Ernstoff, Alexi

Fantke, Peter

Jolliet, Olivier

We present novel methods to incorporate exposure to chemicals within food contact materials (FCM) (e.g. packaging) into life cycle impact assessment (LCIA). Chemical migration into food is modeled as a function of contact temperature, time, and various chemical, FCM, and food properties. In order to reduce computing time and complexity, a double exponential curve was fit (R2≈1) to an exposure model which otherwise requires numeric solutions. The model is modified to evaluate the product intake fraction, PiF, which is a new metric that accounts for exposure to mass of chemicals embodied in a product in a way compatible with intake fraction, iF, a metric traditionally used in LCIA. The model predicts PiF increases with temperature and for compounds with lower octanol-water partition coefficients within more permeable materials which are in contact with foods with high ethanol equivalencies (fatty foods)

Online Research Database In Technology

                       General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal   If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.     Downloaded from orbit.dtu.dk on: Dec 21, 2017Evaluating use stage exposure to food contact materials in a LCA frameworkErnstoff, Alexi; Jolliet, Olivier; Fantke, PeterPublished in:Proceedings. International conference on Life Cycle Assessment as reference methodology for assessing supplychains and supporting global sustainability challengesPublication date:2015Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Ernstoff, A., Jolliet, O., & Fantke, P. (2015). Evaluating use stage exposure to food contact materials in a LCAframework. In S. Scalbi, A. D. Loprieno, & P. Sposato (Eds.), Proceedings. International conference on LifeCycle Assessment as reference methodology for assessing supply chains and supporting global sustainabilitychallenges: LCA for "Feeding the planet and energy for life" (pp. 311-314). ENEA.copertinaLCA FOR FEEDING THE PLANET...:Layout 1  8-10-2015  16:54  Pagina 1  311 Evaluating use stage exposure to food contact materials in a LCA framework Alexi Ernstoff1, Olivier Jolliet2, Peter Fantke1 1Quantitative Sustainability Assessment, Technical University of Denmark, Kgs. Lyngby, DK 2Environmental Health, University of Michigan, Ann Arbor, Michigan, USA E-mail contact: alexer@dtu.dk 1. Abstract  We present novel methods to incorporate exposure to chemicals within food contact materials (FCM) (e.g. packaging) into life cycle impact assessment (LCIA). Chemical migration into food is modeled as a function of contact temperature, time, and various chemical, FCM, and food properties. In order to reduce computing time and complexity, a double exponential curve was fit (R2≈1) to an exposure model which otherwise requires numeric solutions. The model is modified to evaluate the product intake fraction, PiF, which is a new metric that accounts for exposure to mass of chemicals embodied in a product in a way compatible with intake fraction, iF, a metric traditionally used in LCIA. The model predicts PiF increases with temperature and for compounds with lower octanol-water partition coefficients within more permeable materials which are in contact with foods with high ethanol equivalencies (fatty foods). 2. Introduction  Various life cycle assessment (LCA) studies evaluating food contact materials (FCMs), like baby food packaging containers, have found advantages to plastic over glass [1]. Life cycle impact assessment (LCIA) includes human toxicity impacts from exposure to chemicals released throughout product life cycles, but excludes use stage exposures to chemicals migrating from FCM into food. Generally, regulatory risk assessments aim to ensure human exposure to potentially harmful chemicals in food is below certain thresholds of ‘safety’ and rely on submitted industry data or migration modeling of supposed worst-case scenarios [2]. Such efforts help limit dietary exposure, but actual human intake and levels of some phthalates within food nonetheless approach or exceed regulatory thresholds—with indication of FCMs as the chemicals source [3]. Furthermore, regulatory thresholds for toxic substances are continuously subject to change for various reasons and differ between countries [4, 5]. Unlike risk assessment the primary goal of LCIA is not to ensure individual consumer safety with respects to toxicity thresholds, but to indicate products with minimal potential for population-scale impact, and thus LCIA methods rely on linear dose-response relationships (not thresholds) derived from toxicity studies and combine these with average population-scale (not worst-case individual) exposure. Accordingly, LCIA is a promising risk-minimization and product-optimization approach for FCM; however, methods to include exposure to FCM in LCIA are currently lacking although they likely exceed other life cycle exposures [3]. Our goal is to provide LCIA-compatible methods to close this research gap. 3. Methods To be compatible with the scope of LCIA, which defines a reference flow (e.g. a mass of packaging required to contain a volume of food), we built the FCM exposure model to estimate the newly defined product intake fraction, PiF (kgintake/kgin product) [6]. This method quantifies PiF as the chemical-specific mass taken in by users of the FCM product per kilo of chemical in the FCM—where ingestion is assumed to be the   312 dominating route, and food waste, inhalation, dermal contact, and exposure to environmental emissions are assumed negligible.   