Tracing sulfate sources in a tropical agricultural catchment with a stable isotope Bayesian mixing model.

Sulfate (SO42-) is an essential anion in drinking water and a vital macronutrient for plant growth. However, elevated sulfate levels can impact ecosystem or human health and could be an important indicator of acid rock drainage or pollution. Therefore, monitoring SO42- sources and transport is important for water quality assessments. This study focused on exploring the sources and transformations of SO42- as well as estimating the proportional contribution of the potential SO42- pollutant sources to groundwater and surface water in a tropical river basin, the Densu River Basin. The study used major ions combined with stable sulfur and oxygen isotope compositions and a Bayesian isotope mixing model, MixSIAR. The major ion characteristics indicate that SO42- concentrations remain stable throughout the rainy and dry seasons but originate from diverse sources. The multi-isotope model (δ34SSO4, δ18OSO4) identified four potential SO42- sources: detergent, precipitation, sewage, and sulfate fertilizer. However, the δ34SSO4 and δ18OSO4 values of the fertilizer source signatures overlapped with those of precipitation and sewage. Nevertheless, the contributions from each source were disentangled using the MixSIAR model, which revealed sewage as the most dominant SO42- pollutant in the Densu Basin, accounting for ~47 % of sulfate in groundwater and ~ 56 % of sulfate in surface water. Sulfate fertilizer (~33 %) was the second most important source after sewage for groundwater, while detergent (~23 %) was the second most important source for surface water. The redox processes of bacterial sulfate reduction and sulfide oxidation were determined to have a minimal impact on the sulfur isotope fractionation within the basin. This study highlights the benefits of combining major ions, sulfur isotopes and the MixSIAR model for identifying sources of sulfate. This approach accounts for uncertainties in source contributions which allows for more robust and reliable apportionment of sulfate sources. The study emphasizes the need for effective waste management and pollution control measures to protect water quality and provides vital guidelines on how to partition sulfate sources on a large catchment scale and evidence for making pollution management decisions on water resources.

Disentangling nitrate pollution sources and apportionment in a tropical agricultural ecosystem using a multi-stable isotope model.

Fertilizers increase agricultural productivity and farmers' income. However, intensive agriculture frequently overuses fertilizers, which in turn can contaminate surface and groundwater. In this study, hydrochemical and multi-isotope (δ15NNO3, δ18ONO3 and δ18OH2O) data have been combined to identify nitrate pollution sources in Ghana's Densu River Basin, trace the Nitrogen (N) biogeochemical processes in the basin and apportion the contribution of each pollution source. Surface water NO3- ranged from 0.3 to 10.6 mg/L (as N), while groundwater NO3- ranged from 0.9 to 34 mg/L. Hierarchical cluster analysis classified the water samples into three spatial categories: upstream, midstream, and downstream, reflecting river and land use patterns. The multi-isotope model considered five primary NO3- sources: atmospheric deposition, manure/sewage, NH4+ in fertilizers, other NO3- based fertilizers and soil N. Nitrification was identified as the major biogeochemical process upstream, whereas mixing of sources and denitrification dominate the midstream to downstream sections of the basin. Nitrate source apportioning using a MixSIAR model reveal that N fertilizers (40 %) and soil N (34 %) contribute the most to nitrate pollution upstream of the river. From the midstream to downstream sections, manure/sewage (43 %) become the dominant nitrate source, reflecting the transition from agriculture to peri-urban and urban land use. This study has shown that soil erosion and runoff contribute to nitrate pollution in the Densu River, at levels comparable to N fertilizers, and groundwater across the basin is impacted mainly by manure/sewage. The multi-isotope analyses allowed the partitioning of N sources in other ways not possible using only classical hydrochemical methods.

Ellucidating the incidence and the prevalence of Schistosomiasis spp infection in riparian communities of the Bui dam.

The flow rate of rivers are affected when modifications are made for the benefit of mankind. Some man-made alterations carried out include dam construction. The aim of this study was to investigate the health impact of the Bui dam with respect to the prevalence and awareness level of schistosomiasis in a typical damming environment. The study was conducted in 4 riparian communities within the dam catchment area. A cross-sectional study design was employed to interview 350 individuals. Urine and stool samples were also collected from 386 participants. Results of the study showed that, knowledge of schistosomiasis was significantly greater in close communities (99.47%) than their far counterparts (50.29%) (p > 0.001; OR = 172). Schistosomiasis infection rate in the close communities (32.57%) were significantly greater in far communities (7.23%; p ≤ 0.0001). The overall prevalence of 82 (21.1%) was recorded for Schistosoma haematobium and 64 (16.1%) for Schistosoma mansoni. A significantly high prevalence of S. haematobium (43.3%) was found in the age group 15-24 with no prevalence reported for age group 5-9 (Close communities) (p = 0.012). When the same age group was further examined for S. mansonii, group 5-9 recorded a prevalence of 0% with age group 10-14 showing a high prevalence of 26.1% (p = 0.047). From the study, it was concluded that, though awareness level of Schistosomiasis knowledge on the cause, mode of transmission and symptoms were high, they were ignorant on personal preventive strategies. In addition, the study also revealed that, S. haematobium was more prevalent among inhabitants living closer to the Bui dam with children less than 14 years of age being the worst affected.