Validation and sensitivity of the FINE Bayesian network for forecasting aquatic exposure to nano-silver
Abstract
The adaptive nature of the Forecasting the Impacts of Nanomaterials in the Environment (FINE) Bayesian network is explored. We create an updated FINE model (FINEAgNP-2) for predicting aquatic exposure concentrations of silver nanoparticles (AgNP) by combining the expert-based parameters from the baseline model established in previous work with literature data related to particle behavior, exposure, and nano-ecotoxicology via parameter learning. We validate the AgNP forecast from the updated model using mesocosm-scale field data and determine the sensitivity of several key variables to changes in environmental conditions, particle characteristics, and particle fate. Results show that the prediction accuracy of the FINEAgNP-2 model increased approximately 70% over the baseline model, with an error rate of only 20%, suggesting that FINE is a reliable tool to predict aquatic concentrations of nano-silver. Sensitivity analysis suggests that fractal dimension, particle diameter, conductivity, time, and particle fate have the most influence on aquatic exposure given the current knowledge; however, numerous knowledge gaps can be identified to suggest further research efforts that will reduce the uncertainty in subsequent exposure and risk forecasts.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Toxicity prediction models trained on these attribute combinations can sometimes identify causal relationships, which can be effectively achieved with the Bayesian Network approach.
“Toxicity prediction models trained on these attribute combinations can sometimes identify causal relationships,239 which can be effectively achieved with the Bayesian Network (BN, also called a Bayesian Belief network, BBN) approach.249”
From QSAR without borders (2020) · cites this paper as [249]
Cites
(1988) · Cited by 16,582 · see citing works
Bayesian Networks and Decision Graphs
Jensen, F. V., & Nielsen, T. D. (2007) · Information science and statistics · doi:10.1007/978-0-387-68282-2 · Cited by 4,157 · see citing works
Gottschalk, F., Sonderer, T., Scholz, R. W., & Nowack, B. (2009) · Environmental Science & Technology · doi:10.1021/es9015553 · Cited by 2,388 · see citing works
Exposure Modeling of Engineered Nanoparticles in the Environment
Mueller, N. C., & Nowack, B. (2008) · Environmental Science & Technology · doi:10.1021/es7029637 · Cited by 1,828 · see citing works
Advantages and challenges of Bayesian networks in environmental modelling
Uusitalo, L. (2007) · Ecological Modelling · doi:10.1016/j.ecolmodel.2006.11.033 · Cited by 1,110 · see citing works
Blaser, S. A., Scheringer, M., MacLeod, M., & Hungerbühler, K. (2007) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2007.10.010 · Cited by 971 · see citing works
The release of engineered nanomaterials to the environment
Gottschalk, F., & Nowack, B. (2011) · Journal of Environmental Monitoring · doi:10.1039/c0em00547a · Cited by 795 · see citing works
The EM algorithm for graphical association models with missing data
Lauritzen, S. L. (1995) · Computational Statistics & Data Analysis · doi:10.1016/0167-9473(93)e0056-a · Cited by 761 · see citing works
Marcot, B., Steventon, J. D., Sutherland, G., & Mccann, R. K. (2006) · Canadian Journal of Forest Research · doi:10.1139/x06-135 · Cited by 688 · see citing works
Nanomaterials and the environment: A review for the biennium 2008–2010
