Microplastic pollution in surface sediments of Coromandel coastline, South-East Coast, India: Diversity index, carbonyl index, pollution load index, risk fraction and MPs inventory.

The investigation along the Coromandel coastline of South-east India focused on assessing microplastics abundance using Simpson's diversity index (DIMP), Degradation-carbonyl index (DgCIMP), Pollution load index (PLIMP) and Ecological risk fraction (RfMP). These indices evaluated the dissemination and transportation of MPs across a 1076 km stretch divided into five zones from Chennai to Kanyakumari. During the wet season, average microplastics abundance (101 ± 36.6 items/kg dw) was lower compared to the dry season (143 ± 56.2 items/kg dw). Notably, 54% and 45% of microplastics were found in the 0.1-0.5 mm size range, with 45% and 64% being colored microplastics, and 80% and 71% being fibers during the wet and dry seasons respectively. Micro-Fourier-transform infrared spectroscopy (µFTIR) analysis showed rayon (34%) and PE (64%) dominance in ports and estuaries during both seasons. Kottaipattinam Port exhibited higher diversity indices (DIMPsh=0.56,DIMPsz=0.66,DIMPco=0.50andDIMPpo=0.65) compared to other zones, with an overall diversity index IDIMP of 0.57. Notably, among the DgCIMP values (n = 96), only 12 fell within the moderate photo-chemical oxidation range (0.16-0.35), while the majority (n = 60) surpassed 0.35 indicating higher oxidation levels, with some (n = 24) exceeding 0.50, signifying extreme oxidation. PLIMP revealed that 42% of sampling stations had very low to negligible MP contamination levels in ports and estuaries. However, ecological risk fraction RfMP values ranged from 10.2 to 13,670, with 27% of values exceeding 1500, indicating higher coastal ecological risk in 13 sampling stations.

Investigation and comparative analysis of ecological risk for heavy metals in sediment and surface water in east coast estuaries of India.

The sediments and surface water from 8 stations each from Dhamara and Paradeep estuarine areas were sampled for investigation of heavy metals, Cd, Cu, Pb, Mn, Ni, Zn, Fe, and Cr contamination. The objective of the sediment and surface water characterization is to find the existing spatial and temporal intercorrelation. The sediment accumulation index (Ised), enrichment index (IEn), ecological risk index (IEcR) and probability heavy metals (p-HMI) reveal the contamination status with Mn, Ni, Zn, Cr, and Cu showing permissible (0 ≤ Ised ≤ 1, IEn Ë‚ 2, IEcR ≤ 150) to moderate (1 ≤ Ised ≤ 2, 40 ≤ Rf ≤ 80) contamination. The p-HMI reflects the range from excellent (p-HMI = 14.89-14.54) to fair (p-HMI = 22.31-26.56) in off shore stations of the estuary. The spatial patterns of the heavy metals load index (IHMc) along the coast lines indicate that the pollution hotspots are progressively divulged to trace metals pollution over time. Heavy metal source analysis coupled with correlation analysis and principal component analysis (PCA) was used as a data reduction technique, which reveals that the heavy metal pollution in marine coastline might originate from redox reactions (FeMn coupling) and anthropogenic sources.

Significance between air pollutants, meteorological factors, and COVID-19 infections: probable evidences in India.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors, and the daily reported infected cases caused by novel coronavirus in India. The daily positive infected cases, concentrations of air pollutants, and meteorological factors in 288 districts were collected from January 30, 2020, to April 23, 2020, in India. Spearman's correlation and generalized additive model (GAM) were applied to investigate the correlations of four air pollutants (PM2.5, PM10, NO2, and SO2) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS, and WD) with COVID-19-infected cases. The study indicated that a 10 µg/m3 increase during (Lag0-14) in PM2.5, PM10, and NO2 resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01), and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of Coronavirus Disease 2019 (COVID 19)-infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19-infected cases respectively. In addition, SO2 and relative humidity were negatively associated with COVID-19-infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO2 and for relative humidity respectively. The study recommended that there are significant correlations between air pollutants and meteorological factors with COVID-19-infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease.

Modified heavy metal Pollution index (m-HPI) for surface water Quality in river basins, India.

India is bestowed with huge amount of surface water resources. However, India lacks the quality monitoring of surface water and comprehensive management for sustainable surface water development. A new approach for indexing has been proposed to represent pollution due to heavy metals in surface water. Heavy metal pollution indices (m-HPI) for 60 surface water samples in the peninsular stretch were evaluated during pre-drought, drought and post-drought condition. The Index will be represented by a Positive Index (PI) and a Negative Index (NI), where PI represents the level pollution exceeding the maximum desirable limit and NI reflects the index within the required limit. The PI is assigned as 0 when indicators are present below the detection limit or equal to the maximum required limit. However, the value calculated for NI could be 0 to -1 when the indicators are equal to or less than the suggested maximum desirable limit, and the value could be -1 when the indicators are present below the suggested detection limit. The spatiotemporal variation of water quality pattern was studied by the interpolation maps extracted from ArcGIS. The results are compared with WHO standard to validate the drinking water quality. The calculated indices indicated the suitability of water for domestic and irrigation purposes. The developed indexing system is user friendly, robust, flexible and may evaluate the index considering any water quality standard.