| dc.creator |
Cipullo, Sabrina |
|
| dc.creator |
Snapir, Boris |
|
| dc.creator |
Prpich, George |
|
| dc.creator |
Campo Moreno, Pablo |
|
| dc.creator |
Coulon, Frederic |
|
| dc.date |
2018-10-22T10:42:26Z |
|
| dc.date |
2018-10-22T10:42:26Z |
|
| dc.date |
2018-09-11 |
|
| dc.date.accessioned |
2022-05-25T16:39:08Z |
|
| dc.date.available |
2022-05-25T16:39:08Z |
|
| dc.identifier |
S. Cipullo, B. Snapir, G. Prpich, et al., Prediction of bioavailability and toxicity of complex chemical mixtures through machine learning models. Chemosphere, Volume 215, January 2019, pp. 388-395 |
|
| dc.identifier |
0045-6535 |
|
| dc.identifier |
https://doi.org/10.1016/j.chemosphere.2018.10.056 |
|
| dc.identifier |
http://dspace.lib.cranfield.ac.uk/handle/1826/13556 |
|
| dc.identifier |
21801894 |
|
| dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/182411 |
|
| dc.description |
Empirical data from a 6-month mesocosms experiment were used to assess the ability and performance of two machine learning (ML) models, including artificial neural network (NN) and random forest (RF), to predict temporal bioavailability changes of complex chemical mixtures in contaminated soils amended with compost or biochar. From the predicted bioavailability data, toxicity response for relevant ecological receptors was then forecasted to establish environmental risk implications and determine acceptable end-point remediation. The dataset corresponds to replicate samples collected over 180 days and analysed for total and bioavailable petroleum hydrocarbons and heavy metals/metalloids content. Further to this, a range of biological indicators including bacteria count, soil respiration, microbial community fingerprint, seeds germination, earthworm's lethality, and bioluminescent bacteria were evaluated to inform the environmental risk assessment. Parameters such as soil type, amendment (biochar and compost), initial concentration of individual compounds, and incubation time were used as inputs of the ML models. The relative importance of the input variables was also analysed to better understand the drivers of temporal changes in bioavailability and toxicity. It showed that toxicity changes can be driven by multiple factors (combined effects), which may not be accounted for in classical linear regression analysis (correlation). The use of ML models could improve our understanding of rate-limiting processes affecting the freely available fraction (bioavailable) of contaminants in soil, therefore contributing to mitigate potential risks and to inform appropriate response and recovery methods. |
|
| dc.language |
en |
|
| dc.publisher |
Elsevier |
|
| dc.rights |
Attribution-NonCommercial-NoDerivatives 4.0 International |
|
| dc.rights |
http://creativecommons.org/licenses/by-nc-nd/4.0/ |
|
| dc.subject |
Risk assessment |
|
| dc.subject |
Machine learning |
|
| dc.subject |
Bioavailability |
|
| dc.subject |
Complex chemical mixtures |
|
| dc.subject |
Compost |
|
| dc.subject |
Biochar |
|
| dc.title |
Prediction of bioavailability and toxicity of complex chemical mixtures through machine learning models |
|
| dc.type |
Article |
|