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Addressing temporal variability when modeling bioaccumulation in plants.
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).
2009 (English)In: Environmental Science and Technology, ISSN 0013-936X, E-ISSN 1520-5851, Vol. 43, no 10, 3751-6 p.Article in journal (Refereed) Published
Abstract [en]

Steady state models are commonly used to predict bioaccumulation of organic contaminants in biota. However, the steady state assumption may introduce errors when complex dynamic processes such as growth, temperature fluctuations, and variable environmental concentrations significantly affect the major chemical uptake and elimination processes. In this study, a strategy for addressing temporal variability in bioaccumulation modeling is proposed. Chemical partitioning space plots are used to show the time necessary for organic contaminants to approach steady state in plant leaves and roots as well as the dominant uptake/elimination fluxes of chemicals as a function of the contaminants' physical chemical properties. The plots were produced with a novel nonsteady state model of bioaccumulation in plants, which is presented, parameterized, and evaluated. The first prerequisite identified for using a steady state model is that the duration of chemical exposure exceeds the time to approach steady state. Next, the dominant chemical transport processes for the chemical in question should be identified and the variability of parameters affecting these processes compared to the time to approach steady state. A major systematic variation in one of these parameters on a time scale similar to the time to approach steady state may cause an unacceptable deviation between the predicted and true chemical concentrations in vegetation. In such cases a nonsteady state model such as the one presented here should be used. The chemical partitioning plots presented provide guidance for understanding the dominant uptake/elimination processes and the time to approach steady state in relation to the partitioning properties of organic compounds.

Place, publisher, year, edition, pages
American Chemical Society , 2009. Vol. 43, no 10, 3751-6 p.
National Category
Environmental Sciences
Research subject
Applied Environmental Science
Identifiers
URN: urn:nbn:se:su:diva-34628DOI: 10.1021/es900265jISI: 000266046700056PubMedID: 19544883OAI: oai:DiVA.org:su-34628DiVA: diva2:285318
Available from: 2010-01-11 Created: 2010-01-11 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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