprovedbyms

39 Atmospheric Deposition

40 Atmospheric Deposition - II Only the atmospheric deposition directly to surface water. Major source for N-total, heavy metals, pesticides and PAH. A comple source, influenced by weather, climate, emissions to air. (Often) models are used for the quantification of loads.

41 Atmospheric Deposition - III Model studies of EMEP: Flues in kg per km 2 per year Per km grid Substances: > 6 Metals: Hg, Cd, Pd, Ni, Cu, Zn > 6PAHs: Anthracene Fluoranthene Benzo(a)pyrene Benzo(b)fluoranthene Benzo(ghi)perylene Indeno(1,2,3-cd)pyrene > 2 Pesticides: a-hch, g-hch

42 Atmospheric Deposition - IV Calculation per RBD in five steps: 1. Grids are divided over the NUTS3 regions. 2. The average flu is calculated for every NUTS3 region. 3. The water surface per NUTS region in km 2 is determined. 4. Formula: Emission per NUTS3 region = flu (kg/ km 2 ) surface of water (km 2 ). 5. NUTS3 regions are added up to RBDs.

43 Atmospheric Deposition - V Methodology of the spatial allocation Gridded data on atmospheric deposition are derived from the Precipitation Chemistry Database of the Co-operative Programme for the Monitoring and Evaluation of the Long-Range Transmission of Air Pollutants in Europe [EMEP, 2012]. The used data are based on the EMEP Unified model revision 1.7, 50 km grid. The EMEP data will be recalculated from the EMEP50-grid cell level to the RBDSU level and can be weighted using Corine Land Cover information ( water bodies ). 43

44 Atmospheric Deposition - VI Figure 1: SO2 atmospheric deposition data (EMEP) on RBDSU level. 44

45 Atmospheric Deposition - VII Discussion Advantages: > international accepted model > transparant method > spatial allocation: seems detailed enough > easy to update Data can be checked by MS, compared with monitoring results Data can be improved by MS (more substances)

46 Agriculture

47 Agriculture II Major source for nutrients, heavy metals and pesticides. Comple source (related with soil, groundwater) Eisting models used by JRC for: > Nutrients > Pesticides Use of indicator pollutants for pesticides.

48 Agriculture III Methodology of the spatial allocation 1. Regionalization Emission on national level Emission on NUTS 3 level Animal census (EUROSTAT, FAO) 2. Gridding into target high resolution grids (5km 5km) using Corine Land Cover CORINE Land Cover (EEA Data service) 3. Allocation to the RBDSU level Amount of CORINE Land Cover Water bodies classes

49 Agriculture IV proy data Sector_Name Proy Data for Regionalisation Proy Data for Gridding Cattle Dairy Cattle Non-Dairy Buffalo Sheep Animal data (EUROSTAT, 2008) & Animal density (FAO, 2005 (2010)) Goats Swine Horses Mules and Asses --- Laying Hens Broilers Turkeys Animal density (FAO, 2005 (2010)) Other Poultry Other --- Synthetic N-fertilizers Farm-level agricultural operations incl. storage, handling and transport of agricultural products Off-farm storage, handling and transport of --- bulk agricultural products N-ecretion on pasture range and paddock Unspecified CORINE Land Cover (2010) (CLC90) Switzerland (CLC 2000) Europe (CLC 2006) Europe class 2: Agricultural areas 2.1. Arable land 2.2. Permanent crops 2.3. Pastures 2.4. Heterogeneous agricultural areas CORINE Land Cover (2010) (CLC90) Switzerland (CLC 2000) Europe (CLC 2006) Europe class 2: Agricultural areas ecept the sub-classes (rice fields)

50 Agriculture V

51 Agriculture VI Discussion Advantages: > eisting model and data We have to check: > availability of model results (year, coverage) > transparancy of the model, documentation Data not easy to check by MS Some MS have own, often complicated models

52 Road transport

53 Road transport II Different sub sources: > Break wear > Engine oil leaks > Road surface wear > Tyre wear Source for heavy metals and PAH. Data regarding the location of roads and traffic intensity is available on a EU-scale. The emissions are calculated separately for: > various vehicle categories (light and heavy) > road types (urban, rural and highways).

