This study aimed to develop a geochemical database by thoroughly analyzing groundwater and sediments from coastal aquifers of southwest Bangladesh. Moreover, we investigated the source of sediment deposition and the mechanisms behind the presence of arsenic and salinity in groundwater. The seasonal distribution patterns of arsenic among the shallow and deep coastal aquifers were found to be 45.12 µg/l and 20.65 µg/l during dry and wet seasons, respectively. Moreover, the groundwater salinity distribution ranged from 3262.88 mg/l to 1930.88 mg/l during the dry and wet seasons. Cored sediment samples showed fine to medium sands of 92%, with silt and clay particles. The petrographic study of authigenic and heavy minerals revealed that the mineral grains were subangular to angular, indicating their textural immaturity of coastal sediments. The reactivity of goethite (FeOOH) and siderite (FeCO₃) minerals suggests that the aquifers were subjected to slightly oxidized to moderately reducing conditions, with ORP values ranging from +50.40 mv to −149.5 mv. Such redox conditions could potentially result in the enrichment and mobility of arsenic in the groundwater. Although arsenic concentrations in deep aquifers are relatively low, higher salinity values are found in both shallow and intermediate coastal aquifers.

Salinity intrusion in the coastal regions of Bangladesh is particularly challenging for communities living near mangrove forests, which have salinity-dependent ecosystems. The salinity levels within groundwater aquifer systems here vary seasonally, and vegetation has the potential to serve as a mitigation strategy to reduce salinity intrusion. In this study, we focused on the southwest coastal region of Bangladesh, an area characterized by high salinity intrusion. We analyzed groundwater samples collected from monitoring wells during both dry and wet seasons. Additionally, we recorded the necessary physical and chemical parameters to support the analysis. We explored several GIS interpolation methods, including Inverse Distance Weight (IDW), Empirical Bayesian Kriging (EBK), and Kernel interpolation to map salinity while employing remote sensing techniques to map vegetation dynamics. We used spatial regression and bivariate clustering methods to identify and map the seasonal spatial dependence variation of salinity and vegetation. Our analysis indicates a negative linear relationship between salinity and vegetation dynamics, with the correlation being notably stronger in the dry season (Pearson correlation − 0.540) compared to the wet season (Pearson correlation − 0.149). The spatial regression model indicated spatial dependence between these two variables only during the dry season, with an R-squared value of 0.972. Bivariate clustering results identified approximately 20% of the area during the dry season with high average salinity and low average vegetation index values. The spatial context of this study is crucial for developing mitigation strategies for salinity intrusion, considering the vegetation dynamics of coastal regions of Bangladesh.