28-03-2017 The ScorePP Approach to Predict Releases of Priority Pollutants From Urban Sources Hans-Christian Holten Lützhøft1, Erica Donner2, Veerle Gevaert3,

The ScorePP Approach to Predict Releases of Priority Pollutants From Urban Sources Hans-Christian Holten Lützhøft1, Erica Donner2, Veerle Gevaert3, Webbey De Keyser3, Tonie Wickman4, Matej Cerk5, Eva Eriksson1, André Lecloux6, Primož Banovec5 and Anna Ledin1 1DTU Environment, Technical University of Denmark, Kgs. Lyngby, Denmark 2Urban Pollution Research Centre, Middlesex University, London, UK 3BIOMATH, Ghent University, Gent, Belgium 4Environmental Monitoring, Stockholm Stad, Stockholm, Sweden 5Faculty of civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia 6Envicat Consulting, Avenue Montesquieu 36, B-1300 Wavre, Belgium EEA Seminar Copenhagen Monday 6 December 2010 Yes, my name is Hans-Christian and I am going to tell you about the a part of the ScorePP project that deals with sources and source characterisation. I have given my presentation the title: “The ScorePP Approach to Predict Releases of PPs from Urban Sources”. The work I will present has developed in close collaboration with 5 other partners, 3 universities, 1 private company and 1 municipality. My name is Hans-Christian and as just mentioned I work at the Technical University of Denmark. I am going to tell you about the project that I mainly work on. It is called ScorePP and is an EU project financed by the 6th FP. Today you will hear five presentations concerning this project, and mine is the first. The work I will present has developed in close collaboration with 5 other universities, private companies and municipalities. Mit navn er Hans-Christian Holten Lützhøft og jeg kommer fra DTU Miljø. Det jeg skal fortælle jer om, handler om det projekt jeg primært arbejder på og som hedder ScorePP. Det er et EU-projekt, finansieret af Eu’s 6 rammeprogram og som det fremgår er disse resultater fremkommet i tæt samarbejde 5 andre universiteter, private virksomheder og kommuner.

Presentation MSc in pharmacy (1991-1996) PhD (1996-2000)
MSc in pharmacy ( ) Medicinal chemistry, organic synthesis of AMPA-receptor ligands PhD ( ) Environmental Risk Assessment of Antimicrobials; experimental work on ecotoxicity and environmental fate; literature study of ecotoxicity and occurrence in relation to fish farming activities PostDoc at KU-Life ( ) Environmental fate of antimicrobials in soil and porewater PostDoc at Novo Nordisk ( ) Stability testing of tablets – development of super enhanced stability testing methods AssProf at KU-Life ( ) Intestinal absorption of pharmaceuticals Pharmacist ( ) AssocProf at DTU Environment (2006- ) Source characterisation of (organic) priority substances; inherent properties, source tracking, source dynamics, urban releases Sampling, extraction, purification and analysis or organic substances Monitoring; Stakeholder interaction

Background European Water Framework Directive (EU WFD) implemented in 2000 with Environmental Quality Standards implemented in 2008 Aim of EU WFD is to improve water quality of European water courses Both pollution source inventories as well as monitoring programmes have to be established EU member states are obliged to improve water quality through more than one measure, for instance Legislation Improved handling and treatment of waste streams (municipal/industrial wastewater or stormwater) Voluntary initiatives The EU 6th framework programme financed the DTU Environment coordinated ScorePP (Source control options for reducing the emissions of Priority Pollutants) project 9 partners with 30+ collaborators across Europe and 4 case cities SEP2006-MAR2010, 3½ years The main purpose within the ScorePP project is to develop Source Control Options for Reducing Emissions of Priority Pollutants from urban areas. ScorePP is an abbreviation of Source Control Options for Reducing Emissions of Priority Pollutants. To be able to develop these control options and strategies to reduce the emission of PPs, it is a necessity to have a thorough understanding of the sources to the PPs and preferably also knowledge about how much substance that is released from the individual source. Therefore, the specific aim of this work was to identify potential sources and to quantify the releases of PPs from these sources. Hovedformålet med ScorePP projektet er at udvikle kildekontrolforanstaltninger for at reducere udledningen af prioriterede stoffer fra bymiljøer. ScorePP er en forkortelse for Source Control Options for Reducing Emissions of Priority Pollutants. For at kunne udvikle disse kontrolforanstaltninger og strategier for at mindske udledningen af prioriterede stoffer er det en nødvendighed at have et indgående kendskab til kilder til stofferne og gerne helst også viden om hvor meget stof der udledes fra den enkelte kilde.

