Assessing heavy metal toxicity in sediments of Chennai Coast of Tamil Nadu using Energy Dispersive X-Ray Fluorescence Spectroscopy (EDXRF) with statistical approach

Tholkappian, M.; Ravisankar, R.; Chandrasekaran, A.; Jebakumar, J. Prince Prakash; Kanagasabapathy, K.; Prasad, M. V. R.; Satapathy, K. K.

doi:10.1016/j.toxrep.2017.12.020

Cited by 56 publications

(22 citation statements)

References 46 publications

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“…There were no elements with mean EF values less than 1. Th and Cr had the least values of EF with 7.26 and 9.79 respectively and had significant enrichment according to the classification of categories (Latif et al 2015, Tholkappian et al 2017. Uranium also had significant enrichment as the EF value was in the range 5-20 according to the classification.…”

Section: Dust Fallout Rates Variation With Meteorological Parametersmentioning

confidence: 93%

“…The trend shown in Table 3 shows that of all the six elements investigated there is none with EF value less than 1. Accordingly, all the elements presented in Table 3 have EF greater than 1 and are influenced by anthropogenic emissions (Latif et al 2015, Tholkappian et al 2017 though the degree of contributions to the different metals differ. The order of the mean EF values of the elements in the study area from the highest to lowest is Cd > Pb > Hg > U > Cr > Th.…”

Section: Concentration and Enrichment Factors (Ef) Of Metals And Radimentioning

confidence: 98%

“…Uranium also had significant enrichment as the EF value was in the range 5-20 according to the classification. Mercury (Hg) with mean EF value of 29.22 had very high enrichment according to the (Latif et al 2015, Tholkappian et al 2017. Cd and Pb have extremely high enrichment as the EF value is greater than 40.…”

Section: Dust Fallout Rates Variation With Meteorological Parametersmentioning

confidence: 99%

“…Usually Al has relatively high natural concentrations and hence is not expected to be substantially enriched from anthropogenic sources (Abrahim and Parker 2008). The categories for EF values are described as follows according to (Latif et al 2015, Tholkappian et al 2017: EF < no enrichment EF = 1-2 low enrichment EF = 2-5 moderate enrichment EF = 5-20 significant enrichment EF = 20-40 very high enrichment EF > 40 extremely high enrichment Although there have been various studies on dust fall out in mining areas of South Africa (e.g. Oguntoke et al 2013), very few studies have focussed on radionuclides in the dust from soil and mine tailings in the West Rand mining area (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of heavy metals and radionuclides in dust fallout in the West Rand mining area of South Africa

2018

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Windblown dust can contain radioactive materials from mining. These radionuclides when inhaled in dust produce ionizing radiation which damages the cells and tissues in the body. The aim of this study was to assess dust fallout radionuclides in the West Rand mining area of South Africa. Dust fallout monitoring was done using the method (ASTM) D-1739 of 1998 employing multi-directional buckets. Results show that all 9 locations investigated in the different seasons recorded dust fallout rates within stipulated residential limit according to the National Dust Control Regulations (NDCR) (Government Gazette 36974, 1 Nov 2013). Locations 3 in spring (301.93 mg/ m 2 /day) and 6 in winter (589.8 mg/m 2 /day) recorded dust fallout rates with values above the target value of 300 mg/m 2 /day according to guidelines by South African Standards (SANS 1929:2011). The ANOVA tests (p-value < 0.05) indicate that the mean dust fallout rate is significantly different across the seasons, mean dust fallout rate in summer is significantly (p-value < 0.05) more than the autumn by 103.15 mg/m 2 /day. Moderate, positive correlations (0.4 ≤ r < 0.7) exist between average dust fallout rate and wind speed. A weak, negative correlation exist between average dust fallout rate and rainfall with a value of-0.393. All the investigated elements had Enrichment Factor (EF) greater than 1. Cadmium (Cd) and lead (Pb) were extremely enriched (68.36 and 43.48, respectively), whereas Thorium (Th) (7.26) and Chromium (Cr) (9.79) had significant enrichment. Activity concentrations obtained for 226

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Section: Dust Fallout Rates Variation With Meteorological Parametersmentioning

confidence: 93%

Section: Concentration and Enrichment Factors (Ef) Of Metals And Radimentioning

confidence: 98%

Section: Dust Fallout Rates Variation With Meteorological Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of heavy metals and radionuclides in dust fallout in the West Rand mining area of South Africa

2018

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“…A técnica de fluorescência de raios-X é utilizada de forma não destrutiva, para qualificar e quantificar diversos elementos, podendo ser a amostra sólida, líquida ou em pó e com diversas aplicações, como na área acadêmica, ambiental, dentre outras (ARDID et al, 2004;ANGEYO et al, 2012;THOLKAPPIAN et al, 2018;MOHAMMED et al, 2016).…”

Section: Determinação De Elementosunclassified

Poluição em ambientes internos. Caracterização de espécies no material particulado de atmosferas escolares

Pereira¹

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Machine learning approach for measuring water quality of coastline and estuaries in Chennai coastal area

Kamarajan¹,

Mohan²,

Shanmugam³

et al. 2023

Environmental Quality Mgmt

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Chennai has had the longest coastline over other major cities of India. It is decidedly vital to monitor seawater quality due to the increased coastline population. This study presents an Android mobile application based on a machine learning approach to perform basic testing parameters of seawater by applying convolutional neural network concepts. A commercially available “saltwater master test kit” was used in this study to test the level of pH, Ammonia, Nitrite, Nitrate, and Total Phosphate (T.P.) in seawater. Six water samples collected from every 10 regions, including the coastline and coastal estuaries, were tested with the test kit in a microplate. Images were captured in the solely designed mobile app, and they were pre‐processed, and RGB (Red, Green, and Blue) were recorded from the Region of Interest (R.O.I.) of the image. A supervised Convolutional Neural Network Image Classifier (SCNNIC) algorithm was developed to classify the RGB pixel values. CIEDE2000 (C2K) color difference algorithm was applied over the recorded RGB values with the datasets stored previously to result in the nearest color match between the ideal dataset and R.O.I. of the captured image. Grayscale and RGB methods results were compared with the standard APHA method. This C2K color difference algorithm produced a percent accuracy of over 98% compared with other methods used, and R2 recorded by curve fitting method for pH, T.N. and T.P. were above 0.98. Disquieting results were reported in this study, especially in Muttukkadu backwater and Adayar river backwater estuaries, reported high values of pH (7.82 and 8.17), TN (13.74 mg/L and 13.45 mg/L) and T.P. (0.266 μg/L and 0.724 μg/L). The mean for all 10 regions of 110 km chosen in this study for the 2 years was calculated, and values were obtained as pH‐8.33, TN‐5.321 mg/L, and TP‐0.143 μg/L.

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Assessing heavy metal toxicity in sediments of Chennai Coast of Tamil Nadu using Energy Dispersive X-Ray Fluorescence Spectroscopy (EDXRF) with statistical approach

Abstract: Graphical abstractPlot of locations versus EF values of heavy metals.

Cited by 56 publications

References 46 publications

Assessment of heavy metals and radionuclides in dust fallout in the West Rand mining area of South Africa

Assessment of heavy metals and radionuclides in dust fallout in the West Rand mining area of South Africa

Poluição em ambientes internos. Caracterização de espécies no material particulado de atmosferas escolares

Machine learning approach for measuring water quality of coastline and estuaries in Chennai coastal area

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