Groundwater quality in Kadaladi is a critical concern due to contamination from natural and human activities. This study assesses heavy metal concentrations across pre-monsoon and post-monsoon seasons at 44 sampling sites, using the Heavy Metal Pollution Index (HPI), Metal Index (MI), and non-carcinogenic risk (HQ) assessments. Key heavy metals analysed include Cu, Pb, Zn, Ni, Cd, Mn, and Fe, in Pre-monsoon and Post-monsoon with comparisons to WHO (2017) standards using atomic absorption spectrometry. The Heavy Metal Pollution Index (HPI) and Metal Index (MI) were computed to assess contamination levels, and health risks were evaluated through non-carcinogenic hazard quotient (HQ) models for adults and children. Results revealed that Mn and Fe concentrations exceeded WHO permissible limits in 20% of the samples, with Site 33 showing the highest pollution in HPI (99.1) and MI (17.89). Post-monsoon samples showed notably higher contamination, attributed to monsoonal leaching and runoff from agricultural and saltpan activities. GIS-based spatial analysis identified persistent hotspots at Sites 6, 24, and 33. Children were particularly vulnerable, with HQ values exceeding 2.0 in affected zones, especially due to Mn exposure. HQ values indicated that children face higher health risks, particularly from Mn, exceeding permissible limits in 4.55% of samples. Pearson correlation revealed strong Mn-Fe geogenic associations, while Cd-Mn correlations post-monsoon pointed to anthropogenic sources. The integration of spatial mapping using GIS and statistical methods provides insight into contamination hotspots, emphasizing the critical role of monsoonal hydrology in mobilizing pollutants. These findings underscore the need for region-specific mitigation strategies and regular water quality monitoring. The study fills a regional knowledge gap and contributes globally relevant insights on managing groundwater quality in vulnerable coastal systems.

The present study aims to evaluate the seasonal variations of geochemistry and groundwater quality in the Vallanadu region. Groundwater samples collected during the pre-monsoon and post-monsoon periods were analyzed using spatial analysis, the Water Quality Index (WQI), rock-water interaction, Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA). The WQI analysis revealed a significant deterioration in groundwater quality, with 60% of pre-monsoon samples classified as “Good,” 30% as “Fair,” and 10% as “Poor,” compared to 15% “Good,” 30% “Fair,” and 55% “Poor” or “Hazardous” post-monsoon, highlighting seasonal changes' influence. Hydrogeochemical facies analysis, through Piper diagrams, identified mixed Ca²⁺-Mg²⁺-Cl⁻ water as the dominant type, indicating marine intrusion and carbonate dissolution as major influencing factors. Rock-water interaction analysis highlighted silicate and carbonate weathering as primary geochemical processes, with seasonal recharge and evaporation shaping ion distributions. Statistical analyses including HCA and PCA, revealed distinct geochemical zones, with pre-monsoon periods showing higher ionic concentrations due to evaporation and reduced recharge, while post-monsoon periods were influenced by dilution and increased recharge. The findings underscore significant seasonal variations in groundwater quality and emphasize the growing impact of agricultural runoff, saline intrusion, and anthropogenic activities. The study highlights the urgent need for groundwater quality management in the Vallanadu region to ensure sustainable drinking and irrigation use.

This study attempts a detailed assessment of the quality of groundwater in the coastal region of Trivandrum District, Kerala where groundwater is the main source of drinking water. Forty groundwater samples were collected during the pre-monsoon and post-monsoon periods. The collected samples were analyzed for physical properties such as electrical conductivity (EC), pH, total dissolved solids (TDS), and total hardness, along with chemical properties, including major cations (Ca²⁺, Mg²⁺, Na⁺, K⁺) and anions (Cl⁻, SO₄²⁻, HCO₃⁻, NO₃⁻). The analysis of groundwater quality reveals significant spatial and seasonal variations caused by both natural and manmade influences. Water Quality Index (WQI), hydrogeochemical plots, and Principal Component Analysis (PCA) were used to analyses the data. The results show that Vakkom, Kazhakottam, Veli-Attipara, and Pozhiyoor show significant deterioration, and areas such as Varkala, Ayroor, and Edava generally maintain good water quality. The Water Quality Index (WQI) assessment indicates that approximately 22.5% of the studied area falls under excellent quality, while 17.5% is classified as poor. The WHO standard and BSI standards were used to derive the WQI. Principal Component Analysis (PCA) identified electrical conductivity, total dissolved solids, and total hardness as the primary factors affecting groundwater quality, explaining 65.17% and 61.03% of the total variance in the pre-monsoon and post-monsoon periods, respectively. Hydrochemical plots collaborate these results, emphasize the influence of rock-water interactions as the main geochemical process, further compounded by pollution from agricultural runoff and urban development. These findings highlight the need for sustainable groundwater management strategies in coastal communities. Effective measures, including pollution mitigation, sustainable agricultural practice, proper waste management, and preservation of freshwater ecosystems, are essential for ensuring the sustainability of groundwater resources.

The primary objective of this research is to delineate potential groundwater recharge zones in the Kadaladi taluk of Ramanathapuram, Tamil Nadu, India, using a combination of remote sensing and Geographic Information Systems (GIS) with the Analytical Hierarchical Process (AHP). Various factors such as geology, geomorphology, soil, drainage, density, lineament density, slope, rainfall were analyzed at a specific scale. Thematic layers were evaluated for quality and relevance using Saaty's scale, and then integrated using the weighted linear combination technique. The weights assigned to each layer and features were standardized using AHP and the Eigen vector technique, resulting in the final groundwater potential zone map. The AHP method was used to normalize the scores following the assignment of weights to each criterion or factor based on Saaty's 9-point scale. Pair-wise matrix analysis was utilized to calculate the geometric mean and normalized weight for various parameters. The groundwater recharge potential zone map was created by mathematically overlaying the normalized weighted layers. Thematic layers indicating major elements influencing groundwater occurrence and recharge were derived from satellite images. Results indicate that approximately 21.8 km² of the total area exhibits high potential for groundwater recharge. Groundwater recharge is viable in areas with moderate slopes, particularly in the central and southeastern regions.