Aim Approach Substances Results Conclusions Aim The main aim of the ScorePP project was to develop Source Control Options for Reducing Emissions of Priority Pollutants from urban areas The specific aim of this task was to identify potential sources and to quantify releases of priority pollutants The main purpose within the ScorePP project is to develop Source Control Options for Reducing Emissions of Priority Pollutants from urban areas. ScorePP is an abbreviation of Source Control Options for Reducing Emissions of Priority Pollutants. To be able to develop these control options and strategies to reduce the emission of PPs, it is a necessity to have a thorough understanding of the sources to the PPs and preferably also knowledge about how much substance that is released from the individual source. Therefore, the specific aim of this work was to identify potential sources and to quantify the releases of PPs from these sources. Hovedformålet med ScorePP projektet er at udvikle kildekontrolforanstaltninger for at reducere udledningen af prioriterede stoffer fra bymiljøer. ScorePP er en forkortelse for Source Control Options for Reducing Emissions of Priority Pollutants. For at kunne udvikle disse kontrolforanstaltninger og strategier for at mindske udledningen af prioriterede stoffer er det en nødvendighed at have et indgående kendskab til kilder til stofferne og gerne helst også viden om hvor meget stof der udledes fra den enkelte kilde.

Approach Develop Source Classification Framework
Aim Approach Substances Results Conclusions Approach Develop Source Classification Framework Compile data on sources & releases Classifying using the Emission String concept To do so we first developed a source classification framework. Hereafter we compiled data on sources and releases. Then we classified all this knowledge using the ES concept, which I will explain in a moment. Finally we have made some calculations of releases based on combining release factors and catchment data. Vi valgte først at udvikle et kildeklassifikationssystem. Dernæst indsamlede vi data som derefter blev klassificeret ved at bruge ESs princippet. Establish releases based on the compiled data

Source Classification Framework
Aim Approach Substances Results Conclusions Source Classification Framework Requirements Content should be structured and organised in a harmonised way Ensure that the different sources could be distinguished from each other To be dynamic and valid EU wide Inspiration US EPA Source Classification Code (US EPA SCC) The Technical Guidance Document on Risk assessment (TGD) Harmonised codes like the Common Nomenclature (CN), the National Classification of Economic Activities (NACE) and the NOmenclature for Sources Emissions (NOSE) EINECS, CAS# What is a SCF? We have had many and long discussions back and forth about what such a system is and how it should look like. A picture that illustrates it quite well is this box, which I guess is known by most of us. It may be a little negative to use a black box, but the idea is that we have the different sources, symbolised by these cubes, cylinders etc. which have to be put into the system in the correct way – the cube can only fit into the squared entrance and the cylinder only into the round entrance and so on. Amongst others we had the following requirements to the system: Content should be structured and organised in a harmonised way It should be ensured that the different sources could be distinguished from each other It should be valid throughout EU It should be dynamic, so that it can be used also when our project ends This means no long text documents where you have to search for the important details or spreadsheets, where only the one that produced the spreadsheet can find the way around in it. We have been inspired by the US EPA, who has such a Source Classification Code. However, the US do not use standardised and harmonised European classification systems … We have looked in the TGD and various harmonised European classification systems like the Common Nomenclature, the National Classification of Economic Activities and the Nomenclature for Source Emissions. These are systems that some European companies already report there activities and emissions to. Finally we discussed whether we should use the EINECS or the CAS #. Hvad er et kildeklassifikationssystem? Vi har haft mange diskussioner frem og tilbage om hvad sådan et system er og hvordan det skulle se ud. Et billede der illustrerer det meget godt er nok denne puttekasse som de fleste af os kender. Det er måske lidt negativt med en sort boks, men ideen er at vi har forskellige kilder, her symboliseret med klodser, der skal puttes ind i et system på den rigtige måde – kuben kan kunne komme gennem det firkantede hul, cylinderen kun gennem det runde hul og så videre. Blandt andet havde vi følgende krav til systemet: Indholdet skulle gemmes på en struktureret, organiseret og harmoniseret måde, De enkelte kilder skulle kunne blive skelnet fra hinanden, Systemet skulle være gyldigt i hele EU, Og så skulle det være så dynamisk at det vil kunne bruges efter dette projekts levetid ALTSå, ikke noget med meget lange tekstdokumenter hvor man skal lede for at finde detaljerne eller regneark, som kun den der har arbejdet med det, kan finde rundt i. Vi har hentet inspiration fra USA, hvor miljøstyrelsen har en såkaldt Source Classification Code. Vi har kigget i TGDen og forskellige harmoniserede europæiske klassifikationssystemer som the Common Nommenclature, National Classification of Economic Acitivites og Nomenclature for Source Emissions. Til sidst diskuterede vi om vi skulle bruge EINECS eller CAS# som det kemiske ID.

