Preliminary Evidence of Transport‐Limited Chemical Weathering and Element Immobility in the Ganges Tidal Delta Plain of Bangladesh

Abstract

Previous studies have documented a weathering‐limited regime in the upper reaches of the Ganges River Basin. Chemical weathering and element mobility at six sites in the lower reaches of the Ganges‐Brahmaputra tidal floodplain of southwest Bangladesh were investigated by comparing compositions of rice paddy soils, precursor tidal channel sediments, surface waters, and extract solutions, which represent the soluble fraction of solids. Little spatial variation in water and solid compositions is observed in each season, indicating similar processes are acting to transport elements across this region. Roughly one to several decades after deposition, rice paddy soils are not significantly different in mineralogy or composition from precursor tidal channel sediments, and both are similar to the composition of average upper continental crust. Soil and sediments contain mostly stable, cation depleted minerals, and coexisting waters are saturated in those minerals. There is no detectable change in composition of tidal channel water between upstream and downstream sites. Together, these observations indicate the dominance of weathered material and weak chemical weathering in the tidal floodplain, consistent with a transport‐limited regime. Multiple lines of evidence indicate a lack of exchange equilibrium between surface waters and coexisting solids, which may be a common feature in tidal river deltas where transport‐limited regimes likely dominate.

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

“…Thus, Se may be sourced from sewage, oil refineries, or agricultural runoff in the area. It is also interesting that Se concentrations are much higher in our samples from 2018 and 2019 (regardless of season) compared to results from surface waters in the same general study area in 2016 analyzed using the same methods [ 8 ]. One possible explanation for this is that Se is anthropogenic and the source of its release into the dissolved load began after sampling in 2016, potentially from industrial centers in Khulna [ 25 ], while another possible explanation is that the overall less saline samples taken in 2016 were more diluted with rainwater or other sources than ours in 2018 and 2019, thus lowering Se concentrations.…”

“…Arsenic concentrations in this study are also much greater than surface waters sampled in 2016 in the same geographic region [ 8 ]. The differences in As concentrations between [ 8 ] and both this study and [ 6 ] may be attributed to small sample size, overall less saline (and thus potentially more diluted) samples in Ayers et al [ 8 ], and sample location heterogeneity, as all three studies used similar methodology.…”

“…Arsenic concentrations in this study are also much greater than surface waters sampled in 2016 in the same geographic region [ 8 ]. The differences in As concentrations between [ 8 ] and both this study and [ 6 ] may be attributed to small sample size, overall less saline (and thus potentially more diluted) samples in Ayers et al [ 8 ], and sample location heterogeneity, as all three studies used similar methodology. For example, if groundwater exfiltration is a possible source of As to surface waters, sampling locations from [ 8 ] may receive different exfiltration rates of groundwater because of widespread geologic heterogeneity in the area (e.g., [ 7 ]).…”

“…This extends to upstream (non-headwaters) Ganges River samples [ 15 ], their Table S4), where Cu, Vi, and Ni result in a great multiple linear predictive model fit for As (adjusted R 2 = 0.64, p = 7.2e-15) (Additional file 1 : Table A8). Additionally, when examining tidal channel samples from Ayers et al [ 8 ], predicting As with Cu, V, Ni, and P results in a great overall fit as well (adjusted R 2 = 0 0.74, p = 0.024) (Additional file 1 : Table A9). However, Se predictive modeling for tidal channels in Ayers et al [ 8 ] is much poorer, with DOC, V, and Ni only resulting in an adjusted R 2 of 0.24 (p = 0.17).…”

“…Additionally, when examining tidal channel samples from Ayers et al [ 8 ], predicting As with Cu, V, Ni, and P results in a great overall fit as well (adjusted R 2 = 0 0.74, p = 0.024) (Additional file 1 : Table A9). However, Se predictive modeling for tidal channels in Ayers et al [ 8 ] is much poorer, with DOC, V, and Ni only resulting in an adjusted R 2 of 0.24 (p = 0.17). Nevertheless, the general approach of multiple linear regression with several important predictor variables such as Ni and V or other geochemical analogues appears to be applicable to other surface water bodies, particularly rivers, and can be especially useful in better understanding and predicting the concentration of As in solution.…”