Evaluation of trace metals in seawater, sediments, and bivalves of Nellore, southeast coast of India, by using multivariate and ecological tool

Jha, Dilip Kumar; Ratnam, Krupa; Rajaguru, S. N.; Dharani, G.; Devi, M. Prashanthi; Kirubagaran, R.

doi:10.1016/j.marpolbul.2019.05.044

Cited by 26 publications

(17 citation statements)

References 31 publications

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“…The coast is prone to heavy rainfall, the likely discharges from the nearby aquaculture activities in the inner stations (BC, SR2, and SR1) of the SRE region which was supported with previous studies (Sreenivasulu et al 2018). The results of this study are in agreement with Jha et al (2019) and Pandey et al (2021) in the same region.…”

Section: J Ttsupporting

confidence: 90%

“…With the increase in nearby aquaculture, agricultural, and anthropogenic activities, the effluent discharges find their way into the nearby coastal areas which provides an advantageous environment to the organisms for proliferation. Similar activities have been reported in the Swarnamukhi River Estuary (SRE) region, fewer studies have been carried out to assess the tidal variations (Reddi et al 1993), hydrographic properties of water (Sreenivasulu et al 2015), contamination studies on the presence of heavy metal in seawater, sediments, & organisms (Reddy et al 2016;Sreenivasulu et al 2018;Jha et al 2019), and the benthic organisms (Pandey et al 2021). However, an elaborate study for the plankton communities is not available for the SRE region.…”

Section: Introductionmentioning

confidence: 78%

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Seasonal variations influencing the abundance and diversity of plankton in the Swarnamukhi River Estuary, Nellore, India

et al. 2022

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An integrated approach was used to study the seasonal influence on the abundance and diversity of phytoplankton and zooplankton in the Swarnamukhi River Estuary (SRE) and the adjacent coast covering five stations by collecting monthly samples from the years 2014 to 2017. A total of 54 phytoplankton species conforming to four families and 58 zooplankton species conforming to nine families were recorded. Phytoplankton abundance and richness were high during pre-monsoon (PRM - 56410 cells/L) followed by monsoon (MON – 42210 cells/L). A similar trend was observed in the case of zooplankton, where abundance was recorded high during PRM (124261 ind./m3) followed by MON (111579 ind./m3). Moreover, phytoplankton and zooplankton were dominated by the diatoms and copepods, respectively. Both phytoplankton and zooplankton exhibited significant temporal (F= 26.4, p <0.05) and spatial (F= 32.1, p <0.05) variations. The higher density and abundance were recorded in the inner stations compared to the open sea. The present study reveals that the SRE have a rich diversity which could be attributed to a higher nutrient influx in the inner stations. The anthropogenic discharge from the surrounding aqua farms, agricultural land, and human settlement area could cause concerns for the local flora and fauna if a proper mitigation plan is not evolved through long-term monitoring study in this coastal region.

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Section: J Ttsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 78%

Seasonal variations influencing the abundance and diversity of plankton in the Swarnamukhi River Estuary, Nellore, India

et al. 2022

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“…However, the concentration was optimal toward the OS. Although anthropogenic discharge (from aquafarms and settlement areas) is high at the BC, tidal amplitude (0.59 m) (Jha et al, 2019) helps to diminish this effect. An earlier study from the area (Pandey et al, 2021) also suggests that anthropogenic disturbances at BC are considerably higher than other stations, which resulted in poor environmental and ecological quality status.…”

Section: Resultsmentioning

confidence: 99%

“…Previous scientific records on the seawater quality parameters are minimal in this region. Although a few environmental contamination studies have been carried out on metal pollution (foraminifera, seawater, sediment, bivalves, and benthos) in this area (Sundara Raja Reddy et al, 2016;Patel et al, 2018;Jha et al, 2019;Pandey et al, 2021), long-term studies on the physicochemical parameters are sparse (Sreenivasulu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Physicochemical Characteristics of Coastal Waters of Nellore, Southeast Coast of India, by a Multivariate Statistical Approach

et al. 2022

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The Swarnamukhi river estuary (SRE), and the surrounding sea in Nellore, southeast coast of India, is one of the least studied marine environments, notably for physicochemical characteristics. Seawater samples were collected from five stations every month from 2014 to 2017 to assess physicochemical characteristics. The open sea (OS) station was significantly different from the inner stations, according to non-metric multidimensional scaling and cluster analysis. The variability was shown by strong factor loadings of atmospheric temperature (0.87), water temperature (0.84), biochemical oxygen demand (0.77), ammonia (0.85), and total nitrogen (0.78). Furthermore, one-way ANOVA and box-whisker plots facilitated simplifying and corroborating multivariate results that showed high concentration in the inner stations. Based on the N/P and Si/N ratios, nitrate and silicate were the key limiting factors in this study. The findings are critical for establishing reference conditions for comparison studies with other comparable ecosystems in the tropical region for better environmental conservation and management.

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“…Therefore, the number of studies on beach sands increased [1][2][3][4][5]. In these studies, as well as many other studies, statistical evaluations were conducted [6][7][8][9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Data Analysis of Beach Sands’ Chemical Analysis Using Multivariate Statistical Methods and Heavy Metal Distribution Maps: The Case of Moonlight Beach Sands, Kemer, Antalya, Turkey

Yalçın

2020

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Multivariate statistical methods are widely used in several disciplines of fundamental sciences. In the present study, the data analysis of the chemical analysis of the sands of Moonlight Beach in the Kemer region was examined using multivariate statistical methods. This study consists of three parts. The multivariate statistical analysis tests were described in the first part, then the pollution indexes were studied in the second part. Finally, the distribution maps of the chemical analyses and pollution indexes were generated using the obtained data. The heavy metals were mostly observed in location K1, while they were sorted as follows based on their concentrations: Mg > Fe > Al > Ti > Sr > Mn > Cr > Ni > Zn > Zr > Cu > Rb. Also, strong positive correlations were found between Si, Fe, Al, K, Ti, P. According to the results of factor analysis, it was found that four factors explained 83.5% of the total variance. On the other hand, the coefficient of determination (R2) was calculated as 63.6% in the regression model. Each unit increase in the value of Ti leads to an increase of 0.022 units in the value of Si. Potential Ecological Risk Index analysis results (RI < 150) revealed that the study area had no risk. However, the locations around Moonlight Beach are under risk in terms of Enrichment Factor and Contamination Factor values. The index values of heavy metals in the anomaly maps and their densities were found to be successful; and higher densities were observed based on heavy metal anomalies.

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Evaluation of trace metals in seawater, sediments, and bivalves of Nellore, southeast coast of India, by using multivariate and ecological tool

Cited by 26 publications

References 31 publications

Seasonal variations influencing the abundance and diversity of plankton in the Swarnamukhi River Estuary, Nellore, India

Seasonal variations influencing the abundance and diversity of plankton in the Swarnamukhi River Estuary, Nellore, India

Evaluation of Physicochemical Characteristics of Coastal Waters of Nellore, Southeast Coast of India, by a Multivariate Statistical Approach

Data Analysis of Beach Sands’ Chemical Analysis Using Multivariate Statistical Methods and Heavy Metal Distribution Maps: The Case of Moonlight Beach Sands, Kemer, Antalya, Turkey

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