National Classification of Economic Activities (NACE)
Aim Approach Substances Results Conclusions National Classification of Economic Activities (NACE) 18 main classes and about 850 subclasses

NOmenclature for Sources of Emissions (NOSE)
Aim Approach Substances Results Conclusions NOmenclature for Sources of Emissions (NOSE) 14 main classes and about 750 subclasses

Aim Approach Substances Results Conclusions ScorePP classification of the Urban Structure – the Urban Structure Descriptor (USD) 22 classes in use

Source Classification Framework – the Emission String concept
Aim Approach Substances Results Conclusions Source Classification Framework – the Emission String concept CAS #: unique identification of each substance NOSE: unique identification of emission processes NACE: unique identification of economic activities related with the source The ScorePP classifications Urban Structure Descriptor (USD), comprising e.g. Construction sites Facilities; e.g. factories, dentists, slaughter houses (i.e. legal entities) Households Rivers Roads Waste sites/landfills Release Profile Descriptor (RPD) Temporal releases on a daily, weekly and yearly basis Release Factor (RF) All data are stored in a database The final decision was to develop the ES concept which consist of the following parameters: The CAS # to identify the substance The NOSE classification to state the emission process (for instance combustion of coal in relation to central heating, disposal of waste in incineration plants or emission of chemicals in relation to production of a certain product) The NACE classification to state the economical activity, which could be transport, different kinds of manufacturing or retail business Then our own developed Urban Structure which describes the urban structure, for instance households, construction sites, roads or facilities which comprise all kinds of legal entities like factories, dentists etc. Then a Release Pattern descriptor to identify potential releases on a daily, weekly and yearly basis, dependent on weekends, holidays and other temporal dependencies. Finally we associated as many release data as possible. And something that is really nice in this work, is that all data are nicely stored in ONE database. This makes information sharing within our project and also later very easy and furthermore, because we throughout the database have used the CAS # as the central identification and also used harmonised classification systems as the NOSE and NACE systems and connected it with GIS-coordinates, we can easily produce electronic maps that show where we have this and that pollution. In this way an ES is more or less similar to a source or an activity. In our database there can be examples of two or more ESs representing one source or activity. This can be seen for example for benzene originating from combustion in vehicles and is because we in the literature found release factors for both vehicles with and without catalyst and for both diesel and gasoline driven vehicles and so on. Den endelige beslutning landede på brugen af Emission Strings, med følgende hovedparametre: CAS# for at identificere kemikaliet, NOSE for at angive emissionsprocessen (det kan være relateret til forbrænding af kul og koks i forbindelse med fjernvarme, bortskaffelse af affald i forbrændingsanlæg eller udledning af kemikalier i forbindelse med produktion af et givent produkt), NACE for at angive den økonomiske aktivitet (hvilket kan være transport, forskellige former for fremstilling eller detailhandel) og til sidst Vores egen definerede bymæssige struktur (eks. husholdninger, byggepladser eller faciliteter der omfatter enhver form for lovlig drevet virksomhed). Og noget af det rigtigt lækre i denne opgave, det er at alle data er pænt lagret i EN database! Det gør vidensdeling i vores projekt og senere hen meget let, og ydermere fordi vi har bundet det hele op på CAS# og brugt harmoniserede klassifikationssystemer som NOSE og NACE og angivet GIS-koordinater, så kan vi fremstille elektroniske kort der viser hvor vi har hvilken form for forurening. Således angiver en ES i store træk en kilde eller en aktivitet. I vores database forekommer der nogle gange dog flere ESs for en kilde. Det gør sig gældende for eks. udledning af benzen fra forbrænding af brændstof i biler, eftersom litteraturen angav både værdier for biler med og uden katalysator, for diesel og for benzin mv.