Peralta-Videa, J. R., Zhao, L., López-Moreno, M. L., de la Rosa, G., Hong, J., & Gardea-Torresdey, J. L. (2010) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2010.11.020 · Cited by 593 · see citing works
Lowry, G. V., Espinasse, B. P., Badireddy, A. R., Richardson, C. J., Reinsch, B. C., Bryant, L. D., Bone, A. J., Deonarine, A., Chae, S.-R., Thérézien, M., et al. (2012) · Environmental Science & Technology · doi:10.1021/es204608d · Cited by 400 · see citing works
Self-citationMetrics for evaluating performance and uncertainty of Bayesian network models
Marcot, B. G. (2012) · Ecological Modelling · doi:10.1016/j.ecolmodel.2012.01.013 · Cited by 380 · see citing works
Wiesner, M. R., Lowry, G. V., Jones, K., Hochella, M. F., Giulio, R. T. D., Casman, E. A., & Bernhardt, E. S. (2009) · Environmental Science & Technology · doi:10.1021/es803621k · Cited by 350 · see citing works
Self-citationAschberger, K., Micheletti, C., Sokull‐Klüttgen, B., & Christensen, F. M. (2011) · Environment International · doi:10.1016/j.envint.2011.02.005 · Cited by 327 · see citing works
Gottschalk, F., Scholz, R. W., & Nowack, B. (2009) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2009.08.011 · Cited by 293 · see citing works
Categorization framework to aid hazard identification of nanomaterials
Hansen, S. F., Larsen, B. H., Olsen, S. I., & Baun, A. (2007) · Nanotoxicology · doi:10.1080/17435390701727509 · Cited by 232 · see citing works
Using Bayesian belief networks in adaptive management
Nyberg, J., Marcot, B., & Sulyma, R. (2006) · Canadian Journal of Forest Research · doi:10.1139/x06-108 · Cited by 220 · see citing works
Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (2000)Preprint
Boutilier, C. E., & Goldszmidt, M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1304.3842 · Cited by 204 · see citing works
A Bayesian belief network analysis of factors influencing wildfire occurrence in Swaziland
Dlamini, W. M. D. (2009) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2009.08.002 · Cited by 175 · see citing works
Hristozov, D. R., Gottardo, S., Critto, A., & Marcomini, A. (2012) · Nanotoxicology · doi:10.3109/17435390.2011.626534 · Cited by 168 · see citing works
Linkov, I., & Seager, T. P. (2011) · Environmental Science & Technology · doi:10.1021/es100959q · Cited by 148 · see citing works
Exposure Assessment Approaches for Engineered Nanomaterials
Abbott, L. C., & Maynard, A. (2010) · Risk Analysis · doi:10.1111/j.1539-6924.2010.01446.x · Cited by 145 · see citing works
Value of information analysis: the state of application
Keisler, J. M., Collier, Z. A., Chu, E., Sinatra, N., & Linkov, I. (2013) · Environment Systems & Decisions · doi:10.1007/s10669-013-9439-4 · Cited by 137 · see citing works
Research Priorities to Advance Eco-Responsible Nanotechnology
Alvarez, P. J. J., Colvin, V. L., Lead, J. R., & Stone, V. (2009) · ACS Nano · doi:10.1021/nn9006835 · Cited by 134 · see citing works
Nanomaterials: Risks and Benefits
Linkov, I., & Steevens, J. A. (2009) · NATO science for peace and security series. C, Environmental security · doi:10.1007/978-1-4020-9491-0 · Cited by 130 · see citing works
Reflections on the use of Bayesian belief networks for adaptive management
Henriksen, H. J., & Barlebo, H. C. (2007) · Journal of Environmental Management · doi:10.1016/j.jenvman.2007.05.009 · Cited by 124 · see citing works
Hendren, C. O., Badireddy, A. R., Casman, E. A., & Wiesner, M. R. (2013) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2013.01.078 · Cited by 120 · see citing works
