Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Simplifying complex models: Application of modeling tools in exposure assessment of organic pollutants
Stockholm University, Faculty of Science, Department of Applied Environmental Science (ITM).
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 [en]
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: urn:nbn:se:su:diva-42331ISBN: 978-91-7447-131-1 (print)OAI: oai:DiVA.org:su-42331DiVA: diva2:345349
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
List of papers
1. Evaluation of a novel high throughput screening tool for relative emissions of industrial chemicals used in products
Open this publication in new window or tab >>Evaluation of a novel high throughput screening tool for relative emissions of industrial chemicals used in products
2011 (English)In: Chemosphere, ISSN 0045-6535, E-ISSN 1879-1298, Vol. 82, no 7, 996-1001 p.Article in journal (Refereed) Published
Abstract [en]

Tens of thousands of chemicals are currently marketed worldwide, but only a small number of these compounds has been measured in effluents or the environment to date. The need for screening methodologies to select candidates for environmental monitoring is therefore significant. To meet this need, the Swedish Chemicals Agency developed the Exposure Index (EI), a model for ranking emissions to a number of environmental matrices based on chemical quantity used and use pattern. Here we evaluate the EI. Data on measured concentrations of organic chemicals in sewage treatment plants, one of the recipients considered in the EI model, were compiled from the literature, and the correlation between predicted emission levels and observed concentrations was assessed by linear regression analysis. The adequacy of the parameters employed in the EI was further explored by calibration of the model to measured concentrations. The EI was found to be of limited use for ranking contaminant levels in STPs; the r2 values for the regressions between predicted and observed values ranged from 0.02 (= 0.243) to 0.14 (= 0.007) depending on the dataset. The calibrated version of the model produced only slightly better predictions although it was fitted to the experimental data. However, the model is a valuable first step in developing a high throughput screening tool for organic contaminants, and there is potential for improving the EI algorithm.

Keyword
Emission estimates, Exposure, Organic chemicals, Sewage treatment plants, Use pattern, Emission factor
National Category
Environmental Sciences
Research subject
Applied Environmental Science
Identifiers
urn:nbn:se:su:diva-42349 (URN)10.1016/j.chemosphere.2010.10.069 (DOI)000287563800008 ()
Available from: 2010-08-25 Created: 2010-08-25 Last updated: 2017-12-12Bibliographically approved
2. Addressing temporal variability when modeling bioaccumulation in plants.
Open this publication in new window or tab >>Addressing temporal variability when modeling bioaccumulation in plants.
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
National Category
Environmental Sciences
Research subject
Applied Environmental Science
Identifiers
urn:nbn:se:su:diva-34628 (URN)10.1021/es900265j (DOI)000266046700056 ()19544883 (PubMedID)
Available from: 2010-01-11 Created: 2010-01-11 Last updated: 2017-12-12Bibliographically approved
3. Modeling bioaccumulation in humans using poly-parameter linear free energy relationships (ppLFERs)
Open this publication in new window or tab >>Modeling bioaccumulation in humans using poly-parameter linear free energy relationships (ppLFERs)
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.

Keyword
ppLFER, Human bioaccumulation, Partition coefficients
National Category
Environmental Sciences
Research subject
Applied Environmental Science
Identifiers
urn:nbn:se:su:diva-42345 (URN)10.1016/j.scitotenv.2011.01.044 (DOI)000288738900015 ()
Available from: 2010-08-25 Created: 2010-08-25 Last updated: 2017-12-12Bibliographically approved
4. The susceptibility of human populations to environmental exposure to organic contaminants
Open this publication in new window or tab >>The susceptibility of human populations to environmental exposure to organic contaminants
2010 (English)In: Environmental Science and Technology, ISSN 1086-931X, E-ISSN 1520-6912, Vol. 44, no 16, 6249-6255 p.Article in journal (Refereed) Published
Abstract [en]

Environmental exposure to organic contaminants is a complex function of environmental conditions, food chain characteristics, and chemical properties. In this study the susceptibility of various human populations to environmental exposure to neutral organic contaminants was compared. An environmental fate model and a linked bioaccumulation model were parameterized to describe ecosystems in different climatic regions (temperate, arctic, tropical and steppe). The human body burden resulting from constant emissions of hypothetical chemicals was estimated for each region. An exposure susceptibility index was defined as the body burden in the region of interest normalized to the burden of the same chemical in a reference human from the temperate region eating an average diet. For most persistent chemicals emitted to air, the Arctic had the highest susceptibility index (max 520). Susceptibility to exposure was largely determined by the food web properties. The properties of the physical environment only had a marked effect when air or water, not food, was the dominant source of human exposure. Shifting the mode of emission markedly changed the relative susceptibility of the ecosystems in some cases. The exposure arising from chemical use clearly varies between ecosystems, which makes an understanding of ecosystem susceptibility to exposure important for chemicals management.

Place, publisher, year, edition, pages
Washington, D.C.: American Chemical Society, 2010
National Category
Environmental Sciences
Research subject
Applied Environmental Science
Identifiers
urn:nbn:se:su:diva-42339 (URN)10.1021/es1009339 (DOI)000280727400041 ()
Available from: 2010-08-25 Created: 2010-08-25 Last updated: 2017-12-12Bibliographically approved

Open Access in DiVA

No full text

Search in DiVA

By author/editor
Undeman, Emma
By organisation
Department of Applied Environmental Science (ITM)
Environmental SciencesEnvironmental Sciences

Search outside of DiVA

GoogleGoogle Scholar

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 153 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf