A comparative study on mesozooplankton abundance and diversity between a protected and an unprotected coastal area of Andaman Islands

The study was carried out to understand the variability in phytoplankton production (Chlorophyll a) and mesozooplankton diversity from two different shallow coastal regions of south Andaman viz. Port Blair Bay (PBB), the only real urban area among the islands and Mahatma Gandhi Marine National Park, a Marine Protected Area (MPA) at Wandoor. Seasonal sampling was carried out during the Northeast monsoon (NEM—November 2005), Intermonsoon (IM—April 2006), and Southwest monsoon (SWM—August 2006). Significant (P?<?0.05) seasonal variation was observed in the environmental variables at both the regions. Higher average chlorophyll a (Chl. a) and mesozooplankton standing stock were observed at PBB compared to MPA, but the seasonal variation observed was marginal at both the study areas. Chl. a showed a steep increasing gradient from outer to the inner regions of the PBB. The number of zooplankton taxa recorded at both areas was quite similar, but marked differences were noticed in their relative contribution to the total abundance. Eventhough the Copepoda dominated at both the areas, the non-copepod taxa differed significantly between the regions. Dominance of carnivores such as siphonophores and chaetognaths were noticed at PBB, while filter feeders such as appendicularians and decapod larvae were more abundant at MPA. A total of 20 and 21 copepod families was recorded from PBB and MPA, respectively. Eleven species of chaetognaths were observed as common at both areas. Larval decapods were found to be predominant at MPA with 20 families; whereas, at PBB, only 12 families were recorded. In the light of the recent reports on various changes occurring in the coastal waters of the Andaman Islands, it is suspected that the difference in Chl. a as well as the mesozooplankton standing stock and community structure observed between the two study areas may be related to the various anthropogenic events influencing the coastal waters.     

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.