Time series (2003-15) analysis of selected physicochemical parameters in Indian Ocean: Cumulative impacts prediction on coral bleaching using machine learning.

Coral bleaching is an important ecological threat worldwide, as the coral ecosystem supports a rich marine biodiversity to survive. Sea surface temperature was considered a major culprit; however, later it was observed that other water parameters like pH, tCO2, fCO2, salinity, dissolved oxygen, etc. also play a significant role in bleaching. In the present study, all these parameters of the Indian Ocean area for 15 years (2003-2017) were collected and analysed using machine learning language. The main aim is to see the cumulative impacts of various ocean parameters on coral bleaching. Introducing machine learning in environmental impact assessment studies is a new approach, and the prediction of coral bleaching using simulation of physico-chemical parameters interactions shows 94.4 % accuracy for the prediction of the future bleaching event. This study can be probably the first step in the application of the machine learning language for the prediction of coral bleaching in the field of marine science.

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.

Impact of 2nd wave of COVID-19-related lockdown on coastal water quality at Diu, western coast of India and role of total alkalinity on bacterial loads.

A detailed coastal water monitoring near Diu coast, western part of India was performed from October, 2020 to May, 2021 covering the 2nd lockdown time. Average monthly fluctuation from 7 different sampling stations of total 9 physico-chemical parameters such as pH, salinity, turbidity, nitrite (NO2), nitrate (NO3), ammonia (NH3), phosphate (PO4), total alkalinity and silicate were recorded. Initially, Mann-Kendall trend test for all the 9 parameters showed non-zero trend, which may be either linear or non-linear. During 2nd lockdown period, there was a fluctuation of value for parameters like pH, salinity, nitrate, nitrite and phosphate. Average total bacterial count and differential bacterial count also gradually decreased from March, 2021 sampling. Principal component analysis (PCA) plot covering all the physico-chemical parameters as well as the differential bacterial count showed a distinct cluster of all bacterial count with total alkalinity value. Subsequently, mathematical equation was formulated between total alkalinity value and all differential bacterial count. Upto our knowledge, this is the first report where mathematical equation was formulated to obtain value of different bacterial load based on the derived total alkalinity value of the coastal water samples near Diu, India.

In target areas where human mosquito-borne diseases are diagnosed, the inclusion of the pre-adult mosquito aquatic niches parameters will improve the integrated mosquito control program.

In human communities inhabiting areas-such as West Bengal- India-where perpetuate the pre-imago & adult developmental stages of mosquitoes; many infectious diseases are still diagnosed such as Dengue, Malaria and Acute Encephalitis Syndrome. The control of the aquatic developmental stages is one of the easiest way to prevent the emergence of adults-the blood feeding adult females being thus prevented to sample their blood meal and to lay their eggs in the aquatic milieu where develop the aquatic pre-imaginal developmental stages. Moreover, reducing the adult population size also the probability of for the blood feeding adult female mosquitoes to act as hosts and vectors of the arboviruses such as dengue virus & Japanese encephalitis virus as well as of Plasmodium. Several environmental factors including water quality parameters are responsible for the selection of oviposition sites by the female mosquitoes. In our study, larval densities of three important mosquitoes (Aedes/A. albopictus, Anopheles/An. stephensi and Culex/C. vishnui) were measured and water qualities of their habitat i.e. pH, Specific Conductance, Dissolved Oxygen, Chemical Oxygen Demand, Total alkalinity (Talk), Hardness, Nitrate nitrogen and Ammonia nitrogen were analyzed in 2017 and 2018 in many districts of West Bengal where humans beings are suffering from arboviruses and /or malaria. Whereas we have found positive correlation of density of C. vishnui and A. albopictus with the water factors except Chemical Oxygen Demand (COD) and Talk, for An. stephensi all these factors except pH, COD and Talk have positive correlation. Hardness of the water shows positive correlation with the density of An. stephensi and C. vishnui but negative correlation with density of A. albopictus. Contour plot analysis demonstrates that occurrence of each mosquito species lies in between specific range of water factors. Inter- correlation analysis revealed that mosquitoes were negatively correlated with each other. A positive correlation of the water quality parameters and larval density, over two successive years, was also noticed. In conclusion, the increasing level of pollution due to industrial and other irresponsible waste management system which changes the water quality parameters may also influence mosquito population.