Compiling data Online Risk Assessment Reports from EU
Aim Approach Substances Results Conclusions Compiling data Online Risk Assessment Reports from EU Hazardous Substance Data Bank and Household Product Database from US NLM Handbooks and electronic compilations, e.g. the Merck Index, Rippen, the e-Pesticide Manual, Kirk-Othmer’s Encyclopedia of Chemical Technology Research articles We have compiled our data by going through different reports, databases, hand books, electronic compilations of data and also when necessary, original research articles. Most of the aforementioned sources of course comprise research articles. As far as possible we have searched for both sources and releases of chemicals from these sources. We preferred to get information about the release factors from the sources, but when this was not possible we have compiled any other kinds of quantitative release data. The two data sources shown in pink do not contain RFs, but have information about product content or how to dose a given pesticide. We have the opinion, that if we could not have a RF, a dosing regime could be used as a worst case estimate for the release of the given substance. Indsamling af data er foregået ved gennemgang af div. rapporter, databaser, håndbøger og elektroniske samlinger af data og også, når det var nødvendigt, videnskabelige artikler. Langt de fleste andre datakilder bygger ligeledes på videnskabelige artikler. Så vidt muligt har vi i samme datakilder søgt kvantitative data om frigivelse fra de enkelte kilder, det vi senere hen kalder for Release Factor. De to datakilder der er angivet med pink indeholder ikke RFs, men derimod information om et givent produkts indhold af et givent stof. Såfremt man ikke har en RF, vil indhold kunne bruges til at estimere en udledning. Anvender man totalt indhold, vil det selvfølgelig føre til et worst-case scenario.

Classifying sources using the ES concept
Aim Approach Substances Results Conclusions Classifying sources using the ES concept The last exercise is then to classify the compiled knowledge about the source by using this ES concept. This slide shows you how we have envisaged the various sources/activities/processes in our project. Bla bla bla bla This slide shows how we look at possible releases to the urban environment. First you can see the production of a given product, then we have the storage of the product before it is sold and finally we have the use and the disposal. In this case it is a shoe, where the sole could contain a plasticiser. Further, there could be by-products or impurities in the sole. The substances can evaporate, but as the sole is designed to be used, the substance will be released, maybe as particles containing the substance. At last the shoe is disposed. We have for the individual activities, processes and sources produced theses ESs by stating the CAS#, the NOSE, the NACE and the urban structure. When we found the data, we have also associated RFs. Den sidste øvelse er så at klassificere informationen om kilden ved at bruge det her ES-koncept. Den følgende slide viser jer hvordan vi har anskuet mulige udledninger i bymiljøet. Først har vi produktionen af et givent produkt, så har vi opbevaring af det samme produkt og til sidst har vi anvendelsen og bortskaffelsen af produktet. I det her tilfælde er det en sko, hvis sål kunne indeholde en blødgører. Ligeledes kan der være urenheder og andre kontamineringer i sålen. Stofferne kan fordampe, men da sålen er designet til at blive brugt vil der frigives, om ikke det rene stof, så partikler med stoffet i. Og til sidst bortskaffes skoen så. For de enkelte aktiviteter og processer har vi konstrueret ESs ved at angive CAS#, NOSE, NACE og bystrukturen. I bedste fald har vi også knyttet information om frigivelsen.