Parameter  Parameter  Parameter a octanol-water partition coefficient, Kow b root of tan qn = -αqn c volume of FCM a molecular weight, MW c initial concentration (migrant) c density of FCM b diffusion coefficient (migrant) c temperature c thickness of FCM b diffusion parameter (polymer) c time of contact c density of food b ethanol equivalency of food, E-eq c activation energy (polymer) c mass of food b package-food partition coefficient, KP,F c contact area  c volume of food  Table 1: Required parameters for migration model, and their classification a, b, or c  We adapted a numeric migration model commonly used in regulatory risk assessment and compliance testing [7, 8, 10], by deriving an analytical solution and providing average/realistic diffusion and partition coefficients for use in LCIA, instead of the default values used by risk assessors. Specifically, the model is for chemicals in plastic packaging (see [8]) relying on 19 input parameters (Table 1) which we classified as a) available in open-source platforms (e.g. molecular weight), b) estimable (e.g. by a linear regression), and c) default assumptions given by regulatory documents [8] which can be updated by the LCA practitioner. The model was programmed in MATLAB® and we developed approximation strategies when needed, e.g. the plastic-food partition coefficient (KP,F) is a function of ethanol-equivalency (e.g. food fat content) and the octanol-water partition coefficient (Kow) of the chemical migrant [9]. We also extracted data from [8] to calculate average polymer-specific diffusion parameters. Further, we investigated fitting a double exponential to the model: PiF(kgintake/kgin product) =a*exp(b*t) + c*exp(d*t) which could then be programmed in a spreadsheet where computing time and required input parameters are reduced.  Hypothetical migrants across Kow and at two molecular weights (MW) within polyethylene terephthalate (PET) were modeled at 5oC for 10 days. PiF was also modeled for diethylhexyl phthalate (DEHP) in PET and high-density polyethylene (HDPE) as FCMs for milk, clear drinks, and dough (spanning ethanol equivalencies), at 125oC for in-bottle pasteurization.  4. Results and Discussion We used a regulatory risk assessment FCM migration model, and derived a nearly identical but analytically computational solution, solved for the LCIA-compatible PiF metric, and estimated average (not worst-case) diffusion and partition coefficients. Our results and work by [7,8,9,10] identify KP,F as an important parameter. We developed a linear approximation for KP,F for all ethanol equivalencies (E-eq) i.e. content of organic phase, such as fat within foods, from data available in [9] for only three E-eq. More data may be needed for an improved approximation, e.g. as provided in background calculations in FACET [10]. When regulators apply the migration model [8] parameters classified as b) estimatable, are often set to a fixed value. Although stated in regulatory documents that package-food partition coefficient, KP,F=1 is a fixed “worst-case,” we found evidence KP,F<1 may occur experimentally [9], and that conveniently, when KP,F=1   313 dependent parameters are also fixed, simplifying the calculation. For various plastic-food combinations we found often KP,F >1, which supports the need to better approximate KP,F for model application in LCIA as well as for realistic exposure estimates [10].   Figure 1: PiF model results across Kow for hypothetical migrants at MW=300 and 50 g/mol, for foods  with various ethanol equivalencies (E-eq).   Figure 2: Example of the PiF model results for DEHP, where exposure potential was found to be high for DEHP in HDPE for foods with 90%E-eq, mid for HPDE for foods with 50%E-eq, and low for DEHP in PET  with all food types as well as HPDE with 10%E-eq.  Further, via MATLAB® we fit and parameterized a double exponential curve (R2≈1), where for example parameter c=1-a, and a, b, d are functions of easily obtainable input parameters. In this manor, the difficult to obtain input parameters, e.g. iterative solutions of transcendental equations, were no longer needed and computational time was decreased.  