Arsenic in Agricultural Soils of a Historically Mined and Industrial Region of Southern Serbia and Northern Kosovo: Bioavailability and Uptake by Plants Species<i>Zea mays L.</i>and<i>Solanum tuberosum L.</i>

Barać, Nemanja; Škrivanj, Sandra; Bukumirić, Zoran; Barać, Milan; Manojlović, Dragan; Petrović, Rada; Ćorac, Aleksandar

doi:10.1080/15320383.2015.997868

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…High importance to fluvisols probability, including a strong positive correlation with the proportion of As concentrations >10 µg/L, indicates that the occurrence of high As concentrations is limited to floodplains. Fluvisols are very young soils, typically associated with alluvial deposits, which are more enriched with As than soils at a distance from the river flows (Barać et al, 2015). Likewise, a positive correlation between gleysols’ probability and As concentration indicates reducing environments favoring As release in the aquifer (Podgorski et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

A geospatial machine learning prediction of arsenic distribution in the groundwater of Murshidabad district, West Bengal, India: spatio-temporal pattern and human health risk

Nath

Das

Roychowdhury

et al. 2022

Preprint

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Arsenic (As) contamination of groundwater in parts of South and Southeast Asia is a public health disaster. Millions of people living in these regions could be chronically exposed to drinking water with As concentrations above the World Health Organizations provisional guideline of 10 μg/L. Recent field investigations have shown that the distribution of groundwater As in many shallow aquifers in India and Bangladesh is evolving rapidly due to massive irrigation pumping. This study compares a decade-old dataset of As concentration measurements in groundwater with a dataset of recent measurements using geospatial machine learning techniques. We observed that the probability of As concentrations >10 μg/L was much greater in the regions between two major rivers than in the regions close to the Ganges River on the eastern border of the study area, where As concentrations >10 μg/L had been measured prior to 2005. The greater likelihood that As is present away from the river channel and is found instead in the interfluvial regions could be attributed to the transport and flushing of aquifer As due to intense groundwater pumping for agriculture. We estimated that about 2.8 million people could be chronically exposed to As concentrations >10 μg/L. This high population-level exposure to elevated As concentrations could be reduced through targeted well-testing campaigns, promoting well-switching, provisions for safe water access, and developing plans for raising public awareness. Policymakers could use the ternary hazard map to target high-risk localities for priority house connections of piped water supply schemes to help reduce human suffering.

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Section: Resultsmentioning

confidence: 99%

A geospatial machine learning prediction of arsenic distribution in the groundwater of Murshidabad district, West Bengal, India: spatio-temporal pattern and human health risk

Nath

Das

Roychowdhury

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The high importance of fluvisol probability, including a strong positive correlation with the proportion of As concentrations >10 μg/L, indicates that the occurrence of high As concentrations is limited to floodplains. Fluvisols are very young soils, typically associated with alluvial deposits, which are more enriched with As than soils formed at a greater distance from the rivers . Likewise, a positive correlation between gleysols’ probability and As concentration indicates reducing environments favoring As release in the aquifer .…”

Section: Resultsmentioning

confidence: 99%

“…Fluvisols are very young soils, typically associated with alluvial deposits, which are more enriched with As than soils formed at a greater distance from the rivers. 61 Likewise, a positive correlation between gleysols' probability and As concentration indicates reducing environments favoring As release in the aquifer. 53 The possible confounding could be a human-driven change in subsurface groundwater flow dynamics which could transport and/or flush the dissolved As with time, changing temporal patterns of As distribution.…”

Section: Modeled Probability Of As Exceedance and Model Uncertaintymentioning

confidence: 99%

Geospatial Machine Learning Prediction of Arsenic Distribution in the Groundwater of Murshidabad District, West Bengal, India: Analyzing Spatiotemporal Patterns to Understand Human Health Risk

et al. 2022

View full text Add to dashboard Cite

Arsenic (As) contamination of groundwater in parts of South and Southeast Asia is a public health disaster. Millions of people living in these regions could be chronically exposed to drinking water with As concentrations above the World Health Organization's provisional guideline of 10 μg/L. Recent field investigations have shown that the distribution of groundwater As in some shallow aquifers in India and Bangladesh is evolving rapidly because of massive irrigation pumping. This study compares a decadeold dataset of As concentration measurements in groundwater with a dataset of recent measurements using geospatial machine learning techniques. We observed that the probability of As concentrations >10 μg/L was much greater in the regions between two major rivers than in the regions close to the Ganges River on the eastern border of the study area, where higher proportions of As concentrations >10 μg/L had been observed prior to 2005. The greater likelihood that toxic concentrations of As are present away from the river channel and is found instead in the interfluvial regions could be attributed to the transport and flushing of aquifer As from intense irrigation pumping. We estimated that about 2.8 million people could be chronically exposed to As concentrations >10 μg/L. This high population-level exposure to elevated As concentrations could be reduced through targeted well-testing campaigns, promoting well-switching, provisions for safe water access, and developing plans for raising public awareness. Policymakers could use the ternary hazard map presented here to target high-risk localities for priority implementation of piped water supply strategies to help reduce human suffering.

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Distribution and mobility of heavy elements in floodplain agricultural soils along the Ibar River (Southern Serbia and Northern Kosovo). Chemometric investigation of pollutant sources and ecological risk assessment

Barać

Škrivanj

Bukumirić

et al. 2016

Environ Sci Pollut Res

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This work investigates the influence of a high-magnitude flood event on heavy elements (HEs) pollution and mobility in the agricultural soils along Ibar River in Southern Serbia and Northern Kosovo. The study area was one of the most important Pb/Zn industrial regions in Europe. Soil samples (n = 50) collected before and after the floods in May 2014 were subjected to the sequential extraction procedure proposed by the Community Bureau of Reference (BCR). The results indicated that the floods significantly increased not only the pseudo total concentrations of HEs in the soil but also their mobile and potentially bioavailable amounts. Moreover, higher concentrations (both pseudo total and potentially bioavailable) were found in the agricultural soils closer to the industrial hotspots. Principal component analysis and hierarchical cluster analysis successfully grouped the analyzed elements according to their anthropogenic or natural origin. The floods significantly increased the potential ecological risk of HEs associated with Pb/Zn industrial activities in the study area. The potential ecological risk of Cd after the floods was highest and should be of special concern.

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Arsenic in Agricultural Soils of a Historically Mined and Industrial Region of Southern Serbia and Northern Kosovo: Bioavailability and Uptake by Plants SpeciesZea mays L.andSolanum tuberosum L.

Cited by 4 publications

References 30 publications

A geospatial machine learning prediction of arsenic distribution in the groundwater of Murshidabad district, West Bengal, India: spatio-temporal pattern and human health risk

A geospatial machine learning prediction of arsenic distribution in the groundwater of Murshidabad district, West Bengal, India: spatio-temporal pattern and human health risk

Geospatial Machine Learning Prediction of Arsenic Distribution in the Groundwater of Murshidabad District, West Bengal, India: Analyzing Spatiotemporal Patterns to Understand Human Health Risk

Distribution and mobility of heavy elements in floodplain agricultural soils along the Ibar River (Southern Serbia and Northern Kosovo). Chemometric investigation of pollutant sources and ecological risk assessment

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