Assessing spatiotemporal variability in the concentration and composition of dissolved organic matter and its impact on iron solubility in tropical freshwater systems through a machine learning approach.
- DOI
- 10.1016/j.scitotenv.2023.166892
- Published
- 2023 Dec 15
- Container
- The Science of the total environment
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.scitotenv.2023.166892,
title = {Assessing spatiotemporal variability in the concentration and composition of dissolved organic matter and its impact on iron solubility in tropical freshwater systems through a machine learning approach.},
author = {Kikuchi T and Anzai T and Ouchi T},
year = {2023},
journal = {The Science of the total environment},
doi = {10.1016/j.scitotenv.2023.166892},
url = {https://doi.org/10.1016/j.scitotenv.2023.166892}
}RIS
TY - JOUR TI - Assessing spatiotemporal variability in the concentration and composition of dissolved organic matter and its impact on iron solubility in tropical freshwater systems through a machine learning approach. AU - Kikuchi T AU - Anzai T AU - Ouchi T PY - 2023 JO - The Science of the total environment DO - 10.1016/j.scitotenv.2023.166892 UR - https://doi.org/10.1016/j.scitotenv.2023.166892 ER -
APA
T, K., T, A., & T, O. (2023). Assessing spatiotemporal variability in the concentration and composition of dissolved organic matter and its impact on iron solubility in tropical freshwater systems through a machine learning approach.. The Science of the total environment. https://doi.org/10.1016/j.scitotenv.2023.166892
Source records
- pubmed · retrieved 2026-09-26T17:08:57.041Z