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Cun Liu
Institute of Soil Science, Chinese Academy of Sciences
Corresponding Author
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Root concentration factor is an important substance-specific characterization parameter for plant uptake of organic contaminants from soils in life cycle impact assessment (LCIA); however, the availability of a reliable dataset and building of robust predictive models remain challenging due to the complexity of chemical-soil-plant root interactions. Here we developed end-to-end machine learning models to devolve the interaction complexity by training on a unified dataset with 341 data points covering 72 chemicals. The gradient boosting regression tree (GBRT) model based on the extended connectivity fingerprints (ECFP) demonstrated a superior prediction performance with R-squared of 0.77 and Mean Absolute Error (MAE) of 0.22. In addition, partial dependence analysis was used to determine the nonlinear relationships in the chemical-soil-plant root system. Feature importance analysis revealed the relationship between and chemical topological structures. Stemming from its simplicity and universality, the GBRT-ECFP model provides a promising tool for LCIA to better characterize the human and ecological impacts of chemicals in the environment.
Keywords
root concentration factor, organic contaminants, soils
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The full text of this article is available to read as a PDF.
There is NO Competing Interest.
This is a list of supplementary files associated with this preprint. Click to download.
- DirectpredictionofRCFsoilMLSItableS2heatmapdataframe.pdf
SI Table S2 heatmap spredsheets
- DirectpredictionofRCFsoilMLSI.docx
Supplementary Information
- NATSUSTAIN21028965SI.pdf
Supplementary Information - updated 05/03
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A new preprint design is coming!
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Article
Cun Liu
Institute of Soil Science, Chinese Academy of Sciences
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
View the published article at Environmental Science & Technology.
Version 1
Posted 08 Mar, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Environmental Science & Technology.
Root concentration factor is an important substance-specific characterization parameter for plant uptake of organic contaminants from soils in life cycle impact assessment (LCIA); however, the availability of a reliable dataset and building of robust predictive models remain challenging due to the complexity of chemical-soil-plant root interactions. Here we developed end-to-end machine learning models to devolve the interaction complexity by training on a unified dataset with 341 data points covering 72 chemicals. The gradient boosting regression tree (GBRT) model based on the extended connectivity fingerprints (ECFP) demonstrated a superior prediction performance with R-squared of 0.77 and Mean Absolute Error (MAE) of 0.22. In addition, partial dependence analysis was used to determine the nonlinear relationships in the chemical-soil-plant root system. Feature importance analysis revealed the relationship between and chemical topological structures. Stemming from its simplicity and universality, the GBRT-ECFP model provides a promising tool for LCIA to better characterize the human and ecological impacts of chemicals in the environment.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
The full text of this article is available to read as a PDF.
There is NO Competing Interest.
This is a list of supplementary files associated with this preprint. Click to download.
- DirectpredictionofRCFsoilMLSItableS2heatmapdataframe.pdf
SI Table S2 heatmap spredsheets
- DirectpredictionofRCFsoilMLSI.docx
Supplementary Information
- NATSUSTAIN21028965SI.pdf
Supplementary Information - updated 05/03
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