Pre-to-post COVID-19 lockdown and their environmental impacts on Ghoghla beach and Somnath beach, India

Comparative analysis of temporal variation of heavy metal accumulation by two sea urchin species from a harbour region, including pre and post COVID 19 lock down period

Sea urchins are marine invertebrates belonging to phylum Echinodermata, recognized as relevant biological tool for assessing environmental pollution. In the present study, we assessed the bioaccumulation potential of different heavy metals by two sea urchin species, Stomopneustes variolaris Lamarck, 1816 and Echinothrix diadema Linnaeus, 1758, collected from a harbour region, along the south west coast of India, during four different sampling periods for 2 years, from the same sea urchin bed. Heavy metals like Pb, Cr, As, Cd, Co, Se, Cu, Zn, Mn and Ni were analysed from water, sediment and different body parts of sea urchins, such as shell, spine, tooth, gut and gonad. The sampling periods also included the pre and post COVID 19 lockdown period during which the harbour activities were closed. The bio-water accumulation factor (BWAF), bio-sediment accumulation factor (BSAF) and the metal content/test weight index (MTWI) were calculated, in order to compare the bioaccumulation of metals by both the species. The results showed that S. variolaris had higher bioaccumulation potential than E. diadema, for metals like Pb, As, Cr, Co and Cd especially in the soft body parts like gut and gonad. The hard parts of S. variolaris like shell, spine, and tooth also accumulated more Pb, Cu, Ni and Mn than E. diadema. Following the lockdown period, there was a decline in the concentration of all heavy metals in water, whereas in sediment, Pb, Cr, and Cu levels were reduced. The gut and gonad tissues of both the urchins showed a decrease in the concentration of most of the heavy metals following the lockdown phase and no significant reduction was observed in the hard parts. This study reveals the use of S. variolaris as an excellent bioindicator of heavy metal contamination in the marine environment which can be employed for coastal monitoring programs.

Interaction of physico-chemical parameters with Shannon-Weaver Diversity Index based on phytoplankton diversity in coastal water of Diu, India

Phytoplankton acts as carbon sinks due to photosynthetic efficacy and their diversity is expressed by SWDI (Shannon-Weaver Diversity Index), which depends on water quality parameters. The coastal water of Diu was studied for three seasons, and the relationship between different parameters and SWDI was established. Subsequently, an attempt was made to build up a prediction model of SWDI based on multilayer perceptron Artificial neural network (ANN) using the R programme. Analysis shows interrelationship between the water quality parameters and phytoplankton diversity is same in linear principal component analysis (PCA) and neural network model. Variations of different parameters depend on seasonal changes. The ANN model shows that ammonia and phosphate are key parameters that influence the SWDI of phytoplankton. Seasonal variation in SWDI is related to variation in water quality parameters, as explained by both ANN and PCA. Hence, the ANN model can be an important tool for coastal environmental interaction study.