A data-driven modeling approach for the sustainable remediation of persistent arsenic (As) groundwater contamination in a fractured rock aquifer through a groundwater recirculation well (IEG-GCW®).
- DOI
- 10.1016/j.envres.2022.114827
- Published
- 2023 Jan 15
- Container
- Environmental research
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.envres.2022.114827,
title = {A data-driven modeling approach for the sustainable remediation of persistent arsenic (As) groundwater contamination in a fractured rock aquifer through a groundwater recirculation well (IEG-GCW®).},
author = {Ciampi P and Esposito C and Bartsch E and Alesi EJ and Rehner G and Morettin P and Pellegrini M and Olivieri S and Ranaldo M and Liali G and Papini MP},
year = {2023},
journal = {Environmental research},
doi = {10.1016/j.envres.2022.114827},
url = {https://doi.org/10.1016/j.envres.2022.114827}
}RIS
TY - JOUR TI - A data-driven modeling approach for the sustainable remediation of persistent arsenic (As) groundwater contamination in a fractured rock aquifer through a groundwater recirculation well (IEG-GCW®). AU - Ciampi P AU - Esposito C AU - Bartsch E AU - Alesi EJ AU - Rehner G AU - Morettin P AU - Pellegrini M AU - Olivieri S AU - Ranaldo M AU - Liali G AU - Papini MP PY - 2023 JO - Environmental research DO - 10.1016/j.envres.2022.114827 UR - https://doi.org/10.1016/j.envres.2022.114827 ER -
APA
P, C., C, E., E, B., EJ, A., G, R., P, M., M, P., S, O., M, R., G, L., & MP, P. (2023). A data-driven modeling approach for the sustainable remediation of persistent arsenic (As) groundwater contamination in a fractured rock aquifer through a groundwater recirculation well (IEG-GCW®).. Environmental research. https://doi.org/10.1016/j.envres.2022.114827
Source records
- pubmed · retrieved 2026-09-25T18:14:53.426Z