The water quality of Rudrasagar Lake, the second-largest natural reservoir of Tripura is of great ecological and economic importance as it serves a diverse range of purposes, including fishing, irrigation, aquaculture, domestic use, and recreation activities. This study investigates the water quality of the study area, an esteemed Ramsar site in North Eastern India, using a combined application of multivariable statistical and geospatial techniques. In this study, 24 water samples were designed based on their use and collected along the periphery and the inner areas of the lake employing the Latin Square Matrix. This research also examines the spatial variations of water quality involving quartile-based water quality categorization of parameters, with Pearson’s Correlation analysis, Principal Component Analysis (PCA), and Hierarchy Cluster Analysis (HCA) applied for dimension reduction. The analysis involved quartile-based water quality categorization of parameters, with PCA and HCA applied for dimension reduction. Meanwhile, the Inverse distance weighted (IDW) approach was used to interpolate the spatial distribution of the quartile score using the ArcGIS platform. The Bureau of Indian Standards (BIS) was followed for water quality assessment. The results revealed significant spatial variation, providing valuable insights for future water management strategies. PCA indicates 57.26% of the variance in the dataset, whereas samples were classified into three subgroups and two groups in a dendrogram representing the result of the HCA. This study demonstrates the utility of PCA, HCA, and IDW interpolation in water quality assessment, highlighting the effect of human-induced activities in the lake’s vicinity.

Climate disasters have become increasingly frequent in India, severely affecting the railway infrastructure every year. Physical damages to railway tracks, bridges, and signaling systems, caused by floods, cyclones, and landslides, are well documented. However, the impact of these disasters on the railway infrastructure was beyond direct physical damages. This paper aimed to explore the impact of climate disasters on railway infrastructure in Northeast India using case study approach. Three cases were studied to analyse the impact of climate disasters on railway infrastructure, including geological disasters and extreme weather. Infrastructure development and operation of railway transport system in Assam, Mizoram, and Manipur proved to be challenging, especially when coping with natural disasters, such as floods, landslides, and earthquakes. This paper found that disruption of railway services was associated with geo-physical structure of the region, which triggered the disaster vulnerability. The results showed that climate disasters had a significant impact on railway infrastructure in Northeast India in many aspects. Formulation and implementation of strategic policies might reduce the disaster risks. Therefore, policymakers and Ministry of Railways, Government of India should consider this possible probability approach over environmental determinism.

This study delves into the crucial task of embedding climate change resilience within the sphere of railway infrastructure planning and design in India. As climate change continues to threaten global transportation systems, the creation of robust, sustainable infrastructure becomes indispensable for minimizing its impacts. Initial investigation entails assessing both existing and anticipated climate change scenarios in India, encompassing elements like temperature fluctuations, changes in precipitation, and severe weather phenomena. Following this, the study proceeds to pinpoint the specific risks and vulnerabilities that the Indian railway system stands to confront due to these climatic shifts. A thorough exploration of current adaptation policies and strategies provides a framework to merge these into railway infrastructure planning and design, using a mix of literary review, best practices, and international case studies as resources. The Indian railway network undergoes a meticulous analysis to evaluate its vulnerability, leading to the identification of key adaptation measures like devising new railway tracks, enhancing the existing infrastructure, adopting resilience-based technologies, and implementing nature-centric solutions. The research probes the economic, social, and environmental ramifications of these measures, underlining the long-term sustainability and beneficial impacts on the transportation industry. Expert interviews, stakeholder consultations, and policy analysis culminate in a set of recommendations for policymakers, urban planners, and transportation authorities. These recommendations aim to shape the progression of a climate-resilient railway infrastructure in the light of India’s distinct challenges. Such an integration of climate change adaptation strategies contributes towards a more robust and sustainable transportation system. This study enriches the existing body of knowledge on climate change adaptation in transportation, offering valuable perspectives for policymakers, practitioners, and researchers aiming for climate resilience in the railway sector.

Wetlands are dynamic and robust ecosystems that require comprehensive monitoring and assessment to safeguard water resources for irrigation and consumption. Recently, surface water has been severely degraded by several anthropogenic activities that persistently disrupt both public health as well as ecological integrity. This study employed multivariate statistical analysis (MSA) to examine spatial heterogeneity in hydro-chemical parameters and elucidate the dominant factors controlling water quality degradation. Water samples were strategically collected from peripheral and core areas o f the Rudrasagar wetland following standard protocol. An integrated framework was utilised incorporating geospatial techniques and water quality indices such as Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) and Weighted Arithmetic Water Quality Index (WAWQI) to provide a holistic snapshot of hydro-chemical properties. Spatial variation mapping effectively demonstrated dynamics of physico-chemical fingerprints and contamination risks of the wetland ecosystem. Multidimensional assessment revealed significant spatial heterogeneity in Rudrasagar wetland. CCME WQI classified 76% of the area as having excellent and 19% as good water quality while WAWQI indicated 66% as poor quality and 11% as unsuitable for domestic utilisation. Concurrently, Comprehensive Pollution Index (CPI) specified consistent pollution levels with nominal significant variation. This study exhibited pollution levels under three distinct categories; sub-clean water, slightly polluted water and moderately polluted water. CPI exposed that 68% of the samples were slightly polluted, while 14% were approaching heavily polluted water quality. The central to south-eastern zones exhibited substantial contamination by various pollutants. Pearson correlation matrix (r) demonstrated the significance interdependence among hydro-chemical parameters. Principal Component Analysis (PCA) identified five factors accounting for 65% of the extraction sums of squared loadings indicating that both anthropogenic activities and geogenic mechanism significantly accelerated water quality deterioration. Hierarchical cluster analysis (HCA) classified the sampling sites based upon similar hydro-chemical characteristic. These findings effectively emphasized the urgent need to develop sustainable management strategies and strict land-use regulations for protecting wetland ecosystems. Furthermore, establishing integrated GIS-based monitoring systems is essential for implementing effective conservation policies and ensures long-term ecosystem integrity. Graphical Abstract