32 citations as recorded by crossref.

1. Delayed and asymmetric groundwater responses to meteorological forcing across Germany revealed by lagged mutual information F. Cai et al. https://doi.org/10.1016/j.jhydrol.2026.136019
2. Unveiling the biogeochemical mechanism of nitrate in the vadose zone-groundwater system: Insights from integrated microbiology, isotope techniques, and hydrogeochemistry D. Wang et al. https://doi.org/10.1016/j.scitotenv.2023.167481
3. Machine learning models for prediction of nutrient concentrations in surface water in an agricultural watershed A. Elsayed et al. https://doi.org/10.1016/j.jenvman.2024.123305
4. Future groundwater drought analysis under data scarcity using MedCORDEX regional climatic models and machine learning: The case of the Haouz Aquifer E. Imane et al. https://doi.org/10.1016/j.ejrh.2025.102249
5. Seasonal nitrate variation as a tracer of preferential flow in bedrock aquifers S. Worthington https://doi.org/10.1016/j.jhydrol.2024.132015
6. Distribution, sources and main controlling factors of nitrate in a typical intensive agricultural region, northwestern China: Vertical profile perspectives D. Wang et al. https://doi.org/10.1016/j.envres.2023.116911
7. Unveiling hidden threats: Nitrate pollution in agricultural catchments with deep vadose zone M. Ren et al. https://doi.org/10.1016/j.agee.2025.109819
8. Shallow Groundwater Hydrochemical Facies, Nitrate Sources and Potential Health Risks in Southern Baoding of North China Using Hydrochemistry and Positive Matrix Factorization Y. Zhao et al. https://doi.org/10.3390/su172310834
9. Prediction of sulfate concentrations in groundwater in areas with complex hydrogeological conditions based on machine learning Y. Tian et al. https://doi.org/10.1016/j.scitotenv.2024.171312
10. Interplay of legacy irrigation and nitrogen fertilizer inputs to spatial variability of arsenic and uranium within the deep vadose zone A. Malakar et al. https://doi.org/10.1016/j.scitotenv.2023.165299
11. Effects of the Japanese Nitrate Directive Plan (NDP) to curb groundwater nitrate-nitrogen content in the Miyakonojo River basin Z. Yu et al. https://doi.org/10.1016/j.jhydrol.2022.128563
12. Predictive modelling benchmark of nitrate Vulnerable Zones at a regional scale based on Machine learning and remote sensing A. Cardenas-Martinez et al. https://doi.org/10.1016/j.jhydrol.2021.127092
13. Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives X. Wu et al. https://doi.org/10.1016/j.jhazmat.2022.129487
14. Prediction of groundwater nitrate concentration in a semiarid region using hybrid Bayesian artificial intelligence approaches K. Alkindi et al. https://doi.org/10.1007/s11356-021-17224-9
15. Stable isotopic characterization and estimation of canal recharge to the groundwater in a part of the Ganga Plain, North India D. Rai et al. https://doi.org/10.1016/j.jhydrol.2025.132962
16. Characterization and determination of soil unsaturated hydraulic conductivity by integrating time-lapse geophysical data with hydrogeological measurements C. Zou et al. https://doi.org/10.1016/j.geoderma.2026.117742
17. Application of classification machine learning algorithms for characterizing nutrient transport in a clay plain agricultural watershed A. Elsayed et al. https://doi.org/10.1016/j.jenvman.2023.118924
18. Spatial patterns, driving mechanisms, and source-tracing approaches for groundwater nitrate contamination: A review with a European perspective W. Zhang & J. Wu https://doi.org/10.1016/j.pce.2026.104354
19. Predictive modeling and analysis of key drivers of groundwater nitrate pollution based on machine learning Y. Deng et al. https://doi.org/10.1016/j.jhydrol.2023.129934
20. Nitrate concentrations tracking from multi-aquifer groundwater vulnerability zones: Insight from machine learning and spatial mapping S. Abba et al. https://doi.org/10.1016/j.psep.2024.02.041
21. Geographical disparities in nitrate trends: Mapping transfer times in French aquifers N. Surdyk et al. https://doi.org/10.1016/j.gsd.2025.101467
22. Review on the distribution and influencing factors of nitrate and ammonia in regional groundwater: current status, differences and analytic techniques M. Zhang et al. https://doi.org/10.1007/s12665-025-12378-y
23. Scale effects and time-lag responses of groundwater hydrochemistry to land use change: A spatiotemporal analysis of a rapidly urbanizing typical agricultural area in the Lower Yellow River alluvial plain J. Feng et al. https://doi.org/10.1016/j.ejrh.2026.103630
24. Use of machine learning and geographical information system to predict nitrate concentration in an unconfined aquifer in Iran V. Gholami & M. Booij https://doi.org/10.1016/j.jclepro.2022.131847
25. Addressing Nitrate Contamination in Groundwater: The Importance of Spatial and Temporal Understandings and Interpolation Methods M. Zaresefat et al. https://doi.org/10.3390/w15244220
26. Transformation of inorganic reactive nitrogen species in a simulated vadose zone Y. Ukwishaka et al. https://doi.org/10.1016/j.scitotenv.2026.181412
27. Decadal Hydrochemical Monitoring Reveals Characteristics, Genetic Mechanisms and Health Risks of High-Nitrate Groundwater Q. Yang et al. https://doi.org/10.3390/app16094524
28. Regression-based machine learning models for nitrate and chloride prediction in surface water in a small agricultural sand plain sub-watershed in southwestern Ontario, Canada A. Elsayed et al. https://doi.org/10.3389/fenvs.2025.1543852
29. Degassing, dating and deeper insights into the groundwater dynamics of an arsenic contaminated aquifer in Vietnam A. Lightfoot et al. https://doi.org/10.1016/j.jhydrol.2026.135372
30. Ecosystem service value of groundwater in Gansu, China: Correlation with land use/land cover and mode of development W. Zhao et al. https://doi.org/10.1016/j.pce.2026.104667
31. Unveiling nitrate contamination and health risks: Insights from groundwater quality assessment and Monte Carlo simulation along the Southern Caspian Sea Coasts M. Zazouli et al. https://doi.org/10.1016/j.gsd.2024.101340
32. Enhancing local-scale groundwater quality predictions using advanced machine learning approaches A. Yadav et al. https://doi.org/10.1016/j.jenvman.2024.122903