Self-citationEnvironmental risk analysis for nanomaterials: Review and evaluation of frameworks
Grieger, K., Linkov, I., Hansen, S. F., & Baun, A. (2011) · Nanotoxicology · doi:10.3109/17435390.2011.569095 · Cited by 111 · see citing works
Linkov, I., Steevens, J. A., Adlakha-Hutcheon, G., Bennett, E., Chappell, M. A., Colvin, V. L., Davis, J. M., Davis, T., Elder, A. C. P., Hansen, S. F., et al. (2008) · Journal of Nanoparticle Research · doi:10.1007/s11051-008-9514-9 · Cited by 86 · see citing works
Hendren, C. O., Lowry, M., Grieger, K., Money, E. S., Johnston, J. M., Wiesner, M. R., & Beaulieu, S. M. (2013) · Environmental Science & Technology · doi:10.1021/es302749u · Cited by 84 · see citing works
Self-citationMoney, E. S., Reckhow, K. H., & Wiesner, M. R. (2012) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2012.03.064 · Cited by 72 · see citing works
Self-citationO’Brien, N., & Cummins, E. (2010) · Risk Analysis · doi:10.1111/j.1539-6924.2010.01540.x · Cited by 69 · see citing works
A weight of evidence approach for hazard screening of engineered nanomaterials
Hristozov, D. R., Zabeo, A., Foran, C. M., Isigonis, P., Critto, A., Marcomini, A., & Linkov, I. (2012) · Nanotoxicology · doi:10.3109/17435390.2012.750695 · Cited by 68 · see citing works
Estimating leaf area index from satellite imagery using Bayesian networks
Kalácska, M., Sánchez-Azofeifa, A. G., Caelli, T., Rivard, B., & Boerlage, B. (2005) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2005.848412 · Cited by 62 · see citing works
Exploiting Qualitative Knowledge in the Learning of Conditional Probabilities of Bayesian NetworksPreprint
Wittig⋆, F., & Jameson, A. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.3903 · Cited by 38 · see citing works
A risk forecasting process for nanostructured materials, and nanomanufacturing
Wiesner, M. R., & Bottero, J. (2011) · Comptes Rendus Physique · doi:10.1016/j.crhy.2011.06.008 · Cited by 35 · see citing works
Self-citationClassifying Nanomaterial Risks Using Multi-Criteria Decision Analysis
Linkov, I., Steevens, J. A., Chappell, M. A., Tervonen, T., Figueira, J. R., & Mérad, M. (2009) · NATO science for peace and security series. C, Environmental security · doi:10.1007/978-1-4020-9491-0_13 · Cited by 11 · see citing works
Application of a Bayesian network for land-cover classification from a Landsat 7 ETM+ image
Dlamini, W. M. D. (2011) · International Journal of Remote Sensing · doi:10.1080/01431161.2010.512934 · Cited by 7 · see citing works
Foltran, F., Berchialla, P., Bigi, R., Migliaretti, G., BESTETTI, A., & Gregori, D. (2010) · Journal of Evaluation in Clinical Practice · doi:10.1111/j.1365-2753.2010.01366.x · Cited by 3 · see citing works
Proceedings of the French Academy of Sciences
(1852) · Scientific American · doi:10.1038/scientificamerican10301852-54 · Cited by 2 · see citing works
VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY
BRIER, G. W. (1950) · Monthly Weather Review · doi:10.1175/1520-0493(1950)078
Flagged first, then by citations
4 of the 45 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works
Nanotechnology and artificial intelligence to enable sustainable and precision agriculture
Zhang, P., Guo, Z., Ullah, S., Melagraki, G., Afantitis, A., & Lynch, I. (2021) · Nature Plants · doi:10.1038/s41477-021-00946-6 · Cited by 506 · see citing works
Bayesian Networks in Environmental Risk Assessment: A Review
