Syamaprasad College

Rapid urbanization significantly influences the quantity and quality of natural water resources. This study investigates the impact of urban growth on the water quality of the Balason and Mahananda rivers in Siliguri. To evaluate the Water Quality Index (WQI), water samples for eleven physicochemical parameters were collected from seventeen selected sites. An upper - urban - downstream gradient approach was adopted, integrating statistical and GIS-based techniques for comprehensive analysis. WQI results indicated a marked decline in water quality during the lean season, particularly in densely populated urban stretches. Average WQI scores ranged from good to poor, with the most degraded sites located within the central urban zone. Statistical assessments revealed significant spatial variation in water quality across upstream, urban, and downstream segments. This research highlights that improving urban water quality aligns directly with SDG 6 (clean water and sanitation), suggesting the urgent need for integrated water resource management and sustainable urban planning.

UGA-SSMRPT2, the spin-free perturbative analogue of Mukerjee's State-Specific Multireference Coupled Cluster Theory (MkMRCC), is known to be successful for size-extensive and intruder-free construction of dissociation curves. This work demonstrates that UGA-SSMRPT2 is also an accurate and computationally inexpensive framework for computing the excitation energies. The method achieves near-chemical accuracy for the vast majority of π → π*, n → π*, charge-transfer, valence-Rydberg, and Rydberg excited states commonly used for benchmarking electronic structure theories for excited states. Our results demonstrate that UGA-SSMRPT2 excitation energies lie within 0.20 eV of EOM-CCSD and/or well-established theoretical best estimates, often surpassing the popular MRPT2 approaches like NEVPT2, CASPT2, and MCQDPT while typically requiring smaller active spaces. Its state-specific formulation circumvents the well-known intruder-state problem and eliminates the need for empirical parameters, such as IPEA shifts in CASPT2. This work proposes UGA-SSMRPT2 as a robust and scalable approach for modeling challenging electronically excited states.

Groundwater depletion and land subsidence have emerged as critical environmental challenges in rapidly urbanizing cities due to excessive groundwater extraction, increasing population pressure, and uncontrolled urban expansion. The present study investigates the relationship between groundwater depletion and potential land subsidence in the Kolkata Municipal Corporation (KMC), West Bengal, India, using geospatial techniques, Remote Sensing, Geographic Information System (GIS), and Analytical Hierarchy Process (AHP)-based Multi-Criteria Decision Analysis (MCDA). Groundwater fluctuation data for pre-monsoon and post-monsoon periods were collected from the Groundwater Year Book (GWYB) for the years 2010, 2013, and 2020. Various thematic layers, including aquifer depletion, geology, land use/land cover (LULC), urbanization growth, tectonic activity, and drainage pattern, were prepared and integrated to delineate groundwater potential and subsidence-prone zones. Inverse Distance Weighting (IDW) interpolation was applied to analyse spatial groundwater variability, while hydrochemical parameters were evaluated using the coefficient of variation (CV). The results reveal significant groundwater depletion in several parts of KMC, particularly within highly urbanized and densely populated zones. Urban expansion, reduced recharge potential, and unconsolidated geological formations were identified as major contributors to groundwater stress and potential land subsidence. The study highlights the effectiveness of integrating geospatial techniques and AHP-based MCDA for groundwater assessment and emphasizes the urgent need for sustainable groundwater management, artificial recharge measures, and continuous monitoring to reduce future subsidence risks in Kolkata.

West Bengal has a unique diversity of geomorphological landscapes ranging from the Eastern Himalaya and Sub-Himalayan piedmont to plateau, fluvial, deltaic and coastal environments. However, comparative and spatially explicit assessment of geosites across these contrasting landscapes is limited. The present study employs the Modified Geosite Assessment Model (M-GAM) to assess and rank 42 representative geosites in the major geomorphic provinces of West Bengal. The evaluation comprises expert assessment of 27 standardised sub-indicators and visitor opinions collected through a structured survey of 202 respondents. The expert-based assessment incorporated visitor-derived importance factors (Im) to reflect the perceived importance of individual attributes. The visitor instrument demonstrated acceptable internal consistency for the Main Value (MV; Cronbach’s α = 0.79) and Additional Value (AV; Cronbach’s α = 0.84) subscales. The results show high variability among geosites in scientific/educational, scenic/aesthetic, protection, functional and touristic attributes. The mean AV score (11.90) was higher than the mean MV score (8.53), indicating the importance of functional and tourism-supporting attributes in the relative M-GAM scores. The 42 geosites were ranked into relatively high-13, moderate-18 and low-11 priority groups using quartile-based thresholds within the study inventory. Correlation analysis revealed positive correlations between some of the M-GAM components, for example, between accessibility and number of visitors (r = 0.74). The VTr – AV correlation (r = 0.81) is treated with caution since VTr is a component of AV itself. Further, a comparison of expert and visitor assessments identifies sites where geomorphological significance and visitor perceived importance are convergent or divergent. Overall, the study demonstrates the value of M-GAM as a comparative decision support framework for geosite prioritization across contrasting landscapes, whilst acknowledging the uncertainties associated with visitor sampling, expert scoring, weighting, aggregation and relative classification.

The fast rate of urban expansion in the ecologically fragile Northeastern Himalayas is related to the rise in anthropogenic pressure and the changes in vegetation composition, surface thermal conditions, atmospheric quality, and demographic intensity. However, the existing environmental indices are typically focused on individual environmental dimensions or provide a limited representation of the combined urban-ecological system. In this research, we developed an Urban-Ecological Impact Index (UEII) using Google Earth Engine and multi-source remote sensing and ancillary datasets to characterize the spatial-temporal co-occurrence of urban pressure and environmental stress across the Northeastern Himalayan region. Multi-temporal Landsat observations for 2005, 2015 and 2025 were used to derive the Normalised Difference Vegetation Index (NDVI), Normalised Difference Built-up Index (NDBI) and Land Surface Temperature (LST). Nighttime-light intensity, Air Quality Index (AQI) and population density were incorporated to represent additional anthropogenic and environmental pressures. Within the framework, these indicators are integrated into an Urbanization Index (UI) and an Ecological Degradation Index (EDI), and combined to produce values of UEII on a normalised 0–1 scale. The Getis-Ord Gi* hotspot analysis was used to assess spatial clustering, and the correlation analysis was used to investigate the relationships between the main environmental and anthropogenic indicators. Moreover, the revised assessment considers sensitivity to weighting, multicollinearity, spatial dependence, and uncertainty from heterogeneous spatial resolutions and temporal sensor differences. Results indicate that urban–ecological pressure is spatially significantly differentiated, and high UEII concentrations are particularly around major urban and fast-developing corridors such as Guwahati–Shillong and the Siliguri region. The temporal comparison also identifies the change of vegetation condition, built-up intensity, thermal environment, nighttime illumination, atmospheric quality and population pressure between 2005 and 2025. The temporal comparison also identifies the change of vegetation condition, built-up intensity, thermal environment, nighttime illumination, atmospheric quality and population pressure between 2005 and 2025. Importantly, the UEII is interpreted as a relative measure of combined urban pressure and environmental stress, rather than as a probabilistic ecological-risk metric or a causal measure of urbanisation-induced degradation. This study illustrates the potential of cloud-based geospatial analysis for integrated environmental assessment and decision support in fast-transforming and environmentally sensitive Himalayan landscapes.