Silica sources for arsenic mitigation in rice: machine learning-based predictive modeling and risk a...
Silica sources for arsenic mitigation in rice: machine learning-based predictive modeling and risk assessment
About this item
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Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
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Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Scope and Contents
Contents
Arsenic (As) is a well-known human carcinogen, and the consumption of rice is the main pathway for the South Asian people. The study evaluated the impact of the amendments involving CaSiO
3
, SiO
2
nanoparticles, silica solubilizing bacteria (SSB), and rice straw compost (RSC) on mitigation of As toxicity in rice. The translocation of A...
Alternative Titles
Full title
Silica sources for arsenic mitigation in rice: machine learning-based predictive modeling and risk assessment
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Record Identifier
TN_cdi_proquest_miscellaneous_3153548637
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_3153548637
Other Identifiers
ISSN
1614-7499,0944-1344
E-ISSN
1614-7499
DOI
10.1007/s11356-023-30339-5