54 Road transport III Activity Rates traffic performance EU road network Emission factor Use of international literature EU policy measures EF = Emissions 2009*/Traffic performance 2009 Pathways (urban/rural/highways) in % Emissions in uban aereas are assumed to go to the sewer system and are not considered anymore Formula: Emission = AR EF pathway

55 Road transport IV Methodology of the spatial allocation emission i, = emission t i value i, value i, emission i, : emission of road transport (for each vehicle and road type) at the specific road segment emission t: national total emission of road transport (per vehicle, and road category) value i,: surrogate data value for each individual road segment within each target grid cell as traffic volume fraction of the sum of the total traffic volume Overview of the applied methodology for the spatial distribution of the road transport

56 Road transport V Road network covered in Trans-Tools Trans Tools considers especially highway and rural roads. Road classes: ME - European motorway M - Motorway DE - European dual carriageway D - Dual carriageway OE - Other European road F Ferry

57 Road transport VI Road Network Supplementation Trans-Tools road network cover is not completely Supplementation of the Trans-Tools network using GISCO data Identification of the missing traffic volume based on TREMOVE data Population data based weighting of the added road segments

58 Road transport VII

59 Road transport VIII Discussion Advantages: > international EF s > international AR s Spatial allocation rather comple Weakest link: % of loads to surface water

60 Inland navigation

61 Inland navigation II Different sub sources: > Coatings > Sacrificial anodes > Bilge water > Sanitary wastewater Source can be relevant for: > nutrients > PAH > Copper EU-wide data on activity rates

62 Inland navigation III Activity Rates Ton kilometers inland navigation Emission Factors Use of international literature EU policy measures EF = Emissions 2009*/Traffic performance 2009 * = all sub sources added Formula: Emission = AR EF

63 Inland navigation IV Methodology of the spatial emission i, = emission t i value i, value i, i: geographic feature like inland waterway segments, harbours or grid cell within the grid layer emission i, : domestic or national shipping emission for e.g. the specific inland waterway segment i emission t: national total emissions for inland navigation value i, - are the surrogate data values for each geographical feature like inland waterway segments Overview of the applied methodology for the spatial distribution of the inland navigation

64 Inland navigation V Proy data sets for inland navigation Sector description Proy Dataset Data Source Year Etend Traffic dataon the sections of the waterways Voies Navigables de France (VNF) 2010 Selected countries Shipping Navigable inland waterways Traffic volume Trans-Tools 2010 Selected countries River geo data/geospatial inland waterways GISCO Eurostat 2010 EU 27 + EFTA Statistical data on harbor movements Eurostat 2009 Ports in Europe Georeferenced ports Gisco Eurostat 2010 World Econimic zones available from GISCO 2007 EU 27 Location of harbour areas (not applied) Corine Land cover 2000/ EU 27 + EFTA Water bodies Corine Land cover 2000/ EU 27 + EFTA VNF Traffic data Trans-Tools Economical zones

65 Inland navigation VI Inland navigation in Trans-Tools Trans-Tools covering not all rivers Enhancement of the Trans-Tools network by river related geodata Modelling of missing rivers by regional administrations (=>VNF data set)

66 Inland navigation VII Coastal water bodies

67 Inland navigation VIII Coastal water bodies

68 Inland navigation IX

69 Inland navigation X Discussion Advantages: > international EF s > international AR s use of types of coatings can be quite diffentent in MS collection of bilgewater can be quite different in MS Spatial allocation rather comple there are some RBDSU which are overlapping the 12nm EEZ (Eclusive Economic Zones) and other which are overlapping only the continental areas => after the intersection of the RBDSU and 12nm EEZ the results will concern only the values from the overlapping areas.