Substances on the Water Framework Directive
Aim Approach Substances Results Conclusions

Substances on the Water Framework Directive – continued
Aim Approach Substances Results Conclusions

SCF tested on a selection of WFD substances
Our SCF has been tested on a range of substances listed on the WFD. The number of substances on the WFD is a story in it self, as some substances are listed as TBT and its compounds or PAHs, thus there are more individual substances than the 33 originally listed on the WFD. We identified as many as 67, but found out that we needed to reduce these 67 substances. For many of the substances it has been possible to group them, and by this exercise one substance could represent one or more other substances. For instance, endring has more or less identical inherent properties and uses as aldrin, dieldrin and isodrin, why we found it justifiable to let endrin represent the other three. Similar can be done for chlorpyrifos and chlorfenvinfos, atrazin and simazin, trichloroethylene and perchloroethylene. Some of the substances can not be grouped. This is mainly the metals and is due to their many applications. The substances shown in pink are the substances classified as PHS, which means that they have to be phased out of discharges within 20 years after adopting the WFD. Vores klassifikationssystem har vi afprøvet på en række af de stoffer der er listet på vandrammedirektivet. Antallet af stoffer på vandrammedirektivet er en fortælling i sig selv og da vi havde identificeret flere end vi umiddelbart mente vi kunne arbejde videre med i henhold til kilder og frigivelse, udvalgte vi disse 26 stoffer. For langt de fleste af dem har det været muligt at gruppere dem, således at et stof kunne repræsentere et eller flere andre. Eks. kan nævnes at da endrin har stort set samme fysisk-kemiske egenskaber og anvendelser som aldrin, dieldrin og isodrin, fandt vi det anvendeligt at lade endrin repræsentere de andre 3 og dermed reducere antallet af stoffer. Det samme gør sig gældende for eks. chlorpyrifos og chlorfenvinphos, atrazin og simazin, trichlorethylen og perchlorethylen. Der er flere af stofferne der ikke kan grupperes. Det er primært metallerne og det grunder i de meget brede anvendelsesmuligheder der er for metallerne. De stoffer der er angivet i pink er dem der er klassificeret som PHS.

Number of ESs for each PP (ab 900 ESs in total)
Aim Approach Substances Results Conclusions Number of ESs for each PP (ab 900 ESs in total) The following slides shows you some overviews of the data that easily can be extracted from our database. This slide shows how many ESs we could establish for each of the substances we have been working with. På X-aksen af denne graf ses de stoffer vi har arbejdet med, dem jeg viste jer lige før. Y-aksen angiver antallet af de her ESs vi har indsamlet for hvert enkelt stof. Dvs. hele baren angiver at der eks. for anthracene er omkring 50 ES, som i store træk kan oversættes til kilder. For bly er der godt 60 og for flere af pesticiderne er der omkring 10. Det må ikke misforstås som at det er en udtømmelig liste over kilder til det enkelte stof. Det er et udtryk for de kilde VI gennem litteraturstudier har fundet der eksisterer for de enkelte stoffer. Der kan selvfølgelig være nogle kilder vi ikke kender til. Farverne repræsenterer således de ESs hvor vi enten har eller ikke har viden om de kvantitative udledninger forbundet med den givne kilde. Eks. har vi for DCM godt 40 ESs. Heraf er godt 10 med kvantitative data på frigivelse (den grønne farve), nogle stykker angiver hvilken mængde man i et givent område har fundet i forbindelse med en given aktivitet (den blå farve), nogle enkelte angiver indholdet i et givent produkt (den orange farve) og den omkring 20 angiver hvor mange ESs der ikke er knyttet viden om den kvantitative frigivelse. Igen i henhold til vores litteraturestudier. Alt i alt har vi fundet kvantitative data på ca. 17% og ingen data på ca. 65%.

Which sources were identified for a particular PP – DEHP
Aim Approach Substances Results Conclusions Which sources were identified for a particular PP – DEHP Handling of the pure substance Undercoating of motor vehicles Production of electricity Release from electrical cables – indoor and outdoor Treatment of waste; land fills Various manufacturing; sealants, paint, ink, ceramic, plastic, DEHP Release from floor and wall covering Various building materials; tubes, profiles, coated metal sheets Textiles, clothing, footwear, shoes Eftersom alle vores data er lagret i en database, kan man forholdsvis let spørge databasen om hvilke kilder der er til eks. DEHP. Herved fremkommer 9 grupper. Fælles for flere af dem er at kilden er svær at lokalisere, idet der både frigives noget fra biler, fra elektriske kabler både inde og ude, fra forskellige byggematerialer og fra beklædningsdele. Dette var en forholdsvis simpel forespørgsel.