Kaikkonen, L., Parviainen, T., Rahikainen, M., Uusitalo, L., & Lehikoinen, A. (2020) · Integrated Environmental Assessment and Management · doi:10.1002/ieam.4332 · Cited by 268 · see citing works
Modeling nanomaterial fate and uptake in the environment: current knowledge and future trends
Baalousha, M., Cornelis, G., Kuhlbusch, T. A. J., Lynch, I., Nickel, C., Peijnenburg, W. J. G. M., & van den Brink, N. W. (2016) · Environmental Science Nano · doi:10.1039/c5en00207a · Cited by 132 · see citing works
Marvin, H. J. P., Bouzembrak, Y., Janssen, E. M., van der Zande, M., Murphy, F., Sheehan, B., Mullins, M., & Bouwmeester, H. (2017) · Nanotoxicology · doi:10.1080/17435390.2016.1278481 · Cited by 82 · see citing works
Isigonis, P., Hristozov, D. R., Benighaus, C., Giubilato, E., Grieger, K., Pizzol, L., Semenzin, E., Linkov, I., Zabeo, A., & Marcomini, A. (2019) · Nanomaterials · doi:10.3390/nano9050696 · Cited by 62 · see citing works
Bayesian Network Resource for Meta‐Analysis: Cellular Toxicity of Quantum Dots
Bilal, M., Oh, E., Liu, R., Breger, J. C., Medintz, I. L., & Cohen, Y. (2019) · Small · doi:10.1002/smll.201900510 · Cited by 50 · see citing works
Marvin, H. J. P., & Bouzembrak, Y. (2019) · Agricultural Systems · doi:10.1016/j.agsy.2019.102760 · Cited by 49 · see citing works
Ecotoxicity of silver nanoparticles on plankton organisms: a review
Kalantzi, I., Mylona, K., Toncelli, C., Bucheli, T. D., Knauer, K., Pergantis, S. A., Pitta, P., Tsiola, A., & Tsapakis, M. (2019) · Journal of Nanoparticle Research · doi:10.1007/s11051-019-4504-7 · Cited by 45 · see citing works
Adam, V., & Nowack, B. (2017) · Environmental Science Nano · doi:10.1039/c7en00487g · Cited by 44 · see citing works
Characterization of engineered TiO2 nanomaterials in a life cycle and risk assessments perspective
Adam, V., Loyaux‐Lawniczak, S., & Quaranta, G. (2015) · Environmental Science and Pollution Research · doi:10.1007/s11356-015-4661-x · Cited by 41 · see citing works
Review of decision analytic tools for sustainable nanotechnology
Subramanian, V., Semenzin, E., Hristozov, D. R., den Beuken, E. Z.-V., Linkov, I., & Marcomini, A. (2015) · Environment Systems & Decisions · doi:10.1007/s10669-015-9541-x · Cited by 38 · see citing works
A Tractable Method for Measuring Nanomaterial Risk Using Bayesian Networks
Murphy, F., Sheehan, B., Mullins, M., Bouwmeester, H., Marvin, H. J. P., Bouzembrak, Y., Costa, A. L., Das, R., Stone, V., & Tofail, S. A. M. (2016) · Nanoscale Research Letters · doi:10.1186/s11671-016-1724-y · Cited by 34 · see citing works
Impact assessment of water and nutrient reuse in hydroponic systems using Bayesian Belief Networks
Zimmermann, M., & Fischer, M. (2020) · Journal of Water Reuse and Desalination · doi:10.2166/wrd.2020.026 · Cited by 21 · see citing works
Fate models of nanoparticles in the environment: a critical review and prospects
Zhang, R., Zheng, X., Fan, W., Wang, X., Zhao, T., Zhao, X., Peijnenburg, W. J. G. M., Vijver, M. G., & Wang, Y. (2025) · Environmental Science Nano · doi:10.1039/d5en00342c · Cited by 18 · see citing works
Atal, M. K., Palei, S. K., Chaudhary, D. K., Kumar, V., & Karmakar, N. C. (2020) · International Journal of Occupational Safety and Ergonomics · doi:10.1080/10803548.2020.1828551 · Cited by 17 · see citing works
Nanoinformatics and artificial intelligence for nano-enabled sustainable agriculture
Varsou, D., Zhang, P., Afantitis, A., Guo, Z., Lynch, I., & Melagraki, G. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91233-4.00015-6 · Cited by 16 · see citing works
