Health risk assessment of heavy metal accumulation in the food fish, Channa striata from Krishna river, Andhra Pradesh

Pv, Krishna; Mounika, M Sai; Sarma, Bvl Aradhya; Padmaja, B

doi:10.22271/fish.2021.v9.i2c.2448

Int. J. Fish. Aquat. Stud.

2021

DOI: 10.22271/fish.2021.v9.i2c.2448

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Health risk assessment of heavy metal accumulation in the food fish, Channa striata from Krishna river, Andhra Pradesh

Krishna Pv¹,

M Sai Mounika²,

Bvl Aradhya Sarma³

et al.

Abstract: The new threats to human health from heavy metals are associated with exposure to some selected metals. The study identifies and assessment of the accumulation and levels of five heavy metals (Cd, Cr, Ni, Pb and Zn) in food fish Channa striata of Krishna river, collected from three stations during four seasons of the year i.

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Cited by 2 publications

References 17 publications

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Multi-Year Monitoring of the Toxicological Risk of Heavy Metals Related to Fish Consumption by the Population of the Kendari Region (Southeast Sulawesi, Indonesia)

Saputri

Yusnaini

Sara

et al. 2023

Toxics

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This study measured the concentrations of Hg, As, Ni, Cd, and Pb in six fish species commonly consumed in Kendari. Samples were bought within local markets from 2012 to 2017 at the end of the dry season. Results showed that mercury concentrations fluctuated between years and within species, except in the Caranx sexfasciatus, which showed no significant differences (Kruskall–Wallis, p-value > 0.05, df = 5) and an average concentration of 0.371 ± 0.162 µg g−1 DW. Arsenic was found in high concentrations across species and years and varied widely in C. sexfasciatus, the lowest value being 0.32 ± 0.01 µg g−1 DW in 2012 and the highest was 5.63 ± 1.89 µg g−1 DW in 2017. The highest nickel concentrations were found in 2016 across four of the six species. The fish samples displayed very low cadmium and lead concentrations throughout the study. In addition, the potential human health risk due to fish consumption was assessed. This showed that mercury is the only one of the five metals present in concentrations high enough to individually pose a potential hazard, the only metal likely to be accumulated beyond a safe concentration in Kendari. Chanos chanos never posed a toxicological risk based on the results of this research.

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Multi-Year Monitoring of the Toxicological Risk of Heavy Metals Related to Fish Consumption by the Population of the Kendari Region (Southeast Sulawesi, Indonesia)

Saputri

Yusnaini

Sara

et al. 2023

Toxics

View full text Add to dashboard Cite

show abstract

Identification of Salt-Affected Soils in the Coastal Area of Krishna District, Andhra Pradesh, Using Remote Sensing Data and Machine Learning Techniques

Anuradha,

Chivukula,

Kothangundla

2024

IAPGOS

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In agricultural soil analysis, the challenge of soil salinization in regions like Krishna District, Andhra Pradesh, profoundly impacts soil health, crop yield, and land usability, affecting approximately 77,598 hectares of land. To address this issue, three machine learning algorithms are compared for classifying salinity levels in the coastal area of Krishna district, Machilipatnam. This study utilizes Landsat-8 images from 2014 to 2021, correcting for cloud cover and creating a true-color composite. The study area is defined and visualized. Twelve indices, derived from Landsat imagery, are incorporated into the analysis. These indices, including spectral bands and mathematical expressions, are added as image bands. The median of these indices is calculated, and sample points representing both non-saline and saline areas are used for supervised machine learning. The data is divided into two sets: training and validation. The study evaluates Random Forest, Classification and Regression Trees, and Support Vector Machines for classifying soil salinity levels using these indices. The RF algorithm produced an accuracy of 92.1%, CART produced 91.3%, and SVM produced 86%. Results are displayed on the map, representing predicted salinity levels with distinct colors. Performance metrics are evaluated, and they assess algorithm performance. The research involved gives insights into the classification of soil salinity using machine learning, which could represent an efficient solution to the problem of soil salinization in Machilipatnam.

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Health risk assessment of heavy metal accumulation in the food fish, Channa striata from Krishna river, Andhra Pradesh

Cited by 2 publications

References 17 publications

Multi-Year Monitoring of the Toxicological Risk of Heavy Metals Related to Fish Consumption by the Population of the Kendari Region (Southeast Sulawesi, Indonesia)

Multi-Year Monitoring of the Toxicological Risk of Heavy Metals Related to Fish Consumption by the Population of the Kendari Region (Southeast Sulawesi, Indonesia)

Identification of Salt-Affected Soils in the Coastal Area of Krishna District, Andhra Pradesh, Using Remote Sensing Data and Machine Learning Techniques

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