To which compartment are the substances released to?
Aim Approach Substances Results Conclusions To which compartment are the substances released to? Air Groundwater Generally to the urban surface To an impervious urban surface To a permeable urban surface Generally to water (wastewater or receiving waters) Directly to water (receiving waters) Indirectly to water (wastewater going to WWTP) Hernæst kan man spørge om hvilke compartments stofferne udledes til. Vores ESs indeholder nemlig også et estimat/skøn/viden om hvilket compartment en given kilde udleder stof til og i hvilket forhold. Dette vil i flere tilfælde være skønnet ud fra viden om brug og fysisk-kemiske egenskaber af stoffer. Vi har opdelt compartments i: Luft, Grundvand, Forskellige bymæssige overflader, de gennemtrængelige og de ikke gennemtrængelig og en generel og Forskellige former for vand, vand generelt, gennem renseanlæg og direkte til miljøet.

Substances are released to …
Aim Approach Substances Results Conclusions Substances are released to … Denne tabel giver en oversigt over hvilke stoffer vi har fundet udledes til hvilke compartments. Man kan sige at vi ikke har fundet så meget information om en given kilde udleder til gennemtrængelig eller ikke gennemtrængelig bymæssig struktur, idet stort set alle stoffer angives generelt at blive udledt den bymæssige struktur. Hvad vandet angår, så har vi en række stoffer der udledes direkte til vandmiljøet og stort set alle har kilder der udleder til en form for renseanlæg.

Number of ESs in each urban structure (ab 900 ESs in total)
Aim Approach Substances Results Conclusions Number of ESs in each urban structure (ab 900 ESs in total) This slide shows the numbers of ESs within each urban structure. Denne graf viser i princippet samme data. Denne gang bare fordelt på bystruktur. Hvad der nok ikke undrer, så har vi fundet flest kilde i forbindelse med fremstilling, produktion og andre erhverv, eks. tandlæger. Men også mange kilder findes i husholdninger, landbruget og infrastrukturen

Archetype sources Facilities Households
Aim Approach Substances Results Conclusions Archetype sources Agriculture Construction sites and buildings Facilities Households Roads Waste disposal Diffuse and other not immediately classifiable sources From the previous slides we can identify some archetype sources. They are to some extent related to the urban structure or to some activities that we perform. Roads is an urban structure which release substances that are characteristic for transport. Similar can be argued for construction sites and buildings, which is an archetype source comprising typical substances used in building materials. The largest group is the facilities. I will not deal with them here, but will in the following focus on households and roads.

Number of ESs within households (ab 85 ESs in total – 18 substances)
Aim Approach Substances Results Conclusions Number of ESs within households (ab 85 ESs in total – 18 substances) Kigger man lidt dybere i de kilder der hører til husholdningerne får man følgende billede. De to største grupper udleder til luften og til spildevand. Nogle kilder går til bymæssige overflader og enkelte går generelt til vand.

Substances from households are released to …
Aim Approach Substances Results Conclusions Substances from households are released to … Denne tabel angiver hvilke stoffer der udledes til de enkelte compartments. De stoffer der frigives til luften oprinder primært fra forbrændingsprocesser, men både DCM og DEHP oprinder fra afdampning af byggematerialer. Stoffer der udledes til den bymæssige overflade er primært pesticider, urenheder eller oprinder fra byggematerialer. Og stoffer der udledes til spildevand er nogle af de samme som fra indendørs udledning plus dem der eks. udledes fra transport.

Environmental releases due to households
Aim Approach Substances Results Conclusions Environmental releases due to households Heating Anthracene: 0,8-102 mg/kg wood Benzo(a)pyrene: 2,7 mg/kg coal Benzo(a)pyrene: 27 µg/kg wood Smoking Anthracene: 34 ng/cigaret Benzene: µg/cigaret Benzo(a)pyrene: ng/cigaret Clothes and building materials DEHP:250 kg DCM: µg/m2/h TCE: 3,6 µg/m2/h Fertilizers and pest control Diuron: 7,5-25 mg/application Cd: from fertilizers Building materials Ni: 0,3-0,8 mg/m2 stainless steel/yr Cd: 0,01-10 kg/yr from Zn-materials DEHP: 16 tonnes/yr Clothes DEHP: 950 kg/yr Greywater Hg: 17µg/PE/d Cd: 5 kg/yr TCE: µg/L Ni: jewellery, coins, washing etc. Benzo(a)pyrene: 1,8 µg/PE/d Painting and car wash DEHP: 12 kg/yr Fertilizers Cd: 500 g/yr Building materials DEHP:600 kg/yr On this slide you see what we could dig out of the literature relating to releases from households. First we have releases going into air with associated quantitative data. Then we have releases going to the urban surface. Then we have releases going to water, whether directly to surface water or indirectly through the sewer system. Finally we have non-quantified releases. Pay attention to all the different units – which makes it difficult to make an accountant of the actual loads in a certain catchment, as you need to know exactly how much wood is burned, how much clothes is washed etc. Plus releases of HCB, HCH, PeCB, TBTs, chlorpyrifos, endrin, Pb, trifluralin and NPs