70 Industry not in E-PRTR

71 Industry not in E-PRTR II Reliable estimation is etremely comple: > Heterogeneity of companies very large > Treatment of separate waste water > Effluents of industry sewer or surface water? A case study; a first step in quantification Paper industry > Good defined sector > Sector wide data available > Results can be checked with E-PRTR

72 Industry not in E-PRTR III Activity Rates (AR) Production of paper and paperboard (Eurostat) > Mass unit per country per year Emission factor (EF) Concentration ranges (lower and upper value) in BREFs N, P and TOC (= COD/3) Connection rate (CF) to collective sewer systems Formula: Emission = AR EF (1 - CF)

73 Industry not in E-PRTR IV Results: Emissions total EU (E-PRTR and non E-PRTR) > Lower value > Upper value Reported E-PRTR emissions EU27 E-PRTR reported (kg) average emissions calculated (kg) Difference kg) total TOC Nitrogen Phosphorus

74 Industry not in E-PRTR V Conclusions: Differences between reported E-PRTR and calculated emissions are large Big differences per MS > Eample nitrogen MS E-PRTR reported (kg) average emission calculated (kg) difference Czech Republic Germany Netherlands Non E-PRTR emissions can be estimated, but detailed information is necessary for: > Employee numbers (spatial allocation) > Waste Water Technology in MS

75 Industry not in E-PRTR VI Total Nitrogen emissions from E-PRTR and calculated according to production numbers EU27 Member state Number of facilities reporting to E-PRTR for 2009 Reported EPRTR emissions* (ton/year) Emissions calculated (ton/year) Emission factor: 5 g/adt Emission factor: 100 g/adt Difference between EPRTR emissions and emissions calculated (ton/year) Emission factor: 5 g/adt Emission factor: 100 g/adt Austria Belgium Czech Republic Finland France Germany Hungary Italy Netherlands Poland Portugal Romania Slovakia Slovenia Spain United Kingdom Bulgaria Denmark Estonia Ireland Greece Cyprus Latvia Lithuania Luembourg Malta Sweden EU27 total ** 6747** * Reporting limit (threshold) for total Nitrogen: 50 tonnes/year. ** This is the difference between the calculated total and the EU27 total.

76 Industry not in E-PRTR VI Methodology of the spatial allocation emission i, = emission t i value i, value i,

77 Industry not in E-PRTR VII Spatial allocation of the diffuse industrial releseases (E- PRTR Diffuse Emissions to Air) Industrial emissions Non-diffuse emissions Diffuse emissions Distribution parameters on admininstrative level, e.g number of employees Grid distribution parameters, e.g landcover of industrial areas Emissions Regionalization Grid distribution Gridded emissions

78 Industry not in E-PRTR VIII

79 Industry not in E-PRTR IX 2. Gridding Allocation of the emissions distributed to NUTS3 units to each polygon/grid cell according to the defined 5 km 5 km grid cell resolution.

80 Industry not in E-PRTR X Proy data sets Land use data CORINE Land Cover data (CLC2000, CLC2006 and CLCCH1990 for Switzerland) merged in order to cover the EU27 and EFTA4 countries. Population data Population data from EUROSTAT for 2008 (population and employees by activity branches) Facility data Facility data from E-PRTR 2008 (geographical location)

81 Industry not in E-PRTR XI

82 Industry not in E-PRTR XII

83 Industry not in E-PRTR XIII Discussion Reporting limits E-PRTR systematic error Currently available data not detailed enough > A wide range in Emission Factors > For many MS calculated emissions are lower than E-PRTR reported BREFs from 2001 Emission factor ranges for Pulp and Paper industry (source: BREF, 2001), COD: chemical oygen demand; ADt; air-dried metric tonne. Pollutant Unit Emission factor (lower value) (upper value) COD* kg/adt 2 6 N g/adt P g/adt 1 30

84 Industry not in E-PRTR XIV General Conclusion The eploratory eercise shows that in principle non PRTR emissions from industrial sectors can be estimated. The results for the paper industry indicates that the non PRTR facilities may contribute substantially to the total emissions of the paper and pulp industry. But to come to an reliable estimate detailed information on processes, water treatment technology and employee numbers are required. The eercise showed that even for an relative uniform sector the required data are not available at EU level so no maps could be generated.