A district-level susceptibility and vulnerability assessment of the COVID-19 pandemic's footprint in India.

In this study, we trace the COVID-19 pandemic's footprint across India's districts. We identify its primary epicentres and the outbreak's imprint in India's hinterlands in four separate time-steps, signifying the different lockdown stages. We also identify hotspots and predict areas where the pandemic may spread next. Significant clusters in the country's western and northern parts pose risk, along with the threat of rising numbers in the east. We also perform epidemiological and socioeconomic susceptibility and vulnerability analyses, identifying resident populations that may be physiologically weaker, leading to a high incidence of cases and pinpoint regions that may report high fatalities due to ambient poor demographic and health-related factors. Districts with a high share of urban population and high population density face elevated COVID-19 risks. Aspirational districts have a higher magnitude of transmission and fatality. Discerning such locations can allow targeted resource allocation to combat the pandemic's next phase in India.

Gauging the effects of the COVID-19 pandemic lockdowns on atmospheric pollution content in select countries.

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Some respite for India's dirtiest river? Examining the Yamuna's water quality at Delhi during the COVID-19 lockdown period.

The Yamuna's stretch within Delhi is considered as the dirtiest river reach in India and despite numerous restoration plans, pollution levels have risen unabated. However, the enforcement of a nationwide lockdown due to the ongoing COVID-19 pandemic can possibly provide a ray of hope. We analyze the lockdown's impact on the water quality status of this stretch using a combination of measured parameters and satellite image derived indices. Class C Water Quality Index estimates of nine stations indicate an improvement of 37% during the lockdown period. The Biological Oxygen Demand and Chemical Oxygen Demand values reduced by 42.83% and 39.25%, respectively, compared to the pre-lockdown phase, while Faecal Coliform declined by over 40%. Similar analysis of 20 major drains that meet the Yamuna revealed declining effluent loads and discernable improvements in drain contaminant status were ascertained via a hierarchical cluster analysis. Reach-wise suspended particulate matter content, turbidity and algal signatures were derived from multi-temporal Landsat-8 images of prior and ongoing lockdown periods for 117 channel segment zones. These parameters also declined notably within most stretches, although their extents were spatially varied. While the partial/non-operational status of most industries during the lockdown enabled significant reduction in effluent loads and a consequent betterment in the river water quality, its spatial variations and even deterioration in some locations resulted from the largely undiminished inflow of domestic sewage through multiple drains. This study provides an estimate of possible river recovery extents and degree of improvement if deleterious polluting activities and contaminants are regulated properly.

Reading the geography of India's district-level fertility differentials: a spatial econometric approach.

India has gradually progressed into fertility transition over the last few decades. However, the timing and pace of this transition has varied notably in terms of both its geography and the demographic groups most affected by it. While much literature exists on the relationships between fertility level and its influence on demographic, economic, socio-cultural and policy-related factors, the potential spatial variations in the effects of these factors on the fertility level remain unaddressed. Using the most recent district-level census data (of 2011) for India, this nationwide study has identified plausible spatial dependencies and heterogeneities in the relationships between the district-wise Total Fertility Rates (TFRs) and their respective demographic, socioeconomic and cultural factors. After developing a geocoded database for 621 districts of India, spatial regression and Geographically Weighted Regression (GWR) models were used to decipher location-based relationships between the district-level TFR and its driving forces. The results revealed that the relationships between the district-level TFR and the considered selected predictors (percentage of Muslims, urbanization, caste group, female mean age at marriage, female education, females in the labour force, net migration, sex ratio at birth and exposure to mass media) were not spatially invariant in terms of their respective strength, magnitude and direction, and furthermore, these relationships were conspicuously place- and context-specific. This study suggests that such locality-based variations and their complexities cannot be explained simply by a single narrative of either socioeconomic advancement or government policy interventions. It therefore contributes to the ongoing debate on fertility research in India by highlighting the spatial dependence and heterogeneity of the impacts made by demographic, socioeconomic and cultural factors on local fertility levels. From a methodological perspective, the study also discerns that the GWR local model performs better, in terms of both model performance and prediction accuracy, compared with the conventional global model estimates.

Evaluation of different digital elevation models for analyzing drainage morphometric parameters in a mountainous terrain: a case study of the Supin-Upper Tons Basin, Indian Himalayas.

BACKGROUND: With myriad geospatial datasets now available for terrain information extraction and particularly streamline demarcation, there arises questions regarding the scale, accuracy and sensitivity of the initial dataset from which these aspects are derived, as they influence all other parameters computed subsequently. In this study, digital elevation models (DEM) derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER V2), Shuttle Radar Topography Mission (SRTM V4, C-Band, 3 arc-second), Cartosat -1 (CartoDEM 1.0) and topographical maps (R.F. 1:250,000 and 1:50,000), have been used to individually extract and analyze the relief, surface, size, shape and texture properties of a mountainous drainage basin. RESULTS: Nestled inside a mountainous setting, the basin is a semi-elongated one with high relief ratio (>90), steep slopes (25°-30°) and high drainage density (>3.5 km/sq km), as computed from the different DEMs. The basin terrain and stream network is extracted from each DEM, whose morphometric attributes are compared with the surveyed stream networks present in the topographical maps, with resampling of finer DEM datasets to coarser resolutions, to reduce scale-implications during the delineation process. Ground truth verifications for altitudinal accuracy have also been done by a GPS survey. CONCLUSIONS: DEMs derived from the 1:50,000 topographical map and ASTER GDEM V2 data are found to be more accurate and consistent in terms of absolute accuracy, than the other generated or available DEM data products, on basis of the morphometric parameters extracted from each. They also exhibit a certain degree of proximity to the surveyed topographical map.