Environmental releases due to vehicular transport on roads
Aim Approach Substances Results Conclusions Environmental releases due to vehicular transport on roads Anthracene Combustion: 5,2-28 µg/kg fuel burned, depending on vehicle and fuel type Benzene Combustion: 4-10 mg/km driven, depending on vehicle type Benzo(a)pyrene Combustion: 1-8 µg/km driven, without and with catalyst Cadmium (from both break linings, tyres, fuel and asphalt) 7 kg/year is released in Stockholm with inhabitants DEHP (from undercoating) 200 kg/year is released in Stockholm with inhabitants Mercury Tyres: µg/km depending on vehicle type Roads: 3-17 µg/km depending on vehicle type Nickel Combustion: and 3, ng/km driven, for gasoline and diesel, respectively Brake-linings, tyres and asphalt: ng/km Kigger man dybere i kilder fra transport på veje fås følgende data: Anthracene udledes fra forbrænding. De data vi har fundet viser afhængighed af både køretøj og brændstof. Benzene udledes ligeledes fra forbrænding og er også afhængig af køretøj. Det samme gælder BaP Cd har mange kilder, lige fra asfalten over bremseskiver til forbrænding. DEHP udledes fordi stoffet er i det man undervognsbehandler køretøjer med. Hg udledes pga. slid på veje og dæk. Og Ni udledes lige som Cd fra mange kilder.

Statistics for Denmark year 2007
Aim Approach Substances Results Conclusions Statistics for Denmark year 2007 Jeg har været i DKs statistik og fundet følgende informationer fra 2007. Desværre er data blandet sammen, så man ikke kan dele op på brændstofstype, som det fremgår for både personbiler, taxaer og varevogne er det en fællesopgivelse for både diesel og benzin. I DK blev der kørt ca. 36 milliarder km i lette køretøjer og ca. 11 milliarder i tunge køretøjer. Hvad angår brændstoftype, så var det heller ikke muligt at gøre det samlede forbrug af brændstof op i andelen af benzin og diesel til lette og tunge køretøjer. Hvis man samlet set dividerer den kørte distance med forbruget af brændstof får man små 14 km/L. Adaptation matrix: Everything you need to combine with ES that you need to apply. Release factor multiplier and location.

Environmental releases due to vehicular transport on roads
Aim Approach Substances Results Conclusions Environmental releases due to vehicular transport on roads Depending on fuel and vehicle type: Anthracene: kg Nickel: 4,4-117 kg Benzene from busses, lorries etc: 105 tonnes Cadmium: 49 kg Mercury: 0,3-12 tonnes Release of nickel from Danish highways: 108 kg Benzene from cars: 154 tonnes Benzo(a)pyrene: 360 kg DEHP: 1,41 tonnes Så for DK og samlet set for biler, busser, lastbiler og et enkelt hus fås at der udledes: 12-67 kg anthracene og 0, kg Ni afhængig af køretøj og brændstoftype Ca. 105 tons benzene fra tunge køretøjer og ca. 154 tons benzene fra lette køretøjer Ca 360 tons BaP i alt. Hertil kommer DEHP der oprinder fra undervognsbehandling, anthracene, Cd, Hg og Ni fra slid på dæk og veje og hvad der måtte komme fra lækage og spild. LEL: localised emission(release) load Plus releases of anthracene from wear & tear of tyres and asphalt and release of anthracene, benzene, benzo(a)pyrene due to leakage & spillage Thomas Ruby Bentzen, PhD thesis (2008)

