Assessment of natural arsenic in groundwater in Cordoba Province, Argentina.

Environ Geochem Health

National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina.

Published: December 2009

Groundwater in the central part of Argentina contains arsenic concentrations that, in most cases, exceed the value suggested by international regulations. In this region, Quaternary loessical sediments with a very high volcanic glass fraction lixiviate arsenic and fluoride after weathering. The objectives of this study are to analyze the spatial distribution of arsenic in different hydrogeological regions, to define the naturally expected concentration in an aquifer by means of hydrogeochemistry studies, and to identify emergent health evidences related to cancer mortality in the study area. The correlation between arsenic and fluoride concentrations in groundwater is analyzed at each county in the Cordoba Province. Two dimensionless geoindicators are proposed to identify risk zones and to rapidly visualize the groundwater quality related to the presence of arsenic and fluoride. A surface-mapping system is used to identify the spatial variability of concentrations and for suggesting geoindicators. The results show that the Chaco-Pampean plain hydrogeologic region is the most affected area, with arsenic and fluoride concentrations in groundwater being generally higher than the values suggested by the World Health Organization (WHO) for drinking water. Mortality related to kidney, lung, liver, and skin cancer in this area could be associated to the ingestion of arsenic-contaminated water. Generated maps provide a base for the assessment of the risk associated to the natural occurrence of arsenic and fluoride in the region.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10653-008-9245-yDOI Listing

Publication Analysis

Top Keywords

arsenic fluoride
20
arsenic
8
cordoba province
8
fluoride concentrations
8
concentrations groundwater
8
groundwater
5
fluoride
5
assessment natural
4
natural arsenic
4
arsenic groundwater
4

Similar Publications

Improving groundwater vulnerability assessment using machine learning.

J Environ Sci (China)

July 2025

Division of Analytical and Environmental Toxicology, Department of Laboratory Medicine and Pathology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta T6G 2G3, Canada. Electronic address:

View Article and Find Full Text PDF

A comprehensive analysis of the impact of arsenic, fluoride, and nitrate-nitrite dynamics on groundwater quality and its health implications.

J Hazard Mater

January 2025

Third World Center (TWC) for Science and Technology, H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi 75270, Pakistan; H.E.J. Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi 75270, Pakistan. Electronic address:

Groundwater contamination is a growing global concern. The objective of the present study is to assess the groundwater quality of Khairpur district, Sindh, Pakistan-a region which is emblematic of broad environmental and public health challenges prevalent in South Asian countries. The study also aims to comprehend the impact of arsenic (As), fluoride (F), and nitrate (NO) dynamics and its health implications.

View Article and Find Full Text PDF

The hydrodynamics, water temperature, and water quality model for the Dan River and Renzhuang Reservoir continuum were developed using field monitoring data and the Environmental Fluid Dynamics Code (EFDC). An in-situ water discharge experiment enabled the calculation of water propagation time using a simulated flood progression method and the hydrodynamics module of EFDC. Based on these model results, degradation coefficients for chemical oxygen demand, biochemical oxygen demand, nitrogen (N), phosphorus (P), fluoride, arsenic were determined, revealing significantly higher values when the wetland barrage was opening.

View Article and Find Full Text PDF

Groundwater pollution has become a global challenge, posing significant threats to human health and ecological environments. Machine learning, with its superior ability to capture non-linear relationships in data, has shown significant potential in addressing groundwater pollution issues. This review presents a comprehensive bibliometric analysis of 1462 articles published between 2000 and 2023, offering an overview of the current state of research, analyzing development trends, and suggesting future directions.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!