Use environment –where?E.g. –* (e.g. Indoor)

- home- office- industry- car cabin

* (e.g. Outdoor)- rural/urban- road

Co CoCo

Component

Material M M M

Material properties* Composition* Density* (Surface/volume)* Thickness

Product properties * Material composition* (Lifetime)* (Surface area)* (Surface materials)

Product-category

PC

P

Product

PP

ALL PRODUCTS

PC

P

P

P

PC PC

PC

PC

Product categoryproperties* Net annual supply* Average lifetime* Surface area* Accumulated stock* (Average material composition)

PC

Use(Use time)

[kg], [pcs]

Emission drivers* Temperature* (Light)* Ventilation, wind

speed* (Hydrology)* (Biofilm)* ...

Chemical Ch Ch Ch Ch ChChemical properties* (Formula)* (Function)* Molecular weight* (Kow)* (Reaction half-life) * (LD50)* ...

[%]

Quantity Q[ton]

[ton]

[ton]

ChEmiTecs concept model

Use type – how?E.g. -* (“New product” phase)* (Human direct contact)* (Passive e.g. storage)* (Intentional emission)* (Abrasion)* ...

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T e c h n o s p h e r e

Sverker Molander, Johan Tivander, Filippa Fuhrman – Environmental Systems Analysis, Chalmers University of Technology, Gothenburg, SwedenPatrik L. Andersson, Peter Haglund, Tomas Holmgren – Department of Chemistry, University of Umeå, Umeå, SwedenElisabeth Hallberg, Tomas Rydberg, Jenny Westerdahl, Andreas Öman – IVL Swedish Environmental Research Institute, Gothenburg, Sweden

Objective First results

On-going work

BackgroundEstimating the size of the problem with release, fate, exposure and effects from the human use of chemical substances of materials and consumer products is daunting. More than 100 000 chemical substances are in commercial use and a reasonable description of their existence in, and release from, plastic polymers, glues, paints, fibres, lubricants and so on comprise a big challenge.Still there is a need to cover the vast range of substances in order to get an initial indication of relative amounts emitted and to know what chemicals to focus regarding risk reduction measures.

The objective of this research was to develop and apply a simple method for an initial approximation of emissions of a set of organic chemicals from products containing a selected number of materials, mostly plastics, used in Sweden.

Emissions of additives from all goods containing plastic non-fibre polymers were based on estimates of total area for each plastic type and an empirical diffusion equation (OECD 2009) or a new equation based on a Piringer-type diffusion coefficient and convection mass transfer predictions. Below are further specifications of the algorithm given in conjunction to its place in the conceptual model’s calculation hierarchy. Bracketed factors were not included in this first iteration of the calculations.

Regardless of diffusion equation used the 8 substances with the highest emissions from the list of chosen additives are sorbitan monolaurate, triphenylphosphate 2,4-dibromophenol, 2,4,6-tribromophenol, oleic acid amide, di(ethylhexyl)phthalate, benzylbuthylphthalate, dibutylphthalate and di(n-hexyl,n-octyl,n-decyl)phthalate. All of these substances are to be expected due to their ubiquitous use and chemical properties. In general the OECD equation overestimates emissions 300-500 times (extreme is 10000 times).

The uncertainty is currently huge due to several factors. The diffusion model is e.g. under improvement based on empirical work. Furthermore are the total areas of polymers uncertain due to uncertain amounts of different polymers and their thicknesses in various goods. The amount of unbound additive in materials is another considerable uncertainty. However these and other uncertainties will be brought down during the development of the model and its database, which include further work based on case-studies and the on-going refined identification of additives in the materials and their amounts.

Model & Algorithm

ReferencesOECD (2009). OECD Series on Emission Scenario Documents, Number 3 – Emission Scenario Documents on Plastic Additives. OECD Environment Health and Safety Publications, Report ENV/JM/MONO(2004)8/REV1

Jansson, E. (2008). Kartläggning av kemiska ämnens förekomst i materialslagen plast, gummi och textil. Individual work 15 hp. 2008:1 Environmental Chemistry, Stockholm University, KEMI (in Swedish)

Sörme L, Brolinson H (2010). PVC-golv och metodik för varuflöden, Chemitscs report P4-D3a, Avdelningen för regioner och Miljö, Statistiska Centralbyrån, SCB, Stockholm Sverige. http://www.chemitecs.se/publicationer (in Swedish).

Swedish EPA and the Swedish Chemicals Inspectorate (1999). Att finna farliga flöden: kemikalier i samhället: rapport från ett regeringsuppdrag. Naturvårdsverket (in Swedish)

Zweifel, H., Maier, R.D. and Schiller, M. (2009). Plastics Additives Handbook, 6th edition. Carl Hanser Verlag, Munich.

ChEmiTecs Research program - http://www.chemitecs.se/

MW for each additive in respective polymer material (needed for the

diffusion equation)

Material composition (content of additives) was based on Zweifel et al (2009) and Jansson (2008)

Density of polymer est. 1000 kg/m3

The following broad categories of polymers were covered:Acetals, Acrylics, Acrylonitrile butadiene styrene (ABS), Amino resins, Epoxy resins, Phenolics, Polyamide, Polycarbonate, Polyester, Polyethylene, Polypropylene, Polystyrene, Polyurethane, Polyvinyl chloride (PVC), Unsaturated polyester resins

Thickness was categorized into 5 broad ranges (0,00001 to 1 m) based on product descriptions.

Accumulated stock = Net annual supply × Average lifetime

Average lifetime was obtained from Swedish EPA and the Swedish Chemicals Inspectorate (1999)

Temperature was 40oC for OECD eq. and 25oC for Piringer diffusion/mass transfer eq.

Material compositions of products were based on product descriptions

Total surface area of a specific polymer material (pm) was calculated asMasspm/(Thicknesspm × Densitypm)

Wind speed was set to 0,15 m/s turbulent flow or 0,000333 m/s laminar flow

Net annual supply = Manufacturing + Import – Export

Net annual supply was compiled for the year of 2006 from the trade and production statistics using databases of Statistics Sweden, applying statistical categories according to the Combined Nomenclature and thus gathering highly aggregated CN-4 statistics as ton/year for the 45 CN-categories containing significant amounts of plastics and with stocks > 100 000 tons. Supply and stocks were calculated according to Sörme and Brolinsson (2010).

Modelling chemical emissions from products– a first iteration of a Swedish case study of polymers and related chemicals