<Emphasis Type="Italic">Noctiluca</Emphasis> and copepods grazing on the phytoplankton community in a nutrient-enriched coastal environment along the southwest coast of India

The relative grazing impact of Noctiluca scintillans (hereafter referred only Noctiluca) and copepods (Acrocalanus gracilis, Paracalanus parvus, Acartia danae and Oithona similis) on the phytoplankton community in an upwelling–mudbank environment along the southwest coast India is presented here. This study was carried out during the Pre-Southwest Monsoon (April–May) to the Late Southwest Monsoon (August) period in 2014. During the sampling period, large hydrographical transformation was evident in the study area (off Alappuzha, Southwest coast of India); warmer Pre-Southwest Monsoon water column condition got transformed into cooler and nitrate-rich hypoxic waters during the Southwest Monsoon (June–August) due to intense coastal upwelling. Copepods were present in the study area throughout the sampling period with a noticeable increase in their abundance during the Southwest Monsoon. On the other hand, the first appearance of Noctiluca in the sampling location was during the Early Southwest Monsoon (mid-June) and thereafter their abundance increased towards the Peak Southwest Monsoon. The grazing experiments carried out as per the food removal method showed noticeable differences in the feeding preferences of Noctiluca and copepods, especially on the different size fractions of phytoplankton. Noctiluca showed the highest positive electivity for the phytoplankton micro-fraction (av. 0.49 ± 0.04), followed by nano-fraction (av. 0.17 ± 0.04) and a negative electivity for the pico-fraction (av. ?0.66 ± 0.06). In total ingestion of Noctiluca, micro-fraction contribution (83.7%) was significantly higher compared to the nano- (15.7%) and pico-fractions (0.58%). On the other hand, copepods showed the highest positive electivity for the phytoplankton nano-fraction (av. 0.38 ± 0.04) followed by micro- (av. -0.17 ± 0.05) and pico-fractions (av. ?0.35 ± 0.05). Similarly, in total ingestion of copepods, nano-fraction (69.7%) was the highest followed by micro- (28.9%) and pico-fractions (1.37%). The grazing pressure of Noctiluca on the total phytoplankton was found to be 27.7% of the standing stock and 45.6% of the production, whereas in the case of copepods, it was 9.95% of the standing stock and 16.6% of the production. The study showed that the grazing pressure of Noctiluca on the total phytoplankton as well as larger phytoplankton fraction was 2.8- and 8-folds higher than that of the copepods. This suggests the leading role of Noctiluca as an effective grazer of larger phytoplankton along the southwest west coast of India, especially during the Peak/Late Southwest Monsoon.     

Variability in the phytoplankton community of Kavaratti reef ecosystem (northern Indian Ocean) during peak and waning periods of El Niño 2016

El Niño, an interannual climate event characterized by elevated oceanic temperature, is a prime threat for coral reef ecosystems worldwide, owing to their thermal threshold sensitivity. Phytoplankton plays a crucial role in the sustenance of reef trophodynamics. The cell size of the phytoplankton forms the “master morphological trait” with implications for growth, resource acquisition, and adaptability to nutrients. In the context of a strong El Niño prediction for 2015–2016, the present study was undertaken to evaluate the variations in the size-structured phytoplankton of Kavaratti reef waters, a major coral atoll along the southeast coast of India. The present study witnessed a remarkable change in the physicochemical environment of the reef water and massive coral bleaching with the progression of El Niño 2015–2016 from its peak to waning phase. The fluctuations observed in sea surface temperature, pH, and nutrient concentration of the reef water with the El Niño progression resulted in a remarkable shift in phytoplankton size structure, abundance, and community composition of the reef waters. Though low nutrient concentration of the waning phase resulted in lower phytoplankton biomass and abundance, the diazotroph Trichodesmium erythraeum predominated the reef waters, owing to its capability of the atmospheric nitrogen fixation and dissolved organic phosphate utilization.     

Source identification and apportionment of PM2.5 and PM2.5−10 in iron and steel scrap smelting factory environment using PMF,PCFA and UNMIX receptor models

To identify the potential sources responsible for the particulate matter emission from secondary iron and steel smelting factory environment, PM_2.5 and PM_2.5?10 particles were collected using the low-volume air samplers twice a week for a year. The samples were analyzed for the elemental and black carbon content using x-ray fluorescence spectrometer and optical transmissometer, respectively. The average mass concentrations were 216.26, 151.68, and 138. 62 μg/m³ for PM_2.5 and 331.36, 190.01, and 184.60 μg/m³ for PM_2.5?10 for the production, outside M1 and outside M2 sites, respectively. The same size resolved data set were used as input for the positive matrix factorization (PMF), principal component factor analysis (PCFA), and Unmix (UNMIX) receptor modeling in order to identify the possible sources of particulate matter and their contribution. The PMF resolved four sources with their respective contributions were metal processing (33 %), e-waste (33 %), diesel emission (22 %) and soil (12 %) for PM_2.5, and coking (50 %), soil (29 %), metal processing (16 %) and diesel combustion (5 %) for PM_2.5?10. PCFA identified soil, metal processing, Pb source, and diesel combustion contributing 45, 41, 9, and 5 %, respectively to PM_2.5 while metal processing, soil, coal combustion and open burning contributed 43, 38, 12, and 7 %, respectively to the PM_2.5?10. Also, UNMIX identified metal processing, soil, and diesel emission with 43, 42 and 15 % contributions, respectively for the fine fraction, and metal processing (71 %), soil (21 %) and unidentified source (1 %) for the coarse fraction. The study concluded that metal processing and e-waste are the major sources contributing to the fine fraction while coking and soil contributed to the coarse fraction within the factory environment. The application of PMF, PCFA and UNMIX receptor models improved the source identification and apportionment of particulate matter drive in the study area.     

The use of sensory perception indicators for improving the characterization and modelling of total petroleum hydrocarbon (TPH) grade in soils

This paper proposes a multistep approach for creating a 3D stochastic model of total petroleum hydrocarbon (TPH) grade in potentially polluted soils of a deactivated oil storage site by using chemical analysis results as primary or hard data and classes of sensory perception variables as secondary or soft data. First, the statistical relationship between the sensory perception variables (e.g. colour, odour and oil–water reaction) and TPH grade is analysed, after which the sensory perception variable exhibiting the highest correlation is selected (oil–water reaction in this case study). The probabilities of cells belonging to classes of oil–water reaction are then estimated for the entire soil volume using indicator kriging. Next, local histograms of TPH grade for each grid cell are computed, combining the probabilities of belonging to a specific sensory perception indicator class and conditional to the simulated values of TPH grade. Finally, simulated images of TPH grade are generated by using the P-field simulation algorithm, utilising the local histograms of TPH grade for each grid cell. The set of simulated TPH values allows several calculations to be performed, such as average values, local uncertainties and the probability of the TPH grade of the soil exceeding a specific threshold value.