Determining nitrate and sulfate pollution sources and transformations in a coastal aquifer impacted by seawater intrusion—A multi-isotopic approach combined with self-organizing maps and a Bayesian mixing model
Publication type: Journal Article
Publication date: 2021-09-01
scimago Q1
wos Q1
SJR: 3.078
CiteScore: 24.6
Impact factor: 11.3
ISSN: 03043894, 18733336
PubMed ID:
34229392
Environmental Chemistry
Environmental Engineering
Health, Toxicology and Mutagenesis
Pollution
Waste Management and Disposal
Abstract
Over the past few decades, the La Paz aquifer system in Baja California Sur, Mexico, has been under severe pressure due to overexploitation for urban water supply and agriculture; this has caused seawater intrusion and deterioration in groundwater quality. Previous studies on the La Paz aquifer have focused mainly on seawater intrusion, resulting in limited information on nitrate and sulfate pollution. Therefore, pollution sources have not yet been identified sufficiently. In this study, an approach combining hydrochemical tools, multi-isotopes (δ 2 H H2O , δ 18 O H2O , δ 15 N NO3 , δ 18 O NO3 , δ 34 S SO4 , δ 18 O SO4 ), and a Bayesian isotope mixing model was used to estimate the contribution of different nitrate and sulfate sources to groundwater. Results from the MixSIAR model revealed that seawater intrusion and soil-derived sulfates were the predominant sources of groundwater sulfate, with contributions of ~43.0% ( UI 90 = 0.29) and ~42.0% ( UI 90 = 0.38), respectively. Similarly, soil organic nitrogen (~81.5%, UI 90 = 0.41) and urban sewage (~12.1%, UI 90 = 0.25) were the primary contributors of nitrate pollution in groundwater. The dominant biogeochemical transformation for NO 3 - was nitrification. Denitrification and sulfate reduction were discarded due to the aerobic conditions in the study area. These results indicate that dual-isotope sulfate analysis combined with MixSIAR models is a powerful tool for estimating the contributions of sulfate sources (including seawater-derived sulfate) in the groundwater of coastal aquifer systems affected by seawater intrusion. • Multiple isotopes and Bayesian mixing model traced nitrate and sulfate sources. • First-time use of Bayesian mixing model to assess sulfate from seawater intrusion. • Fertilizer - ẟ15 N is affected by the mineralization–immobilization–turnover process. • Groundwater overuse promotes nitrification, seawater intrusion and sulfate increase.
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Total citations:
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Citations from 2024:
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(47.41%)
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Torres Martínez J. P. et al. Determining nitrate and sulfate pollution sources and transformations in a coastal aquifer impacted by seawater intrusion—A multi-isotopic approach combined with self-organizing maps and a Bayesian mixing model // Journal of Hazardous Materials. 2021. Vol. 417. p. 126103.
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Torres Martínez J. P., Mora A., Mahlknecht J., Kaown D., Barceló D. Determining nitrate and sulfate pollution sources and transformations in a coastal aquifer impacted by seawater intrusion—A multi-isotopic approach combined with self-organizing maps and a Bayesian mixing model // Journal of Hazardous Materials. 2021. Vol. 417. p. 126103.
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TY - JOUR
DO - 10.1016/j.jhazmat.2021.126103
UR - https://doi.org/10.1016/j.jhazmat.2021.126103
TI - Determining nitrate and sulfate pollution sources and transformations in a coastal aquifer impacted by seawater intrusion—A multi-isotopic approach combined with self-organizing maps and a Bayesian mixing model
T2 - Journal of Hazardous Materials
AU - Torres Martínez, Juan Pablo
AU - Mora, Abrahan
AU - Mahlknecht, Jürgen
AU - Kaown, Dugin
AU - Barceló, Damiá
PY - 2021
DA - 2021/09/01
PB - Elsevier
SP - 126103
VL - 417
PMID - 34229392
SN - 0304-3894
SN - 1873-3336
ER -
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@article{2021_Torres Martínez,
author = {Juan Pablo Torres Martínez and Abrahan Mora and Jürgen Mahlknecht and Dugin Kaown and Damiá Barceló},
title = {Determining nitrate and sulfate pollution sources and transformations in a coastal aquifer impacted by seawater intrusion—A multi-isotopic approach combined with self-organizing maps and a Bayesian mixing model},
journal = {Journal of Hazardous Materials},
year = {2021},
volume = {417},
publisher = {Elsevier},
month = {sep},
url = {https://doi.org/10.1016/j.jhazmat.2021.126103},
pages = {126103},
doi = {10.1016/j.jhazmat.2021.126103}
}