Kumari, A., Singh, J., & Yadav, S. K. (2026) · Bioscience Nanotechnology · doi:10.1186/s44331-026-00011-z · Cited by 13 · see citing works
Brandmayr, C., Kerber, H., Winker, M., & Schramm, E. (2015) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2015.05.074 · Cited by 13 · see citing works
Bilal, M., Liu, H., Liu, R., & Cohen, Y. (2017) · Nanoscale · doi:10.1039/c6nr08583k · Cited by 11 · see citing works
Lasat, M. M., Chung, K. F., Lead, J. R., McGrath, S. P., Owen, R., Rocks, S. A., Unrine, J. M., & Zhang, J. J. (2018) · Journal of Environmental Protection · doi:10.4236/jep.2018.94025 · Cited by 11 · see citing works
Marvin, H. J. P., Bouzembrak, Y., Asselt, E. D. V., Meijer, N., Kleter, G. A., Lorentzen, G., & Johansen, L. (2019) · EFSA Supporting Publications · doi:10.2903/sp.efsa.2019.en-1619 · Cited by 9 · see citing works
Environmental Bioindication Studies by Bayesian Network with Use of Grey Heron as Model Species
Sujak, A., Andrzej, K. U. S. Z., Rymarz, M., & Kitowski, I. (2016) · Environmental Modeling & Assessment · doi:10.1007/s10666-016-9524-4 · Cited by 4 · see citing works
Quantifying uncertainty in dose–response screenings of nanoparticles: a Bayesian data analysis
Simeone, F. C., & Costa, A. L. (2022) · Nanotoxicology · doi:10.1080/17435390.2022.2038298 · Cited by 3 · see citing works
Bayesian network modelling for predicting the environmental hazard of silver nanomaterials in soils
Furxhi, I., Roberts, S., Cross, R. K., Morel, E., Costa, A. L., & Lahive, E. (2025) · NanoImpact · doi:10.1016/j.impact.2025.100553 · Cited by 2 · see citing works
Sabaraya, I. V., Li, X., Perreault, F., Dolocan, A., Incorvia, J. A. C., Kirisits, M. J., & Saleh, N. B. (2022) · Environmental Science Nano · doi:10.1039/d2en00707j · Cited by 2 · see citing works
Khan, B., & Cohen, Y. (2022) · doi:10.1002/9781119817512.ch9 · Cited by 2 · see citing works
Brzeziński, S. S., Kalapus, M., & Puzyn, T. (2025) · Environmental Science Nano · doi:10.1039/d5en00950b
- Authors
- Money, E. S., Barton, L. E., Dawson, J., Reckhow, K. H., & Wiesner, M. R.
- Affiliations
- Duke University (US)
- Oberlin College (US)
- Corresponding author
- Money, Eric S.
- Venue
- Science of The Total Environment
- Publisher
- Elsevier BV
- Published
- 2014-03
- Volume
- 473-474
- Pages
- 685-691
- ISSN
- 0048-9697 (ISSN-L)
- OpenAlex
- W2037431260
- Cited by
- 30
- References
- 45
- Citations by year
- 2026: 1 · 2025: 3 · 2023: 1 · 2022: 3 · 2021: 1 · 2020: 4 · 2019: 5 · 2018: 1 · 2017: 3 · 2016: 4 · 2015: 4
- FWCI
- 1.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Science of The Total Environment (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (NSF) (EF-0830093)
- Environmental Protection Agency (EPA)
- Center for the Environmental Implications of NanoTechnology (CEINT)
- Transatlantic Initiative for Nanotechnology and the Environment (TINE)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Nanoparticles: synthesis and applications
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Environmental Toxicology and Ecotoxicology
- Physical Sciences › Environmental Science › Pollution › Pharmaceutical and Antibiotic Environmental Impacts
- Keywords
- silver nanoparticles, aquatic exposure, Bayesian networks, parameter learning, sensitivity analysis, fractal dimension, particle diameter
- MeSH terms
- Bayes Theorem, Environmental Monitoring, Sensitivity and Specificity, Silver, Water Pollutants, Chemical, Water Pollution, Chemical, Risk Assessment, Metal Nanoparticles
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
