RESUMEN
The analysis of plutonium (Pu) in soil samples can inform the understanding of soil erosion processes globally. However, there are specific challenges associated for analysis in tropical soils and so an optimal analytical methodology ensuring best sensitivity is critical. This method aimed to demonstrate the feasibility of sample preparation and analysis of Pu isotopes in African soils, considering the environmental and cost implications applicable to low-resource laboratories. The separation procedure builds upon previous work using TEVA columns, further demonstrating their usefulness for the reduction of uranium (U) interference in ICP-MS analysis with enhanced selectivity for Pu. Here several steps were optimised to enhance Pu recovery, reducing method blank concentration, and improving the separation efficiency through the determination of the elution profiles of U and Pu. The elimination of the complexing agent in the eluent, increased the spike recovery by improving matrix tolerance of the plasma, and simplified the separation procedure, improving throughput by 20%. The subsequent method was validated through the analysis of Certified Reference Material IAEA-384, where high accuracy and improved precision of measurement were demonstrated (measured value 114 ± 12 versus certified value 108 ± 13 Bq kg-1). Optimisation of the column separation, along with the analysis of the samples using O2 gas in ICP-MS/MS mode to mass shift Pu isotopes away from interfering molecular U ions provided a simple, robust, and cost-effective method with low achievable method detection limits of 0.18 pg kg-1 239+240Pu, applicable to the detection of ultra-trace fallout Pu in African soils.
RESUMEN
Soil erosion accelerated by poor agricultural practices, land degradation, deprived infrastructure development and other anthropogenic activities has important implications for nutrient cycling, land and lake productivity, loss of livelihoods and ecosystem services, as well as socioeconomic disruption. Enhanced knowledge of dynamic factors influencing soil erosion is critical for policymakers engaged in land use decision-making. This study presents the first spatio-temporal assessment of soil erosion risk modelling in the Winam Gulf, Kenya using the Revised Universal Soil Loss Equation (RUSLE) within a geospatial framework at a monthly resolution between January 2017 and June 2020. Dynamic rainfall erosivity and land cover management factors were derived from existing datasets to determine their effect on average monthly soil loss by water erosion. By assessing soil erosion rates with enhanced temporal resolution, it is possible to provide greater knowledge regarding months that are particularly susceptible to soil erosion and can better inform future strategies for targeted mitigation measures. Whilst the pseudo monthly average soil loss was calculated (0.80 t ha-1 month-1), the application of this value would lead to misrepresentation of monthly soil loss throughout the year. Our results indicate that the highest erosion rates occur between February and April (average 0.95 t ha-1 month-1). In contrast, between May and August, there is a significantly reduced risk (average 0.72 t ha-1 month-1) due to the low rainfall erosivity and increased vegetation cover as a result of the long rainy season. The mean annual gross soil loss by water erosion in the Winam Gulf catchment amounts to 10.71 Mt year-1, with a mean soil loss rate of 9.63 t ha-1 year-1. These findings highlight the need to consider dynamic factors within the RUSLE model and can prove vital for identifying areas of high erosion risk for future targeted investigation and conservation action.
