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  • Author: Swades Pal x
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Abstract

Dwarka River basin (3882.71 km2) of Eastern India in the Chotonagpur Plateau and Gangetic Plain is highly affected by stone mining and crushing generated dust. In the middle catchment of this basin, there are 239 stone mines and 982 stone crushing units. These produce approximately 258120 tons of dust every year and this dust enters into the river and coats the leaves of plants. On the one hand, this is aggrading in the stream bed, increasing sediment load, decreasing water quality, specifically increasing total dissolved solid, pH, water colour, and it also degrades the vegetation quality. Vegetation quality is also degraded as indicated by decreasing of NDVI values (maximum NDVI in 1990 was 0.70 and in 2016 it was 0.48). Considering all these issues, the present paper intends to identify dust vulnerable zones based on six major driving parameters and the impact of the dust on river morphology, water quality and vegetation quality in different vulnerable zones. Weighted linear combination method (in Arc Gis environment) is used for compositing the selected parameters and deriving vulnerable zones. Weight to the each parameter is assigned based on analytic hierarchy process, a semi quantitative method. According to the results, 579.64 km2 (14.93%) of the catchment area is very highly vulnerable: Here 581 rivers have a length of 713 km and these riversare prone to high dust deposition, increased sediment load and water quality deterioration.

Abstract

Rapid urbanization and change of landuse/landcover results in changes of the thermal spectrum of a city even in small cities like English Bazaar Municipality (EBM) of Malda district. Monitoring the spatio-temporal surface temperature patterns is important, therefore, the present paper attempts to extract spatio-temporal surface temperature from thermal band of Landsat imageries and tries to validate it with factor based Land Surface Temperature (LST) models constructed based on six proxy temperature variables for selected time periods (1991, 2010 and 2014). Seasonal variation of temperature is also analyzed from the LST models over different time phases. Landsat TIRS based LST shows that in winter season, the minimum and maximum LST have raised up 2.32°C and 3.09°C in last 25 years. In pre monsoon season, the increase is much higher (2.80°C and 6.74°C) than in the winter period during the same time frame. In post monsoon season, exceptional situation happened due to high moisture availability caused by previous monsoon rainfall spell. Trend analysis revealed that the LST has been rising over time. Expansion and intensification of built up land as well as changing thermal properties of the urban heartland and rimland strongly control LST. Factor based surface temperature models have been prepared for the same period of times as done in case of LST modeling. In all seasons and selected time phases, correlation coefficient values between the extracted spatial LST model and factor based surface temperature model varies from 0.575 to 0.713 and these values are significant at 99% confidence level. So, thinking over ecological growth of urban is highly required for making the environment ambient for living.