Simulation and prediction of sulfamethazine migration, transformation and risk diffusion during cross-media infiltration from surface water to groundwater driven by dynamic water level : Machine learning coupled HYDRUS-GMS model
Zhu, Siyu LU ; Liu, Bingxin ; Li, Sinuo ; Zhang, Linus LU
; Rene, Eldon R.
and Ma, Weifang
(2025)
In Journal of Environmental Management
373.
- Abstract
Seasonal water level fluctuations in rivers significantly influenced the cross-media migration, transformation, and risk diffusion of antibiotics from the vadose zone into groundwater. This study developed a coupled model integrating machine learning (ML) with HYDRUS-3D and GMS to accurately predict sulfamethazine migration under dynamic water levels. The predictive accuracy (E≥0.98) of this ML-HYDRUS-GMS model was enhanced by accounting for seasonal water level fluctuations and biogeochemical variability. Significant seasonal differences presented with sulfamethazine diffusion in the vadose zone with the migration rate decreased from 0.06 m/d to 0.02 m/d with the transition from wet to dry seasons. After 6 years of infiltration, it... (More)
Seasonal water level fluctuations in rivers significantly influenced the cross-media migration, transformation, and risk diffusion of antibiotics from the vadose zone into groundwater. This study developed a coupled model integrating machine learning (ML) with HYDRUS-3D and GMS to accurately predict sulfamethazine migration under dynamic water levels. The predictive accuracy (E≥0.98) of this ML-HYDRUS-GMS model was enhanced by accounting for seasonal water level fluctuations and biogeochemical variability. Significant seasonal differences presented with sulfamethazine diffusion in the vadose zone with the migration rate decreased from 0.06 m/d to 0.02 m/d with the transition from wet to dry seasons. After 6 years of infiltration, it reached groundwater, where lateral migration rates, influenced by seasonal flow variations, were 0.12 m/d in the wet season and decreased to 0.07 m/d in the dry season, with a diffusion range extending to 217 m over 100 years. This discrepant continuous filtration of sulfamethazine and the succession of metabolic pathways induced toxicity range to expand by 65.6 m and the risk to increase to warning level. Sulfamethazine underwent oxidative breakdown in aerobic vadose zone conditions, while anaerobic groundwater conditions led to hydrogenation and reduction, increasing its migration distance.
(Less)
- author
- Zhu, Siyu
LU
; Liu, Bingxin
; Li, Sinuo
; Zhang, Linus
LU
; Rene, Eldon R.
and Ma, Weifang
- organization
- publishing date
- 2025-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Dynamic prediction, Loosely-coupled model, Migration and transformation, Risk diffusion, Sulfamethazine
- in
- Journal of Environmental Management
- volume
- 373
- article number
- 123484
- pages
- 13 pages
- publisher
- Academic Press
- external identifiers
-
- pmid:39615474
- scopus:85210281223
- ISSN
- 0301-4797
- DOI
- 10.1016/j.jenvman.2024.123484
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2024 Elsevier Ltd
- id
- ef3d78ab-daaa-4fe3-9fb4-01d29cfde327
- date added to LUP
- 2025-01-12 11:27:46
- date last changed
- 2025-06-30 01:28:19
@article{ef3d78ab-daaa-4fe3-9fb4-01d29cfde327,
abstract = {{<p>Seasonal water level fluctuations in rivers significantly influenced the cross-media migration, transformation, and risk diffusion of antibiotics from the vadose zone into groundwater. This study developed a coupled model integrating machine learning (ML) with HYDRUS-3D and GMS to accurately predict sulfamethazine migration under dynamic water levels. The predictive accuracy (E≥0.98) of this ML-HYDRUS-GMS model was enhanced by accounting for seasonal water level fluctuations and biogeochemical variability. Significant seasonal differences presented with sulfamethazine diffusion in the vadose zone with the migration rate decreased from 0.06 m/d to 0.02 m/d with the transition from wet to dry seasons. After 6 years of infiltration, it reached groundwater, where lateral migration rates, influenced by seasonal flow variations, were 0.12 m/d in the wet season and decreased to 0.07 m/d in the dry season, with a diffusion range extending to 217 m over 100 years. This discrepant continuous filtration of sulfamethazine and the succession of metabolic pathways induced toxicity range to expand by 65.6 m and the risk to increase to warning level. Sulfamethazine underwent oxidative breakdown in aerobic vadose zone conditions, while anaerobic groundwater conditions led to hydrogenation and reduction, increasing its migration distance.</p>}},
author = {{Zhu, Siyu and Liu, Bingxin and Li, Sinuo and Zhang, Linus and Rene, Eldon R. and Ma, Weifang}},
issn = {{0301-4797}},
keywords = {{Dynamic prediction; Loosely-coupled model; Migration and transformation; Risk diffusion; Sulfamethazine}},
language = {{eng}},
publisher = {{Academic Press}},
series = {{Journal of Environmental Management}},
title = {{Simulation and prediction of sulfamethazine migration, transformation and risk diffusion during cross-media infiltration from surface water to groundwater driven by dynamic water level : Machine learning coupled HYDRUS-GMS model}},
url = {{http://dx.doi.org/10.1016/j.jenvman.2024.123484}},
doi = {{10.1016/j.jenvman.2024.123484}},
volume = {{373}},
year = {{2025}},
}