Influences on tidal channel and aquaculture shrimp pond water chemical composition in Southwest Bangladesh.

Detailed geochemical studies of both major and minor elements in Bangladesh surface waters are sparse, particularly in shrimp aquaculture pond environments. Therefore, water samples from shrimp aquaculture ponds and tidal channels were collected in high precipitation (July) and low precipitation (May) months from 2018-2019 in Southwest Bangladesh and analyzed for complete water chemistry. Selenium (Se) and arsenic (As) were elevated above WHO guidelines in 50% and ~ 87% of samples, respectively, but do not show any recognizable spatial patterns. Shrimp pond and tidal channel water compositions in the dry season (May) are similar, illustrating their connectivity and minimal endogenous effects within shrimp ponds. Tidal channels are less saline in July than shrimp ponds still irrigated by tidal channels, suggesting that either farmers limit irrigation to continue farming saltwater shrimp, or the irrigation flux is low and leads to a lag in aquaculture-tidal channel compositional homogenization. δO and δH isotopic compositions from samples in May of 2019 reveal tidal channel samples are closer to the local meteoric water line (LMWL) than shrimp pond samples, because of less evaporation. However, evaporation in May shrimp ponds has a minimal effect on water composition, likely because of regular drainage/exchange of pond waters. Dissolved organic carbon (DOC) is positively correlated with both δO and δH in shrimp ponds, suggesting that as evaporation increases, DOC becomes enriched. Multiple linear regression reveals that As and Se can be moderately predicted (adjusted R values between 0.4 and 0.7, p < 0.01) in surface waters of our study with only 3-4 independent predictor variables (e.g., Ni, V and DOC for Se prediction; Cu, V, Ni and P for As prediction). Thus, this general approach should be followed in other regions throughout the world when measurements for certain hazardous trace elements such as Se and As may be lacking in several samples from a dataset.