Preliminary results, e.g. for PET demonstrate that when contact temperature, T= 5oC, PiF<10-3 kgintake/kgin product and is largely influenced by KP,F which is a function of Kow of the migrant and E-eq of the contacted (packaged) food. This reflects that chemicals tend to remain partitioned in plastic when in contact with foods with low ethanol-equivalencies (e.g. clear drinks), but increasingly partition into foods with higher ethanol   314 equivalencies (e.g. milk and dough) (Figure 1). A migrant’s MW was unimportant for the T=5oC scenarios (Figure 1), but has a major influence at T=125oC for in-bottle pasteurization (results not shown) because of the influence of the diffusion coefficient. For high temperatures, PiF can approach 20% for contact times of 30 minutes (Figure 2) for foods with high ethanol equivalencies (e.g. >90%) when the model was run for DEHP within FCM made of HDPE (which is typically not legally allowed, however may occur at low levels via recycling processes and/or contamination). As our model is based on a regulatory model, the trends we observed with respect to chemical, food, material, and scenario properties are also considered by risk assessors, e.g. reduction factors for certain fatty foods are applied assuming that modeling with a high E-eq greatly overestimates exposure [2]. 5. Conclusion We developed a modeling strategy that adapts and parameterizes a numerical FCM migration model normally used to ensure risk-based regulatory compliance, to be operational in LCIA by analytically estimating an average/realistic PiF. While risk assessment based on supposed worst-case scenarios is required to evaluate FCM safety compliance, including FCM migration modeling in LCIA has a different goal of comparative risk minimization which accounts for impact trade-offs due to the entire FCM life cycle. Including use stage exposures to FCM in LCIA—which judging by preliminary calculation of PiFs has the potential for exposure exceeding environmentally mediated exposures by orders of magnitude—may help minimize exposure to chemicals within FCM, which is especially important for those which already exceed regulatory statutes, like DEHP, and may be due to recycling or other processes along the products’ life cycles.  6. References [1] Humbert, S., Rossi, V., Margni, M., Jolliet, O. and Loerincik, Y., 'Life cycle assessment of two baby food packaging alternatives: glass jars vs. plastic pots' Int J Life Cycle Assess 14 (2) (2009) 95–106. [2]  Simoneau, C. Applicability of generally recognised diffusion models for the estimation of specific migration in support of EU Directive 2002/72/EC. European Commission, JRC (2010). [3] Lee, J., Pedersen, A. B. and Thomsen, M., 'The influence of resource strategies on childhood phthalate exposure – The role of REACH in a zero waste society' Environ. Int.  73 (2014) 312–322. [4] Magnuson, B., Munro, I., Abbot, P., Baldwin, N.,  Lopez-Garcia, R., Ly, K., McGirr, L., Roberts, A. and Socolovsky, S., 'Review of the regulation and safety assessment of food substances in various countries and jurisdictions' Food Addit Contam Part A Chem Anal Control Expo Risk Assess. 30 (7) (2013) 1147–1220. [5] Muncke, J., Myers, J. P., Scheringer, M. and Porta, M., 'Food packaging and migration of food contact materials: will epidemiologists rise to the neotoxic challenge? J Epidemiol Community Health (2013). [6] Jolliet, O., Ernstoff, A., Csiszar, S. and Fantke, P., 'Defining the Product Intake Fraction to Quantify and Compare Exposure to Consumer Products'. Environ Sci and Technol (in press, doi: 10.1021/acs.est.5b01083). [7] Begley, T., Castle, L., Feigenbaum, A., Franz, R., Hinrichs, K., Lickly, T., Mercea, P., Milana, M., O’Brien, A., Rebre, S. and et al., 'Evaluation of migration models that might be used in support of regulations for food-contact plastics' Food Additives & Contaminants 22 (1) (2005) 73–90. [8]  Ozak, A., Gruner, A., Störmer, A., Brandsch, R., and Franz, R., 'Correlation between Partition Coefficients Polymer/Food Simulant, KP,F, and Octanol/Water, Log POW ― a New Approach in support of Migration Modeling and Compliance Testing' Deutsche Lebensmittel-Rundschau 106 (4) (2010) 203–208. [9]Oldring, P. K. T., Castle, L., O’Mahony, C., Dixon, J., 'Estimates of Dietary Exposure to Bisphenol A (BPA) from Light Metal Packaging using Food Consumption and Packaging usage Data: A Refined Deterministic Approach and a Fully Probabilistic (FACET) Approach' Food Addit Contam Part A Chem Anal Control Expo Risk Assess. 31 (3) (2014) 466–489.  

Evaluating use stage exposure to food contact materials in a LCA framework

http://orbit.dtu.dk/ws/files/118303347/Evaluating_use_stage_exposure.pdf

Evaluating use stage exposure to food contact materials in a LCA framework

Abstract

Similar works

Full text

Available Versions

Online Research Database In Technology