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Modeling bioaccumulation in humans using poly-parameter linear free energy relationships (ppLFERs)
Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
2011 (English)In: Science of the Total Environment, ISSN 0048-9697, E-ISSN 1879-1026, Vol. 409, no 9, 1726-1731 p.Article in journal (Refereed) Published
Abstract [en]

Chemical partition coefficients between environmental media and biological tissues are a key component of bioaccumulation models. The single-parameter linear free energy relationships (spLFERs) commonly used for predicting partitioning are often derived using apolar chemicals and may not accurately capture polar chemicals. In this study, a poly-parameter LFER (ppLFER) based model of organic chemical bioaccumulation in humans is presented. Chemical partitioning was described by an air–body partition coefficient that was a volume weighted average of ppLFER based partition coefficients for the major organs and tissues constituting the human body. This model was compared to a spLFER model treating the body as a mixture of lipid(≈octanol) and water. Although model agreement was good for hydrophobic chemicals (average difference 15% for log KOWN4 and log KOAN 8), the ppLFER model predicted ~90% lower body burdens for hydrophilic chemicals (log KOWb0). This was mainly due to lower predictions of muscle and adipose tissue sorption capacity for these chemicals. A comparison of the predicted muscle and adipose tissue sorption capacities of hydrophilic chemicals with measurements indicated that the ppLFER and spLFER models' uncertainties were similar. Consequently, little benefit from the implementation of ppLFERs in this model was identified. hydrophilic chemicals with measurements indicated that the ppLFER and spLFER models' uncertainties were similar. Consequently, little benefit from the implementation of ppLFERs in this model was identified.

Place, publisher, year, edition, pages
2011. Vol. 409, no 9, 1726-1731 p.
Keyword [en]
ppLFER, Human bioaccumulation, Partition coefficients
National Category
Environmental Sciences
Research subject
Applied Environmental Science
Identifiers
URN: urn:nbn:se:su:diva-42345DOI: 10.1016/j.scitotenv.2011.01.044ISI: 000288738900015OAI: oai:DiVA.org:su-42345DiVA: diva2:345458
Available from: 2010-08-25 Created: 2010-08-25 Last updated: 2017-12-12Bibliographically approved
In thesis
1. Simplifying complex models: Application of modeling tools in exposure assessment of organic pollutants
Open this publication in new window or tab >>Simplifying complex models: Application of modeling tools in exposure assessment of organic pollutants
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Thousands of chemicals are used in society, but the exposure to humans and other organisms has been measured only for a small number of compounds. Modeling tools constitute low-cost and effective alternatives to measurements for the assessment of exposure. In this thesis, the prerequisites for the application of modeling tools in environmental exposure assessment of organic pollutants were explored. The first aspect discussed was emission estimates, which are crucial for any quantitative modeling study. In Paper I, the only currently existing high throughput tool for ranking emissions was evaluated and found to have limited predictive power, suggesting that further research is necessary to enable exposure based screening. The second aspect was the model’s treatment of dynamic processes. A strategy for deciding on the temporal resolution required for the description of dynamic processes was proposed in Paper II, which involved identification of major transport routes and time to approach steady state. The third aspect was prediction of partition coefficients for use in bioaccumulation models. The traditional single parameter regressions (spLFER) employed for this purpose were compared to the more mechanistically sound ppLFER equations in Paper III. The two methods had a similar accuracy when compared to measured data, implying that the choice of approach should be based on other factors than methodology (e.g. availability of accurate input data). The fourth aspect was the influence of system characteristics on human exposure. The susceptibilities of several ecosystems with diverging characteristics to exposure to organic chemicals were compared in Paper IV. The strong variation in exposure susceptibilities found suggests that the choice of model system can be relevant for exposure assessment and that models may have to be tailored to the ecosystem of interest. In the broader context, this work provides methodologies for handling model complexity in exposure modeling.

Place, publisher, year, edition, pages
Stockholm: Department of Applied Environmental Science (ITM), Stockholm University, 2010. 42 p.
Keyword
mass balance model, environmental modeling, exposure modeling, bioaccumulation, organic contaminants, emission estimate, plant model, steady state, poly parameter linear free energy relationship, PPLFER, ecosystem susceptibility
National Category
Environmental Sciences Environmental Sciences
Research subject
Applied Environmental Science
Identifiers
urn:nbn:se:su:diva-42331 (URN)978-91-7447-131-1 (ISBN)
Public defence
2010-10-01, William-Olssonsalen, Geovetenskapens hus, Svante Arrhenius väg 14, Stockholm, 10:00 (English)
Opponent
Supervisors
Note
At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: Submitted. Paper 3: Submitted.Available from: 2010-09-09 Created: 2010-08-24 Last updated: 2010-08-26Bibliographically approved

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