Aim Approach Substances Results Conclusions Conclusions SCF established – based on literature knowledge about sources About 900 ESs established for the 25 WFD substances Ranging from 5 ESs for atrazine and 133 ESs for nickel Overall 16% with concrete knowledge about release quantities Overall 65% without any quantitative data on release into the technosphere WFD substances occur in a wide variety of sources and activities in urban settings and are released to all studied compartments Most sources are related to production activities Other large categories are households, waste disposal, agriculture, construction and transport Substances from households are mainly released to air, wastewater and to the urban surface Substances released to air (both from household and transport) are mainly bi-products from combustion Vi udviklede et kildeklassifikationssystem der nu indeholder information fra litteraturen om kilder til prioriterede stoffer på vandrammedirektivet. Vi fandt omkring 900 ESs for de 25 stoffer. Vi fandt kvantitative frigivelsesdata for 16% af ESs. For 65% af ESs fandt vi ingen kvantitative data om frigivelse. Stofferne på WFD optræder i en bred række kilde og aktiviteter og frigives til alle de undersøgte compartments

Conclusions – continued
Aim Approach Substances Results Conclusions Conclusions – continued Classifying the sources according to the Urban Structure descriptor enables Sources to be linked to GIS, thus enhancing visualisation Definition of archetype sources and thus a better targeting of mitigation options and Emission Control Strategies Vi udviklede et kildeklassifikationssystem der nu indeholder information fra litteraturen om kilder til prioriterede stoffer på vandrammedirektivet. Vi fandt omkring 900 ES for de 25 stoffer, heraf 5 for atrazine og 133 for Ni. Vi fandt kvantitative frigivelsesdata for 16% af ESs. For 65% af ESs fandt vi ingen kvantitative data om frigivelse. Stofferne på WFD optræder i en bred række kilde og aktiviteter og frigives til alle de undersøgte compartments Stoffer fra kilder i husholdningen frigives hovedsageligt til luft, spildevand og bymæssig overflade. Stoffer der frigives til luft er hovedsageligt biprodukter fra forbrænding og vil formentlig kunne tilbageholdes hvis skorstene og udstødning blev udstyret med passende filtre eller hvis man overgik til ikke fossilbrændstofkilde.

Aim Approach Substances Results Conclusions Outlook SCF has been used on national scale to calculate releases from a particular archetype source, i.e. roads (Holten Lützhøft et al., 2009) SCF has been used to identify pollution sources in catchments of Copenhagen (Holten Lützhøft et al., submitted) SCF can be used to establish a pollution inventory for a given catchment in the process of either identifying pollution sources or identifying the most appropriate emission control strategy (Eriksson et al., 2010) Implemented European wide it will help in the process of prioritising finances and work power It would be interesting to perform a comparison of pollution inventories with human health statistics, i.e. the spatial incidents of cancer compared with for instance the expected releases of benzo[a]pyrene Vi udviklede et kildeklassifikationssystem der nu indeholder information fra litteraturen om kilder til prioriterede stoffer på vandrammedirektivet. Vi fandt omkring 900 ESs for de 25 stoffer. Vi fandt kvantitative frigivelsesdata for 16% af ESs. For 65% af ESs fandt vi ingen kvantitative data om frigivelse. Stofferne på WFD optræder i en bred række kilde og aktiviteter og frigives til alle de undersøgte compartments

Acknowledgement The presented results have been obtained within the framework of the project ScorePP - “Source Control Options for Reducing Emissions of Priority Pollutants”, contract no , a project coordinated by Department of Environmental Engineering, Technical University of Denmark within the Energy, Environment and Sustainable Development section of the European Community’s Sixth Framework Programme for Research, Technological Development and Demonstration.

28-03-2017 The ScorePP Approach to Predict Releases of Priority Pollutants From Urban Sources Hans-Christian Holten Lützhøft1, Erica Donner2, Veerle Gevaert3,

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28-03-2017 The ScorePP Approach to Predict Releases of Priority Pollutants From Urban Sources Hans-Christian Holten Lützhøft1, Erica Donner2, Veerle Gevaert3,

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Presentation on theme: "28-03-2017 The ScorePP Approach to Predict Releases of Priority Pollutants From Urban Sources Hans-Christian Holten Lützhøft1, Erica Donner2, Veerle Gevaert3,"— Presentation transcript:

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